Optimal allocation of biomass distributed generation in distribution systems using equilibrium algorithm

Abstract

Summary The majority of power utilities have targeted pretentious clean and efficient energy plans. These plans are accompanied in driving down the cost of biomass distributed generation units (BDGs). This paper proposes an optimal allocation procedure of BDGs to enhance the performance of the distribution systems and to reduce the related environmental emissions. The proposed procedure aims to maximize the power utilities' benefits in terms of power loss reduction, energy sales excess, and pollutant emission reduction. It also takes into consideration the minimization of the annual operational and maintenance costs of the BDGs. The load growth with several loading levels over the BDGs planning period is presented. For achieving this target, an adaptive equilibrium optimizer (EO) technique is developed which is characterized by simple structure and dynamic control parameters. The proposed procedure is applied to IEEE 33‐bus and practical large‐scale 141‐bus system of AES‐Venezuela in the metropolitan area of Caracas. The simulation results declare the effectiveness and capability of the employed EO technique in achieving great benefits in terms of energy sales and environmental emissions with a high improvement of the voltage profile and minimizing power losses. In addition, a comparative and statistical analysis is executed with several recent techniques to show the superior capability of the proposed procedure using the EO technique.

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

“…This is elaborated in Table 2, which tabulates the comparative results of GTO. It achieved the minimum RMSE value and standard deviation values of 6.367E−4 and 4.405E−8, respectively, with respect to other recent optimization techniques, which were EO [47,55], FBI [46], HEAP [49,56], jellyfish search (JFS) optimizer [48,57], and EMPA [58,59], and other reported optimization techniques, which were PSO [12], ALO [18], flexible PSO (FPSO) [29], PGJAYA [34], classified perturbation mutation PSO (CPMPSO) [60], Hybrid Firefly and Pattern Search (HFAPS) [61], Lightning Attachment Procedure Optimization (LAPO) [62], Barnacles Mating Optimizer (BMA) [63], neighborhood scheme-based Laplacian MBA (NLBMA) [64], hybrid PSO-GWO algorithm (PSOGWO) [65], Enriched Harris Hawks optimization (EHHO) [66], and multi-verse optimizer (MVO) [67]. As shown in Table 2, the GTO technique for the SDM of KC200GT had the minimum error value compared with the various reported algorithms in the literature.…”

Optimal management of static volt‐ampere‐reactive devices and distributed generations with reconfiguration capability in active distribution networks

“…A schematic diagram that shows all these modifications of the second network is depicted in Figure 4. In this article, the DG‐based biomass gas turbine unit is a synchronous generator 15 . However, it is modeled to operate on a unity power factor as recommended by the IEEE 1547 standard.…”

“…In the literature, there are various methods employed to get the optimal allocations, such as modified multiobjective coyote optimization, 13 particle swarm optimization, 3 and multiobjective opposition‐based chaotic differential evolution 14 . The authors in Reference 15 employed the equilibrium optimizer to get the best allocations of biomass DGs where the objective function contains the minimization of the maintenance and operating costs. Besides, Monte‐Carlo simulation has been combined with the sunflower optimization algorithm was proposed in Reference 16 to allocate the wind DGs where the uncertainties of wind power are considered, whereas the CBs re‐allocation is considered to get the minimum value of power loss.…”

Effective Automation of Distribution Systems With Joint Integration of DGs/ SVCs Considering Reconfiguration Capability by Jellyfish Search Algorithm

“…Several techniques have been utilized to find the best allocations of the DGs such as particle swarm optimization, modified moth flame optimization techniques, and multiobjective opposition based chaotic differential evolution [3], [13] and [14]. Abo El-Ela et al [15] utilized the equilibrium optimizer to allocate DGs of biomass type in the distribution systems. In this paper, minimizing the operating and maintenance costs of the DGs have been augmented with the power utilities' benefits and handled as single target.…”