Optimization Approaches for Microgrid Energy Management: History
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Contributor:
Md Shafiullah
,
Akib Mostabe Refat
,
Md Ershadul Haque
,
Dewan Mabrur Hasan Chowdhury
,
Md Sanower Hossain
,
Abdullah G. Alharbi
,
Md Shafiul Alam
,
Amjad Ali
,
Shorab Hossain

The microgrids (MG) is a single controllable local power network with distributed energy sources (solar Photovoltaics (PV), wind energy, diesel generator, fuel cell, wave energy, etc.), ESS, controllable distributed loads (households, commercial, industrial, etc.), and advanced energy management systems (EMSs). It can be operated alone or in connection with the utility grid. 

  • energy management
  • functions
  • metaheuristic approaches

1. Introduction

Many studies have been conducted based on the system’s topologies, architectures, and operating modes [1][2][3]. For instance, the stochastic character of installed RESs can be controlled and optimized by a dependable power supply to customers while maintaining appropriate operating conditions for the storage system, electricity bill, and occupancies. Suggested energy management systems (EMS) optimization mechanisms are shown in Figure 1. 
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Figure 1. Control and optimization approach of EMS.

2. Objective Functions and Constraints

The deployment of EMS optimization techniques outlines the primary target functions, including power quality, dependability, pollution, and costs [4][5][6][7]. The fundamental goal of utilizing economic objective functions, for example, is to reduce the price of power. For cost reduction in  microgrids (MGs), many formulations have been investigated. For example, the cost minimization problem was framed as a dynamic economic load dispatch problem [8]. Jafari et al. [9] suggested an electrical market approach for improving the dependability of islanded multi-MG networks. A techno-economic goal function was used to account for the MG owners’ profit while enhancing the system’s reliability. For the probabilistic modeling of renewable energy resources (RER) and loads, distribution functions were employed, and an electrical market strategy was developed to increase the profit of the MG owners.
However, power quality, particularly power loss, continues to be a significant concern for system dependability. Murty et al. [10], who examined a multi-objective EM in a MG system, enhanced a helpful literature study for multi-objective EM. Techno-economic analysis and energy dispatch were given for independent and grid-connected MG infrastructure with hybrid renewable energy resources (RER) and storage devices. Following the definition of the system’s restrictions and goal functions, appropriate optimization methods are needed to guarantee power flows between the installed RER/storage and the MGs and between the MGs and the utility grid. The remainder of this section is devoted to a review of critical approaches found in the literature. The energy management system is a computerized system comprised of a software platform that provides essential support for the effective generation and transmission of electrical energy [11]. This platform also ensures adequate security of the energy supply with minimal cost.

