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Salkuti, S.R. Energy Management System in Microgrids. Encyclopedia. Available online: (accessed on 17 June 2024).
Salkuti SR. Energy Management System in Microgrids. Encyclopedia. Available at: Accessed June 17, 2024.
Salkuti, Surender Reddy. "Energy Management System in Microgrids" Encyclopedia, (accessed June 17, 2024).
Salkuti, S.R. (2021, September 22). Energy Management System in Microgrids. In Encyclopedia.
Salkuti, Surender Reddy. "Energy Management System in Microgrids." Encyclopedia. Web. 22 September, 2021.
Energy Management System in Microgrids

This entry gives a brief introduction to microgrids, their operations, and further, a review of different energy management approaches. In a microgrid control strategy, an energy management system (EMS) is the key component to maintain the balance between energy resources (CG, DG, ESS, and EVs) and loads available while contributing the profit to utility. This article classifies the methodologies used for EMS based on the structure, control, and technique used.

renewable energy sources microgrid energy management system communication technologies microgrid standards

1. Introduction

Over the last few decades, with an increasing population, the world has gone through an exponential consumption of energy which has led to the depletion of conventional resources like coal, crude oil, and natural gas. The exploitation of these resources has a severe impact on the environment with an increase in greenhouse gases [1][2]. To mitigate these effects, a policy has been adopted by different countries to introduce non-conventional/renewable sources to support the fields of electrification and transportation. In electrification, the existing power grid uses conventional sources for generation and lacks power quality. The poor power quality of supply leads to load shedding and blackouts, thereby interrupting the day-to-day activities of the consumers. The conventional grid uses one-third of the total generation fuel to convert into electricity and, with an eight percent loss in transmission lines of the generated electricity, is used to meet the peak demand that also has a five percent probability of occurring, with reduced reliability [3]. Conventional generation does not utilize the heat produced by itself for any application. These drawbacks of the conventional grid could be compensated with penetration of renewable sources at local areas or distributed generation (DG) there by reducing the transmission losses and maximum utilization of the output including heat generated [4][5][6]. Integration of dispatchable energy sources like wind and PV introduces the problem of intermittent power generation as they generally depend on climatic and meteorological conditions. A hybrid energy system consisting of storage elements and renewable energy sources is used for the continuous supply of power. The future power grid needs to be intelligent to maintain a reliable supply of economical and sustainable power for consumers [7][8][9][10]. To overcome the existing challenges in the grid, a smart grid needs to be adopted which controls the complex process of power exchange and plans as well for the growing energy demand. The future grid requires the support of communication technologies and local microgrids (MG) for efficient control of the system. The integration of renewable energy resources at the load side requires a two-way flow of power and data with the capability of adapting to management applications that can leverage the technology [11]. During a fault condition, the local microgrid isolates itself from the main grid, creating a standalone/islanding mode of supply to the consumers [12][13]. This feature is known as plug and play, which allows the local generation to meet the demand by balancing the energy available. The microgrid consists of a microgrid control center (MGCC) and local controllers (LCs) to balance the energy demand. The microgrid takes the inputs from forecasted parameters (weather, generation, and market prices) to meet the uncertain load demand and also participates in the energy market. The MGCC is supported by communication technologies and equipped with processing algorithms to overcome the challenges in the generation–demand balance [14][15][16][17]. The energy management in microgrids controls the power supply of storage elements, demand response, and local controllers/local generation sources. Figure 1 shows a typical structure of a microgrid.

Figure 1. Structure of a typical microgrid.

The contributions of this paper are shown as below: This paper provides a brief introduction about the architecture of microgrids, different classifications in microgrids, components of a microgrid, communication technologies used, standards available for the implementation, and auxiliary services required. This paper provides a review of the recent analysis of the different energy management strategies consisting of classical, heuristic, and intelligent algorithms. The article analyzes each approach and its applications in that methodology. The paper addressed applications in energy management which include forecasting, demand response, data handling, and the control structure. This article provides insight on areas in which the scope of research and their contribution to energy management is in the nascent stage.

