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

用于大规模整合可再生能源的储能系统的最佳和稳健组合 - 专注于水电/水力存储解决方案

• 批准号: 351135640
• 负责人: Professor Dr.-Ing. András Bárdossy
• 金额: --
• 依托单位: Departamento de Ingeniería Eléctrica
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/351135640?language=en
• 关键词: Optimal; robust; combination; energy; storage

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.

为了减少我们能源匮乏的社会的环境足迹，需要从传统能源过渡到可再生能源。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。储能系统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，能量及时转移、功率斜坡、不确定性下的灵活性、电网稳定性和拥塞管理）。然而，没有理想的个人ESS来完成这项任务。因此，与其只采用一种特定的技术，ESS自然应该在精心选择的组合中共存并相互补充。第一次尝试计划ESS混合已经阻碍了解决由此产生的复杂的，动态的和随机的优化问题所涉及的大量计算成本。本研究旨在开发一种新的优化模型，寻找ESS（电池，氢，飞轮...）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的建模细节进行系统分析是了解它们可以为可再生能源整合提供多种服务的关键，（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）水务部门可以提供更大的灵活性，但要理解其协同作用，需要进行联合水电规划;（4）这种ESS组合可以通过优化找到，但前提是计算时间显著减少。我们的方法及其结果有四个创新之处：（1）为了找到最佳的ESS规模，我们考虑了ESS可以提供的众多服务;（2）我们研究了它们处理天气预报和气候变化引起的不确定性的能力;（3）鉴于未来的能源系统，部门之间的相互作用变得更加重要。因此，我们在模型中包括了水和电力部门之间的相互作用，例如，饮用水供应的基础设施（水箱、水泵、海水淡化厂）和多用途水库可有助于能源转型以及如何控制水电厂的水力调峰;（4）为了抵消相关联的计算负担，我们将开发和评估一系列的算法，以找到一个好的初始解，并减少优化问题的搜索空间。我们的方法允许在系统层面上确定每个ESS的作用以及ESS之间的协同作用，包括水部门和水电的灵活性。这样的优化框架是透明的决策支持的先决条件，当能源当局调查不同的能源政策。