Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications

This paper proposes a methodology for an efficient generation of correlated scenarios of Wind, Photovoltaics (PV) and small Hydro production considering the power system application at hand. The merits of scenarios obtained from a direct probabilistic forecast of the aggregated production are compared with those of scenarios arising from separate production forecasts for each energy source, the correlations of which are modeled in a later stage with a multivariate copula. It is found that scenarios generated from separate forecasts reproduce globally better the variability of a multi-source aggregated production. Aggregating renewable power plants can potentially mitigate their uncertainty and improve their reliability when they offer regulation services. In this context, the first application of scenarios consists in devising an optimal day-ahead reserve bid made by a Wind PV-Hydro Virtual Power Plant (VPP). Scenarios are fed into a two-stage stochastic optimization model, with chance-constraints to minimize the probability of failing to deploy reserve in real-time. Results of a case study show that scenarios generated by separately forecasting the production of each energy source leads to a higher Conditional Value at Risk than scenarios from direct aggregated forecasting. The alternative forecasting methods can also significantly affect the scheduling of future power systems with high penetration of weather-dependent renewable plants. The generated scenarios have a second application here as the inputs of a two-stage stochastic unit commitment model. The case study demonstrates that the direct forecast of aggregated production can effectively reduce the system operational cost, mainly through better covering the extreme cases. The comprehensive application-based assessment of scenario generation methodologies in this paper informs the decision-makers on the optimal way to generate short-term scenarios of aggregated RES production according to their risk aversion and to the contribution of each source in the aggregation.