"Impact of Cross-Border Flow Forecast Alignment on Flow-Based Market Coupling Efficiency"

"The developed model is based on a test network, retrieved from Barrios et al. It is adjusted to fit the purpose of this analysis. Transformers are not considered but all 220 kV and 380 kV lines are. The conventional power plants are provided by the dataset. The types of renewable energy capacities and their assignments to nodes is retrieved visually from Barrios et al.. The resulting network is depicted in Fig. 14. Fig. 16 provides an annotated overview of the network with an indication of all nodes and lines. Zones 1\u20133 are considered as the flow-based market coupling area. The adjacent zones represent consolidated zones, giving the possibility of trade with the FBMC area. This trade, however, is not modeled through FBMC but with static bilateral trade capacities (NTCs), which is the current way the CWE region conducts trade with all neighboring countries not part of FBMC. Weather data for renewable energy sources of France, Belgium and Germany are used for zones 1\u20133 (see below). It is important to note, however, that the generation capacities of zones 1\u20133 do not represent these countries. In terms of installed renewable energy capacities, zone 1 is dominated by conventional generation with small solar and wind capacities. Zone 2 is a solar-dominated zone and zone 3 shows large capacities of wind energy. For the case studies, a scenario with higher variable renewable energy source (vRES) is considered (Fig. 15). In zone 1, all renewable capacities are doubled, zone 2 further expands solar energy and reduced lignite capacities and zone 3 expands onshore wind as well as solar energy.22 The capacity factors for renewable energy sources are retrieved from SETIS [31] for the year 2015. Zones 1\u20133 are assigned the capacity factors of France, Belgium and Germany, respectively. The generation capacities for the remaining three zones are not provided. Here, the generation capacity structures of Italy, the Netherlands and Poland are used to mimic these zones Import\/Export 1\u20133, respectively. The capacities are obtained from ENTSO-E [14]. They are scaled down to power plant fleets with sizes similar to zone 1\u20133. The resulting generating capacities are depicted in Fig. 15. While node-specific load time series are provided for zones 1\u20133 (year 2012), the load time series for the remaining zones are created artificially. Here, the aggregated load time series of zones 1\u20133 is taken as a basis and scaled so the ratio of maximum zonal load to zonal installed capacities matched the real ratio of the countries. Concretely, the ratio of maximum load to installed capacity in the zone Import\/Export 1 matched the ratio of Italy. This has the effect, that in every zone a \u201crealistic\u201d load time series is created and the maximum load never exceeds the conventional generating capacities. Despite all attentiveness, uncertainty of input data from the external sources cannot be completely excluded. This includes the test network, originating from Barrios et al. [8], on which the derived model is based. At present, no weaknesses are known concerning this data source. Furthermore, weather data comes from the open access platform SETIS [31], consequently, the accuracy of the capacity factors depends on the quality of the dataset. Considering that the test network is hypothetical, the impact of possibly existing uncertainties on the results is low. Moreover, the installed capacities of adjacent zones are taken from ENTSO-E [14]. Since those capacities do not emerge from time series, the related uncertainties can be considered rather low."

本研究构建的模型基于取自Barrios等人的测试网络，并针对本次分析的目标进行了适配调整。本模型未纳入变压器元件，但所有220千伏与380千伏输电线路均被保留。
数据集提供了常规发电厂的相关信息；可再生能源装机容量的类型及其在各节点的分配情况，则通过目视方式从Barrios等人的研究中提取得到。
最终构建的测试网络如图14所示；图16则为该网络带标注的概览图，标注了所有节点与输电线路。
1-3号区域被划定为基于流的市场耦合（Flow-Based Market Coupling, FBMC）区域；相邻区域则为整合区域，可与FBMC区域开展电力交易。不过此类交易并非通过FBMC机制建模，而是采用静态双边交易容量（净传输容量，Net Transfer Capacities, NTCs），这也是CWE区域与所有未加入FBMC的周边国家开展电力贸易的现行运作方式。
1-3号区域的可再生能源发电相关气象数据取自法国、比利时与德国的公开数据，但需特别说明的是，1-3号区域的发电装机容量并不对应上述三国。
从可再生能源装机结构来看，1号区域以常规发电为主，仅配备少量太阳能与风电装机；2号区域以太阳能发电装机为主；3号区域则拥有大规模风电装机。
针对本研究的案例分析，本研究设置了高比例可变可再生能源（Variable Renewable Energy Source, vRES）的情景（如图15所示）：1号区域的所有可再生能源装机容量翻倍；2号区域进一步扩大太阳能装机规模并削减褐煤装机容量；3号区域则扩大陆上风电与太阳能装机容量。22 可再生能源的容量系数取自2015年的SETIS数据库[31]；1-3号区域分别对应法国、比利时与德国的容量系数。
剩余三个区域的发电装机容量未在原始数据中给出，本研究分别采用意大利、荷兰与波兰的发电装机结构来模拟这三个进出口区域（即1-3号进出口区域）。装机容量数据取自ENTSO-E数据库[14]，并将其缩放至与1-3号区域机组规模相近的水平。最终得到的发电装机容量如图15所示。
1-3号区域已提供了节点级的负荷时间序列数据（2012年），但剩余区域的负荷时间序列则通过人工合成得到。本研究以1-3号区域的总负荷时间序列为基础，进行缩放调整，使得各区域的最大区域负荷与区域装机容量的比值与对应国家的实际比值一致。具体而言，1号进出口区域的最大负荷与装机容量的比值与意大利的该比值匹配。通过该方式，可在每个区域生成符合实际情况的负荷时间序列，且最大负荷不会超过区域常规发电装机的承载能力。
尽管本研究已尽可能严谨，但仍无法完全排除外部数据源带来的输入数据不确定性。本研究基于的测试网络取自Barrios等人的研究[8]，目前尚未发现该数据源存在明显缺陷。此外，气象数据取自开放获取平台SETIS[31]，因此容量系数的准确性取决于该数据集的质量。鉴于本测试网络为假想网络，现有不确定性对研究结果的影响相对有限。此外，相邻区域的装机容量数据取自ENTSO-E[14]，由于该类数据并非源自时间序列，相关的不确定性可认为处于较低水平。