The energy management strategies proposed for the microgrid in the paper are structured into six sections. Section 1 is the introduction to microgrids and energy management. Section 2 provides a brief overview of microgrid elements, architecture, classification, and communication. Section 3 gives an overview of different control structures in energy management. Section 4 provides reviews on different numerical algorithms used in energy management strategies in microgrids based on the classification, control, and methods of approach. Remarks on each paper for different controls of the EMS application are given. Section 5 discusses the support infrastructure of microgrids for their efficient operation. Section 6 provides the conclusion of the paper.

2. Overview of Microgrid

A microgrid is a small or medium distribution system comprised of smart infrastructure capable of maintaining equilibrium in demand–supply while providing security, autonomy, reliability, and resilience. Sourced distributed generations (DGs) like photovoltaics (PV), wind turbines (WT), microturbine (MT), fuel cells (FC), and energy storage units (ESU) are expected to deliver electricity without interference from the main grid. This high penetration of DGs can cause challenges in the performance of power system stability in large areas. To minimize the risks, the concept of microgrids is proposed [18][19]. A microgrid is a small-scale low- or medium-level voltage distribution system consisting of distributed energy resources (DERs), intermittent storage, communication, protection, and control units that operate in coordination with each other to supply reliable electricity to end-users [20].

Conventional generation (CG), such as coal-based thermal power plants, hydro power plants, wind-generation farms, and large-scale solar and nuclear power plants, are centralized to supply electricity for long distances. A decentralized generation is energy generated by the end-users by using small-scale energy resources [21][22]. Local generation when compared with the conventional power system reduces the transmission losses and the cost associated with it. The generation could be from 1 kW to a few 100 MW; the generation units are mostly used to support the peak load of the demand. Distributed generation sources consist of both renewable and non-renewable sources, i.e., wind generators, PV panels, small hydro power plants, and diesel generators [23]. Combined heat and power (CHP) is where heating is added along with electricity in the application. The sources that are being used in CHP systems are Stirling engines, internal combustion engines, and micro-turbines (MT) using biogas, hydrogen, and natural gas [24]. CHP technology stores excess allowing optimum performance, thereby attaining efficiency of more than 80%, to that of about 35% for centralized power plants [25]. Table 1 shows characteristics of distributed generation sources.

Table 1. Characteristics of distributed generation sources.
Characteristics Solar Wind Micro-Hydro Diesel CHP
Availability Location-Based Location-Based Location-Based Anywhere Source-Based
Carbon emission Nil Nil Nil High Source-Based
Interface Converter Converter + IG/SG IG/SG Generator Generator
Flow control MPPT/DC Voltage MPPT/Torque and Pitch Controllable Controllable AVR and Governor

Energy storage is a device that is capable of converting the electrical energy to a storable form and converting it back to electricity when it is needed. Based on the form of stored energy, there are four main categories for energy storage technologies: mechanical energy storage (MES), thermal energy storage (TES), chemical energy storage (CES), and electrical energy storage (EES). The key components for the working of MG EMS are the energy storage units, which regulate the supply–demand balance during the operation of DGs. In [26][27][28], a conclusion is drawn that a system with several micro sources is modeled to support an island mode where storage systems are needed to maintain the balance of the intermittent sources. The energy storage devices that are included in microgrid systems that provide continuous power supply are batteries, flywheels, and supercapacitors [29]. In terms of the current economy, batteries are less expensive and have a high negative environmental effect compared to other storage devices. Storage in fuel cells is also another option that converts the fuel into electricity through a chemical process. These fuel cells require oxygen and hydrogen for continuous supply without discharge. A variety of fuels available for the fuel cell are propane, natural gas, anaerobic digester gas, methanol, and diesel hydrogen [30], while hydrogen has become prominent in recent years for its clean and safe operation. Table 2 shows commonly used energy storage and their characteristics.

