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Impact of energy management system on the sizing of a grid-connected PV/Battery system
Abstract
A genetic algorithm can be applied to optimize the sizing of a grid-connected hybrid photovoltaic/battery energy system deployed in conjunction with a home energy management system under different charging/discharging scenarios of a plug-in electric vehicle.
... Battery is used for storage in [4], [5], [9]. Grid-tied hybrid energy systems are proposed in [10] - [18]. In order to cater for reliability of the hybrid supply, some studies propose battery storage [4], [10], [15]. ...
... Grid-tied hybrid energy systems are proposed in [10] - [18]. In order to cater for reliability of the hybrid supply, some studies propose battery storage [4], [10], [15]. On the other hand, other researches propose diesel generator as back up [6], [7], or a combination of both diesel generator and battery storage [5], [9]. ...
... Optimal sizing of hybrid system ensures that the designed hybrid system will meet the load requirements of the building [1] with possible minimum operating costs. Optimal sizing techniques are required for designing [10], [25] reliable and economical hybrid energy systems. Sizing techniques have been reviewed in [1], [10], [19]. ...
... Recently, HOMER ® software optimization tool, which is developed by National Renewable Energy Laboratory (NREL), USA [31] has been used in countless studies for optimization of different configurations of HRESs (e.g. PV/WT/Biomass/Battery/Converter [5],DG/PV/WT/Battery/ Converter [9], [32], WT/PV/Battery/Converter [4], [33], PV/Biomass gasifier/DG /Battery/Converter [16], PV/WT /DG/Battery/Converter [31] PV-battery [34], PV/DG/ battery/Converter [23], PV-WT [14], [35], [36], only PV [37], [38], PV/Hydro/DG/Battery/Converter [39], PV-WTbattery/FC [3], [40]- [42]) for electrification of rural [4], [16], [35], [40] and urban [5], [9], [32], [36] areas with countrybased study cases [4], [5], [38], [40], [9], [13], [14], [16], [32], [35]- [37] either for grid-connected [3], [13], [42]- [44], [16], [34]- [38], [40], [41], [45] or stand-alone [4], [5], [9], [13]- [16], [40] systems. HOMER fundamentally is a robust techno-economic optimization model [9]. ...
... Recently, HOMER ® software optimization tool, which is developed by National Renewable Energy Laboratory (NREL), USA [31] has been used in countless studies for optimization of different configurations of HRESs (e.g. PV/WT/Biomass/Battery/Converter [5],DG/PV/WT/Battery/ Converter [9], [32], WT/PV/Battery/Converter [4], [33], PV/Biomass gasifier/DG /Battery/Converter [16], PV/WT /DG/Battery/Converter [31] PV-battery [34], PV/DG/ battery/Converter [23], PV-WT [14], [35], [36], only PV [37], [38], PV/Hydro/DG/Battery/Converter [39], PV-WTbattery/FC [3], [40]- [42]) for electrification of rural [4], [16], [35], [40] and urban [5], [9], [32], [36] areas with countrybased study cases [4], [5], [38], [40], [9], [13], [14], [16], [32], [35]- [37] either for grid-connected [3], [13], [42]- [44], [16], [34]- [38], [40], [41], [45] or stand-alone [4], [5], [9], [13]- [16], [40] systems. HOMER fundamentally is a robust techno-economic optimization model [9]. ...
... The parameters of battery inductance design are as follows [34]: ΔIbat =20%Ibat, Ibat < Ibat(max), DBat=VDC/VBat ...
Renewable energy sources (RESs) offer a promising prospect for covering the fundamental needs of electricity for remote and isolated regions. To serve the customers with high power quality and reliability, design optimization methodology and a possible power management strategy (PMS) for winddiesel-battery-converter hybrid renewable energy system (HRES) is proposed in this paper. The analysis is applied to a real case study of a standalone residential load located in a remote rural area in Pakistan. Firstly, optimal component sizing is investigated according to actual meteorological and load profile data. Different hybrid configurations are modeled, analyzed, and compared in terms of their technical, economic and environmental metrics with the aid of HOMER® software. The main objective is to determine the most feasible and cost-effective solution with least life-cycle cost, keeping in view the impact of carbon emissions. Secondly, a suitable PMS based on the state of charge (SOC) of the battery is proposed and implemented in MATLAB/Simulink® software for the designed HRES. The PMS is targeted to maintain load balance and extract maximum wind power while keeping the battery SOC within the safe range. Model predictive control (MPC) approach is applied to improve the output voltage profile and reduce the total harmonic distortion (THD). The boost converter is used for maximum power extraction from the wind. The DC-DC buck-boost battery controller is utilized to stabilize the DC bus voltage. The design optimization results show that the hybridization of wind, battery, and converter presents optimal configuration plan with minimum values of total net present cost (14,846$)andcostofenergy(0.309$/kWh), which means 76.7% reduction in both total system cost and energy cost and 100% saving in harmful emissions when compared to the base case using diesel generator. The proposed system is able to support hundred percent of the load demand with excess energy of 30.1%. Performance analysis of PMS under variable load and fluctuating wind power generation is tested, and promising results with efficient load voltage profile is observed. Further, THD is reduced significantly to 0.26%as compared to 2.62% when the conventional PI controller is used. The output of this work is expected to open a new horizon for researchers, system planners for efficient design and utilization of HRES to curb drastic increase in load demand for urban as well as rural areas.
... Optimal sizing of the hybrid system ensures that the designed hybrid system meets the load requirements of the building, with possible minimum operating costs. Optimal sizing techniques are required for designing reliable and economical hybrid energy systems (Askarzadeh, 2017;Abushnaf and Rassau, 2018). Sizing techniques have been reviewed in (Erdinc and Uzunoglu, 2012;Upadhyay and Sharma, 2014;Abushnaf and Rassau, 2018). ...
... Optimal sizing techniques are required for designing reliable and economical hybrid energy systems (Askarzadeh, 2017;Abushnaf and Rassau, 2018). Sizing techniques have been reviewed in (Erdinc and Uzunoglu, 2012;Upadhyay and Sharma, 2014;Abushnaf and Rassau, 2018). ...
... Optimality is developed according to the sizing [8], the extraction of maximum power [9]- [11] or even energy flows management [12], [13]. The latter is an important aspect because it is strongly influenced by the application and have a direct impact on other aspects [14]. Energy management can be developed either according to mathematical programming, metaheuristic or heuristic programming or even according to flowcharts that directly adapts to the selected application [15], which have been proven to be efficient and easy to implement. ...
... Indeed, we seek to minimize as much as possible the energy that will be consumed from the grid and the amount of energy that probably could be lost during energy and control. For this purpose the objective function is given by Eq. (14). ...
In this paper, we propose a new hybrid architecture of a renewable PY-Wind-Battery hybrid system. This architecture will be connected to the grid to alternatively supply a Moroccan house in a smart city environment. The energy management source-side is modeled according to a linear programming optimization with an objective function that aims to minimize the energy purchased from the utility grid and to ensure the energy balance of the HRES-HOME system. Otherwise, the demand side was optimized with a mixed integer GA programming based on smart home loads categorization. This will ensure an autonomous internal management system and a grid interconnection just to inject the surplus or during critical shortages after demand side management. The resolution of this program has been realized with MATLAB platform minimization toolbox. The simulation of the results proved the accuracy of the proposed methodology. Effectively, 82.42% was well reduced with the use of the hybrid system and linear optimization system. A study on the categorization of smart home loads revealed that for a house with 25% of the energy consumed is devoted to controllable loads, 75% of the rest should be assigned to reducible controllable loads.
... The unit sizing of RESs is a complex task because each component of the system needs to be modeled and optimally sized (Fathy et al. 2016). In this context, several optimization techniques like hybrid optimization model for electric renewable (HOMER) (Syed et al. 2017), iterative method (Giallanza et al. 2018), meta-heuristics (Huang et al. 2018), (Abushnaf et al. 2018), and other schemes are reported in the literature for solving unit sizing problem in remote areas. ...
... On the other side, meta-heuristic algorithms are powerful optimization schemes used in the literature for solving the unit sizing problem (Huang et al. 2018) and (Abushnaf et al. 2018). However, meta-heuristic algorithms proposed for unit sizing (Ogunjuyigbe et al. 2016)- (Bingham et al. 2019) require algorithmic-specific parameters, which, if not tuned properly may halt in local optimum or result in a high computational time )- ). ...
In a stand-alone environment, a system comprising of non-renewable source, renewable energy sources (RESs), and energy storage systems like fuel cells (FCs) provide an effective and reliable solution to fulfill the user's load. In this paper, a diesel generator (DG), pho-tovoltaics (PVs), wind turbines (WTs) and FCs are modeled, optimally sized, and compared in three scenarios: PV-WT-FC-DG, PV-FC-DG, and WT-FC-DG in terms of environmental emission and total annual cost (TAC) for a home, located in Hawksbay, Pakistan. The optimal size of hybrid RESs and their components is achieved using a novel TAC minimization algorithm (TACMA). The TACMA achieves superior results in terms of TAC when it is compared to two algorithm-specific parameter-less schemes: Jaya and teaching learning-based optimization. Further, the PV-WT-FC-DG and PV-FC-DG hybrid systems are found as the most economical and nature-friendly scenarios, respectively.
... The authors used various software, linear programs, nonlinear programs, and smart algorithms. From that popular software used, Hybrid2, E Huga, TRNSYS, Homer, and RETScreen software, and from the famous algorithms used in achieving the optimization are GA algorithms, PSO algorithms, and Artificial Bee Colony (ABC) Algorithm, etc. [7][8][9][10][11][12]. ...
... The battery capacity is based on load demand and battery daily autonomy . The battery capacity is expressed as follows [9,20]: ...