3. Classical Methods

The classical approaches mainly concentrate on optimizing the energy resources and transmission with the mother grid. Still, a lot of work is needed to focus on the battery depth of discharge, greenhouse gas emissions, the privacy of customers, and system reliability. Two well-known classical solution methods of the EMS optimization approaches are (a) linear and nonlinear programming methods and (b) dynamic programming and rule-based methods. Several researchers used these strategies to solve the EMS control approaches. For example, Sukumar et al. [12] presented an EMS combining continuous run mode, power-sharing mode, and on/off mode. While the constant run and power-sharing modes were solved using the linear programming optimization method, the on/off mode was solved using the mixed-integer linear programming method (MILP). In most linear programming approaches, the constraints and objective functions are linear functions with whole-valued and real-valued choice variables. This methodology is frequently used for system analysis and optimization because it is a versatile and powerful tool for tackling big and complicated issues, such as distributed generating and MG systems. Vergara et al. [13] used a non-linear programming method to minimize the cost of a residential three-phase MG. The initial development of the non-linear model was then converted into the MILP. The results showed that the converted method experienced less error and computational time than the nonlinear three-phase optimal power flow formulation. Heymann et al. [14] presented a dynamic programming optimization method. The comparative results showed that this method was more effective in operational cost and computation time than the classical non-linear and MILP methods. Wang et al. [15] presented a Lagrange-programming neural networks (LPNN) approach for the efficient control and administration of MG systems, with the primary goal of lowering the total cost of the MG. This study divided the load into four categories: controlled load, thermal load, price-sensitive load, and critical load, with variable neurons and Lagrange neurons coupled to provide optimal MG operation scheduling.
More complicated problems that can be discretized and sequenced are solved using dynamic programming approaches. The investigated issues are generally divided into subproblems that are addressed optimally, and the acquired answers are then overlaid to produce an optimum solution for the original problem [16]. A rule-based solution approach is utilized for grid-connected and islanded modes of the MG [17]. As a result, rule-based approaches are commonly used to implement the EM system, since they do not require any future data profiles to decide, making them more suited to real-time applications. Bukar et al. [18], for instance, provided a rule-based EMS in which a rule-based algorithm was utilized to implement RER use priority and control the power flow of the suggested MG components. A nature-inspired optimization method was employed to optimize the MG system’s operations with respect to long-term capacity planning. The proposed objective function’s primary purpose was to reduce the cost of energy in MG systems and the chance of power supply failure. Rule-based techniques for controlling and optimizing energy flow in MG systems have been presented in other papers. Merabet et al. [19] devised a control method to ensure power compatibility with the EMS for various resources in the MG. The hybrid system in the MG was experimentally validated using a real-time control system. The findings indicated that the suggested technique kept the MG subsystems running smoothly under various power-generating and consumption situations. Luu et al. [20] investigated a method for constructing the optimal EM for a MG-connected system that considered the cost of energy trading with the main grid, as well as the cost of battery aging. The authors employed a dynamic programming technique to reduce the system’s cash flow while optimizing the power supply from the main grid. Unlike traditional techniques, dynamic programming algorithms may be thought of as mathematical optimization methods that can break down a complex issue into smaller sub-problems that can then be addressed recursively. They can make the best judgments. However, they come at a high cost in processing, making them challenging to implement in embedded devices. Table 1 demonstrates the review of classical techniques for the microgrid EMS in recently published articles.
Table 1. Review of some recent literature on classical techniques for microgrid energy management.
Ref. Method Contributions Application Key Findings
Moazeni and Khazaei [21] MILP Minimizing the daily cost of energy Water-energy microgrid system Water sector energy demand was decreased by maintaining a cleaner supply to the energy sector
Vitale et al. [22] Dynamic programming Development of a fast reduced-order sub-model Islanded and grid-tied microgrids Achieved reduced payback period through the proper capacity exploration
Pedro et al. [23] MINLP Modeled an unbalanced three-phase electrical distribution system with droop control Islanded microgrids Reduced average maintenance load and cost curtailments
Balderrama et al. [24] Linear programming Identified an open-source modeling framework to bridge the gap between field practices and two-stage stochastic modeling approaches Community microgrid Recommended robust and optimal system configurations with a minimal impact on the final costs for the community
Iqbal et al. [25] Non-linear programming Modelling of a peer-to-peer energy-sharing strategy Community microgrid Minimized overall device errors (~25%) compared to traditional sharing mechanisms
Liu et al. [26] Stochastic programming Development of a multi-period investment planning scheme Islanded microgrid Achieved better economic and synergetic performances compared to the traditional model.
Restrepo et al. [27] Optimization- and rule-based EMS Development, implementation, and commissioning of different EMS strategies for testbed microgrid Canadian Renewable Energy Laboratory Yielded better overall performance over the rule-based EMS using similar communication links while maintaining stability.
Bukar et al. [28] Rule-based Development of a rule-based energy management scheme based on queuing theory. Long-term capacity planning for MG The optimization problem minimizes energy costs and maximizes system reliability.
Almada et al. [18] Rule-based Management and control of a microgrid with distributed energy resources under standalone operation, grid connection, and transition between the aforementioned operating modes AC microgrid MG control and management techniques work well under all operating conditions
Rippia et al. [17] Rule-based Enhancing the energy management of a grid-connected microgrid comprised of renewable energy sources, loads, and ESS Grid-connected microgrids The rule-based method performed better than the MILP while ensuring nearly no performance loss by offering a sizable decrease in computation time

