DOI: 10.5281/zenodo.1220934
title: The P2P-Ieee 14 Bus System Data Set

description: This data set models the IEEE 14-bus system for studies on P2P electricity markets, including real data of consumption, solar and wind power from Australia. This data set is characterized by 30 minutes time-step over one year, i.e. from July 2012 to June 2013.

The transmission system comprises 14 buses and 20 lines, and its characteristics are based on [1]. The original number of generators was increased to 8 generators, i.e. 1 coal-based generator, 2 gas-based generators, 3 wind turbines and 2 PV plants. The data set uses the original number of 11 loads.

The bus 1 represents the upstream connection to the main grid, where the generator assumes an infinite power. The market price from the Australian Energy Market Operator is used in this generator. It is assumed the same period from July 2012 to June 2013 [4]. This data set supposes a tariff of 10$/MWh for using the main grid. The energy imported and exported in bus 1 has to account this extra cost. Thus, the exportation price is equal to the market price minus this grid tariff. On the other hand, the importation price is equal to the market price plus this grid tariff.

The wind production has been based on the data set from [2]. The time resolution has been converted from 5 minutes to 30 minutes. The authors would like to acknowledge that the data set in [2] was processed by Stefanos Delikaraoglou and Jethro Dowell. The solar production and load consumption are taken from [3]. The load consumption is split into fixed and flexible consumption per time-step. Since there is no access to the total capacity of the flexible consumption, we split the daily flexible consumption over each time-step. In this way, the maximum consumption is equal to the fixed consumption plus twice this flexible consumption per time-step. The minimum consumption is equal to the fixed consumption in each time-step.

The wind, solar and load data sets have been normalized, i.e. values relative to rated power. Then, these normalized sequences were multiplied by the capacity of each element. The data is intended for use in studies related to consumer-centric electricity markets, e.g.:


	Validate new market designs or business models;
	Assess the impact of new grid operation strategies;
	Test the effect of strategic behavior by producers or consumers.
description: The authors are partly supported by the Danish ForskEL programme through the Energy Collective project (grant no. 2016-1-12530), and by the EU Interreg programme through the Smart City Accelerator project (grant no. 20200999). The post-doctoral grant of Tiago Soares was financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project ESGRIDS - Desenvolvimento Sustentável da Rede Elétrica Inteligente/SAICTPAC/0004/2015-POCI-01-0145-FEDER-016434.
description: {"references": ["[1] IEEE common data repository: http://www.ee.washington.edu/research/pstca/.", "[2] J. Dowell, P. Pinson, \"Very-short-term probabilistic wind power forecasts by sparse vector autoregression\", IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 763-770, 2016, doi:10.1109/TSG.2015.2424078.", "[3] E. L. Ratnam, S. R. Weller, C. M. Kellet, A. T. Murray, \"Residential load and rooftop PV generation: an Australian distribution network data set\", International Journal of Sustainable Energy, vol. 36, no. 8, pp. 787-806, 2017, doi: 10.1080/14786451.2015.1100196.", "[4] AEMO webpage: https://www.aemo.com.au/, accessed December 2017."]}