Optimization of microgrids short term operation based on an enhanced genetic algorithm

Abstract

This paper outlines the optimization problem of unit commitment (UC) and economic dispatch (ED) in microgrids (MG). An improved real coded genetic algorithm (GA) has been developed to schedule the active and reactive powers of integrated controllable generators, battery storage systems (BSS) and shiftable loads in the system. In the proposed GA method, both network restrictions (voltages and loadings) and unit constraints have been considered, and minimization of the operation costs and pollutant treatment costs have been formulated as objective functions. For network analysis, comprehensive load flow calculations based on Matpower program are conducted in the optimization process. The proposed GA method features a highly flexible set of sub-functions, intelligent convergence behavior, as well as diversified searching approaches and penalty methods for constraint violations. Moreover, a new Li-Ion BSS model with an event-driven ageing behavior has been introduced to the GA structure. In the end, the performance and effectiveness of the developed GA method is verified by a number of optimization study cases applied to a typical test microgrid. The simulation results have demonstrated the capabilities of the optimizer to detect feasible global optimal solution for microgrid UC and ED problem

A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics

“…[34], maintenance scheduling, e.g. [45], scheduling energy resources (unit commitment) and economic dispatch [4,24,27,28,31,32,35,36,43,46], to name a few. In recent extensive overviews, Zia et al [49] and Alvarado-Barrios et al [2] presented comprehensive studies about different methods and techniques used in energy management systems to optimize and schedule the operations.…”

“…For the problem of scheduling DERs, the authors of [4,27,28,31,32,36] implemented GAs to schedule the power generation in microgrids. Several microgrids with sizes ranging from six to 12 DERs and a wide variety of generators e.g.…”

An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

“…After that, both GA and interior-point algorithms are used to solve the IP and NLP problems, respectively. An Enhanced Genetic Algorithm (EGA) is proposed in [33] to address both the UC and ED problems simultaneously. The performance and effectiveness of the developed GA is verified in a set of case-study optimization scenarios, which are applied to a typical test microgrid.…”

“…To optimize the UC problem, the priority list method (PL algorithm) [38] is applied. [31] min OC SO GC DUC GA + IP (UC), NLP(ED) [33] min OC SO SA DUC EGA [34] min OC SO SA DUC MGA [35] min OC, max ER SO SA and GC DUC GA + SA [36] min EC, min NL SO SA SUC BPSO, CBPSO [37] min GC SO GC DUC, SUC GA [32] min GC, min SUC + SDC SO GC SUC PL…”

Impact of scaled fitness functions on a floating‐point genetic algorithm to optimise the operation of standalone microgrids

“…Conversely, Bilil et al [37] use non-dominant sorting GA-II (which is a fast and elitist type of GA) to solve a multiobjective optimisation problem of MGs, by controlling the load imbalance in the MG. Similarly, various types of GAs such as the real coded GA, hybrid-fuzzy GA, and floating-point GA (FP-GA) are used in [38][39][40] to solve the optimisation problem for the standalone MGs and the power systems. In contrast to most GAbased techniques that consider binary numbers in their genes and chromosomes, a floating-point number is used in each gene and chromosome of an FP-GA [41].…”