Solar Forecasting And Future Sustainable Power Systems

太阳能预测和未来可持续电力系统

• 批准号: 2908163
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2908163
• 关键词: Solar; Forecasting; Future; Sustainable; Power

The contribution of PV energy to the electricity generation mix continues to grow. Installed capacity in the UK in 2020 was 13.4 GW and is expected to increase to 40 GW by 2030. Accelerating the adoption of solar energy will present significant challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction of solar energy production.To address this issue, the proposed project aims to enhance the forecasting accuracy of solar radiation, thereby improving our understanding of solar power generation and its impact on the grid. The PhD student will focus on developing an economic model for electricity price variation over time, considering the availability of electricity from the solar system as influenced by solar availability. Subsequently, they will develop optimal strategies for purchasing and selling solar electricity for end users.Year 1: Conduct a literature review of current grid operation strategies and the models utilised by end users for purchasing and selling electricity.Year 2: Develop the initial model for optimal strategies concerning the purchase and sale of solar electricity for end users, incorporating improved solar forecasting data.Year 3: Refine and finalise the developed model to ensure optimal performance and usability.Year 4: Thesis writing up.

光伏能源对发电结构的贡献持续增长。 2020年英国装机容量为13.4吉瓦，预计到2030年将增至40吉瓦。加速太阳能的采用将对输配电系统带来重大挑战，因为太阳能不可调度，因此将其纳入电力系统发电组合的一个重要组成部分需要准确预测太阳能产量。为了解决这个问题，拟议项目旨在提高太阳辐射的预测精度，从而提高我们对太阳能发电和发电的了解。其对电网的影响。博士生将专注于开发电价随时间变化的经济模型，考虑太阳能系统电力的可用性受太阳能可用性的影响。随后，他们将制定为最终用户购买和销售太阳能电力的最佳策略。第一年：对当前电网运营策略以及最终用户用于购买和销售电力的模型进行文献综述。第二年：开发初始模型为最终用户购买和销售太阳能电力的最佳策略，结合改进的太阳能预测数据。第 3 年：完善和最终确定开发的模型，以确保最佳性能和可用性。第 4 年：撰写论文。