Distribution asset management through coordinated microgrid scheduling

Mahoor, Mohsen; Majzoobi, Alireza; Khodaei, Amin

doi:10.1049/iet-stg.2018.0076

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…Many studies have investigated about failure mode and effect analysis with focus on transformer as a critical component of the system, some of them are [22,23,44]. In [71] transformers are the most critical component of the system by default and it proposed a model in which the micro grid exchanged power with the utility grid which is reshaped in such a way that the distribution transformer lifetime is maximized.…”

Section: Components Of Distribution Systemsmentioning

confidence: 99%

Asset management and maintenance programming for power distribution systems: A review

Mirhosseini

Keynia

2021

IET Generation Trans & Dist

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Security and continuous proper functioning of the power system is a vital issue for supplying demands. Providing an acceptable level of reliability and necessity of that in all parts of the power system, from generation to distribution, has always been, and remains a major concern for network managers. Asset management and performing regular and routine maintenance activities has a considerable impact on ensuring system reliability, reducing expensive crashes, and preventing shutdowns. Up to now, much research has been done on the proper planning of maintenance in different parts of the network with a focus on cost savings, improved system performance and reliability, and reduced shutdowns. The purpose of this article is to review the findings of the study and to provide a more comprehensive view of the maintenance planning issue.

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Section: Components Of Distribution Systemsmentioning

confidence: 99%

Asset management and maintenance programming for power distribution systems: A review

Mirhosseini

Keynia

2021

IET Generation Trans & Dist

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“…In the second step, the power on the target day is forecasted by the weighted average of the solar PV generation during the identified k similar days. More specifically, the weights are calculated as the inverse of the distances as in (2). The forecasted PV power generation on day D+1 (denoted bŷ P D+1 ) is calculated using (3), where P sj represents the PV power generation on day S j .…”

Section: A Basic Sbfm (Bsbfm)mentioning

confidence: 99%

Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

2020

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Accurate forecasting of solar photovoltaic (PV) power for the next day plays an important role in unit commitment, economic dispatch, and storage system management. However, forecasting solar PV power in high temporal resolution such as five-minute resolution is challenging because most of PV forecasting models can only achieve the same temporal resolution as their predictors(i.e., weather variables), whose temporal resolution is usually low (i.e., hourly). To address this challenge, similaritybased forecasting models (SBFMs) are advocated in this paper to forecast PV power in high temporal resolution using low temporal resolution weather variables. To effectively generalize the model for different scenarios of available weather data, three forecasting models (i.e., basic SBFM, categorical SBFM, and hierarchical SBFM) are proposed. As a case study, the PV power generated by the solar panels on the rooftop of a commercial building is forecasted for the next day with a five-minute resolution under three different scenarios of available weather data. The leave-one-out cross-validation analysis reveals that using only two or three weather variables, the proposed SBFMs can achieve higher forecasting accuracy than several benchmark models.

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“…However, the integration of PV systems into the distribution system presents unique challenges that necessitate flexible power system options to ensure reliable service during rapid supply and demand fluctuations [3]. Concerns for power system stability are raised by the variability and low predictability of PV generation, as grid operators must account for the intermittent character of solar-generated electricity in generation planning and dispatch operations [3][4][5]. As the prevalence of distributed PV systems increases, utility grids are undertaking a transition to modern, digitally-enhanced technologies that enable the monitoring and control of distributed energy resources (DERs) to actively integrate intermittent renewable generation into network planning models and optimisation procedures.…”

Section: Introductionmentioning

confidence: 99%

PV generation forecasting utilizing a classification-only approach

Theocharides,

Makrides,

Georghiou

2024

EPJ Photovolt.

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The increasing use of photovoltaic (PV) systems in electricity infrastructure poses new reliability challenges, as the supply of solar energy is primarily dependent on weather conditions. Consequently, to mitigate the issue, enhanced day-ahead PV production forecasts can be obtained by employing advanced machine learning techniques and reducing the uncertainty of solar irradiance predictions through statistical processing. The objective of this study was to present a methodology for accurately forecasting day-ahead PV production using novel machine learning techniques and a classification-only forecasting approach. Specifically, the central component of the proposed method is a classifier model based on an Extreme Gradient Boosting (XGBoost) ensemble algorithm that classifies the respective daily 30-min profiles of the forecasted global horizontal irradiance (GHI), the measured incident irradiance (Gi), and the AC power (PAC) into a predetermined number of classes. The formed classifier model was used as a dictionary to designate the newly arrived forecasted GHI to a particular class and ultimately identify the corresponding forecasted PAC. The results demonstrated that the proposed forecasting solution provided forecasts with a daily normalised root mean square error (nRMSE) of 8.20% and a mean absolute percentage error (MAPE) of 6.91% over the test set period of one year, while the model's reproducibility was also evaluated and confirmed. Additionally, a comprehensive evaluation based on clear-sky index categories revealed that the model's performance was notably accurate on clear-sky days, while maintaining acceptable accuracy levels on moderate and overcast days. These findings underscore the versatility and robustness of the proposed methodology in handling diverse weather conditions and hold promise for improved PV production forecasts.

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Distribution asset management through coordinated microgrid scheduling

Cited by 7 publications

References 34 publications

Asset management and maintenance programming for power distribution systems: A review

Asset management and maintenance programming for power distribution systems: A review

Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

PV generation forecasting utilizing a classification-only approach

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