@article{Shao2026, 
author = {Linlin Shao and Guoqing Li and Song Zhang},
title = {Distribution Market Trading Optimization for High-Energy-Consuming Industrial Park Microgrids via Distributionally Robust Chance Constraints},
year = {2026},
journal = {Power and Energy Future},
volume = {1},
number = {3},
pages = {9650022},
keywords = {HEIMG, production process demand response, distributionally robust chance constraints, mathematical programs with equilibrium constraints},
url = {https://www.sciopen.com/article/10.26599/PEF.2026.9650022},
doi = {10.26599/PEF.2026.9650022},
abstract = {Uncertainty in photovoltaic (PV) output disrupts energy management decisions for a high-energy-consuming industrial park microgrid (HEIMG) and increases operational risk when it participates in distribution market transactions. This paper proposes an HEIMG energy management scheme adapted to distribution market contexts by leveraging distributionally robust chance constraints (DRCC) and fully considering the adjustable demand response characteristics of industrial production processes. An HEIMG energy management formulation integrating production-side demand response constraints is developed, and a bi-level optimization model is further derived under distribution market interaction. Wasserstein-distance-based DRCC is introduced to quantify PV generation uncertainty, thereby balancing the over-reliance of stochastic optimization on complete probability information and the excessive conservatism of traditional robust optimization. Conservative conditional value-at-risk (CVaR) approximation combined with duality theory is adopted to transform DRCC into tractable second-order cone programming (SOCP) formulations. Karush–Kuhn–Tucker (KKT) optimality conditions combined with the big-M method are then utilized to linearize complementary slackness constraints, transforming the bi-level formulation into a tractable mixed-integer second-order cone programming (MISOCP) model. Numerical simulations verify that the proposed method achieves a favorable trade-off between operational risk control and economic performance for park energy management.}
}