4. Heuristic and Metaheuristic Approaches

Heuristic and metaheuristic methods are frequently used in the literature to solve complex and non-differentiable optimization problems from various engineering fields, including transportation, communication, power systems, product distribution, and microgrid energy management [29]. Among many approaches, the genetic algorithm and particle swarm optimization methods are two popular meta-heuristic methods to solve the EMS of the MG, due to their parallel computational ability. Chalise et al. [30] formulated a multi-objective EMS concentrating on a remote MG’s economic load dispatch and battery degradation cost. This work considered day-ahead scheduling using a genetic algorithm and real-time operation using a rule-based approach. A PSO-based optimal EMS for both islanded and grid-connected modes of the MG has been proposed in [31]. The objective functions for the islanded and grid-connected modes were to minimize the operational and maintenance costs and maximize the energy trading profit with the primary grid. The results show that this technique provided a better solution than the genetic algorithm in terms of the global optimum solution and the time of computation. Apart from these two well-known solution approaches, the genetic algorithm and PSO methods of the EMS, there are other approaches, such as differential evolution [32], gray wolf optimization (GWO) [33], ant colony optimization (ACO) [34], etc.
Paperi et al. [35] presented a heuristic technique for determining the best functioning and EMS of the MG system. The research topic was framed as a single-objective optimization problem, only focusing on cost reduction. Khan et al. [36] presented a metaheuristic-based system by combining the Harmony search algorithm with improved differential evolution. During peak periods, several knapsacks were utilized to ensure that power consumption was below a predetermined threshold value—the suggested system beat existing metaheuristic approaches in terms of cost and peak-to-average ratio. Ref. [37] suggested a genetic algorithm-based optimum EMS system for a grid-connected MG system that considered electricity price, power usage, and RER generation uncertainty. The multi-period gravitational search method solves a deterministic EM issue [38]. Aghajani et al. [39] employed a multi-objective PSO method while addressing the EMS of the MG system. Wei et al. [40] created a standalone modular microgrid model to shorten the feasible economic dispatch regions, formulate an optimization model, and define optimum microgrid system operating strategies. An improved genetic algorithm was proposed to investigate this problem. The employed strategy was capable of solving the EMS problems with many constraints and produced a high-quality solution. Prasant and Joseph [41] developed a methodology for evaluating the techno-economic and environmental efficiency of supplying uninterrupted electricity to a microgrid composed of seven components—wind turbines, solar PV, lead-acid batteries, fuel cells, biodiesel generators, electrolyzes, and small-scale hydrogen tanks—in Tucson, Lubbock, and Dickinson, TX, USA. They measured the configurations with the lowest levelized cost of energy (LCOE) using the genetic algorithm. Table 2 summarizes the meta-heuristic methods for the microgrid EMS. It was evident from the presented analysis that the various meta-heuristic approaches showed satisfactory performances in achieving optimal solutions (minimal costs or maximum profits) while solving microgrid EMS problems with various constraints and uncertainties. Besides, in most of the cases the authors reported better or competitive efficacy for their employed algorithms compared to others. However, it is challenging to come up with solid conclusions about the superiority of any specific algorithm, as they all should provide similar results ideally due to their stochastic nature. Besides, their efficacy also depends on the proper selection of the hyper-parameters.
Table 2. Recent literature on meta-heuristic techniques for microgrid energy management.
Ref. Algorithm Contributions Application Key Findings
Quazi et al. [42] NSWOA Hybridization of WOA with a non-dominated sorting technique Islanded microgrid Achieved optimal solutions with lower computational expenses compared to other reported algorithms
Leonori et al. [43] GA Investigation of strategies to synthesize rule-based fuzzy inference systems Demand response services Reduced the system complexity and maximized profit generation by 10% compared to the referenced solution
Hussein et al. [44] SFOA Formulation of the multi-objective problem for controller parameter tuning Inverter based microgrid Enhancement of system performance and flexibility compared to particle swarm optimization
Almadhor et al. [45] BAPSO Determination of optimal locations and sizes for the solar generation systems PV-based microgrid Reduced transmission power loss and achieved faster convergence with less computational burden
Singh and Gope [46] GWO Optimization problem formulation for load frequency control Two-area multi-microgrid Achieved superior performance with the GWO-tuned controller over the cuckoo search algorithm-tuned controller
Roslan et al. [47] LSA Development of an optimal power scheduling strategy Scheduling controller Savings of 62.5% in overall costs and 61.98% in carbon dioxide emission reduction
Shafiullah et al. [33] GWO Formulation of the multi-objective problem considering ESS degradation cost Community microgrid Generation of quality solutions with a competitive computational effort
Suman et al. [48] PSO-GWO Formulation of the optimal planning problem Rural microgrid Obtained a reduced average cost of electricity by meeting a good portion of the load demand
Soham and Kamal [49] CE-DE Optimization problem formulation for minimization of running costs and reduction of pollution. Benchmarked microgrids Avoided pre-mature convergence and generated competitive solutions
Perol et al. [50] Evolutionary algorithms Development of control strategies for power and energy management Low voltage microgrids Reduced operation costs and energy losses, thus improving overall efficiency
Hossain et al. [51] Modified PSO Proposed an optimal battery control strategy for real-time management Grid-tied microgrids Achieved a 12% reduction in operation costs for a time horizon of 96 h
Kavitha et al. [52] MPO Solved supply-demand problems to minimize production costs Islanded and grid-tied microgrids Generated around 8% higher profit with better optimization ability and faster convergence
Tomin et al. [53] Monte-Carlo tree search algorithm Developed a unified approach for optimal energy and benefits management Community microgrids Improved supplied energy quality and reduced the levelized cost of energy index from 20% to 40%

