Search

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution for ACOPF with short-term injection uncertainties is essential. Robust Optimization (RO), when used for the conventional non-linear ACOPF, may result in a computationally intractable Robust Counterpart (RC), which is undesirable as ACOPF is an operational problem. Hence, this article proposes a single-stage, non-integer, non-recursive, RC of ACOPF using a dual transformation for short-term injection uncertainties. The proposed RC is convex, tractable, and provides base-point active power generations and terminal voltage magnitudes (setpoints) of conventional generators that satisfy all constraints for all realizations of considered injection uncertainties. The non-linear impact of uncertainties on other variables is inherently modeled. The budget of uncertainty constraints are also considered for low conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based participation factored AC power flows validate the robustness of the setpoints on NESTA and 9241pegase systems for different injection uncertainties. Comparison with previous approaches indicates the efficacy of the proposed approach in terms of low cost, robustness, and computation time.

Demand Response (DR), along with the Volt-Var Optimization (VVO), can be effectively utilized to reduce the loss and unbalances in an Electric Distribution Network (EDN). Network Reconfiguration (NR), a reliability enhancement strategy, is also carried out in limited numbers for a specific time and duration along with DR and VVO. This study addresses the challenges of this integration using a fast and efficient graph-theory-based topology pre-processor. It eliminates all non-radial configurations beforehand, reduces the curse of dimensionality, and improves the convergence of the proposed optimization formulation. Further, a novel variable reduction concept is applied to integrate the topologies into the optimization effectively. A hybrid Multi-Objective Particle Swarm Optimization (MOPSO) is used to fetch the optimal network topology, VVO devices’ settings, and the schedule of flexible loads for every 15 minutes interval in a day. With the initial elimination of non-feasible topologies and the subsequent variable reduction technique, the proposed scheme significantly improves the supply reliability during outages and results in better peak load reduction, loss minimization, and unbalance management. A comparison of load management using the proposed three-phase valley filling index method and the conservation voltage reduction is also given. The proposed method’s effectiveness is demonstrated on a modified active IEEE 123-bus EDN. Note to Practitioners—Supply reliability, power quality, and energy efficiency are essential objectives in an EDN. Ensuring power flow through alternate paths during line outages, maintaining electricity supply with stringent voltage standards, and minimizing energy loss, unwarranted current and voltage imbalances are vital to such goals. NR re- routes power flow during line outages to ensure no consumer is isolated from the source. Dynamic NR can efficiently support other listed targets if exercised in limited numbers. Load redistribution through DR helps maintain supply-demand balance, reduces peak load, and minimizes voltage violations in terms of voltage drop and rise. In VVO, voltage regulator taps, VAR support from PV inverters, and capacitor banks’ settings are varied simultaneously to reduce energy loss and unbalances while ensuring an acceptable voltage profile. A co- optimization of dynamic NR, DR, and VVO in an unbalanced active EDN provides cumulative benefits with limited and shared stress on each control device. This work projects an efficient scheme for such an integration. Novel topology pre-processing and associated formulations are devised to reduce the computational burden of such a simultaneous approach. The practical significance of the proposed approach is demonstrated through various case studies. It can fetch maximum solar power hosting without source curtailments, enhance regulator taps and switches lifespan, avoid load shedding, relieve peak load-related challenges, and improve consumer satisfaction. Future research will include the stochastic nature of solar PVs and loads like electric vehicles.