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首页> 外文会议>IAEE International Conference;International Association for Energy Economics >Flexibility Demand Meets Flexibility Supply - The influence of the future renewable energy portfolio on the optimal investment in flexibility options
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Flexibility Demand Meets Flexibility Supply - The influence of the future renewable energy portfolio on the optimal investment in flexibility options

机译：灵活性需求满足灵活性供应-未来可再生能源组合对灵活性选项最佳投资的影响

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OverviewThe extension of renewable energies leads to a transformation of the energy systems. With an increasing share ofvariable renewable energy (VRE) sources the balancing of electricity consumption and generation becomes morechallenging (Huber, et al., 2014). Additionally to the flexibility of conventional power plants, new flexibility optionsare necessary in future energy systems (Evans, et al., 2012). With an increasing share of VRE on total demand, thedifferent generation characteristics of the weather dependent energy sources photovoltaic (PV), wind onshore andoffshore have an increasing influence on the flexibility need. Thus, the chosen VRE extension path can lead to verydifferent energy systems. To observe the influence of the variability in VRE generation, a few studies analysed costminimal VRE shares (Rodriguez, et al., 2015; Huber, et al., 2014) as well as different solar-to-wind ratios (Gils, etal., 2017; Lund, 2006). The present papers analysis the uncertainty of future PV and wind capacity extensions bysetting up two scenarios with different solar-to-wind ratios in a VRE portfolio with an overall share of 80 % in totaldemand. To integrate the resulting variable generation it is further of high importance to assess the required typesand capacities of flexible technologies, since different available options may become more appropriate whenpursuing different VRE extension paths. Therefore the impact of different flexibility needs on optimal investmentsin flexibility options is analysed. By including a broad range of flexible technologies (on electricity supply anddemand side as well as electricity shifting technologies) with different applications possible synergies andcompetitions will be analysed in an isolated view as well as in a combined approach. Therefore a coupled linearcost-minimizing investment and a dispatch energy system model is used, to calculate endogenously requiredcapacities and system cost. The question evaluated is: how does a varying flexibility need, resulting from uncertainsolar-to-wind ratios, interact with a cost-optimal flexibility provision?MethodsIn a first step the flexibility need is calculated and analysed in VRE portfolio scenarios. The solar-to-wind ratio iscalculated from the theoretical annual power generation of PV and wind. For the analysis of the uncertain solar-towindratio two scenarios are developed with a PV-share of 20 and 80 % respectively. The weather and base year forthe scenario data is 2014. VRE and demand data is based on ENTSO-E data (2018). The model region includes theEU 15 countries1 plus Norway and Switzerland. For this region a theoretical overall VRE share of 80 % onelectricity consumption is assumed. While the electricity demand is kept constant on 2014 level the extension ofVRE in the different countries is based on the mean of the full load hours (FLH) of the years 2006 to 2016. Thisincludes the assumption, that the distribution of PV and wind is proportional to their power production potential,reflecting cheaper cost for higher potentials.The above presented