The wind power scheduling problem

OPSEARCH ◽  
2021 ◽  
Author(s):  
T. R. Lalita ◽  
G. S. R. Murthy
Keyword(s):  
Wind Power ◽  
Scheduling Problem ◽  
Power Scheduling

scholarly journals Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

2017 ◽  
Vol 7 (5) ◽  
pp. 2349 ◽  
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Scheduling Problem ◽  
Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

Short-term Hydro-Thermal-Wind-Solar Power Scheduling: A Case Study of Kanyakumari Region of India

2021 ◽  
Author(s):  
Sunimerjit Kaur ◽  
Yadwinder Singh Brar ◽  
Jaspreet Singh Dhillon
Keyword(s):  
Solar System ◽  
Power System ◽  
Simplex Method ◽  
Solar Power ◽  
Test System ◽  
Scheduling Problem ◽  
Fuel Cost ◽  
Thermal Wind ◽  
Power Scheduling

In this paper, a multi-objective hydro-thermal-wind-solar power scheduling problem is established and optimized for the Kanyakumari (Tamil Nadu, India) for the 18th of September of 2020. Four contrary constraints are contemplated for this case study (i) fuel cost and employing cost of wind and solar power system, (ii) NOx emission, (iii) SO2 emission, and (iv) CO2 emission. An advanced hybrid simplex method named as-the -constrained simplex method (ACSM) is deployed to solve the offered problem. To formulate this technique three amendments in the usual simplex method (SM) are adopted (i) -level differentiation, (ii) mutations of the worst point, and (iii) the incorporation of multi-simplexes. The fidelity of the projected practice is trailed upon two test systems. The first test system is hinged upon twenty-four-hour power scheduling of a pure thermal power system. The values of total fuel cost and emissions (NOx, SO2, CO2) are attained as 346117.20 Rs, 59325.23 kg, 207672.70 kg, and 561369.20 kg, respectively. In the second test system, two thermal generators are reintegrated with renewable energy resources (RER) based power systems (hydro, wind, and solar system) for the same power demands. The hydro, wind, and solar data are probed with the Glimn-Kirchmayer model, Weibull Distribution Density Factor, and Normal Distribution model, respectively. For this real-time hydro-thermal-wind-solar power scheduling problem the values of fuel cost and emissions (Nox, SO2, CO2) are shortened to 119589.00 Rs, 24262.24 kg, 71753.80 kg, and 196748.20 kg, respectively for the specified interval. The outturns using ACSM are contrasted with the SM and evolutionary method (EM). The values of the operating cost of solar system, wind system, total system transmission losses, and computational time of test system-2 with ACSM, SM, and EM are evaluated as 620497.40 Rs, 1398340.00 Rs, 476.6948 MW & 15.6 seconds; 620559.45 Rs, 1398479.80 Rs, 476.7425 MW & 16.8 seconds; and 621117.68 Rs, 1399737.80 Rs, 477.1715 MW and 17.3 seconds, respectively. The solutions portray the sovereignty of ACSM over the other two methods in the entire process.

A Min-conflict Algorithm for Power Scheduling Problem in a Smart Home Using Battery

2020 ◽  
pp. 489-501
Author(s):  
Sharif Naser Makhadmeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Syibrah Naim ◽  
Zaid Abdi Alkareem Alyasseri ◽  
...  
Keyword(s):  
Smart Home ◽  
Scheduling Problem ◽  
Power Scheduling

Multi-objective power scheduling problem in smart homes using grey wolf optimiser

2018 ◽  
Vol 10 (9) ◽  
pp. 3643-3667 ◽  
Author(s):  
Sharif Naser Makhadmeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Syibrah Naim
Keyword(s):  
Smart Homes ◽  
Scheduling Problem ◽  
Grey Wolf ◽  
Multi Objective ◽  
Power Scheduling

Research on the Power Balanced Scheduling Model and Methods for the Intelligent Community

2012 ◽  
Vol 614-615 ◽  
pp. 1685-1688
Author(s):  
Ling Juan Li ◽  
Xiong Li
Keyword(s):  
Model Simulation ◽  
Electrical Power ◽  
Scheduling Problem ◽  
Scheduling System ◽  
Power Scheduling ◽  
Scheduling Model ◽  
Proposed Model ◽  
Simulation Results ◽  
Intelligent Community ◽  
Working Method

Power scheduling of the Intelligent Community is an important issue, and it is also an important part of the Smart Grid. With the target of saving and rationally distributing energy, this paper studies the power scheduling problem of the Intelligent Community. Firstly it designs a framework of power scheduling system. Then, based on the framework, it proposes a power balanced scheduling model among users of the Intelligent Community, and uses the classical simplex method and the improved table-working method respectively to solve the model. Simulation results show that the proposed model and the designed methods can realize power balanced scheduling and minimize the loss in the power scheduling process, and therefore make the electrical power fully utilized.

