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Energy management system (EMS) and control for the proposed MG. WNN: wavelet neural network; ISA: interior search algorithm.
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Recently, significant development has occurred in the field of microgrid and renewable energy systems (RESs). Integrating microgrids and renewable energy sources facilitates a sustainable energy future. This paper proposes a control algorithm and an optimal energy management system (EMS) for a grid-connected microgrid to minimize its operating cost...
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Citations
... In [3], the authors explored the evolution of the microgrid and energy management system and also reviewed the existing technologies and challenges faced in microgrids and energy management systems. In [4], an economic analysis of a gridconnected microgrid has been proposed using 24-h ahead forecast data to minimize the operating cost. However, another significant challenge that microgrids face is the potential loss of data in the communication channels. ...
This study presents a multi-layered microgrid system with an optimization-based energy management system, where the impact of renewable energy penetration and data loss in battery command is investigated. Data loss in battery command can cause voltage instability, energy supply loss, and increased operational costs in microgrid systems, especially in electricity markets. The simulation results show that on average, more data loss results in higher operational costs, but there are situations where less data loss can be more detrimental to microgrid operation than higher levels of data loss. This research provides valuable insights into the effects of data loss in battery command and its potential economic impact on microgrid operation.
... Furthermore, the economic analysis of RESs also considers the potential savings from reducing GHG emissions and avoiding the adverse effects of climate change [7], [8]. By transitioning to cleaner energy sources, societies can mitigate the economic costs associated with environmental damages, public health impacts, and the need for costly adaptation measures [9]. ...
The economic analysis of the contribution of Renewable Energy Sources (RESs) to the decarbonization of power systems involves evaluating the financial implications and overall benefits of transitioning towards cleaner energy sources. As traditional power systems heavily rely on fossil fuels, which contribute to Greenhouse Gas (GHG) emissions, the integration of RESs plays a crucial role in reducing carbon footprints and addressing Climate Change (CC). Economic analysis assesses the costs and benefits associated with the adoption of RESs in power systems. It involves considering factors such as the initial investment required for deploying renewable energy technologies, Operational and Maintenance (O&M) costs, potential revenue generation, and the long-term environmental benefits. This analysis enables policymakers, investors, and stakeholders to evaluate the financial viability of transitioning to cleaner energy systems
... At present, researchers have done a lot of research about grid-connected MGs and renewable energy systems [59,60]. The combination of MG and renewable energy contributes to sustainable energy development [61]. Barani et al. [62] assessed the impact of ICT integration into the system and the impact of non-dispatchable renewable energy by examining the reliability of grid-connected MGs. ...
Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids.
... Nowadays, renewable energy sources (RESs) are increasingly being integrated into utility power systems. RESs have the advantages of being clean, infinite, and inexpensive energy sources [1,2]. However, RESs can cause some issues and challenges for utility grids. ...
Since modern power systems are susceptible to undesirable frequency oscillations caused by uncertainties in renewable energy sources (RESs) and loads, load frequency control (LFC) has a crucial role to get these systems’ frequency stability back. However, existing LFC techniques may not be sufficient to confront the key challenge arising from the low-inertia issue, which is due to the integration of high-penetration RESs. Therefore, to address this issue, this study proposes an optimized intelligent fractional-order integral (iFOI) controller for the LFC of a two-area interconnected modern power system with the implementation of virtual inertia control (VIC). Here, the proposed iFOI controller is optimally designed using an efficient metaheuristic optimization technique, called the gray wolf optimization (GWO) algorithm, which provides minimum values for system frequency deviations and tie-line power deviation. Moreover, the effectiveness of the proposed optimal iFOI controller is confirmed by contrasting its performance with other control techniques utilized in the literature, such as the integral controller and FOI controller, which are also designed in this study, under load/RES fluctuations. Compared to these control techniques from the literature for several scenarios, the simulation results produced by the MATLAB software have demonstrated the efficacy and resilience of the proposed optimal iFOI controller based on the GWO. Additionally, the effectiveness of the proposed controller design in regulating the frequency of interconnected modern power systems with the application of VIC is confirmed.