scenarios are used to find optimal investments in power plants and further flexibletechnologies. Therefore ELTRAMOD, a linear European electricity transhipment market model, is used as basis andextended by equations constraining the investment and dispatch of the included flexibility options. In general thebasic version of ELTRAMOD cost minimizes the power plant dispatch per region. The regions (here 17 countries)are treated as one node each, connected by Net Transfer Capacities (NTC), while the electricity grid within onecountry is neglected. The main restriction is the energy balance defining to meet the residual load in each time stepand region. Further constraints of the basic version limit the output of power plants by upper bounds for capacityand availability as well as load change costs. As a first extension the endogenous investment in conventional powerplants is introduced. With this modification ELTRAMOD becomes a greenfield approach model. This is reasonableregarding the long-term scenarios. Furthermore equations concerning model-endogenous investments in NTC,demand-side-management (DSM) processes, storages as well as so called Power-to-X technologies are introduced.Regarding the DSM applications seven aggregated processes (for both load reduction as well as load increase) arerepresented including their technical constraints, temperature dependencies (if existing) as well as resulting countryspecific yearly and hourly potentials as data input. Furthermore three storage types (namely hourly, daily andseasonal storages) and their corresponding techno-economical characteristics are included in the presented model.The Power-to-X technologies include electric boilers and heat pumps in combination with heat storages as well aselectrolyzers.To reduce the computational time the presented model is divided in an investment model with reduced time frame(representative weeks selected by a hierarchical clustering algorithm) and a dispatch model in hourly resolution for afull year. The investment model is used to calculate optimal capacity investments and the corresponding costs. Thedispatch model calculates resulting dispatch costs as well as further results like curtailed VRE and emissions for thetwo scenarios. The presented model can be used to analyse optimal investments in a single technology or in differentcombinations (e.g. only electricity shifting technologies) to give insights in the interplay between availableflexibility options.ResultsRegarding the flexibility requirements it can be noted in general, that with high wind shares longer periods withsurplus energy out of VRE can be observed. Nevertheless in comparison to a PV-dominated energy system otherresidual load parameters (in the present paper residual load is defined as difference between the load and theelectricity generation out of PV, wind -onshore and –offshore) are less extreme. This is due to both, the loweravailability as well as the higher daily (and seasonal) variability of PV compared to wind. With an increasing shareof PV in the solar-to-wind ratio in particular electricity surplus peaks on midday combined with correspondingresidual load gradients become very huge.In general the optimal investments in flexible technologies show a strong sensitivity to the flexibility needs. In thefollowing the results for a combination of all included flexibility options is presented briefly. While in a winddominatedenergy system NTC extensions are the major source of flexibility provision, this is true for storages andgas power plants in a PV-dominated energy system. Additionally seasonal storages are more beneficial with highinstalled wind capacities