Optimal energy scheduling for multi-energy network based on incentive integrated demand response

Abstract

In recent years, the rapid expansion of data center construction has led to rising energy demand, forming new energy-intensive industrial clusters supported by integrated energy systems (IES). This study investigates the use of integrated demand response (IDR) to manage the complex coordination of electricity, heating, and cooling in such systems. A framework is proposed that connects data centers with surrounding residential and commercial buildings through an IES, enabling joint scheduling of multiple energy flows. A two-stage bilevel optimization model is developed to capture the interaction between the integrated energy service provider (IESP) and users. The upper level minimizes total operating costs, while the lower level maximizes user benefits under comfort constraints. Simulation results show that the proposed IDR strategy effectively balances multi-energy demands, enables rapid load adjustments, smooths the energy supply curve, and reduces dependence on costly peak-hour energy. The strategy achieves an average daily cost reduction of approximately 3.25 %. Compared with conventional approaches focused only on electricity price response, this work presents a generalized and scalable IDR framework. It incorporates energy conversion relationships, user-side flexibility, and incentive design, making it more applicable to complex, multi-energy environments. The results highlight the potential of this approach to enhance economic efficiency and operational reliability in future energy-intensive clusters.