Stochastische Fundamentalmodellierung von Day-Ahead- und Intraday-Elektrizitätsmärkten
| Autor: | Nobis, Moritz |
|---|---|
| Přispěvatelé: | Schnettler, Armin, Weber, Christoph |
| Jazyk: | němčina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Aachen 1 Online-Ressource (xii, 154 Seiten) : Illustrationen, Diagramme (2020). doi:10.18154/RWTH-2020-10818 = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020 |
| DOI: | 10.18154/rwth-2020-10818 |
| Popis: | Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020; Aachen 1 Online-Ressource (xii, 154 Seiten) : Illustrationen, Diagramme (2020). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020 The decarbonization of energy generation is leading to a steady increase in generation capacity from weather-dependent and thus forecasting error-prone wind energy and photovoltaic plants. As a result, trading volumes on the intraday market, which enables the intraday trading of forecast errors from renewable energies and the electrical load until a few minutes before physical fulfillment, are also gradually increasing. The volume uncertainty on the intraday market leads to price uncertainty. This makes the intraday market increasingly relevant in unit commitment decisions for all market participants. The bottom-up modeling of techno-economic causal relationships in electricity markets via computer models is of central importance for energy system planning. However, no method is currently able to adequately reflect forecasting uncertainties and risk metrics in large-scale systems due to complexity limitations. Thus, it is not yet possible to realistically map power plant operations for future energy systems. Within the scope of this work, a method is being developed which simulates the electricity market bottom-up by making block-specific commitment decisions for the entire power plant fleet of the European interconnected grid, taking into account all relevant technical and economic boundary conditions. Forecasting errors of windenergy and photovoltaic plants, as well as the electrical load, are taken into account and thus, in addition to a day-ahead market, an intraday market is also implicitly represented. The marketing risk resulting from forecasting uncertainty is included in the marketing decision by including conditional value-at-risk. Endogenous market coupling on the day-ahead and intraday markets is also integrated into the model. The method becomes applicable only through the development of a novel nested mathematical decomposition, which couples an extended Benders formulation with a Lagrangian relaxation. In a backtest, the method shows realistic market prices and unit commitment decisions. By applying it to scenarios for 2023 and 2035, rising spot prices and a rising intraday-day-ahead spread can be derived. Intraday market coupling, which is currently being successively expanded, shows a welfare-optimizing and pricedampening effect. In addition, based on the scenarios, increasing contribution margins for gas-fired power plants can be expected, which will partly exceed annuity investment costs in 2035 and thus potentially set investment signals. Published by Aachen |
| Databáze: | OpenAIRE |
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