Evaluating peer-to-peer energy sharing mechanisms for residential customers in present and future scenarios of Great Britain

Peer-to-peer (P2P) energy sharing involves novel technologies and business models at the demand-side of power systems, which is able to manage the increasing connection of distributed energy resources (DERs). In P2P energy sharing, prosumers directly trade energy with each other to achieve a win-win outcome. A research paper titled "Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework" has been published on Applied Energy regarding this topic. In the paper, a general multiagent framework was established to simulate P2P energy sharing, with two original techniques proposed to facilitate simulation convergence. Furthermore, a systematic index system was established to evaluate P2P energy sharing mechanisms from both economic and technical perspectives. In case studies of the paper, two sets of cases were conducted to validate the proposed simulation and evaluation methods and to give some practical implications on applying P2P energy sharing in Great Britain (GB) at present and in the future. The household demand dataset and electric vehicle (EV) dataset used in the paper has been provided for researchers to reproduce the results in the paper or to conduct further related studies. Also, the original numerical data of the results in the case studies of the paper have been provided, for researchers to better understand the results or to use the results for other purposes. The whole dataset includes 9 excel files in total. The detailed description for them are presented as follows: 1. “CREST_Demand_Model_v2.2 (Great Britain).xlsm” is a high-resolution stochastic integrated thermal-electrical domestic demand simulation tool developed by Centre for Renewable Energy Systems Technology (CREST) of Loughborough University (refering to http://www.lboro.ac.uk/research/crest/demand-model/). It contains a lot of sheets and VBA codes, which are used to generate “fake” demand curves of domestic customers sampled from statistical distributions that are based on real-life data. In the “Main Sheet”, input parameters like “day of month”, “month of year”, “latitude”, “longitude”, etc. can be entered, and then the “Run simulation” button can be clicked to start the simulation. After the simulation, daily curves like “occupancy and activity”, “total electrical demand”, “total gas demand”, etc. are generated and visualized, with very high time resolution. 2. “Electric_Vehicle_Dataset (Great Britain).xlsx” is a dataset based on the research conducted jointly by Centre for Integrated Renewable Energy of Cardiff University and Key Laboratory of Smart Grid of Ministry of Education of Tianjin University (referring to https://doi.org/10.1016/j.apenergy.2015.10.159). It contains two sheets, which provide the parameters of 1000 typical electric vehicles of Great Britain respectively. For each electric vehicle, the parameters include: (1) “Time starting charging / returning home (hour)”, (2) “Time finishing charging / leaving home (hour)”, (3) “Battery capacity (kWh)”, (4) “Energy consumption due to travel (measured by SOC)”, (5) “Lowerlimit of SOC”, (6) “Upperlimit of SOC”, (7) “Maximum charging/discharging power”, (8) “Charging efficiency”, and (9) “Discharging efficiency”. 