5. Artificial Intelligence Methods

Artificial neural networks are an example of a method that is created artificially. They are stochastic approaches that may be utilized to address optimization issues for systems that contain random variables. The fluctuating nature of RER in MG systems is caused by meteorological conditions, which impact electricity generation. Solanki et al. [54] presented a mathematical approach for intelligent load control in a stand-alone MG system. Neural networks were utilized to simulate the loads examined, and a predictive control was applied to manage the energy, based on expected load fluctuation. The EMS based on artificial intelligence primarily concentrated on Fuzzy logic and neural networks [55], as well as multi-agent systems. A fuzzy logic-based EMS with a battery and hydrogen energy storage system for a microgrid has been proposed [56]. The authors claimed that this solution could respond well to required load demands and meet the established technical and economic criteria. Wang et al. [16] formulated a neural network-based EMS for the MG. The objective function is to reduce the overall fuel, operation, maintenance, and emission cost of the generation units. Ghorbani et al. [57] proposed a multi-agent-based EMS approach, where consumers, storage units, generation units, and the grid are considered agents for a grid-connected MG. In this work, the objective function was to reduce power imbalance costs. Results showed that the time required to take the decision was better for the decentralized approach than that of the centralized approach. Among the other known artificial intelligence solution techniques, game theory, the Markov decision process, and theadaptive intelligence technique are remarkable.
Neural networks are primarily used to regulate, optimize, and detect system characteristics in online and offline applications. Given their capacity to address the system’s stability through self-learning and prediction skills, neural networks, unlike prior techniques, can handle issues with nonlinear data in large-scale MG systems [58][59][60]. Despite the solutions’ efficacy, intelligent energy management in smart MG systems still needs real-time and predictive control methodologies. Table 3 illustrates various artificial intelligence methods related to microgrid EMSs.
Table 3. Review of artificial intelligence methods related to microgrid energy management.
Ref. Technique Contributions Application Key Findings
Dong et al. [61] Fuzzy logic Developed day-ahead fuzzy rules for real-time energy management under various operational uncertainty Multi-energy microgrid Exhibited superior performance compared to the online rule-based and meta-heuristic optimization-based offline scheduling schemes
Zehra et el. [62] Fuzzy logic Proposed the control strategies for renewable energy resources and battery storage systems DC microgrid Achieved better controllability compared to the sliding and integral sliding mode controllers
Singh and Lather [63] HBSANN Addressed demand-generation disparity for effective power-sharing between various ESS. Low-voltage DC microgrid Exhibited lower voltage overshoot and settling time compared to conventional strategy
Nakabi and Toivanen [64] Reinforcement learning Outlined various flexible resources for coordination with priority Microgrid Exhibited the highest model performance and convergence to near-optimal policies
Tan and Chen [65] NN Designed a multi-objective optimization model for multiple microgrid systems Multiple microgrids Achieved a 36.86% lower forecasting error; obtained better pareto solutions and faster convergence
Tayab et al. [66] HHO-FNN Developed a hybrid approach for short-term load forecasting Queensland electric market Reduced the mean absolute percentage error ranging from 33.30% to 60.76%
Priyadarshini et al. [67] EE-RRVFLN Proposed a maximum power point tracking model for multiple PV-integrated MG under partial shading conditions and load uncertainty. PV-BESS integrated microgrid Established the superiority of the employed approach over the conventional and random vector functional link neural networks

6. Other Methods

Proactive control is one of the most intriguing EMS methods. This technique is based on a mixed-integer optimal control issue, which can be expressed as a mixed-integer nonlinear programming problem [21]. According to the literature, the notion of proactive control for EM in MG systems is seldom applied. For control-based prediction judgments, the notion is particularly appealing. Proactive control can be enhanced in future investigations for EM in MG systems, thanks to the advancement of information and communication technologies, particularly microcontrollers. In addition, the approach can improve the system’s performance in existing system disruptions. Amirioun et al. [68] provided a MG proactive control method for dealing with the adverse effects of severe windstorms. When the anticipated windstorm alerts arrived, the method discovered a conservative MG schedule with the fewest susceptible branches operating while the whole load was serviced. The cautious timetable guaranteed that the MG functioned normally before the windstorm, while decreasing the MG’s susceptibility when the event arrived. This technique benefited from generation rescheduling, network reconfiguration, parameter tuning of the droop-controlled units, and conservation voltage regulation. Panteli et al. [69] talked about unified resilience evaluation, the operational enhancement method, and a technique for evaluating the impact of severe weather conditions. Another study by Amin et al. [70] combined BESS and PV systems under a hierarchical transactive EM method to lower customer power costs. A cost-benefit analysis technique that integrated PV units with battery storage systems was created for proactive residences. The control algorithm managed the battery’s charge/discharge cycle based on a cost-benefit analysis of real-time energy rates and battery costs, providing users with a precise estimate of their investment returns and annual savings. When a proactive system is handled utilizing predictive techniques, the performance of this method may be improved. Reactive Feedback Control and Model Predictive Control were compared with respect to energy used, energy error, and management effort for a specific data center by Rahmani et al. [71]. The research suggested a data center model-based feedback control method to improve service quality, energy consumption, and managerial effort. Moreover, the combination of different approaches mentioned earlier was also used in a few cases for energy management in microgrid systems [72][73][74][75].

