School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, Shandong, China.
Comput Intell Neurosci. 2022 Aug 21;2022:1722848. doi: 10.1155/2022/1722848. eCollection 2022.
In order to actively respond to the "14th Five-Year Plan," the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the "14th Five-Year" energy plan.
为积极响应“十四五”规划，本文运用 PGA 算法制定新能源规划策略。该项目可以充分利用我国丰富的可再生能源，鼓励节能减排，提高能源结构的低碳水平，支持智能绿色能源发展，实现生态文明建设。本方案可以向用户展示某些环境变化可减少多少温室气体排放，以及满足未来能源需求的基本问题。它可以展示未来不同情景下的效益、成本和排放数据，并采用情景展示方法使能源规划更生动。它可以让人们、技术人员和决策者了解中国未来 15 年的碳排放情况。本文创新性地将粒子群算法与遗传算法相结合，设计了用于路径优化的 PGA 算法。在减排方面，对比试验表明，PGA 算法的路径优化比遗传算法高 58.06%；在成本方面，PGA 算法的路径优化比遗传算法便宜 15.72%。本文为未来能源规划方案选择最佳结果提供了参考路径，为“十四五”能源规划提供了新策略。
