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

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.

[1]  Lynn Powell,et al.  Power System Load Flow Analysis , 2004 .

[2]  Valentin Bertsch,et al.  The impact of microeconomic decisions in electricity market modelling on load flows in transmission grids , 2016, 2016 13th International Conference on the European Energy Market (EEM).

[3]  Jutta Geldermann,et al.  What drives the profitability of household PV investments, self-consumption and self-sufficiency? , 2017 .

[4]  Valentin Bertsch,et al.  Regionalizing Input Data for Generation and Transmission Expansion Planning Models , 2017 .

[5]  Wolf Fichtner,et al.  Cost-potentials for large onshore wind turbines in Europe , 2015 .

[6]  Reza S. Abhari,et al.  Large scale technical and economical assessment of wind energy potential with a GIS tool: Case study Iowa $ , 2012 .

[7]  Muireann Á. Lynch,et al.  Competition and the single electricity market: Which lessons for Ireland? , 2016 .

[8]  Gregory P. Swinand,et al.  Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland , 2015 .

[9]  J. Cohen,et al.  Re-focussing research efforts on the public acceptance of energy infrastructure: A critical review , 2014 .

[10]  Prasanta Kumar Dey,et al.  Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis , 2016, Comput. Oper. Res..

[11]  W. Fichtner,et al.  Public acceptance and preferences related to renewable energy and grid expansion policy: Empirical insights for Germany , 2016 .

[12]  Bernd Resch,et al.  GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues , 2014, ISPRS Int. J. Geo Inf..

[13]  Valentin Bertsch,et al.  A participatory multi-criteria approach for power generation and transmission planning , 2015, Annals of Operations Research.

[14]  E. Whelan,et al.  The nearshore wind and wave energy potential of Ireland: A high resolution assessment of availability and accessibility , 2016 .

[15]  Valentin Bertsch,et al.  What drives people's opinions of electricity infrastructure? Empirical evidence from Ireland , 2017 .

[16]  E. Dunlop,et al.  Potential of solar electricity generation in the European Union member states and candidate countries , 2007 .

[17]  Valentin Bertsch,et al.  Network constraints in techno-economic energy system models: towards more accurate modeling of power flows in long-term energy system models , 2013 .

[18]  Valentin Bertsch,et al.  The Role of Community Involvement Mechanisms in Reducing Resistance to Energy Infrastructure Development , 2018 .

[19]  Nikolaos S. Thomaidis,et al.  Optimal management of wind and solar energy resources , 2016, Comput. Oper. Res..

[20]  Valentin Bertsch,et al.  A Multi-objective Time Segmentation Approach for Power Generation and Transmission Models , 2015, OR.

[21]  Nikos D. Hatziargyriou,et al.  Optimal Distributed Generation Placement in Power Distribution Networks : Models , Methods , and Future Research , 2013 .

[22]  Muireann Á. Lynch,et al.  Carbon dioxide (CO2) emissions from electricity: The influence of the North Atlantic Oscillation , 2015 .

[23]  E. Schmid,et al.  The European renewable energy target for 2030 – An impact assessment of the electricity sector , 2015 .

[24]  Mohammad Yusri Hassan,et al.  Optimal distributed renewable generation planning: A review of different approaches , 2013 .

[25]  W. Fichtner,et al.  A high-resolution determination of the technical potential for residential-roof-mounted photovoltaic systems in Germany , 2014 .

[26]  Mostafa Nick,et al.  Wind power optimal capacity allocation to remote areas taking into account transmission connection requirements , 2011 .

[27]  Sakti Prasad Ghoshal,et al.  Optimal sizing and placement of distributed generation in a network system , 2010 .