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welfare cost of redispatch. A grid model also allows us to propose grid reinforcements where the grid congestions occur and evaluate if the reinforcements reduce redispatch cost sufficiently to be economically sensible. The grid modelling does not only compare redispatch costs in a fixed grid in the different scenarios, but also considers how the grid may be reconfigured in order to match the differences in generation and trade between the scenarios.

For this study, we used THEMA’s The-GRID model.

In Section 6.1 we describe the methodology used for the grid modelling and the calculation of redispatch costs. We proceed to present the results from the grid modelling in terms of utilization of internal grid elements in the relevant countries and regions, in Section 6.2. The impact of offshore wind power on internal grid costs is quantified through a simple calculation of redispatch costs. These calculations create the basis for a discussion of the need for internal grid upgrades due to the offshore wind deployment in the scenarios described in Task 2.

Finally, we collect the results from all countries and conclude our findings in Section 6.3.

The impact of redispatch and grid upgrades on total system costs is included in the cost benefit analysis in Chapter 1. For all countries, the results should be interpreted in the context of a system undergoing profound changes towards a system with a high penetration of renewable power generation, electrification of transportation and heating sector, as well as increased cross-border interconnector capacity. Such a system will challenge the internal grids regardless of offshore deployment, as we will see in the model results.

according to the market solution in each scenario. The purpose is to identify congestion patterns as a basis for calculation of redispatch costs and identification of candidates for grid investments.

4. Calculation of initial redispatch costs: Redispatch costs for each scenario are calculated as the difference in generation costs between the dispatch in the copper plate model and the dispatch in the grid model, including the cost of load shedding, fuel substitution in the heating sector, and countertrade costs and revenues.

5. Assessment of grid investments: Where redispatch costs increase, we qualitatively assess a number of possible grid reinforcements. For each proposed upgrade and each scenario, we compute if the cost of that grid investment is lower than the associated redispatch costs. If so, we include the upgrade in our final grid model. As a result, the initial grid configuration is amended to the congestion patterns for each scenario. The costs for grid upgrades and the now-reduced cost for redispatch are then used in the cost-benefit analysis presented in Chapter 1.

Note: We have explored upgrades based on where offshore wind is connected, and what areas seem to be the most affected by the offshore wind development. No formal grid optimization procedure has been applied, thus more cost-efficient options could exist.

6.1.1 Populating the grid model

The grid is modelled for the years 2030 and 2050. We have used a variety of publicly available sources to gain information about the grid for the different countries, such as TYNDP data from ENTSO-E, data from transparency platforms, and data from national TSOs. We start by defining an initial grid configuration. For a given year, the initial grid configuration is assumed to be identical in the six scenarios. We then study how congestion patterns in the initial grid are affected when the offshore wind farms and hubs are connected to the transmission grid.

The initial grid configuration in 2030 includes plans for grid development from TYNDP 2018 and national grid development plans (where available), but no additional grid reinforcements that are not already proposed by the TSOs. Some of the initial grid upgrades assumed to take place by 2030 are aimed at strengthening the connection between Poland and the Baltic States, to facilitate the Baltic States’ synchronisation with the Continental Grid. We note that some of this strengthening could alternatively be achieved through the development of an offshore grid, which could also then be used to support offshore wind development. However, we have not assumed this in the baseline scenario, which instead undertakes the necessary reinforcement through connections on land.

With regards to grid development towards 2050, we have included some of the projects marked as suggested in TYNDP 2018 and national grid development plans. According to the assumptions used in the market modelling, electricity demand is expected to increase towards 2050, particularly in Poland and Germany, while there is a simultaneous shift from conventional energy sources towards wind and/ or solar power in all market areas. For the grid to be able to accommodate the associated changes in flows, additional reinforcements will be needed, independently of any increase in offshore wind power deployment. As current grid development plans often do not reach very far into the future, we have assumed that additional grid upgrades

are carried out in the long run. The choice of such upgrades is based on shadow prices on specific grid elements from earlier iterations of the grid model.37

In addition to populating the grid model in accordance with the six scenarios explored in this report, we have created a base case scenario, see Figure 6-1. In the base case scenario, we assume the same generation mix and other parameters as those applied in the Low National Policies scenario for 2030 and 2050 respectively. Offshore wind power development is excluded from the base case scenario. The base case scenario is used as a reference to make it possible to analyse the effect of offshore wind development on internal grid costs independently of the impact of other factors.

Figure 6-1 Scenario setup for deployment of Baltic offshore wind, including base case scenarios.

For each scenario, we have also created a “copper plate” model with no constraints in the internal grids within each price zone as a reference case (grid elements crossing the border between price zones are still subject to capacity constraints). Comparing results from the copper plate model with the full grid model allows the computation of redispatch costs, as elaborated below.

6.1.2 Connection to market modelling and other assumptions

To ensure consistency between the market and grid modelling, and hence allow for a comprehensive cost-benefit analysis across all scenarios and dimensions, the grid model is also populated to match the output from the market modelling in Task 2. Input data obtained from the market modelling include generation mix, demand assumptions, fuel price assumptions, inflow assumptions (hydro power), power prices in adjacent countries and trade capacities between price zones.

The market modelling is based on a bidding zone configuration, whereas the grid model is nodal. We have therefore distributed demand and generation from each energy source among the nodes in the grid. The assumptions about the spatial distribution of demand and generation

37 Shadow prices reflect the marginal value of increased capacity of grid elements in terms of the frequency and severity of congestions.

are based on historical demand and generation data, and the distribution is similar between scenarios.

6.1.3 Redispatch calculations

Redispatch costs are defined as the cost associated with any deviations from the market solution due to physical constraints in the grid. Redispatch costs are costs related to interventions made by TSOs in order to ensure that the physical flows do not violate grid capacity and stability limits. Redispatch can include changes in generation at different nodes, demand side management, substitution of electric heating with fossil-fuel heating, as well as countertrading. Such interventions represent changes in social welfare compared to the unconstrained solution.

If a line in the The-GRID model is fully utilised, the model will automatically adjust the dispatch or trade to prevent any further loading of the line. Hence, we see higher dispatch costs if some lines in the system are fully utilised (the optimal copper plate solution is not feasible). The model ensures that the dispatch solution is feasible and that no lines are physically overloaded.

We calculate the redispatch cost as the difference in the sum of generation costs, cost of load shedding due to demand response or fuel switching, and countertrading cost/income between the grid and the copper plate model. This calculation yields the welfare economic cost of redispatch, that is, it computes the actual increase in fuel costs, CO2 emission costs and load shedding costs (value of lost load). The calculation does not identify redistribution effects associated with redispatch – depending on the local regulation regarding reimbursements for up- and down-regulation due to redispatch, generators may profit from being redispatched while consumers may have to pay additional costs beyond the actual increase in fuel costs.

However, the redistribution of welfare does not affect the total welfare itself and depends mainly on the market design. It is therefore outside the scope of this study.

The redispatch costs are used 1) as a measure of changes in system costs due to offshore wind development, and 2) to evaluate the value of suggested internal grid upgrades.

1. The redispatch costs for each scenario are compared to the redispatch costs of the base case scenario of 2030 and 2050 respectively. The difference in redispatch costs is then attributed to the offshore wind power development.

2. If the annual social welfare costs decrease by an amount larger than the annualized investment cost of the internal grid upgrade, the investment is considered economically sensible and is included in the modelling.

Note that although high utilisation of lines indicates a need for redispatch, the redispatch costs are not necessarily high. Only the computation of the change of actual redispatch costs following an upgrade of the grid as described in 2) can determine if that grid upgrade would be net beneficial in welfare economic terms.