Efficient Energy Management for Smart Homes with Electric Vehicles Using Scenario-Based Model Predictive Control

Xinchen Deng, Jiacheng Li, Huanhuan Bao (), Zhiwei Zhao, Xiaojia Su and Yao Huang
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Xinchen Deng: China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
Jiacheng Li: College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Huanhuan Bao: China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
Zhiwei Zhao: China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
Xiaojia Su: China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
Yao Huang: China Post Group Co., Ltd., Chongqing 400001, China

Sustainability, 2025, vol. 17, issue 17, 1-17

Abstract: Model predictive control (MPC) is a commonly used online strategy for maximizing economic benefits in smart homes that integrate photovoltaic (PV) panels, electric vehicles (EVs), and battery energy storage systems (BESSs). However, prediction errors associated with PV power and load demand can lead to economic losses. Scenario-based MPC can mitigate the impact of prediction errors by computing the expected objective value of multiple stochastic scenarios. However, reducing the number of scenarios is often necessary to lower the computation burden, which in turn causes some economic loss. To achieve online operation and maximize economic benefits, this paper proposes utilizing the consensus alternating direction method of multipliers (C-ADMM) algorithm to quickly calculate the scenario-based MPC problem without reducing stochastic scenarios. First, the system layout and relevant component models of smart homes are established. Then, the stochastic scenarios of net load prediction error are generated through Monte Carlo simulation. A consensus constraint is designed about the first control action in different scenarios to decompose the scenario-based MPC problem into multiple sub-problems. This allows the original large-scale problem to be quickly solved by C-ADMM via parallel computing. The relevant results verify that increasing the number of stochastic scenarios leads to more economic benefits. Furthermore, compared with traditional MPC with or without prediction error, the results demonstrate that scenario-based MPC can effectively address the economic impact of prediction error.

Keywords: smart home; scenario-based model predictive control; prediction error; consensus alternating direction method of multiplier; scenario generation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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