Collaborative risk-resistant distributionally robust dispatch and benefit allocation scheme for interconnected distribution systems

Abstract

To further exploit the potential of interconnected active distribution networks in reducing overall operational risks through coordinated scheduling under source-load uncertainties, this paper proposes a distributionally robust dispatch and benefit allocation scheme. First, a collaborative risk-resistant distributionally robust optimization model based on the conditional value at risk measure is established to minimize the whole risk cost associated with the heavy-tailed distribution of uncertain variables, trading a small loss in expected economic performance for a significant gain in tail robustness. Then, by employing piecewise linearization techniques and duality theory, the introduction of integer variables is avoided, and the original semi-infinite programming problem is equivalently transformed into an efficiently solvable semidefinite programming problem. Finally, an asymmetric Nash bargaining model that accounts for risk-reduction contributions is employed to ensure a fair and equitable allocation of cooperative benefits. Simulation results based on the modified 119-bus system demonstrate that the proposed method can effectively improve the economic and robust operation of interconnected active distribution networks under source-load uncertainties. Compared with the non-cooperative distributionally robust optimization model, the day-ahead risk and intraday cost of the interconnected microgrids are reduced by 27.48% and 14.34%, respectively.