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Probabilistic curtailment analysis for transmission grid planning using Active Network Management

par Faghihi, Farshid
Président du jury Kinnaert, Michel
Promoteur Labeau, Pierre-Etienne
Co-Promoteur Maun, Jean Claude
Publication Non publié, 2015-04-27

Thèse de doctorat

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Résumé :

According to the EU Council in 2007, a target of 20% Renewable Energy Sources (RES) energy share was determined by the year 2020. Maximizing RES penetration, whilst simultaneously ensuring grid stability and security of electric supply, has become a major challenge for the grid operators. The aggregated effect of Distributed Generation (DG) units will affect increasingly the transmission grid operation and planning. More and more, the High Voltage (HV) grid has to export the excess of power produced at the Medium Voltage (MV) level, where DG units are connected. The energy flows become variable both in value and direction in substations at the interface with distribution networks, which is a complete change for the grid operator. Power flow congestions and voltage problems are particularly more likely to arise. Systematically reinforcing the network in order to absorb the last MWh produced by DG units located in unfavorable areas, while maintaining the traditional operation of the grid, is not efficient, i.e. neither economically viable for the community nor acceptable from the point of view of environmental impact. The intermittency of DG units makes it irrelevant to define the amount of connectable units on the basis of their installed power and the N-1 criterion. New paradigms to increase the grid capacity of accepting DG units before reinforcement are to be considered. And new methodologies for long-term and operational grid planning, giving allowance to this inherent variability in the generation, are therefore necessary.Active Network Management (ANM) allows to moving away from conventional grid operation towards a new approach, comprising (almost) real-time supervision and control of the DG units and network elements. Thanks to this new management of the system and accounting for the intermittent (i.e. weather-dependent) RES production, more DG units can be connected to an existing grid: the power produced by some DG units can be curtailed to eliminate possible congestions encountered for specific combinations of loads, generations and weather conditions. In others words, the use of an ANM scheme makes possible to maximize the grid utilization in enhancing the required flexibility of system operation to maintain power system security margins.A reasonable level of security in applying ANM is however required and it must be assessed before any possible application to the grid. This assessment can be performed based on a probabilistic approach: the uncertain parameters, i.e. each load and power produced by a DG unit, are modeled with probability density functions (pdf’s); the latter are then randomly sampled, to create so-called variants. These variants serve as input data for an Optimal Power Flow (OPF) module to find the possible redispatching or curtailment that could be necessary in each case. The state space is extremely vast, however, due to combinatorial explosion. Creating a sufficiently large sample of variants to cover all significant situations the grid can face appears intractable, and alternative approaches, combining a systematic search in the state space with an acceptable computation time, are to be developed.This research proposes a pragmatic methodology to handle the high dimensionality of the problem and estimate the impact of connecting a new DG unit, via the computation of several risk indices. A systematic approach guarantees searching all over the plausible congestion zones of the state space, while an on-target sampling drives the computational effort towards the direction of interest. This combined approach allows managing the computation time without falling into oversimplification or losing too much accuracy.