Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm

In this paper, a Modified Adaptive Selection Cuckoo Search Algorithm (MASCSA) is proposed for solving the Optimal Scheduling of Wind-Hydro-Thermal (OSWHT) systems problem. The main objective of the problem is to minimize the total fuel cost for generating the electricity of thermal power plants, where energy from hydropower plants and wind turbines is exploited absolutely. The fixed-head short-term model is taken into account, by supposing that the water head is constant during the operation time, while reservoir volume and water balance are constrained over the scheduled time period. The proposed MASCSA is compared to other implemented cuckoo search algorithms, such as the conventional Cuckoo Search Algorithm (CSA) and Snap-Drift Cuckoo Search Algorithm (SDCSA). Two large systems are used as study cases to test the real improvement of the proposed MASCSA over CSA and SDCSA. Among the two test systems, the wind-hydro-thermal system is a more complicated one, with two wind farms and four thermal power plants considering valve effects, and four hydropower plants scheduled in twenty-four one-hour intervals. The proposed MASCSA is more effective than CSA and SDCSA, since it can reach a higher success rate, better optimal solutions, and a faster convergence. The obtained results show that the proposed MASCSA is a very effective method for the hydrothermal system and wind-hydro-thermal systems.

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Short–Term Planning of Hybrid Power System

Journal of Electrical Engineering ◽

10.1515/jee-2016-0035 ◽

2016 ◽

Vol 67 (4) ◽

pp. 234-245 ◽

Cited By ~ 1

Author(s):

Goran Knežević ◽

Zoran Baus ◽

Srete Nikolovski

Keyword(s):

Power System ◽

Power Plants ◽

Thermal Power ◽

Optimization Process ◽

Thermal Power Plants ◽

Short Term ◽

Hybrid Power System ◽

Hybrid Power ◽

Hydropower Plants ◽

Pumped Storage

Abstract In this paper short-term planning algorithm for hybrid power system consist of different types of cascade hydropower plants (run-of-the river, pumped storage, conventional), thermal power plants (coal-fired power plants, combined cycle gas-fired power plants) and wind farms is presented. The optimization process provides a joint bid of the hybrid system, and thus making the operation schedule of hydro and thermal power plants, the operation condition of pumped-storage hydropower plants with the aim of maximizing profits on day ahead market, according to expected hourly electricity prices, the expected local water inflow in certain hydropower plants, and the expected production of electrical energy from the wind farm, taking into account previously contracted bilateral agreement for electricity generation. Optimization process is formulated as hourly-discretized mixed integer linear optimization problem. Optimization model is applied on the case study in order to show general features of the developed model.

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Short-term Load Forecasting Model Based on RBF Neural Network Optimized by Artificial Bee Colony Algorithm

2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) ◽

10.1109/icbaie52039.2021.9390043 ◽

2021 ◽

Author(s):

Han Aoyang ◽

Zhou Shengqi ◽

Jian Xuehui ◽

Zhang Zhisheng

Keyword(s):

Neural Network ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Rbf Neural Network ◽

Load Forecasting ◽

Forecasting Model ◽

Short Term ◽

Model Based ◽

Bee Colony ◽

Short Term Load Forecasting

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Optimal scheduling of flexible thermal power plants with lifetime enhancement under uncertainty

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2021.116794 ◽

2021 ◽

Vol 191 ◽

pp. 116794

Author(s):

Jairo Rúa ◽

Adriaen Verheyleweghen ◽

Johannes Jäschke ◽

Lars O. Nord

Keyword(s):

Power Plants ◽

Thermal Power ◽

Optimal Scheduling ◽

Thermal Power Plants ◽

Lifetime Enhancement

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Optimal short-term hydro-thermal scheduling using multi-function global particle swarm optimization

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i1.pp537-544 ◽

2020 ◽

Vol 20 (1) ◽

pp. 537

Author(s):

Surender Reddy Salkuti

Keyword(s):

Power Generation ◽

Particle Swarm Optimization ◽

Power Plants ◽

Thermal Power ◽

Particle Swarm ◽

Test System ◽

Thermal Power Plants ◽

Fuel Cost ◽

Short Term ◽

Swarm Optimization

<p>An optimal short-term hydro-thermal scheduling (ST-HTS) problem is solved in this paper using the multi-function global particle swarm optimization (MF-GPSO). A multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The ST-HTS problem determines the optimal power generation of hydro and thermal generators which is aimed to minimize total fuel cost of thermal power plants during a determined time period. Effects of valve point loading and prohibited operating zones in the fuel cost function of the thermal power plants is examined. Power balance, reservoir volume, water balance and operation constraints of hydro and thermal plants are considered. The effectiveness and feasibility of MF-GPSO algorithm is examined on a standard test system, and the simulation results are compared with other algorithms presented in the literature. The results show that the MF-GPSO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques reported in the literature.</p>

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Combined PSO and IBF Algorithm for Short Term Hydro Thermal Scheduling

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d1093.1284s219 ◽

2019 ◽

Vol 8 (4S2) ◽

pp. 418-424

Keyword(s):

Power Generation ◽

Power Plant ◽

Convergence Rate ◽

Power Plants ◽

Large Scale ◽

Thermal Power ◽

Research Work ◽

Thermal Power Plants ◽

Scheduling Problems ◽

Short Term

Hydro and thermal power plants are planned to reduce the overall operation cost of the thermal power plants by optimally allocating the hydro units and thermal units in the power generation system. In this research work a combined particle swarm optimization (PSO) and improved bacterial foraging algorithm (IBFA) is proposed for short term hydro thermal scheduling (STHTS) with prohibited operating zones (POZs). The PSO algorithm yields the fastest convergence rate and possesses maximum capability of finding the global optimal solutions to the HTS (Hydro Thermal Scheduling) problems. Also BFA has succeeded in solving several issues in optimization, but it demonstrates poor convergence characteristics for large-scale issues such as the STHTS problem. Critical improvements to the basic BFA are implemented to tackle this complex issue in view of its high-dimension search space. The chemotactic step is changed in IBF, so that the convergence becomes dynamic rather than static. The combined PSO-IBF algorithm is assessed on a typical power generation plants consists of a hydroelectric power plant and an equivalent thermal power plant with a time slot of six 12-hour intervals and simulated using the MATLAB software. The simulation result shows that the combined PSO-IBF algorithm yields minimum cost value and optimal convergence rate than the existing algorithms.

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