Dataset to accompany paper: "Can battery deployment avoid network reinforcement in an unbundled electricity system? A Great Britain case study"

This dataset accompanies manuscript entitled "Can battery deployment avoid network reinforcement in an unbundled electricity system? A Great Britain case study".
The research is in the context of a recognised need for flexible resources on Great Britain (GB)'s electricity system, as greater penetrations of less flexible or inflexible generation are deployed to aid system decarbonisation. The work explores research questions:
"Will deployment of battery energy storage, engaged in wholesale trading, reduce peak flows across Great Britain’s electrical networks, and thus reduce or defer the need for network reinforcement?
Or, conversely, might deployment of batteries add to maximum network flows and reinforcement needs?"

The data are arranged in folders which follow the structure of the manuscript. All folders and subfolders contain "read_me" text files with more information about data themselves, their sources and their interpretation. All data were sourced from open sources or were created as part of the research.
Folder 1 has data for auction prices of frequency regulation and response services procured by the Electricity System Operator during 2022, which show a decline in prices in the later part of the year. In these circumstances, alternative activities were becoming more attractive for battery owners.
Folder 2 contains timeseries data for wholesale trading and Balancing Mechanism activity during 2022 of around 20 commercial batteries active in GB's Balancing Mechanism. These datasets illustrate increasing interest in wholesale trading during the year.
Folder 3 contains data relating to a battery simulation model created for this research, and its outputs. The model simulates a battery agent, engaged in wholesale trades (i.e. arbitrage), and intent on accruing maximum overall net revenues. It contains the day-ahead spot market price data used in this work (during selected periods during 2022), model outputs for the most lucrative battery simulations, including timeseries of simulated charging and discharging activity. These timeseries are later used together with other data to assess the effect of battery activity on distribution and transmission network congestion.
Folder 4 has data for the distribution network case study. It contains data relating to the topology and thermal limits of the networks studied, timeseries of demands and distributed wind generator outputs, used to calculate aggregated flows on 33kV networks, and then the effect of simulated battery activity on these flows.
Folder 5 accompanies the transmission network study. It contains timeseries data of wind generation availability and curtailment, in Scotland, which are viewed together with simulated battery activity to determine batteries' likely effect on transmission congestion.
Folder 6 contains timeseries of GB system demand, and of embedded solar and wind generation from the ESO, which the manuscript's Discussion section refers to.

本数据集配套发表于题为《非捆绑式电力系统中电池部署能否避免电网补强？一项英国案例研究》的学术手稿。
本研究立足于英国电力系统对灵活性资源的公认需求背景：为助力电力系统脱碳，当前正接入更多低灵活性或不可灵活性发电机组，行业对灵活性资源的需求已得到广泛认可。本研究旨在解答如下科研问题：
“参与批发电价交易的储能电池部署，是否会降低英国电力网络的峰值潮流，从而减少甚至推迟电网补强的需求？
反之，电池部署是否会加剧网络最大潮流及补强需求？”

本数据集按照手稿的章节结构组织为多个文件夹。所有文件夹及子文件夹均配有“read_me”文本文件，详细说明数据集内容、数据来源及解读方法。所有数据均来自开源渠道，或为本研究原创生成。

文件夹1收录了2022年英国电力系统运营商（Electricity System Operator, ESO）采购的调频与响应服务拍卖价格数据，数据显示该类服务价格在年内后期呈下降趋势。在此背景下，储能电池运营商开展其他业务的吸引力逐渐提升。

文件夹2包含2022年英国平衡机制（Balancing Mechanism）中约20台活跃商用储能电池的批发电价交易与平衡机制运行时序数据，该数据集反映出年内市场主体对批发电价交易的关注度持续攀升。

文件夹3包含为本研究开发的电池仿真模型及其输出结果数据。该模型模拟了一款电池智能体，其参与批发电价套利交易，以实现总净收益最大化为目标。本数据集包含本研究使用的2022年部分时段日前现货市场价格数据，以及收益最优的电池仿真模拟输出结果，包括模拟充放电活动的时序数据。后续研究将结合该时序数据与其他数据集，评估电池活动对配电网及输电网拥塞的影响。

文件夹4包含配电网案例研究相关数据，涵盖所研究网络的拓扑结构与热稳定限值、用于计算33kV网络聚合潮流的负荷与分布式风电出力时序数据，以及模拟电池活动对上述潮流的影响数据。

文件夹5配套输电网案例研究，包含苏格兰地区风电并网可用率与弃电情况的时序数据，研究将结合该数据与模拟电池活动，分析电池对输电网拥塞的潜在影响。

文件夹6包含英国电力系统总负荷、以及来自电力系统运营商（ESO）的分布式光伏与风电出力时序数据，对应手稿讨论章节的相关分析内容。