Optimal scheduling the wind-solar-storage hybrid generation system considering wind-solar correlation

Yang, Guangya; Zhou, Ming; Lin, Bing; Du, Wangyang

doi:10.1109/appeec.2013.6837181

Cited by 14 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, the load tracking coefficient based on volatility consistency is usually used to quantitatively evaluate the timing matching of wind power output and grid load [14][15][16][17][18], as shown in Formula (6).…”

Section: The Timing Matching Degree Evaluation Indicator Of Wind Power Output and Grid Load 41 The Existing Source-load Timing Matching Dmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid

et al. 2021

View full text Add to dashboard Cite

The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load.

show abstract

Section: The Timing Matching Degree Evaluation Indicator Of Wind Power Output and Grid Load 41 The Existing Source-load Timing Matching Dmentioning

confidence: 99%

“…Yang et al [17] proposed a coordinated optimal dispatching scheme to minimize the dynamic sourceload tracking coefficient. Yang et al [18] proposed an optimal scheduling approach on the wind-solar-storage generation system, which considers the correlation among wind power, photovoltaic output and load.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Moreover, the load is variable, and the power system operator is responsible to fulfill this load demand by scheduling the generation sources at the optimal generation point. In a hybrid system, an optimal scheduling approach is presented by Yang et al (2013) to manage a wind, solar, and storage system. Gayme and Topcu (2012) figured an OPF issue to track down the ideal estimations of factors of a power system having a capacity limit.…”

Section: Problem Overview and Motivationmentioning

confidence: 99%

(DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

et al. 2021

View full text Add to dashboard Cite

The performance of a power system can be measured and evaluated by its power flow analysis. Along with the penetration of renewable energies such as wind and solar, the power flow problem has become a complex optimization problem. In addition to this, constraint handling is another challenging task of this problem. The main critical problem of this dynamic power system having such variable energy sources is the intermittency of these VESs and complexity of constraint handling for a real-time optimal power flow (RT-OPF) problem. Therefore, optimal scheduling of generation sources with constraint satisfaction is the main goal of this study. Hence, a renewable energy forecasting–based, day-ahead dynamic optimal power flow (DA-DOPF) is presented in this paper with the forecasting of solar and wind patterns by using artificial neural networks. Moreover, contribution factors are calculated using triangular fuzzy membership function (T-FMF) in the sub-interval time slots. Furthermore, the superiority of feasible (SF) solution constraint handling approach is used to avoid the constraint violation of inequality constraints of optimal power flow. The IEEE 30-bus transmission network has been amended to integrate a solar photovoltaic and wind farm in different buses. In this approach, the computing program is based on MATPOWER which is a tool of MATLAB for load flow analysis which uses the Newton–Raphson technique because of its rapid convergence. Meteorological information has been gathered during the time frame January 1, 2015, to December 31, 2017, from Danyore Weather Station (DWS) at Hunza, Pakistan. A Levenberg–Marquardt calculation–based artificial neural network model is utilized to foresee the breeze speed and sunlight-based irradiance in light of its versatile nature. Finally, the results are discussed analytically to select the best generation schedule and control variable values.

show abstract

“…The direct search algorithms utilize the function values alone and necessitate numerical values without the structure of a problem. A method for optimal scheduling of wind‐solar‐storage system to meet the demand has been presented 7 . An optimal power management scheme for microgrid with energy storage devices has been estimated 8 .…”

Section: Introductionmentioning

confidence: 99%

“…A method for optimal scheduling of wind-solar-storage system to meet the demand has been presented. 7 An optimal power management scheme for microgrid with energy storage devices has been estimated. 8 Recently, OPF problem incorporated with voltage stability has been solved using successive linear programming (SLP) technique.…”

Section: Introductionmentioning

confidence: 99%

Implementation of differential evolution algorithm and its variants for optimal scheduling of distributed generations

Shilaja

2020

Int J Communication

View full text Add to dashboard Cite

Summary Modern power systems are being shifted its attention towards distributed generations (DGs). DGs have allured more concern as an auspicious opportunity with considerable economic profits. To minimize the power generation costs of DGs, day‐to‐day operation scheduling is essential. The role of this study is to offer an optimal operation schedule for DG with several energy sources including renewable energy sources (RES), considering economic facets. In order to achieve the cost minimization along with optimal scheduling, an objection function has been formulated and solved using the optimization algorithms. This study aims to present the applications of differential evolution (DE) algorithm and its variants such as opposition‐based differential evolution (ODE), self‐adaptive differential evolution (SaDE), improved differential evolution (IDE), and cultivated differential evolution (CuDE) for scheduling DG optimally. A scheme for optimal scheduling of thermal, wind power, and solar PV generators has been evaluated. The simulations have been carried out on IEEE 14 bus system, IEEE 30 bus system, IEEE 57 bus system, and Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO), as a real part of 62 bus Indian utility system (IUS). The novelty of this study lies in simulating a real‐time system for solving optimal scheduling problem, in that way helping decision makers to choose the optimal operation points. The results indicate that the SaDE outperformed other DE variants by giving the best fitness value and convergence rate.

show abstract

Optimal scheduling the wind-solar-storage hybrid generation system considering wind-solar correlation

Cited by 14 publications

References 5 publications

Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid

Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid

(DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

Implementation of differential evolution algorithm and its variants for optimal scheduling of distributed generations

Contact Info

Product

Resources

About