Micro-grids bidding Strategy in a Transactive energy market

Document Type : Article


Department of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran


This paper proposes microgrid (MG) bidding strategy in the transactive energy market (TEM), in which market participants are able to negotiate and trade by a new-designed smart contract (SC) in a peer-to-peer way. In such a market, MG can balance its deviations, which are the resultant of the intermittency of the renewable energy sources, and the volatility of the load. In this paper, the uncertainty is handled by interval optimization. By participation in the TEM, the MG bidding problem is a bi-level optimization with interval coefficient, in which the MG’s profit maximizes in the upper level and the rivals’ behaviour in the TEM are modelled in the lower level. In order to solve the aforementioned problem, the proposed model recasts as a single-level interval optimization problem by the Karush-Kuhn-Tucker (KKT) conditions and the interval optimization concept. Simulation results show the applicability of the proposed model and realize the 1.7% increase in the MG profit for a 4-hour duration basis TEM.


1. Rahimi, F., Ipakchi, A., and Fletcher, F. \The changing  electrical landscape: end-to-end power system  operation under the transactive energy paradigm",  IEEE Power and Energy Magazine, 14(3), pp. 52-62  (2016).  2. Kristov, L., Martini, P.D., and Taft, J.D. \A Tale  of two visions: Designing a decentralized transactive  electric system", IEEE Power and Energy Magazine,  14(3), pp. 63-69 (2016).  3. Nezamabadi, H. and Vahidinasab, V. \Market bidding  strategy of the microgrids considering demand  response and energy storage potential exibilities",  IET Generation, Transmission & Distribution, 13(8),  pp. 1346-1357 (2019).  4. Morstyn, T., Farrell, N., Darby, S.J., et al. \Using  peer-to-peer energy-trading platforms to incentivize  prosumers to form federated power plants", Nature  Energy, 3(2), pp. 94-101 (2018).  5. Morales, J.M., Conejo, A.J., Madsen, H., et al. \Integrating  renewables in electricity markets: operational  problems", Springer Science & Business Media, New  York, USA (2013).  6. Nikolaidis, A.I., Charalambous, C.A., and Mancarella,  P. \A graph-based loss allocation framework for transactive  energy markets in unbalanced radial distribution  networks", In IEEE Transactions on Power Systems,  34(5), pp. 4109-4118 (2019).  7. DiSilvestre, M.L., Gallo, P., Ippolito, M.G., et al. \A  technical approach to the energy blockchain in microgrids",  IEEE Transactions on Industrial Informatics,  14(11), pp. 4792-4803 (2018).  8. Moret, F., Baroche, T., Sorin, E., et al. \Negotiation  algorithms for peer-to-peer electricity markets:  Computational properties", In 2018 Power Systems  Computation Conference (PSCC), Dublin, Ireland, pp.  1-7 (2018).  9. Morstyn, T., Teytelboym, A., and McCulloch, M.D.  \Bilateral contract networks for peer-to-peer energy  trading", IEEE Transactions on Smart Grid, 10(2),  pp. 2026-2035 (2018).  10. Morabito, V., Business Innovation Through  Blockchain, Springer International Publishing, Cham,  Switzerland (2017).  11. Liu, G., Xu, Y., and Tomsovic, K. \Bidding strategy  for microgrid in day-ahead market based on hybrid  stochastic/robust optimization", IEEE Transactions  on Smart Grid, 7(1), pp. 227-237 (2016).  12. Kazempour, S.J. and Zareipour, H. \Equilibria in  an oligopolistic market with wind power production",  IEEE Transactions on Power Systems, 29(2), pp. 686-  697 (2014).