The hybrid small grid system is a solution to many economic and environmental problems. The pre-feasibility of the project is a necessary step to validate the implementation of any project. Microgrid hybrid systems (consisting of PV, wind turbines, diesel generators, and battery storage) were examined in two countries to determine their optimal economic and size. In this paper, the technical-economic was implemented as an objective function based on net present cost NPC, with respecting many constraints such as LPSP, availability, and the renewable fraction. The optimization performed using a smart and efficient algorithm called the PSO algorithm. The results indicate that the building of a microgrid hybrid system in Baghdad is more economical compared to Rabat with the same corresponding components of renewable energies and load capacity. The resulting total showed that the cost of the project reached 31K dollars for the city of Baghdad, while the cost touched 43K dollars for the city of Rabat.
... In [4], [13], [14], the life cycle cost of a gridconnected hybrid system and an off-grid hybrid system are minimized through obtaining the optimal battery size or PV size or combination of PV, wind and battery sizes. In [15], [16], grid-connected PV and battery size are determined to minimize the annualised system cost. However, [15] does not include weather effects such as temperature in the PV model and [16] does not consider the battery charging and discharging constraints. ...
... In [15], [16], grid-connected PV and battery size are determined to minimize the annualised system cost. However, [15] does not include weather effects such as temperature in the PV model and [16] does not consider the battery charging and discharging constraints. Reference [17] minimizes the overall electricity cost without considering losses in the battery storage and the associated converter. ...
... In this research, the nanogrid for EV charging, which is depicted in Figure 1, comprises 22 kW AC charging stations which can provide up to 32 A to charge the EV. Photovoltaic (PV) panels are used to generate renewable energy They are preferred to other RERs like a small scale wind turbine, because of their power production during daytime, their low maintenance cost and their easy set-up [6] [7]. Moreover, with the continuous downward trend of the price of PV modules, solar power is considered as the most competitive technology for a nanogrid parking [8]. ...
... Next to an EMS, it is also important to have an appropriate sizing of the system components (PV array, BESS and grid connection) in order to guarantee the lowest overall cost of the nanogrid while ensuring the highest possible reliability [7]. The variables that should be optimized are the number of parallel strings of the PV array, the number of parallel strings of the battery pack and the limited amount of power that the grid can supply, as only these three variables have on influence on the current flow inside the nanogrid. ...
This paper presents an energy management strategy (EMS) and infrastructure-sizing algorithm for charging electric vehicles (EVs) from a nanogrid, comprising a photovoltaic array and a stationary battery pack. This to the utility grid connected system can be a solution for the integration of EVs in the future. The EMS is developed with a fuzzy logic controller that controls the power flow within the nanogrid with the objective to satisfy a weekly load demand of 250 kWh per charging station. The system’s components are optimally sized with a genetic algorithm that minimizes a cost function including the capital and operating costs. The results prove the correct functioning of the EMS, but it was found that a nanogrid parking is not yet attractive from the economical point of view.
... The reported BEV powertrain control and energy management usually focus on appropriate battery energy management, efficient motor traction control, and regenerative braking control [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Battery energy management is an indispensable part of electric vehicles to ensure an optimal and reliable operation of the battery packs [23]. ...
... The battery energy management studies support not only battery fault estimation (including battery health monitoring and life estimation) [24][25][26], but also vehicle toque or power demand estimation [28][29], battery SOC and range estimation [29][30] etc. Recent studies have even evaluated the effect of connecting the vehicle energy management system with the electric grid to maximize energy efficiency [31]. Novel motor traction controls improve BEV performance and energy savings by utilizing the electric motor's quick and precise torque response. ...
... To minimize this impact, numerous researchers use renewable energy systems to support the charging stations and reduce the stress on the grid [6]- [9]. A photovoltaic (PV) system with battery storage demonstrated excellent performance improvement for the charging stations [10], [11]. The energy storage captures the surplus energy produced from the PVs during the maximum power output and will discharge it when the PV system cannot meet the load demand. ...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network.
... The use of direct current sources in driving a device is limited. But, it is quite common and desirable to obtain direct current energy from renewable sources [1,2]. This direct current energy source needs to be transformed for using it more widely in electronic applications [3][4][5][6][7]. ...
... Information about batteries with different technologies can be found in [22][23][24][25]. Ref. [26] used the genetic optimization algorithm in order to find the optimal size of the grid-connected compound system under different charging and discharging scenarios for electric vehicles. ...
This study aims to develop an optimization strategy for determining the optimal type and capacity of batteries in a building‐applied photovoltaic system, taking into account battery degradation, consumption profiles, and regional solar irradiation. Key performance indicators such as peak shaving, savings, net present value, self‐consumption, return on investment, and payback period are examined. The best trade‐off among these indicators is determined using the fuzzy decision‐making method. A study was conducted using real data from Kpenergy Company, focusing on a building with a 50 kW photovoltaic system located in Stockholm. Three cases were examined in MATLAB software, each categorized based on the type of contract between the utility (Vattenfall Company) and the subscriber. The results of these case studies highlight the effectiveness of the proposed optimization approach. Using the proposed approach, optimal batteries are determined, minimizing subscriber costs while maximizing profit.
... The performance of a solar PV system can be affected by several things, such as; The use of solar PV control systems, controller Pulse Width Modulation (PWM) [18,19], controller Maximum Power Point Tracking (MPPT) [20,21,22], Solar tracker [23], hybrid solar PV with battery power supply [24], solar PV and Storage for household consumer using Agent Based modeling [25], and hybrid solar PV with other energy sources ongrid or off-grid [26,27,28]. The system solar PV on-grid or offgrid with PLN (PT. ...
The use of solar PV as an alternative to fulfill household-scale electricity needs has begun to be widely developed. However, the problem of investment costs and the location of solar PV placement for household scale is still a challenge in its implementation. The construction model of rooftop solar PV can affect the investment cost and performance of solar PV. In this paper, the triangle model of rooftop solar PV on grid with PLN (PT. Perusahaan Listrik Negara) electricity network is studied in terms of technology and economics to determine the feasibility of implementing 900 VA household-scale power plants. Testing the application of solar PV technology under solar radiation conditions in the city of Surabaya, Indonesia as a case study. Calculation of electricity production, energy savings, energy sales, and energy purchases to determine technological feasibility as well Net Present Value (NPV), Benefit Cost Ratio (BCR), and Payback Period (PP) to determine the level of economic feasibility. The results of the research of 1,5 KWP (kilowatt peak) solar PV technology on a household scale are able to meet energy needs and reduce PLN electricity purchases to 0% and can sell electrical energy by 13.96% / year of the total electrical energy produced. In addition, the NPV, BCR with a value greater than zero, and PP of 8.6 is less than 15 years which is the service life of solar PV, so solar PV/ PLN on grid is feasible to be implemented for household scale power generation models.
... Another important variable to be considered in these systems is the connection mode. In the case of a grid-connected system, different examples are available in the literature showing energy management strategies to supply optimally the energy sources [24][25][26][27][28][29], even under daily blackouts, contingencies [30,31], or random contingencies due to extreme climate events or other causes. In all these cases, efforts are addressed to optimize the energy flow in power distribution networks to increase resilience [32][33][34]. ...
Electric substations (ESS) are important facilities that must operate even under contingency to guarantee the electrical system’s performance. To achieve this goal, the Brazilian national electricity system operator establishes that alternating current (AC) auxiliary systems of ESS must have, at least, two power supplies, and in the case of failure of these sources, an emergency generator (EG) must at least supply energy to the essential loads. In order to improve the availability of auxiliary systems, a microgrid with other sources, such as photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), can be an alternative. In this case, an economical optimization of the PV/BESS system must be addressed considering the costs associated with the installation and maintenance of equipment, and the gains from the credits generated by the photovoltaic system in the net metering scheme. In this paper, the size of the BESS system was determined to supply energy to the load of auxiliary systems of an ESS, as well as a PV system to achieve a null total cost. Furthermore, multi-objective optimization using the genetic algorithm technique was employed to optimize the size of the hybrid PV/BESS to minimize the investment cost and time when the demand was not met. Simulations under different scenarios of contingency were allowed to obtain the Pareto frontier for the optimal sizing of a PV/BESS system to supply energy to AC auxiliary systems in an ESS under contingency.
... As per International Renewable Energy Agency (IRENA) reports, the Global Levelized cost of electricity (GLCE) on utility-scale perspectives for renewable power generation technologies is shown in Fig 1. It could be noted that the GLCE of Solar PV generation system (SPVGS) has decreased up to 400% between 2010-17 [3] [4]. As of now, solar PV systems are having the decidedly less Global Levelized cost of electricity (GLCE) compared to other renewable energy sources [5]. ...
Solar Photovoltaic (PV) generation systems have a less Levelized cost of electricity (LCoE). As such, when solar energy is available, the demand response is scheduled in such a way that maximum utilization of solar energy is practised.But the power generation from a solar PV system is highly uncertain and unpredictable due to irregular solar irradiation. Also, the power generation is limited to a time fraction of a day.The impact of these negative traits in a power system is studied with the help of an analytical curve called “Duck curve”. “Solar Duck curve” is a graphical representation of time scaled imbalances between a SPV generation to peak demand. A steep or rugged part in a duck curve indicates sudden shortcoming of SPV generation with respect to the peak demand. Hence, during this period, the loads are shifted between solar PV sources and the main grid with respect to the insufficiency of solar power from peak demand. The proposed system is a machine learning-based multistage demand response system for meeting demand response of a SPV dominant duck curve. The model has four layers/stages.The primary layer is used to analyse the behaviour of the duck curve with the help of a Support Vector regression algorithm and the second layer is used for determining theoperating parameters based on the economic constraints imposed. The third layer is a demand response model based on the previous layer, and the fourth layer is aadaptive signal-processing model used to improve the stability of the system.The obtained demand response model is updated continuously in an adaptive manner so as to improve the stability of the system.A hardware experimental setup is made with eighteen numbers of 24V/2kW interconnected solar PV real-time system which is used for validating and analysing the method.
