Multi-Objective Short-Term Hydro-Thermal Scheduling Using Meta-Heuristic Approaches

Every day humans face new challenges to survive in this world. It is a big challenge to utilize hydro and thermal generating unit properly. Researchers are trying to explore new techniques to improve scheduling of generating units. Environmental matter is a big issue to modern society. This chapter suggests a well-organized and effective approach using concept of grey wolf optimization (GWO) to deal with non-linear, multi-objective, short-term, hydro-thermal scheduling (MOHTS) problem. Moreover, authors have incorporated oppositional based learning (OBL) to enhance characteristics of GWO to achieve solution more consistently and accurately. To explore authenticity of our proposed algorithms, GWO and OGWO (oppositional based GWO) are applied to multi-chain cascade of 4-hydro and 3-thermal test system. Effective constraints like valve-point loading, water discharge, water storage, etc., are considered here. Statistical comparisons with other enlisted heuristic methods are done. The projected methods solve MOHTS problem quickly and efficiently.

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Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

Journal of Engineering ◽

10.31026/j.eng.2018.03.07 ◽

2018 ◽

Vol 24 (3) ◽

pp. 84

Author(s):

Hassan Abdullah Kubba ◽

Mounir Thamer Esmieel

Keyword(s):

Genetic Algorithm ◽

Objective Function ◽

Power Flow ◽

Optimal Power Flow ◽

Reactive Power ◽

Test System ◽

Multi Objective ◽

Optimal Power ◽

Blending Method ◽

Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.

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Index of surface-water discharge, water-quality, sediment, and biological records through September 30, 1990, for Wyoming

Open-File Report ◽

10.3133/ofr91497 ◽

1991 ◽

Author(s):

N.K. Ruby ◽

R.J. Sagmeister ◽

S.L. Green ◽

J.D. Walgreen

Keyword(s):

Water Quality ◽

Surface Water ◽

Water Discharge ◽

Discharge Water

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Voltage Stability Enhancement with Optimal Placement of IPFC

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9748.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 9465-9471

Keyword(s):

Transmission Line ◽

Objective Function ◽

Search Algorithm ◽

Cuckoo Search ◽

Test System ◽

Optimal Location ◽

Optimal Placement ◽

Utilization Factor ◽

Location Selection ◽

Multi Objective

This paper presents a novel technique based on Cuckoo Search Algorithm (CSA) for enhancing the performance of multiline transmission network to reduce congestion in transmission line to huge level. Optimal location selection of IPFC is done using subtracting line utilization factor (SLUF) and CSA-based optimal tuning. The multi objective function consists of real power loss, security margin, bus voltage limit violation and capacity of installed IPFC. The multi objective function is tuned by CSA and the optimal location for minimizing transmission line congestion is obtained. The simulation is performed using MATLAB for IEEE 30-bus test system. The performance of CSA has been considered for various loading conditions. Results shows that the proposed CSA technique performs better by optimal location of IPFC while maintaining power system performance

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Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

Processes ◽

10.3390/pr8121581 ◽

2020 ◽

Vol 8 (12) ◽

pp. 1581

Author(s):

Wenqiang Zhu ◽

Jiang Guo ◽

Guo Zhao ◽

Bing Zeng

Keyword(s):

Power Supply ◽

Tidal Current ◽

System Optimization ◽

Energy System ◽

Energy Sources ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Hybrid Energy System ◽

Hybrid Energy ◽

Multi Objective

The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

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Stochastic Multi-objective Short-term Hydro-thermal Self-scheduling in Joint Energy and Reserve Markets Considering Wind-Photovoltaic Uncertainty and Small Hydro Units

Journal of Electrical Engineering and Technology ◽

10.1007/s42835-021-00688-7 ◽

2021 ◽

Vol 16 (3) ◽

pp. 1327-1347

Author(s):

Mohammad Reza Behnamfar ◽

Hassan Barati ◽

Mahdi Karami

Keyword(s):

Short Term ◽

Multi Objective

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Multi-Objective Sizing Optimization of Hybrid Renewable Energy Microgrid in a Stand-Alone Marine Context

Electronics ◽

10.3390/electronics10020174 ◽

2021 ◽

Vol 10 (2) ◽

pp. 174

Author(s):

Wenqiang Zhu ◽

Jiang Guo ◽

Guo Zhao

Keyword(s):

Renewable Energy ◽

Optimization Problem ◽

System Optimization ◽

Economic Benefits ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Energy Management Strategy ◽

Sizing Optimization ◽

Multi Objective ◽

Hybrid Renewable Energy

Islands are the main platforms for exploration and utilization of marine resources. In this paper, an island hybrid renewable energy microgrid devoted to a stand-alone marine application is established. The specific microgrid is composed of wind turbines, tidal current turbines, and battery storage systems considering the climate resources and precious land resources. A multi-objective sizing optimization method is proposed comprehensively considering the economy, reliability and energy utilization indexes. Three optimization objectives are presented: minimizing the Loss of Power Supply Probability, the Cost of Energy and the Dump Energy Probability. An improved multi-objective grey wolf optimizer based on Halton sequence and social motivation strategy (HSMGWO) is proposed to solve the proposed sizing optimization problem. MATLAB software is utilized to program and simulate the optimization problem of the hybrid energy system. Optimization results confirm that the proposed method and improved algorithm are feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. The proposed HSMGWO shows better convergence and coverage than standard multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO) in solving multi-objective sizing problems. Furthermore, the annual operation of the system is simulated, the power generation and economic benefits of each component are analyzed, as well as the sensitivity.

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