Two-Level Optimisation and Control Strategy for Unbalanced Active Distribution Systems Management

Abstract

This article proposes a new approach to the operation of unbalanced Active Distribution Systems (ADS) using an economic dispatch optimisation model for Active Distribution Systems Management (ADSM). The model proposes a two-level control strategy. The first one poses an optimisation problem with the objective of minimising total active power losses in the ADS and the second one proposes an algorithm that controls the position of the taps of three-phase on-load tap-changer (OLTC) transformers to ensure compliance with the technical constraints imposed by the Distribution System Operator (DSO). The optimisation problem is solved by MATLAB and DIgSILENT PowerFactory for power systems static simulations. This paper includes a novel peer to peer communication framework between MATLAB /DIgSILENT . The control and optimisation strategy is validated on the IEEE 34-Node Distribution Test Feeder. This network incorporates balanced and unbalanced three-phase loads, single-phase loads in the different phases, and two-phase loads. In this scientific paper, photovoltaic (PV) and wind power generation (WT) have been integrated to test feeder operation, with the support of battery energy storage systems (BESS). The correct operation of the proposed ADSM is demonstrated using numerical simulation on five scenarios considering several configurations of the renewable generation units and the batteries. The strategy has also been validated in a more extensive distribution network, proving its good performance.

An Improved Crow Search Algorithm Applied to the Phase Swapping Problem in Asymmetric Distribution Systems

“…In order to expose the iterative swept TPPF method developed by [15], in any n−node system, we used the relationship between the nodal voltage and the injected current that is presented using the equivalent between the admittance matrix and the incidence matrix [32], as shown in Equation (7).…”

“…(ii) The nature of the loads may be 1ϕ, 2ϕ, or 3ϕ, which generates unbalances in voltages at the nodes and in the line currents [6]. (iii) The arbitrary location of single-phase transformers on the phases of the system causes an unbalance in the currents through the lines [7]. Load unbalances in distribution systems create undesirable scenarios such as the increase of current in any phase system, which produces an increase in power losses through its constituent elements [8].…”

“…Load unbalances in distribution systems create undesirable scenarios such as the increase of current in any phase system, which produces an increase in power losses through its constituent elements [8]. These power losses can exceed the capacity required to supply the demand, cause equipment to age, and increase investment and operating costs for network operators [7,9]. The importance of reducing power losses in distribution networks has established multiple approaches, such as (i) optimal placement and sizing of distributed generation [10], (ii) optimal capacitor placement and sizing [11], (iii) optimal network reconfiguration [12], (iv) optimal conductor sizing in distribution networks [13], (v) optimal power system restoration [14], and (vi) optimal phase swapping [15,16].…”

Reduction of Losses and Operating Costs in Distribution Networks Using a Genetic Algorithm and Mathematical Optimization

“…Distribution networks are a fundamental component of power systems involved in warranty power supply to end customers (i.e., residential, commercial, mixed, and industrial customers) [1,2]. Due to their radial topology, in these systems, a large percentage of power is often lost (i.e., when power energy is transformed to heat energy).…”

A Mixed-Integer Quadratic Formulation of the Phase-Balancing Problem in Residential Microgrids

“…The problem of the optimal-phase balancing in electrical distribution networks and microgrids is complex for three main reasons, namely: (i) Power balance equations constitute a high-dimensional non-linear and non-convex set of constraints; (ii) the problem is combinatorial due to the binary variables, associated with the possible options for balancing loads and generators at each node [16,18]; and (iii) the problem is stochastic due to the high variation of the load and generation of renewable sources [15]. These complications in the solution of the phase-balancing problem motivate the development of the current research.…”