This paper presents the assessment of stakeholder impacts of European electricity transmission network investments for a set of future system development scenarios. A techno-economic analysis is adopted, which quantifies the economic impacts on different stakeholders including electricity producers, consumers, and network investors, under the future de-carbonisation pathways described in the IRENE-40 scenarios, ranging from 2010 to 2050. To quantify the impact of transmission infrastructure development, for each pathway, two distinct scenarios of future European transmission development are assessed: “low” (no new transmission between 2010 and 2050) and “high” (optimal transmission development between 2010 and 2050 to accommodate the generation pathway). The geographical scope of the analysis covers the EU 27+2 nations. A detailed case study of Spain and France is presented to illustrate the so-called ‘asymmetric’ impacts towards different stakeholders in different importing and exporting zones. The resulting arbitrage trades shift the market equilibriums, which eventually affect asymmetrically the welfare of stakeholders. @en

Variable renewable energies (VRE), in particular wind and solar PV, constitute a key option to reduce global greenhouse gas emissions. Future policy scenarios therefore propose a dominant role for VRE. However, relying almost entirely on the stochastic weather-determined output of VRE will require a transformation of the way power systems are planned and operated: a growing amount of flexibility will be needed to match variable demand with increasingly variable supply. Due to the complexity of power systems as well as their long investment cycles, it is crucial to prepare the strategic development of flexibility now. The key question for the transition to energy systems based on variable renewables becomes: “How can we ensure that future power systems have the flexibility needed to match demand and variable supply?” Power system operators and regulators need to assess the current flexibility level in their system, analyze all possible flexibility options, and clearly prioritize the needed actions. This paper presents the Flexibility Tracker, an assessment methodology developed to monitor and compare the readiness of power systems for high VRE shares. The Flexibility Tracker builds 14 flexibility assessment domains, by screening systems across the possible flexibility sources (supply, demand, energy storage) and enablers (grid, markets), via 80 standardised Key Performance Indicators (KPIs) scanning the potential, deployment, research activities, policies and barriers regarding flexibility. The methodology allows monitoring the progress made in individual power systems with respect to their potential for integrating VRE, comparing and ranking of different systems, and identifying best practices, common challenges and needed actions to enable and advance flexibility. It ensures that the complex flexibility question has a clear reference which looks at all relevant flexibility options, without being restricted to a single technology scope. As such it provides a useful instrument for market actors operating in multiple countries, as well as policy makers. As case study, the paper presents a comparative assessment of key European systems using this methodology. The results show that the although flexibility deployment depends on the specifics of each system, a coordinated approach would be beneficial as there are clear no-regret options that face barriers in some systems.@en

In this paper, a risk-based security assessment methodology is presented, which allows the assessment of operational security of a power system’s future state under uncertainty deriving from varying topology scenarios (contingencies) and forecast errors (loads and renewable infeeds). The methodology models input uncertaintywith a copula function-based Monte–Carlo (MC) framework. Furthermore, it provides the highest level of accuracy on initiating causes of failures through an AC power flow (AC PF) framework. Finally, it achieves speed in solution by the combination of twomeasures of risk. A fast screening tool, based on severity functions, allows us to quickly screen the system for the most severe states. A detailed analysis tool, based on an AC optimal power flow (AC OPF) framework and the notion of lost load, provides additional valuable information, including remedial actions to steer away from severe system states. This paper presents results from the application of the methodology proving the necessity of such a framework. It is shown that not taking into account stochastic dependence through a proper MC setup seriously underestimates system risk and that an AC framework is needed, as voltage deviations are shown to often be initiators of system collapse.@en

The increasing penetration of renewable generation in power systems necessitates the modeling of this stochastic system infeed in operation and planning studies. The system analysis leads to multivariate uncertainty analysis problems, involving non-Normal correlated random variables. In this context, the modeling of stochastic dependence is paramount for obtaining accurate results; it corresponds to the concurrent behavior of the random variables, having a major impact to the aggregate uncertainty (in problems where the random variables correspond to spatially spread stochastic infeeds) or their evolution in time (in problems where the random variables correspond to infeeds over specific time-periods). In order to investigate, measure and model stochastic dependence, one should transform all different random variables to a common domain, the rank/uniform domain, by applying the cumulative distribution function transformation. In this domain, special functions, copulae, can be used for modeling dependence. In this contribution the basic theory concerning the use of these functions for dependence modeling is presented and focus is given on a basic function, the Normal copula. The case study shows the application of the technique for the study of the large-scale integration of wind power in the Netherlands.@en

