Modelling the Finnish Transmission Grid for Power Flow Studies

Sähköverkko, voimalaitosten generaattorit ja kuluttajien kuormat muodostavat sähköjärjestelmän. Sähköjärjestelmän tarkastelussa tarvitaan erilaisia sähkömarkkinoita ja sähköverkkoja kuvaavia simulointimalleja. Kantaverkon tarkastelussa eniten käytetty verkostolaskennan muoto on tehonjakolaskenta. Tehonjakolaskennan avulla selvitetään sähköverkon tehonsiirrot pysyvässä, symmetrisessätilanteessa. Tämän diplomityön tavoitteena oli rakentaa tehonjakolaskentaan soveltuva simulointimalli Suomen kantaverkosta. Tietoja kantaverkon rakenneosista kerättiin julkisista lähteistä, kuten sähkövoimatekniikan oppi- ja käsikirjoista, vanhoista diplomitöistä sekäenergia-alan yhtiöiden ja järjestöjen Internet-sivuilta. Kerätyt tiedot koottiin tämän diplomityön yhteydessä rakennettuun verkkotietokantaan, josta tiedot voidaan hakea sopivassa muodossa simulointimalliin, joka syötetään verkostolaskentaohjelmistoon. Simulointimalli validoitiin vertaamalla sillä saatuja simulaatiotuloksia Suomen kantaverkkoyhtiön simulointimallilla saatuihin julkisiin tuloksiin. Validoinnin johtopäätöksenä todettiin, että diplomityössä rakennettu verkkomalli kuvaa Suomen kantaverkkoa suuntaa antavasti, kun tarkastellaan pätötehonsiirtoja 400 kV:n ja 220 kV:n verkoissa. Verkkomalli tuo merkittävän parannuksen tutkimukseen, jossa tarvitaan kuvausta Suomen kantaverkosta ja jossa ei ole käytettävissäSuomen kantaverkkoyhtiön simulointimalleja. Kovin tarkkaan loistehotarkasteluun tai 110 kV:n verkkojen tarkasteluun verkkomallia ei kuitenkaan nykyisellään voi käyttää.Power system is formed by power grid, generators at power plants and consumers' loads. Power system analysis requires different types of simulation models describing power markets and power grids. At power transmission grid level, the most widely used network calculation form is power flow analysis. It is used to determine the power flows in the grid in a symmetric, steady-state situation. The aim of this thesis was to build a simulation model of the Finnish transmission grid for power flow studies. Information on power grid components were collected from public sources, such as power engineering textbooks and handbooks, old theses as well as the websites of energy sector companies and organizations. The grid data were entered into a database that was build side by side with this thesis. The data can be fetched from the database to a simulation model, which is fed to a power system simulation software. The simulation model was validated by comparing the simulation results obtained by it to the public results obtained by the grid model of the Finnish national grid company. The conclusion was that the grid model developed in this thesis describes Finnish transmission grid with moderate accuracy when studying active power flows in 400 kV and 220 kV grids. The grid model will bring a significant improvement in research that requires a description of the Finnish transmission grid and in which simulation models of the Finnish national grid company are not available. However, the grid model as such is not suitable for accurate reactive power studies or 110 kV network analysis

The impact of sector coupling and demand-side flexibility on electricity prices in a close to 100% renewable power system

Since variable renewables with low marginal costs will constitute the dominant source of power in a fully renewable European power system, wholesale electricity prices could be expected to decrease due to the resulting shift in the marginal cost curve for the power supply. Yet, this effect can be mitigated by the increasing elasticity of demand. We model scenarios of fully renewable European power systems with varying levels of flexibility on the demand side and thermal capacity on the supply side. First, we apply the open-source energy system modelling framework Backbone to optimise investments in new capacities in the scenarios. We enforce the desired level of thermal capacity by adding respective constraints to the model. On the demand side, we include other energy sectors by introducing industrial hydrogen demand, energy demand for electric vehicles, and heating demand for buildings. Using the resulting optimal capacity mixes, we subsequently optimise operations to simulate the European electricity market. As a result, we find that the flexible actors on the demand side can help stabilise wholesale electricity prices in renewable power systems, particularly with very high shares of variable renewables that incur very low marginal costs

Temporal flexibility options in electricity market simulation models: Deliverable D4.1

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: This report covers the implementation of temporal flexibility options in TradeRES’ agent-based electricity market simulations models. Within this project, the term “temporal flexibility option” was defined as an asset or measure supporting the power system to balance electric demand and supply and compensate for their stochastic fluctuations stemming from, e.g., weather or consumer behaviour by adjusting demand and/or supply as a function over time or by reducing their forecast uncertainty. Other reports from the same work package of TradeRES are published almost simultaneously, each focussing on another aspect of market model enhancements. These accompanying reports address sectoral flexibility, spatial flexibility, actor types, and modelling requirements for market designs. Flexibility options covered in this report were selected with regard to a predominantly temporal characteristic, a contribution to TradeRES’ assessment of market designs, and the feasibility to be implemented in at least one of the agent based models (ABM) during the project’s lifetime. The technical aspects of “Load shedding”, “Load shifting”, “Electricity storage”, and “Real-time pricing” were selected for implementation. In addition, the following new electricity market products were selected for implementation: “Rolling market clearing”, “Trading with shorter time units”, and “Variable market closure lead times”.N/