A stochastic MPC-based energy management system for integrating solar PV, battery storage, and EV charging in residential complexes

被引:1
|
作者
Saleem, M. I. [1 ]
Saha, S. [1 ]
Izhar, U. [1 ]
Ang, L. [1 ]
机构
[1] Univ Sunshine Coast, Sippy Downs, QLD, Australia
关键词
Solar photovoltaics; Energy management system; Battery energy storage system; Model predictive control; Electric vehicle charging; POWER-SYSTEMS; ALGORITHM; VEHICLE;
D O I
10.1016/j.enbuild.2024.114993
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper presents a Stochastic Model Predictive Control (SMPC)-based energy management system (EMS) for residential complexes with integrated solar photovoltaics (PV), battery energy storage systems (BESS), and electric vehicle (EV) charging infrastructure. The EMS coordinates BESS operations, integrating solar generation, residential load demand, and EV charging. It optimizes BESS charging/discharging based on solar power, load demands, electricity pricing, and feed-in tariffs over a finite horizon, while considering uncertainties through multiple scenarios of load and EV charging demand, as well as solar generation. By accounting for battery degradation, cost savings, and revenue from energy transactions, the proposed EMS enhances BESS longevity and profitability. The EMS also manages reactive power provision from the BESS inverter, ensuring voltage stability in the presence of uncertainties. Extensive case studies on Matlab Simscape Electrical and real-time validation on the OPAL-RT simulator demonstrate the effectiveness of the proposed SMPC-based EMS in optimizing energy use, operational efficiency, and economic returns, contributing significantly to the sustainable energy management of the residential complex.
引用
收藏
页数:23
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