five

Simulating Peer-to-Peer Energy Trading in a Microgrid

收藏
DataCite Commons2024-12-05 更新2025-04-09 收录
下载链接:
https://research-data.cardiff.ac.uk/articles/dataset/Simulating_Peer-to-Peer_Energy_Trading_in_a_Microgrid/27052300
下载链接
链接失效反馈
官方服务:
资源简介:
Peer-to-Peer (P2P) energy trading represents direct energy trading between peers, where energy from small-scale Distributed Energy Resources (DERs) in dwellings, offices, factories, etc, is traded among local energy prosumers and consumers. A research paper titled “Peer-to-Peer Energy Trading in a Microgrid” has been published on Applied Energy regarding this topic. In the paper, a hierarchical system architecture model was proposed to identify and categorize the key elements and technologies involved in P2P energy trading. A P2P energy trading platform was designed and P2P energy trading was simulated using game theory. In case studies of the paper, two sets of cases were conducted in order to validate the proposed P2P energy trading platform using the simulation method illustrated in the paper. 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 4 excel files in total. The detailed descriptions are presented as follows: 1. “Numerical results and figures _ Case 1 _ Inputs.xlsx” provides the numerical inputs of Case 5.1 of the paper. It contains three sheets, providing the data behind Fig. 7(a), Fig. 7(b) and Fig. 7(c) of the paper respectively. In the “Fig. 7(a)” sheet, the “Percentage of Peak Generation (%)” is provided. In the “Fig. 7(b)” sheet, the “Load Profile of Non-flexible Demand Type 1 (kW)” and “Load Profile of Non-flexible Demand Type 2 (kW)” are provided. In the “Fig. 7(c)” sheet, the “Total Power Consumption of Non-flexible Demands in the Microgrid (kW)” is provided. 2. “Numerical results and figures _ Case 1 _ Outputs.xlsx” provides the numerical results of Case 5.1 of the paper. It contains three sheets, providing the data behind Fig. 8(a), Fig. 8(b) and Fig. 8(c) of the paper respectively. In the “Fig. 8(a)” sheet, the “ON/OFF Status of Flexible Demand of Peers 1 (Boolean)” and “ON/OFF Status of Flexible Demand of Peers 2 (Boolean)” are provided. In the “Fig. 8(b)” sheet, the “Total Power Consumption of Peers 1 without P2P (kW)”, “Total Power Consumption of Peers 1 with P2P (kW)”, “Total Power Consumption of Peers 2 without P2P (kW)”, and “Total Power Consumption of Peers 2 with P2P (kW)” are provided. In the “Fig. 8(c)” sheet, the “Net Load of the Microgrid without P2P (kW)” and “Net Load of the Microgrid with P2P (kW)” are provided. 3. “Numerical results and figures _ Case 2 _ Inputs.xlsx” provides the numerical inputs of Case 5.2 of the paper. It contains one sheet, providing the data behind Fig. 9 of the paper. In the “Fig. 9” sheet, the “Percentage of Peak PV Generation (%)” and “Percentage of Peak Wind Generation (%)” are provided. 4. “Numerical results and figures _ Case 2 _ Outputs.xlsx” provides the numerical results of Case 5.2 of the paper. It contains three sheets, providing the data behind Fig. 10(a), Fig. 10(b) and Fig. 10(c) of the paper respectively. In the “Fig. 10(a)” sheet, the “ON/OFF Status of Flexible Demand of Peers 1 (Boolean)”, “ON/OFF Status of Flexible Demand of Peers 2 (Boolean)”, and “ON/OFF Status of Flexible Demand of Peers 3 (Boolean)” are provided. In the “Fig. 10(b)” sheet, the “Total Power Consumption of Peers 1 without P2P (kW)”, “Total Power Consumption of Peers 1 with P2P (kW)”, “Total Power Consumption of Peers 2 without P2P (kW)”, “Total Power Consumption of Peers 2 with P2P (kW)”, “Total Power Consumption of Peers 3 without P2P (kW)”, and “Total Power Consumption of Peers 3 with P2P (kW)” are provided. In the “Fig. 10(c)” sheet, the “Net Load of the Microgrid without P2P (kW)” and “Net Load of the Microgrid with P2P (kW)” are provided.
提供机构:
Cardiff University
创建时间:
2018-04-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作