Work Package 2: Model Adjustment and Linking
Description
For the analyses in Fair-Grid, three models are used that are coupled following a soft link approach (see WP3): the macroeconomic model DYNK, the bottom-up energy system model BALMOREL and the distribution grid simulation framework Femto.jl.
For Fair-Grid, the three models are adapted as follows:
Task 2.1 Integration of electricity generation, trade and distribution in DYNK
In this task, the sector "electricity generation, trade and distribution" (35A) is first disaggregated into the electricity generation technologies as differentiated in the BALMOREL model using information from the BALMOREL database as well as from EXIOBASE and a literature review (WP1). The remainder of sector 35A, after the extraction of electricity generation, comprises electricity trade and distribution (grid). We disaggregate this remainder into the separate sectors trade and distribution in order to allow for different developments. The input structure of the sectors is based on a literature review (see WP1). In addition, we investigate the investment cost structure of each generation technology to be able to economically evaluate distinctive investments in the electricity system and link the investments of sector 35A to grid and power plant investments (from BALMOREL).
Task 2.2 Adaptation of the household module in DYNK
The household module in DYNK can differentiate various household types, the number of which can be altered flexibly. For Fair-Grid, we differentiate households according to their equivalised income. For the five household income groups, the consumption structure and income are derived from the Austrian household budget survey and EU SILC respectively. Within each quintile, we introduce a differentiation between mere consumers and prosumers as well as between households owning a heat pump or an electric vehicle.
Task 2.3 Implementation of grid financing options in DYNK
The change in grid costs is implemented as a mark-up on the electricity price of different agents (commercial or private) or is subsidized by public means, with the subsidy being financed fully by one of the sources of public revenues as taxes and transfers. This means that DYNK must be expanded
- in the tax and subsidy structure to allow endogenous (depending on grid costs) changes in subsidies of the electricity distribution sector,
- in the price system to allow price discrimination between the agents, and
- with respect to endogenous reactions of a public revenue source (e.g. tax) to fund an eventual subsidy.
Task 2.4 Implementation of WEM Scenario in DYNK
A baseline development of DYNK is designed that resembles the latest version of the official Austrian energy scenario WEM (with existing measures) in terms of economic development, energy demand, energy-related emissions as well as electricity demand. For this task, the energy intensity and fuel mix of each sector have to be adjusted.
Task 2.5 Adjustment of the BALMOREL model
For BALMOREL, the database is adapted in accordance with the scenario definition derived as output of WP1 to assure consistency and to incorporate the latest trend developments within the energy sector and the economy.
The model serves for assessing wholesale electricity market developments, specifically wholesale price trends for analysed scenarios. Where suitable we also incorporate the impact of grid charging strategies developed in WP1 on investment and operation decisions for key power system assets, including storages.
Task 2.6: Set-up of the flexibility optimization framework Femto.jl
In this task, several use cases are defined, representing different customer types within an electricity grid branch. The customer types include conventional consumers as well as prosumers with local PV generation and various flexible technologies like batteries, heat pumps, electric vehicle charging stations or electric boilers. The use cases differ in terms of available technologies in order to represent rural, urban and sub-urban regions and different grid expansion pathways, defined in WP1.
Subsequently, optimization models are set up in the Femto.jl optimization framework and solved for different grid tariff design options to evaluate their impact on the cost of different customer types and on their net grid consumption. The outcome of the model simulation runs are the optimized load for each use case as well as the cost for each customer type, including cost for energy, grid cost and fees and taxes.
