Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

Chen, Kai; Han, Li; Wang, Shuhuan; Lu, Junjie; Shi, Liping

doi:10.1155/2019/5831362

Cited by 6 publications

(5 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…− The losses from the transmission line have an impact on the optimal flow of power in the power system. These losses can be mathematically written as in (2).…”

Section: Economic Dispatch Problemmentioning

confidence: 99%

“…In the power systems, the economic dispatch (ED) issue portrays the expected level of load that must be partitioned between the generators to ensure minimal operating cost. The concept of optimization demands minimization of the objective functions while maintaining a reasonable and acceptable level of system performance [1], [2]. Typically, ED is considered an  ISSN: 2088-8708 important area of the control and operation of power systems as its main objective of the ED plant is to schedule the operations of the generating units to ensure maximum performance at the minimum operation cost; this amounts to low-cost electricity on the side of the customers and possible gain on the side of the service provider in the electricity market.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary algorithm solution for economic dispatch problems

Hussein

2022

IJECE

View full text Add to dashboard Cite

<span>A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints; these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed; the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.</span>

show abstract

“…− The losses from the transmission line have an impact on the optimal flow of power in the power system. These losses can be mathematically written as in (2).…”

Section: Economic Dispatch Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithm solution for economic dispatch problems

Hussein

2022

IJECE

View full text Add to dashboard Cite

show abstract

“…The problem is solved using two metaheuristic optimization algorithms that take into account both the variable production of the wind turbines and the energy price and charging power of the EVs. In [19], a PSO-based metaheuristic optimization method is applied to solve the economic dispatch problem in a power system with thermal and wind power plant DG. Variable load and DG production are considered in the study.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation and Energy Management of Units in Distribution Networks with Multiple Renewable Energy Sources and Battery Storage Based on Computational Intelligence

Barukčić,

Kurtović,

Benšić

et al. 2023

Energies

View full text Add to dashboard Cite

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively.

show abstract

“…But due to various drawbacks such as approximation used in these techniques and early convergence to local minima, these algorithms proved to be computationally inefficient. To have better solutions to the optimization problem, algorithms such as differential evolution (Peng et al, 2012),genetic algorithm (Sahay et al, 2018) (Nadakuditi et al, 2019), artificial bee colony (He, et al, 2013) (Jadav et al, 2013), particle swarm optimization (Yao et al, 2012) (Gupta et al, 2020) (Chen et al, 2019),gravitational search algorithm (Mondal et al, 2013)(Sarkar et al, 2018, and artificial neural networks has been used by various scholars.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Combined Economic Emission Dispatch Including Wind Generators by Real Coded Genetic Algorithm

Kumari

Pal

2022

International Journal of Applied Metaheuristic Computing

View full text Add to dashboard Cite

With the growing environmental depletion, the shift in the focus towards minimizing the emissions of gases released in the conventional generators and further incorporation of a cleaner alternate renewable source of energy such as wind or solar to the existing system is of utmost importance. The research paper aims to build an environmentally resilient electric power system. Real coded genetic algorithm- powerful optimization technique is employed to solve the dynamic combined economic emission dispatch i.e. DCEED strategy for two proposed algorithm. The first proposed DCEED algorithm includes fuel cost of only conventional generators while in the second algorithm along with conventional generators, wind powered generators with varying power output characteristic is added. A comparative analysis of both the algorithms in terms of total combined cost, emission level and fuel cost is taken into account and it is observed that in spite of wind uncertainty the proposed method is more economical.

show abstract

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

Cited by 6 publications

References 69 publications

Evolutionary algorithm solution for economic dispatch problems

Evolutionary algorithm solution for economic dispatch problems

Optimal Allocation and Energy Management of Units in Distribution Networks with Multiple Renewable Energy Sources and Battery Storage Based on Computational Intelligence

Dynamic Combined Economic Emission Dispatch Including Wind Generators by Real Coded Genetic Algorithm

Contact Info

Product

Resources

About