Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch

Dynamic programming (DP) is one of classic and sophisticated optimization methods that have successfully been applied to solve the problem of hydro unit load dispatch (HULD). However, DP will be faced with the curse of dimensionality with the increase of unit number and installed generating capacity of hydropower station. With the appearance of the huge hydropower station similar to the Three George with 26 generators of 700 MW, it is hard to apply the DP to large scale HULD problem. It is crucial to seek for other optimization techniques in order to improve the operation quality and efficiency. Different with the most of literature about power generation scheduling that focused on the comparisons of novel PSO algorithms with other techniques, the paper will pay emphasis on comparison study of PSO with DP based on a case hydropower station. The objective of study is to seek for an effective and feasible method for the large scale of hydropower station of the current and future in China. This paper first compares the performance of PSO and DP using a sample load curve of the Wujiangdu hydropower plant located in the upper stream of the Yangtze River in China and contained five units with the installed capacity of 1250 MW. Next, the effect of different load interval and unit number on the optimal results and efficiency of two methods has also been implemented. The comparison results show that the PSO is feasible for HULD. Furthermore, we simulated the effect of the magnitude of unit number and load capacity on the optimal results and cost time. The simulation comparisons show that PSO has a great advantage over DP in the efficiency and will be one of effective methods for HULD problem of huge hydropower stations.     

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.     

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.     

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.     

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.     

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 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.     

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.     

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.     

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.     

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources, multiobjective optimal dispatch of MGs need to be studied in detail. In this study, a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines, photovoltaics, diesel engine unit, load, and battery energy storage system. The economic cost, environmental concerns, and power supply consistency are expressed via subobjectives with varying priorities. Then, the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives. The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG. Finally, the validity of the proposed model and solution methodology are confirmed by case studies. This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.     

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.     

Application of the invasive weed optimization algorithm to economic dispatch problems

In this paper the invasive weed optimization algorithm has been applied to a variety of economic dispatch (ED) problems. The ED problem is concerned with minimizing the fuel cost by optimally loading the electrical generators which are committed to supply a given demand. Some involve prohibited operating zones, transmission losses and valve point loading. In general, they are non-linear non-convex optimization problems which cannot be directly solved by conventional methods. In this work the invasive weed algorithm, a meta-heuristic method inspired by the proliferation of weeds, has been applied to four numerical examples and has resulted in promising solutions compared to published results.     

Particle swarm optimization for AC-coupling stand alone hybrid power systems

The main purpose of this study was to present a technique on how to optimize the configuration of a typical AC-coupling stand alone hybrid power system (SAHPS). The design was posed as an optimization problem whose solution allowed obtaining the configuration of the SAHPS that minimized the total cost through the useful life of the system. To verify the system component models, an existing PV/wind/diesel hybrid power system at Chik Island, Thailand, was selected as a reference system, and the in situ monitoring results were compared with the simulation results. The minimization of the objective function was evaluated using TRNSYS 16 in assistance with GenOpt (optimization program). The result showed that the overall best cost reduction has been achieved by the particle swarm optimization (PSO) with constriction coefficient algorithm. This method requires just a few seconds to give the best results (where the number of generations in the algorithm is 46). It is thus believed that the present method would decrease the time required by design engineers to find the SAHPS optimum solution.     

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.     

Development of hybrid energy system with cycle charging strategy using particle swarm optimization for a remote area in India

In recent years, renewable energy can be seen as one of the important prospect of today's research, as it is likely to enlighten the lives of millions of people by fulfilling demand of electricity in their daily life. The present work focuses on the development of optimal hybrid energy system sizing model based on comparative analysis of particle swarm optimization, genetic algorithm and Homer software for energy index ratio of 1. The model also incorporates renewable fraction, emissions of carbon di oxide from diesel generator, net present cost and cost of energy. The system is developed to supply the demand of 7 un-electrified villages of Dhauladevi block of Almora district in Uttarakhand, India with the help of the available resources of solar, hydro, biomass and biogas energy along with the addition of diesel generator, for meeting out the energy deficit. From the optimization results, minimum cost of energy and maximum renewable fraction are obtained as 5.77 Rs/kWh and 92.6% respectively.     

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.     

Prediction of natural gas hydrate inhibitor vaporization rate using particle swarm optimization approach

Natural gas is a very important source of energy. In natural gas processing, accurate prediction of methanol loss to the vapor phase during natural gas hydrate inhibition is necessary to compute the total methanol injection rate required to effectively prevent the formation of natural gas hydrate. A reliable prediction tool that has the capability to accurately predict methanol losses to the vapor phase is thus needed. In order to address this matter, the current research was aimed at assessing the ability and feasibility of a robust computational intelligence paradigm. Based on a total of 326 dataset collected from the reliable literature, methanol loss to the vapor phase was predicted using artificial neural network (ANN) linked with particle swarm optimization (PSO) which is employed to determine the optimal values of the ANN weights. Success of the introduced hybrid intelligence model (or PSO-ANN) was confirmed with overall mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) values of 0.16421, 0.33210, and 0.99696, respectively.