3. “Numerical results and figures _ Case 1-1.xlsx” provides the numerical results of Case 1-1 of the paper. It contains three sheets, providing the data behind Fig. 6, Fig. 7 and Fig. 8 of the paper respectively. In the “Fig. 6” sheet, the “Total Net Consumption (kWh)” and “Total PV Generation (kWh)” under “SDR mechanism” and “conventional paradigm” are provided. In the “Fig. 7” sheet, the “Net energy cost under SDR mechanism (£)” and “Net energy cost under conventional paradigm (£)” of each prosumer are provided. In the “Fig. 8” sheet, the “Internal selling price (£/MWh)”, “Internal buying price (£/MWh)” and “Total Net Energy Cost (£)” of each iteration are provided. 4. “Numerical results and figures _ Case 1-2.xlsx” provides the numerical results of Case 1-2 of the paper. It contains two sheets, providing the data behind Fig. 9, Fig. 10 and Fig. 11 of the paper. In the “Fig. 9 and 10” sheet, for Fig. 9, the “The iteration at which the simulation stopped” given different ramping rates are provided; for Fig. 10, the “Overall Performance Index” with different ramping rates given different demand profiles are provided. In the “Fig. 11” sheet, the “Total net energy cost (ramping rate = 0.3) (£)” and “Total Net Energy Cost (ramping rate = 0.6) (£)” at each iteration are provided. 5. “Numerical results and figures _ Case 1-3.xlsx” provides the numerical results of Case 1-3 of the paper. It contains only one sheets, providing the data behind Fig. 12 of the paper. In the “Fig. 12” sheet, the “Overall Performance Index” with different learning rates given different demand profiles are provided. 6. “Numerical results and figures _ Case 1-4.xlsx” provides the numerical results of Case 1-4 of the paper. It contains two sheets, providing the data behind Fig. 13 and Fig. 14 of the paper. In the “Fig. 13” sheet, the “Overall Performance Index” with different ramping rates given different initial values are provided. In the “Fig. 14” sheet, the “Overall Performance Index” with different learning rates given different initial values are provided. 7. “Numerical results and figures _ Case 1-5.xlsx” provides the numerical results of Case 1-5 of the paper. It contains only one sheet, providing the data behind Fig. 15 and Fig. 16 of the paper. In the “Fig. 15 and 16” sheet, for Fig. 15, the number of iterations when the simulation stopped given different maximum number of iterations and ramping rates are provided; for Fig. 16, the overall performance given different maximum number of iterations and ramping rates are provided. 8. “Numerical results and figures _ Case 2-2.xlsx” provides the numerical results of Case 2-2 of the paper. It contains only one sheet, providing the data behind Fig. 17 of the paper. In the “Fig. 17” sheet, the overall performance scores of the three mechanisms (SDR, MMR and BS) and conventional paradigm in scenarios with different PV and EV penetration levels are provided. 