Jump to Main Content
Multi-objective optimization scheduling of wind–photovoltaic–hydropower systems considering riverine ecosystem
- Liu, Weifeng, Zhu, Feilin, Chen, Juan, Wang, Hao, Xu, Bin, Song, Peibing, Zhong, Ping-an, Lei, Xiaohui, Wang, Chao, Yan, Mengjia, Li, Jieyu, Yang, Minzhi
- Energy conversion and management 2019 v.196 pp. 32-43
- algorithms, ecosystems, models, multi-criteria decision making, power generation, solar energy, uncertainty, water power, wind, wind power
- Hydropower can aid in compensating for wind and photovoltaic power output fluctuations and uncertainties. In this study, a multi-objective optimization model was established by integrating wind and photovoltaic power with hydropower scheduling considering the total power generation, power output stability, and influence of hydropower on a downstream riverine ecosystem. An improved adaptive reference point-based multi-objective evolutionary algorithm was employed to solve the wind–photovoltaic–hydropower system problem with various complicated constraints. Moreover, the large-scale system decomposition principle was used to decouple a wind–photovoltaic–hydropower system into a wind–photovoltaic compensated subsystem and a hydropower system. A combined solution method was developed according to the subsystem characteristics to improve the model efficiency. Considering that direct crossover and mutation of hydropower systems may not yield feasible solutions, dynamic feasible regions for crossover and mutation were constructed for multi-objective optimal scheduling. Furthermore, a stochastic multi-criteria decision making model that accounts for the uncertainty of criterion information was established, and the non-dominated solution obtained using the improved multi-objective evolutionary algorithm was employed for decision-making. The results showed that the total power generation, power output stability, and downstream riverine ecosystem have strong competitive relationships, and the improved adaptive reference point-based multi-objective evolutionary algorithm can produce superior quality Pareto optimal solutions with uniform distribution. Subsequently, the stochastic multi-criteria decision making model was used to rank the Pareto optimal solutions, where each solution can obtain several ranks with different probabilities, providing extensive information for use in decision-making.