... Todavia, observa-se que a obtenção do equilibrio de carga, e da compensação de crédito (na conta de consumo de energia do prosumidor) tem um tempo médio de retorno a mediano e curto prazo.Notando-se que, em paises em desenvolvimento e emergentes há ainda, poucos incentivos e políticas públicas de implementação em massa de MG via FGDs, nos clientes de BT. Tornado-se uma solução interessante, porém ainda inviável de aplicação em massa, na redução do desbalanceamento de carga na rede secundária, El-Hendawi et al. (2018). ...
The load imbalance in the low-voltage grid phases compromises the load stability also power quality provided to the final consumer. In this, sense, the phase-load-balance of grid by dynamic switching, is an efficient way for this problem. However, is important to establish a load stability for a long-lasting. This article proposes a phase balance local controller model based on combined algorithms, that considers beyond the current state of monthly load consumption (of final consumers) its twelve-step forward forecast. Providing, an optimal switching selection based on, the load consumption future state in the final consumer’s phases. The model is developed in Hierarchical- Petri nets. The results point to an efficient attenuation of phase imbalance, applied to three kinds of consumers (single-phase, bi-phase, and three-phase). Therefore, ensuring in low- voltage circuits, the load balance for long-duration.
... The autoregressive or machine learning model is used to predict the weather conditions by utilizing past values. A Wavelet Neural Network (WNN) was developed for the prediction of load demand day-ahead to reduce the Prosumer Microgrids' operating expenses [10]. The day-ahead operation of the microgrid was predicted using Reinforcement Learning in [11]. ...
Prosumer microgrids are playing a significant role in the electrical grid system. In the recent decade the injection of renewable energy sources is rapidly increasing. Whereas the output power of photovoltaic sources can be influenced by the unpredictable behaviour of weather factors like solar irradiance and ambient temperature. Under such bad weather conditions, the prosumer fulfils their energy gap from the grid system, which causes an extra compulsion to the utility, concerning the grid instability. Also, by using the energy of peak period, consumers have to bear an additional cost. The underlying study identifies the mitigation method and proposed a Smart Battery scheduling system based on weather forecasting data for achieving an affordable and reliable energy supply. The battery scheduling system collects weather information from the weather station through HTTP protocol using thingspeak server. The information gathered can be used to schedule battery charges and establish a demand response scheme. The obtained test result illustrates that the suggested system's forecasting mechanism may significantly reduce grid dependence.
... The main characteristic of μG is its functioning either in a grid-tied mode or in an islanded mode (Marnay et al., 2008;Li et al., 2019;. Based on the IoT system, the evolving grid is well controlled and has the main capability of μG such as self-healing and remote monitoring and its controlling techniques (Medina et al., 2010;El Rahi and El Rahi, 2017;El-hendawi et al., 2018). Similarly, μG offers different possibilities for the sustainability and utilization of renewable energy to prosumer μGs through the implementation of energy management systems (EMSs). ...
Electrical energy is very necessary for human life in the modern era. The rising energy prices, depletion of fossil fuels, and instability of the grid is an alarming situation. So needs a smart solution to ensure the balance between pricing and saving natural resources. Some other issues like environmental change, limitations on installation of new transmission lines, reliability concerns, considering the expansion in distributed energy generation technologies, etc. are promising the implementation of distributed generation extensively. The in-tegration of two or more energy supplies in a power system is known as distributed energy resources system. In this paper, a university campus is taken as a case study to reduce the energy cost while considering the above-mentioned issues. The intelligent source-load-storage coordination scheme is proposed to utilize the available renewable energy resources with storage systems. The proposed linear model is solved in MATLAB using the exact method technique considering the economic parameters. The campus microgrid analysis was not addressed considering the internet-of-thing (IoT) based building, especially in the scenario of Pakistan. The results show the efficacy of the proposed model and can be implemented on the existing campus for Source-Load-Storage Coordination as an economical solution.