This entry is adapted from the peer-reviewed paper 10.3390/su142214794

References

  1. Yu, H.; Niu, S.; Zhang, Y.; Jian, L. An integrated and reconfigurable hybrid AC/DC microgrid architecture with autonomous power flow control for nearly/net zero energy buildings. Appl. Energy 2020, 263, 114610.
  2. Wu, T.; Wang, J. Artificial intelligence for operation and control: The case of microgrids. Electr. J. 2020, 34, 106890.
  3. Guichi, A.; Mekhilef, S.; Berkouk, E.; Talha, A. Optimal control of grid-connected microgrid PV-based source under partially shaded conditions. Energy 2021, 230, 120649.
  4. Li, B.; Roche, R.; Miraoui, A. Microgrid sizing with combined evolutionary algorithm and MILP unit commitment. Appl. Energy 2017, 188, 547–562.
  5. Hannan, M.; Tan, S.Y.; Al-Shetwi, A.Q.; Jern, K.P.; Begum, R. Optimized controller for renewable energy sources integration into microgrid: Functions, constraints and suggestions. J. Clean. Prod. 2020, 256, 120419.
  6. Cannata, N.; Cellura, M.; Longo, S.; Montana, F.; Sanseverino, E.R.; Luu, Q.L.; Nguyen, N.Q. Multi-Objective Optimization of Urban Microgrid Energy Supply According to Economic and Environmental Criteria. In Proceedings of the 2019 IEEE Milan PowerTech, Milan, Italy, 23–27 June 2019; pp. 1–6.
  7. Chen, C.; Duan, S.; Cai, T.; Liu, B.; Hu, G. Smart energy management system for optimal microgrid economic operation. IET Renew. Power Gener. 2011, 5, 258–267.
  8. Kamboj, V.K.; Bath, S.K.; Dhillon, J.S. Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. Neural Comput. Appl. 2015, 27, 1301–1316.
  9. Jafari, A.; Ganjehlou, H.G.; Khalili, T.; Bidram, A. A fair electricity market strategy for energy management and reliability enhancement of islanded multi-microgrids. Appl. Energy 2020, 270, 115170.
  10. Murty, V.V.S.N.; Kumar, A. RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Prot. Control Mod. Power Syst. 2020, 5, 2.
  11. Zia, M.F.; Elbouchikhi, E.; Benbouzid, M. Microgrids energy management systems: A critical review on methods, solutions, and prospects. Appl. Energy 2018, 222, 1033–1055.
  12. Sukumar, S.; Mokhlis, H.; Mekhilef, S.; Naidu, K.; Karimi, M. Mix-mode energy management strategy and battery sizing for economic operation of grid-tied microgrid. Energy 2017, 118, 1322–1333.
  13. Vergara, P.; López, J.C.; da Silva, L.C.; Rider, M.J. Security-constrained optimal energy management system for three-phase residential microgrids. Electr. Power Syst. Res. 2017, 146, 371–382.
  14. Heymann, B.; Bonnans, J.F.; Martinon, P.; Silva, F.J.; Lanas, F.; Jiménez-Estévez, G. Continuous optimal control approaches to microgrid energy management. Energy Syst. 2018, 9, 59–77.
  15. Wang, T.; He, X.; Deng, T. Neural networks for power management optimal strategy in hybrid microgrid. Neural Comput. Appl. 2019, 31, 2635–2647.
  16. García Vera, Y.E.; Dufo-López, R.; Bernal-Agustín, J.L. Energy Management in Microgrids with Renewable Energy Sources: A Literature Review. Appl. Sci. 2019, 9, 3854.
  17. Pippia, T.; Sijs, J.; De Schutter, B. A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids. IEEE Trans. Control Syst. Technol. 2020, 28, 2364–2376.
  18. Almada, J.; Leão, R.; Sampaio, R.; Barroso, G. A centralized and heuristic approach for energy management of an AC microgrid. Renew. Sustain. Energy Rev. 2016, 60, 1396–1404.
  19. Merabet, A.; Ahmed, K.T.; Ibrahim, H.; Beguenane, R.; Ghias, A.M.Y.M. Energy Management and Control System for Laboratory Scale Microgrid Based Wind-PV-Battery. IEEE Trans. Sustain. Energy 2017, 8, 145–154.
  20. An, L.N.; Quoc-Tuan, T. Optimal energy management for grid connected microgrid by using dynamic programming method. In Proceedings of the 2015 IEEE Power & Energy Society General Meeting, Denver, CO, USA, 26–30 July 2015; pp. 1–5.
