Compound microgrid installation operation planning of a PEFC and photovoltaics with prediction of electricity production using GA and numerical weather information

Obara, Shin’ya; El-Sayed, Abeer Galal

doi:10.1016/j.ijhydene.2009.08.006

Cited by 24 publications

(9 citation statements)

References 7 publications

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“…NWP systems have been utilized for forecasting applications for many years and perform best for the time horizons from 6 h to 2 weeks [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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“…NWP systems have been utilized for forecasting applications for many years and perform best for the time horizons from 6 h to 2 weeks [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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“…Obara and E.G. El-Sayed in [89] develop an optimal operation algorithm of a compound microgrid using numerical weather information (NWI) which is freely available and a GA is developed to minimize system fuel consumption. L Ricalde et al introduce in [90] some forecasting methods depending on temporal range of look-ahead times, and they address ANN as excellent approximations for nonlinear and stochastic models.…”

Section: Operation Scheduling: Economic Load Dispatch Problemmentioning

confidence: 99%

Computational optimization techniques applied to microgrids planning: A review

Gamarra

Guerrero

2015

Renewable and Sustainable Energy Reviews

332

143

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Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new microgrid planning approaches are pointed out.

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“…For example, the power demand at an office is usually large on weekdays and small at weekends. Thus, the day factor is included in (1). With the past demand and climate data, the regression coefficients are optimized using the least squares method.…”

Section: A) Prediction Of Power Demandmentioning

confidence: 99%

“…However, energy generated from such energy resources is usually intermittent and dependent on the weather, which makes it difficult to integrate RE at large scales and use it as the main energy source. Thus, energy storage systems have been proposed to overcome this problem [1]. With energy storage systems, the energy generated from RE can be stored and be used when in need.…”

Section: Introductionmentioning

confidence: 99%

Dynamic energy management method for power systems with large-scale storage batteries and photovoltaic cells

Tanaka

Maeda

Imanishi

2013

2013 International Conference on Clean Electrical Power (ICCEP)

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A dynamic energy management method for power systems with large-scale storage batteries and PV cells is developed. With the proposed method, storage batteries charge and discharge the appropriate amount of energy at the appropriate time in order to gain economic benefits, including the reduction in electricity bills. Within the method, the power demand and power generation are predicted based on the past data, and the prediction is dynamically modified by feeding back the prediction errors. The charging/discharging plan of the storage battery is also modified based on the prediction errors. The case studies are conducted for an imaginary community, which show that CO 2 emissions are reduced and that peak-cut is achieved, proving the effectiveness of the proposed method. The case studies also infer that the optimal battery management algorithm is dependent not only on the pattern of power demand but also the sizes of storage batteries and PV cells.Index Terms-Energy management, smart grid, storage battery. 978-1-4673-4430-2/13/$31.00 ©2013 IEEE

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Compound microgrid installation operation planning of a PEFC and photovoltaics with prediction of electricity production using GA and numerical weather information

Cited by 24 publications

References 7 publications

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Computational optimization techniques applied to microgrids planning: A review

Dynamic energy management method for power systems with large-scale storage batteries and photovoltaic cells

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