Two-stage optimal scheduling of air conditioning resources with high photovoltaic penetrations

Abstract

Abstract Large-scale of controllable air conditioning loads have the potential to participate in distribution network operation via demand side control scheme. At the same time, distributed renewable energy resources are being deployed in distribution networks with increasing penetration rate. In this paper, a two-stage optimal scheduling method is proposed for distribution system with high photovoltaic penetration. The proposed scheduling method is decomposed into two stages to alleviate intra-day random variations of PV generation, electricity prices, and end-user load. In the day-ahead stage, this paper employs a mixed integer linear programming (MILP) method to achieve coordinated control of air conditioning loads, solar photovoltaic (PV) resources and battery energy storage systems (BESSs) under the target of minimizing system overall operation costs. A novel two-parameter lumped thermal model is introduced to more accurately describe the thermal dynamic process of the buildings, which is critical to meet the end users’ thermal comfort in control process. In the real-time stage, a rolling horizon optimization approach is applied for minimization of imbalance costs between the day-ahead energy market and real-time energy market. Simulations are conducted on a radial distribution network, whose results verify that the proposed method can effectively reduce operation cost, lower peak demand and improve the PV penetration level in the distribution network.