Optimization of Distributed Energy Resources in Distribution Networks: Applications of Convex Optimal Power Flow Formulations in Distribution Networks

Abstract

The increasing penetration of distributed energy resources (DER) has imposed several challenges in the analysis and operation distribution networks. In the last decade, the implementation of battery energy storage systems (BESS) in electric networks has caught the interest in research since the results have shown multiple positive effects when deployed optimally. However, a complex formulation of the optimization problem regarding DER implementations can easly become nonconvex, thus limiting the scope of the results (quality) and affecting the computational efficiency. In this paper, convex formulations of the optimal power flow (OPF) problem for DER (PV and BESS) installations in San Andres distribution network were implemented to minimize power losses. Four main simulation cases were stablished covering convex power flow analysis, convex optimal power flow to locate and size dispatchable and nondispatchable DER (PV) with demand and irradiation profiles, and the inclusion of aggregated and distributed PV and BESS systems. The computational realization of those formulations was made with three different software packages: CVX in python, CPLEX, and MATLAB (MATPOWER/PSO). The results show that installing DER capacity (both PV and/or BESS) can substantially improve the power losses in distribution networks, especially if considered distributed instead of aggregated, achieving reductions up to 75% for power losses and up to 57% for conventional generation utilization. Besides power losses, it was observed that optimal DER (PV and BESS) implementations behaved similarly as demand response signals do, potentially increasing economic benefits for DISCOs. In terms of computational efficiency, convex formulations help substantially to reduce computation times, especially if scalability is to be considered when distributed DER installations were studied.

Convex Stochastic Approaches for the Optimal Allocation of Distributed Energy Resources in AC Distribution Networks with Measurements Fitted to a Continuous Probability Distribution Function

“…Under these conditions, network planners and operators implement AC-OPF to optimize either individual or combined, technical, economical, or environmental objectives, such as power losses minimization [10], voltage regulation [11], emissions reduction [12], operational and infrastructure investment cost reductions [13,14], or a combination, resulting in techno-economic-environmental multiobjective formulations [15]. This requires the study of newer methodologies involving active distribution network modeling [16], adequate mathematical formulation of optimization problems aiming to increase efficiency and solution quality [17] and the proper implementation of optimization techniques to solve the problem [18]. Additionally, recent multidisciplinary research has been directed to increase computational efficiency in the solution of the OPF problem [18], showing results particularly in newer metaheuristic techniques (e.g., the arithmetic optimization algorithm (AOA) [19], the hybrid Harris hawks optimizer-AOA (hHHO-AOA) [20], and the mutation improved grey wolf optimizer (MIGWO) [21]), and in the convexification of the problem with relaxations based on second-order cones [22,23] or positive semi-definite expressions [24].…”