  21. Moazeni, F.; Khazaei, J. Optimal operation of water-energy microgrids; a mixed integer linear programming formulation. J. Clean. Prod. 2020, 275, 122776.
  22. Vitale, F.; Rispoli, N.; Sorrentino, M.; Rosen, M.; Pianese, C. On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids. Energy 2021, 225, 120304.
  23. Vergara, P.P.; López, J.C.; Rider, M.J.; Shaker, H.R.; da Silva, L.C.; Jørgensen, B.N. A stochastic programming model for the optimal operation of unbalanced three-phase islanded microgrids. Int. J. Electr. Power Energy Syst. 2019, 115, 105446.
  24. Balderrama, S.; Lombardi, F.; Riva, F.; Canedo, W.; Colombo, E.; Quoilin, S. A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the “El Espino” community. Energy 2019, 188, 116073.
  25. Iqbal, S.; Nasir, M.; Zia, M.F.; Riaz, K.; Sajjad, H.; Khan, H.A. A novel approach for system loss minimization in a peer-to-peer energy sharing community DC microgrid. Int. J. Electr. Power Energy Syst. 2021, 129, 106775.
  26. Liu, Y.; Guo, L.; Hou, R.; Wang, C.; Wang, X. A hybrid stochastic/robust-based multi-period investment planning model for island microgrid. Int. J. Electr. Power Energy Syst. 2021, 130, 106998.
  27. Restrepo, M.; Cañizares, C.A.; Simpson-Porco, J.W.; Su, P.; Taruc, J. Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility. Appl. Energy 2021, 290, 116760.
  28. Bukar, A.L.; Tan, C.W.; Yiew, L.K.; Ayop, R.; Tan, W.-S. A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm. Energy Convers. Manag. 2020, 221, 113161.
  29. Shafiullah, M.; Abido, M.A.; Al-Mohammed, A.H. Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach, 1st ed.; Elsevier: Amsterdam, The Netherlands, 2022; ISBN 9780323884297.
  30. Chalise, S.; Sternhagen, J.; Hansen, T.M.; Tonkoski, R. Energy management of remote microgrids considering battery lifetime. Electr. J. 2016, 29, 1–10.
  31. Li, H.; Eseye, A.T.; Zhang, J.; Zheng, D. Optimal energy management for industrial microgrids with high-penetration renewables. Prot. Control Mod. Power Syst. 2017, 2, 12.
  32. Rana, J.; Zaman, F.; Ray, T.; Sarker, R. Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling. IEEE Access 2020, 8, 76500–76515.
  33. Shafiullah, M.; Haque, M.E.; Hossain, S.; Hossain, M.S.; Rana, M.J. Community Microgrid Energy Scheduling Based on the Grey Wolf Optimization Algorithm. In Artificial Intelligence-Based Energy Management Systems for Smart Microgrids; Khan, B., Sanjeevikumar, P., Alhelou, H.H., Mahela, O.P., Rajkumar, S., Eds.; CRC Press: Boca Raton, FL, USA, 2022; pp. 47–73. ISBN 9781003290346.
  34. Rahim, S.; Iqbal, Z.; Shaheen, N.; Khan, Z.A.; Qasim, U.; Khan, S.A.; Javaid, N. Ant Colony Optimization Based Energy Management Controller for Smart Grid. In Proceedings of the 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, Switzerland, 23–25 March 2016; pp. 1154–1159.
  35. Papari, B.; Edrington, C.; Vu, T.V.; Diaz-Franco, F. A heuristic method for optimal energy management of DC microgrid. In Proceedings of the 2017 IEEE Second International Conference on DC Microgrids (ICDCM), Nuremburg, Germany, 27–29 June 2017; pp. 337–343.
  36. Vacca, J. Solving Urban Infrastructure Problems Using Smart City Technologies; Elsevier: Amsterdam, The Netherlands, 2021; ISBN 9780128168165.
  37. Radosavljević, J.; Jevtić, M.; Klimenta, D. Energy and operation management of a microgrid using particle swarm optimization. Eng. Optim. 2015, 48, 811–830.
  38. Marzband, M.; Ghadimi, M.; Sumper, A.; Domínguez-García, J.L. Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode. Appl. Energy 2014, 128, 164–174.
