Optimizing solar farm interconnection networks using graph theory and metaheuristic algorithms with economic and reliability analysis

Optimal configuration of photovoltaic power plant using grey wolf optimizer: A comparative analysis considering CdTe and c-Si PV modules

In photovoltaic power plants, operating environmental conditions have a profound effect on the behavior and the quality of the photovoltaic (PV) energy produced. The non-homogenous insolation reduces the power producing capacity, introduces multiple peaks in the PV curve, and produces hot spots. Metaheuristic algorithms have emerged as an effective tool in reconfiguring the panels to disperse the shading uniformly. The actual locations of the modules remain unchanged during reconfiguration while the electrical connection is changed. Therefore, this paper presents a comprehensive study on two different metaheuristic optimization algorithms, namely particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). These optimization tools provide a connecting matrix for the new electrical connection that gives high output power to the same PV array. In addition, a comparison of these reconfiguration methods is done to assess their suitability of these methods. Based on the result, PSO gives better output as compared to GOA both in terms of power produced as well as the PV curve.

Optimal online battery power control of grid-connected energy-stored quasi-impedance source inverter with PV system

Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are two commonly employed techniques in designing maximum power point tracking systems in photovoltaic (PV) farms. A mathematical formulation of the objective function is derived by implementing the maximum power theorem for load matching using the relationship between input and output impedances. This paper also proposes a novel Center-based Latin Hypercube (CLHS) initialization scheme for population-based algorithms; it is shown that for population initialization, the newly proposed technique of CLHS gives better results with a small population size. A comprehensive comparative study is conducted on DE and PSO algorithms in terms of control parameters, search components, and population initialization methods to determine the best algorithm with its corresponding optimal parameters settings and population initialization to solve a family of maximum power point tracking problems. The work shows that both algorithms are capable of tracking the maximum power point although the PSO is more effective over a small population size. In this study, in overall, 15,876 and 96,228 settings possibilities for DE and PSO respectively are investigated.

Flexible alternating current transmission system (FACTS) devices are becoming part of the modern network because of their importance in power network parameter control. However, the optimal device placement and parameter settings are crucial to achieving the set objectives. Therefore, this study investigates the most efficient metaheuristic optimization algorithm between particle swarm optimization (PSO) and the firefly algorithm (FA) in allocating a static synchronous compensator (STATCOM) controller for the multi-objectives of loss minimization and voltage violation mitigation. The efficient algorithm is the one whose applications in device allocation will result in effective system loss and bus voltage violation minimizations in the network. Meanwhile, the choice of a static synchronous compensator out of FACTS devices was as a result of its reactive power compensation capabilities, while PSO and FA were considered because of their computational efficiencies among other metaheuristic algorithms. The simulations were implemented on an IEEE 14-bus system utilizing the MATLAB package. Active power loss minimizations of 0.43 and 0.73 MW were achieved when STATCOM was optimized with PSO and FA, respectively. Therefore, the achievements in power losses and voltage deviation reductions in this case indicate some advantages of FA over PSO. Besides, the effectiveness of metaheuristic algorithms in FACTS device allocation has also been demonstrated in this study.

Allocation and smart inverter setting of ground-mounted photovoltaic power plants for the maximization of hosting capacity in distribution networks

The particle swarm optimization (PSO) is a population-based algorithm belonging into metaheuristic algorithms and it has been used since many decades for handling and solving various optimization problems. However, it suffers from premature convergence and it can easily be trapped into local optimum. Therefore, this study presents a new algorithm called multi-mean scout particle swarm optimization (MMSCPSO) which solves reactive power optimization problem in a practical power system. The main objective is to minimize the active power losses in transmission line while satisfying various constraints. Control variables to be adjusted are voltage at all generator buses, transformer tap position and shunt capacitor. The standard PSO has a better exploitation ability but it has a very poor exploration ability. Consequently, to maintain the balance between these two abilities during the search process by helping particles to escape from the local optimum trap, modifications were made where initial population was produced by tent and logistic maps and it was subdividing it into sub-swarms to ensure good distribution of particles within the search space. Beside this, the idle particles (particles unable to improve their personal best) were replaced by insertion of a scout phase inspired from the artificial bee colony in the standard PSO. This algorithm has been applied and tested on IEEE 118-bus system and it has shown a strong performance in terms of active power loss minimization and voltage profile improvement compared to the original PSO Algorithm, whereby the MMSCPSO algorithm reduced the active power losses at 18.681% then the PSO algorithm reduced the active power losses at 15.457%. Hence, the MMSCPSO could be a better solution for reactive power optimization in large-scale power systems.

