Title: Simulating ancillary service provision from a peer-to-peer energy trading community - data

Citation
Zhou Y, Wu J (2020). Simulating ancillary service provision from a peer-to-peer energy trading community - data. Cardiff University. http://doi.org/10.17035/d.2020.0114344992


Access Rights: Data is provided under a Creative Commons Attribution (CC BY 4.0) licence
Access Method: Click to email a request for this data to opendata@cardiff.ac.uk

Cardiff University Dataset Creators

Dataset Details
Publisher: Cardiff University
Date (year) of data becoming publicly available: 2020
Data format: .xlsx
Software Required: Microsoft EXCEL
Estimated total storage size of dataset: Less than 100 megabytes
Number of Files In Dataset: 7
DOI: 10.17035/d.2020.0114344992

Description

The whole dataset includes 9 EXCEL files in total, providing the data used and produced in the relevant paper titled as ‘Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community’, which has been published on the journal Applied Energy. The detailed description for them is presented as follows:

1. “Smart_Home_Parameters.xlsx” provides the parameters of various types of electrical devices in smart homes considered in the case study section of the paper. It contains 5 sheets. In the first sheet ‘General Parameters’, the general parameters of a lot of electrical devices are presented, including those of noninterruptible appliances, thermal appliances (i.e. electric water heaters) and electric vehicles. In the second sheet ‘P_PV’, the PV power generation of each smart home at each hour is presented. In the third sheet ‘P_Mustrun’, the must-run electric loads of each smart home at each hour is presented. In the fourth sheet ‘dn’, the hot water use of two homes at each hour is presented. In the fifth sheet ‘sita_en’, the temperature of the cold water inlet of two homes at each hour is presented.

2. “Numerical_Results_Case_1_Performance_Evaluation.xlsx” provides data produced in Case 1 of the relevant paper, which is to assess the performance of the proposed mechanisms for facilitating ancillary service provision from a P2P energy trading community. It contains 3 sheets. The first sheet ‘Fig. 4’ presents the daily revenues of each customer in three different scenarios (‘P2G’, ‘P2P’, and ‘P2P+AS’). The second sheet ‘Fig. 5’ presents the daily social welfare of the whole community in three different scenarios. The third sheet ‘Fig. 7’ presents the volume of ancillary service provided during 4 different time periods.

3. “Numerical_Results_Case_2_Different_Service_Types.xlsx” provides data produced in Case 2 of the relevant paper, which is to assess the performance if a different type of ancillary service is provided. It contains 3 sheets. The first sheet ‘Fig. 8’ presents the daily revenues of each customer in three different scenarios (‘P2G’, ‘P2P’, and ‘P2P+AS’). The second sheet ‘Fig. 9’ presents the daily social welfare of the whole community in three different scenarios. The third sheet ‘Fig. 10’ presents the volume of ancillary service provided during 4 different time periods.

4. “Numerical_Results_Case_3_Service_Provision_with_limited_amount.xlsx” provides data produced in Case 3 of the relevant paper, which is to assess the performance if a limit is posed on the total amount of ancillary service to be procured. It contains 2 sheets. The first sheet ‘Fig. 11’ presents the volume of ancillary service provided during 4 different time periods with the limit imposed. The second sheet ‘Fig. 12’ presents the ancillary service provision with and without the limit imposed from each customer.

5. “Numerical_Results_Case_4_Impact_of_Service_Prices.xlsx” provides data produced in Case 4 of the relevant paper, which is to assess the impact of ancillary service prices. It contains 2 sheets. The first sheet ‘Fig. 13’ presents the daily social welfare of the whole community of three scenarios given different levels of price of ancillary service. The second sheet ‘Fig. 14’ presents the total service provided during 13:00-17:00 given different levels of price of ancillary services.

6. “Numerical_Results_Case_5_Impact_of_EV_Penetration.xlsx” provides data produced in Case 5 of the relevant paper, which is to assess the impact of installation rate of electric vehicles. It contains 2 sheets. The first sheet ‘Fig. 15’ presents the daily social welfare of the whole community of three scenarios given different installation rates of electric vehicles. The second sheet ‘Fig. 16’ presents the total service provided during 13:00-17:00 given installation rates of electric vehicles.

7. “Numerical_Results_Case_6_Impact_of_PV_Penetration.xlsx” provides data produced in Case 6 of the relevant paper, which is to assess the impact of installation rate of PV systems. It contains 2 sheets. The first sheet ‘Fig. 17’ presents the daily social welfare of the whole community of three scenarios given different installation rates of PV systems. The second sheet ‘Fig. 18’ presents the total service provided during 13:00-17:00 given installation rates of PV systems.

Research results based upon these data are published at https://doi.org/10.1016/j.apenergy.2020.115671



Keywords

Demand Response, Peer-to-peer energy sharing

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Last updated on 2020-04-09 at 14:13