Stochastic Power Generation From Renewables: Forecasting and Optimization Challenges for its Optimal Integration
Seminar Room 1, Newton Institute
AbstractRenewable energy sources and demand are directly influenced by the weather. In contrast to the electric load, whose variations at an aggregated level are quite smooth and fairly predictable based on key meteorological variables, the variability and (lack of) predictability of renewable power generation induce new challenges in power systems and market operations. This variability and lack of predictability obviously depend upon the type of renewable energy source, location, time of the year and prevailing weather conditions. They are of crucial importance at a wide range of temporal and spatial scales, e.g., local and short-term for control problems or region-wide and longer-term for planning and investment problems. In practice, the operational management of renewable energy in power systems and electricity markets requires dedicated forecasts of power generation, which may be of deterministic or probabilistic nature. They are used as input to decision-making problems which are more or less advanced depending upon the decisions to be made and the expertise of the decision-maker. In view of the uncertainties involved the forecasts should optimally be probabilistic, in the form of marginal predictive densities or even space-time scenarios, while decision-making problems should be solved in a stochastic optimization framework. In that respect maybe one of the key to successful decision-making lies at the interface between forecasting and optimization. Indeed, the more advanced stochastic optimization problems, supposed to yield better decisions in view of uncertainties, necessitate forecasts of ever-increasing complexities. These get quite difficult to generate and verify, potentially leading to less valuable decisions overall. Also, practical aspects might step in, preventing the application and operational implementation of the advanced approaches proposed by academics. The interface between forecasting and optimization will be discussed based on real-world test cases, but also toy models in power systems operations and electricity markets.
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