Selection of Cost-Effective and Energy-Efficient Storages with Respect to Uncertain Nature of Renewable Energy Sources and Variations of Demands

Khalili, Tohid; Bidram, Ali; Nojavan, Sayyad; Jermsittiparsert, Kittisak

doi:10.1007/978-3-030-42420-6_2

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…In [10], optimal microgrid's scheduling has been studied with the implementation of the incentive-based DR program considering the uncertainty of renewable energy sources and load. The performance and efficiency of renewable energy sources and storages have been studied from the uncertainty point of view in [11]. In [12] time-based DR program for commercial, residential, academia, and industrial load curves is implemented.…”

Section: A Literature Reviewmentioning

confidence: 99%

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Optimal Power Flow Considering Time of Use and Real-Time Pricing Demand Response Programs

Nojavan

Ajoulabadi

Khalili

et al. 2021

2021 IEEE Green Technologies Conference (GreenTech)

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In recent years, the implementation of the demand response (DR) programs in the power system's scheduling and operation is increased. DR is used to improve the consumers' and power providers' economic condition. That said, optimal power flow is a fundamental concept in the power system operation and control. The impact of exploiting DR programs in the power management of the systems is of significant importance. In this paper, the effect of time-based DR programs on the cost of 24-hour operation of a power system is presented. The effect of the time of use and real-time pricing programs with different participation factors are investigated. In addition, the system's operation cost is studied to analyze the DR programs' role in the current power grids. For this aim, the 14-bus IEEE test system is used to properly implement and simulate the proposed approach.

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Section: A Literature Reviewmentioning

confidence: 99%

“…Equation ( 10) is used to maximize the customer's profitability. After simplification of (10); the final formula is shown in (11). However, multi-period loads can be transferred at different time intervals and can be used at different times of the day.…”

Section: B Responsive Loads Modelingmentioning

confidence: 99%

Optimal Power Flow Considering Time of Use and Real-Time Pricing Demand Response Programs

Nojavan

Ajoulabadi

Khalili

et al. 2021

2021 IEEE Green Technologies Conference (GreenTech)

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“…Due to the decline in cost, photovoltaic (PV) power generation has developed rapidly. PV power generation has intermittent and stochastic nature due to external factors that cannot be controlled [2]. Therefore, accurate PV power generation forecasts are critical to ensure the balance between power generation and electricity consumption [3].…”

Section: Introductionmentioning

confidence: 99%

Solar Power Forecasting Based on Domain Adaptive Learning

et al. 2020

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Solar power forecasting is critical to ensure the safety and stability of the power grid with high photovoltaic power penetration. Machine learning methods are compelling in solar forecasting. These methods can capture the complex coupling relationship between different meteorological factors without physical modeling. Most of the existing machine learning based forecasts follow the batch learning manner. Once the training is completed, the structure and parameters of the model are usually no longer adjusted. However, the climate is complex and dynamic. It is difficult for a fixed model to adapt to the climate characteristics of different regions or periods. Therefore, an online domain adaptive learning approach is proposed in this paper. Knowledge can be selectively accumulated or forgotten in its iterative process. As weather changes, the model can dynamically adjust its structure to adapt to the latest weather conditions. Unlike existing adaptive iterative methods, the proposed adaptive learning approach does not rely on the labels of the test data in the updating process. Experiments show that this method can effectively track changes in data distribution and obtain reliable prediction results. INDEX TERMS Solar power forecasting, adaptive learning, neural networks, ensemble learning.

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Energy, Economic and Environmental Analysis of a Hybrid Power Plant for Electrification, and Drinking and Irrigation Water Supply

Bertsiou

Baltas

2022

Environ. Process.

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Selection of Cost-Effective and Energy-Efficient Storages with Respect to Uncertain Nature of Renewable Energy Sources and Variations of Demands

Cited by 10 publications

References 46 publications

Optimal Power Flow Considering Time of Use and Real-Time Pricing Demand Response Programs

Optimal Power Flow Considering Time of Use and Real-Time Pricing Demand Response Programs

Solar Power Forecasting Based on Domain Adaptive Learning

Energy, Economic and Environmental Analysis of a Hybrid Power Plant for Electrification, and Drinking and Irrigation Water Supply

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