Search

• Surprisingly efficient decomposition approach is used to decompose unknown parameters for many different photovoltaic models, such as the SDM, DDM and PV module model. • Self-adaptive enhanced learning differential evolution—is designed to identify the nonlinear parameters obtained by decomposition, and then, by computing the derived matrix equations to obtain the linear parameters. • Considering the installation scenario of real photovoltaic generator sets, photovoltaic module models under different ambient temperatures and different irradiances are tested using the SaELDE. In order for the photovoltaic power generation system to convert energy with the highest efficiency after installation, the unknown parameters of the photovoltaic model need to be accurately set in advance. Therefore, in order to identify unknown parameters of photovoltaic models more reliably, effectively, and accurately, this paper first uses the decomposition approach that is extremely effective for different types of photovoltaic models, called matrix rotation decomposition. The unknown parameters of different photovoltaic models are decomposed into linear parameters and nonlinear parameters through matrix rotation decomposition approach. Then a simple and effective algorithm—Self-adaptive enhanced learning differential evolution (SaELDE)—is proposed to identify the decomposed nonlinear parameters. In SaELDE, there are three improvements: (1) the classification mutation learning mechanism is designed to make classified individuals evolve towards the promising direction; (2) the introduced crossover rate sorting approach effectively makes up for the neglected relationship between individuals and crossover rates; (3) combined with the population reduction strategy, it avoids the impact of the initial population setting on algorithm performance and balances the transition between the exploration and exploitation phases. Finally, the linear parameters are calculated by calculating the derived matrix equations according to the nonlinear parameters. Experimental results from a variety of common photovoltaic models, and even photovoltaic module models that need to face different temperatures and irradiance, show that the proposed SaELDE performs more accurately and has better robustness. In addition, the correlation coefficient of SaELDE is 1 under all conditions, which further proves the accuracy of the parameters extracted by SaELDE. [ABSTRACT FROM AUTHOR]

Locating multiple roots of nonlinear equation systems (NESs) remains a challenging and meaningful task in the numerical optimization community. Although a large number of NES-solving approaches have been put forward, they can only find the roots of one NES at a time. In this paper, we develop a novel NES-solving algorithm based on evolutionary multitasking referred to as EMNES, the goal of which is to effectively find the multiple roots of multiple different NESs simultaneously in a single run through knowledge sharing and transfer. Specifically, firstly a NES-solving framework based on evolutionary multitasking is proposed. Then an efficient multi-task evolutionary algorithm based on neighborhood-based speciation differential evolution for NESs is designed. Finally, combining Gaussian distribution and uniform distribution, a novel resource release strategy is proposed to release the found roots to improve resource utilization and increase population diversity. Numerous experimental results reveal that the proposed EMNES algorithm can achieve a higher root rate and success rate when compared with several well-established algorithms on thirty NESs. Furthermore, simulation results on a more complex test set show that the proposed EMNES is able to locate more roots than most comparison algorithms. • An evolutionary multitasking NES-solving framework is proposed. • Relationship of different NESs is established by a unified search space. • An adaptive mutation strategy with knowledge transfer is presented. • A novel resource release strategy is developed to improve the utilization of computing resources. [ABSTRACT FROM AUTHOR]

• A memetic adaptive DE is proposed for parameter estimation of different PV models. • Ranking-based elimination strategy is developed to accelerate convergence speed. • The performance of our method is verified through different PV models. Parameter estimation of photovoltaic (PV) models plays an important role in the simulation, evaluation, and control of PV systems. In the past decade, although many meta-heuristic methods have been devoted to parameter estimation of PV models and achieved satisfactory results, they may suffer from consuming large computational resources to get promising performance. In order to fast and accurately estimate the parameters of PV models, in this paper, a memetic adaptive differential evolution, namely MADE, is developed. The proposed MADE can be featured as: (i) the success-history based adaptive differential evolution is used for the global search; (ii) the Nelder-Mead simplex method is employed for the local search to refine the solution; and (iii) the ranking-based elimination strategy is proposed to maintain the promising solutions in the external archive. To verify the performance of our approach, it is applied to estimate the unknown parameters of different PV models, i.e., the single diode model, the double diode model, and the PV module. Experimental results obtained by MADE are compared with several state-of-the-art methods reported in the literature. Comparison analysis demonstrates that the proposed MADE exhibits remarkable performance on accuracy and reliability. It also consumes less computational resources than other compared methods. [ABSTRACT FROM AUTHOR]