... The results indicate that grid connectivity is economically advantageous. Abushnaf and Rassau (2018) determined the optimal size of a gridconnected photovoltaic/battery hybrid system and selected a number of photovoltaic panels, batteries, and inverters as decision variables. Cingoz and Sozer Badawy et al. (2016) pinpointed battery storage sizing for a grid-connected photovoltaic/battery hybrid system for various scenarios and the optimal size was derived for each scenario. ...
Renewable energy systems, particularly in countries with limited fossil fuel resources, are promising and environmentally sustainable sources of electricity generation. Wind, solar Photovoltaic (PV), and biomass gasifier-based systems have gotten much attention recently for providing electricity to energy-deficient areas. However, due to the intermittent nature of renewable energy, a completely renewable system is unreliable and may cause operation problems. Energy storage systems and volatile generation sources are the best way to combat the problem. This paper proposes a hybrid grid-connected wind-solar PV generation Microgrid (MG) with biomass and energy storage devices to meet the entire value of load demand for the adopted buildings in an intended region and ensure economic dispatch as well as make a trade in the electricity field by supplying/receiving energy to/from the utility grid. The control operation plan uses battery storage units to compensate energy gap if the priority resources (wind turbine and solar PV) are incapable of meeting demand. Additionally, the biomass gasifier is used as a fallback option if the batteries fail to perform their duty. At any time, any excess of energy can be utilized to charge the batteries and sell the rest to the utility. Additionally, if the adopted resources are insufficient to meet the demand, the required energy is acquired from the utility. A Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO) algorithm is adopted for achieving optimal sizing of the proposed grid-connected MG. To assess the proposed technique’s robustness, the results are compared to those obtained using the Grey Wolf Optimization (GWO) algorithm. The GWCSO method yielded a lower total number of component units, annual cost, total Net Present Cost (NPC), and Levelized Cost Of Energy (LCOE) than the GWO algorithm, whereas the GWCSO algorithm has the lowest deviation, indicating that it is more accurate and robust than the GWO algorithm.
... Nowadays, Renewable Energy Sources (RES) are used widely to fulfill the higher power demand of the modern world. The usage of non-renewable energy sources is gradually decreased due to the impacts of global warming and the usage of RES [1]. The different types of RES are tidal power, the solar, wind, hydroelectric, and geothermal energy [2]. ...
. Renewable Energy Sources (RES) are currently being used on a much larger scale to support and satisfy the higher energy demands caused by industrialization and population growth. Due to this rise in the number of consumers of power systems and the unpredictable nature of the electric load, the vast power demand proves to be a tough challenge for electric utilities and system operators. So, power demands have occurred over many periods and become a threat to the system's functionality. Therefore, an effective Energy Management System (EMS) name called Golden Eagle Optimization with Incremental Conductance (GEO-INC) is proposed to meet the load demand. Three different systems, namely: RES Photovoltaic (PV) module, wind turbine, and battery create an effective EMS. The proposed method extracts more power from the PV panel and effectively controls the switching between the wind turbine and the battery storage system. The proposed method achieves 1.98 % distortion from the results, which is less than the existing methods.
... Electricity produced from PV structures is vital renewable energy, which encloses zero greenhouse gas emission and no fossil fuel consumption [3]. A 60% average annual progress rate of PV capacity was seen from 2004-2009, and an 80%-90% development was estimated in 2011. ...
In Renewable energy schemes, Solar photovoltaic (PV) systems provide effective incorporation of generating electrical energy. Many current control techniques such as Hysteresis control, predictive control and Sliding mode control are available to improve the performance of PV systems. However, the current tracking of the existing controllers is suffered when connected to the grid-connected system due to the lack of constant switching frequency in the three-phase inverter control. Furthermore, the complexity of these systems is very high. An Artificial Neural Network (ANN) based Sliding Mode Control approach has been used to solve the voltage regulation in the grid-connected solar system. This model has been employed to implement a two-loop controller using a voltage controller as an outer loop and a current controller as an inner loop. The ANN-SMC-based three-phase inverter is simulated to diminish the load current's harmonics and provide robustness in the inverter control. The proposed system results show the robustness improvement of the grid-connected solar system with a low level of Total Harmonic Distortion (THD).
... In Ref. [ In Ref. [39], the size and design of the grid-connected system, including PV/WT/FC, were optimized to supply a recreational place load in Egypt using Hybrid Firefly (HFA), HS, and PSO with LPSP, COE, and TNPC targets. In Ref. [40], the GA was used to optimize the size of the grid-connected PV/battery system to reduce costs. In Ref. [41], a grid-based PV/WT energy system was designed with PSO to reduce costs. ...
The optimization of distributed generation technologies and storage systems are essential for a reliable, cost-effective, and secure system due to the uncertainties of Renewable Energy Sources (RESs) and load demand. In this study, two algorithms, the Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-Dominant Sorting Genetic Algorithm II (NSGA-II) were utilized to design five different case studies (CSs) (photovoltaic (PV)/wind turbine (WT)/battery/diesel generator (DG), PV/WT/battery/fuel cell (FC)/electrolyzer (EL)/hydrogen tank (HT), PV/WT/battery/grid-connected, PV/WT/battery/grid-connected with Demand Response Program (DRP), and PV/WT/battery/electric vehicle (EV)) to minimize life cycle cost (LCC), loss of power supply probability (LPSP), and CO2 emissions. In fact, different backups are provided for (PV/WT/battery), which is considered as the base system. Further, the uncertainties in RES and load were modeled by the Taguchi method, and Monte Carlo simulation (MCS) was used to model the uncertainties in EV to achieve accurate results. In addition, in CS4, a Demand Response Program (DRP) based on Time-of-Use (TOU) price is considered to study the effect on the number of specific components and other parameters. Finally, the simulation results verify that the NSGA-II calculation provides accurate and reliable outcomes compared to the MOPSO method, and the PV/WT/battery/EV combination is the most suitable option among the five designed scenarios.
... Furthermore, according to Table 6 and Figure 17A, the majority of the DE occurred at approximately 13 hours at 12.23 kW. And also, as shown in Figure 17B, at 1,8,10,16,19, and 20 hours, the system purchased additional power at 0.09 kW, 0.14 kW, 2.7 kW, 0.13 kW, 0.85 kW, and 2.45 kW, respectively. In the remaining hours, it shared its extra power with other systems. ...
Currently, the ideal sizing of hybrid technologies is one of the vital aspects of power system design. In this article, the design and optimization of the sizing of hybrid renewable energy systems (HRESs) with power‐sharing capabilities in conjunction with electric vehicles (EVs) were proposed in two case studies. Two algorithms, namely, multi‐objective particle swarm optimization (MOPSO) and multi‐objective crow search (MOCS), have been formulated and were used to solve the problem being investigated. In case study 1 (CS1), four different HRESs are designed in the presence of EVs, meaning that for each HRES an EV and the power‐sharing capability is employed. And also, the stochastic behavior of the EV using Monte Carlo simulation (MCS) is modeled. In case study 2 (CS2), four HRESs are designed with power‐sharing capabilities, but in this case, for any of the HRESs, EV is not considered. This idea can be considered a novel breakthrough for the potential of power‐sharing has been incorporated with the integration of EVs and HRESs. This approach improves the life cycle cost and loss of power supply probability indices. In summary, both cases in the presence and absence of EVs were compared with the simulation results. The results show that the use of the proposed EV significantly reduces the total cost of the engineered system. Furthermore, two meta‐heuristic techniques were compared, and it was concluded that MOPSO had performed better than MOCS. Optimal sizing and power sharing of hybrid renewable systems with EVs. Proposed novel heuristic optimization approach using MOPSO and MOCS. Optimization of uncertainty parameters using 100 different scenarios using MCS. Economic and reliability benefits of the proposed system.
... Most sizing strategies used different objective functions to be minimized; some of these objective functions are economical, technical, or environmentally-based. Some studies design the HRES for minimum cost as a single objective function [13][14][15][16][17][18][19] and some other studies (multi-objective optimization ones) use more than one objective function to size and design the HRES [20][21][22][23][24][25]. The use of multi-objective optimization permits better operation of the HRES concerning different techno-economic factors. ...
The use of hybrid renewable energy systems (HRES) has become the best option for supplying electricity to sites remote from the central power system because of its sustainability, environmental friendliness, and its low cost of energy compared to many conventional sources such as diesel generators. Due to the intermittent nature of renewable energy resources, there is a need however for an energy storage system (ESS) to store the surplus energy and feed the energy deficit. Most renewable sources used battery storage systems (BSS), a green hydrogen storage system (GHSS), and a diesel generator as a backup for these sources. Batteries are very expensive and have a very short lifetime, and GHSS have a very expensive initial cost and many security issues. In this paper, a system consisting of wind turbines and a photovoltaic (PV) array with a pumped hydro energy storage (PHES) system as the main energy storage to replace the expensive and short lifetime batteries is proposed. The proposed system is built to feed a remote area called Dumah Aljandal in the north of Saudi Arabia. A smart grid is used via a novel demand response strategy (DRS) with a dynamic tariff to reduce the size of the components and it reduces the cost of energy compared to a flat tariff. The use of the PHES with smart DRS reduced the cost of energy by 34.2%, and 41.1% compared to the use of BSS and GHSS as an ESS, respectively. Moreover, the use of 100% green energy sources will avoid the emission of an estimated 2.5 million tons of greenhouse gases every year. The proposed system will use a novel optimization algorithm called the gradually reduced particles of particle swarm optimization (GRP-PSO) algorithm to enhance the exploration and exploitation during the searching iterations. The GRP-PSO reduces the convergence time to 58% compared to the average convergence time of 10 optimization algorithms used for comparison. A sensitivity analysis study is introduced in this paper in which the effect of ±20% change in wind speed and solar irradiance are selected and the system showed a low effect of these resources on the Lev-elized cost of energy of the HRES. These outstanding results proved the superiority of using a pumped-storage system with a dynamic tariff demand response strategy compared to the other energy storage systems with flat-rate tariffs.
