The current era in sustainable development is focused on the rapid integration of renewable energy sources driven by a wide range of socioeconomic objectives. Due to the inherent property of time-varying weather conditions, the intermittent sources, that is, Solar PV and Wind Energy, are considered as variable energy resources. The uncertainty and variability problem of these sources has brought many complications to distributed network operators to operate and control the complex or multi-microgrids with limited fast-ramping resources in order to maintain the power system flexibility. It led many researchers to find an alternative strategy since the conventional approaches are no longer adequate to handle the economic implications of operational decision making. At first, the brief review of various deterministic and probabilistic approaches, stochastic programming and robust optimisation strategies to address the uncertainty of variable energy resources are discussed. Furthermore, in the energy management point of view, the optimal scheduling problem of distributed sources of the microgrid is considered, and a brief review of optimisation models, advanced control strategies and demand response strategies to maximise economic benefits of microgrids are also elaborately presented. Finally, the multiagent-based distributed and decentralised control strategies for seamless integration of distributed generator units are reviewed under various configurations of the power grid along with communication network topologies.

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Summary Microgrid (MG) energy management is a complex task for MG operators to integrate and utilize consumer‐based power sources. The MG energy management systems’ problem will become tedious by considering distributed generation (DG) units' nonconvex characteristics. Therefore, a novel attempt is made to solve the day‐ahead dispatch problem of grid‐connected MG with the nonconvex cost function of DG units, including weekend and weekday load dynamics. At first, the utility‐induced flexible load shaping strategy is implemented to enhance the DG units' operation cost and reduce the peak loads. Then, demand‐side management (DSM) programs are plausibly the essential form of energy management to regulate the consumers' energy usage without violating grid price policies. Next, the DSM program is implemented to study the impact of DSM participation levels with convex and nonconvex cost functions. Further, the day‐ahead scheduling time duration with a resolution of 15 minutes is considered to examine the impact of a typical weekend and weekday load dynamics on DG units' nonconvex cost function. Finally, the Quantum Teaching‐Learning‐Based Optimization algorithm (QTLA) is devised to handle the nonconvex cost function of DG units and optimize MG's total operating costs for the first time. The proposed QTLA algorithm is compared with other metaheuristic optimization techniques such as differential evolution (DE), real‐coded genetic algorithm (RCGA), and Teaching‐Learning‐based Optimization (TLBO). The results show that the proposed strategy reduces the MG operating cost by 3.14% compared to the case study, where no DSM participation is considered. Finally, the QTLA algorithm outperforms in terms of efficacy, convergence characteristics, and computational time.

Summary Strategic planning for optimal operation of modern power systems has always been a significant task to satisfy the techno‐social aspects in the sustainable energy scenario. In the light of restructured power systems and decentralized generation, the economic dispatch (ED) problem has remained the utmost important economic assignment for the network operators. The conventional techniques have failed to address the nonlinear characteristics of generators for solving the practical ED problem. The complications identified in the literature are valve point loading, prohibited operating zones, multiple fuels, ramp rate limits, and spinning reserve. Therefore, a diverse range of traditional and modern optimization techniques have been adopted to tackle those complexities and nonlinearities. With the recent advancements in centralized optimization techniques, several research works were reported in the past decade to accomplish better results. This review paper endeavors to cover the pioneering research on centralized approaches as well as present‐day research trends on decentralized and distributed approaches by focusing on the economic aspects. First, a detailed survey of several deterministic, stochastic, nature‐inspired, and meta‐heuristic based centralized approaches for solving the ED problem is reviewed. A brief analysis based on the performance evaluation of centralized algorithms on six standard test systems is presented. Then, the recent literature on decentralized and distributed optimization approach and the consensus protocols with respect to various network topologies is briefly reviewed. The prime objective of this review is to summarize the centralized, decentralized, and distributed approaches to solve the classic dispatch, dynamic dispatch, economic emission dispatch, and multi‐area ED problems.