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

为了减少我们这个能源匮乏的社会对环境的影响，需要从传统能源向可再生能源过渡。然而，可再生能源受到天气驱动的波动和不确定性的影响。这些需要通过高度灵活的传统发电技术、输电加固或储能系统（ESS）来平衡。ESS被广泛认为是可再生能源整合的解决方案：它们可以提供广泛的服务（例如，及时的能量转移、功率坡道、不确定性下的灵活性、电网稳定性和拥堵管理）。然而，对于该任务，没有理想的单个ESS。因此，与其只部署一种特定的技术，ESS自然应该在一个精心选择的组合中共存并相互补充。最初计划ESS混合的尝试受到了大量计算成本的阻碍，这些计算成本涉及解决由此产生的复杂、动态和随机优化问题。本研究旨在开发一种新的优化模型，以寻找ESS（电池、氢气、飞轮等）的最佳组合，重点是水电解决方案（水库、抽水蓄能等）。我们遵循以下假设：(1)对水电技术和ESS建模细节的系统分析是了解它们可以为可再生能源整合提供的多种服务的关键；(2)现有电力系统必须配备强大且精心选择的ESS组合，其中许多水电解决方案发挥相关作用；(3)水务部门可以提供进一步的灵活性，但要了解其协同效应，需要联合水电规划；(4)通过优化可以找到这种ESS组合，但前提是计算次数显著减少。我们的方法和结果有四个新颖之处：(1)为了找到最优的ESS规模，我们考虑了ESS可以提供的众多服务；(2)研究它们处理天气预报和气候变化带来的不确定性的能力；(3)考虑到未来的能源系统，部门间的互动将变得更加相关。因此，我们将水和电力部门之间的相互作用纳入我们的模型，例如，饮用水供应基础设施（水箱、水泵、海水淡化厂）和多用途水库如何有助于能源转型，以及如何控制水电站的水力调峰；(4)为了应对相关的计算负担，我们将开发和评估一系列启发式方法，以寻找优化问题中的良好初始解并减少搜索空间。我们的方法可以在系统层面上确定每个ESS的作用以及ESS之间的协同作用，包括来自水务部门和水电的灵活性。这种优化框架是能源当局在调查不同能源政策时提供透明决策支持的先决条件。

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.