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

TL;DR

This paper presents a chance-constrained scheduling model for community energy systems that optimizes costs and enhances flexibility by integrating demand response, renewable uncertainties, and multi-energy carriers, using advanced linearization techniques.

Contribution

It introduces a novel chance-constrained programming approach for integrated demand response-enabled community energy systems, incorporating power-to-gas and micro-gas turbines for improved flexibility.

Findings

Improved operational economy through coordinated demand response and renewable uncertainty management.

Enhanced system flexibility and user satisfaction with multi-energy carrier integration.

Outperforms existing algorithms in optimization quality and computational efficiency.

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 micro-gas turbine (MT), as links of multi-energy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory (SOT) and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. 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 trade-off between 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.