In this paper we study and compare the environmental efficiency of 118 photovoltaic (PV) plants in China. Drawing on the nonparametric data envelopment analysis (DEA) method, our study takes the initiative to take the insolation, annual sunshine duration, and covering area as input variables into account, as well as the installed capacity, annual electricity generation, CO2 emission reduction, and coal saving as output variables, to provide a unified measure of environmental efficiency of PV plants in China. We find widespread inefficiencies in roughly 95% of the PV plants, and the performance of different economic zones and types of PV plants are quite different. Specifically, those PV plants in eastern China are the least satisfying performers among three different economic zones. The surprising result indicates that eastern China has room for improvement by overcoming the inefficiencies caused by serious aerosol pollution and the high urbanization rate. We also find rooftop PV plants have the highest efficiencies among the four types of PV plants due to very little power loss. However, complementary PV plants have the lowest efficiencies most likely because of high operating temperatures during the process of power generation.

The interconnection of utility-scale photovoltaic (PV) power plants with the electric grid is a crucial factor that requires comprehensive analysis and assessment. The focus of this research article is on a specific photovoltaic (PV) power plant that is planned for construction in the X Power System located in Indonesia which has 150 MW capacity which has intermittent behavior, experiencing fluctuations in power generation based on the availability of sunlight and the cloud movement. The objective of this paper is to explore the feasibility, technical prerequisites, and potential solutions for the successful integration of the PV power plant into the existing power system. Multiple investigations will be carried out, which is Load Flow, Short-Circuit, and Transient Stability analyses, with the aim of assessing the consequences of linking the PV power plant to the existing power system. Consequently, it is vital to model the X power system conditions prior to the interconnection process. Moreover, modeling an intermittent PV power plant necessitates different approaches compared to conventional power plants. According to the research findings from load flow analysis, the voltage levels near the interconnection point, both prior to and after linking the PV Power Plant, remain within permissible bounds of +5% and -10%. Furthermore, there are no constraints on the load capacity of the transmission lines and Interbus Transformers (IBT) either prior to or following the integration of the PV Power Plant. The short-circuit current around the point of interconnection experiences a marginal increase, and it is advisable to employ circuit breakers (CB) rated at 40 kA for both the PV Power Plant and the switching station. Furthermore, the power system exhibits resilience in preserving its stability, even in scenarios involving abrupt power loss or intermittent generation from the PV Power Plant. These situations can result from unexpected outages or variations in solar radiation. The interconnection of the 150 MW PV Power Plant can be implemented without significant adverse effects on the system's voltage, loading capacity, and stability.

The selection of features is used to obtain a subset of features by the removal of irrelevant features with no or less predictive output. Meta-heuristic algorithms are appropriate for the selection of features because feature subset representation is direct and the evaluation is easily accomplished. This paper performed a comparative study on the impact of meta-heuristic optimization algorithms on breast cancer diagnosis using Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The two feature selection algorithms were used to obtain the relevant attributes from the Wisconsin breast cancer (original) dataset. The selected attributes were passed to seven learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naïve Bayes (NB), K Nearest Neighhood (KNN), Neural Network (NN), Logistic Regression (LR), and Random Forest (RF). The diagnostic model was evaluated based on accuracy, precision, recall, and F1-measure. Experimental showed that the highest accuracy of 97.1388% was obtained in both PSO and ACO using RF classifier, the highest precision value of 0.9720 was recorded in ACO using RF classifier, the highest recall value of 0.9750 was achieved in PSO using RF classifier, the highest F1-measure value of 0.9700 was obtained in PSO using SVM, the highest kappa statistic of 0.9370 was obtained in both PSO and ACO using RF and the lowest time of 0s was taken to build a model was recorded in PSO using KNN and NB, and also in ACO using KNN. The paper concluded that the breast diagnostic model using PSO and ACO with different learning algorithms revealed that the accuracy of RF outperformed other algorithms. Also, it was shown that ACO produced better precision using RF compared with PSO and PSO gave better recall using RF compared with ACO, PSO recorded an efficient F1-measure using SVM. The best time used to build a model was obtained in PSO for KNN and NB, and ACO with KNN.Keywords— Breast cancer, Data mining, Diagnosis, Feature selection, Meta-heuristic.

