Bi-level electricity-carbon collaborative transaction optimal model for the rural electricity retailers integrating distributed energy resources by virtual power plant

To resolve a large number of discrete distributed energy resources clusters in rural areas, the electricity retailer (ER) is set as the agent of the distributed energy resources clusters by the way of virtual power plant (VPP), namely the electricity retailer (ER) integrated with virtual power plant (VPP-ER). Then, the electricity-carbon collaborative transaction mode is discussed, and a bi-level purchase-sale transaction optimal model. The upper-level model applies the conditional value-at-risk method (CVaR) to establish an electricity-carbon coordinated transaction model for rural VPP-ER. The lower-level model applies the robust optimization theory to measure the uncertainty risk of the power output of WPP, PV to establishes an optimal dispatching model for VPP. Thirdly, the model is converted into the Karush- Kuhn-Tucker (KKT) optimality conditions to solve the bi-level purchase-sale transaction model. Finally, the Henan Lankao industrial cluster is taken as an example, the results show (1) the proposed bi-level model can establish an optimal electricity-carbon coordinated trading scheme. (2) when the confidence level beta belongs to [0.8, 0.9], the decision-maker is willing to bear risk to achieve excess trading returns. At this time, the equivalent carbon emissions of the total transaction electricity should be 1 & PLUSMN; 5% as the initial carbon emission quota and the risk weight lambda should be 0.5. (3) When & UGamma; belongs to [0.80, 0.90], the decision-makers are willing to measure the relationship between risks and returns and formulate a dispatching plan that conforms to their risk attitude. When the forecast error rho fluctuates between (0.05, 0.15), the amplification effect of the forecast error on the uncertainty risk can be minimized. Overall, the findings of this study could provide an effective decision-making tool for rural VPP-ER in China's electricity market.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).