Multi-Objective Generation Scheduling for Energy-Saving and Emission Reduction

2013 ◽  
Vol 392 ◽  
pp. 582-585
Author(s):  
Li Jun Qin ◽  
Yu Xing Hao
Keyword(s):  
Energy Saving ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Thermal Power ◽  
Pso Algorithm ◽  
Load Curve ◽  
Multi Objective ◽  
Satisfaction Degree ◽  
Generation Scheduling ◽  
To Receive

This paper based on the idea of dynamic division of the load curve with the monotonicity of load curve and the PSO algorithm and gets the characteristics section. It fuses the sub-processes with the energy trajectory methods in order to describe the climbing peak and descending valley of the load curve closely, and characterizes the actual load curve. The multi-objective daily generation scheduling model is built based on the lowest cost of thermal power purchase of electricity and the lowest emissions of pollution gas. The optimal power flow based on combination of satisfaction degree and close degree are introduced to transform multi-objective into the single-objective optimization model. Decision makers can make interactive solution via adjusting goal satisfaction degree and close degree,so as to receive satisfactory results considering all aspects and make the daily generation scheduling based on energy-saving and environmental protection.

Multi-Objective Optimal Power Flow of Integrated Renewable Systems Using a Novel Evolutionary Algorithm

2020 ◽  
pp. 160-194
Author(s):  
Barun Mandal ◽  
Provas Kumar Roy
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Thermal Power ◽  
Power Generators ◽  
Optimal Dispatch ◽  
Multi Objective ◽  
Optimal Power ◽  
Previous Decade ◽  
Generation Costs ◽  
Grasshopper Optimization

This chapter introduces an approach to explain optimal power flow (OPF) for stochastic wind and conventional thermal power generators-based system. In this chapter, grasshopper optimization algorithm (GOA) is implemented to efficiently prove its superiority for solving wind-based OPF problem. Diminishing carbon emissions is a significant goal for the entire world; a tremendous penetration of unpredictable wind energy can assist in reducing emissions. In the previous decade, the access of renewable energy opening for energy production has improved significantly. WE has become an important source that has begun to be used for energy all over the world in recent years. The optimal dispatch between thermal and wind units to minimize the total generating costs and emission are considered as multi-objective (MO) model. In MO optimization, whole electrical energy generation costs and burning emissions are concurrently minimized. The performance of aforesaid approach is exercised and it proves itself as a superior technique as compared to other algorithms revealed in the literature.

scholarly journals Multi-objective optimal power flow in the presence of intermittent renewable energy sources

2018 ◽  
Vol 7 (4) ◽  
pp. 2766 ◽  
Author(s):  
S. Surender Reddy
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Wind Farms ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost

This paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function. In this paper, three objective functions i.e., total generation cost, transmission losses and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the MO-OPF problems are solved using the multi-objective glowworm swarm optimiza-tion (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 bus system with two wind farms located at two different buses in the system.  

scholarly journals A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques

2021 ◽  
Vol 13 (9) ◽  
pp. 4979
Author(s):  
Hatem Diab ◽  
Mahmoud Abdelsalam ◽  
Alaa Abdelbary
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Control Technique ◽  
Heuristic Optimization ◽  
Multi Objective ◽  
Optimal Power

Optimal power flow (OPF) is considered one of the most critical challenges that can substantially impact the sustainable performance of power systems. Solving the OPF problem reduces three essential items: operation costs, transmission losses, and voltage drops. An intelligent controller is needed to adjust the power system’s control parameters to solve this problem optimally. However, many constraints must be considered that make the design process of the OPF algorithm exceedingly tricky due to the increased number of limitations and control variables. This paper proposes a multi-objective intelligent control technique based on three different meta-heuristic optimization algorithms: multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawks optimization (HHO) to solve the OPF problem. The proposed control techniques were validated by applying them to the IEEE-30 bus system under different operating conditions through MATLAB simulations. The proposed techniques were then compared with the particle swarm optimization (PSO) algorithm, which is very popular in the literature studying how to solving the OPF problem. The obtained results show that the proposed methods are more effective in solving the OPF problem when compared to the commonly used PSO algorithm. The proposed HHO, in particular, shows that it can form a reliable candidate in solving power systems’ optimization problems.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

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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