Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids

Sáez, Doris; Ávila, Fernanda; Olivares, Daniel; Cañizares, Claudio A.; Marín, Luis G.

doi:10.1109/tsg.2014.2377178

Cited by 178 publications

(95 citation statements)

References 27 publications

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“…The probabilistic prediction can be superior in terms of accounting for the variability of the power and load demand (Sáez, Ávila, Olivares, Cañizares, & Marín, 2015), due to the intermittency and uncertainty of renewable resources and user behaviours. On the other hand, development of new forecasting techniques concentrating on improving model efficiency and accuracy under online and real-time scenarios will be one of the future research topics in this field.…”

Section: Future Trendmentioning

confidence: 99%

A review of forecasting algorithms and energy management strategies for microgrids

2018

Systems Science & Control Engineering

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As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed. ARTICLE HISTORY

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Section: Future Trendmentioning

confidence: 99%

A review of forecasting algorithms and energy management strategies for microgrids

2018

Systems Science & Control Engineering

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“…For the microgrid, the power predictions of PVs and WTs have inevitable uncertainties, since the power of PVs and WTs is subject to the local weather conditions, such as irradiance, temperature and wind speed [40]. In addition, load prediction is also inaccurate due to the randomness in load demand.…”

Section: Scheduling Problem Formulationmentioning

confidence: 99%

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

et al. 2017

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Abstract:In this paper, an optimal day-ahead scheduling problem is studied for a microgrid with multiple distributed resources. For the sake of coping with the prediction uncertainties of renewable energies and loads and taking advantage of the time-of-use price for buying/selling electricity, an interval-based optimization model for maximum profits is developed. To reduce the computational complexity in solving the model, the possibility degree comparison between an interval and a real number is used to convert the interval constraints into the general ones; meanwhile, some slack variables and complementary conditions are introduced to eliminate the absolute-value operation. Unlike the stochastic optimization, the interval optimization only needs the upper-lower bounds of the uncertain variables instead of their probability distribution functions, which is beneficial to the practical application. Furthermore, the possible profit interval and the expected optimal profit can be determined by solving the optimization model. Numerical simulations are performed on a microgrid system modified from the benchmark low voltage network in the European Union project "Microgrid", and the results demonstrate the effectiveness of the proposed method.

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“…An energy management system architecture for a microgrid control system was proposed by Saez et al [15]. The fuzzy prediction interval models generalized the wind and solar power generation as well as the microgrid's electrical load behaviors.…”

Section: Introductionmentioning

confidence: 99%

Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks

et al. 2018

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Many of rural Alaskan communities operate their own, stand-alone electrical microgrids as there is no state-wide power distribution network. Although the fossil fuel-based power generators are the primary energy source in these isolated communities, an increasing number of microgrids have started to diversify their power supply by including renewable wind, hydro and photovoltaic energy sources. The integration and optimization of the multiple energy sources requires a design of a new power management system that can anticipate how much electricity will be drawn by the community and how much electricity will be generated from the renewable sources in order to control the fossil fuel-based generators to meet the community power demand. To address this problem, we designed a hybrid machine learning algorithm to predict community power draw as one module for the next generation microgrid power management system. The algorithm divides the task of a power load prediction into two sub-models: the first temporal model predicts the future weather conditions and the second model is trained to associate the predicted weather conditions with the community power demand. The results illustrate (1) the feasibility of building a machine learning algorithm that uses relatively small data for model training and validation, (2) the ability to predict the near-future community power load for the microgrids operating in the environments with highly dynamic weather conditions and (3) how the integration of multiple low quality future weather conditions produced high accuracy power load prediction.

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Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids

Abstract: Artículo de publicación ISIMillennium Institute Complex Engineering Systems ICM: P-05-004-F CONICYT: FBO16 National Fund for Science and Technology 1140775 CONICYT/FONDAP/15110019Millennium Institute Complex Engineering Systems, National Fund for Science and Technolog

Cited by 178 publications

References 27 publications

A review of forecasting algorithms and energy management strategies for microgrids

A review of forecasting algorithms and energy management strategies for microgrids

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks

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