A nodal pricing analysis of the future German electricity market

Abstract

The electricity market in Germany is likely to undergo several significant structural changes over the years to come. Here one may think of Germany's ambitious renewable agenda, the disputed decommissioning of nuclear facilities, but also unbundling of TSO's as enforced by European regulation. This study is a scenario-based analysis of the impact of different realizations of known investment plans for transmission and generation capacity on the future German power market while accounting for internal congestion. For this analysis the static equilibrium model of the European electricity market COMPETES is deployed, including a 10-node representation of the German high-voltage grid. Results for the multi-node analysis indicate that price divergence and congestion are likely to arise in the German market as renewable additions affecting mainly the North of Germany, the debated decommissioning of nuclear facilities in the South, and the expected decommissioning of coal-fired facilities in Western Germany appear to render current investment plans for transmission capacity insufficient. The current system of single-zone pricing for the German market may therewith be compromised. However, transmission additions would not benefit all market parties, with producers in exporting regions and consumers in importing regions being the main beneficiaries. Vertical unbundling of German power companies could increase the incentive for constructing transmission lines if generation capacity would cause Germany to be a net-importing country. In case Germany remains a net-exporting country, the effects of vertical unbundling on cross-border capacity are less clearcut.Index Terms-complementarity problem, electricity market, investment, market power, nodal pricing, partial equilibrium model, transmission system unbundling,

Regionalization of a national integrated energy system model: A case study of the northern Netherlands

“…We constructed a multi-node network infrastructure in which each node represented an explicit region. This approach is also used in other models, such as COMPETES, the pan-European electricity market model [55,85], and ESME [24]. The data requirements for our method were relatively low compared with the data requirements of GIS-based methods because of the modest geographical resolution.…”

“…This scenario favors electrification through renewable power generation and a relatively high demand and supply of domestically produced green H 2 . National-level imports/exports of electricity in our model were based on an import/export profile derived from COMPETES [55,85] using the NM2050 scenario [91]. Table 5 presents the newly added activities under the demand category and the supply potentials/capacities, standardized units, and regional allocation of activities.…”

“…In addition, we allowed an onshore HV transmission grid expansion of 2.5× the current capacity in 2050 as compared to 2× considered in [90]. Since, the electricity grid is internationally connected, we used trade flows from the COMPETES model [85,112] based on the LSES study [91]. Additionally in [90], H 2 admixing in the NG network is unclear for 2050.…”

Zonal and Nodal Models of Energy Market in European Union

“…Thus, the energy market has an obligation to organize in an impartial and non-discriminatory manner the exchange of data concerning basic information such demand of energy, supply of energy, and potential flexibility [36]. For example, the planned energy transformation in Germany is to enable the generation of 100% of all energy from renewable sources [37,38]. By 2050, Germany will be a climate neutral country, i.e., with zero emissions [39].…”

Linear estimation approach for including strategic competition in market simulations

“…It is developed by ECN in corporation with B.F. Hobbs, who is a professor with the Department of Geography and Environmental Engineering of the Johns Hopkins University, Baltimore, USA and Scientific Advisor to ECN. Some of the references documenting formulations and applications of COMPETES are [19]- [21]. In order to analyze whether the regression model is a good fit and that there is a statistical significance between LI and RSI, the following variables has been calculated: P-value (P-val): it is an indication of the probability that the coefficient estimated for a certain regressor could have been obtained by chance.…”