Highly resolved optimal renewable allocation planning in power systems under consideration of dynamic grid topology

Abstract

The system integration of an increasing amount of electricity generation from decentralised renewable energy sources (RES-E) is a major challenge for the transition of the European power system. The feed-in profiles and the potential of RES-E vary along the geographical and temporal dimension and are also subject to technological choices and changes. To support power system planning in the context of RES-E expansion and allocation planning required for meeting RES-E targets, analyses are needed assessing where and which RES-E capacities are likely to be expanded. This requires models that are able to consider the power grid capacity and topology including their changes over time. We therefore developed a model that meets these requirements and considers the assignment of RES-E potentials to grid nodes as variable. This is a major advancement in comparison to existing approaches based on a fixed and pre-defined assignment of RES-E potentials to a node. While our model is generic and includes data for all of Europe, we demonstrate the model in the context of a case study in the Republic of Ireland. We find wind onshore to be the dominating RES-E technology from a cost-efficient perspective. Since spatial wind onshore potentials are highest in the West and North of the country, this leads to a high capacity concentration in these areas. Should policy makers wish to diversify the RES-E portfolio, we find that a diversification mainly based on bioenergy and wind offshore is achievable at a moderate cost increase. Including solar photovoltaics into the portfolio, particularly rooftop installations, however, leads to a significant cost increase but also to a more scattered capacity installation over the country.-2 -

The role of power-to-gas in the future energy system: Market and portfolio effects

“…Moreover, the power system described by EirGrid (2016) includes 4800 MW of installed wind capacity, which are assumed to be installed and operated on the supply side. In order to meet the long-term RES-E and decarbonisation targets beyond 2025, the installed wind capacity will need to be further increased (Slednev et al, 2017). Therefore, in order to analyse how increasing levels of wind capacity affect optimal investment decisions in the power system, in particular in power-to-gas, the model is run several times while exogenously increasing the wind capacity in 10% steps (in relation to the 4800 MW installed, i.e.…”

Spatial Distribution of Overhead Power Lines and Underground Cables in Germany in 2016

“…Worldwide, energy systems are transforming towards low-carbon and renewable-based systems. The increasingly decentralized supply structure raises many research questions regarding structure, dimension and impact of the electricity transmission and distribution network [1][2][3][4][5][6][7][8][9][10]. Spatial grid data on overhead power lines, but also underground cables, are essential for these research areas [11].…”

“…Nonetheless, the existing public data on the electricity transmission and distribution networks in Germany [12] does not provide information on crucial parameters, such as voltage levels, route length, number of circuits, and circuit length. These parameters are, however, necessary for a sophisticated applicability of the dataset in spatial planning, energy system analysis, and impact assessments [3]. For example, to estimate the impact of overhead power lines on the avifauna, the number of cables mounted on the power line determines the risk of birds colliding with the power line.…”

Impact of Flow Based Market Coupling on the European Electricity Markets

“…The market model covers a unit-wise representation of the European thermal power plant fleet, which is based on Platts (2016), including the member states of the EU-27, except for Malta, Cyprus and Ireland, extended for Albania, Bosnia and Herzegovina, Great Britain, Kosovo, North Macedonia, Montenegro, Norway, Serbia and Switzerland. For the regionalization of renewable energy sources and demand, the methodology and weather data presented in Slednev et al (2017) and Slednev et al (2018) are applied. The NTC results of the model are used as base case from which power flows on the relevant grid elements are determined in normal operation and contingency cases for relevant outages.…”