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Under China&#x2019;s vigorous development of integrated energy services, the Integrated Energy Service Agency (IESA) is responsible for purchasing energy from external markets and selling energy to multi-energy users (MEUs). Currently, an increase in the various forms of energy in industrial parks has caused great uncertainty for MEUs participating in an integrated demand response (IDR) but has also provided an opportunity for industrial parks to optimize energy conservation. Therefore, determining how to build an elastic energy cloud model with IDR uncertainty and add the uncertainty and randomness of the cloud model to the optimal scheduling of an industrial park integrated energy system is a key problem. In this paper, an optimal economic dispatch model of an industrial park is proposed and considers the uncertain elastic energy of IDR. In this model, the IESA is responsible for the reasonable scheduling of equipment for optimal operation, the establishment of integrated energy retail prices for MEUs, and the goal of maximizing the net income of the IESA. First, the functional relationship among the self-elastic coefficient, retail energy prices, and IDR variation is considered. A cloud model of the self-elastic coefficient is constructed to indirectly represent the multiple uncertainties of the elastic energy in the industrial park. Second, this paper compares and analyzes the economic benefits and IDR potential of the industrial park by considering only single power users in different intervals and the selection of cloud drop elements of MEUs in all intervals. Finally, a new scene random sampling method based on interval contributions (SRS-IC) is employed to solve the optimization model, and a typical example is used to demonstrate that the model and method can guarantee the overall economy of the industrial park, improve the computational efficiency, and explore the IDR potential of MEUs.

Optimal Economic Dispatch for an Industrial Park With Consideration of an Elastic Energy Cloud Model With Integrated Demand Response Uncertainty