Table 2. Different energy storage systems in microgrids.
Characteristics Charge/Discharge Rate (MW) Discharge Duration Response Time Energy Density (Wh/kg) Power Density (W/kg) Environmental Impact Service (Years) Efficiency (%)
Battery 0–40 msec–hours msec 10–250 70–300 High 5 70–90
Flywheel 0.001–0.005 msec–1 h msec 0.005–5 500–10,000 Low 20 75–95
Supercapacitor 0.002–0.25 msec–15 min instantaneous 5–130 400–1500 Low >10 90–95
Fuel Cell 0.001–50 sec-day+ m sec 800–10,000 500–1000 Moderate >15 20–90
CES 0.1–300 Hour–day+ min 3–60 - Low 15 40–90
SMES 0.1–10 msec–10 sec instantaneous 0.5–5 500–2000 Low 10 >95
Pumped storage 0.1–5000 Hour–day+ Sec–min 0.5–1.5 - Low 25 >85

Loads can be categorized as residential, commercial, industrial, and others (agriculture and public offices) from the statistical data of feeder consumption in the distribution system. Measurement-based and component-based approaches are considered for load model identification [31]. The measurement-based approach needs the measured data from the smart meters or measuring devices which derives into load model structure. The capturing of data for load characteristics needs to be composed of different environmental conditions. The data obtained from the smart devices are used to form the load model structure as static, ZIP (constant impedance-resistive components or heating, constant current-street lighting, and constant power motors), and exponential [32][33]. Then, the structure is estimated and validated with field measurements by correcting the errors using intelligent detection techniques (artificial intelligence and pattern detection). The component-based approach aggregates the load model by combining the load consumption of individual components, acquired by the information or rating of each load in the load composition. This approach needs three different datasets: (i) individual component load model, (ii) percentage of each component’s load consumption, and (iii) share of the load contribution from each load class—residential, commercial, and industrial. The individual component model parameters are obtained from experiments [34][35][36][37]. Figure 2 has shown different loads classification is based on identification and control.

Figure 2. Loads classification is based on identification and control.

3. Energy Management System Control Structure

According to the International Electro-Technical Commission (IEC) standard application program about power systems, IEC-61,970 defines an energy management system as a “computer system comprising a software platform providing basic support services and a set of applications providing the functionality needed for the effective operation of electrical generation and transmission facilities to assure adequate security of energy supply at minimum cost” [38].

Energy management in microgrids is a complex automated system that is aimed at optimal scheduling of available resources (CG, DGs, ESS) to meet the day-to-day demand while considering the meteorological data and market price. There are three control approaches in energy management of the microgrid which are: (i) centralized, (ii) decentralized, and (iii) distributed.

With an increase in the geographical area, the system control in centralized mode becomes difficult due to the delay or lag in the communication, which leads to delay control. This process is not feasible as well as not economical; hence, we choose the decentralized mode of control. In decentralized control, each unit has its own local controller that works in an autonomous state where it receives the voltage and frequency data [39]. Here, the decentralized control does not provide the all the information to the other local controllers, but rather exchanges the global information to make the decisions of the overall system. The exchange of information is allowed in a few controllers to take action spontaneously in a state of emergency. A third approach, obtained with a combination of the above two control approaches, is the distributed control [40]. This mode of control scheme provides control to both centralized as well to decentralized property up to a certain degree of control. In this control scheme, each local controller unit uses the local information like voltage and frequency from the neighbors, which helps to obtain a global solution by the central controller while using the two-way communication link by the local controllers. Characteristics of different types of controls in the energy management system are presented in Table 3 .

Table 3. Characteristics of different types of controls in the energy management system.
  Centralized Decentralized Distributed
Information Accessed Microgrids pass information to the central controller Independent control is provided with data from the other local controllers Interoperability and data exchange between every device
Communication Information Synchronized information from the device to the central controller Information among local controllers is asynchronized Communication is both locally and globally asynchronized
Function in real-time Complex Acceptable Easy
Feature of Plug and play The central controller needs to be instructed Can be accessed by central controller Available by the peers
Expenditure More Less Less
Structure of Grid Centrally controlled Locally controlled Both centrally and locally controlled
Tolerance during fault Less tolerance capability One router fault—tolerated
N router fault—expensive
N router fault—tolerated,
Possible self-healing feature
Infrastructure Needs suggestion integrating DERs Integration is modular and possible No change while integration
Size (Number of nodes) Less IPv4-212
Final Nodes No identification Unique identification IP Global unique identifier
Operation Flexibility Very less Available Very much needed
Bandwidth & Latencies Low and high Both are great High and low
QoS Not allowed Allowed Inherent
Connectivity EPA (Physical) TCP/IP (Physical) TCP/IP (Virtual)
Safety measures Less Available High
Individuality No No Possible

Load balance acts as a constraint between generation and demand. Load demand balance problems can be categorized in two ways: the supply-side and the demand-side [41]. Supply-side balance can be obtained by using the hierarchical control scheme for the economic scheduling for consumption by the end-users. Load control can be categorized as: (i) controllable loads, which are the loads that are managed according to the price, and (ii) shiftable loads, also known as deferrable loads, such as charging of electric vehicles, washing machines, dryers, which can provide scheduling flexibility for demand response.

