DOI: 10.17035/d.2018.0046405003
title: Evaluating peer-to-peer energy sharing mechanisms for residential customers in present and future scenarios of Great Britain

description: 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.