A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control

Bracale, Antonio; Caramia, P.; Carpinelli, G.; Fazio, Anna Rita Di; Ferruzzi, Gabriella

doi:10.3390/en6020733

Cited by 119 publications

(63 citation statements)

References 30 publications

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“…Literature [33] used the framework of stochastic differential equations to predict the probability of solar irradiation. Literature [34] first predicted the probability distribution of the hourly clear sky index through Bayesian autoregressive time series model, and then calculated the probability distribution of PV output by random sampling of the clear sky index by Monte Carlo simulation. Literature [35] used the copula function to model the joint probability distribution of PV actual output and point prediction, and realized the estimation of conditional probability distribution of the corresponding photovoltaic actual output by any point prediction.…”

Section: Distributed Photovoltaic Output Forecastmentioning

confidence: 99%

A Review of Distributed PV Operation Analysis and Demand Response Research

Cheng

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. The large-scale access of the distributed photovoltaic power supply to the grid will bring great challenge to the safe operation of power grid, thus adversely affecting the further development of distributed photovoltaic. First of all, from the aspects of structure, power quality, network loss and relay protection, this paper summarizes the influence of distributed photovoltaic system on distribution network. Secondly, the distributed photovoltaic output is summarized and summarized from two aspects of distributed photovoltaic output performance analysis and output forecast. In addition, from the perspective of photovoltaic user demand response can improve the photovoltaic extinction rate, this paper also combs the analysis and research of PV user demand response modelling and electricity price mechanism. Finally, some suggestions on the key areas for future research are put up. IntroductionThe development prospect of distributed photovoltaic power generation is broad [1], it advocates the spontaneous use of electricity, the rest of the internet, there is no step-up and long-distance transportation problem, directly facing the terminal power consumers[2], in the power market reform and energy-saving emission reduction situation, has got more and more attention and development. With the increase of the distributed PV network capacity, the safe and stable operation of the power system will be influenced by the non-negligible [3]. The accurate characteristic analysis and output prediction of distributed PV are the important foundation to solve these problems and ensure the safe operation of the power grid [4]. At the same time, a lot of researches have been done on demand side participation in distribution network, the purpose of which is to adjust shifting and increase PV extinction rate by using demand response of PV users [5][6][7][8][9].Based on the influence of photovoltaic power grid on distribution network, this paper collated the research of PV power generation characteristics analysis and output prediction, and then it has further combed the research on PV user demand response, which has provided some reference for readers.

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Section: Distributed Photovoltaic Output Forecastmentioning

confidence: 99%

A Review of Distributed PV Operation Analysis and Demand Response Research

Cheng

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The expected value can be obtained from a forecasted probability density function (PDF) or historical data [23].…”

Section: Modifications Of the Opfmentioning

confidence: 99%

A Conservation Voltage Reduction Scheme for a Distribution Systems with Intermittent Distributed Generators

2016

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Abstract:In this paper, a conservation voltage reduction (CVR) scheme is proposed for a distribution system with intermittent distributed generators (DGs), such as photovoltaics and wind turbines. The CVR is a scheme designed to reduce energy consumption by lowering the voltages supplied to customers. Therefore, an unexpected under-voltage violation can occur due to the variation of active power output from the intermittent DGs. In order to prevent the under-voltage violation and improve the CVR effect, a new reactive power controller which complies with the IEEE Std. 1547 TM , and a parameter determination method for the controller are proposed. In addition, an optimal power flow (OPF) problem to determine references for the resources of CVR is formulated with consideration of the intermittent DGs. The proposed method is validated using a modified IEEE 123-node test feeder. With the proposed method, the voltages of the test system are maintained to be greater than the lower bound, even though the active power outputs of the DGs are varied. Moreover, the CVR effect is improved compared to that used with the conventional reactive power control methods.

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“…A new multi-model ensemble forecasting method (MEM) is proposed and is used to properly combine two probabilistic base predictors. The base predictors are a Bayesian-based method (BM) and a quantile regression-based method (QM); both were successfully used for the forecasting of PV power in the relevant literature [18][19][20][21]. Numerical applications were performed to validate the method on the basis of actual solar measurements; the performances of the proposed method are quantified numerically in terms of a proper score [i.e.…”

Section: Introductionmentioning

confidence: 99%

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

Bracale¹,

Carpinelli²,

Falco³

2016

Sustainable Energy - Technological Issues, Applications and Case Studies

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The high penetration of photovoltaic (PV) systems led to their growing impact on the planning and operation of actual distribution systems. However, the uncertainties due to the intermittent nature of solar energy complicate these tasks. Therefore, high-quality methods for forecasting the PV power are now essential, and many tools have been developed in order to provide useful and consistent forecasts. This chapter deals with probabilistic forecasting methods of PV system power, since they have recently drawn the attention of researchers as appropriate tools to cope with the unavoidable uncertainties of solar source. A new multi-model probabilistic ensemble is proposed; it properly combines a Bayesian-based and a quantile regression-based probabilistic method as individual predictors. Numerical applications based on actual irradiance data give evidence of the probabilistic performances of the proposed method in terms of both sharpness and calibration.

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A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control

Cited by 119 publications

References 30 publications

A Review of Distributed PV Operation Analysis and Demand Response Research

A Review of Distributed PV Operation Analysis and Demand Response Research

A Conservation Voltage Reduction Scheme for a Distribution Systems with Intermittent Distributed Generators

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

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