4. Numerical Methodologies of EMS

Different EMS techniques are differentiated according to the numerical methods used for controlling the energy management system. These methods are broadly classified into three categories: (i) classical methods, (ii) metaheuristic methods, and (iii) intelligent methods.

Classical methods are the mathematical programming or classical programming methods that choose certain variables to maximize or minimize a given function subject to a given set of constraints. Branch and bound are the classic components that are used for solving the classical method approach to find the optimal solution in an iterative process without integer constraints. Classical methods use both linear and nonlinear optimization models to solve the problem. The classical methods are divided into certainty- and uncertainty-constrained problems.

An optimal solution can be found in the distinct search space as used in combinatorial optimization. Metaheuristic method is an iterative method that is unlikely to guarantee a global optimum solution due to its convergence properties. This can be compensated with finding the mean of the solutions; the use of Monte Carlo simulation improves the convergence of the solution. Stochastic implementation of optimization is dependent on the random variables created [42]. The metaheuristic approach works on two concepts, namely intensification and diversification. Intensification is searching a local area to find an optimal solution when we know that solution could be found in the prescribed region. The diversification process is searching the space on a global scale with no limits in the search pattern using the randomly generated variables, while randomization increases the diversity of solution when the search space exceeds the local optima. To find the global optimal or the best solution, both the intensification and diversification processes need to be in proper balance, which increases the rate of convergence in the algorithm [43][44][45][46][47]. A few metaheuristic algorithms are particle swarm optimization (PSO), genetic algorithm (GA), modified PSO (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II), enhanced velocity differential evolutionary PSO (EVDEPSO), priority PSO, multi-voxel pattern analysis (MVPA), grey wolf optimization (GWO), artificial bee colony (ABC), adaptive differential evaluation (ADE), crow search algorithm (CSA), rule-based bat optimization (BO), gravitational search algorithm (GSA), alternating direction method of multipliers (ADMM) using modified firefly algorithm (MFA), teaching–learning optimization (TLA), social spider algorithm (SSO), and whale optimization algorithm (WOA). Table 4 provides a critical review of the metaheuristic methods used in EMS.

