Optimal operational planning considering uncertainties for energy plants

In recent years, cogeneration systems (CGS) have been installed in various factories and buildings. In order to generate optimal operational planning for CGS, various load, for example, electric loads, air-conditioning loads, heating loads, and hot water loads, should be forecasted, and startup and shutdown status and input values for the facilities at each control interval should be determined using facility models. The authors have already developed optimal operational planning for CGS using particle swarm optimization (PSO), which is one of the meta-heuristic optimization methods. However, there have always been uncertainties such as load forecasting errors or sensor errors. Therefore, generated operational planning does not always be optimal considering uncertainties. This paper proposes optimal operational planning of energy plants by PSO considering load forecasting error. And this paper also introduces the sensor diagnosis functions based on the concept of power system state estimation. The proposed method is applied to a typical cogeneration system with promising results.