Operating Cost Reduction in Distribution Networks Based on the Optimal Phase-Swapping including the Costs of the Working Groups and Energy Losses

Abstract

The problem of optimal phase-balancing in three-phase asymmetric distribution networks is addressed in this research from the point of view of combinatorial optimization using a master–slave optimization approach. The master stage employs an improved sine cosine algorithm (ISCA), which is entrusted with determining the load reconfiguration at each node. The slave stage evaluates the energy losses for each set of load connections provided by the master stage by implementing the triangular-based power flow method. The mathematical model that was solved using the ISCA is designed to minimize the annual operating costs of the three-phase network. These costs include the annual costs of the energy losses, considering daily active and reactive power curves, as well as the costs of the working groups tasked with the implementation of the phase-balancing plan at each node. The peak load scenario was evaluated for a 15-bus test system to demonstrate the effectiveness of the proposed ISCA in reducing the power loss (18.66%) compared with optimization methods such as genetic algorithm (18.64%), the classical sine cosine algorithm (18.42%), black-hole optimizer (18.38%), and vortex search algorithm (18.59%). The IEEE 37-bus system was employed to determine the annual total costs of the network before and after implementing the phase-balancing plan provided by the proposed ISCA. The annual operative costs were reduced by about 13% with respect to the benchmark case, with investments between USD 2100 and USD 2200 in phase-balancing activities developed by the working groups. In addition, the positive effects of implementing the phase-balancing plan were evidenced in the voltage performance of the IEEE 37-bus system by improving the voltage regulation with a maximum of 4% in the whole network from an initial regulation of 6.30%. All numerical validations were performed in the MATLAB programming environment.

Application of the Hurricane-Based Optimization Algorithm to the Phase-Balancing Problem in Three-Phase Asymmetric Networks

“…To represent the system of the active power balance equilibrium at each node, we use Equation (2) as presented in [34]:…”

Approximated Mixed-Integer Convex Model for Phase Balancing in Three-Phase Electric Networks

“…The parametric information of this test feeder is reported in Table 3. It is worth mentioning that the information of the impedance matrices for this system is the same used in the 8-bus test feeder [18].…”

Dynamic optimisation of unbalanced distribution network management by model predictive control with Markov reward processes

“… [28] presents a real-time control model for Active Distribution System Management (ADSM) in low voltage (LV) networks, focusing on controlling the temperature of controlled loads and EES. Medium and low voltage networks are very sensible to voltage fluctuations due to active power from distributed generation resources, as highlighted in [29] . Consequently, these problems couple two objectives: minimization of power losses and compliance with voltage limits [30] , [31] .…”

“…The analysis of the literature reveals certain limits in the application of different strategies and mathematical tools used in the management of ADS, both for their feasibility, performance, and scalability. Some of them: i) not addressing the uncertainties caused by the presence of RES in a comprehensive way (e.g., in [23] , [24] , the authors acknowledge that the use of time series for DERs scheduling and grid asset control with variable generation cannot be the only way to deal with the intrinsic uncertainty); ii) low level of penetration of RESs in ADS [28] ; iii) failure to consider the variability of power flows (upstream export of energy to the grid is not taken into account in [28] , which significantly limits the scope and the need to manage the impact of uncertainties in planning and operations; iv) failure to consider intra-hourly variations of wind speed and demand in the optimization model ( [29] and [30] ). Regarding the use of MPC techniques, it is necessary to point out that they require random variable forecasts, which is a limitation for environments where such forecasts are not available for practical or cost reasons [39] .…”