An Improved Differential Evolution Algorithm for Dynamic Economic Dispatch of Power Systems

2011 ◽  
Vol 84-85 ◽  
pp. 706-710 ◽  
Author(s):  
Hong Feng Zheng
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Population Based ◽  
Vector Population ◽  
Open Questions ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch

Differential Evolution (DE), a vector population based stochastic optimization method has been introduced to the public in 1995. During the last 25 years research on and with DE has reached an impressive state, yet there are still many open questions, In this paper ,An improved differential evolution (IDE) algorithm was presented for power system Dynamic economic dispatch(IDED), Dynamic economic dispatch (DED), an extension of the economic dispatch problem, is a method of scheduling the online generators with a predicted load demand over acertain period of time taking into account the various constraints imposed on the system operation. The results indicate that IDE algorithm outperforms GA ,PSO and DE algorithms in solving DED problems.

The Economic Dispatch of Wind Integrated Power System based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 13 (3) ◽  
pp. 384-395
Author(s):  
Haiqing Liu ◽  
Jinmeng Qu ◽  
Yuancheng Li
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Bee Colony ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch ◽  
Integrated Power System

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

A Modified ABC Algorithm for Solving Non-Convex Dynamic Economic Dispatch Problems

2016 ◽  
Vol 6 (6) ◽  
pp. 2621
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Optimization Method ◽  
Population Based ◽  
Test System ◽  
Time Interval ◽  
Ramp Rate ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Dynamic Economic Dispatch

<p>In this paper, a modified artificial bee colony (MABC) algorithm is presented to solve non-convex dynamic economic dispatch (DED) problems considering valve-point effects, the ramp rate limits and transmission losses. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The feasibility of the proposed method is validated on 5 and 10 units test system for a 24 h time interval. The results are compared with the results reported in the literature. It is shown that the optimum results can be obtained more economically and quickly using the proposed method in comparison with the earlier methods.</p>

scholarly journals Dynamic Economic Dispatch in Thermal-Wind-Small Hydropower Generation System

2018 ◽  
Vol 246 ◽  
pp. 01085
Author(s):  
Guangbiao Liu ◽  
Jianzhong Zhou ◽  
Xiaogang Xiao ◽  
Li Mo ◽  
Yang Yuqi ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Wind Power ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Small Hydropower ◽  
Hydro Power ◽  
Thermal Wind ◽  
Hydropower System ◽  
Evolution Algorithm ◽  
Dynamic Economic Dispatch

With the large-scale wind power integration, the uncertainty of wind power poses a great threat to the safe and stable operation of the system. This paper proposes dynamic economic dispatch problem formulation in thermal power system incorporating stochastic wind and small-hydro (run-in-river) power, called thermal-wind-small hydropower system (TWSHS). Weibull and Gumbel probability density functions are used to calculate available wind and small-hydro power respectively. An improved differential evolution algorithm based on gradient descent information (DE-GD) is proposed to solve the dynamic economic dispatch (DED) problem considering uncertainty of wind power and small-hydro power, as well as complicated constraints in TWSHS. Based on the traditional differential evolution algorithm, the gradient information of the objective function is introduced after the mutation process to enrich the diversity of the population, thus increasing the possibility of convergence to the global optimization. Generation scheduling is simulated on a TWSHS with the proposed approach. Simulation results verify feasibility and effectiveness of the proposed method while considering various complex constraints in the thermal-windsmall hydropower system.

scholarly journals A Modified ABC Algorithm for Solving Non-Convex Dynamic Economic Dispatch Problems

2016 ◽  
Vol 6 (6) ◽  
pp. 2621
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Optimization Method ◽  
Population Based ◽  
Test System ◽  
Time Interval ◽  
Ramp Rate ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Dynamic Economic Dispatch

<p>In this paper, a modified artificial bee colony (MABC) algorithm is presented to solve non-convex dynamic economic dispatch (DED) problems considering valve-point effects, the ramp rate limits and transmission losses. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The feasibility of the proposed method is validated on 5 and 10 units test system for a 24 h time interval. The results are compared with the results reported in the literature. It is shown that the optimum results can be obtained more economically and quickly using the proposed method in comparison with the earlier methods.</p>

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