2019 journal article
Optimal day-ahead scheduling for commercial building-level consumers under TOU and demand pricing plan
ELECTRIC POWER SYSTEMS RESEARCH, 173, 240–250.
In this paper, we solve the day-ahead scheduling programming problem for commercial building-level consumers with combined time-of-use ($/kWh) and demand ($/kW) pricing plans. Aiming at minimizing the monthly charge, the problem formulation is proposed as a generic algorithm that produces day-ahead building operation schedules. It considers the influences of daily peak load during on-peak hours and daily energy consumption on the monthly charge. The aggregation model of building-level space conditionings is built for scheduling demo. To obtain the near global optimum, a multi sub-swarms particle swarm optimization (MSPSO) is proposed by introducing the ideas of mutation operation, work hierarchy and iterative regrouping into particle swarm optimization (PSO). It improves the population diversity for enhancing the global searching ability and the ability of escaping from local optimum. The PSO comparison shows that MSPSO has better convergence performance with higher stability compared with some classical PSOs. Furthermore, a commercial office-style building with space conditionings is simulated. Using TOU and demand pricing plan from Duke Energy, numerical results demonstrate that the proposed day-ahead scheduling algorithm and the improved MSPSO can reduce the monthly charge by 30% and 17%, respectively.