Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Nespoli, Alfredo; Mussetta, Marco; Ogliari, Emanuele; Leva, Sonia; Fernández‐Ramírez, Luis M.; García‐Triviño, Pablo

doi:10.3390/electronics8121434

Cited by 22 publications

(18 citation statements)

References 24 publications

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“…The PV power forecast is computed two times a day, at 11 AM and PM, when updated information is available from the weather service. A comprehensive list of the parameter provided to the PHANN both in the training and test phase can be found in [34].…”

Section: Day Aheadmentioning

confidence: 99%

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

et al. 2021

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Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

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Section: Day Aheadmentioning

confidence: 99%

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

et al. 2021

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“…Similarly, different methods (models) can be used for very short-term or short-term solar (photovoltaic) power prediction. The examples are: using smart persistence and random forests for forecasts of photovoltaic energy production [31]; an ensemble model for short-term photovoltaic power forecasts [32]; hybrid method based on the variational mode decomposition technique, the deep belief network and the auto-regressive moving average model (for short-term solar power forecasts) [33]; a model which combines the wavelet transform, adaptive neuro-fuzzy inference system, and hybrid firefly and particle swarm optimization algorithm (for solar power forecasts) [34]; a physical hybrid artificial neural network for the 24 h ahead photovoltaic power forecast in microgrids [35]; a hybrid solar and wind energy forecasting system on short time scales [36].…”

Section: Related Workmentioning

confidence: 99%

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

et al. 2021

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This paper addresses very short-term (10 s) forecasting of power demand of highly variable loads. The main purpose of this study is to develop methods useful for this type of forecast. We have completed a comprehensive study using two different time series, which are very difficult to access in practice, of 10 s power demand characterized by big dynamics of load changes. This is an emerging and promising forecasting research topic, yet to be more widely recognized in the forecasting research community. This problem is particularly important in microgrids, i.e., small energy micro-systems. Power demand forecasting, like forecasting of renewable power generation, is of key importance, especially in island mode operation of microgrids. This is due to the necessity of ensuring reliable power supplies to consumers. Inaccurate very short-term forecasts can cause improper operation of microgrids or increase costs/decrease profits in the electricity market. This paper presents a detailed statistical analysis of data for two sample low voltage loads characterized by large variability, which are located in a sewage treatment plant. The experience of the authors of this paper is that very short-term forecasting is very difficult for such loads. Special attention has been paid to different forecasting methods, which can be applied to this type of forecast, and to the selection of explanatory variables in these methods. Some of the ensemble models (eight selected models belonging to the following classes of methods: random forest regression, gradient boosted trees, weighted averaging ensemble, machine learning) proposed in the scope of choice of methods sets constituting the models set are unique models developed by the authors of this study. The obtained forecasts are presented and analyzed in detail. Moreover, qualitative analysis of the forecasts obtained has been carried out. We analyze various measures of forecasts quality. We think that some of the presented forecasting methods are promising for practical applications, i.e., for microgrid operation control, because of their accuracy and stability. The analysis of usefulness of various forecasting methods for two independent time series is an essential, very valuable element of the study carried out. Thanks to this, reliability of conclusions concerning the preferred methods has considerably increased.

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“…Figure 9 shows the block diagram representation of overall closed-loop MG system. The augmented plant P(s) is given by Equation (22). The external disturbance input is w and z 1 , z 2 , z 3 denotes the desired output signals that have to be maintained by rejecting the effects of all the disturbances in operating frequency band that imparts robust stability and performance of overall MG system.…”

Section: H ∞ Controller Synthesismentioning

confidence: 99%

“…The stability analysis of nonlinear DC microgrid is presented in Reference 21 with robust optimization and order reduction. The work presented in Reference 22 proposes a proactive frequency control method to control the traditional synchronous generators in advance by predicting the short term sudden power fluctuation. Here the ensemble‐forecasting model based on the extreme learning machine algorithm is used to predict short term power fluctuations that act as an additional reference signal for automatic generation control.…”

Section: Introductionmentioning

confidence: 99%

Forecast‐based modeling and robust frequency control of standalone microgrids considering high penetration of renewable sources

Kumar

Mukherjee

Mathur

et al. 2020

Int Trans Electr Energ Syst

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Summary This article proposes forecast‐based modeling and robust frequency control strategy in isolated microgrids (MGs) to improve its stability. The intermittency and variability in renewable generation is problem for its smooth integration to MGs considering frequency stability. Continuous rise in penetration levels of renewable energy sources (RESs) is the main motivation behind forecast‐based modeling and controller design for MGs. The disturbances that affect the frequency in the MG may come from the load side and/or the generation sides. In MGs, at the generation side, the forecast of power from RESs is usually obtained to get a rough estimate of available renewable power. The forecasted power always differs from the actual one, so the secondary frequency controller may get overburdened due to forecast error resulting in abnormal frequency deviation that may lead to unstable power system. The proposed H∞ based robust control design considers the forecast error which improves the system stability and performance against disturbances coming from load/generation side. First, a long short term memory based recurrent neural network has been used to forecast the renewable power availability. Thereafter a H∞ controller is designed, and it is shown through extensive simulation studies that the H∞ controller outperforms optimized PID controllers so far as rejecting the effects of uncertainties in RESs, forecasting errors, and model parameter variations are concerned. The proposed robust controller design is also validated with real time simulator (OP4510) made by Opal‐RT. The controller hardware in the loop (CHIL) test has been done taking the simulation time step of 50 μS.

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Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Cited by 22 publications

References 24 publications

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

Forecast‐based modeling and robust frequency control of standalone microgrids considering high penetration of renewable sources

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