Short-term optimal operation of wind-solar-hydro hybrid system considering uncertainties

Zhang, Zhendong; Hui, Qing; Li, Jie; Liu, Yongqi; Yao, Liqiang; Wang, Yongqiang; Wang, Chao; Pei, Shaoqian; Zhou, Jianzhong

doi:10.1016/j.enconman.2019.112405

Cited by 70 publications

(17 citation statements)

References 34 publications

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“…Scott et al [38] carried out a study on the role of long-term uncertainty in electricity market modelling for renewable energy support policy evaluation. There are other studies that consider uncertainty in different aspects of renewable-related projects, such as load demand uncertainty [39,40]; renewable energy resources uncertainty [41,42]; uncertainties of the inflow of the hydropower plant and the PV power output [43]; wind energy uncertainty [44]; uncertainty in the costs of renewable energy [45]; uncertainties of wind speed, solar radiation intensity, and power load [46]; and uncertainties of renewable energy production and load demand [47].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

et al. 2021

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One of the many barriers to decarbonization and decentralization of the energy sector in developing countries is the economic uncertainty. As such, this study scrutinizes economics of three grid-independent hybrid renewable-based systems proposed to co-generate electricity and heat for a small-scale load. Accordingly, the under-study systems are simulated and optimized with the aid of HOMER Pro software. Here, a 20-year average value of discount and inflation rates is deemed a benchmark case. The techno-economic-environmental and reliability results suggest a standalone solar/wind/electrolyzer/hydrogen-based fuel cell integrated with a hydrogen-based boiler system is the best alternative. Moreover, to ascertain the impact of economic uncertainty on optimal unit sizing of the nominated model, the fluctuations of the nominal discount rate and inflation, respectively, constitute within the range of 15–20% and 10–26%. The findings of economic uncertainty analysis imply that total net present cost (TNPC) fluctuates around the benchmark value symmetrically between $478,704 and $814,905. Levelized energy cost varies from an amount 69% less than the benchmark value up to two-fold of that. Furthermore, photovoltaic (PV) optimal size starts from a value 23% less than the benchmark case and rises up to 55% more. The corresponding figures for wind turbine (WT) are, respectively, 21% and 29%. Eventually, several practical policies are introduced to cope with economic uncertainty.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

et al. 2021

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“…However, solar power is affected by natural climate conditions, and its output power is unbalanced in space and unstable in time, showing strong randomness, volatility and intermittent characteristics [2]. The randomness and uncertainty of solar energy resources make it difficult for independent photovoltaic systems to continuously output stable power [3], which not only aggravates the pressure of peak and frequency modulation of the power grid, but also affects the safe and stable operation of the power system, thereby seriously restricting the power grid's ability to absorb solar power [4]. Therefore, obtaining solar radiation intensity deterministic forecasting results with high accuracy and probabilistic forecasting results with high reliability are very important for the application of solar power.…”

Section: Introductionmentioning

confidence: 99%

Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression

et al. 2021

Self Cite

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Solar radiation intensity is intermittent and uncertain under the influence of meteorological conditions. Clustering them and obtaining high-precision and reliable probabilistic forecasting results play a vital role in the planning and management of solar power. In this study, a novel K-means time series clustering (K-MTSC) algorithm is first proposed to cluster solar radiation intensity and compared with astronomy method and K-means. Then, different feature inputs for different categories of solar radiation intensity are screened. Afterwards, the different kernel functions of Gaussian process regression (GPR) are compared and optimal kernel function is selected in terms of deterministic forecasting and probabilistic forecasting for different categories. Finally, the case study in Tibet province, China are performed to verify the validity and practicability of this research model and method. In this experiment, the average accuracy of GPR is 44% higher than that of Artificial Neural Network ANN, and 17% higher than that of Support Vector Regression. The experiments show that (1) the clustering results obtained by the K-MTSC algorithm have a larger inter-group distance and a smaller intra-group distance, and at the same time, it will not destroy the continuity of the time series. (2) The probability forecast results obtained by GPR are reliable and high-accuracy.INDEX TERMS solar radiation intensity; K-means Time Series Clustering; probabilistic forecasting; Gaussian process regression.

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“…Many scholars have studied the impact of the combination of renewable energy and other distributed energy sources on modern power systems [8][9][10]. Zhang et al have considered the uncertainties of wind power and photovoltaic power in a shortterm optimal operation and have proposed a wind-solar-hydro hybrid system optimal scheduling model [11]. Han and Valizadeh have used the copula theory to apprehend the correlation of wind and photovoltaic energy output [12,13].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Electric Vehicles Based on Frank-Copula-GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research

Gao

et al. 2021

Energies

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Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under the objectives of carbon peaking and carbon neutralization. This paper proposes an optimization scheduling method of electric vehicles (EV) combined with wind and photovoltaic power based on the Frank-Copula-GlueCVaR. First, a joint output model based on copula theory was built to describe the correlation between wind and photovoltaic power output. Second, the Frank-Copula-GlueCVaR index was introduced in a novel way. Operators can now predetermine the future wind–photovoltaic joint output range based on this index and according to their risk preferences. Third, an optimal scheduling model aimed at reducing the group charging cost of EVs was proposed, thereby encouraging EV owners to participate in the demand response. Fourth, this paper: proposes the application of a Variant Roth–Serve algorithm; regards the EV group as a multi-intelligent group; and finds the Pareto optimal strategy of the EV group through continuous learning. Finally, case study results are shown to effectively absorb more renewable energy, reduce the consumption cost of the EV group, and suppress the load fluctuation of the whole EV group, which has a practical significance and theoretical value.

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Short-term optimal operation of wind-solar-hydro hybrid system considering uncertainties

Cited by 70 publications

References 34 publications

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression

Optimization of Electric Vehicles Based on Frank-Copula-GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research

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