Novel constrained search-tactic for optimal dynamic economic dispatch using modern meta-heuristic optimization algorithms

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.

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Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

Water Resources Management ◽

10.1007/s11269-020-02756-5 ◽

2021 ◽

Vol 35 (4) ◽

pp. 1149-1166

Author(s):

Hossien Riahi-Madvar ◽

Majid Dehghani ◽

Rasoul Memarzadeh ◽

Bahram Gharabaghi

Keyword(s):

Optimization Algorithms ◽

Heuristic Optimization ◽

River Flows ◽

Long Term Forecasting

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Implementation of meta-heuristic optimization algorithms for interview problem in land consolidation: A case study in Konya/Turkey

Land Use Policy ◽

10.1016/j.landusepol.2021.105511 ◽

2021 ◽

Vol 108 ◽

pp. 105511

Author(s):

Sifa Ozsari ◽

Harun Uguz ◽

Huseyin Hakli

Keyword(s):

Optimization Algorithms ◽

Land Consolidation ◽

Heuristic Optimization

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Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

Sustainability ◽

10.3390/su13031274 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1274

Author(s):

Loau Al-Bahrani ◽

Mehdi Seyedmahmoudian ◽

Ben Horan ◽

Alex Stojcevski

Keyword(s):

Large Scale ◽

Thermal Power ◽

Optimization Technique ◽

Economic Dispatch ◽

Pso Algorithm ◽

Search Space ◽

Economic Benefits ◽

Optimization Techniques ◽

Ramp Rate ◽

Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

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