... A few studies considered the optimization of operation [127], dispatch [128], energy scheduling [129] and energy flow [134] alongside the PV-battery optimal sizing. Genetic algorithm was used as the optimization algorithm in Ref. [131]. The PSO algorithm was only used in Ref. [148] for optimal sizing of PV-BES system in a GCRS. ...
Integration of solar photovoltaic (PV) and battery storage systems is an upward trend for residential sector to achieve major targets like minimizing the electricity bill, grid dependency, emission and so forth. In recent years, there has been a rapid deployment of PV and battery installation in residential sector. In this regard, optimal planning of PV-battery systems is a critical issue for the designers, consumers, and network operators due to high number of parameters that can affect the optimization problem. This paper aims to present a comprehensive and critical review on the effective parameters in optimal planning process of solar PV and battery storage system for grid-connected residential sector. The key parameters in process of optimal planning for PV-battery system are recognized and explained. These parameters are economic and technical data, objective functions, energy management systems, design constraints, optimization algorithms, and electricity pricing programs. A timely review on the state-of-the-art studies in PV-battery optimal planning is presented. The challenges, trends and latest developments in the topic are discussed. At the end, scopes for future studies are developed. It is found that new guidelines should be provided for the customers based on various electricity rates and demand response programs. Also, several design considerations like grid dependency and resiliency need further investigation in the optimal planning of PV-battery systems.
... The study suggested an effective method of forecasting in the design of integrated PV air conditioning systems that can be operated in different regions. (Abushnaf and Rassau, 2018) 2018 Australia 5 kW Polycrystalline silicon Researchers examined the total annual demand and factors affecting the optimal size of a gridconnected PV system. Because there is no solar radiation except in the daytime, any solar system needs a fixed number of photovoltaic cells to meet the demand for pregnancy during the day whether or not auxiliary systems are used. ...
In this study, the aging measurements of a 1.4 kW grid-connected photovoltaic system were analyzed. The system is located at the Solar Energy Laboratory at the College of Engineering, Sohar University, Sohar, Oman. The system variables were monitored and measured for a period of seven years, starting from 1 October 2012 until 30 September 2019, during which the electricity produced to the network was fed. Weather data metrics measurements showed solar irradiation values, ambient temperature, and the PV module temperature were measured for seven years. The performance criteria for the different PV system, which included (the PV module and system efficiency), performance ratio, and capacity factor were measured in addition to the different losses and system productivity. The results showed that the system is exposed to aging, although the amount of its impact is relatively small, as the study was conducted for a seven-year-old, while the effect of aging is more at longer ages. The measured aging caused the system efficiency to decrease by 6.3 % and the production rate to 5.88 % while the mean daily array capture loss and system loss were 6.95 % and 6.13 %, respectively, and the capacity factor decreased by 4.91 %. Measurements during the seven years showed that the rate of degradation is greater during the summer than the rest of the seasons due to the high radiation intensity which causes high temperatures of the PV units in addition to the high dust density during this season. The results showed that the use of GCPV systems in the Sohar region is very successful in terms of the limited aging of the system and its long-term viability with appropriate efficiency.
... The other groups of objective functions are emission and technical objectives, which contain renewable factor (RF), carbon emission (CE), battery lifetime (BL), customer comfort level (CCL), and dumped energy (DE). The RF shows how much of the energy demand in the RAES system is supplied by renewable resources [43]. The CE is the amount of carbon emission by the designed RAES system during the project lifetime [44]. ...
Optimal planning of a remote area electricity supply (RAES) system is a vital challenge to achieve a reliable, clean, and cost-effective system. Various components like diesel generators, renewable energy sources, and energy storage systems are used for RAES systems. Due to the different characteristics and economic features of each component, optimal planning of RAES systems is a challengeable task. This paper presents an overview of the optimal planning procedure for RAES systems by considering the important components, parameters, methods, and data. A timely review on the state of the art is presented and the applied objective functions, design constraints, system components, and optimization algorithms are specified for the existing studies. The existing challenges for RAES systems’ planning are recognized and discussed. Recent trends and developments on the planning problem are explained in detail. Eventually, this review paper gives recommendations for future research to explore the optimal planning of components in RAES systems.
... The paper [17] proposes an optimization model for determining the optimal size of the battery devices of standalone microgrids islanded microgrid. The sizing of a grid-connected hybrid renewable energy system supplying electric power to a household is presented in the paper [18]. Technoeconomic sizing optimization of hybrid RES for high-rise residential buildings is presented in the paper [19]. ...
Renewable energy systems have become more attractive with the increase in energy demand due to demographic growth, industrial development, and conventional sources' cost and their impact on the environment. Finding the most suitable solution to obtain the optimum design of renewable energy systems by considering techno-economic performance is a significant challenge to ensure their efficiency at the lowest cost of energy produced. This paper has developed our Electric System Cascade Extended Analysis with new merits and functionalities to be able to determine the optimum capacities and sizes for different power generation and storage facilities of renewable energy systems in both on-grid and off-grid. The Loss of Power Supply Probability as a system reliability criterion, the Life Cycle Cost and the Levelized Cost of Energy as economic indicators, are implemented together as tri-objective optimization functions into the ESCEA to optimize the sizing results techno-economically. The sizing procedure takes as inputs hourly meteorological data, load profile, and the technical and economic data for the generation and storage units. The algorithm has been demonstrated with a case study on a site located in Oujda city in Morocco, with different electrical energy demands. Validation of the developed methodology is performed by comparing the obtained results with those from the System Advisor Model software. The results from the Electric System Cascade Extended Analysis shows that it successfully identified the optimal configuration with a difference with System Advisor Model of 1.1% in sizing results of CSP plants, 1% for PV systems, 0.9% for wind turbines systems, and a maximum difference of 1.5% in annual produced energy. The economic analysis of the ESCEA sizing results shows that it achieved viable levels cost of energy for all studied on-grid and off-grid renewable energy systems and provided a comprehensive evaluation that help to choose the suitable RES for any site worldwide.
... The PV consists of several diodes which form a single cell. The mathematical equation of a single diode can be obtained from the Shockley equation as follow [32]: ...
... The PV consists of several diodes which form a single cell. The mathematical equation of a single diode can be obtained from the Shockley equation as follow [32]: ...
This study presents a detailed feasibility analysis of technical and financial assessment for grid-connected Hybrid Renewable Energy System (HRES) configurations by including grid-only, HRES-only and grid-HRES at four different provinces in the Kingdom of Saudi Arabia (KSA), namely; (Al Baha University, University of Jeddah, Prince Sattam Bin Abdulaziz University, and Tabuk University). The objective of this paper is to search the possibility of supplying the load demand with the optimum system that has the lowest net present cost (NPC) and greenhouse emission CO 2. The simulation results show that NPC of a proposed grid/PV system, at the current grid's tariff, is more sufficient than other configurations with a result in a renewable fraction of more than 50%, a payback time of 17 years, and 54.3% reduction in CO 2. The results also show that the integration of 62 kW PV array with the main grid is the best configuration that leads to the minimum cost of energy (COE) of 0.0688 $/kWh and the sell back energy of 9.16% of total energy consumption at Al Baha University. Besides, optimization modeling addresses that HRES-only system can supply the full load demand without power shortage (<0.1%) with a major contribution from solar PV by 78.5%, wind energy shares 11.3% of load demand , and 10.2% from battery banks. The developed analysis concludes that the objective function is feasible for the selected locations. The study has three novelties. Firstly, the required load at different locations of the university's buildings at KSA is supplied by minimizing COE. The objective function is achieved by considering a combination of HRES. Then, it applies the sensitivity analysis for several cases such as payback time, gird's tariff variation, and load demand change. Finally, the current analyses are applicable to any university at KSA and around the world.
... The performance evaluation protocol reveals that on-peak demand aggressively increases for a number of appliances. The research [122] proposes RESs based energy model for HEMS. The efficiency of RESs is analyzed using GA for charging and discharging of the battery storage system. ...
The reliable, efficient, sustainable and optimal management of city resources to facilitate the inhabitants defines the Smart City (SC). The resources of every sector of a SC are managed for their efficient utilization. The power sector is the backbone of a SC, which should be well planned in its design and structure to optimize power utilization. The integration of Information and Communication Technology (ICT) with conventional power grid allows two-way communication between supply and demand sides, which is defined as Smart Grid (SG). The intelligent monitoring and control systems for SG optimize the power generation and power consumption on supply and demand sides, respectively. On supply-side, fossil fuel is used to run the power generators to fulfill power demand, which is expensive and also emits Carbon-dioxide (CO2) in the environment. High power demand requires more generation as a result more CO2 is released in the environment, which causes the greenhouse effect. Optimized energy demand (power management on demand-side) ensures the optimized power production, which reduces the energy cost and emission of CO2. The demand-side is divided into industrial, commercial and residential sectors. The energy management programs optimize the power demand for these sectors. The industrial and commercial sectors are rigid for their energy demand due to their business portfolio; however, the residential sector is flexible. A energy management program of a home optimizes the energy demand by shifting its load demand of from on-peak to off-time time-slots. This optimization reduces energy cost of the home and power production on supply-side. Moreover, the integration of Renewable Energy Sources (RESs) on demand-side mitigates power demand from the utility (supply-side). The residential sector is further classified into islanded Smart Homes(SHs) and
smart community for energy management. In an islanded SH, the load is shifted from on-peak to off-peak time to reduce power consumption cost while avoiding the peak demand for the supply-side. However, when multiple SHs in a community shift load to avoid peak demand, it may generate a rebound peak and the problem of inefficient power demand persists. So, a global solution is required to be proposed for a smart community by considering power sources and power demand.
