Decentralized Demand Management Based on Alternating Direction Method of Multipliers Algorithm for Industrial Park with CHP Units and Thermal Storage

• Published in: Journal of modern power systems and clean energy Vol. 10; no. 1; pp. 120 - 130
• Main Authors: Jingdong Wei, Yao Zhang, Jianxue Wang, Lei Wu, Peiqi Zhao, Zhengting Jiang
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2022
• Subjects: Alternating direction method of multipliers (ADMM); combined heat and power (CHP) unit; demand management; industrial park; integrated demand response (IDR); thermal storage
• ISSN: 2196-5420; 2196-5625; 2196-5420
• DOI: 10.35833/mpce.2020.000623

This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains. A demand management model for industrial park considering the integrated demand response of combined heat and power (CHP) units and thermal storage is firstly proposed. Specifically, by increasing the electricity outputs of CHP units during peak-load periods, not only the peak demand charge but also the energy charge can be reduced. The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units. The heat dissipation of thermal storage, thermal delay effect, and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park. The proposed model is formulated as a multi-period alternating current (AC) optimal power flow problem via the second-order conic programming formulation. The alternating direction method of multipliers (ADMM) algorithm is used to compute the proposed demand management model in a distributed manner, which can protect private data of all participants while achieving solutions with high quality. Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge, and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.