In this paper, an improved slime mould algorithm (ISMA) is proposed for finding the optimal location and sizing of photovoltaic distributed generation units (PVDGUs) in unbalanced distribution systems (UDSs). The proposed method is developed by changing the control variable update mechanism of the original slime mould algorithm (SMA). Total power losses on distribution lines and voltage deviation index of all buses are minimized under the consideration of various constraints. The location and sizing of PVDGUs found by ISMA are added into the cosimulation between MATLAB and OpenDSS for reaching other remaining parameters of UDSs. In addition, sunflower optimization (SFO), social-ski drive (SSD), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), bonobo optimizer (BO), and SMA are also run for finding PVDGUs placement solution on the unbalanced three-phase IEEE 123-bus test feeder. As a result, the proposed ISMA can reduce the power loss up to 78.88% and cut the voltage deviation up to 1.4779 pu while that of others is from 69.10% to 78.87% and from 1.5759 to 1.4996 pu. Thus, ISMA should be used to place PVDGUs in UDSs meanwhile the cosimulation between MATLAB and OpenDSS should be applied as a power flow calculation tool for UDSs.

Determining the optimal integration of renewable energy–based distributed generation units (DGUs) in electric distribution systems brings many positive technical and economic impacts and contributes significantly for improving the performance of the distribution system. The suitable installation of DGUs is always a challenging problem because the output behaviors of DGUs, specifically photovoltaic units (PVUs) and wind turbine units (WTUs) are strongly affected by stochastic natural conditions. In this study, the main purpose is to address optimal nonlinear constrained problems with three targets in the multiobjective function (MOF) for minimizing (1) total power loss, (2) voltage deviation, and (3) the cost of purchasing energy from the main grid considering the uncertainties of solar irradiance and wind speed in the actual region in Binh Thuan Province, Vietnam. This paper solves three problems related to optimal connection of DGUs in the distribution system considering constant power generation and consumption with unity power factor (PF) (Problem 1), constant power generation and consumption with optimal PF (Problem 2), and time‐varying power generation and consumption with optimal PF (Problem 3). Besides, this research also proposes a novel meta‐heuristic optimization algorithm called revised coyote optimization algorithm (RCOA) for addressing the optimization problems of the simultaneous integration of DGUs in IEEE 69‐bus radial distribution system. The obtained results in above three problems are compared with the original and published methods to demonstrate the superior economic and technical benefits of the proposed method.

In this paper, a bonobo optimizer (BO) and two other methods, particle swarm optimization (PSO) and salp swarm algorithm (SSA), are implemented to determine the location and sizing of photovoltaic distributed generators (PDGs) and capacitors in IEEE 69-bus radial distribution system with many nonlinear loads. The objective of the study is to minimize the costs for purchasing energy from main grid for load demand and power loss on transmission lines as well as cost for emission fines from fossil fuel generation units of the grid under considering strict constraints on penetration, voltage, current and harmonic distortions. The results have shown that BO is the best and most stable method in solving the considered optimization problem. With the use of the optimal solution from BO, the total cost is significantly reduced up to 80.52%. As compared to base system without capacitors and PDGs, the obtained solution can reduce power loss up to 94.48% and increase the voltage profile from the range of [0.9092 1.00] pu to higher range of [0.9907 1.0084] pu. In addition, total harmonic distortion (THD) and individual harmonic distortion (IHD) are also much improved and satisfied under the IEEE Std. 519. Thus, BO is a suitable method for the application of installing capacitors and PDGs in distribution systems.

This paper proposes an improved equilibrium optimizer (IEO) for determining optimal location and effective size of distributed generation units (DGUs) in the distribution systems in order to minimize the total power loss on distribution branches, investment cost, and operation and maintenance cost. In a good obtained solution, limits of voltage, current, and harmonic flows are also seriously considered, exactly satisfying predetermined ranges. Especially, individual harmonic distortion (IHD) and total harmonic distortion (THD) of bus voltage must fall into IEEE Std. 519. The proposed IEO is developed from the original equilibrium optimizer (EO), which was motivated by control volume mass balance models. This novel algorithm can effectively expand the search area and avoid the premature convergence to low‐quality solution spaces. With the determined solutions from IEO, not only are the voltages well improved but also the harmonics are mitigated from the violated values down to the allowable values of IEEE Std. 519. Moreover, the total power loss is significantly reduced from 0.2110 MW to 0.0815 MW, 0.2245 MW to 0.07197 MW, and 0.3161 MW to 0.1515 MW for IEEE 33‐bus, IEEE 69‐bus, and IEEE 85‐bus radial distribution systems, respectively. Not only that, the total cost of DGUs is also more economical and consumes only $3.4753 million, $3.2840 million, and $3.0593 million corresponding to the three systems for a 20‐year planning period. The performance of the proposed algorithm is compared to three other implemented methods consisting of artificial bee colony (ABC) algorithm, salp swarm algorithm (SSA), and EO and eight previously published methods for the three test systems. The comparisons of results imply that IEO is better than other methods in terms of performance, stability, and convergence characteristics.