... The study suggested an effective method of forecasting in the design of integrated PV air conditioning systems that can be operated in different regions. (Abushnaf and Rassau, 2018) 2018 Australia 5 kW Polycrystalline silicon Researchers examined the total annual demand and factors affecting the optimal size of a gridconnected PV system. Because there is no solar radiation except in the daytime, any solar system needs a fixed number of photovoltaic cells to meet the demand for pregnancy during the day whether or not auxiliary systems are used. ...
In this study, the aging measurements of a 1.4 kW grid-connected photovoltaic system were analyzed. The system is located at the Solar Energy Laboratory at the College of Engineering, Sohar University, Sohar, Oman. The system variables were monitored and measured for a period of seven years, starting from 1 October 2012 until 30 September 2019, during which the electricity produced to the network was fed. Weather data metrics measurements showed solar irradiation values, ambient temperature, and the PV module temperature were measured for seven years. The performance criteria for the different PV system, which included (the PV module and system efficiency), performance ratio, and capacity factor were measured in addition to the different losses and system productivity. The results showed that the system is exposed to aging, although the amount of its impact is relatively small, as the study was conducted for a seven-year-old, while the effect of aging is more at longer ages. The measured aging caused the system efficiency to decrease by 6.3% and the production rate to 5.88% while the mean daily array capture loss and system loss were 6.95% and 6.13%, respectively, and the capacity factor decreased by 4.91%. Measurements during the seven years showed that the rate of degradation is greater during the summer than the rest of the seasons due to the high radiation intensity which causes high temperatures of the PV units in addition to the high dust density during this season. The results showed that the use of GCPV systems in the Sohar region is very successful in terms of the limited aging of the system and its long-term viability with appropriate efficiency.
... The energy assets combination is based on the specific application needs and the load demand criticality (i.e., supply reliability increases in hybrid diesel systems against increased operating and fuel cost and the frequent need for a fuel supply). Theoretically, an isolated load demand can be fully met (24/7) by a combination of PV and battery storage, though relying only on such resources can lead to oversized systems at a very high cost and wasted excess energy [5,6]. Instead, the PV and battery systems may be sized reasonably to supply the critical load continuously in relevant applications, while accepting a reasonable shedding margin to the non-critical loads at the demand side due to the lack of consistent supply resources. ...
... However, grid blackout is another main issue, which can affect intensively on the reliability and security of the energy system [8]. To address these issues of electricity access and grid reliability, numerous studies on establishing stand-alone and grid-connected HRESs have been conducted worldwide [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Most of these studies have been based on hybrid optimization model for electric renewables (HOMER) optimization tool [27]. ...
Fast-growing load demand, environmental issues, high network losses, and low reliability are the main factors to penetrate hybrid renewable energy systems (HRESs) to the electrical grid. HRESs are able to provide an eco-friendly and cost- efficient alternative to conventional energy systems. This paper provides a feasibility study for grid-connected electrification of a residential area located at Suhag city, Egypt. Different hybridization scenarios of solar, wind, diesel, and converter technologies are modelled, analyzed and compared in terms of their technical, economic and environmental indices. Grid blackouts and intermittent nature of renewable resources are also modelled and investigated in order to obtain the optimal design of the hybrid energy system with the least cost and high-reliability level. The simulation results show that solar/wind/diesel grid-connected system is the best configuration for the investigated area with least system cost. Also, the total load demand is successfully served by the proposed system topology with realistic carbon emission and zero capacity shortage.
... Optimal capacities of PV and BES in grid-connected households are commonly investigated in [3][4][5][6][7][8][9][10]. However, there is limited published work addressing all the indices during the optimisation and providing a general guideline to the residential customers for PV and BES procurement. ...
... Specifically, DG brings the power of its utilization; this likewise incorporates low warmth misfortunes and maintaining a strategic distance from points of confinement forced by a packed transmitter network. [1][2][3] Besides, the expanding inclination towards the high entrance of RES originates from their condition neighborly and cost upper hands over ordinary generation. [4][5][6] MG works on grid coupled or island mode, organizing systems with private or business clients in rustic or urban territories. ...
This paper proposes an efficient hybrid approach–based energy management strategy
(EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed
technique is to reduce the operational electricity cost and enhanced power flow
between the source side and load side subject to power flow constraints. The proposed
control scheme is a consolidated execution of both the random forest (RF) and quasioppositional‐
chaotic symbiotic organisms search algorithm (QOCSOS), and it is named
as QOCSOS‐RF. Here, the QOCSOS can have the capacity to enhance the underlying
irregular arrangements and joining to a superior point in the pursuit space. Likewise,
the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert
and extrapolate the arbitrary information with high exactness. Here, the required load
demand of the grid‐connectedMGsystem is continuously tracked by the RF technique.
The QOCSOS optimized the perfect combination of the MG with the consideration of
the predicted load demand. Furthermore, in order to reduce the influence of renewable
energy forecasting errors, a two‐strategy for energy management of the MG is
employed. At that point, proposed model is executed in MATLAB/Simulink working
platform, and the execution is assessed with the existing techniques
... For example, the battery scheduling problem for microgrid operation is investigated by [12][13][14]. Some of the studies also assume a connection to a larger grid [12] or mainly focus on the optimal size of a battery [15,16]. ...
By installing a battery storage system in the power grid, Distribution Network Operators (DNOs) can solve congestion problems caused by decentralized renewable generation. This paper provides the necessary theory to use such a community battery for grid congestion reduction, backed up by experimental results. A simple network model was constructed by linearizing the load flow equations using a constant impedance load model. Using this model, an accurate estimate of voltage and overload problems is fed into a receding horizon charge path optimizer. The charge path optimization problem is posed as a linear problem and subsequently solved by an LP solver. The algorithms have been applied and validated on a real-world community battery installation. It was found that the voltages and currents can be controlled to a great degree, increasing the grid capacity significantly. The proposed control framework can be used to safeguard network constraints and is compatible with other battery control goals, such as energy trading or energy independence. Network design formulas are described with which a DNO can quickly estimate the potential (de) stabilization of a community battery on the steady-state voltages and currents in the grid.
... Moreover, a high PV penetration scenario leads to a locally less flexible grid. Abushnaf and Rassau [7] presented a numerical model to study the optimal size of grid connected hybrid PV/battery system with home energy management system. The system was tested under different cases of charging and discharging. ...
This paper presents a design for a grid connected PV system with the capacity of 1.5 MVA, as well as a standalone PV system with the capacity of 50 kVA in the West Bank industrial zone, Palestine. The factors affecting the design and size of the system are also presented and evaluated. To ensure adequate, reliable and economical system design, over and under sizing energy has been avoided. Furthermore, a numerical analysis will be carried out to evaluate the effects of the grid-connected system on the network, with respect to the voltage profile, power flow, and energy losses. The results have shown a good enhancement in the total energy losses and voltage profile in reference to load and generation capacity. However, the voltage profile is enhanced on buses located close to the proposed PV plant while the profile enhancements decrease when moving closer to the source buses. The maximum increase in voltage profile is 0.4% which is satisfactory in our case. Furthermore, the power losses were clearly decreased with installed of PV systems. Finally, an economic analysis for both systems will be presented. It was found that the total income of the project is expected to be around $616,923 per year. Further, the payback period is expected to be .5 years and the Net Present Value (NPV) is about $3,885,125.91. The Internal Rate of Return (IRR) for the project during a 20-year lifespan is about 26%.
... The performance evaluation protocol reveals that peak demand is aggressively increasing for unlimited (huge number of) appliances. The authors in [78] propose RES based energy model for HEMS. The size of RESs is analyzed using GA for charging and discharging of battery storage. ...
The reliable, efficient, sustainable and optimal management of city resources to facilitate the inhabitants define the Smart City (SC). A SC has many management sectors; however, the power sector is the backbone, which has a complex and expensive structure. The integration of the conventional power grid with Information and Communication Technologies (ICTs) defines the Smart Grid (SG). Two-way communication between supply and demand sides provides an opportunity to optimize power production and consumption. In this synopsis, Demand Side Management (DSM) is proposed for the residential sector. The residential sector is divided into islanded Smart Homes (SHs) and smart communities. In SHs, the appliances are scheduled to shift the load from on-peak
to off-peak hours to reduce the energy cost and mitigate the load peak demand. In this synopsis, four scheduling algorithms are proposed for a SH’s appliances. The cost efficient energy consumption using the algorithms shall be analyzed by implementing with three pricing schemes; Critical Peak Pricing (CPP), Day Ahead- Real Time Pricing (DA-RTP) and Inclined Block Rates (IBR) with three Operation Time Intervals (OTIs); 10, 30 and 60 minutes for a SH. The habitants of a set of SHs in a region or building in a city share the things on common grounds, which define the community. When SHs of a region schedule their load from on-peak to off-peak hours, the load peak is generated in off-peak hours. In the synopsis, four cloud-fog based system models are proposed for communities to optimize energy consumption with time efficiency. The Response
Time (RT), Processing Time (PT) and computing cost of the system are optimized with Service Broker Policies (SBPs) and load balancing algorithms (managing the load of requests and tasks) to balance the load of physical and virtual resources for the fog. SBPs route the requests on potential data center and load balancers (heuristic techniques) manage the load of requests on virtual resources efficiently. The quality of resource utilization effects the RT, PT and computing cost. Cloud-fog based models for realistic environments, in which Fog-as-a-Power-Economy-Sharing and Fog based Energy Management as a Service (FEMaaS) for prosumers are proposed for communities. The cost efficient energy consumption and efficient computing cost will validate the feasibility of proposed system. The proposed research will strengthen the concept of SC with cost efficient energy management in SG.
... In [14], optimal management strategy of ESSs is illustrated in medium voltage dis- tribution grids. In [15] genetic algorithm is used to optimize the sizing of a grid-connected hybrid photovoltaic/ battery energy system with a home energy management system under the different charging/dis- charging scenarios of a plug-in electric vehicle. In [16], wind turbines and ESSs are used together in the power system, and finally, the ad- vantages of the ESSs application are presented. ...
