Optimal Scheduling of Integrated Demand Response-Enabled Community-Integrated Energy Systems in Uncertain Environments

• Published in: IEEE transactions on industry applications Vol. 58; no. 2; pp. 2640 - 2651
• Main Authors: Li, Yang, Wang, Bin, Yang, Zhen, Li, Jiazheng, Li, Guoqing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-03-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Chance-constrained programming (CCP); community integrated energy system (CIES); Confidence intervals; Constraints; Electric power demand; Electric vehicles; electric vehicles (EVs); Energy management; flexible load; Hybrid systems; Integer programming; integrated demand response (IDR); Integrated energy systems; Linear programming; Load management; Load modeling; Mixed integer; Natural gas; Optimal scheduling; Optimization; power to gas (P2G); Resistance heating; Scheduling; Spinning; Subsystems; System reliability; Turbines; Uncertainty
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2021.3106573

The community-integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR) enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity-gas-heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and microgas turbine (MT), as the links of multienergy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. The simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a tradeoff between the economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the hybrid intelligent algorithm in terms of both optimization results and calculation efficiency.