The rapid expansion of renewable generation has drastically increased the planning complexity of modern power systems as additional uncertainties, environmental concerns, and technical-economic issues should be accounted for. Within this context, the best operation performance of contemporary power system operators (SOs) depends not just on tractable realistic optimal power flow (OPF) formulations, but also on powerful optimization approaches. In this work, a tractable lifelike multi-objective probabilistic OPF-based model for the SO's medium-term operation considering high penetration of renewable resources is proposed. This model includes an explicit formulation of the operation of dispatchable and non-dispatchable generation, shunt reactive power sources, and under-load tap-changing (ULTC) transformers. The resulting model is a large-scale probabilistic multi-objective non-convex nonlinear mixed-integer programming (NLMIP) problem with continuous, discrete, and binary variables. To ensure tractability, uncertainties are modeled through a fast and efficient 2m probabilistic approach. To handle the nonlinearities and non-continuous variables that characterize the problem, a modified non-dominated sorting genetic algorithm (NSGA)-II solution approach is proposed and effectively tested. Keywords Multi-objective optimization • NSGA-II • Optimal power flow • Renewable generation 1 Notation The main notation used throughout this paper is reproduced below for quick reference.
This work presents a probabilistic sequential framework for short-term operation of distribution companies (DisCos) participating in the day-ahead (DA) and real-time (RT) markets. In the proposed framework, the DisCo's operating decisions are sequentially optimised; first, in a DA operation stage, and then in RT. The DA decisions are driven by the DisCo's profit maximisation, while the DisCo aims to minimise the actions required to accommodate deviations from forecasted quantities (i.e. the DA decisions) in the RT operation stage. This sequential approach considers realistic voltage-sensitive loads and full ac power flow equations to represent the realistic network's active and reactive power injections. In addition, the operation of stationary batteries and the demand elasticity under time-varying retail prices are explicitly modelled. The two resulting models are large-scale highly non-linear non-convex mathematical problems with continuous and discrete variables. A pseudo-dynamic tabu-search-based solution algorithm is used as an alternative to conventional optimisation solvers in order to tackle the problem in an effective manner, without linearisations. Numerical results from 69-and 135-bus distribution systems illustrate the performance and the scalability of the proposed approach.
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