Integrated Particle Swarm Optimization Algorithm Research Articles

Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval Optimization

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model's predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system.

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Objective. To study energy consumption and QoS optimization coverage mechanism in wireless sensor networks based on swarm sensing algorithm. Methods. The swarm sensor algorithm is reported to optimize the configuration of wireless sensor nodes, improve network service performance, and provide feedback on effective sensor placement. Results. Two WSN coverage optimization schemes were proposed in this essay. The first scheme applied drosophila intelligent algorithm to WSN coverage optimization, and the second scheme integrated particle swarm optimization algorithm and improved firefly algorithm to complement each other. Both schemes can achieve good results in practical application. Scheme one can increase the coverage rate by 6.36% compared with previous schemes, while scheme two can increase it by 6.48%. Both schemes are basically the same, so they can be applied in the optimization of WSN node deployment. Conclusion. As an optimization method, group perception algorithm is inspired by biology. Through the continuous enrichment and development of this algorithm, it has been effectively used in the coverage optimization of the wireless sensor networks.

Flexible energy load identification in intelligent manufacturing for demand response using a neural network integrated particle swarm optimization

Various demand response programs have been widely established by many utility companies as a critical load management tool to balance the demand and supply for the enhancement of power system stability in smart grid. While participating in these demand response programs, manufacturers need to develop their optimal demand response strategies so that their energy loads can be shifted successfully according to the request of the grid to achieve the lowest energy cost without any loss of production. In this paper, the flexibility of the electricity load from manufacturing systems is introduced. A binary integer mathematical model is developed to identify the flexible loads, their degree of flexibility, and corresponding optimal production schedule as well as the power consumption profiles to ensure the optimal participation of the manufacturers in the demand response programs. A neural network integrated particle swarm optimization algorithm, in which the learning rates of the particle swarm optimization algorithm are predicted by a trained neural network based on the improvement of the fitness values between two successive iterations, is proposed to find the near optimal solution of the formulated model. A numerical case study on a typical manufacturing system is conducted to illustrate the effectiveness of the proposed model as well as the solution approach.

Dynamic programming integrated particle swarm optimization algorithm for reservoir operation

The present study suggests an integrated approach for determining the optimal release policy for the Mula reservoir situated at the Godavari basin, India. The proposed integrated algorithm named DP-PSO is a hybridization of Dynamic Programming (DP) and Particle Swarm Optimization (PSO). The reservoir operation problem is demonstrated in the form of a nonlinear optimization model subject to various constraints. Two case studies are considered. In the first case the efficiency of the proposed algorithm is tested on a small data set of 1 year and in the second case, data set taken is for 10 years. The results obtained are compared in terms of objective function value as well CPU time for performance evaluation of the integrated methods.

Double layer metaheuristic based energy management strategy for a Fuel Cell/Ultra-Capacitor hybrid electric vehicle

This paper highlights the use of metaheuristic approaches to perform the energy management of a hybrid Fuel Cell/Ultra-Capacitor Electric Vehicle considering hydrogen consumption, Fuel Cell durability and computational time as key performance criteria. The considered architecture is an integrated rule-based metaheuristic approach that combines the simplicity and the effectiveness of rule based and optimization approaches. Online results compared with benchmark offline Ant Colony Optimization results, present near to optimal performance, with slightly better performance for Particle Swarm Optimization versus Genetic Algorithm. The durability of the main energy source which is a crucial issue for Fuel Cell Hybrid Electric Vehicles is considered by limiting the Fuel Cell power variation rate in both layers providing a high protection level in order to respect its slow dynamics that represent a critical operational limitation. The rule-based layer which is a strategic layer reduces computational effort and time and restricts the search space of the optimization layer allowing a rapid convergence and thus enabling real time applications. Integrated Particle Swarm Optimization algorithm achieved lower computational time with an average of 0.65 ms versus 43.09 ms for the integrated Genetic Algorithm for each time step, which makes it more suitable for real time applications.

