Two-stage stochastic operation considering day-ahead and real-time scheduling of microgrids with high renewable energy sources and electric vehicles based on multi-layer energy management system

• Published in: Electric power systems research Vol. 201; p. 107527
• Main Authors: Azarhooshang, Alireza, Sedighizadeh, Davoud, Sedighizadeh, Mostafa
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2021; Elsevier Science Ltd
• Subjects: Alternative energy sources; Constraints; Day-ahead; Distributed generation; Electric vehicles; Electric vehicles (EVs); Energy industry; Energy management; Energy storage; Grid connected microgrid (GCMG); Monte Carlo simulation; Multi-layer energy management system; Multilayers; Operating costs; Real time; Real time energy markets; Real time operation; Renewable energy sources; Scheduling; Scheduling algorithms; Smart grid technology; Stochastic models; Stochastic programming; Storage systems; Two-stage operation; Uncertainty; Renewable energy sources; Real time energy markets; Two-stage operation; Day-ahead; Grid connected microgrid (GCMG); Electric vehicles (EVs); Multi-layer energy management system
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2021.107527

•Modeling the multi-layer EMS in the GCMGs based on coordination between individual MGs and MGC, and MGC and DSO to reduce the data volume in the DSO and obtain high performance speed.•Presenting two-stage operation according to hourly day-ahead and 5 min real time energy markets model to achieve the accurate and optimal economic formulation.•Considering optimal energy management model for GCMGs including high penetration rate of RESs and EVs, and.•Modeling the different uncertain parameters by stochastic programming that is coupled the MCS and fast backward/forward approach. In this paper, two- stage operation of a grid-connected microgrid (GCMG) with high penetration rate of renewable energy sources (RESs) and electric vehicles (EVs) is presented based on day-ahead and real time energy markets, where GCMG follows multi-layer energy management system (EMS). In the proposed method, all microgrids (MGs) are categorized to individual MGs and an MG community (MGC) connected to the distribution network which manages other MGs. Hence, the first/second layer of EMS is applied to individual MGs/MGC according to hourly operation in the day-ahead market at the first stage of the problem. The first layer model minimizes the operating cost of the MG subject to network model, distributed generations (DGs), energy storage systems (ESSs) and EVs parking lot constraints in individual MGs. The second layer model minimizes the sum of expected operation and risk costs of the MGC, limited by the same constraints of the first layer problem. In the second stage, the imbalance cost between day-ahead and real time operation is minimized, constrained to MGs and their devices model based on 5 min real-time dispatch. Stochastic programming based on coupling Mont Carlo Simulation (MCS) and fast backward/forward approach is used to model uncertainties of load, renewable power, energy price, and EVs parameters. Therefore, multi-layer energy management, coordination and RESs and EVs in GCMG, two-stage operation including day-ahead and real-time scheduling, and the procedure used for stochastic modeling of uncertainties are among the contributions of the proposed scheme. Finally, to evaluate the efficacy of proposed approach, it is tested on a standard system in GAMS software. [Display omitted]