Multi-objective Differential Evolution Method Research Articles

EVCS Demand Management using Multi-Objective Power Flow Analysis with Renewable Distributed Generators

Abstract The optimal placement and sizing of multiple distributed generator units in strategic locations are essential for effective energy management of Electric vehicle charging station (EVCS) in modern power systems. This study aims to minimize power losses, optimize Renewable based Distributed Generator (RDG) sizing, and reduce fuel costs of conventional generators while also considering the impact on voltage stability by using multi objective Newton-Raphson (NR) power flow method. Two meta-heuristic multi-objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Differential Evolution (MODE) techniques have been merged with NR power flow analysis. In this paper two scenarios are analyzed: the base case under normal conditions and with EVCS where the load is increased by 1.1 times the base load. The results demonstrate that the MOGWO approach outperforms the MODE method, yielding more favorable outcomes in both scenarios. Notably, the MOGWO method results in smaller RDG sizes compared to MODE in both normal and EVCS loading conditions. Transmission losses are reduced by 68.12% under normal conditions and by 72.7% under EVCS loading conditions with the MOGWO approach. The effectiveness of the proposed method has been evaluated using MATLAB software on the standard IEEE 57-bus system. Additionally, the base case results have been compared with other algorithms, including SCA, DA, NSGA-II, and MOCS, to validate the performance of the proposed MOGWO approach.

MULTI-OBJECTIVE OPTIMAL REACTIVE POWER DISPATCH USING DIFFERENTIAL EVOLUTION

Reactive power optimization is a major concern in the operation and control of power systems. In this paper a new multi-objective differential evolution method is employed to optimize the reactive power dispatch problem. It is the mixed–integer non linear optimization problem with continuous and discrete control variables such as generator terminal voltages, tap position of transformers and reactive power sources. The optimal VAR dispatch problem is developed as a nonlinear constrained multi objective optimization problem where the real power loss and fuel cost are to be minimized at the same time. A conventional weighted sum method is inflicted to provide the decision maker with a example and accomplishable Pareto-optimal set. This method underlines non-dominated solutions and at the same time asserts diversity in the non-dominated solutions. Thus this technique treats the problem as a true multi-objective optimization problem. The performance of the suggested differential evolution approach has been tested on the standard test system IEEE 30-bus.

A Multi-Objective Differential Evolutionary Method for Constrained Crowd Judgment Analysis

Crowdsourcing has already been shown to be a promising tool in solving many real-life problems in time and cost-effective way. For example, in city planning, to install some specific facilities it is required to acquire knowledge about various factors like demand, demographic information, suitability of the resources in that area, etc. However, obtaining this information is a tedious and time-consuming job. Now-a-days, this process can be accelerated by utilizing the enormous power of crowd while outsourcing it to the general people. Basically, seeking opinions from multiple non-experts instead of a single expert can be advantageous in terms of time, cost and accuracy. Although, in most of the crowdsourcing models, the questions posted to crowd consist of a single component. Interestingly, in many real-life applications like city planning, the questions can have multiple components. To exemplify, the posted question can be seeking opinions about 2D coordinates of $k$ best possible locations (i.e., $k$ components) to install $k$ facilities. Moreover, there exist some constraints which are needed to be satisfied by the crowd while providing their opinions. Thus, it introduces a new kind of judgment analysis problem recently termed as ‘Constrained Judgment Analysis’. Most of the state-of-the-art judgment analysis problems deal with the question without multiple components and constraints as well. In this article, we address this emerging problem and propose a multi-objective differential evolution method to obtain better decision guided by the crowd. The effectiveness of the proposed method is demonstrated by applying it over two real-life crowd opinion datasets.

Adaptive multi objective differential evolution with fuzzy decision making in preventive and corrective control approaches for voltage security enhancement

Abstract This paper presents a multi objective differential evolution (MODE) based voltage security enhancement through combined preventive-corrective control strategy. Load shedding, generation rescheduling and optimal utilization of FACTS devices are considered for security enhancement. Maximum l -index of load buses is taken as the indicator of voltage stability. Minimization of cost of FACTS devices, minimization of amount of load shedding along with improvement in voltage stability are the objectives of this multi objective optimization problem. The optimal location of FACTS devices are selected using modal analysis technique. The buses for load shedding are selected based on the minimum eigen value of load flow Jacobian. The proposed MODE algorithm employs DE/randSF/1/bin strategy scheme with self tuned parameter which employs binomial crossover and difference vector based mutation. A fuzzy based decision making algorithm is employed to get the best compromise solution from the non dominated solutions. The proposed MODE is also tested with statistical performance metrices. The proposed methodology is implemented on IEEE 30 bus and IEEE 57 bus test systems. The proposed MODE method provides better solutions in the pareto optimal front than the other optimization techniques such as MOGA and NSGA II under combined preventive–corrective control approach. In IEEE 30 bus system, the amount of load shedding is reduced by 40% and voltage stability is improved by 15% and in IEEE 57 bus system, the amount of load shedding is reduced by 15.4% and voltage stability is improved by 13% by the proposed approach. Hence the simulation results show that the proposed approach provides considerable reduction in the amount of load shedding and enhancement of voltage stability by including generation rescheduling and utilization of FACTS devices.

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene\u2013gene interactions

Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.

Application and Comparative Analysis of Various Classical and Soft Computing Techniques for Model Reduction of MIMO Systems

Model reduction techniques are simplification methods based on mathematical approaches employed to realize reduced models for the original high order systems. Some existing classical model reduction techniques for multivariable system are considered and compared for their performances. Interlacing property and coefficients matching (IPCM) method gives overall minimum integral square error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE) values compared to other methods. Though the IPCM method is efficient, it may not guarantee for minimization of all objective functions simultaneously. In this paper, model reduction approach based on objectives like ISE, IAE and ITAE using multi-objective differential evolution (MODE) method is proposed for reducing the numerator and the denominator is reduced by interlacing property. MODE method minimizes the small, normal and large errors persisting for long time between original and reduced models. This multi-objective approach is applied for model reduction of 10th order multivariable linear time invariant power system model. Simulation results are demonstrated for single and multi-objective model reduction and compared with multi-objective particle swarm optimization (MOPSO) method to prove the validity of proposed MODE technique.

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California. A new multi objective optimization algorithm, entitled MOSPD, is developed.Comparison study is carried out over eight complex test functions.MOSPD is effective and efficient in searching global Pareto optimal.A reservoir system model is built for Oroville-Thermalito complex in California.MOSPD provides flexible reservoir release strategies to support decision making.

A hybrid multiobjective differential evolution method for electromagnetic device optimization

PurposeThe purpose of this paper is to show that the performance of differential evolution (DE) can be substantially improved by a combination of techniques. These enhancements are applicable to both single and multiobjective problems. Their combined use allows the optimization of complex 3D electromagnetic devices.Design/methodology/approachDE is improved by a combination of techniques which are applied in a cascade way and their single and combined effect is tested on well‐known benchmarks and domain‐specific applications.FindingsIt is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems.Research limitations/implicationsThe increased speed of convergence may come at the price of a somewhat decreased robustness. However, such behavior is justified by the CPU time constraints under which the optimization has to be performed.Practical implicationsThe proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.Originality/valueThis paper explorers the combined use of many of the most recent and successful algorithmic improvements to DE and applies them to both single and multiobjective problems.