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With the deterioration of the environment and the depletion of fossil fuel energy, renewable energy has attracted worldwide attention because of its continuous availability from nature. Despite this continuous availability, the uncertainty of intermittent power is a problem for grid dispatching. This paper reports on a study of the scheduling and o...
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Providing of energy is one of the most important issues for each country. Also, environmental issues due to fossil fuel depletion are other serious concern of them. In this regard, moving toward energy sustainability is a constructive solution for each country. This paper studies the short-term planning of generating units in renewable energy-based...
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... The charging cost of the EVs Deep reinforcement learning [9] EVs and grid The charging cost of the EVs. Linear programming algorithm [10] PV, BESS, and grid Total cost Multi-agent PSO [11] Wind, BESS, and grid Wind curtailment rate and profit NSGA-II and VIKOR [12] Wind, EVs, and thermal power units Rate of change of load, the total cost of generating electricity, and the wind curtailment rate Modified PSO algorithm [13] PV, EVs, and grid Total revenue Stackelberg and GA [14] EVs and wind Total revenue A game theory [15] EVs and wind Total revenue Linear programming algorithm [16] PV, EVs and grid Total cost Robust chaotic optimization algorithm [17] BESS and grid Total cost of the BESS PSO-based frequency control [18] PV, EVs, BESS, and grid Total revenue Linear programming algorithm [19] PV, EVs, and grid Electricity purchasing cost Linear programming algorithm [20] PV, BESS, EVs, and grid Total cost Deep learning algorithm [21] EVs, and grid The charging cost of the EVs and the busbar voltage deviation NSGA-II Ours PV, BESS, EVs, and grid ...
... The grid can also provide energy to the charging stations and the PV system can deliver energy to the grid. The constraints of the PV system and the BESS can be found in Equations (9)- (16). The differences between the above three cases are shown in Table 5. MOMUS can be used to calculate all three cases mentioned above. ...
... Energies 2023, 16,5216 ...
Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.
... The improvement of robustness can make the system maintain its balance when it is disturbed, thereby reducing the loss caused by disturbances [8]. Meanwhile, some other power consumption units are introduced, such as electric vehicles (EVs) and fuel cells, on the basis of wind-photovoltaic-storage microgrid architecture, which can ensure the system maintains a stable operation by interacting power with other components when it is impacted [9,10]. In terms of the objective function, some microgrid systems, including fossil fuels, need to be considered regarding carbon emissions to reduce the environmental pollution caused by them [11]. ...
... EV Robustness Algorithm Pollution Market Remark [6] √ √ ANN-based scheduling control approaches [7] √ √ Proposes a robust model predictive control approach [9] √ √ √ Addresses the uncertainty of PV output and EV charging [10] √ Solves the sub-problems with fitted Q-iteration [11] √ √ √ Uses improved algorithm to mine magnesium energy [12] √ Introduces a non-cooperative framework [17] √ Improvements and comparisons of algorithms [18] √ √ √ Uses ASAPSO algorithm in multi-objective optimization [19] √ √ Plans two-stage form of multi-energy supply optimization This paper √ √ √ Improves MA algorithm and designs a scheduling model Through the above table, it can be seen that the robustness and stability of the system are studied by some scholars. In these studies, robust model predictive control (RMPC) and other methods are used to deal with the uncertainties of renewable energy. ...
