Implementation of a price-driven demand response in a distributed energy system with multi-energy flexibility measures

Distributed energy systems are a promising integrated energy technology due to their energy efficiency and environment benefits. However, the increasing complexity of distributed energy systems, integrated with variable renewable energy, requires more intelligent operational methods to achieve economic efficiency, and reduce negative effects on the power grid. In this study, multi-flexibility measures are used to facilitate interaction between a distributed energy system and the power grid. First, a mixed-integer and linear programming model is proposed for optimizing the dispatch of a distributed energy system with minimum operational costs. Then, the price-driven demand response is performed by coordinating flexibility measures optimally in the operation of a distributed energy system in Guangdong province, China. A detailed case study is conducted in which three types of flexibility measures are modeled, and their effects on end-users and power grid are discussed. The optimal results show that each flexibility measure can well response to the time-of-use price. The distributed energy system's operating costs were reduced by 1.7-12.9% when individual flexibility measures were applied and 19.6% when all the flexibility measures were implemented. However, the price-driven demand response program significantly decreases the tie-line's (i.e. the power line connecting the distributed energy system to the main power grid) power stability. Three types of ancillary services based on multi-flexibility measures in a distributed energy system are proposed in this paper and optimized based on the epsilon - constraint method to facilitate smooth interaction between the power grid and a distributed energy system. The Pareto frontiers indicate that all the operational costs decrease along with the ancillary service. The technical and economic boundaries of each ancillary service are further determined, which can help distributed energy system operators make more informed operational decisions.