Table 4. A review of metaheuristic methods used in EMS.
Ref No Method Power Sources Ev Dr Grid/Island Ems Remarks
[48] NSGA-II PV, WT, BT     G/I C A multi-objective optimization problem is proposed to maximize the economy. Intelligent power marketing is adapted to improve the economic dispatch of the microgrid.
[49] NSGA-II PV, WT, BT *   G/I C This paper establishes an integral objective function considering the demand response and user satisfaction constraints, which has an effect on the economy and operation of the system with the DR strategy.
[50] PSO PV, MT, BT, TES     G/I C An optimal energy planning is proposed for the recently modeled energy hub. An efficient microgrid structure is discussed along with technical and economic prospects with optimization.
[51] CVCPSO PV, WT, DE *   G/I C Minimizing the operating costs while maximizing the utility benefit using the CVCPSO algorithm, which yielded the Pareto-optimal set for each objective, and the fuzzy-clustering technique was adopted to find the best compromise solution.
[52] MPVA PV, WT, MT, BT     G/I C A sports metaheuristic algorithm to minimize the overall running cost of MG while studying four different MG scenarios.
[53] GWO PV, WT     G/I C A sine cosine optimizer is used to optimally participate in the trading of energy, i.e., selling or buying the power while bringing the capital cost of the microgrid.
[54] ABC PV, WT, DE, BT, FC *   G/I C An EMS application of the V2G economic dispatch problem is optimized in the MG while converting the multi-objective problem to a single objective using the judgment matrix methodology.
[55] EBC PV, WT, MT, BT     G/I C Different TOUs are evaluated to minimize MG operational costs and to analyze the efficiency of a typical distribution system, considering all relevant technical constraints.
[56] ADE DG, BT     G C An ADE-based optimization is proposed for the DC microgrid modeling the active power sources under real-time pricing to minimize the total operating cost.
[57] MOPSO PV, MT, BT, TES   * G/I C EMS application is proposed to reduce the carbon dioxide emissions and payback period of the microgrid structure.
[58] EVDEPSO PV, BT * * G/I C A day-ahead planning schedule is determined to improve the energy market trading while managing the resources available. Includes the electric vehicles participating in the energy market, G2V and V2G.
[59] Rule base BO PV, WT, MT, FC, BT   * G C A bat algorithm is used to optimize the MG operation by forecasting the load power and uncertainties in RES using probabilistic methods. The weight factors are taken for tuning.
[60] CSA PV, FC, DE, HY   * G/I C The Pareto front is considered to investigate the operating cost, solar power uncertainty, carbon emission, and the cost of the parameters. Hydrogen fuel is considered in reducing operating costs.
[61] GSA PV, WT, BT * * G/I C Optimization of the overall cost considering the carbon emission and weekly generation scheduling for the small dispatchable systems.
[62] ADMM-MFA PV, WT, MT, FC, BT   * G/I C EMS is modeled for the MG to optimize the electricity price by considering the load profile, PV irradiance, and market prices with certain constraints.
[63] TLA PV, WT, MT, FC * * G/I C Hybrid MG reducing the operating cost considering thermal power recovery and hydrogen generation; V2G technology helps to convert the PEVs into active storage.
[64] SSO PV, WT, DE, FC   * I C Optimal sizing of the renewable energy sources with conventional sources to minimize the cost of energy (COE) and power loss supply probability while analyzing the reliability.
[65] WOA PV, WT, DE, BT   * I C EMS is proposed to optimize the load demand of the MG by minimizing the operating cost with improved reliability of the power.

PV—Photo voltaic; WT—Wind Turbine; MT—Micro Turbine; TES—Thermal energy storage; DE—Diesel; FC—Fuel Cell; HY—Hydro; C—Centralized, DC—Decentralized, DT—Distributed, *—Availability.

Neuro-fuzzy is a combination of fuzzy approach and neural network, where fuzzy inference system (FIS) is adjusted by the data provided to NN learning rules. Improved speed, accuracy, and strong learning skills along with simple execution are the advantages of this approach [66].