Short‐Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short‐term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

Sizing and layout design of truss structures under dynamic and static constraints with an integrated particle swarm optimization algorithm

Natural frequencies offer useful knowledge on the dynamic response of the structures. It is possible to avoid from the destructive effects of dynamic loads on the structures by optimizing layout and size of their subject to constraints on natural frequencies. Since optimization problems including frequency constraints are highly nonlinear, this kind of problems forms a challenging area to test the performance of the different optimization techniques. This study tests the performance of an integrated particle swarm optimization algorithm (iPSO), a new particle swarm optimizer integrated with the improved fly-back mechanism and the weighted particle concept, in four weight minimization of truss structures with sizing and layout variables under multiple frequency constraints. Optimization results demonstrate that the new algorithm is competitive with other state-of-the-art metaheuristic algorithms in dynamic and static structural optimization problems.

Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot

This paper presents a new particle swarm optimization (PSO) algorithm, called the PSO-IAC algorithm, to resolve the goal of reaching with the obstacle avoidance problem for a 6-DOF manipulator of the home service robot. The proposed PSO-IAC algorithm integrates the improved adaptive inertia weight and the constriction factor with the standard PSO. Both the free-space and obstacle avoidance states are established for evaluations in computer simulations and real-time experiments. The performance comparisons of the PSO-IAC algorithm with respect to the existing inertia weighted PSO (PSO-W), constriction factor based PSO (PSO-C), constriction factor and inertia weighted PSO (PSO-CW), and adaptive inertia weighted PSO (PSO-A) algorithms are examined. Simulation results indicate that the PSO-IAC algorithm provides the fastest convergence capability. Finally, the proposed control scheme can make the manipulator of the home service robot arrive at the goal position with and without obstacles in all real-time experiments.

Improved Taguchi method based contract capacity optimization for industrial consumer with self-owned generating units

Contract capacity setting for industrial consumer with self-owned generating units (SOGUs) is a highly discrete and nonlinear optimization problem considering expenditure on the electricity from the utility and operation costs of the SOGUs. This paper proposes an improved Taguchi method that combines existing Taguchi method and particle swarm optimization (PSO) algorithm to solve this problem. Taguchi method provides fast converging characteristics in searching the optimal solution through quality analysis in orthogonal matrices. The integrated PSO algorithm generates new solutions in the orthogonal matrices based on the searching experiences during the evolution process to further improve the quality of solution. To verify feasibility of the proposed method, the paper uses the real data obtained from a large optoelectronics factory in Taiwan. In comparison with the existing optimization methods, the proposed improved Taguchi method has superior performance as revealed in the numerical results in terms of the convergence process and the quality of solution obtained.

Probabilistic Optimization of Laminated Composites Considering Both Ply Failure and Delamination Based on PSO and FEM

Reliability-based design is one of major strategies for cost-effective designs under uncertainties in either engineering designs or manufacturing processes. This paper presents an approach integrating particle swarm optimization algorithm (PSO) and finite element method (FEM) into the reliability-based design optimization (RBDO). The total weight of the laminate was taken as the objective function, the fiber directional angle and thickness ratio as the design variables. Both in-plane damage and delamination were taken into account. The result shows that the value of object function using the proposed method is much greater than those without considering delamination in literature and demonstrate that the proposed method produces more conservative result.

Basin-wide Water Resources Planning by Integrating PSO Algorithm and MODSIM

Water resources planning and management at basin scale is such a large and complex problem that makes it essential to use effective modeling tools in order to obtain an optimum plan for river basins development. In this paper, a methodology is presented for optimized design and operation of the upstream Sirvan basin in Iran. The model proposed integrates MODSIM generalized river basin network flow model, with the capability of simulating various characteristics and features of water resources in a river basin, and Particle Swarm Optimization (PSO) algorithm. In the developed PSO-MODSIM model, the size of planned dams and water transfer systems, as design variables, and the relative priorities for meeting reservoir target storages, as operational variables, are varied and evolved using PSO algorithm. MODSIM is called to simulate the system performance and to evaluate the fitness of each set of those design and operational variables with respect to the model’s objective function. The PSO objective function is to maximize the total net benefit consisting of benefits due to supplying water to different types of water uses and construction costs of dams and water transfer and/or pumping systems. Varying the design and operational variables in MODSIM 8 is done using the MODSIM’s custom coding feature in VB.NET routine. The PSO-MODSIM model is used to size the planed water storage and transfer components of a river basin system and to allocate water resources optimally over time and space among competing demands, considering coordinated operation of the system components. The model results has been analyzed for different scenarios of water transfer from Sirvan to neighboring basins.