Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique

This paper presents performance comparative study of various particle swarm optimization (PSO) techniques for the placement of generator units in the distributed generation (DG) system. For the installation of generator units in the distributed generation system, it is very important to know the generator sizing and its placement in the network system for reducing the line losses and hence the cost. Various PSO techniques such as Canonical PSO, Hierarchical PSO (HPSO), Time varying acceleration coefficient (TVAC) PSO, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), Stochastic inertia weight (Sto-IW) PSO and Time varying inertia weight (TVIW) PSO have been used for comparative study. Here the main objective function (OF) is to minimize the system cost. These techniques have been tested on the standard IEEE-14 bus, IEEE-30 bus and IEEE-57 bus network system by the use of MATLAB software.

This paper proposes a self‐organizing hierarchical particle swarm optimization (SPSO) with time‐varying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with non‐smooth functions including multiple fuel options (MFO) and valve‐point loading effects (VPLE). The proposed SPSO with TVAC is the new approach optimizer and good performance for solving ED problems. It can handle the premature convergence of the problem by re‐initialization of velocity whenever particles are stagnated in the search space. To properly control both local and global explorations of the swarm during the optimization process, the performance of TVAC is included. The proposed method is tested in different ED problems with non‐smooth cost functions and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed SPSO with TVAC is effective in finding higher quality solutions for non‐smooth ED problems than many other methods.

This paper presents a new improved self adaptive particle swarm optimization technique to avoid premature convergence for economic load dispatch problem. Many evolutionary techniques such as particle swarm optimization (PSO), differential evolution (DE) have been applied to solve this problem and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. In this method, the inertia weight is made self adaptive depending on the population size and the fitness rank of the particle along with time variant acceleration coefficients. A thirteen-unit test system is considered to demonstrate the effectiveness of the proposed method. The results obtained by the proposed algorithm are compared with other classical as well as modern heuristic techniques. It is found that the proposed method can produced improved results.KeywordsParticle Swarm OptimizationDifferential EvolutionFuel CostInertia WeightPremature ConvergenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper presents an efficient approach for solving economic dispatch(ED) problem with non-convex cost functions using particle swarm optimization with time varying acceleration coefficients. PSO_TVAC is applied to 3, 13, & 14 unit test system with and without valve point loading effects. The results of PSO_TVAC are compared with PSO From simulation results, it is clear that PSO_TVAC performance is better than many well settled nature inspired algorithms.Economic dispatch is one of the key functions of the modern energy management system. Classical particle swarm optimization (PSO) algorithm is capable of achieving near global solutions for such problems but it tends to converge prematurely. The practical Non Convex Economic Dispatch problem is solved here using PSO with a novel parameter automation strategy in which time varying acceleration coefficients (TVAC) are employed to efficiently control the local and global search, such that premature convergence is avoided and global solutions are achieved.

This chapter proposes a pseudo-gradient based particle swarm optimization (PGPSO) method for solving nonconvex economic dispatch (ED) including valve point effects, multiple fuels, and prohibited operating zones. The proposed PGPSO is based on the self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) with position of particles guided by a pseudo-gradient. The pseudo-gradient here is to determine an appropriate direction for the particles during their movement so that they can quickly move to an optimal solution. The proposed method has been tested on several systems and the obtained results are compared to those from many other methods available in the literature. The test results have indicated that the proposed method can obtain less expensive total costs than many others in a faster computing manner, especially for the large-scale systems. Therefore, the proposed PGPSO is favorable for online implementation in the practical ED problems.KeywordsEconomic dispatchMultiple fuel optionsParticle swarm optimizationProhibited zonesPseudo gradientValve point effects

This article presents day-in-advance microgrid heat and electric power scheduling with and without demand-side management. The objective is to minimize the cost of heat and electric power generation, considering solar and wind power uncertainty. This scheduling problem is solved using a social group entropy optimization (SGEO) technique. The proposed microgrid comprises diesel generators, biomass-fuel-fired combined heat and power (BCHP) units, mini-hydro power plants, solar photovoltaic plants, wind turbine generators (WTGs), a hydrogen storage system (HSS) and plug-in electric vehicles (PEVs). A system with three diesel generators, three BCHP units, one mini-hydro power plant, two WTGs, two solar photovoltaic plants, one HSS and PEVs is studied to evaluate the suggested SGEO technique. Test results of the proposed SGEO technique are compared with those from self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution, and the recommended SGEO is shown to produce superior results.

This paper proposes a self-organizing hierarchical particle swarm optimization (SOHPSO) with time-varying acceleration coefficients (TVAC) for solving security constrained optimal power flow (SCOPF) problem. The SCOPF is to minimize a total generation cost while satisfying generator operational constraints and network security constraints in which the minimum severity index is used to alleviate line loading. The proposed SOHPSO-TVAC re-initializes particle velocities whenever the population stagnates at local optima during the search and TVAC is used to enhance convergence to the global optimum solution. Test results on the IEEE 30 and 118 bus systems indicate that the proposed SCOPF renders a lower total generation cost in a faster convergence rate than the other heuristic methods, which is favorable for online implementation.