to balance longer periods of VRE surplus or deficits. In contrast there are higher installedcapacities of daily storages with a high PV-share in the observed region. Besides further results in a PV-dominatedenergy system less Power-to-X capacity is optimal with a higher share of heat-pumps compared to the high windsharescenario.ConclusionsThe presented approach combines analyses on both sides of the future flexibility challenge in energy systems. Onthe one side the VRE extension in Europe as crucial uncertainty is observed. Resulting from the dissimilaravailabilities and generation characteristics of solar and wind energy, the uncertainty regarding a future VREportfolio can lead very different flexibility needs. On the other side this flexibility need is used to analyse optimalflexibility provision. The presented model set-up including the holistic as well as extensive representation of flexibletechnologies allows to answer to a broad range of questions regarding the competition of flexibility options to meetthe flexibility demand. The results show a close interaction between these two sides. It can be shown, that theoptimal combination of flexibility options differs strongly between wind- and PV-dominated energy systems.
机译：概述 可再生能量的延伸导致能量系统的转换。随着份额的增加 可变可再生能源（VRE）来源的电力消耗和一代的平衡变得更多 挑战（Huber，等，2014）。另外，传统发电厂的灵活性，新的灵活性选项 在未来的能源系统中是必要的（埃文斯，et al。，2012）。随着总需求的增加，越来越多的百分比 不同的一代特征，天气依赖能源光伏（PV），风陆上和 离岸对灵活性的影响越来越大。因此，所选的VRE延伸路径可能导致非常 不同的能量系统。为了观察VRE生成的变异性的影响，一些研究分析了成本 最小的vre股票（Rodriguez，等，et al。，2015; Huber，等，2014）以及不同的太阳能到风力比（Gils，Et al。，2017; Lund，2006）。本文分析了未来光伏和风力容量延长的不确定性 在VRE投资组合中建立两个具有不同太阳能差值的场景，总份额总额为80％ 要求。要集成所产生的变量，因此评估所需类型的重要性是高度重要的 灵活技术的能力，因为不同的可用选项可能会更适合 追求不同的VRE延长路径。因此，不同的灵活性需求对最佳投资的影响 在灵活性选项中进行了分析。通过包括广泛的灵活技术（电力供应和电力供应） 需求方面以及电力转换技术）不同的应用可能的协同作用和 竞争将在孤立的视图中分析以及以组合的方法分析。因此耦合线性 使用成本最小化投资和调度能源系统模型，以计算内源性所需的 容量和系统成本。评估的问题是：如何在不确定的情况下改变灵活性 太阳能到风量，与成本最佳的灵活性提供交互？ 方法 在第一步中，在VRE投资组合方案中计算并分析灵活性。太阳能到风比是 从PV和风的理论年发电量计算。用于分析不确定的太阳筒 比率两种情况分别具有20％和80％的PV-份额。天气和基准年 方案数据是2014年。VRE和需求数据基于Etento-E数据（2018）。模型区域包括 欧盟15个国家1加上挪威和瑞士。对于这个地区，理论上的总体v次占80％的份额 假设电力消耗。虽然电力需求在2014年保持不变，但延伸的延长 在不同国家的vere是基于2006年至2016年的全部负载时间（FLH）的平均值。这 包括假设，PV和风的分布与其电力生产潜力成比例， 反映更高潜力的更便宜的成本。 上述情况用于在发电厂找到最佳投资，进一步灵活 技术。因此，Eltramod是一种线性欧洲电转换市场模型，被用作基础和 通过限制投资和调度所包含的灵活性选项的方程扩展。一般来说 Eltramod成本的基本版本最大限度地减少了每个区域的电厂调度。该地区（这里有17个国家） 被视为每个节点，通过净传输容量（NTC）连接，而一个电网内 国家被忽视了。主要限制是定义能量平衡，以满足每次步骤中的残余负载 和地区。基本版本的进一步约束限制了容量的上限的发电厂的输出 和可用性以及负载变化成本。作为首次延长传统能力的内源性投资 介绍了植物。通过这种修改，Eltramod成为一个绿地方法模型。这是合理的 关于长期情景。此外，关于NTC的模型 - 内源性投资的方程， 介绍了需求方面管理（DSM）流程，存储以及所谓的Power-to-X技术。 关于DSM应用七个聚合过程（对于负载减少以及负载增加）都是 代表包括他们的技术限制，温度依赖性（如果存在）以及由此产生的国家/地区 特定年次和每小时潜力作为数据输入。此外，还有三种存储类型（每小时，每天和 季节性存储）及其相应的技术经济特征都包含在该模型中。 Power-to-X技术包括电锅炉，热泵以及蓄热器，以及 电解槽。 为了减少计算时间，将提出的模型划分为具有较短时间范围的投资模型 （由分层聚类算法选择的代表周）和按小时解析的调度模型 整整一年。投资模型用于计算最佳产能投资和相应的成本。这 调度模型可以计算出最终的调度成本以及进一步的结果，例如减少了VRE和排放的排放量。 两种情况。提出的模型可用于分析单一技术或不同技术中的最佳投资 组合（例如仅电力转换技术）以提供有关可用之间相互影响的见解 灵活性选项。 结果 关于柔韧性要求，通常可以注意到，高风速时，风速与风速成正比。 可以观察到VRE中有多余的能量。然而，与以光伏为主的能源系统相比，其他 残余载荷参数（在本文中，残余载荷定义为载荷与载荷之间的差值）。 光伏发电，陆上风电和近海风电的发电量并不是那么极端。这是由于两者都较低 与风相比，PV的可用性以及每日（和季节性）PV的较高变化性。随着份额的增加 太阳能在风/风比中的比例，尤其是中午的电力过剩高峰以及相应的 残余载荷梯度变得非常大。 通常，对柔性技术的最佳投资显示出对灵活性需求的强烈敏感性。在里面 简要介绍了所有包含的灵活性选项的组合的结果。在狂风中 能源系统NTC扩展是提供灵活性的主要来源，这对于存储和 光伏为主的能源系统中的天然气发电厂。此外，季节性存储在高存储空间下更有利 安装风能以平衡较长时间的VRE盈余或赤字。相比之下，有更高的安装 观察区域中具有高PV份额的日常存储的容量。除了进一步导致以PV为主 与高风量分享相比，能源系统少Power-to-X容量是最佳的，热泵份额更高 设想。 结论 提出的方法结合了能源系统未来灵活性挑战双方的分析。在 一方面，由于存在重大不确定性，VRE在欧洲的扩展。由于不同 太阳能和风能的可用性和发电特性，有关未来VRE的不确定性 投资组合可以带来非常不同的灵活性需求。另一方面，这种灵活性需求用于分析最优 灵活性条款。提出的模型设置包括灵活的整体以及广泛的表示形式 技术可以回答有关灵活性选择竞争的广泛问题 灵活性需求。结果显示了这两个方面之间的紧密相互作用。可以看出， 灵活选择的最佳组合在以风为主和以光伏为主的能源系统之间存在很大差异。

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