Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review

Mohseni, Soheil; Brent, Alan C.; Kelly, Scott; Browne, Will N.

doi:10.1016/j.rser.2022.112095

Cited by 73 publications

(9 citation statements)

References 329 publications

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“…The graphical tool's strengths lie in illustrating the correlation between the measured and predicted data, uncovering biases and systematic deviations based on radiation conditions, and depicting the range of deviations associated with the predictors. The fact that being a hybrid model validates the total response of the model and not by individual stages [34] is highlighted.…”

Section: Model Validationmentioning

confidence: 99%

Machine Learning Model for Primary Solar Resource Assessment in Colombia

Obando Paredes

2023

TecnoL.

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This work introduces a Machine Learning (ML) model designed to predict solar radiation in diverse cities representing Colombia's climatic variability. It is crucial to assert that the amount of solar energy received in a specific region is directly related to solar radiation and its availability, which is influenced by each area's particular climatic and geographic conditions. Due to the high variability and resulting uncertainty, various approaches have been explored, including the use of numerical models to estimate solar radiation. The primary objective of this study was to develop and validate an ML model that accurately predicts solar radiation in cities. The methodology employed was specific to data treatment and ML model development. It was structured into three fundamental stages: clustering, estimation, and response, considering that the model is based on historical data. The obtained results were assessed using appropriate statistical definitions, not only determining the model's efficiency in terms of prediction but also considering interactions between data for the approximation and prediction of solar radiation. In this context, it is crucial to emphasize that the research contributes to understanding solar radiation in Colombia. This study underscores the importance of developing ML models to predict solar radiation, emphasizing the need to consider the country's climatic diversity. The results obtained, following the model's application, provide valuable information for comprehending and anticipating the availability of this primary resource.

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Section: Model Validationmentioning

confidence: 99%

Machine Learning Model for Primary Solar Resource Assessment in Colombia

Obando Paredes

2023

TecnoL.

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“…Take advantage of users' side load control technology to deal with the fluctuation of renewable energy has become a research focus on the whole world [132]. Usually, the loads in MMG can be divided into flexible and inflexible loads.…”

Section: Load Demand Response and Regulationmentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

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“…Однако чтобы свести к минимуму разрывы между моделированием и реальностью, необходимы исследования более интегрированных моделей оптимизации факторов, которые характеризуют более широкий спектр неопределенностей. В частности, установлено, что исследования в этой области обычно связаны с использованием упрощенных моделей и предположений, которые отделяют соответствующие подходы от реальности и тем самым не раскрывают реальные проблемы переноса моделирования в реальный мир [21].…”

Section: обзор литературыunclassified

Forecasting investments in fixed assets

Terekhov

2022

Vestnik universiteta

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The article details the issues of forecasting investments in fixed assets. The theoretical aspects (foreign experience) of using various forms of predictive models based on the analysis of foreign scientific literature are considered. Two different forecasting methods have been practically implemented using the example of building multiplicative and ARIMA models. Quarterly forecasts of the cost of investments in fixed assets for 2022–2023 in the Russian Federation are formulated. A comparison of the simulation results showed that the calculated values for both models describe the dynamics of the indicator well, the forecast values differ, and therefore an average (combined) forecast is built based on the prediction results for both implemented methods. The results of the study can be used in the practical activities of state and municipal bodies, enterprise management for quarterly forecasting of investments in fixed assets.

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Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review

Cited by 73 publications

References 329 publications

Machine Learning Model for Primary Solar Resource Assessment in Colombia

Machine Learning Model for Primary Solar Resource Assessment in Colombia

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Forecasting investments in fixed assets

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