Chance-constrained optimization for integrated local energy systems operation considering correlated wind generation

Energy hubs, which integrate multiple energy vectors through converters, can enhance the value of Integrated Local Systems (ILES) via increased flexibility and reduced costs. However, uncertain renewable non-convex, non-linear properties flows complicate modelling operation hub systems. This paper develops chance-constrained optimization methods for planning systems under uncertainty. The formulations power gas are relaxed by convexification methods, leading to a formulation Second Order Cone Problem (SOCP), be efficiently solved global optimality. correlation between geographically close wind generators connected is modelled establishing their relation using Gaussian copula. proposed demonstrated on six-hub system within multi-vector distribution network with 7 electrical buses nodes. different levels integration installation hubs investigated. results show that combining chance constrained operation, method reduce operating costs increase yields, thereby benefitting operators customers infrastructure investment