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

Dyfyniad
Zhou Y, Wu J, Long C (2018). Evaluating peer-to-peer energy sharing mechanisms for residential customers in present and future scenarios of Great Britain. Cardiff University. http://doi.org/10.17035/d.2018.0046405003


Hawliau Mynediad: Gall data fod ar gael yn rhad ac am ddim yn amodol ar briodoli
Dull Mynediad: I anfon cais i gael y data hwn, ebostiwch opendata@caerdydd.ac.uk

Crewyr y Set Ddata o Brifysgol Caerdydd

Manylion y Set Ddata
Cyhoeddwr: Cardiff University
Dyddiad (y flwyddyn) pryd y daeth y data ar gael i'r cyhoedd: 2018
Fformat y data: .xlsx
Meddalwedd ofynnol: Microsoft Excel
Amcangyfrif o gyfanswm maint storio'r set ddata: Llai na 100 megabeit
Nifer y ffeiliau yn y set ddata: 9
DOI: 10.17035/d.2018.0046405003

Disgrifiad

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.

Research results based upon these data are published at https://doi.org/10.1016/j.apenergy.2018.02.089




Allweddeiriau

Peer-to-peer energy sharing, Residential electric demand model

Prosiectau Cysylltiedig
Flexis East (01.07.2015 - 28.02.2021)

Diweddarwyd y tro diwethaf ar 2019-14-01 am 09:49