  1. Hu, H.; Xie, N.; Fang, D.; Zhang, X. The role of renewable energy consumption and commercial services trade in carbon dioxide reduction: Evidence from 25 developing countries. Appl. Energy 2018, 211, 1229–1244.
  2. Hussain, A.; Bui, V.-H.; Kim, H.-M. Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience. Appl. Energy 2019, 240, 56–72.
  3. Farhangi, H. The path of the smart grid. IEEE Power Energy Mag. 2009, 8, 18–28.
  4. Qi, F.; Wen, F.; Liu, X.; Salam, A. A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles. Energies 2017, 10, 1159.
  5. Pascual, J.; Sanchis, P.; Marroyo, L. Implementation and Control of a Residential Electrothermal Microgrid Based on Renewable Energies, a Hybrid Storage System and Demand Side Management. Energies 2014, 7, 210–237.
  6. Cai, J. Optimal Building Thermal Load Scheduling for Simultaneous Participation in Energy and Frequency Regulation Markets. Energies 2021, 14, 1593.
  7. Hatziargyriou, N. Microgrids: Architectures and Control; Wiley: Hoboken, NJ, USA, 2014.
  8. Hossain, A.; Pota, H.R.; Issa, W. Overview of AC Microgrid Controls with Inverter-Interfaced Generations. Energies 2017, 10, 1300.
  9. Nejabatkhah, F.; Li, Y.W. Overview of Power Management Strategies of Hybrid AC/DC Microgrid. IEEE Trans. Power Electron. 2014, 30, 7072–7089.
  10. Patrao, I.; Figueres, E.; Garcerá, G.; González-Medina, R. Microgrid architectures for low voltage distributed generation. Renew. Sustain. Energy Rev. 2015, 43, 415–424.
  11. Pashajavid, E.; Shahnia, F.; Ghosh, A. A decentralized strategy to remedy the power deficiency in remote area microgrids. In Proceedings of the 50th International Universities Power Engineering Conference, Stoke on Trent, UK, 1–4 September 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 1–6.
  12. Hussain, N.; Nasir, M.; Vasquez, J.C.; Guerrero, J.M. Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review. Energies 2020, 13, 2149.
  13. Javaid, N.; Hafeez, G.; Iqbal, S.; Alrajeh, N.; Alabed, M.S.; Guizani, M. Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management. IEEE Access 2018, 6, 77077–77096.
  14. Bintoudi, A.; Zyglakis, L.; Tsolakis, A.; Gkaidatzis, P.; Tryferidis, A.; Ioannidis, D.; Tzovaras, D. OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration. Energies 2021, 14, 2752.
  15. Takano, H.; Goto, R.; Hayashi, R.; Asano, H. Optimization Method for Operation Schedule of Microgrids Considering Uncertainty in Available Data. Energies 2021, 14, 2487.
  16. Hussain, A.; Bui, V.-H.; Kim, H.-M. Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties. Energies 2016, 9, 278.
  17. Cha, H.-J.; Won, D.-J.; Kim, S.-H.; Chung, I.-Y.; Han, B.-M. Multi-Agent System-Based Microgrid Operation Strategy for Demand Response. Energies 2015, 8, 14272–14286.
  18. Helmi, A.M.; Carli, R.; Dotoli, M.; Ramadan, H.S. Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization. IEEE Trans. Autom. Sci. Eng. 2021, PP, 1–17.
  19. Muhammad, M.A.; Mokhlis, H.; Naidu, K.; Amin, A.; Franco, J.F.; Othman, M. Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm. J. Mod. Power Syst. Clean Energy 2020, 8, 86–93.
  20. Li, F.; Xie, K.; Yang, J. Optimization and Analysis of a Hybrid Energy Storage System in a Small-Scale Standalone Microgrid for Remote Area Power Supply (RAPS). Energies 2015, 8, 4802–4826.
  21. Prehoda, E.; Pearce, J.M.; Schelly, C. Policies to Overcome Barriers for Renewable Energy Distributed Generation: A Case Study of Utility Structure and Regulatory Regimes in Michigan. Energies 2019, 12, 674.
  22. Moradi, H.; Esfahanian, M.; Abtahi, A.; Zilouchian, A. Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties. Energies 2017, 10, 1430.
  23. Shayeghi, H.; Alilou, M. 3—Distributed generation and microgrids. In Hybrid Renewable Energy Systems and Microgrids; Kabalci, E., Ed.; Academic Press: Cambridge, MA, USA, 2021; pp. 73–102.