An Ecological Context That Scars: Reflections on Three Nature Documentaries of 2020

Solar photovoltaic (PV) power plants consist of a large number of series-connected modules with a high voltage range to reduce unwanted power losses. But, the power plants encounter a serious power loss due to one of the unavoidable scenarios in the environment i.e. cloud passage. This paper focuses on the investigation of the horizontal cloud passage impact on the performance of a 34.1kW power plant. The study has been performed in the MATLAB environment and the performance analysis has been done based on power-voltage characteristics analysis and various parameters such as power generation, losses, efficiency and performance ratio. Also, cloud passing (or movement) impact on PV plant performance has been studied using time-domain analysis. The study concluded that the PV power plants can encounter power losses of higher than 25% due to cloud passage in the environment.

This paper presents a particle swarm optimization (PSO) method to deal with reactive power optimization problem in a province power system in China. The objective the optimization problem is to minimize the real power losses whilst maintaining acceptable voltage profiles. Particle swarm optimization, a new evolutionary computation method, relieves the derivative assumptions of objective function and deals with continuous and discrete variables conveniently. The effects of PSO parameters, such as inertia weight, learning factors and population size have been empirically studied. The successful application to the practical Heilongjiang power system indicates the possibility of PSO as a practical tool for various optimization problems in power system.

Power Plants Economic Dispatch Optimization on IEEE 30 Bus System using Metaheuristic Algorithms. Economical scheduling or also known as economic dispatch (ED) of power plants is an important approach in electric power system to reduce fuel costs while still considering the system technical constraints. ED problems are nonlinear and complex because they involve quadratic functions, generation limits and power losses in the system. In this study, metaheuristic algorithm are used, namely Particle Swarm Optimization (PSO) and Novel Bat Algorithm (NBA) which are applied to an IEEE 30-bus sytem with 6 generator units. The data used are generator cost function data, minimum and maximum generator limit data, load data for each bust and transmission line parameters. Both metaheuristic algorithms are simulated in MATLAB software, with the objective function of minimizing the total generation cost and considering power losses as an additional evaluation parameter. Based on the simulation results, NBA shows a total generation cost of $488.07/hour while PSO is $501.54/hor. By using the NBA method, the total fuel cost is cheaper than PSO. Power allocation using NBA is more economically efficient, although it results in slightly higher power losses of 4.76 MW compared to PSO 4.16 MW. However, this difference is still with acceptable limits. Therefore, this algorithm can be an effective alternative for optimizing generator scheduling in electric power system.

This paper presents distributed and asynchronous active fault management (DA-AFM) to manage renewable energy upon faults. Addressed here are two challenges in fault management for photovoltaic (PV) farms and wind farms. The first one is the activation of crowbars in doubly-fed induction generator (DFIG) wind turbine systems during fault ride-though. The activation undesirably makes DFIG-based wind farms lose control and absorb reactive power. The second challenge is implementation of distributed fault management for distinct PV farms with different objectives and constraints. Coordination for large number of PV farms facilitates integration of themselves and other renewable energy. To prevent crowbars from being activated, DA-AFM controls nearby PV farms' interface converters to smooth voltage drops so that DFIGs experience voltages with a lower dropping speed. To enable distributed computation of DA-AFM's optimization formulation, a distributed and asynchronous surrogate Lagrangian relaxation (DA-SLR) method is devised to coordinate a cluster of PV farms. Simulation results have demonstrated DA-AFM 's effectiveness in preventing crowbars' activation in wind farms and in coordinating diverse PV farms.