Abstract
The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources.
Highlights
The global societal goal of limiting global warming leads to policies for increasing the share of renewable energies (REN) in the gross final energy consumption to 27% in the EU and 30% in the Federal Republic of Germany by 2030 [1,2,3,4]
When considering the risk measure conditional value-at-risk (CVaR) in the decision process of the power plants, the classic Benders decomposition framework exhibits a significant disadvantage in the sense that for some model instances the number of required algorithm iterations is inadmissibly high
We presented an unit commitment optimization model that allows the simulation of day-ahead market (DA) and intraday market (ID) electricity markets for large-scale energy systems
Summary
The global societal goal of limiting global warming leads to policies for increasing the share of renewable energies (REN) in the gross final energy consumption to 27% in the EU and 30% in the Federal Republic of Germany by 2030 [1,2,3,4]. There is currently no procedure for simulating the electricity market, taking into account short-term forecast uncertainties, and depicting real decision-making processes of market participants The modeling of this two-stage decision situation requires the integration of a risk measure, so that the indifference between uncertain and secure returns can be resolved. This paper presents an approach to model large-scale, detailed electricity spot markets, taking into account the uncertainties of renewable energy forecasts and the electrical system load. For this purpose, the unit commitment decisions are modeled bottom-up, i.e., for each hydro-thermal power plant greater than ten megawatts.
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