  24. Lidula, N.W.A.; Rajapakse, A. Microgrids research: A review of experimental microgrids and test systems. Renew. Sustain. Energy Rev. 2011, 15, 186–202.
  25. Mariam, L.; Basu, M.; Conlon, M.F. A Review of Existing Microgrid Architectures. J. Eng. 2013, 2013, 937614.
  26. Karki, S.; Kulkarni, M.; Mann, M.D.; Salehfar, H. Efficiency Improvements through Combined Heat and Power for On-site Distributed Generation Technologies. Cogener. Distrib. Gener. J. 2007, 22, 19–34.
  27. Li, J.; Wei, W.; Xiang, J. A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids. Energies 2012, 5, 5307–5323.
  28. Dinh, H.T.; Kim, D. An Optimal Energy-Saving Home Energy Management Supporting User Comfort and Electricity Selling With Different Prices. IEEE Access 2021, 9, 9235–9249.
  29. Lasseter, R. MicroGrids. In Proceedings of the IEEE Winter Meeting Power Engineering Society, New York, NY, USA, 27–31 January 2002; IEEE: Piscataway, NJ, USA, 2003; Volume 1, pp. 305–308.
  30. Li, Z.; Zheng, Z.; Xu, L.; Lu, X. A review of the applications of fuel cells in microgrids: Opportunities and challenges. BMC Energy 2019, 1, 8.
  31. Katiraei, F.; Iravani, R.; Hatziargyriou, N.; Dimeas, A. Microgrids management. IEEE Power Energy Mag. 2008, 6, 54–65.
  32. Ruiz, N.; Cobelo, I.; Oyarzabal, J. A Direct Load Control Model for Virtual Power Plant Management. IEEE Trans. Power Syst. 2009, 24, 959–966.
  33. Graditi, G.; Di Silvestre, M.L.; Gallea, R.; Sanseverino, E.R. Heuristic-Based Shiftable Loads Optimal Management in Smart Micro-Grids. IEEE Trans. Ind. Inform. 2014, 11, 271–280.
  34. Asimakopoulou, G.E.; Dimeas, A.L.; Hatziargyriou, N.D. Leader-Follower Strategies for Energy Management of Multi-Microgrids. IEEE Trans. Smart Grid 2013, 4, 1909–1916.
  35. Fischer, D.; Härtl, A.; Wille-Haussmann, B. Model for electric load profiles with high time resolution for German households. Energy Build. 2015, 92, 170–179.
  36. Mohsenian-Rad, A.-H.; Leon-Garcia, A. Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. IEEE Trans. Smart Grid 2010, 1, 120–133.
  37. Ângelos, E.W.S.; Saavedra, O.R.; Cortés, O.A.C.; De Souza, A.N. Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems. IEEE Trans. Power Deliv. 2011, 26, 2436–2442.
  38. Bose, A. Power System Stability: New Opportunities for Control. In Stability and Control of Dynamical Systems with Applications: A Tribute to Anthony N. Michel; Liu, D., Antsaklis, P.J., Eds.; Birkhäuser Boston: Boston, MA, USA, 2003; pp. 315–330.
  39. Zhuo, W.; Savkin, A.V.; Meng, K. Decentralized Optimal Control of a Microgrid with Solar PV, BESS and Thermostatically Controlled Loads. Energies 2019, 12, 2111.
  40. Wang, J.; Li, K.-J.; Javid, Z.; Sun, Y. Distributed Optimal Coordinated Operation for Distribution System with the Integration of Residential Microgrids. Appl. Sci. 2019, 9, 2136.
  41. Onishi, V.C.; Antunes, C.H.; Trovão, J.P.F. Optimal Energy and Reserve Market Management in Renewable Microgrid-PEVs Parking Lot Systems: V2G, Demand Response and Sustainability Costs. Energies 2020, 13, 1884.
  42. Teo, T.T.; Logenthiran, T.; Woo, W.L.; Abidi, K.; John, T.; Wade, N.S.; Greenwood, D.M.; Patsios, C.; Taylor, P.C. Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II. IEEE Trans. Cybern. 2020, 1–12.
  43. Ali, H.; Hussain, A.; Bui, V.-H.; Jeon, J.; Kim, H.-M. Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy. Energies 2019, 12, 3701.
  44. Teekaraman, Y.; Kuppusamy, R.; Nikolovski, S. Solution for Voltage and Frequency Regulation in Standalone Microgrid using Hybrid Multiobjective Symbiotic Organism Search Algorithm. Energies 2019, 12, 2812.
  45. Jumani, T.A.; Mustafa, M.W.; Rasid, M.M.; Mirjat, N.H.; Leghari, Z.H.; Saeed, M.S. Optimal Voltage and Frequency Control of an Islanded Microgrid using Grasshopper Optimization Algorithm. Energies 2018, 11, 3191.
  46. Nimma, K.S.; Al-Falahi, M.D.A.; Nguyen, H.D.; Jayasinghe, S.D.G.; Mahmoud, T.S.; Negnevitsky, M. Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids. Energies 2018, 11, 847.
