In market conditions where program responsible parties are penalized for deviations from proposed bids, energy storage can be used for compensating the energy imbalances induced by limited predictability of wind power. The energy storage capacity necessary for performing this task will differ between delivery periods, according to the magnitude and the evolution of forecast errors in each delivery period. A methodology is presented for the assessment of the necessary storage capacity for each delivery period, based on the degree of risk that the power producer accepts to be exposed to. This approach leads to a dynamic assessment of the energy storage capacity for different delivery periods. In such a context, energy storage is used as a means of risk hedging against penalties from the regulation market. The application of the algorithm on real data (both measurements and forecasts) of the yearly output of a wind farm shows that the application of a dynamic daily sizing of the necessary storage leads to a significant reduction of the storage capacity used, without affecting the producer's profit significantly. The method proposed here may provide the basis for the introduction of storage as an independent market entity, where each producer may rent the necessary daily storage capacity for hedging the risk of the wind power limited predictability.@en

DC power flow is a commonly used tool for contingency analysis. Recently, due to its simplicity and robustness, it also becomes increasingly used for the real-time dispatch and techno-economic analysis of power systems. It is a simplification of a full power flow looking only at active power. Aspects such as voltage support and reactive power management are possible to analyse. However, such simplifications cannot always be justified and sometimes lead to unrealistic results. Especially the implementation of power flow controlling devices is not trivial since standard DC power flow fundamentally neglects their effects. Until recently, this was not an issue as the application of power flow controlling devices in the European grid was limited. However, with the liberalisation of European electricity market and the introduction of large wind energy systems, the need for real power flow control has emerged and therefore, the use of these devices has been reconsidered. Several phase shifting transformers (PST) are being installed or planned in order to control flows. Therefore, it is important to fundamentally re-validate the fast, but less accurate, DC power flow method. In this paper the assumptions of DC power flow are analysed, and its validity is assessed by comparing the results of power flow simulations using both the DC and AC approaches on a modified IEEE 300 bus system with PSTs.@en

In a liberalized electricity market, the use of phase shift ing transformers or other power ¿ow controlling devices allows the transmission system operator to utilize the avail able grid infrastructure in a more optimal way. However, each phase shifter adds a degrees of freedom to the control problem, making optimization more dif¿cult. In this paper, the coordination problem is solved by using Particle Swarm Optimization (PSO). The goal is to give an overview of how PSO is used to solve this particular problem, and to demon strate a new area of application for the method.@en