The incorporation of renewable energy sources for the sustainable electricity production is undertaking a more prominent role in electric power systems. Thus, it will be an indispensable incident that the characteristics of future power networks, their prospective stability for instance, get influenced by the imposed features of sustainable energy sources. One of the distinctive attributes of the sustainable energy sources is exhibiting the stochastic behavior. This paper investigates the impacts of this stochastic behavior on the small disturbance rotor angle stability in the upcoming electric power networks. Considering the various types of renewable energy sources and the vast variety of system configurations, the sensitivity analysis can be an efficient breakthrough towards generalizing the effects of new energy sources on the concept of stability. In this paper, the definition of small disturbance angle stability for future power systems and the iterative-stochastic way of its analysis are presented. Also, the effects of system parameters on this type of stability are described by performing a sensitivity analysis for an electric power test system.@en

Stochastic generation, i.e., electrical power production by an uncontrolled primary energy source, is expected to play an important role in future power systems. A new power system structure is created due to the large-scale implementation of this small-scale, distributed, non-dispatchable generation; the `horizontally-operated¿ system. Modeling methodologies that can deal with the operational uncertainty introduced by these power units should be used for analyzing the impact of this generation to the system. In this contribution, the principles for this modeling are presented, based on the decoupling of the single stochastic generator behavior (marginal distribution-stochastic unit capacity) from the concurrent behavior of the stochastic generators (stochastic dependence structure-stochastic system dispatch). Subsequently, the stochastic bounds methodology is applied to model the extreme power contribution of the stochastic generation to the system, based on two new sampling concepts (comonotonicity¿countermonotonicity). The application of this methodology to the power system leads to the definition of clusters of positively correlated stochastic generators and the combination of different clusters based on the sampling concepts. The stochastic decomposition and clustering concepts presented in this contribution provide the basis for the application of new uncertainty analysis techniques for the modeling of stochastic generation in power systems. Keywords: Stochastic power generation; Distributed generation; Steady-state analysis; Uncertainty analysis; Monte-Carlo simulation; Risk management@en

This paper contributes a Markov chain Monte Carlo (MCMC) method for the direct generation of synthetic time series of wind power output. It is shown that obtaining a stochastic model directly in the wind power domain leads to reduced number of states and to lower order of the Markov chain at equal power data resolution. The estimation quality of the stochastic model is positively influenced since in the power domain, a lower number of independent parameters is estimated from a given amount of recorded data. The simulation results prove that this method offers excellent fit for both the probability density function and the autocorrelation function of the generated wind power time series. The method is a first step toward simple stochastic black-box models for wind generation.@en

Higher participation levels of wind power in power systems will increase the need for flexible back-up generation to balance the differences between predicted and realized wind power production. This is often an expensive solution. With distributed energy resources and more ICT at the demand side, novel, and possibly cheaper, ways for imbalance minimization arise. Micro combined heat-and-power (micro-CHP) is a novel domestic-level generation technology, producing heat and power simultaneously. Clusters of micro-CHPs can function as flexible virtual power plants (VPPs). This paper presents the design of an online coordination scheme that can substantially reduce the imbalance volumes and the associated costs for wind power traders by actively controlling a VPP comprising micro-CHP systems. It is shown that the imbalance volume and associated cost can be reduced by 73 % and 38 %, respectively.@en

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

Stochastic Generation is the electrical power production by the use of an uncontrollable prime energy mover, corresponding mainly to renewable energy sources. For the large-scale integration of stochastic generation in power systems, methods are necessary for the modeling of power generation uncertainty. In this thesis, new tools for the uncertainty analysis of such power systems are developed.@en