Abstract:
This paper proposes a comprehensive planning model for enhancing accommodation of renewable energy sources into a combined heat and power based multi-energy system by utilizing the flexibility of a demand response program. As distinct from existing works, demand response has been implemented through a price-based program not only in terms of its effects on system operation but also for the planning decisions, and the potential correlations among uncertainties (i.e., renewable energy sources availability, load demand, and demand-side responsiveness) have been explicitly considered in our study. The concerned problem is interpreted into a two-stage optimization problem, where the placement of advanced metering infrastructures, the installation of renewable generation units along with the relevant pricing strategy for the demand side are jointly optimized to minimize the overall economic costs of the system. The flexibility of customers’ energy demands in response to real-time price changes is represented by using a generalized elasticity model, through which the demand response in terms of both load shifting in the temporal dimension, and the switching of energy source on the demand side can be properly captured under a unified framework. An efficient scenario generation method leveraging a series of correlation-handling techniques is employed to address the uncertainties in the proposed problem and a comprehensive scenario reduction method based on a novel optimal clustering technique is further introduced to alleviate the computational burden of the resultant model with the correlated uncertainties. Compared with the existing literatures, the novelty of this paper is in three-fold: 1) This study investigates the potential value of demand response for promoting renewable energy exploitation from a long-term planning perspective, instead of the conventional operation aspect. 2) The impacts from both system uncertainties and their potential correlations have been explicitly considered. 3) A comprehensive scenario reduction method based on optimal clustering analysis is introduced and employed to alleviate the solving complexity and improve the computational efficiency of the proposed model. The proposed planning framework is demonstrated on an illustrative test case and the simulation results verified the effectiveness of the developed model.