Highlights • A new TLBO (ITLBO) is proposed for parameters estimation of solar cells/modules. • The ITLBO is based on improved teaching and learning strategies. • The accuracy and reliability of ITLBO is verified through different PV models. • The ITLBO performs better than most reported algorithms. Abstract Accurate and reliable parameter extraction of photovoltaic (PV) models is urgently desired for the simulation, evaluation, control, and optimization of PV systems. Although many meta-heuristic algorithms have been used to extract the PV parameters, the extracted parameters are usually not very accurate and reliable. To accurately and reliably extract the parameters of different PV models, an improved teaching-learning-based optimization (ITLBO) algorithm is proposed in this paper. The novelty of ITLBO lies primarily in the improved teaching and learning strategies with two improvements: (i) the teacher adopts different teaching strategies according to learner levels in the teacher phase; and (ii) in the learner phase, a new learning strategy is proposed to balance exploration and exploitation. The performance of ITLBO is verified by extracting the parameters of the single diode model, the double diode model, and three PV modules. The experimental results indicate that ITLBO obtains better performance with respect to accuracy and reliability compared to the other algorithms. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem featured as a non-linear, non-convex, large-scale and constrained, still remains a popular and challenging work in power systems optimization. Although various optimization algorithms have been devoted to solving this problem, they suffer from some weak points such as insufficient accuracy as well as most of them are unconstrained optimization algorithms that result in optimal solutions that violate certain security operational constraints. To this end, this paper presents an adaptive constraint differential evolution (ACDE) algorithm, in which the novelty lies primarily in these three points: i) the crossover rate (CR) sorting mechanism is employed to build the relationship of CR and individual fitness values; ii) reusing successful evolution direction is proposed to guide the individual evolution towards promising regions; iii) an advanced constraint handling technique named superiority of feasible solutions (SF) is introduced to effectively deal with constraints in power systems. In order to verify the performance of the presented approach to the OPF problem, the standard IEEE-30 bus system is selected as the test case, in which six optimization objectives including total fuel cost, total fuel cost considering the valve-point effect, real active power losses, voltage deviation, voltage stability and emission are studied. The experimental results demonstrate that the presented approach can provide the smaller cost (800.41132$/h), reducing by up to 3.76% compared to the MPIO-COSR. In terms of the emission, ACDE emits the least emissions (0.204817ton/h). In addition, the proposed method also obtains the best results on the real active power losses (3.084041 MW) and voltage deviation (0.085636p.u.) when compared with other state-of-the-art methods. • Crossover rate sorting mechanism is used to improve the performance of adaptive differential evolution. • Reusing successful evolution direction strategy is proposed. • Constraints in OPF are effectively handled through superiority of feasible solutions. • The proposed algorithm can be an effective alternative for constraint OPF problems. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) cells are widely used for their clean and sustainable advantages, forcing researchers to accurately model their characteristics. The behavior of PV cells can be derived from their current–voltage characteristics, depending on their unknown circuit model parameters. Due to the simulation, evaluation, control, and optimization of PV systems, it is essential to accurately and reliably extract the parameters of PV models. However, because of the non-linear, multi-variable, and multi-modal characteristics, it is still a very challenging task. With the rapid development of intelligent computing, various meta-heuristic algorithms have been devoted to extracting the parameters of different PV models. The purpose of this paper is to comprehensively review the meta-heuristic algorithms and their related variants that have been used to extract the parameters of different PV models. Different from the existing research works, this paper presents a comprehensive review based on the reliability, robustness, computational resources, and time complexity of the algorithm. These features are essential to design an algorithm for efficient parameter extraction of PV models. Based on the conducted review, some useful recommendations are provided, which have important reference significance when designing the new parameter extraction methods of PV models and are of great significance for further improving the performance, control, and design of PV cells. • Some common PV models and their parameter optimization problems are described. • Various meta-heuristic methods for parameter extraction of PV models are reviewed. • Important insights and future research directions are summarized. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems play an important role in today's power systems because they can convert solar energy directly into electricity. However, theirs conversion performance depends mainly on the PV models unknown parameters. Due to the complex characteristics of the equivalent circuit equation of the PV model, parameter identification of PV models remains a very popular and challenging task in PV system optimization. In this paper, a hybrid adaptive teaching–learning-based optimization (TLBO) with differential evolution (DE), referred to as ATLDE, is proposed to accurately and reliably identify the unknown parameters of PV models. In ATLDE, three improvements are introduced: i) the learners' ranking probability is presented to adaptively choose the teacher or learner phase of TLBO; ii) based on the learners' ranking probability, an enhanced teaching manner with an adaptive teaching factor T F is proposed to make use of the exploitation abilities of better learners in the teacher phase; iii) DE is embedded in the learner phase to improve population diversity and encourage wider exploration of the search space. In order to verify the performance of ATLDE, it is applied to parameter identification of different PV models, including the single diode model, the double diode model, and two PV panel module models. The experimental results demonstrate that our approach has great competitiveness in terms of accuracy and reliability. Therefore, the proposed algorithm can be an effective and efficient alternative for PV model parameter identification problems. • A hybrid adaptive TLBO with DE algorithm (ATLDE) is proposed. • Learners ranking probability is used to adaptively select the teacher or learner phase. • DE is embedded in TLBO to improve population diversity and exploration ability. • The superiority of ATLDE is verified by solving PV parameters identification problems. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) problem is a large-scale, non-convex, multi-modal, and non-linear constrained optimization problem, which has been widely used in power system operation. Because of these features, solving the OPF problem is a very popular and challenging task in power system optimization. In recent years, many advanced optimization methods are employed to deal with the OPF problem. However, most of these methods are unconstrained. In this paper, an enhanced adaptive differential evolution (JADE) with self-adaptive penalty constraint handling technique, referred to as EJADE-SP, is proposed to obtain the optimal solution of the OPF problem. The EJADE-SP is an enhanced version of JADE, where four improvements are proposed to enhance the performance of JADE when solving the OPF problem: i) crossover rate (C R) sorting mechanism is introduced to allow individuals to inherit more good genes; ii) re-randomizing parameters (C R and scale factor F) to maintain the search efficiency and diversity; iii) dynamic population reduction strategy is used to accelerate convergence; and iv) self-adaptive penalty constraint handling technique is integrated to deal with the constraints. To verify the effectiveness of the proposed method, it is applied to the OPF problem on a modified IEEE 30-bus test system, which combines stochastic wind energy and solar energy with conventional thermal power generators. The simulation results demonstrate that the proposed approach can be an effective alternative for the OPF problem. • An enhanced adaptive differential evolution was proposed for optimal power flow. • Four improvements were introduced in the proposed method to obtain promising results. • Its performance was verified by optimizing various objectives of optimal power flow. • Our proposal obtained highly competitive results compared with related methods. • Our proposal can be an effective alternative for optimal power flow problem. [ABSTRACT FROM AUTHOR]

