The many-objective optimal design of renewable energy cogeneration system

To alleviate the issues of climate warming and fossil resources depletion, this paper proposes a wind-photovoltaic-thermal energy storage-electric heater cogeneration system, which adopts the structure of concentrated solar power plant but replaces the expensive collector field and tower with wind and photovoltaic farms and electric heater that converts the excess electricity as thermal energy source. The cogeneration system can effectively regulate the fluctuating wind and photovoltaic output to satisfy electricity load by electric heater based on resistive heating of molten salt, two-tank molten salt thermal energy storage and power block based on steam Rankine cycle, and supply heat load directly from the molten salt heat transfer fluid and heat exchanger. This paper considers minimizing levelized cost of energy, loss of power and heat supply probability, energy reduction rate and lifecycle equivalent CO2 emissions as optimized objectives, and investigates the many-objective (having four or more objectives) capacity optimization problem by Nondominated Sorting Genetic Algorithm-Ⅲ coupled with Principal Component Analysis and Technique for order preference by similarity to ideal solution. Moreover, residual analysis is applied to consider the uncertainty of wind power prediction. The detailed optimization process and performance comparison of many-objective algorithms, the all-year energy flow and seasonal output characteristics analysis of the optimal-compromise solution are presented. Finally, the proposed cogeneration system is compared to the power-only system with the same installed capacity and equivalent load demand, and the comparison results show that the proposed cogeneration system performs better in all evaluation metrics. Therefore, the proposed renewable energy cogeneration system is feasible and cost-effective for practical applications.