Simeon Hagspiel

Spatial dependencies of wind power and interrelations with spot price dynamics

Wind power has seen strong growth over the last decade and increasingly affects electricity spot prices. In particular, prices are more volatile due to the stochastic nature of wind, such that more generation of wind energy yields lower prices. Therefore, it is important to assess the value of wind power at different locations not only for an investor but for the electricity system as a whole. In this paper, we develop a stochastic simulation model that captures the full spatial dependence structure of wind power by using copulas, incorporated into a supply and demand based model for the electricity spot price. This model is calibrated with German data. We find that the specific location of a turbine – i.e., its spatial dependence with respect to the aggregated wind power in the system – is of high relevance for its value. Many of the locations analyzed show an upper tail dependence that adversely impacts the market value. Therefore, a model that assumes a linear dependence structure would systematically overestimate the market value of wind power in many cases. This effect becomes more important for increasing levels of wind power penetration and may render the large-scale integration into markets more difficult.

Reliability with interdependent suppliers

Abstract We study reliability and the role of interdependent suppliers with reference to electricity systems. Individual availability of supply is uncertain and may exhibit dependencies with other suppliers as well as with the stochastic demand. Aiming at a comprehensive and consistent reliability assessment, we first investigate the system as a whole, and then derive a general solution for an individual supplier’s contribution. Implicitly, we identify changing returns to scale, gains of diversification, and non-additivity. As these properties are often undesirable, e.g., for the purpose of accounting or in specific auction formats, we build on concepts from cooperative game theory to provide the Shapley value as the unique consistent reliability allocation rule. We then illustrate practical relevance and applicability of our approach for the case of wind power contributing to the reliability of Germany’s electricity system, and discuss how today’s reliability mechanisms may be improved by considering interdependencies between suppliers.

Benefits of coordinated control reserve activation and grid management — A probabilistic load flow analysis

Joint control reserve procurement and activation by several Transmission System Operators can have significant technical and economic benefits. In congested transmission networks, however, the activation of control reserve can be constrained by impending overloads. In this paper the benefits of an optimization to coordinate control reserve activation and grid management considering uncertainties caused by forecast errors is outlined. The effect of these uncertainties are measured by using probabilistic load flow methods based on convolution technique. A joint optimization is presented to minimize the costs of supplying control reserve and conducting redispatch, taking into account HVDC lines, variable costs of power plants and the impact of control reserve activation on lines instead of optimizing it separately as it is the current proceeding by European Transmission System Operators. In addition, a method to determine the solution space for permissible control reserve distributions is presented. In a case study of the German transmission grid for the year 2024 the costs of the joint optimization are contrasted to the costs using the normal merit order and additional redispatch as it is the current market scheme. With the simulation for one year, the solution spaces are calculated to show, which power plants can supply control reserve in every hour without causing redispatch measures and whether the existing power plants in 2024 are sufficient to provide control reserve activation without impending overloads.