Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

“…A generalized architecture proposed for energy management in MGs based on multiagent system. The MGEM problem formulations were often aimed at minimizing operating costs or at minimizing both the operating cost and emissions, cost of generation and BES, fuel consumption, system cost, power loss, net present cost (NPC), power fluctuations, electricity cost, energy exchange between main grid and MG, reliability and security margin, maximizing revenue, and renewable power production . Although the objective function of the energy management problem in Carpinelli et al includes several objectives, such as minimizing grid voltage deviations, power losses, security margins, and energy imported from the main grid and the objective function presented in Zheng et al includes minimizing customer's costs, emissions, load peak, and load curve fluctuations, but the proposed MG configuration only consists of renewable sources and electrical vehicles, and controllable DGs or ESS are not considered.…”

“…Different optimization techniques have been used to solve the MGEM problem. These techniques includes robust optimization, evolutionary approach, linear programming, nonlinear programming, dynamic programming, stochastic programming, multi‐period imperialist competition, Lyapunov optimization, multi‐objective cross entropy, distributed algorithm, nondominated sorting genetic algorithm (GA), Particle Swarm Optimization (PSO), model predictive control, heuristic approach, fuzzy logic, multistep hierarchical, chance constrained programming, artificial intelligence, tabu search, graph theory, SOC‐based control strategy, MATPOWER, GA, flexible time frame, column and constraint generation algorithm, chaotic group search optimizer, Whale Optimization Algorithm (WOA), water cycle algorithm (WCA), Moth‐Flame Optimizer (MFO), and hybrid Particle Swarm‐Gravitational Search Algorithm (PSO‐GSA) . MGEM problem has been studied in conjunction with demand response (DR) program .…”

Predictive energy management for a wind turbine with hybrid energy storage system

“…The development of predictive strategies to control hybrid energy systems was addressed in several studies . In Reference , the authors implemented an optimal control technique for an islanded microgrid based on non‐linear MPC.…”

“…Furthermore, when an excess of energy surplus occurred in the hybrid system (eg, battery and UC fully charged), the absence of an EZ made it necessary to dissipate energy in a braking resistor at some periods. In Reference , the authors employed distributed MPC to regulate the cooperation among several microgrids. With their approach, the authors conditioned the energy exchange between microgrids to an improvement of both the local (microgrid level) and the global (network of microgrids) status.…”

“…The development of predictive strategies to control hybrid energy systems was addressed in several studies. [24][25][26][27][28][29][30][31][32][33] In Reference 24, the authors implemented an optimal control technique for an islanded microgrid based on non-linear MPC. The microgrid consisted of different renewable sources, battery energy storage, as well as residential and industrial loads.…”