  39. Aghajani, G.R.; Shayanfar, H.A.; Shayeghi, H. Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers. Manag. 2015, 106, 308–321.
  40. Yeh, W.-C.; He, M.-F.; Huang, C.-L.; Tan, S.-Y.; Zhang, X.; Huang, Y.; Li, L. New genetic algorithm for economic dispatch of stand-alone three-modular microgrid in DongAo Island. Appl. Energy 2020, 263, 114508.
  41. Nagapurkar, P.; Smith, J.D. Techno-economic optimization and environmental Life Cycle Assessment (LCA) of microgrids located in the US using genetic algorithm. Energy Convers. Manag. 2019, 181, 272–291.
  42. Islam, Q.N.U.; Ahmed, A.; Abdullah, S.M. Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA). Ain Shams Eng. J. 2021, 12, 3677–3689.
  43. Leonori, S.; Paschero, M.; Mascioli, F.M.F.; Rizzi, A. Optimization strategies for Microgrid energy management systems by Genetic Algorithms. Appl. Soft Comput. 2020, 86, 105903.
  44. Hussien, A.; Hasanien, H.M.; Mekhamer, S. Sunflower optimization algorithm-based optimal PI control for enhancing the performance of an autonomous operation of a microgrid. Ain Shams Eng. J. 2021, 12, 1883–1893.
  45. Almadhor, A.; Rauf, H.T.; Khan, M.A.; Kadry, S.; Nam, Y. A hybrid algorithm (BAPSO) for capacity configuration optimization in a distributed solar PV based microgrid. Energy Rep. 2021, 7, 7906–7912.
  46. Singh, K.M.; Gope, S. Renewable energy integrated multi-microgrid load frequency control using grey wolf optimization algorithm. Mater. Today Proc. 2021, 46, 2572–2579.
  47. Roslan, M.; Hannan, M.; Ker, P.J.; Begum, R.; Mahlia, T.I.; Dong, Z. Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction. Appl. Energy 2021, 292, 116883.
  48. Suman, G.K.; Guerrero, J.M.; Roy, O.P. Optimisation of solar/wind/bio-generator/diesel/battery based microgrids for rural areas: A PSO-GWO approach. Sustain. Cities Soc. 2021, 67, 102723.
  49. Mandal, S.; Mandal, K.K. Optimal energy management of microgrids under environmental constraints using chaos enhanced differential evolution. Renew. Energy Focus 2020, 34, 129–141.
  50. Moazeni, F.; Khazaei, J. Dynamic economic dispatch of islanded water-energy microgrids with smart building thermal energy management system. Appl. Energy 2020, 276, 115422.
  51. Hossain, A.; Pota, H.R.; Squartini, S.; Abdou, A.F. Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renew. Energy 2019, 136, 746–757.
  52. Kavitha, V.; Malathi, V.; Guerrero, J.M.; Bazmohammadi, N. Energy management system using Mimosa Pudica optimization technique for microgrid applications. Energy 2022, 244, 122605.
  53. Tomin, N.; Shakirov, V.; Kozlov, A.; Sidorov, D.; Kurbatsky, V.; Rehtanz, C.; Lora, E.E. Design and optimal energy management of community microgrids with flexible renewable energy sources. Renew. Energy 2022, 183, 903–921.
  54. Solanki, B.V.; Raghurajan, A.; Bhattacharya, K.; Canizares, C.A. Including Smart Loads for Optimal Demand Response in Integrated Energy Management Systems for Isolated Microgrids. IEEE Trans. Smart Grid 2017, 8, 1739–1748.
  55. Li, T.; Yang, J.; Cui, D. Artificial-intelligence-based algorithms in multi-access edge computing for the performance optimization control of a benchmark microgrid. Phys. Commun. 2021, 44, 101240.
  56. Vivas, F.J.; Segura, F.; Andújar, J.M.; Palacio, A.; Saenz, J.L.; Isorna, F.; López, E. Multi-objective Fuzzy Logic-based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System. Electronics 2020, 9, 1074.
  57. Ghorbani, S.; Rahmani, R.; Unland, R. Multi-agent autonomous decision making in smart micro-grids’ energy management: A decentralized approach. In Multiagent System Technologies. MATES 2017; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2017.
  58. Mahmoud, M.S.; Alyazidi, N.M.; Abouheaf, M.I. Adaptive intelligent techniques for microgrid control systems: A survey. Int. J. Electr. Power Energy Syst. 2017, 90, 292–305.
