Optimal Control of Energy Systems in Net-Zero Energy Buildings Considering Dynamic Costs: A Case Study of Zero Carbon Building in Hong Kong

Tao Lv, Yuehong Lu, Yijie Zhou, Xuemei Liu, Changlong Wang, Yang Zhang, Zhijia Huang and Yanhong Sun
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Tao Lv: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Yuehong Lu: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Yijie Zhou: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Xuemei Liu: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Changlong Wang: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Yang Zhang: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Zhijia Huang: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China
Yanhong Sun: Department of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243002, China

Sustainability, 2022, vol. 14, issue 6, 1-25

Abstract: Net-zero energy buildings coupled with multiple energy demands on the load side, which utilize renewable energy to a larger extent, are an effective way to consume distributed capacity in situ and need to face the operational challenges brought by the uncertainty of renewable energy while meeting different energy demands. To this end, this paper proposes a Dynamic Cost Interaction Optimization Model (DCI-OM) with Electric Vehicle Charging Station (EVCS) based on dynamic cost (i.e., oil price, electricity price) and considers a larger proportion of renewable energy capacity to be consumed. In this model, the optimized electricity and cooling demand dispatch scheme is given with daily operating cost as the objective function. Using the Zero Carbon Building in Hong Kong, China, as an example, simulations are performed for typical days (i.e., 21 March, 21 June, 22 September, and 21 December) in four seasons throughout the year. The results show that the electric and cooling load demand response scheme given by DCI-OM achieves peak and valley reduction according to the dynamic cost and reduces the original operating costs while ensuring that the customer’s comfort needs are within acceptable limits. The optimized scheduling scheme meets the demand while reducing the daily operating cost.

Keywords: net-zero energy building; dynamic costs; demand response; mixed integer linear programming (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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