scholarly journals A MOPSO-Based Optimal Demand Response Management System for the Integration of Wind-PV-FC-Battery Smart Grid

2019 ◽  
Vol 8 (4) ◽  
pp. 4402-4410
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Smart Grid ◽  
Demand Response ◽  
Particle Swarm ◽  
Response Strategy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results ◽  
The Impact

This paper proposes the Multi-Objective Particle Swarm Optimization to optimize the performance of hybrid WindPV-FC-Battery smart grid to minimize operating costs and emissions. The demand response strategy based on the real-time pricing program with the participation of all kinds of consumers such as residential, commercial and industrial consumers is utilized in order to resolve the power generation uncertainty of renewable energy sources. The multi-objective particle swarm optimization based energy management programming model will be leveraged to reduce the operation costs, emission of pollutants, increase the micro grid operator’s demand response benefits and at the same time satisfying the load demand constraints amongst the others. For the purpose of validating the proposed model, the simulation results are considered for different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The simulation results precisely concluded the impact created by the demand side management in reducing the effects of uncertainty that prevails in forecasted power generation through solar cells and wind turbines.

scholarly journals Decision-Making of Underwater Cooperative Confrontation Based on MODPSO

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2211
Author(s):  
Na Wei ◽  
Mingyong Liu ◽  
Weibin Cheng
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Communication Delay ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Decision Making Model ◽  
The Impact

This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.

scholarly journals A novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm

2020 ◽  
Author(s):  
Ahlem Aboud ◽  
Raja Fdhila ◽  
Amir Hussain ◽  
Adel Alimi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Response Strategy ◽  
Pareto Optimal ◽  
Distributed Architecture ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set

Distributed architecture-based Particle Swarm Optimization is very useful for static optimization and not yet explored to solve complex dynamic multi-objective optimization problems. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm with two optimization levels. In the first level, all solutions are optimized in the same search space and the second level is based on a distributed architecture using the Pareto ranking operator for dynamic multi-swarm subdivision. The proposed approach adopts a dynamic handling strategy using a set of detectors to keep track of change in the objective function that is impacted by the problem’s time-varying parameters at each level. To ensure timely adaptation during the optimization process, a dynamic response strategy is considered for the reevaluation of all non-improved solutions, while the worst particles are replaced with a newly generated one. The convergence and<br>diversity performance of the DPb-MOPSO algorithm are proven through Friedman Analysis of Variance, and the Lyapunov theorem is used to prove stability analysis over the Inverted Generational Distance (IGD) and Hypervolume Difference (HVD) metrics. Compared to other evolutionary algorithms, the novel DPb-MOPSO is shown to be most robust for solving complex problems over a range of changes in both the Pareto Optimal Set and Pareto Optimal Front. <br>

scholarly journals An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4480
Author(s):  
Qun Niu ◽  
Han Wang ◽  
Ziyuan Sun ◽  
Zhile Yang
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Thermal Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Solar Thermal Power ◽  
Bare Bone

Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems.

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

2014 ◽  
Vol 971-973 ◽  
pp. 1242-1246
Author(s):  
Tie Jun Chen ◽  
Yan Ling Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

scholarly journals A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2338
Author(s):  
Emad M. Ahmed ◽  
Rajarajeswari Rathinam ◽  
Suchitra Dayalan ◽  
George S. Fernandez ◽  
Ziad M. Ali ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Smart Grid ◽  
Demand Response ◽  
Optimization Algorithm ◽  
Dynamic Pricing ◽  
Particle Swarm ◽  
Modern World ◽  
Swarm Optimization ◽  
Pricing Scheme ◽  
Load Pattern

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

scholarly journals DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Raja Fdhila ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Changes ◽  
Dynamic Change ◽  
Particle Swarm ◽  
Search Space ◽  
Response Strategy ◽  
Parallel Optimization ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective

Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected, the Pareto ranking operator is used to enable a multiswarm subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman's analysis while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD. <br>

Design of Temperature Controllor in Diamond Synthetic Chamber Based on Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 496-500 ◽  
pp. 1448-1451
Author(s):  
Hong Yan Hua ◽  
Cheng Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Programming ◽  
Strong Convergence ◽  
Control Problem ◽  
Temperature Control ◽  
Particle Swarm ◽  
Nonlinear Programming Problem ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

Temperature control problem is a typical multi-constraint/multi-objective nonlinear programming problem in diamond synthetic chamber and it cannot be solved by conventional methods. This paper proposed a constraint multi-objective particle swarm optimization (CMOPSO) algorithm to solve the problem. Simulation results show that the CMOPSO algorithm can be used effectively in diamond temperature control and it has strong convergence optimization abilities for all different parameters and constraint conditions.

Sign in / Sign up

Export Citation Format

Share Document