... El-Hendawi et al. investigated a real case study of an MG situated in Oshawa (Ontario, Canada) with variable load models using an interior search algorithm (ISA) for optimizing hour-by-hour scheduling of a day ahead power scheduling problem [104]. ISA was also used by Trivedi et al. to address the economic load dispatch and combined economic, emission dispatch problems of an MG's EMS [105], results illustrated that the ISA performed more effective in cost reduction when compared to ant colony optimization, cuckoo search algorithm. ...
Expeditious urbanization, population growth, and technological advancements in the past decade have significantly impacted the rise of energy demand across the world. Mitigation of environmental impacts and socio-economic benefits associated with the renewable energy systems advocate the higher integration of the distributed energy systems into the conventional electricity grids. However, the rise of renewable energy generation increases the intermittent and stochastic nature of the energy management problem significantly. Therefore, an optimal energy management technique is required to achieve a high level of system reliability and operational efficiency. A state-of-the-art systematic review of the different optimization techniques used to address the energy management problems in microgrids is presented in this article. The different optimization techniques used in energy management problems, particularly focusing on forecasting, demand management, economic dispatch, and unit commitment, are identified and critically analyzed in this review. The inferences from the review indicated that the mixed integer programming techniques were widely used, considering their simplicity and performance in solving the energy management problem in microgrids. The multi-agent-based techniques and meta-heuristics algorithms outperformed the other conventional techniques in terms of the efficiency of the system due to the decentralized nature of the EMS problem in microgrids and the capability of these techniques to act effectively in such scenarios. In addition, it was also evident that the use of advanced optimization techniques was limited in the scope of forecasting and demand management. Advocating the need for more accurate scheduling and forecasting algorithms to address the energy management problem in microgrids. Finally, the need for an end-to-end energy management solution for a microgrid system and a transactive/collaborative energy sharing functionality in a community microgrid is presented.
... The smart and efficient grid offers diverse possibilities for renewable energy implementation for prosumer µGs by integrating energy management (EMSs) systems. Various kinds of energy management systems need secure interaction between prosumer and conventional grid to operate the control devices intelligently [6][7][8]. However, the distribution network includes a group of µGs wherein every µG acts as a self-governing distribution node, consequently, µGs consist of onsite DGs, storage systems, and DR programs which may play a significant role in minimizing network overloading and electricity cost. ...
Current energy systems face multiple problems related to inflation in energy prices, reduction
of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy
consumers and the affordability of power for large commercial customers. These kinds of problems
can be alleviated with the help of optimal planning of demand response policies and with distributed
generators in the distribution system. The objective of this article is to give a strategic proposition
of an energy management system for a campus microgrid (�G) to minimize the operating costs
and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a
real-time based campus is considered that currently takes provision of its loads from the utility grid
only. According to the proposed given scenario, it will contain solar panels and a wind turbine as
non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates
an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise
from solar radiation. The resultant problem of linear mathematics was simulated and plotted in
MATLAB with mixed-integer linear programming. Simulation results show that the proposed given
model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which
is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an
optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized
solar PV installation on carbon emissions and energy consumption costs was also determined. The
substantial environmental and economic benefits compared to the present situation have prompted
the campus owners to invest in the DGs and to install large-scale energy storage.
... The reference power, which is obtained by the supervisory controller, is regulated by a PI controller. A second PI controller is used to control the current of the battery to protect the battery [12]. ...
With the impact of increasing load, Renewable power generation and dc loads
are shooting up day by day. Medium Voltage Direct Current (MVDC) concept is a common
platform for integrating the renewables to serve the dc-based and constant power loads at the
distribution level. Moreover, higher reliability and availability of the renewable power supply
can be achieved by the Energy Storage System (ESS). In this paper, the comparative
performance of the MVDC distribution network integrated with ESS is evaluated for wind
energy. A simulation model of hybrid configuration merging both MVDC and ESS for wind
energy have been modeled, simulated, and compared. To get a bidirectional power flow, the
ESS is connected to a Bi-directional DC-DC converter. Voltage source converters are used
for AC to DC and DC to AC conversions.ADCcablehasbeenusedtoconnect voltage source
converters. The control strategies for all converters and a supervisory control scheme have
been implemented. Simulation results show the capability of the hybrid systems to meet the
dynamic load demand regardless of the renewable powergeneration.