Dispersed application of Battery Energy Storages (BESs) can have many benefits in terms of voltage regulation and energy management in Active Distribution Networks (ADNs). The batteries are high-cost technologies, and they must be installed and managed optimally to benefit from their innumerable advantages. In addition, each battery technology has specific economic and technical attributes which can be appropriate or inappropriate in a particular condition and utilization. The purpose of this paper is that the optimal size and location of various battery technologies are specified in the distribution network to minimize total cost and maximize reliability index considering the uncertainty of load demand as well as the output power of the wind and solar. Also, multi-objective particle swarm optimization (MOPSO) algorithm is used to minimize two objective functions. Monte Carlo Simulation (MCS) is used to model the uncertainties of economic and technical characteristics of photovoltaic, wind power and load demand. The suggested planning scheme is tested on the modified IEEE 33-bus system. Finally, the different types of the battery technologies in one, five, ten, fifteen and twenty-year period are compared to present the optimum type.
Tamil Nadu, a state in India, has many households with loads between 1 kW and 2.5 kW and a single-phase power supply of 230V, 50Hz. The bi-monthly energy consumption of these categories of houses crosses the band of 500 units, which leads to the excess payment of energy consumption costs. To utilize the plenty of renewable energy available in this state, we conducted a feasibility analysis to develop the optimal solar PV system for these types of households using HOMER software. Over the years from 2016 to 2018, real-time data from 500 residences in Tirumangalam, Madurai District, Tamil Nadu, were gathered and utilized to design an optimal solar PV system for these households. On completion of the sensitivity analysis, the lowest Cost of Energy (COE), Net Present Cost (NPC), and Annual Operating and Maintenance Costs are $0.104, $158.36, and $4,389.76, respectively. An optimal 2kW on-grid Internet of Things (IoT)-based solar PV system is installed in 2019 for a residential building employed in the HOMER results, and the outcomes are compared to those without solar PV systems. It is been proven that adding a 2kW solar PV system leads to an average annual savings of $79.02 for the study period of 2019 to 2022.
With the increasing adoption of renewable energy sources, researchers are actively engaged in the development of smart grid technologies. This study introduces a home energy management system (HEMS) designed to optimize home microgrid (HMG) operation by integrating electric vehicles (EVs) through a hierarchical three-level distributed control approach. The proposed system governs the power transfer of distributed energy sources through converter control at the primary level, while employing an intelligent optimal power exchange technique for EVs and energy storage (ES) at the secondary level. To achieve optimal energy demand management, the system employs a two-layer strategy, comprising offline and online scheduling, utilizing particle swarm optimization and artificial neural network to reduce computing time. The offline scheduling formulates a deterministic optimization model to minimize HMG operation costs, thereby extending ES lifetime through optimal load-sharing. The online scheduling approach enables real-time performance under uncertainty and accommodates plug-and-play renewable energy sources, HMGs, loads, and batteries. Furthermore, the proposed algorithm is tested using MATLAB/Simulink simulations over a 24-h period, affirming its ability to respond promptly to HEMS status changes and adapt decision-making methods to new conditions with improved training time and reduced mean square error. In addition, experimental studies on two laboratory-based HMGs demonstrate the feasibility of the proposed HEMS.
Due to the damage of fossil fuels to the environment, fossil fuels will finish soon, the interest in electric vehicles has increased. Determining the trend of electric/classic vehicle replacement and additional load on the transformer is vital importance for the investment plans of energy distribution companies. It presents a comprehensive method for including electric vehicle replacements in the investment planning of electric distribution companies. A new model is proposed, used to replace classical vehicles by becoming widespread of electric vehicles. The power density and transformer capacity ratio were examined using the proposed model for scenarios. Electricity consumption, line length, transformer installed power capacity, population, and the number of vehicles data for the last ten years were obtained from ÇEDAŞ and Turkish Statistical Institute. According to scenario comparisons over the 30-year planning, the J value on the grid will exceed the current value between 2029 and 2030, R-value will fall below the current value between 2031 and 2032 in the best case. It is necessary to invest in electricity distribution lines in 2029-2030 and transformer capacities in 2031-2032. The results showed the developed method could be used to determine the line and transformer capacities due to the prevalence of electric vehicles.
Energy management strategies and optimal power source sizing for fuel cell/battery/super capacitor hybrid electric vehicles (HEVs) are critical for power splitting and cost-effective sizing to meet power demand for a good drive range, less energy loss and consumption, and minimal fuel cell and battery degradation for hybrid power sources. This paper presents a comprehensive review of the energy management techniques and their integration with energy source sizing, mainly for fuel cell/battery/supercapacitor hybrid electric vehicles. The paper discussed the benefits of integrating an energy management strategy (EMS) and the sizing of hybrid energy sources. Predictive based energy management strategies such as Artificial Neural Network (ANN), Reinforcement Learning (RL), and Model Predictive Control (MPC) were briefly examined. In addition, the paper reviewed hybrid algorithms or techniques for energy management strategies, that could be the combination of rule-based with a predictive, rule based with real-time and predictive with real-time, and predictive with learning based algorithms to give a good energy management strategy for fuel cell/battery/supercapacitor HEVs to achieve the optimal objective functions. The results show, that in terms of the size of the fuel cell, the evaluation of power demand-based and state of charge (SoC)-based methods used for large capacity batteries and smaller capacity batteries, reveals that the SoC-based method is appropriate for real-time energy management, while small capacity batteries have higher degradation. Fuel economy was improved with RL for battery engine hybrid vehicles than when Dynamic Programming (DP) was used. When the EMS was compared using dynamic programming (DP), Pontryagin’s minimum principle (PMP), and Equivalent Consumption Minimization Strategy (ECMS), the results show that ECMS is more efficient for online optimization than PMP and DP. Further results show that RL-based EMSs help to reduce energy losses and also increases the system efficiency, and help to reduce battery degradation as compared to when rule-based EMSs are used.
A Smart Grid (SG), a network of electricity, connects users like producers, Prosumers and consumers are two different types of people to stake energy in smart way, also facilitate them to choose various scheduling techniques in order to manage the energy usage. In this chapter, we discuss about the domiciliary load management with inclusion of Renewable Energy Resource (RES) like solar energy. A genetic algorithm (GA) based technique is projected to manage electrical appliance power utilization with a goal of minimizing power cost, increasing user comfort, and reducing peak to average ratio (PAR) shares of energy deprived of disturbing priorities of the prosumers’ by considering real time energy price (RTP), user priorities and renewable sources of energy-related parameters as input parameters. In our work, we have combined the pricing models of RTP with the Inclining Block Rate (IBR), which integrates user preferences and RES to schedule load demand appropriately. Adopting a RTP+IBR pricing method should successfully minimize electricity bills and PAR and improve system stability. To assess the proposed algorithm performance, the provided mathematical models of used loads are then used to build a multiobjective optimization problem. Further, simulation was done, and the results shows a substantial drop in the cost of energy, as well as achieving grid stability in terms of reduced peak and high comfort.
Renewable Energy Sources (RES) are currently being used on a larger scale to support and satisfy the higher energy demands caused by industrialization and population growth. Therefore, the power generation has to be increased at a greater speed to meet the daily user needs for improvement in lifestyle. The electric utilities and system operators face a tough challenge due to the rise in the number of consumers of power systems and the unpredictable nature of the electric load. Therefore, an effective Energy Management System (EMS) is developed using a combination of Modified Flower Pollination Algorithm (MFPA) and Modified Perturb and Observe (MP&O) method. Three different systems namely: RES Photovoltaic (PV) module, wind turbine, and battery are used to create an effective EMS. MFPA extracts more power from the PV panel by providing less scaling factor. Then it is utilized to increase the initialized population of particles to enhance step size and reset the position of particles. Furthermore, MFPA is used to effectively control the switching between the wind turbine and battery storage system. Furthermore, the MP&O method is used to trigger the switch of the DC-DC converter to achieve stable power from the PV/wind/battery system. The MFPA-MP&O controller is compared with three standard controller combinations FPA-P&O, FPA-MP&O and MFPA-P&O and is also compared with existing RES designs that are: MPCP-MPVP, Fuzzy Logic Control – Proportional Integral (FLC-PI), and MPPVC. Total Harmonic Distortion (THD) of MFPA-MP&O controller is 0.40%, which is less when compared with the MPCP-MPVP, FLC-PI and MPPVC RES designs.
This study investigates the potential application of Stateflow (SF) to design an energy management strategy (EMS) for a renewable-based hybrid energy system (HES). The SF is an extended finite state machine; it provides a platform to design, model, and execute complex event-driven systems using an interactive graphical environment. The HES comprises photovoltaics (PV), energy storage units (ESU) and a diesel generator (Gen), integrated with the power grid that experiences a regular load shedding condition (scheduled power outages). The EMS optimizes the energy production and utilization during both modes of HES operation, i.e., grid-connected mode and the islanded mode. For islanded operation mode, a resilient power delivery is ensured when the system is subjected to intermittent renewable supply and grid vulnerability. The contributions of this paper are twofold: first is to propose an integrated framework of HES to address the problem of load shedding, and second is to design and implement a resilient EMS in the SF environment. The validation of the proposed EMS demonstrates its feasibility to serve the load for various operating scenarios. The latter include operations under seasonal variation, abnormal weather conditions, and different load shedding patterns. The simulation results reveal that the proposed EMS not only ensures uninterrupted power supply during load shedding but also reduces grid burden by maximizing the use of PV energy. In addition, the SF-based adopted methodology is envisaged to be a useful alternative to the popular design method using the conventional software tools, particularly for event-driven systems.
