Adaptive particle swarm optimization approach for static and dynamic economic load dispatch

This paper presents a novel heuristic optimization approach to constrained economic load dispatch (ELD) problems using the adaptive–variable population – PSO technique. The proposed methodology easily takes care of different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones and also accounts for non-smoothness of cost functions arising due to the use of multiple fuels. Simulations were performed over various systems with different numbers of generating units, and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness, fast convergence and proficiency of the proposed methodology over other existing techniques.     

An improved PSO for dynamic load dispatch of generators with valve-point effects

Dynamic load economic dispatch problem (DLED) is important in power systems operation, which is a complicated nonlinear constrained optimization problem. It has nonsmooth and nonconvex characteristics when generator valve-point effects are taken into account. This paper proposes an improved particle swarm optimization (IPSO) to solve DLED with valve-point effects. In the proposed IPSO method, feasibility-based rules and heuristic strategies with priority list based on probability are devised to handle constraints effectively. In contrast to the penalty function method, the constraint-handling method does not require penalty factors or any extra parameters and can guide the population to the feasible region quickly. Especially, equality constraints of DLED can be satisfied precisely. Furthermore, the effects of two crucial parameters on the performance of the IPSO for DLED are also studied. The feasibility and the effectiveness of the proposed method are demonstrated applying it to some examples and the test results are compared with those of other methods reported in the literature. It is shown that the proposed method is capable of yielding higher-quality solutions.     

A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch

This paper proposes a novel method for solving the Non-convex Economic Dispatch (NED) problems, by the Fuzzy Adaptive Modified Particle Swarm Optimization (FAMPSO). Practical ED problems have non-smooth cost functions with equality and inequality constraints when generator valve-point loading effects are taken into account. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. PSO is one of modern heuristic algorithms, in which particles change place to get close to the best position and find the global minimum point. However, the classic PSO may converge to a local optimum solution and the performance of the PSO highly depends on the internal parameters. To overcome these drawbacks, in this paper, a new mutation is proposed to improve the global searching capability and prevent the convergence to local minima. Also, a fuzzy system is used to tune its parameters such as inertia weight and learning factors.     

Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method

In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO–DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO–DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm.     

Long term electric load forecasting based on particle swarm optimization

This paper presents a new method for annual peak load forecasting in electrical power systems. The problem is formulated as an estimation problem and presented in state space form. A particle swarm optimization is employed to minimize the error associated with the estimated model parameters. Actual recorded data from Kuwaiti and Egyptian networks are used to perform this study. Results are reported and compared to those obtained using the well known least error squares estimation technique. The performance of the proposed method is examined and evaluated. Finally, estimated model parameters are used in forecasting the annual peak demands of Kuwait network.     

A particle swarm optimization based power dispatch algorithm with roulette wheel re-distribution mechanism for equality constraint

In this paper, a particle swarm optimization (PSO)-based power dispatch algorithm is proposed to deal with the energy management problem of the hybrid generation system (HGS). For conventional PSO method, the search space is only defined by inequality constraints. However, as for power dispatch problems, it is vital to maintain power balance, which can be represented as an equality constraint. To address this issue, a roulette wheel re-distribution mechanism is proposed. With this re-distribution mechanism, unbalanced power can be reallocated to more superior element and the searching diversity can be preserved. In addition, the effect of depth of discharge on the life cycle of the battery bank is also taken into account by developing a penalty mechanism. The proposed method is then applied to a HGS consisting of photovoltaic array, wind turbine, microturbine, battery banks, utility grid and residential load. To validate the effectiveness and correctness of the proposed method, simulation results for a whole day will also be provided. Comparing with three other power dispatching methods, the proposed method can achieve the lowest accumulated cost.     

Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search

In this paper, an optimization method for the reactive power dispatch in wind farms (WF) is presented. Particle swarm optimization (PSO), combined with a feasible solution search (FSSPSO) is applied in order to optimize the reactive power dispatch, taking into consideration the reactive power requirement at point of common coupling (PCC), while active power losses are minimized in a WF. The reactive power requirement at PCC is included as a restriction problem and is dealt with feasible solution search. Finally an individual set point, particular for each wind turbine (WT), is found. The algorithm is tested in a WF with 12 WTs, taking into consideration different control options and different active power output levels.     