9. “Numerical results and figures _ Appendix B.xlsx” provides the numerical results of the cases in Appendix B of the paper. It contains two sheets, providing the data behind Fig. B1, Fig. B2, Fig. B3 and Fig. B4 of the paper. In the “Fig. B1 and B2” sheet, for Fig. B1, the EWH power consumption (kW) at t=1 and t=2 for each iteration without any techniques for convergence are provided; for Fig. B2, the Internal buying price (pence/kWh) at t=1 and t=2 without any techniques for convergence are provided. In the “Fig. B3 and B4” sheet, for Fig. B1, the EWH power consumption (kW) at t=1 and t=2 for each iteration with a limitation for its power change are provided; for Fig. B2, the Internal buying price (pence/kWh) at t=1 and t=2 with a limitation for its power change are provided.

点对点（Peer-to-peer, P2P）能源共享是电力系统需求侧的新型技术与商业模式，可应对分布式能源资源（distributed energy resources, DERs）接入规模持续增长的挑战。在P2P能源共享场景中，产消者可直接开展能源交易以实现双赢。针对该主题，一篇题为《基于多智能体仿真框架的点对点能源共享机制评估》的研究论文发表于《应用能源（Applied Energy）》期刊。该论文构建了通用多智能体框架以仿真P2P能源共享场景，并提出两项原创技术以提升仿真收敛性。此外，研究建立了系统化指标体系，从经济与技术双维度对P2P能源共享机制开展评估。在论文的案例研究中，作者开展了两组案例以验证所提出的仿真与评估方法，并针对当前及未来英国（Great Britain, GB）的P2P能源共享应用给出实践启示。论文所使用的家庭需求数据集与电动汽车（electric vehicle, EV）数据集已向研究者开放，以供复现论文研究结果或开展后续相关研究。同时，论文案例研究中得到的原始数值结果数据也已公开，便于研究者更好地理解研究结果或用于其他研究场景。本数据集总计包含9个Excel文件，各文件详细说明如下：
1. "CREST_Demand_Model_v2.2 (英国).xlsm" 为由拉夫堡大学可再生能源系统技术中心（Centre for Renewable Energy Systems Technology, CREST）开发的高分辨率随机集成热-电家庭需求仿真工具（参考链接：http://www.lboro.ac.uk/research/crest/demand-model/）。该文件包含多个工作表与VBA代码，用于基于真实生活数据的统计分布采样生成家庭用户的“虚拟”需求曲线。在"Main Sheet"工作表中，可输入“当月日期”“当年月份”“纬度”“经度”等输入参数，点击"Run simulation"按钮即可启动仿真。仿真完成后，将生成并可视化展示占用与活动、总电力需求、总燃气需求等高时间分辨率的每日曲线。
2. "Electric_Vehicle_Dataset (英国).xlsx" 为基于卡迪夫大学综合可再生能源中心与天津大学智能电网教育部重点实验室联合开展的研究构建的数据集（参考链接：https://doi.org/10.1016/j.apenergy.2015.10.159）。该文件包含两个工作表，分别提供了1000台英国典型电动汽车的参数信息。每台电动汽车的参数包括：(1) 充电开始/返家时刻（小时）；(2) 充电结束/离家时刻（小时）；(3) 电池容量（kWh）；(4) 行驶能耗（以荷电状态（State of Charge, SOC）变化衡量）；(5) SOC下限；(6) SOC上限；(7) 最大充/放电功率；(8) 充电效率；(9) 放电效率。
3. "Numerical results and figures _ Case 1-1.xlsx" 提供了论文案例1-1的数值结果。该文件包含三个工作表，分别对应论文图6、图7、图8的原始数据。其中，"Fig. 6"工作表提供了“SDR机制”与“传统范式”下的“总净耗电量（kWh）”与“总光伏发电量（kWh）”；"Fig. 7"工作表提供了每位产消者在“SDR机制下的净能源成本（英镑）”与“传统范式下的净能源成本（英镑）”；"Fig. 8"工作表提供了每次迭代的“内部售价（英镑/兆瓦时）”“内部购价（英镑/兆瓦时）”与“总净能源成本（英镑）”。
4. "Numerical results and figures _ Case 1-2.xlsx" 提供了论文案例1-2的数值结果。该文件包含两个工作表，分别对应论文图9、图10、图11的原始数据。在"Fig. 9 and 10"工作表中，图9对应不同爬坡速率下的“仿真停止迭代次数”，图10对应不同需求曲线场景下不同爬坡速率对应的“综合性能指标”。在"Fig. 11"工作表中，提供了每次迭代下，爬坡速率为0.3与0.6时的“总净能源成本（英镑）”。
5. "Numerical results and figures _ Case 1-3.xlsx" 提供了论文案例1-3的数值结果。该文件仅包含一个工作表，对应论文图12的原始数据。在"Fig. 12"工作表中，提供了不同需求曲线场景下不同学习速率对应的“综合性能指标”。
6. "Numerical results and figures _ Case 1-4.xlsx" 提供了论文案例1-4的数值结果。该文件包含两个工作表，分别对应论文图13、图14的原始数据。在"Fig. 13"工作表中，提供了不同初始值场景下不同爬坡速率对应的“综合性能指标”；在"Fig. 14"工作表中，提供了不同初始值场景下不同学习速率对应的“综合性能指标”。
7. "Numerical results and figures _ Case 1-5.xlsx" 提供了论文案例1-5的数值结果。该文件仅包含一个工作表，对应论文图15、图16的原始数据。在"Fig. 15 and 16"工作表中，图15对应不同最大迭代次数与爬坡速率下的“仿真停止迭代次数”，图16对应不同最大迭代次数与爬坡速率下的“综合性能”。
8. "Numerical results and figures _ Case 2-2.xlsx" 提供了论文案例2-2的数值结果。该文件仅包含一个工作表，对应论文图17的原始数据。在"Fig. 17"工作表中，提供了三种机制（SDR、MMR与BS）与传统范式在不同光伏与电动汽车渗透场景下的综合性能得分。
9. "Numerical results and figures _ Appendix B.xlsx" 提供了论文附录B中案例的数值结果。该文件包含两个工作表，分别对应论文图B1、图B2、图B3、图B4的原始数据。在"Fig. B1 and B2"工作表中，图B1对应未采用任何收敛加速技术时，每次迭代下t=1与t=2时刻的电热水器（Electric Water Heater, EWH）耗电量（kW）；图B2对应未采用任何收敛加速技术时，t=1与t=2时刻的内部购价（便士/千瓦时）。在"Fig. B3 and B4"工作表中，图B3对应采用功率变化限制收敛技术时，每次迭代下t=1与t=2时刻的电热水器耗电量（kW）；图B4对应采用功率变化限制收敛技术时，t=1与t=2时刻的内部购价（便士/千瓦时）。