  47. Huang, Y.; Masrur, H.; Shigenobu, R.; Hemeida, A.; Mikhaylov, A.; Senjyu, T. A Comparative Design of a Campus Microgrid Considering a Multi-Scenario and Multi-Objective Approach. Energies 2021, 14, 2853.
  48. Zhao, F.; Yuan, J.; Wang, N. Dynamic Economic Dispatch Model of Microgrid Containing Energy Storage Components Based on a Variant of NSGA-II Algorithm. Energies 2019, 12, 871.
  49. Wang, Y.; Huang, Y.; Wang, Y.; Yu, H.; Li, R.; Song, S. Energy Management for Smart Multi-Energy Complementary Micro-Grid in the Presence of Demand Response. Energies 2018, 11, 974.
  50. Wasilewski, J. Optimisation of multicarrier microgrid layout using selected metaheuristics. Int. J. Electr. Power Energy Syst. 2018, 99, 246–260.
  51. Kim, H.-J.; Kim, M.-K. Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response. Energies 2019, 12, 4142.
  52. Ramli, M.A.; Bouchekara, H.; Alghamdi, A.S. Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources. Sustainability 2019, 11, 3839.
  53. Dey, B.; Bhattacharyya, B. Hybrid Intelligence Techniques for Unit Commitment of Microgrids. In Proceedings of the 20th International Conference on Intelligent System Application to Power Systems, New Delhi, India, 10–14 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–6.
  54. Habib, H.U.R.; Subramaniam, U.; Waqar, A.; Farhan, B.S.; Kotb, K.M.; Wang, S. Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization. IEEE Access 2020, 8, 62076–62093.
  55. Lin, W.-M.; Tu, C.-S.; Tsai, M.-T. Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization. Energies 2015, 9, 5.
  56. Qian, X.; Yang, Y.; Li, C.; Tan, S.-C. Operating Cost Reduction of DC Microgrids Under Real-Time Pricing Using Adaptive Differential Evolution Algorithm. IEEE Access 2020, 8, 169247–169258.
  57. Zhang, J.; Cho, H.; Mago, P.J.; Zhang, H.; Yang, F. Multi-Objective Particle Swarm Optimization (MOPSO) for a Distributed Energy System Integrated with Energy Storage. J. Therm. Sci. 2019, 28, 1221–1235.
  58. Dabhi, D.; Pandya, K. Enhanced Velocity Differential Evolutionary Particle Swarm Optimization for Optimal Scheduling of a Distributed Energy Resources with Uncertain Scenarios. IEEE Access 2020, 8, 27001–27017.
  59. Elgamal, M.; Korovkin, N.; Elmitwally, A.; Menaem, A.A.; Chen, Z. A Framework for Profit Maximization in a Grid-Connected Microgrid With Hybrid Resources Using a Novel Rule Base-BAT Algorithm. IEEE Access 2020, 8, 71460–71474.
  60. Jamshidi, M.; Askarzadeh, A. Techno-economic analysis and size optimization of an off-grid hybrid photovoltaic, fuel cell and diesel generator system. Sustain. Cities Soc. 2018, 44, 310–320.
  61. Swief, R.A.; El-Amary, N.H.; Kamh, M.Z. Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties. IEEE Access 2020, 8, 176895–176904.
  62. Papari, B.; Cox, R.; Sockeel, N.; Hoang, P.H.; Ozkan, G. Supervisory Energy Management in Hybrid AC-DC Microgrids Based on a Hybrid Distributed Algorithm. In Proceedings of the 2020 Clemson University Power Systems Conference (PSC), Clemson, SC, USA, 10–13 March 2020; pp. 1–6.
  63. Gong, X.; Dong, F.; Mohamed, M.A.; Abdalla, O.M.; Ali, Z.M. A Secured Energy Management Architecture for Smart Hybrid Microgrids Considering PEM-Fuel Cell and Electric Vehicles. IEEE Access 2020, 8, 47807–47823.
  64. Fathy, A.; Kaaniche, K.; Alanazi, T.M. Recent Approach Based Social Spider Optimizer for Optimal Sizing of Hybrid PV/Wind/Battery/Diesel Integrated Microgrid in Aljouf Region. IEEE Access 2020, 8, 57630–57645.
  65. Diab, A.A.Z.; Sultan, H.M.; Mohamed, I.S.; Kuznetsov, O.N.; Do, T.D. Application of Different Optimization Algorithms for Optimal Sizing of PV/Wind/Diesel/Battery Storage Stand-Alone Hybrid Microgrid. IEEE Access 2019, 7, 119223–119245.
  66. Hernandez, L.; Baladrón, C.; Aguiar, J.M.; Carro, B.; Sanchez-Esguevillas, A.J.; Lloret, J. Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies 2013, 6, 1385–1408.
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