A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead (NM) simplex search called FAPSO-NM. In the resulting hybrid algorithm, the NM algorithm is used as a local search algorithm around the global solution found by FAPSO at each iteration. Therefore, the proposed approach improves the performance of the FAPSO algorithm significantly. The algorithm is tested on two typical systems consisting of 13 and 40 thermal units whose incremental fuel cost functions take into account the valve-point loading effects.     

Hybrid SOA–SQP algorithm for dynamic economic dispatch with valve-point effects

This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with valve-point effects. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, SOA is used as a base level search, which can give a good direction to the optimal global region and SQP as a local search to fine tune the solution obtained from SOA. Thus SQP guides SOA to find optimal or near optimal solution in the complex search space. Two test systems i.e., 5 unit with losses and 10 unit without losses, have been taken to validate the efficiency of the proposed hybrid method. Simulation results clearly show that the proposed method outperforms the existing method in terms of solution quality.     

Combined modeling for electric load forecasting with adaptive particle swarm optimization

Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load data and give more accurate forecasting results. The adaptive particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and the other combined model reported in the literature and its results are promising.     

A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem

The increasing costs of fuel and operation of thermal power generating units warrant development of optimization methodologies for economic dispatch (ED) problems. Optimization methodologies that are based on meta-heuristic procedures could assist power generation policy analysts to achieve the goal of minimizing the generation costs. In this context, the objective of this study is to present a novel approach based on harmony search (HS) algorithm for solving ED problems, aiming to provide a practical alternative for conventional methods. To demonstrate the efficiency and applicability of the proposed method and for the purposes of comparison, various types of ED problems are examined. The results of this study show that the new proposed approach is able to find more economical loads than those determined by other methods.     

Grey Predictor reference model for assisting particle swarm optimization for wind turbine control

This paper proposes an approach of forming the average performance by Grey Modeling, and use an average performance as reference model for performing evolutionary computation with error type control performance index. The idea of the approach is to construct the reference model based on the performance of unknown systems when users apply evolutionary computation to fine-tune the control systems with error type performance index. We apply this approach to particle swarm optimization for searching the optimal gains of baseline PI controller of wind turbines operating at the certain set point in Region 3. In the numerical simulation part, the corresponding results demonstrate the effectiveness of Grey Modeling.     

Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization

The accurate mathematical model is an extremely useful tool for simulation and design analysis of fuel cell power systems. Particle swarm optimization (PSO) is a recently invented high-performance algorithm. In this work, a PSO-based parameter identification technique of proton exchange membrane (PEM) fuel cell models was proposed in terms of the voltage–current characteristics. Using the simulated and experimental voltage–current data, the validity of the proposed method has been confirmed. The results indicate that the PSO is an effective technique for identifying the parameters of PEM fuel cell models even in the presence of measuring noise. Moreover, the proposed method does not particularly necessitate initial guesses as close as possible to the solutions, required only is a broad range specified for each of the parameters. Therefore, the PSO method outperforms the GA and traditional optimization methods.     

New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique

PSO (particle swarm optimization) technique is applied to estimate monthly average daily GSR (global solar radiation) on horizontal surface for different regions of Iran. To achieve this, five new models were developed as well as six models were chosen from the literature. First, for each city, the empirical coefficients for all models were separately determined using PSO technique. The results indicate that new models which are presented in this study have better performance than existing models in the literature for 10 cities from 17 considered cities in this study. It is also shown that the empirical coefficients found for a given latitude can be generalized to estimate solar radiation in cities at similar latitude. Some case studies are presented to demonstrate this generalization with the result showing good agreement with the measurements. More importantly, these case studies further validate the models developed, and demonstrate the general applicability of the models developed. Finally, the obtained results of PSO technique were compared with the obtained results of SRTs (statistical regression techniques) on Angstrom model for all 17 cities. The results showed that obtained empirical coefficients for Angstrom model based on PSO have more accuracy than SRTs for all 17 cities.     

Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions

This paper explains the development of a new algorithm for maximum power point tracking (MPPT) in large PV systems under partial shading conditions (PSC). The new algorithm combines the use of particle swarm optimization (PSO) for MPPT during the initial stages of tracking and then employs the traditional perturb and observe (PO) method at the final stages. The methodology has been first simulated in two different PV configurations under varying shading patterns and experimentally verified using a microcontroller based experimental system. The integration of swarm intelligence with PO algorithm is shown to yield faster convergence to the global maximum power point (GMPP) than when the two methods are individually used. The oscillations in the output power, voltage and current of the PV system with the proposed method are the least when compared to the ones obtained during PSO based MPPT.     

On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization

On-site efficiency determination of induction motor is essential in industrial plants for saving the energy consumption. This paper presents a new application of particle swarm optimization (PSO) approach for field efficiency evaluation of induction motor based on a modified induction motor equivalent circuit. The stray-load loss is considered in the equivalent circuit by adding an equivalent resistor in series with the rotor circuit and its value is derived from the assumed stray-load loss recommended in IEEE Std. 112. The PSO approach uses the information about the stator current, stator voltage, input power, stator resistance and speed of the motor and determines the equivalent circuit parameters. Once these parameters are known, the efficiency of motor can be evaluated. The simulation results on a 3.75 kW motor are presented and compared with the results of torque gauge method (TGM), equivalent circuit method (ECM), slip method (SM), current method (CM) and segregated loss method (SLM). The results reveal that the proposed method can evaluate the efficiencies of motor with less than 3% error under normal load conditions. Consequently, the method can be used in motor energy management system for improving the overall energy savings in industry.     

Parameter identification for solid oxide fuel cells using cooperative barebone particle swarm optimization with hybrid learning

Solid oxide fuel cell (SOFC) has been widely recognized as one of the most promising fuel cells. The SOFC performance is highly influenced by several parameters associated with the internal multi-physicochemical processes. In this work, the optimal modeling strategy is designed to determine the parameters of SOFC using a simple and efficient barebone particle swarm optimization (BPSO) algorithm. The cooperative coevolution strategy is applied to divide the output voltage function into four subfunctions based on the interdependence among variables. To the nonlinear characteristic of SOFC model, a hybrid learning strategy is proposed for BPSO to ensure a good balance between exploration and exploitation. The experimental results illustrate the effectiveness of the proposed algorithm. The comparisons also indicate that cooperative coevolution strategy and hybrid learning improve the performance of original PSO algorithm, offering better approximation effect and stronger robustness.     

Energy management and design of centralized air-conditioning systems through the non-revisiting strategy for heuristic optimization methods

It is getting more and more popular to apply heuristic optimization methods, like genetic algorithm (GA) and particle swarm optimization (PSO), to handle various engineering optimization problems. In this paper, optimization problems of typical centralized air-conditioning systems were solved by the non-revisiting (Nr) strategy, which was proposed to be incorporated into the common heuristic methods for improving the optimization effectiveness and reliability. This approach can store the evaluated fitness values in an archive with minimal computer memory, detect the revisits and prevent them from re-evaluating. It is particularly useful for the problems formulated by dynamic simulation or detailed modeling with very demanding computational time for function evaluation. The non-revisiting strategy can facilitate the search of the global optimum by its parameter-less adaptive mutation capability. In the optimization problems of central air-conditioning systems, it was found that the NrGA and NrPSO could search better solutions at a limited number of function evaluations than the conventional GA and PSO did. The ultimate goal is to determine the required parameters for optimal design and energy management. The proposed strategy can be applied to similar types of air-conditioning or engineering optimization problems, and possibly incorporated into other kinds of heuristic optimization methods.     

基于改进粒子群算法的风力机选型应用研究

风力机的选型是风电场建设的重要内容,它对风电场建设造价、投产后的发电量以及运行维护成本等有直接影响。文章在给定风资源的情况下,综合考虑风电场的容量系数和实际发电量,以风力机性能指数作为选型的依据,针对采用常规方法进行风力机参数线性化求解的缺陷,采用智能化的改进粒子群算法对风力机参数进行寻优。与常规计算方法相比,该方法寻得的风力机性能指数更优。结合具体实例计算候选机型的风速加权标准差,选出最优风力机。该研究结果为风电场的风力机选型提供了一种有效可行的方法,具有一定的应用参考价值。