In this paper, the optimal sizing problem of the micro-grid's resources in two different modes in the presence of the electric vehicle using the multi-objective particle swarm optimization algorithm is investigated. In this regard, the uncertain behavior of the electric vehicle is modeled using the Monte Carlo Simulation. In the first case named as PV/wind/battery, the optimum number of components and the amount of cost at different levels of reliability are determined. Then, the electric vehicle is added to the system regarding which the loss of power supply probability is recalculated in the both deterministic and stochastic states. The results show that the electric vehicle increases the system reliability. In the second system named as PV/wind/battery/EV, the effect of deterministic and stochastic behavior of electric vehicle on the number of components and the loss of power supply probability was investigated for the first time. The results demonstrated that the design of both systems is feasible, but the first system was more efficient than the second, because the latter used more winds in a number of identical LPSPs. Moreover, sensitivity analysis has been performed to show the effect of wind speed and load parameters on decision variables.
Climate change is a very rising topic nowadays since the climate of this world is changing rapidly day by day. In the technical field, it is seen that so many things or techniques used here, which have a very bad impact on our environment like use of non-renewable energy source, emission of greenhouse gases and so on. At present electric power generation is mainly dependent upon non-renewable sources. Due to rapid uses of non-renewable energy sources, its storage reserves are decreasing rapidly. So an alternate source is required and that is the renewable energy source, nowadays renewable sources are utilized but in small amount. Renewable sources are environmentally friendly, so using of renewable energy sources are more preferable than non-renewable sources for the betterment of our environment. Due to rising environmental concerns day by day, the utilization of renewable energy need to be increased as much as possible. There are so many remote or island places in this world where huge numbers of renewable sources are available which can be used for power generation. And the most important thing is that they have no effect (or very less effect) in this environment. So our goal is to model and simulate a grid connected solar-wind hybrid energy system which is used to solve the problems regarding the power generation. In this chapter a 24 h case study analysis is done by taking the real time data of solar radiation and wind speed of a selected location. The results of this analysis indicate that the hybrid system is profitable and environmentally friendly. This analysis simply gives an idea about to what extent there will be the generation of power and how much it will be helpful to this environment. In addition, it includes detailed discussion on climate change, harmful effects of non-renewable energy sources on the environment and the need of renewable energy based hybrid energy system to combat climate change. By this explanation we will get to know more about how renewable energy sources mitigate two problems – climate change & power demand.
In this paper, optimal size and power exchange of a grid-connected diesel generator-photovoltaic-fuel cell (diesel/PV/FC) hybrid energy system is investigated by multi-objective optimization for a community in Kerman, Iran. To optimally size hybrid system, number of system components (PV panels, diesel generators, electrolysers, FCs and hydrogen tanks) are considered as decision variables. In this paper, to optimize power exchange between the hybrid system and grid, two other decision variables are introduced: selling coefficient and purchase coefficient. Selling and purchase coefficients are defined to optimize the amount of electricity sold to the grid and the amount of electricity purchased from the grid, respectively. In order to optimally design the hybrid system, multi-objective optimization has been considered with respect to three objectives: levelized cost of energy (LCOE), loss of power supply probability (LPSP) and renewability. Based on simulation results, it can be drawn that when the hybrid system can sell/buy energy to/from the grid, (1) by decreasing non-renewable energy source usage, the value of LPSP increases, (2) by decrease of non-renewable energy usage, value of LCOE increases and (3) using FC system leads to increasing LCOE.
Compared to the conventional energy resources, photovoltaic systems (PVs) widely uses the solar energy to produce electricity considered as one of the renewable energies available having a considerable potential and developing increasingly fast as compared to its counterparts of renewable energies. Such systems can be as either stand-alone or connected to utility grid. But generally, the disadvantage is that solar PV array depends on variable weather conditions. That's why solar panels show a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on the environmental factors, such as temperature and irradiation/insolation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP with the unavoidable and inescapable changes in the environment. In this paper, performance analysis of maximum power point tracking (MPPT) algorithms for a grid connected PV system has been discussed. The MPPT techniques considered in the proposed work are Perturb and Observe (PO), Incremental Conductance (IC) and PI controller. The simulations carried out for PV array cascaded with boost converter and utility grid. The results in terms of maximum power tracking capability investigated at the utility grid.
This paper analyses residential PV battery systems in order to gain insights into their sizing. For this purpose a simulation model was developed and system simulations on a timescale of one minute were performed. Furthermore, a sensitivity analysis was conducted varying the PV system and battery size to identify appropriate system configurations. Based on the simulation results, an economic assessment of PV battery systems was carried out and the cost-optimal configurations for various cost scenarios were determined. The results show that in the considered long-term scenario the conjunction of PV systems with batteries will be not only profitable but also the most economical solution.
Potou is an isolated site, located in the northern coast of Senegal. The populations living in this area have no easy access to electricity supply. The use of renewable energies can contribute to the improvement of the living conditions of these populations. The methodology used in this paper consists in Sizing a hybrid solar–wind-battery system optimized through multi-objective genetic algorithm for this site and the influence of the load profiles on the optimal configuration. The two principal aims are: the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP).To study the load profile influence, three load profiles with the same energy (94 kW h/day) have been used.The achieved results show that the cost of the optimal configuration strongly depends on the load profile. For example, the cost of the optimal configuration decreases by 7% and 5% going from profile 1 to 2 and for those ones going from 1 to 3.
The overall evaluation of small autonomous hybrid power systems (SAHPS) that contain renewable and conventional power sources depends on economic and environmental criteria, which are often conflicting objectives. The solution of this problem belongs to the field of non-linear combinatorial multiobjective optimisation. In a multiobjective optimisation problem, the target is not to find an optimal solution, but a set of non-dominated solutions called Pareto-set. The present article considers as an economic objective the minimisation of system's cost of energy (COE), whereas the environmental objective is the minimisation of the total greenhouse gas (GHG) emissions of the system during its lifetime. The main novelty of this article is that the calculation of GHG emissions is based on life cycle analysis (LCA) of each system's component. In LCA, the whole life cycle emissions of a component are taken into account, from raw materials extraction to final disposal/recycling. This article adopts the non-dominated sorting genetic algorithm (NSGA-II), which in combination with a proposed local search procedure effectively solves the multiobjective optimisation problem of SAHPS. Two main categories of SAHPS are examined with different energy storage: lead-acid batteries and hydrogen storage. The results indicate the superiority of batteries under both economic and environmental criteria.
This paper presents a systematic analytical approach for the well-being assessment of small autonomous power systems (SAPSs) with wind and solar energy sources. The proposed technique accounts for the uncertainties associated with solar irradiance, wind speed, demand, and outages of various generating units. The impact of wind power fluctuation on the system stability is also assessed by limiting the wind power dispatch to a certain percentage of system load. Well-being assessment and production costing simulation for SAPS are performed using proposed analytical approach and Monte Carlo simulation (MCS) method, and then, obtained results are compared in terms of accuracy and computational time. The comparison shows that the developed technique requires less computational time than MCS method, with reasonable accuracy, and thus, validates the usefulness of proposed analytical method. The impact of renewable energy penetration on a SAPS is also analyzed using the proposed method.
In this paper, we study the problem of determining the size of battery
storage used in grid-connected photovoltaic (PV) systems. In our setting,
electricity is generated from PV and is used to supply the demand from loads.
Excess electricity generated from the PV can be stored in a battery to be used
later on, and electricity must be purchased from the electric grid if the PV
generation and battery discharging cannot meet the demand. Due to the
time-of-use electricity pricing, electricity can also be purchased from the
grid when the price is low, and be sold back to the grid when the price is
high. The objective is to minimize the cost associated with purchasing from (or
selling back to) the electric grid and the battery capacity loss while at the
same time satisfying the load and reducing the peak electricity purchase from
the grid. Essentially, the objective function depends on the chosen battery
size. We want to find a unique critical value (denoted as $C_{ref}^c$) of the
battery size such that the total cost remains the same if the battery size is
larger than or equal to $C_{ref}^c$, and the cost is strictly larger if the
battery size is smaller than $C_{ref}^c$. We obtain a criterion for evaluating
the economic value of batteries compared to purchasing electricity from the
grid, propose lower and upper bounds on $C_{ref}^c$, and introduce an efficient
algorithm for calculating its value; these results are validated via
simulations.
Home energy management system technology can provide a smart and efficient way of optimizing energy usage in residential buildings. This paper presents a home energy management system algorithm that monitors and controls household appliances based on time-of-use (TOU) energy pricing models while accounting for multiple inhabitants sharing a home and its appliances. This algorithm helps to manage and schedule usage by prioritizing multiple users with preferred usage patterns. Two different scenarios will be implemented to develop and test the influence of a multiple-users and load priority (MULP) algorithm on reducing energy consumption, energy cost and carbon footprint. In the first scenario, TOU pricing and different demand limits are used, while the second scenario focuses on the TOU pricing with different demand limits combined with the MULP model. Simulation results show that the combination of the MULP model and the TOU pricing leads to significant reductions in user payments and total energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.
This paper presents two novel hourly energy supervisory controls (ESC) for improving long-term operation of off-grid hybrid systems (HS) integrating renewable energy sources (wind turbine and photovoltaic solar panels), hydrogen system (fuel cell, hydrogen tank and electrolyzer) and battery. The first ESC tries to improve the power supplied by the HS and the power stored in the battery and/or in the hydrogen tank, whereas the second one tries to minimize the number of needed elements (batteries, fuel cells and electrolyzers) throughout the expected life of the HS (25 years). Moreover, in both ESC, the battery state-of-charge (SOC) and the hydrogen tank level are controlled and maintained between optimum operating margins. Finally, a comparative study between the controls is carried out by models of the commercially available components used in the HS under study in this work. These ESC are also compared with a third ESC, already published by the authors, and based on reducing the utilization costs of the energy storage devices. The comparative study proves the right performance of the ESC and their differences.
