Irradiance forecasting for microgrid energy management

Duverger, Emilien; Penin, Carolina; Alexandre, Philippe; Thiery, Frédérik; Gachon, D.; Talbert, Thierry

doi:10.1109/isgteurope.2017.8260223

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Moreover, financial matters of energy trade with other MGs and the main grid can also be improved [63]. Nevertheless, and due to the variability of renewable resources [64] and the need to decide how much generation power is used from controllable assets [65], the main objectives of most forecasting methods at the MG level are focused on estimating both renewable resources and demand simultaneously. Forecasting of energy resources is required by the MGS, since they mainly depend on such sustainable resources.…”

Section: Generation and Demand Forecasting Approachesmentioning

confidence: 99%

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

et al. 2021

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It is expected that distribution power systems will soon be able to connect a variety of microgrids from residential, commercial, and industrial users, and thus integrate a variety of distributed generation technologies, mainly renewable energy sources to supply their demands. Indeed, some authors affirm that distribution networks will propose significant changes as a consequence of this massive integration of microgrids at the distribution level. Under this scenario, the control of distributed generation inverters, demand management systems, renewable resource forecasting, and demand predictions will allow better integration of such microgrid clusters to decongest power systems. This paper presents a review of microgrids connected at distribution networks and the solutions that facilitate their integration into such distribution network level, such as demand management systems, renewable resource forecasting, and demand predictions. Recent contributions focused on the application of microgrids in Low-Voltage distribution networks are also analyzed and reviewed in detail. In addition, this paper provides a critical review of the most relevant challenges currently facing electrical distribution networks, with an explicit focus on the massive interconnection of electrical microgrids and the future with relevant renewable energy source integration.

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Section: Generation and Demand Forecasting Approachesmentioning

confidence: 99%

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

et al. 2021

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“…Here, Lagrange multipliers at different quantiles are α τi , α * τi , ξ τi , ξ * τi ≥ 0 (i = 1, 2, · · · I), then the first partial derivative of w τ , b τ , ϑ i , ϑ * i in function L τ are obtained by formula (6).…”

Section: Support Vector Quantile Regression Based Probablity Predictimentioning

confidence: 99%

“…An offshore microgrid is an effective way to provide energy for offshore platforms, which can deal with the limited fossil resources issues through using renewable energy generators and energy storage devices [1,2]. At present, offshore renewable energy mainly includes solar energy, wind energy, ocean temperature difference energy, tidal current energy and so on, among which the tidal current power has recently received widely concerned due to its vast reserves and high energy density [3][4][5][6]. However, the tide current has strong randomness owing to the wave, sea breeze, temperature, etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Dispatching of Offshore Microgrid Considering Probability Prediction of Tidal Current Speed

et al. 2019

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Oceans contain rich tidal current energy, which can provide sufficient power for offshore microgrids. However, the uncertainty of tidal flow may endanger the operational reliability of an offshore microgrid. In this paper, a probabilistic prediction model of tidal current is established based on support vector quantile regression to reduce the influence of uncertainty. Firstly, the penalty factors and kernel parameters of the proposed prediction model was optimized by the dragonfly algorithm to predict the tidal speed of any time of a day in different quantiles. Secondly, combining the above result with the kernel density to predict the probability density function of the tidal current speed, which is to improve the accuracy of prediction in the absence of information. Thirdly, an optimal generation dispatching strategy with tidal current generators is proposed to minimize the fuel consumption of offshore microgrids. Finally, a case study based on the offshore oil and gas platform in Bohai shows that the mean absolute percent error of the proposed model is 2.8142%, which is better than support vector quantile regression model and support vector regression model optimized by the genetic algorithm.

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“…The result shows less influence of uncertainty in solar than that in wind power on the dispatch cost at the generating end and for the management of the flexible loads. The energy management system for an isolated 126 microgrid with PV, ESS and electric vehicles using the hybrid solar irradiance forecasting methodology for a short period of time has been presented in [4]. The proposed methodology, based on the data available online from satellite and the data of clear sky for the selected microgrid location uses Heliosat algorithm for determining the cloud cover from the satellite data.…”

Section: Introductionmentioning

confidence: 99%

Solar irradiance forecasting using fuzzy logic and multilinear regression approach: A case study of Punjab, India

Mehta

Basak

2019

IJAAS

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<p class="MDPI17abstract">The accurate forecasting of solar irradiance depends on various uncertain parameters like time of day, temperature, wind speed, humidity, and atmospheric pressure. All these play an important role in calculating PV power output. In this paper, a novel approach for forecasting of solar irradiance using flexible and accurate fuzzy logic and robust multi-linear regression approach has been proposed considering the above mentioned five variables. Based on the simultaneous consideration of those five variables, the solar irradiance is forecasted using the proposed methodology at a particular location in India, and the results are compared with the real time measured value of solar irradiance at that location on the days for which solar irradiance are forecasted. The proposed method is validated by comparing the results with real time data. The error analysis of the fuzzy logic based proposed system shows the root mean square error of 10.011 and mean absolute percentage error of 1.703%, while compared with real time data measured by instruments pyranometer, anemometer etc. The same results are found better while compared with the results obtained using multilinear regression approach.</p>

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Irradiance forecasting for microgrid energy management

Cited by 5 publications

References 21 publications

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

Optimal Dispatching of Offshore Microgrid Considering Probability Prediction of Tidal Current Speed

Solar irradiance forecasting using fuzzy logic and multilinear regression approach: A case study of Punjab, India

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