Optimal scheduling of commercial demand response by technical virtual power plants

Abstract

The trend towards a decentralized, decarbonized, and digital energy system is gaining momentum. A key driver of this change is the rapid penetration increase of Distributed Energy Resources (DER). Commercial consumers can offer significant contributions to future energy systems, especially by engaging in demand response services. Virtual Power Plants (VPP) can aggregate and operate DERs to provide the required energy to the local grid and allowing for the participation in wholesale energy markets. This work considers both the technical constraints of the distribution system as well as the commercial consumer's comfort preferences. A stochastic mixed-integer linear programming (MILP) optimization model is developed to optimize the scheduling of various DERs owned by commercial consumers to maximize the profit of the TVPP. A case study on the IEEE 119-bus test system is carried out. Results from the case study show that the TVPP provides optimal DER scheduling, improved system reliability and increase in demand response engagement, while maintaining commercial consumer comfort levels. In addition, the profit of the TVPP increases by 49.23% relative to the baseline scenario.