Optimal Planning of Multitype DGs and D-STATCOMs in Power Distribution Network Using an Efficient Parameter Free Metaheuristic Algorithm

Integration of Distributed Generation (DG) in the Distribution network will reduce the network expansion costs and increase the network reliability in terms of reduction in voltage magnitude deviations.This paper presents Symbiotic Organisms Search (SOS) based technique for finding out the best size and position of DG in Radial Distribution networks to improve the voltage profile and voltage security state of the Distribution network. The primary objectives of the paper are minimization of bus voltage variation and maximization of voltage stability index of the network as a multi objective optimization problem in presence of uncertainty of sources and loads. In this paper the uncertainty of solar power, wind power and load are modelled using 2m Point Estimate Method along with SOS algorithm.To show the effect of DG placement on voltage security state of Distribution system, the system is classified into three states based on the values of voltage stability index (VSI). Simulation results as obtained from two standard (IEEE) radial distribution networks prove the efficiency and accuracy of the proposed SOS method. The results of SOS based method are compared with some other techniques as found in recent literature, which shows that SOS algorithm outperforms other standard optimization techniques.

Snake Optimizer: A novel meta-heuristic optimization algorithm

Low-frequency oscillations in power systems sometimes lead to larger disturbances leading to instability. Addition of power system stabilizer (PSS) is very effective in damping the oscillations. The main purpose of the PSS is to increase damping of synchronous machines in the excitation loop. In this paper a new meta-heuristic algorithm termed Quasi-oppositional symbiotic organism (QOSS) search has been proposed for designing the PSS parameters in an optimal manner. The technique is tested on both single machine infinite bus system as well as a multi-machine system. Various operating conditions like loading effect and line outages are also considered for establishing robustness of PSS. The PSS parameters are tuned with the help of other optimizations like Symbiotic organism search, Genetic algorithm and Particle swarm optimization and are compared with those obtained with the application of QOSOS, to show the superiority of the proposed algorithm.

The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.

In this study, symbiotic organisms search (SOS) stochastic method is proposed to solve the optimal power flow (OPF) problem with valve-point effect and prohibited zones, which is one of the most important problems of the modern power system. The SOS approach is defined as the symbiotic relationships observed between two organisms in the ecosystem, which do not need the control parameters unlike other meta-heuristic algorithms in the literature. The effectiveness of the proposed SOS method is tested on modified IEEE 30-bus test system. The OPF problem is considered with four different test cases, such as (1) without valve-point effect and prohibited zones, (2) with valve-point effect, (3) with prohibited zones and (4) with valve-point effect and prohibited zones. The obtained results from the SOS algorithm are compared with the other optimization techniques in the literature. The obtained comparison results indicate that proposed approach is effective to reach optimal solution for the OPF problem.

Recently metaheuristic algorithms have become popular in solving DG placement problems due to its advantages of simple implementation and ability to find the near optimal solution for complex and large-scale optimization problems. Symbiotic organism search (SOS) is one of the latest metaheuristic algorithms introduced to solve DG placement problems. Unlike many other metaheuristic algorithms, SOS is simple to implement and does not use any control parameters which lead to enhancing performance stability. However, like other optimization algorithms SOS suffers with local optimal and stagnations which affects its accuracy and convergence especially in solving real world problems like DG placement problem. This work attempts to enhance performance of SOS by combining with cloud-based model. The proposed algorithm is named cloud based model symbiotic organism search (CMSOS). In CMSOS, Cloud-based theories have been used to generate random number operator in mutualism phase of the original SOS. To assess the performance of CMSOS in solving optimization problems 26 benchmark functions with different dimensions have been used. The performance of the proposed algorithm has been tested for real world DG placement problems. The performed analysis such as statistical, convergence and complexity measures show superiority of the proposed algorithm compare to the studied algorithms.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

Survival exploration strategies for Harris Hawks Optimizer

The ambulance routing problem is one of the capacitated ambulance routing problem forms. It deals with injuries and their requests for saving. Therefore, the main aim of the ambulance routing problem is to determine the minimum (i.e., optimum) required distances of between: 1) accident places and the ambulance station; 2) the location of the nearest hospital and the accident places. Although of the efforts proposed in the literature, determining the optimum route is crucial. Therefore, this article seeks to tackle ambulance vehicle routing in smart cities using Harris Hawks Optimization (HHO) algorithm. It attempts to take the victims as quickly as possible and confidently. Several engineering optimization problems confirm that HHO outperforms many well-known Swarm intelligence approaches. In our system, let’s use the node approach to produce a city map. Initially, the control station receives accident site information and sends it to the hospital and the ambulance. The HHO vehicle routing algorithm receives data from the driver; the data includes the location of the accident and the node position of the ambulance vehicle. Then, the driver’s shortest route to the accident scene by the HHO. The locations of the accident and hospital are updated by the driver once the car reaches the accident site. The fastest route (which results in the least travel time) to the hospital is then determined. The HHO can provide offline information for a potential combination of the coordinates of destination and source. Extensive simulation experiments demonstrated that the HHO can provide optimal solutions. Furthermore, performance evaluation experiments demonstrated the superiority of the HHO algorithm over its counterparts (SAODV, TVR, and TBM methods). Furthermore, for ten malicious nodes, the PDF of the algorithm was 0.91, which is higher than the counterparts

The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current–voltage (I–V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I–V curves in all the cases; nevertheless, the two metaheuristic optimization methods only reproduce the I–V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.

In today’s competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

A metaheuristic-based efficient strategy for multi-unit production planning with unique process constraints

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, novel meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Harris Hawks’ optimization (HHO) and atom search optimization (ASO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W PEM, 500W SR-12PEM and 250W stack, under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with that obtained in studies. Moreover, the I/V curves obtained by the application of HHO and ASO showed a clear matching with data sheet curves for all the studied cases. Finally, PEMFC model based on HHO technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed.

Abstract. The optimization of aerospace propulsion systems plays a pivotal role in enhancing performance and efficiency. This paper presents a comprehensive analysis of a 2D bypass dual throat nozzle, with a primary focus on optimizing two critical design parameters: expansion ratio and bypass width, to maximize the mass flow rate and facilitate thrust vectoring capabilities. An improved Harris Hawks Optimization (HHO) algorithm was leveraged in this research to achieve an optimal configuration for the nozzle. Furthermore, the complex relationship between design parameters and mass flow rate is approximated using a Radial Basis Function (RBF) surrogate model, allowing for effective optimization. Through extensive simulations and analyses, the effectiveness of the proposed methodology is demonstrated, showcasing significant improvements in nozzle performance compared to conventional approaches. Furthermore, the optimized configuration obtained through the HHO algorithm is systematically compared with other metaheuristic optimization algorithms, highlighting its superiority in achieving optimal solutions for thrust vectoring applications. This study advances aerospace propulsion technology by providing valuable insights into the optimization of bypass dual throat nozzles, thereby paving the way for enhanced performance and maneuverability in aerospace systems.