Xie R, Tan T and Chen Y, Valuation of distributed predictive information in robust economic dispatch, HKIE Transactions, Vol. 31, No. 2 (ICEE Special Issue), Article ICEE23JY036, 2024, 10.33430/V31N2ICEE23-JY036
Robust economic dispatch is an essential way to deal with the uncertainties of renewable generations, but its performance highly depends on the pre-built uncertainty set. This paper proposes a novel robust economic dispatch model in which the operator can buy predictions from distributed forecasters to build a better uncertainty set and enhance its dispatch efficiency. The value of distributed predictive information can be quantified by the operator’s payment for buying it. The proposed model renders a two-stage robust optimisation problem with decision-dependent uncertainty (DDU). By analysing the structure of the uncertainty set, the proposed model is equivalently transformed into a two-stage robust optimisation problem with decision-independent uncertainty (DIU) so that it can be effectively solved by applying the column-and-constraint generation (C&CG) algorithm. Case studies show that the proposed method is effective.
Ben-Tal A and Nemirovski A (2002). Robust optimization–methodology and applications. Mathematical Programming, 92, pp.453-480.
Bertsimas D and Sim M (2004). The price of robustness. Operations Research, 52(1), pp.35-53.
Chen Z, Guo C, Dong S, Ding Y and Mao H (2021). Distributed robust dynamic economic dispatch of integrated transmission and distribution systems. IEEE Transactions on Industry Applications, 57(5), pp.4500-4512.
Chen Y, Li T, Zhao C and Wei W (2020). Decentralized provision of renewable predictions within a virtual power plant. IEEE Transactions on Power Systems, 36(3), pp.2652-2662.
Fang D, Guan X, Hu B, Peng Y, Chen M and Hwang K (2021). Deep reinforcement learning for scenariobased robust economic dispatch strategy in internet of energy. IEEE Internet of Things Journal, 8(12), pp.9654-9663.
Grimmett G and Stirzaker D (2020). Probability and Random Processes. Oxford university press.
Litvinov E, Zheng T, Rosenwald G and Shamsollahi P (2004). Marginal loss modelling in LMP calculation. IEEE Transactions on Power Systems, 19(2), pp. 880- 888.
Liu Y, Wu L and Li J (2020). A fast LP-based approach for robust dynamic economic dispatch problem: A feasible region projection method. IEEE Transactions on Power Systems, 35(5), pp.4116-4119.
Lu S, Gu W, Meng K and Dong Z (2021). Economic dispatch of integrated energy systems with robust thermal comfort management. IEEE Transactions on Sustainable Energy, 12(1), pp.222-233.
Pineda S and Morales JM (2019). Solving linear bilevel problems using big-Ms: Not all that glitters is gold. IEEE Transactions on Power Systems, 34(3), pp.2469-2471.
Rahmaniani R, Crainic TG, Gendreau M and Rei W (2017). The Benders decomposition algorithm: A literature review. European Journal of Operational Research, 259(3), pp.801-817.
Xie R, Pinson P, Xu Y and Chen Y (2024). Robust generation dispatch with purchase of renewable power and load predictions. IEEE Transactions on Sustainable Energy, 15(3), pp.1486-1501.
Zhang Y, Ai X, Wen J, Fang J and He H (2019). Dataadaptive robust optimization method for the economic dispatch of active distribution networks. IEEE Transactions on Smart Grid, 10(4), pp.3791-3800.
Zeng B and Zhao L (2013). Solving two-stage robust optimization problems using a column-and-constraint generation method. Operations Research Letters, 41(5), pp.457-461.
