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

TLDR

The community‑integrated energy system (CIES) is a key energy‑internet carrier that has attracted recent attention. The study proposes a chance‑constrained scheduling model for IDR‑enabled CIES to minimize operating costs under uncertainty. The model integrates P2G and micro‑gas turbines to couple energy carriers, employs sequence‑operation theory and linearization to transform the chance‑constrained problem into a solvable mixed‑integer linear program, and uses an IDR program to exploit interactions among flexible loads and electric vehicles. Simulations on a North China CIES show that coordinating IDR with renewable uncertainty improves operational economy, that P2G and MT enhance flexibility and user satisfaction, and that the proposed method outperforms a hybrid intelligent algorithm in both results and efficiency while achieving a trade‑off between economy and reliability via adjustable confidence levels.

Abstract

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.