The practical economic dispatch (ED) problems have many non-convex characteristics, which makes the searching of the global optimum difficult when using traditional mathematical methods. This paper presents a novel hybrid algorithm (HA) to the ED problems based on the particle swarm optimization (PSO) technique and differential evolution (DE) algorithm. Since the standard PSO has the adversity of premature convergence, the mutation and crossover operators of the DE, as well as the chaotic sequences, are considered to be integrated into the PSO to improve the global searching ability. Moreover, the dynamic penalty function is applied to deal with the constraints. The proposed method is tested on a 15-unit system considering the ramp rate limits, operating zones, and network losses. Also, the results are compared with those of other optimization methods.

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

To get the most power out of photovoltaic (PV) panels, PV systems must utilize a maximum power point tracking (MPPT) controller. In partial shading conditions (PSC), the power-voltage (P-V) characteristic of the PV network may show a single global maximum power point (GMPP) and two or more local maximum power points (LMPP). This indicates that the PV cells and panels do not get uniform illumination. As they converge on the maximum power point (MPP) that makes contact first, which is often one of the LMPPs in this scenario, common MPPT approaches like incremental conductance (InC) and perturb and observe (P&O) are unable to distinguish between a GMPP and LMPPs. In this paper, the extraction of the GMPP of the PV system under PSC based on a suggested particle swarm optimization (PSO) approaches is presented. The particularity of the suggested approach is that it takes into account the calculation of the position of each particle as a function of the duty cycle and the global maximum power. Results on the performance of the suggested PSO method show an advantage over the conventional PSO and the commonly used traditional P&O method. The suggested PSO technique offer better performance in terms of global power extracted, ripple rate of the power and efficiency.

In this paper, an effective and reliable variant of particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC) technique is proposed for the economic load dispatch problem considering valve-point loading effect. Main objective of the economic load dispatch (ELD) problem is to minimize the fuel cost by allocating the generation of the committed units subjected to the equality and inequality constraints. Equation of the economic dispatch objective function gets modified with the addition of a new parameter which represents the effect of valve-point loading. While exploring the use of PSO and its other variants to the economic dispatch problem, a number of research works have not considered the transmission losses properly. This paper represents the usefulness of the proposed PSO-TVAC algorithm to significantly reduce the fuel cost while taking into account the effect of transmission losses along with the non-convex characteristic due to valve loading. The results have been demonstrated for three-generator and ten-generator test systems.

An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimodal and difficult combinatorial optimization problem, this paper presents a novel parameter automation strategy for Particle Swarm Optimization (PSO) called PSO with Time-Varying Acceleration Coefficients (PSO-TVAC). This optimization method has a strong ability to successfully control both global and local search in each iteration process for considerably increasing the probability of finding the global optimum solution. The PSO-TVAC algorithm is applied to optimal tuning PSS parameters problem in order to reduce the PSS design effort and find the best possible solution within a reasonable computation time. For this reason, the robustly selection of PSSs parameters is converted as an optimization problem based on the time domain-based objective function under different operating conditions. The robustness of the proposed method is demonstrated on a multi-machine power system in comparison with the classical PSO and conventional method based designed PSSs. It is shown through the nonlinear time domain simulation and some performance indices for a wide range of loading condition. The analysis of the results shows that the improved PSO-TVAC is not only very effective but also provides an excellent ability for damping low frequency oscillations and greatly enhance the dynamic stability of the power system. Moreover, the proposed PSO-TVAC algorithm is superior than that of the classical PSO one in terms of accuracy, convergence and computational effort. Key words: Power system stabilizer (PSS) design, particle swarm optimization, power system dynamics, time-varying acceleration coefficients.

Optimal reactive power compensation is one of the fundamental issues in the operation of power systems. This paper presents a new improved particle swarm optimization technique called black-hole particle swarm optimization (BHPSO) for optimal reactive power compensation of distribution feeders to avoid premature convergence. The performance of the proposed algorithm is demonstrated on a sample test system. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing comparable results.KeywordsReactive Power CompensationBlack-hole Particle Swarm Optimization (BHPSO)Power LossVoltage Profile

This paper presents a new resolution approach of Economic Dispatch (ED) problem considering wind power penetration planning with specified wind farm locations. Firstly, the total fuel cost of conventional generating units is optimized with a wind power contribution. When the optimal wind power penetration for each wind farm location is specified, a planning process is performed to get the total number and the total cost of wind generators. Three levels of the total wind power penetration are examined, which are 25%, 30% and 35%. The Wind Power Planning (WPP) is effected based on the practical weather report for each wind farm location. The ED problem is solved taking in the account the valve point effects of thermal generating units and using Time Varying Acceleration Coefficients (TVAC) based Particle Swarm Optimization method (PSO) due to its effectiveness overcoming the conventional PSO. The model is applied on the well known IEEE 30 bus test system.

This paper gives a new modified technique for evaluating non-convex economic dispatch (NCED)problem having non-linear, discontinuous cost function.This new particle swarm optimization technique incorporates time varying acceleration coefficients (NPSOTVAC).The highly non-linear cost function due to ramp rate limit, prohibited operating zones and valve point loading effect is considered in the proposed method.The effectiveness of proposed NPSOTVAC algorithm is proved through test on 6 and 15 unit test systems.