Viktor Slednev

Regionalizing Input Data for Generation and Transmission Expansion Planning Models

To support decision making in the context of restructuring the power system, models are needed which allow for a regional, long-term operation and expansion planning for electricity generation and transmission. Input data for these models are needed in a high spatial and temporal granularity. In this paper, we therefore describe an approach aimed at providing regionalized input data for generation and transmission expansion planning models. We particularly focus on a dynamic assignment of renewable energy sources and electrical load to potential buses of the transmission grid. Following a bottom up approach, we model the existing and potential distributed generation and load at the lowest possible spatial resolution based on various databases and models. Besides large power plants, which are directly connected to the transmission grid, a decentralized grid connection is modeled on the distribution grid level based on Voronoi polygons around the corresponding substations. By simplifying the load flow over the distribution grid to a shortest path problem, we model the feed-in into the transmission grid as a variable, depending on the nearest available transmission grid connection. As a result, the connection to the buses at transmission grid level is kept variable in case of grid expansion measures at substation level.

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.

A Multi-objective Time Segmentation Approach for Power Generation and Transmission Models.

The complexity of large-scale power system models often necessitates the choice of a suitable temporal resolution. Nowadays, mainly simple heuristic approaches are used. An adequate decision support related to power generation and transmission optimisation in systems with a high RES share, however, requires preserving the complex intra-period and intra-regional links within and between the volatile electricity demand and supply profiles. Focussing on power systems operation, we are able to show that even an amount of less than 300 time segments may be sufficient for the modelling of a whole year, if chosen carefully.

The impact of microeconomic decisions in electricity market modelling on load flows in transmission grids

Europes transmission grids are confronted with increasing numbers of congestions due to multiple reasons leading to rising distances of generation and demand, such as renewable energy sources, which substitute generation from thermal and hydraulic power. Furthermore, planned measures of grid expansion are frequently delayed, resulting in higher flows within the existing network topology which need to be handled by grid operation measures. Due to these changes, the consideration of the impact of market results on the transmission grid is of rising importance when investigating load flows in transmission grid systems. In this paper, an existing agent-based simulation of the electricity market is extended. First, the integration of electricity storage with hydro power plants is improved by a hydro dispatch optimisation, accounting for cascading of multiple units with multiple reservoirs. Second, an AC load flow approach is incorporated to investigate the resulting load flows in the transmission grid in Germany.

Optimal Storage Operation with Model Predictive Control in the German Transmission Grid

In this paper, a model predictive control approach is presented to optimize generator and storage operation in the German transmission grid over time spans of hours to several days. In each optimization, a full AC model with typical OPF constraints such as voltage or line capacity limits is used. With given RES and load profiles, inter-temporal constraints such as generator ramping and storage energy are included. Jacobian and Hessian matrices are provided to the solver to enable a fast problem formulation, but the computational bottleneck still lies in solving the linear Newton step. The deviation in storage operation when comparing the solution over the entire horizon of 96 h against the model predictive control is shown in the German transmission grid. The results show that horizons of around 24 h are sufficient with today’s storage capacity, but must be extended when increasing the latter.