• An enhanced adaptive differential evolution is proposed to extract photovoltaic parameters. • The accuracy, reliability, and convergence of the proposed method are confirmed. • The proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models. Parameter extraction of photovoltaic models based on measured current-voltage data plays an important role in the simulation, control, and optimization of photovoltaic systems. Although many parameter extraction techniques have been devoted to solving this problem, they may suffer from some deficiencies. In this paper, an enhanced adaptive differential evolution algorithm is proposed to extract photovoltaic parameters fast, accurately and reliably. In proposed method, the crossover rate sorting mechanism is introduced to assign each individual to an adapted crossover rate value according to their fitness values, which allows good elements to be more inherited in next generation. In addition, a dynamic population reduction strategy is used to improve the convergence speed and balance the exploration and exploitation. The performance of proposed method is confirmed by extracting parameters of different photovoltaic models, i.e., single diode, double diode, and photovoltaic modules. The simulated results show that the proposed method exhibits competitive performance on accuracy, reliability and convergence speed compared with other state-of-the-art algorithms. Further, the test results on experimental data from the manufacturers data sheet also indicate that the proposed algorithm can obtain superior solutions at different irradiance and temperature. Therefore, the proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models. [ABSTRACT FROM AUTHOR]

Competitive multitasking optimization (CMTO) is a special multitasking optimization paradigm that has been recently proposed. In the CMTO problems, the objective values of all tasks are competitive, and the purpose of CMTO is to find an optimal solution for multiple tasks. However, existing algorithms designed for the CMTO problems perform poorly since their resource allocation strategies are prone to incorrect task selection. To remedy this drawback, this paper proposes an improved multitasking adaptive differential evolution, which can be featured as: (i) a success-history based resource allocation strategy is proposed; (ii) an adaptive random mating probability control strategy is devised to adapt to different task combinations; (iii) an adaptive multitasking differential evolution operator is designed to enhance the searchability. To evaluate the performance of the proposed method, three CMTO benchmark test suites and two real-world optimization problems are chosen. Compared with other related methods, the results show that the proposed method achieved better performance empirically. • Competitive multitasking optimization has good practical significance. • A powerful competitive multitasking evolutionary algorithm is proposed. • A success-history based resource allocation strategy is designed. • An improved differential evolution operator is used to enhance searchability. • An adaptive RMP control strategy is devised for different task combinations. [ABSTRACT FROM AUTHOR]

