Forecasting Technologies for Operation of Modern Electric Energy Systems Under Uncertainty

在过去的二十年里，电力系统发生了三次重大变化。首先，电力行业的放松管制导致批发电力价格不确定。第二，环境问题引起了风能和太阳能发电的部署，这是自然变化和不确定的。第三，智能电网的新模式有望为消费者管理其能源使用和参与电力市场创造新的机会。智能电网受益于现有电网之上的额外智能层，以增加电力系统的灵活性和可靠性。与今天的静态负荷相比，智能电网预计将带来更动态和价格敏感的负荷。这三大变化带来了电价、发电量和用电量的不确定性。建议的研究建议侧重于技术和工具，以减轻这种不确定性对电力系统运行和规划的影响，通过预测未来值的不确定性变量。特别是，将开发电价、电力需求、太阳能和风力发电的预测技术。价格和需求预测技术将考虑到这两者在智能电网环境中的相互影响。特别是，电力负荷、电动汽车、电力消费者的市场激励措施和智能电网的上网电价的动态将被建模和纳入。对于风力发电预测，重点将放在风力斜升事件上，即预测风力发电在未来特定时间的变化率。此外，风电预测的实际应用将与培训和构建预测引擎相联系，以提高性能。此外，还将研究太阳能发电和太阳能预测，并考虑到辐射模式随时间的变化。这部分研究将建立受益于天空成像和数据分析的混合模型。

Over the past two decades, three major changes have been introduced to electric power systems. First, deregulation of power industry has led to uncertain prices of wholesale electricity. Second, environmental concerns have given raise to deployment of wind- and solar-powered electricity generation, which are by nature variable and uncertain. Third, the new paradigm of smart grid is expected to create new opportunities for consumers to manage their energy use and participate in power markets. Smart grids benefit from an extra layer of intelligence on top of the existing grid to add to power systems flexibility and reliability. Smart grids are expected to lead to more dynamic and price-responsive loads compared to today's static load. These three major changes have introduced uncertainty in electricity prices, power generation, and power consumption. The proposed research proposal focuses on techniques and tools to mitigate the impact of such uncertainties on power systems operation and planning by predicting future values of uncertain variables. In particular, forecasting technologies for electricity prices, electricity demand, solar power and wind power will be developed. Price and demand forecasting techniques will take into account the mutual impact of these two on each other in a smart grid environment. In particular, dynamics of electricity load, electric vehicles, market incentives for electricity consumers and feed-in tariffs in smart grids will be modelled and incorporated. For wind power forecasting, the focus will be on wind ramping events, i.e, to predict the rate at which wind power will change at certain times in the future. Moreover, practical applications of wind power forecasts will be tied to training and building forecasting engines for improved performance. Solar power and solar energy prediction will also be investigated considering the variations in irradiation patterns over time.Hybrid models that benefit from sky-imaging and data analysis will be built for this part of the research.