Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

Wan, Can; Lin, Jin; Song, Yonghua; Xu, Zhao; Yang, Guangya

doi:10.1109/tpwrs.2016.2608740

Cited by 139 publications

(61 citation statements)

References 13 publications

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“…It should be noticed that the upper bound of PV generation and wind power is time varying due to the change of solar irradiance and wind speed in a day. The expected value with given intervals can be obtained via the forecasting techniques for renewable energy as have been proposed in previous works . Constraints to 39 describe the uncertainty budget over the time series and spatial series so as to control the robustness of the proposed model.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The historical data of renewable energy is the power output of real wind farm and solar plant in Australia, which is obtained from “GEFcom2014” . The expected value and upper/lower bound of the RDG outputs are gained by adopting the forecast technology in an existing study . It is noted that the forecasting of the renewable energy is beyond the scope of this work.…”

Section: Case Studiesmentioning

confidence: 99%

“…It is widely recognized that developing accurate models of the uncertainties associated with renewable energy is the prerequisite for reliable decision makings in power systems. To this end, different advanced models, such as point forecasting method, probabilistic forecasting method, and interval forecasting method have been developed recently by researchers based on machine‐learning methods, e.g., neural network, fuzzy logical method, and support vector machine, to quantify the uncertainty of renewable energy . In these models, both historical renewable energy output and meteorological data is required to capture the correlations among different variables.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Wen

et al. 2019

Int Trans Electr Energ Syst

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To counterbalance the significant challenges imposed by renewable distributed generations penetration, this paper discusses the need of distributed energy storage system investment in distribution networks and proposes a robust optimization based storage investment model. The operational constraints of distribution network (e.g., voltage profile and substation capacity limitation) and storage device (e.g., state of energy and charging/discharging limit) are considered to guarantee the technical operation requirements. The proposed model is mathematically formulated as a two‐stage robust optimization with uncertainty of renewable distributed generator that is quantified by a polyhedral uncertainty set. The investment‐decision variables are optimized in the first stage, and the feasibility in the real‐time worst‐case scenario is checked in the second stage. A column‐and‐constraint generation (C&CG) algorithm and the big‐M linearization method are employed to solve the associated optimization problem. Numerical experiments on IEEE‐37‐node and IEEE‐123‐node distribution networks demonstrate the effectiveness of the proposed model.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Wen

et al. 2019

Int Trans Electr Energ Syst

Self Cite

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“…The reliability of the deterministic forecasts relies on historical performance of regression models and it is predetermined, therefore, the deterministic forecasts are difficult to estimate the uncertainties of real-time data. Moreover, the PV power generation depends 2 of 16 highly on the chaotic weather conditions [14]. To solve this problem, several probability forecasting approaches are developed, which use the prediction intervals (PIs) to represent the uncertainties [15].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Zhuang

Sheng

et al. 2017

Energies

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High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

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“…DGs include micro-gas turbine (MT), diesel engine (DE), fuel cell (FC), photovoltaic (PV), wind turbine (WT), small hydropower and some energy-saving technologies, such as flywheels, super capacitors and accumulators [5]. However, the uncontrollability and fluctuation of DGs have caused certain impact to the connected distribution network [6], especially the renewable power generation, such as PV [7][8][9][10] and WT [11][12][13][14]. With the growing penetration of DGs, the negative impact on power grid operation is also increasingly prominent [15].…”

Section: Introductionmentioning

confidence: 99%

Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

et al. 2017

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Abstract:The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation's reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT), a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV), wind turbine (WT), fuel cell (FC), diesel engine (DE), MT and energy storage (ES). Four typical scenarios were designed according to different day types (work day or weekend) and weather conditions (sunny or rainy) in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers' comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO) to propose modified chaos particle swarm optimization (MCPSO) whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

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Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

Cited by 139 publications

References 13 publications

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

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