Optimal and robust combination of energy storage systems for massive integration of renewable energy - a focus on hydropower/hydrostorage solutions

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In order to reduce the environmental footprint of our energy-hungry society, a transition from conventional to renewable power sources is required. However, renewable energies are subject to weather-driven fluctuations and uncertainties. These need to be balanced either by highly flexible conventional power generation technologies, transmission reinforcement, or energy storage systems (ESS).ESS are widely regarded to be a solution for renewable energy integration: they can offer a wide spectrum of services (e.g. energy shifting in time, power ramps, flexibility under uncertainty, grid stability and congestion management). However, there is no ideal individual ESS for that task. Consequently, rather than deploying only one specific technology, it is natural that ESS should coexist and complement each other in a well-chosen mix. First attempts to plan ESS mixes have been hampered by the massive computational costs involved in solving the resulting complex, dynamic and stochastic optimization problem.This research seeks to develop a novel optimization model for finding the optimal combination of ESS (batteries, hydrogen, flywheels...) with focus on hydropower solutions (hydro-reservoirs, pumped storage, others). We follow the hypotheses that (1) a systematic analysis of the modelling details of hydropower technologies and ESS is key to understand the multiple services they can offer to the integration of renewable energies, (2) the existing power system has to be equipped with a robust and well-selected mix of ESS, where many hydropower solutions play a relevant role; (3) the water sector can provide further flexibility, but to understand its synergies a joint water-power planning is needed; and (4) this ESS mix can be found by optimization, but only if computing times are reduced significantly.There are four novelties of our approach and its results: (1) to find the optimal ESS sizes, we consider the numerous services ESS can provide; (2) we study their ability of handling the uncertainties arising from weather forecasts and climate change; (3) in the light of future energy systems, sectorial interactions are becoming more relevant. Hence, we include the interactions between the water and power sector in our model, e.g. how infrastructure for drinking water supply (water tanks, pumps, desalination plants) and multi-purpose water reservoirs can contribute to the energy transition and how the hydropeaking of hydropower plants can be controlled; (4) to counter the associated computational burden, we will develop and evaluate a series of heuristics for finding a good initial solution and reducing the search space in the optimization problem.Our approach allows identifying on a systemic level the role of each ESS and the synergies among ESS, including flexibilities from the water sector and hydropower. Such an optimization framework is a prerequisite for transparent decision support when energy authorities investigate different energy policies.