  59. Roslan, M.; Hannan, M.; Ker, P.J.; Uddin, M. Microgrid control methods toward achieving sustainable energy management. Appl. Energy 2019, 240, 583–607.
  60. Rashidi, R.; Hatami, A.; Abedini, M. Multi-microgrid energy management through tertiary-level control: Structure and case study. Sustain. Energy Technol. Assess. 2021, 47, 101395.
  61. Dong, W.; Yang, Q.; Fang, X.; Ruan, W. Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties. Appl. Soft Comput. 2020, 98, 106882.
  62. Zehra, S.S.; Rahman, A.U.; Armghan, H.; Ahmad, I.; Ammara, U. Artificial intelligence-based nonlinear control of renewable energies and storage system in a DC microgrid. ISA Trans. 2021, 121, 217–231.
  63. Singh, P.; Lather, J.S. Dynamic power management and control for low voltage DC microgrid with hybrid energy storage system using hybrid bat search algorithm and artificial neural network. J. Energy Storage 2020, 32, 101974.
  64. Nakabi, T.A.; Toivanen, P. Deep reinforcement learning for energy management in a microgrid with flexible demand. Sustain. Energy Grids Netw. 2020, 25, 100413.
  65. Tan, B.; Chen, H. Multi-objective energy management of multiple microgrids under random electric vehicle charging. Energy 2020, 208, 118360.
  66. Tayab, U.B.; Zia, A.; Yang, F.; Lu, J.; Kashif, M. Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform. Energy 2020, 203, 117857.
  67. Priyadarshini, L.; Dash, P.; Dhar, S. A new Exponentially Expanded Robust Random Vector Functional Link Network based MPPT model for Local Energy Management of PV-Battery Energy Storage Integrated Microgrid. Eng. Appl. Artif. Intell. 2020, 91, 103633.
  68. Amirioun, M.H.; Aminifar, F.; Lesani, H. Resilience-Oriented Proactive Management of Microgrids Against Windstorms. IEEE Trans. Power Syst. 2018, 33, 4275–4284.
  69. Panteli, M.; Trakas, D.N.; Mancarella, P.; Hatziargyriou, N.D. Boosting the Power Grid Resilience to Extreme Weather Events Using Defensive Islanding. IEEE Trans. Smart Grid 2016, 7, 2913–2922.
  70. Amin, U.; Hossain, M.J.; Lu, J.; Mahmud, M.A. Cost-benefit analysis for proactive consumers in a microgrid for transactive energy management systems. In Proceedings of the 2016 Australasian Universities Power Engineering Conference (AUPEC), Brisbane, QLD, Australia, 25–28 September 2016; pp. 2–7.
  71. Rahmani, R.; Moser, I.; Cricenti, A. Modelling and optimisation of microgrid configuration for green data centres: A metaheuristic approach. Futur. Gener. Comput. Syst. 2020, 108, 742–750.
  72. Diaz, N.L.; Dragicevic, T.; Vasquez, J.C.; Guerrero, J.M. Intelligent Distributed Generation and Storage Units for DC Microgrids—A New Concept on Cooperative Control Without Communications Beyond Droop Control. IEEE Trans. Smart Grid 2014, 5, 2476–2485.
  73. De Nadai, B.; De Souza, A.C.Z.; Costa, J.G.C.; Pinheiro, C.; Portelinha, F.M. A secondary control based on fuzzy logic to frequency and voltage adjustments in islanded microgrids scenarios. In Proceedings of the 2017 IEEE Manchester PowerTech, Manchester, UK, 18–22 June 2017; pp. 1–6.
  74. Ei-Bidairi, K.S.; Nguyen, H.D.; Jayasinghe, S.D.G.; Mahmoud, T.S. Multiobjective Intelligent Energy Management Optimization for Grid-Connected Microgrids. In Proceedings of the 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe), Palermo, Italy, 12–15 June 2018; pp. 1–6.
  75. Jafari, M.; Malekjamshidi, Z.; Zhu, J.; Khooban, M.-H. A Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-Connected and Off-Grid Operation of Residential Smart Microgrids. IEEE J. Emerg. Sel. Top. Power Electron. 2020, 8, 1391–1404.
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