Generation planning for power companies with hybrid production technologies under multiple renewable energy policies

Increased use of environmentally friendly and energy-efficient transportation, such as electric vehicles, results in increased demand for power supply, putting pressure on the electricity infrastructure. One of the key challenges facing power companies operating within this context is effectively planning power production across multiple production technologies. To address this challenge, this study employs fuzzy set theory and proposes a combinatorial optimisation approach for the problem of power generation planning. The planning process is considered as a multi-period optimisation task where uncertain factors in each period are considered as fuzzy variables. The credibility expected value and the lower semi-variance in the final production value are considered as the return and risk objectives of the problem, respectively. Then, a bi-objective optimisation model with complex constraints under the influence of political factors is proposed. A hybrid intelligent algorithm based on fuzzy simulation, artificial neural network and multi-objective genetic algorithm is developed to solve the model. A numerical example in the Chinese energy market is tested to illustrate the effectiveness of the proposed approach. The experimental results highlight seasonal variation in the profits and risks of each production technology. From a practical perspective, the proposed approach can help decision-makers to establish multi-period production planning. Additionally, this study analyses the optimal production planning for companies under different levels of renewable energy and carbon emission standards. The results show that the proposed approach can reflect the influence of these policy factors on utilities’ production decisions, which provides certain guidance for regulators to formulate appropriate policies.