Wind Driven Optimization With Smart Home Battery for Power Scheduling Problem in Smart Home

2021 ◽  
Author(s):  
Sharif Naser Makhadmeh ◽  
Mohammed Azmi Al-Betar ◽  
Ammar Kamal Abasi ◽  
Mohammed A. Awadallah ◽  
Zaid Abdi Alkareem Alyasseri ◽  
...  
Keyword(s):  
Smart Home ◽  
Scheduling Problem ◽  
Power Scheduling

Research on Hybrid Energy Storage Configuration in Grid Wind Power Scheduling Tracking Under Statistics and Frequency Decomposition

2020 ◽  
Vol 18 (3) ◽  
Author(s):  
Jian-hong Zhu ◽  
Wen-xia Pan ◽  
Juping Gu
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Storage Capacity ◽  
Control Methods ◽  
Dynamic Power ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Frequency Decomposition ◽  
Power Scheduling ◽  
Energy Storage Capacity

Abstract The low accuracy of wind power scheduling influences the grid dispatch adversely, increasing the demand for spinning to reserve capacity and obstructing the grid frequency regulation. Considering the throughput characteristics of energy storage system, which can be used to compensate for wind farm power scheduling deviations, and smooth the grid power fluctuations, the hybrid energy storage (HES) is employed to enhance the dispatch ability of wind power generation. As one of the key techniques, desirable energy storage capacity configuration (ESCC) and control methods would accelerate the application of energy storage in the field of new resource. Combined with statistics and frequency decomposition of scheduling power deviation, HES capacity configuration and online dynamic power allocation method are proposed. First, by analysis of grid assessment indexes of wind power, scheduled wind power data are produced by improved adaptive error factor correction particle swarm optimization back-propagation neural network (AEFC-PSO-BPNN) prediction followed by wavelet packet smooth (WPS). After comparing with actual power, scheduling deviation statistics and frequency decomposition are applied in capacity and power configuration of energy storage, as well as dynamic power distribution control. With wind/storage simulation platform, then, feasibility of energy storage embedded in grid wind power scheduling deviation, regulation is verified under several combined methods, and the proposed ESCC methods are tested in application case by grid wind power indexes of root-mean-square error rate (RMSE), average volatility (AV), maximum throughout power and current (MTP, MTC), actual supercapacitor (SC), battery consumption capacity, and the number of crossings of state of charge (SOC) of HES. Finally, analyses and comparison of energy storage capacity requirements are carried out on different scheduling deviation control methods so as to explore the significant factors influencing capacity allocation. Applying these methods can improve the scheduling accuracy of grid wind power, reduce power fluctuations at the power common connected (PCC) point, and minimize the impact of accessed wind power to the grid as much as possible.

Distributionally Robust Solution to the Reserve Scheduling Problem With Partial Information of Wind Power

2015 ◽  
Vol 30 (5) ◽  
pp. 2822-2823 ◽  
Author(s):  
Qiaoyan Bian ◽  
Huanhai Xin ◽  
Zhen Wang ◽  
Deqiang Gan ◽  
Kit Po Wong
Keyword(s):  
Wind Power ◽  
Partial Information ◽  
Scheduling Problem ◽  
Robust Solution ◽  
Distributionally Robust

scholarly journals Synergistic control of plug-in vehicle charging and wind power scheduling

2013 ◽  
Vol 28 (2) ◽  
pp. 1113-1121 ◽  
Author(s):  
Chiao-Ting Li ◽  
Changsun Ahn ◽  
Huei Peng ◽  
Jing Sun
Keyword(s):  
Wind Power ◽  
Power Scheduling ◽  
Vehicle Charging
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