Optimal energy management of community microgrids: A risk-based multi-criteria approach

Abstract

Summary Community microgrids (CMGs) have been developing nowadays as an initiative to operate modern electric distribution systems in a more economical, reliable, and environmentally friendly manner than the existing centralized electricity grid which benefited both distribution system operator and consumers. In this paper, the optimal energy management of CMGs is formulated considering distributed energy resources and thermal and electrical demands in CMGs. The objective function of the proposed methodology consists of the total cost of CMGs operation, the total cost of energy not supplied in CMGs, and total cost of emission produced in CMGs in order to comprehensively manage the CMG operation. Moreover, the uncertainty of renewable energy resources, electricity price, and demanded power of CMGs are considered in the proposed methodology. In addition, two risk evaluation measures are employed to cope with existing risks in the CMG operation. Furthermore, a number of sensitivity studies are accomplished to investigate the effects of important parameters on the performance of the proposed approach. The effectiveness of the proposed methodology is examined on a test system.

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

“…The economic dispatch problem was solved using combination of PSO and fuzzy logic system. Energy management in community microgrids was presented in [39] considering distribution generation and electrical load demand to minimize total cost. Photovoltaic and battery storage system integrated to grid connected microgrid [40].…”

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

“…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. Energy dispatch problem in grid‐connected MG includes multiple objectives of cost of electricity, reserve power, and operational cost; cost of energy (COE) delivered, ancillary service, UC, and reserve power; cost of CHP and grid; cost of MG, energy not supplied, and emission; grid price, operation cost, cost of generation and ESS; and cost and voltage deviation considering uncertainty of generation and load demand . Different optimization techniques have been used to solve the MGEM problem.…”

“…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 .…”

“…MGEM problem has been studied in conjunction with demand response (DR) program . Uncertainty of load demand and RES has been considered in MGEM problem …”

A stochastic frequency security constrained optimization model for day‐ahead energy and reserve scheduling of islanded multi‐microgrids systems

“…Similarly, in Reference 19 a stochastic model is proposed to energy scheduling of MMG systems considering correlated power generation of WTs. In Reference 20, a risk based energy management model is proposed for MMG systems to minimize total cost of operation, total cost of energy not supplied, and total cost of emission. Reference 21 deals with frequency fluctuation and stability concerns in islanded MMG systems.…”