Solving nonlinear equation systems (NESs) is an important yet challenging task in the field of numerical computation. It aims to locate multiple roots in a single run. However, the existing methods lack effective knowledge transfer. In this article, a knowledge transfer-based adaptive differential evolution is proposed to deal with NESs. Its main features are: (i) knowledge transfer between two niching techniques (crowding and speciation) is carried out to balance diversity and convergence; (ii) the variation characteristics of population diversity and convergence are used to judge knowledge transfer intensity; (iii) a knowledge transfer mechanism is designed to ensure that reasonable individuals are selected for the transfer to supplement the deficiencies of crowding and speciation; (iv) a parameter adaptation with niching level is introduced to improve search efficiency. Experiments on classical 30 NES problems have demonstrated that the proposed approach can outperform the state-of-the-art algorithms, in terms of root ratio and success rate. [ABSTRACT FROM AUTHOR]

The balance between multiple objectives and various constraints is the key to solving constrained multi-objective optimization problems (CMOPs). When dealing with CMOPs with complex feasible regions, some evolutionary algorithms suffer from great challenges in converging to the constrained Pareto front (CPF) with well-distributed feasible solutions. To address this issue, this paper proposes a dual-population based bidirectional coevolution algorithm, called DBC-CMOEA, which aims to converge to the CPF using promising solutions explored from both feasible and infeasible regions. To do so, DBC-CMOEA maintains two populations and an archive, where the dual-population is complementary in the search process and the archive is used to retain promising feasible and infeasible solutions, thus facilitating information exchange between these two populations. For updating the archive, a nondominated sorting procedure and an angle-based selected scheme are conducted to store infeasible and feasible solutions, as they can help to maintain the diversity of the search and find more feasible regions. To evolve the CPF from the bidirectional side of the feasible region, a novel mating selection strategy is used to choose appropriate mating parents. In comparison with some related constraint multi-objective optimization algorithms on a number of benchmark problems, experimental results show that the proposed algorithm performs better than the state-of-the-art constrained multi-objective evolutionary optimizers. • A cooperative dual-population algorithm is proposed for solving CMOPs. • Constraints are handled in three different ways to explore the search space. • A brand-new selection strategy is applied to select the mating parents. • A novel archive update strategy is designed to retain the promising information. [ABSTRACT FROM AUTHOR]

The widespread existence of constrained multi-objective optimization problems (CMOPs) in practical applications encourages researchers to devote more efforts to the development of constrained multi-objective evolutionary algorithms (CMOEAs). An efficient way of handling CMOPs is to balance the satisfaction of constrains and the optimization of objective functions. Although many approaches have been designed to this end, most of them are still unable to handle CMOPs with diverse characteristics effectively. To remedy this issue, a simple and generic two-stage framework is proposed in this paper to achieve better efficiency and versatility for CMOPs. Specifically, the whole search process is simply divided into two stages with different purposes: (i) Stage 1 focuses on approaching the unconstrained Pareto front (PF) and storing the obtained feasible solutions in the archive. During stage 1 , the archive is updated to promote convergence and diversity; (ii) Stage 2 is mainly designed to obtain the constrained PF of CMOPs. First, the solutions in the archive are used to form the initial population. Afterwards, the constrained Pareto front (CPF) is extensively explored. Based on this framework, a CMOEA, referred to as C-TSEA, is presented. Fifty-seven instances in five benchmark test suites are chosen to evaluate the performance of our approach. Compared with seven advanced CMOEAs, the results demonstrate the superiority or at least competitiveness of C-TSEA. The generality of the proposed framework is also verified by integrating other methods in the proposed framework. • A simple and generic two-stage framework is proposed for CMOPs. • C-TSEA is presented based on the framework to effectively solve CMOPs. • Fifty-seven CMOPs are chosen to extensively evaluate its performance. • C-TSEA gets better or competitive results compared with other methods. [ABSTRACT FROM AUTHOR]