The effectiveness of energy management systems is a great concern for wind–photovoltaic-storage electric vehicle systems, which coordinate operation optimization and flexible scheduling with the power grid. In order to save system operation cost and reduce the energy waste caused by wind and light abandonment, a time-sharing scheduling strategy based on the state of charge (SOC) and flexible equipment is proposed, and a quantum mayfly algorithm (QMA) is innovatively designed to implement the strategy. Firstly, a scheduling strategy is produced according to the SOC of the battery and electric vehicle (EV), as well as the output power of wind–photovoltaic generation. In addition, the minimum objective function of the comprehensive operation cost is established by considering the cost of each unit’s operation and electricity market sale price. Secondly, QMA is creatively developed, including its optimization rule, whose performance evaluation is further carried out by comparisons with other typical bionics algorithms. The advantages of QMA in solving the low-power multivariable functions established in this paper are verified in the optimization results. Finally, using the empirical value of the power generation and loads collected in enterprise as the initial data, the mayfly algorithm (MA) and QMA are executed in MATLAB to solve the objective function. The scheduling results show that the time-sharing scheduling strategy can reduce the system’s cost by 60%, and the method decreases energy waste compared with ordinary scheduling methods, especially when using QMA to solve the function
... Carbon trading mechanisms have provided economic incentives for market players to reduce carbon emissions and promote the utilisation of renewable energies and electric vehicles [15]. The introduction of carbon trading mechanisms no longer defines carbon as emission costs, but additional economic gains through carbon trading [16]. Regulation has been deployed to encourage the power industry to transform its energy structure and to improve technological innovation, achieving an environmentally economic operation model. ...
With the dynamic development of renewable energies, energy storage devices, and electric vehicles, microgrids have been playing an increasingly vital role in smart power grids. Under the recent development of carbon neutralisation, microgrid systems containing multiple clean energy sources have become significant modules for energy conservation and emission reduction. Considering technological and environmental elements, we investigated the economic operation of microgrids with the integration of electric vehicles. In this paper, carbon trading mechanisms and operation scheduling strategies are analysed in the simulation models. Then, transaction costs and power balance are discussed. Industrial applications and policy implications are also presented.
... They have minimized the total costs of energy consumption by reducing the power supplied from the grid. A robust optimization has been described in [21] and compared with stochastic optimization to minimize the economic and environmental costs of a microgrid, which integrates PV and EVs. They have proposed a mathematical model to study the uncertainty of EV charging behavior and PV power. ...
Electric vehicles (EVs) are expanding quickly and widely, and, therefore, EVs can participate in reducing direct greenhouse gas emissions. The intelligent infrastructure for recharging EVs, which is microgrid-based, includes photovoltaic (PV) sources, stationary storage, and a grid connection as power sources. In this article, the energy cost optimization problem is studied, taking into account the intermittent arrival and departure of EVs. A mixed-integer linear programming is formulated as an optimization problem in a real-time operation to minimize the total energy cost, taking into consideration the physical limitations of the system. The interaction with the human-machine interface provides EV data in real-time operation, and the prediction only communicates the PV prediction profile provided by the national meteorological institute in France. The optimization is executed at each EV arrival, with the actualized data in the DC microgrid. Simulation and real-time experimental results of different meteorological conditions show that the EV user demands are satisfied, proving the feasibility of the proposed optimization problem for real-time power management.
... Considering EV stochastic behavior, predictive analysis could be useful for charging planning [17,22]. Nevertheless, the need for information to construct forecast data could be a limitation for new infrastructure investment. ...
... Rule-based (RB) dispatch. Input: TimeSteps(NTS), EV load(EV), PV power(PV), Tariff(WT), EVCS self load(EVCS l ) Output: Operation log with the dispatch balance 1 for i ← 0 to NTS do 2 Obtain all the variable values for this TS; it supplies EVCS l and if there is still PV Excess it goes to grid; supplies the EVCS l and if there is still PV Excess it goes to grid; PV = EV and PV = 0 then 18 PV serves EV and the grid serves EVCS l ; 19 else 20 All PV serves remmaining EV; 21if WT on higher rates then22 BESS serves EV;23if There is lack of energy then24 Grid serves rest of EV and EVCS l ; Excess goes to EVCS l and if still there is PV Excess it goes to grid; Real and forecast PV production. ...
This paper proposes a flexible framework for scheduling and real time operation of electric vehicle charging stations (EVCS). The methodology applies a multi-objective evolutionary particle swarm optimization algorithm (EPSO) for electric vehicles (EVs) scheduling based on a day-ahead scenario. Then, real time operation is managed based on a rule-based (RB) approach. Two types of consumer were considered: EV owners with a day-ahead request for charging (scheduled consumers, SCh) and non-scheduling users (NSCh). EPSO has two main objectives: cost reduction and reduce overloading for high demand in grid. The EVCS has support by photovoltaic generation (PV), battery energy storage systems (BESS), and the distribution grid. The method allows the selection between three types of charging, distributing it according to EV demand. The model estimates SC remaining state of charge (SoC) for arriving to EVCS and then adjusts the actual difference by the RB. The results showed a profit for EVCS by the proposed technique. The proposed EPSO and RB have a fast solution to the problem that allows practical implementation.
