Siemens-EPSRC: Cloud-based solar forecasting for improved grid management

西门子-EPSRC：基于云的太阳能预测可改善电网管理

• 批准号: EP/W028581/1
• 负责人: Yupeng Wu
• 金额: $ 6.42万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW028581%2F1
• 关键词: Siemens; EPSRC; Cloud; based; solar

The contribution of PV energy to the electric grid continues to grow. Installed capacity in the UK in 2020 was 13.4 GW, (4.1% of total electricity generation compared with only 0.01% in 2010) and is expected to increase to 40 GW by 2030. Accelerating 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 estimation of solar energy production. The accurate estimation/prediction of solar energy generation is a significant challenge, especially in countries with widely varying weather patterns such as the UK, due to a poor understanding of the complex distribution of solar energy in the sky. Solar radiation is intermittent and the solar source at any given position on the plane of a PV array is highly dependent on the position of the sun, atmospheric aerosol levels, cloud cover and motion, etc. This inherent variability in the solar source directly affects solar-derived energy fed into power grids and can create severe imbalances between demand and the capacity/transport/distribution/storage of the grid, which can significantly impair grid reliability.To counter these issues, the long-term aim is to develop a comprehensive digital platform for forecasting solar production (from very short to long term solar radiation forecasting) to significantly improve the prediction accuracy of meteorological parameters, reducing the power mismatch caused by solar forecast errors, and also reducing the continuing requirement for fossil fuel-based generation. To achieve this, the aim for this project is to build on our existing outdoor solar testing facility to significantly improve the prediction accuracy for intra-hour solar forecasting by developing and demonstrating a 'cloud'-based solar measurement and modelling platform to support multiple data sources and intensive prediction algorithms. The target is to achieve a prediction horizon of 20s to 1 hour with temporal resolution of 10s.

光伏能源对电网的贡献持续增长。2020年英国的装机容量为13.4吉瓦（占总发电量的4.1%，而2010年仅为0.01%），预计到2030年将增加到40吉瓦。加速采用太阳能将对电力传输和分配系统提出重大挑战，因为太阳能不能调度，因此将其作为发电组合的主要组成部分需要准确估计太阳能产量。太阳能发电的准确估计/预测是一个重大的挑战，特别是在天气模式变化很大的国家，如英国，由于对天空中太阳能的复杂分布的理解不足。太阳辐射是间歇性的，光伏阵列平面上任何给定位置处的太阳能源高度依赖于太阳的位置、大气气溶胶水平、云层覆盖和运动等。太阳能源的这种固有可变性直接影响馈送到电网中的太阳能，并且可能在需求与电网的容量/运输/分配/存储之间造成严重的不平衡，为了解决这些问题，长期目标是开发一个预测太阳能产量的综合数字平台（从极短期到长期的太阳辐射预报）显著提高气象参数的预报精度，减少太阳预报误差造成的功率失配，并且还减少了对基于化石燃料的发电的持续需求。为了实现这一目标，该项目的目标是建立在我们现有的户外太阳能测试设施的基础上，通过开发和展示一个基于“云”的太阳能测量和建模平台，以支持多个数据源和密集的预测算法，显着提高小时内太阳能预测的准确性。目标是实现20秒至1小时的预测范围，时间分辨率为10秒。

项目成果

Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling

使用平均光子能量进行直接光谱分布表征，以改进光伏性能建模

• DOI: 10.1016/j.renene.2022.11.001
• 发表时间: 2022
• 期刊: Renewable Energy
• 影响因子: 8.7
• 作者: Daxini R

Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach

• DOI: 10.1016/j.renene.2023.118952
• 发表时间: 2023-06
• 期刊: Renewable Energy
• 影响因子: 8.7
• 作者: Liwen Zhang;Robin Wilson;M. Sumner;Yupeng Wu