Model-based multi-objective predictive scheduling and real-time optimal control of energy systems in zero/low energy buildings using a game theory approach

Online optimal control of energy systems plays a significant role in achieving the zero/low energy goal and high energy efficiency for zero/low energy buildings. Increasing amount of studies are conducted on the multi-objective optimal control of building energy systems in recent years since multiple objectives are often concerned in practice. The weighted sum method is widely used to solve the multi-objective optimization problems for online optimal controls. However, it is often difficult and even impractical to determine proper weights for objectives of different natures. Existing studies on online optimal control of energy systems in zero/low energy buildings focused on the predictive scheduling, and very few studies addressed the real-time optimal control aspect which is also essential in operation. In this study, a coordinated online multi-objective control strategy, consisting of two control optimization schemes, is therefore proposed for predictive scheduling and real-time optimal control of energy systems in zero/low energy buildings. A cooperative game theory-based method is adopted for the online multi-objective optimizations. The control strategy was tested and evaluated via simulation of the energy systems in a zero energy building with battery storage on two typical days. Control variables and weights of objectives were optimized to minimize the combined optimization objective involving energy cost and grid impact. The test results show that it is essential and beneficial to coordinate the predictive scheduling and real-time optimal control in actual operation. The cooperative game theory-based method is effective for the online multi-objective optimization without the need of setting weights of different objectives.