An overview of optimization technologies applied in combined cooling, heating and power systems

Combined cooling, heating and power (CCHP) systems have the advantages of higher energy efficiency and lower economic and environmental impacts (3E) than separated systems. Recently, optimization methods have been extensively applied in CCHP system's design and control for further improving 3E performances of CCHP systems. However, a wide range of subsystems selection, nonlinear characteristics of subsystem models and uncertainty in modeling results of CCHP systems cause challenges for successful implementation of optimization studies at both design and control stage. This paper presents a systematic review of all aspects of optimization for CCHP systems from problem formulation and algorithms selection to technical implementation. Genetic algorithm, particle swarm optimization and differential evolution are the most common algorithms used in system optimum design and control. Optimization works in design stage focus on optimization of energy source, prime mover, storage system, energy demand and system configuration. Over 60% of renewable energy integrated CCHP system adopted solar energy. More investigations are needed for small scale prime mover and economic is the development obstacle among 3E objectives. Dynamic characteristics and mal-distribution problem should earn more attention when optimizing storage system in CCHP. For the system control aspects, the baseline control strategy of load-following method as well as optimum control under real-time data and uncertainty condition are discussed. The intermittent feature of renewable energy should be considered at system control stage with uncertainty study. Uncertainty inside of energy demands, subsystems performance and market price should be investigated at the same time during control stage. Overall, this review paper can be used as an optimization reference and guidance for optimum CCHP system design and control.