... In addition, conversion devices consume renewable energy during periods of low electricity consumption, or they can even discharge electricity to the grid while the needs are high. In those cases, conversion devices normally use additional non-renewable energy (for instance, any fuels) [25,26]. ...
Following a new climate and energy plan, the European Union (EU) gives big attention to energy savings. The overall assessment of energy saving measures is very important. Thus, it is crucial to estimate in a proper way the primary energy factor, which is used in calculations of primary energy consumption from renewable energy (RE) sources in a Nearly Zero Energy Building (NZEB). The conduced studies of the literature and national regulations showed that different methods to determine energy from photovoltaic (PV) systems are used. The aim of this paper is to evaluate the primary energy factors of energy from photovoltaics and determine the average value. To achieve this aim, the data of 30 photovoltaic systems from Lithuania were analyzed. The results show a 35% diversification in the values of non-renewable primary energy factor, depending on the PV systems’ capacities, with the average on a level of 1.038.
The integrated energy station of new energy vehicle hydrogenation/charging/power exchange is proposed, which also includes hydrogen production, hydrogen storage, electricity sales to users and the grid (WPIES). To address the efficiency of renewable energy use, this paper proposes a future value competition strategy for wind and photovoltaic (PV) allocation based on goal optimization (FVCS). In order to better realize the distribution of wind power/PV in the integrated energy station and improve the energy utilization efficiency of the integrated energy station, a two-layer optimization model of FVCS-WPIES is proposed, in which the upper layer model aims to maximize the expected income. The goals of the lower-level model are to maximize total profit, minimize battery losses, and minimize pollutant emissions. The model also considers the hydrogen power constraint and the upper-level model penalty. The comparison results show that the Pareto solution set is superior to the traditional model.
The growing number of electric vehicles (EVs) has resulted in increasing availability of battery storage capacities. The energy storage capacity of EVs is used to provide demand flexibility for the supply side. However, the different preferences of EV users will affect the charge and discharge decision of EVs. To overcome this problem, the concept of charging and discharging pressure is proposed to restrict the charging and discharging behavior of EVs. It is mainly dominated by the electricity price. Simultaneously, the charging and discharging time anxiety and state of charge (SoC) of EVs also affect the charging and discharging mode of EVs. This paper proposes a novel industrial microgrid (IMG) structure, which is mainly composed of power demand of industrial production, renewable energy sources (RES), energy storage systems (ESS), EVs and thermal power generation units. The aim of the proposed model is to minimize the operation cost of IMG and maximize the income of EV users. For the management of demand side, the strategy of time of use (ToU) price is adopted. In addition, considering the uncertainty of RES and industrial load, a robust optimization algorithm is proposed, and the operation of IMG under different uncertain scenarios is analyzed. Finally, the robust mixed integer quadratic programming (MIQP) of IMG is studied. The detailed simulation and comparison results verify the effectiveness of the proposed energy system under different charging and discharging pressures based on EVs.
This chapter presents a thorough comparative analysis of the state-of-the-art of addressing all the technical as well as non-technical problems relevant to distribution system utility. The distribution system is an integral part of a power system that distributes electric power to an end user at a low or medium voltage level. A comprehensive survey of the literature available in this field reveals various technical, economical, and environmental problems in the distribution systems. The effective operation of an active distribution network requires regulation and control, load forecast, its analysis and execution of adaptive technologies for resilient system operation, grid security monitoring and parameter optimization monitoring, etc. All these operations require advanced data management methods like energy management systems, SCADA, etc. It is also essential to improve performance and quality of power delivery by a distribution network to consumers.
Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system’s stability, which is the key benefit of this research. The system’s power transmission efficiency and reliability can be greatly enhanced by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC’s are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm.
