Economic dispatch and operating cost optimization for thermal power in 500 KV system using genetic algorithm (GA)

In recent years, many works have been done in order to discuss economic dispatch in which wind farms are installed in electrical grids in addition to conventional power plants. Nevertheless, the emissions caused by fossil fuels have not been considered in most of the studies done before. In fact, thermal power plants produce important quantities of emissions for instance, carbon dioxide (CO2) and sulphur dioxide (SO2) that are harmful to the environment. This paper presents an optimization algorithm with the objective to minimize the emission levels and the production cost. A comparison of the results obtained with different optimization methods leads us to opt for the grey wolf optimizer technique (GWO) to use for solving the proposed objective function. First, the method used to estimate the wind power of a plant is presented. Second, the economic dispatch models for wind and thermal generators are presented followed by the emission dispatch model for the thermal units.Then, the proposed objective function is formulated. Finally, the simulation results obtained by applying the GWO and other known optimization techniques are analysed and compared.

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Economic Dispatch Methods for Smart Grid Based on Improved SPEA2 and Improved NSGA2

Frontiers in Energy Research ◽

10.3389/fenrg.2021.767721 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Li ◽

Jingwen Wang

Keyword(s):

Genetic Algorithm ◽

Fuel Cell ◽

Smart Grid ◽

Evolutionary Algorithm ◽

Smart Grids ◽

Thermal Power ◽

Economic Dispatch ◽

Environmental Crisis ◽

Grid Model ◽

Strength Pareto Evolutionary Algorithm

The severity of the ongoing environmental crisis has prompted the development of renewable energy generation and smart grids integration. The access of enewable energy makes the economic dispatching of smart grid complicated. Therefore, the economic dispatching model for smart grid is very necessary. This paper presents an economic dispatching model of smart power grid, which considers both economy and pollution emission. The smart grid model used for the simulation is construced of wind energy, solar energy, fuel cell, and thermal power, and the use of fuel cell enables the smart grid to achieve multi-energy complementar. To overcome the defect of the traditional centralized communication methods, which are prone to communication jams, this paper adopts a multi-agent inform ation exchange method to improve the stability and efficiency. In terms of the solution method for this model, this paper proposes Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2) and Improved Non-dominated Sorting Genetic Algorithm 2(INSGA2) that solves the economic dispatch problem of a smart grid. The strength Pareto evolutionary algorithm 2(SPEA2),non-dominated sorting genetic algorithm 2(NSGA2) and the improved algorithms are simultaneously applied to the proposed smart grid model for economic dispatching simulation. The simulation results show that ISPEA2 and INSGA2 are effective. ISPEA2 and INSGA2 have shown improvements over SPEA2 and NSGA2 in accuracy or running times.

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Genetic Algorithm-based Cost Optimization Model for Power Economic Dispatch Problem

British Journal of Applied Science & Technology ◽

10.9734/bjast/2016/24347 ◽

2016 ◽

Vol 15 (6) ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Samuel Oluwadare ◽

Gabriel Iwasokun ◽

Olatubosun Olabode ◽

O Olusi ◽

Akintoba Akinwonmi

Keyword(s):

Genetic Algorithm ◽

Optimization Model ◽

Cost Optimization ◽

Economic Dispatch ◽

Economic Dispatch Problem ◽

Power Economic

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Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint

Energies ◽

10.3390/en12010103 ◽

2018 ◽

Vol 12 (1) ◽

pp. 103 ◽

Cited By ~ 1

Author(s):

Xiuyun Wang ◽

Junyu Tian ◽

Rutian Wang ◽

Jiakai Xu ◽

Shaoxin Chen ◽

...

Keyword(s):

Wind Power ◽

Heat Storage ◽

Thermal Power ◽

Economic Dispatch ◽

Operating Cost ◽

Pollutant Emissions ◽

Stable Operation ◽

Economic Costs ◽

Spinning Reserve ◽

Multi Objective

With the increasing expansion of wind power, its impact on economic dispatch of power systems cannot be ignored. Adding a heat storage device to a traditional cogeneration unit can break the thermoelectric coupling constraint of the cogeneration unit and meet the economic and stable operation of a power system. In this paper, an economy-environment coordinated scheduling model with the lowest economic cost and the lowest pollutant emissions is constructed. Economic costs include the cost of conventional thermal power generating units, the operating cost of cogeneration units, and the operating cost of wind power. At the same time, green certificate costs are introduced into the economic costs to improve the absorption of wind power. Pollutant emissions include SO2 and NOx emissions from conventional thermal power units and cogeneration units. The randomness and uncertainty of wind power output are fully considered, and the prediction error of wind power is fuzzy treated according to the fuzzy random theory, and the electric power balance and spinning reserve fuzzy opportunity conditions are established, which are converted into the explicit equivalent. The improved multi-objective particle swarm optimization (MOPSO) was used to solve the model. With this method, the validity of the model is verified by taking a system with 10 machines as an example.

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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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