Optimal Integration of Photovoltaic Sources in Distribution Networks for Daily Energy Losses Minimization Using the Vortex Search Algorithm

Abstract

This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm.

Optimal Design of PV Systems in Electrical Distribution Networks by Minimizing the Annual Equivalent Operative Costs through the Discrete-Continuous Vortex Search Algorithm

“…These areas increase the possibility of integrating PV generation sources in a country like Colombia, which permits the proposal of solutions that are energetically sustainable to meet the demand of even more users, while simultaneously limiting the use of diesel-based generation. Based on the aforementioned opportunity of promoting renewable generation solutions in tropical countries, efficient optimization techniques are required to identify the location and sizing of these systems in distribution networks [ 13 ]. To address this problem, this research document proposes an objective function that simultaneously minimizes generation costs and investment, operation, and maintenance costs of PV sources for a planning horizon of 20 years [ 14 ].…”

“…To confront the aforementioned concern, a master–slave optimization methodology is suggested to solve the mixed-integer nonlinear programming model (MINLP) that represents this problem. Therefore, a continuous-discrete version of the vortex search algorithm, recently proposed by [ 13 ], is used to locate and size PV units in electrical distribution grids considering the annualized investment and operating costs along with the studying period.…”

Parametric estimation in photovoltaic modules using the crow search algorithm

“…The presence of photovoltaic (PV) sources has increased rapidly in the past two decades in low, medium and high-voltage levels, and their accelerated development has decreased their production, maintenance, and operative costs [1]- [3]. Moreover, these renewable energy resources have reduced the energy purchase costs in urban areas and greenhouse gas emissions in rural networks powered by diesel generators [4].…”

Optimal Integration of Dispersed Generation in Medium-Voltage Distribution Networks for Voltage Stability Enhancement

“…Electrical distribution networks provide electrical energy to all end-users at mediumand low-voltage levels [1]. These grids interface the power system in the distribution substation with residential, industrial, and commercial consumers by following a radial connection among nodes [2]. Utilities prefer the radial connection owing to lower investment costs in conductors and protective devices.…”

“…Additionally, distribution networks have high energy losses when compared with the transmission counterparts. In some cases, energy losses can be between 6% and 18%, depending on the utility maintenance practices, whereas, in transmission networks, the energy losses are between 1.5% and 2% [2]. These power losses are primarily caused by the radial structure of the network.…”