In this paper, a new formulation for optimizing the design of a photovoltaic (PV)-wind hybrid energy home system, incorporating a storage battery, is developed. This formulation is carried out with the purpose of arriving at a selection of the system economical components that can reliably satisfy the load demand. Genetic algorithm (GA) optimization technique is utilized to satisfy two purposes. The first is to minimize the formulated objective function, which is the total cost of the proposed hybrid system. Whereas, the second is to ensure that the load is served according to certain reliability criteria, by maintaining the loss of power supply probability (LPSP) of the system lower than a certain predetermined value. Two computer programs are designed, using MATLAB code in a two M-files, to simulate the proposed hybrid system and to formulate the optimization problem by computing the coefficients of the objective function and the constraints. Also, these two programs are utilized together with the GA tool under MATLAB software to yield the optimum PV, wind, and battery ratings. The results verified that PV-wind hybrid systems feature lower system cost compared to the cases where either PV-alone or wind-alone systems are used.
An optimal sizing methodology based on an energy approach is described and applied to grid-connected photovoltaic systems taking into account the photovoltaic module technology and inclination, the inverter type and the location. A model describing the efficiency for m-Si, p-Si, a-Si and CIS is used. The method has been applied on various meteorological stations in Bulgaria and Corsica (France). The main parameter affecting the sizing is the inverter efficiency curve. The influence of the PV module technology seems less important except for amorphous photovoltaic modules for which special remarks have been made. The inclination on the PV system influences the performances particularly when the inverter is undersized compared to the PV peak power.
Compared to the traditional energy resources, photovoltaic (PV) system that uses the solar energy to produce electricity considered as one of renewable energies has a great potential and developing increasingly fast compared to its counterparts of renewable energies. Such systems can be either stand-alone or connected to utility grid. However, the disadvantage is that PV generation depended on weather conditions. Thus there is also a need for developing control techniques for three phase grid connected PV systems including a method for DC link voltage control that can stabilize the voltage at the inverter input. This paper presents detailed modeling of the grid-connected photovoltaic generation system components, in Simulink / MATLAB software‥ Simulation results presented here validate the component models and the chosen control schemes.
This paper presents a sizing method and different control strategies for the suitable energy management of a stand-alone hybrid system based on photovoltaic (PV) solar panels, hydrogen subsystem and battery. The battery and hydrogen subsystem, which is composed of fuel cell (FC), electrolyzer and hydrogen storage tank, act as energy storage and support system. In order to efficiently utilize the energy sources integrated in the hybrid system, an appropriate sizing is necessary. In this paper, a new sizing method based on Simulink Design Optimization (SDO) of MATLAB was used to perform a technical optimization of the hybrid system components. An analysis cost has been also performed, in that the configuration under study has been compared with those integrating only batteries and only hydrogen system. The dynamic model of the designed hybrid system is detailed in this paper. The models, implemented in MATLAB-Simulink environment, have been designed from commercially available components. Three control s
In this paper, a procedure for the probabilistic treatment of solar irradiance and wind speed data is reported as a method of evaluating, at a given site, the electric energy generated by both a photovoltaic system and a wind system. The aim of the proposed approach is twofold: first, to check if the real probability distribution functions (PDFs) of both clearness index and wind speed overlap with Hollands and Huget and Weibull PDFs, respectively; and then to find the parameters of these two distributions that best fit the real data. Further, using goodness-of-fit tests, these PDFs are compared with another set of very common PDFs, namely the Gordon and Reddy and Lognormal functions, respectively.
The results inform the design of a pre-processing stage for the input of an algorithm that probabilistically optimizes the design of hybrid solar wind power systems. In this paper, the validity of the proposed procedure was tested using long-term meteorological data from Acireale (Italy). Copyright
In this paper, a new battery/ultracapacitor hybrid energy storage system (HESS) is proposed for electric drive vehicles including electric, hybrid electric, and plug-in hybrid electric vehicles. Compared to the conventional HESS design, which uses a larger dc/dc converter to interface between the ultracapacitor and the battery/dc link to satisfy the real-time peak power demands, the proposed design uses a much smaller dc/dc converter working as a controlled energy pump to maintain the voltage of the ultracapacitor at a value higher than the battery voltage for the most city driving conditions. The battery will only provide power directly when the ultracapacitor voltage drops below the battery voltage. Therefore, a relatively constant load profile is created for the battery. In addition, the battery is not used to directly harvest energy from the regenerative braking; thus, the battery is isolated from frequent charges, which will increase the life of the battery. Simulation and experimental results are presented to verify the proposed system.
This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.
This paper presents an easy and accurate method of modeling photovoltaic arrays. The method is used to obtain the parameters of the array model using information from the datasheet. The photovoltaic array model can be simulated with any circuit simulator. The equations of the model are presented in details and the model is validated with experimental data. Finally, simulation examples are presented. This paper is useful for power electronics designers and researchers who need an effective and straightforward way to model and simulate photovoltaic arrays.
This paper describes a simulation model for analyzing the probability of power supply failure in hybrid photovoltaic–wind power generation systems incorporating a storage battery bank, and also analyzes the reliability of the systems. An analysis of the complementary characteristics of solar irradiance and wind power for Hong Kong is presented. The analysis of local weather data patterns shows that solar power and wind power can compensate well for one another, and can provide a good utilization factor for renewable energy applications. For the loss of power supply probability (LPSP) analysis, the calculation objective functions and restraints are set up for the design of hybrid systems and to assess their reliability. To demonstrate the use of the model and LPSP functions, a case study of hybrid solar–wind power supply for a telecommunication system is presented. For a hybrid system on the islands surrounding Hong Kong, a battery bank with an energy storage capacity of 3 days is suitable for ensuring the desired LPSP of 1%, and a LPSP of 0% can be achieved with a battery bank of 5 days storage capacity.
An economic evaluation of a hybrid wind/photovoltaic/fuel cell (FC) generation system for a typical home in the Pacific Northwest is performed. In this configuration the combination of a FC stack, an electrolyser, and hydrogen storage tanks is used as the energy storage system. This system is compared to a traditional hybrid energy system with battery storage. A computer program has been developed to size system components in order to match the load of the site in the most cost effective way. A cost of electricity, an overall system cost, and a break-even distance analysis are also calculated for each configuration. The study was performed using a graphical user interface programmed in MATLAB.
This paper presents a probabilistic approach based on the convolution technique to assess the long-term performance of a hybrid solar–wind power system (HSWPS) for both stand-alone and grid-linked applications. To estimate energy performance of HSWPS the reliability analysis is performed by the use of the energy index of reliability (EIR) directly related to energy expected not supplied (EENS). Analytical expressions are developed to obtain the power generated. The hybrid system and the load models employed enable the study period to range from one year to one particular hour-of-day, thus allowing the inclusion of the time-value of energy as appropriate in economic assessments. A numerical example application is included to illustrate the validity of the developed probabilistic model: the results are compared to those resulting from time domain simulations.
In life cycle assessment (LCA) of solar PV systems, energy pay back time (EPBT) is the commonly used indicator to justify its primary energy use. However, EPBT is a function of competing energy sources with which electricity from solar PV is compared, and amount of electricity generated from the solar PV system which varies with local irradiation and ambient conditions. Therefore, it is more appropriate to use site-specific EPBT for major decision-making in power generation planning. LCA and life cycle cost analysis are performed for a distributed 2.7 kWp grid-connected mono-crystalline solar PV system operating in Singapore. This paper presents various EPBT analyses of the solar PV system with reference to a fuel oil-fired steam turbine and their greenhouse gas (GHG) emissions and costs are also compared. The study reveals that GHG emission from electricity generation from the solar PV system is less than one-fourth that from an oil-fired steam turbine plant and one-half that from a gas-fired combined cycle plant. However, the cost of electricity is about five to seven times higher than that from the oil or gas fired power plant. The environmental uncertainties of the solar PV system are also critically reviewed and presented.
In this paper, several designs of hybrid PV-wind (photovoltaic-wind) systems connected to the electrical grid, including the intermittent production of hydrogen, are shown. The objective considered in the design is economical to maximise the net present value (NPV) of the system. A control strategy has been applied so that hydrogen is only produced by the electrolyser when there is an excess of electrical energy that cannot be exported to the grid (intermittent production of hydrogen). Several optimisation studies based on different scenarios have been carried out. After studying the results - for systems with which the produced hydrogen would be sold for external consumption - it can be stated that the selling price of hydrogen should be about 10Â [euro]/kg in areas with strong wind, in order to get economically viable systems. For the hydrogen-producing systems in which hydrogen is produced when there is an excess of electricity and then stored and later used in a fuel cell to produce electricity to be sold to the grid, even in areas with high wind speed rate, the price of electrical energy produced by the fuel cell should be very high for the system to be profitable.
Optimal sizing of a hydrogen-based wind/PV plant considering reliability indices. Electric Power and Energy Conversion Systems
- S Dehghan
Dehghan, S., et al., 2018. Optimal sizing of a hydrogen-based wind/PV plant considering
reliability indices. Electric Power and Energy Conversion Systems, 2009. EPECS'09.
International Conference On. 2009. IEEE.
Developments., l.E. 2016; Available from: http://www.lowenergydevelopments.com.au/
Solar-Panel-250W-Monoycrystalline.
He is currently the Associate Dean Academic for the School of Engineering at Edith Cowan University. Since 1998 he has been actively involved as a researcher and lecturer in the areas of embedded systems
- Dr
- Alexander
Dr. Alexander Rassau received a Bachelor of Science (Cybernetics and Control
Engineering) and a PhD from the University of Reading in the United Kingdom in 1997
and 2000 respectively. He is currently the Associate Dean Academic for the School of
Engineering at Edith Cowan University. Since 1998 he has been actively involved as a
researcher and lecturer in the areas of embedded systems, intelligent control, automation
and robotics.
- J Abushnaf
J. Abushnaf, A. Rassau
The Electricity Journal 31 (2018) 58-66