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

光伏发电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。加速采用太阳能将给电力传输和分配系统带来重大挑战，因为太阳能是不可调度的，因此将其作为发电组合的主要组成部分需要对太阳能产量进行准确估计。对太阳能发电的准确估计/预测是一项重大挑战，特别是在天气模式变化很大的国家，如英国，因为对太阳能在天空中的复杂分布了解不足。太阳辐射是间歇性的，光伏阵列平面上任何给定位置的太阳源高度依赖于太阳的位置、大气气溶胶水平、云量和运动等。太阳能的这种固有的可变性直接影响到输入电网的太阳能，并可能在电网的需求和容量/运输/分配/存储之间造成严重的不平衡，这可能严重损害电网的可靠性。为了解决这些问题，长期目标是开发一个全面的数字平台来预测太阳能生产（从极短期到长期的太阳辐射预测），以显着提高气象参数的预测精度，减少太阳能预测误差造成的功率不匹配，并减少对化石燃料发电的持续需求。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，通过开发和演示基于“云”的太阳测量和建模平台来支持多个数据源和密集的预测算法，从而显著提高小时内太阳预测的预测精度。目标是实现20至1小时的预测范围，时间分辨率为10秒。

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.