Demand Response Implementation: Overview of Europe and United States Status

Silva, Cátia; Faria, Pedro; Vale, Zita

doi:10.3390/en16104043

Cited by 7 publications

(9 citation statements)

References 35 publications

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“…In the following equations, z i represents a sample of the measured variable, while ẑi represents the predicted variable, and n represents the number of observations. The mean absolute error (MAE) (10) measures the average magnitude of errors:…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…By analysing and predicting prosumers' energy consumption patterns, load-shifting capabilities, and distributed energy resources, these maintenance approaches can identify potential grid stress points and predict maintenance needs with greater precision. An overview of the demand response programme implementation has been made in [10] for Europe and the United States. It was concluded that the United States has the advantage, due to the better regulatory and policy environment in some regions of the United States.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

Rober,

Maruša,

Beković

2023

Energies

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This paper presents a step-by-step approach to assess the energy flexibility potential of residential consumers to manage congestion in the distribution network. A case study is presented where a selected transformer station exhibits signs of overloading. An analysis has been performed to evaluate the magnitude of the overloading and the timing of the overload occurrence based on their historical load data. Based on the historical load data, the four most prominent consumers have been chosen for the flexibility assessment. Temperature load dependency has been evaluated for the selected consumers. The paper’s novel approach focuses on selecting individual consumers with the highest energy flexibility potential, and analysing their load patterns to address transformer overloading. To achieve this, machine learning algorithms, specifically, multiple linear regression and support vector machines, were used for load profile forecasting during the overload occurrences. Based on the forecast and measured load patterns, flexibility scenarios were created for each consumer. The generated models were evaluated and compared with the forecasting based on the average load of the past days. In the results, three potential consumers were identified who could resolve the transformer overloading problem. The machine learning models outperformed the average-based forecasting method, providing more realistic estimates of flexibility potential. The proposed approach can be applied to other overloaded transformer stations, but with a limited number of consumers.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

Rober,

Maruša,

Beković

2023

Energies

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“…Customer awareness plays a crucial role in the success and effectiveness of DR programs. The level of awareness and understanding among consumers directly influences their willingness to participate in DR initiatives [127]. Customer awareness empowers consumers to make informed decisions about their energy consumption.…”

Section: Customer Awarenessmentioning

confidence: 99%

A Review of Barriers and Enables in Demand Response Performance Chain

Bogdanova,

Viskuba,

Zemīte

2023

Energies

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The role of demand response increases considerably with a higher share of renewable energy sources in the energy mix, characterized with more frequent energy market price fluctuations due to mismatch between uncontrollable weather-dependent production and currently relatively inflexible energy consumption. Reallocation of energy consumption from high-price hours to lower price hours helps to avoid extra costs to the entire economy and ensures the possibility to minimize fossil-based energy generation, therefore contributing to the achievement of zero-emission goals. The research aims to analyze involvement stimulating factors of demand-side management and demand response from the angles of various energy sector participants, identifying barriers and enabling a successful implementation and wider development of this important energy transition facilitator. The qualitative methods are as follows: utilizing the conducted literature review; and scrutinizing scientific publications, reports and legal acts. As a result, the authors point out the five main aspects: pricing mechanisms, incentives, technology, government regulation and customer awareness. Demand response requires spectacular human and technological resources and administrative, financial and educational efforts. However, the demand response addresses many challenges, such as cost savings, innovation, integration of renewable energy sources, effective, stable and safe power grid performance, reduced need for costly infrastructure modifications, and greenhouse gas emission reduction. The abovementioned benefits influence what the demand-response brings, and are crucial for modern society and a successful energy transition.

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“…Building upon the aforementioned research, scholars have introduced the concept of guiding users to alter their electricity consumption behavior, thereby achieving a reduction in carbon emissions within the power system and facilitating the integration of renewable energy sources [12]. Traditional demand response mechanisms operate within an "electricity perspective", primarily employing price signals or incentives to prompt users to proactively modify their electricity consumption patterns, ultimately fostering the secure and economical operation of the power system [13]. Cui et al (2023) tackled the low-carbon economic dispatch problem in a microgrid.…”

Section: Introductionmentioning

confidence: 99%

Assessing Carbon Emission Reduction Potential: A Case Study of Low Carbon Demand Response Technology in Fangshan District, Beijing

Dai,

Pei,

et al. 2024

Sustainability

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Responding to the low-carbon demands of power users helps the power industry unlock carbon emission reduction potential, transmit carbon reduction responsibilities, and hold significant importance for energy conservation and carbon reduction. Currently, there is relatively limited empirical research on carbon emission demand responses in different industries. Therefore, this article, based on dynamic carbon accounting technology and combined with power planning and operation simulation data in Fangshan District, proposes a low-carbon demand response mechanism guided by user-side carbon reduction, using dynamic carbon emission factors as guiding signals. Furthermore, an assessment model for the carbon reduction potential of a low-carbon demand response technology is constructed. Finally, empirical research is conducted on the estimated carbon emission reduction of 108 enterprises in 6 different industries in Fangshan District based on the assessment model. The results indicate that carrying out low-carbon demand response can reduce enterprise carbon emissions, and changes in enterprise capacity and industry affiliation will have a significant impact on the carbon reduction effect. This study introduces a novel approach to the low-carbon development of the power system load side, offering a fresh perspective on the empirical analysis of low-carbon development for typical receiving-end power grids.

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Demand Response Implementation: Overview of Europe and United States Status

Cited by 7 publications

References 35 publications

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

A Review of Barriers and Enables in Demand Response Performance Chain

Assessing Carbon Emission Reduction Potential: A Case Study of Low Carbon Demand Response Technology in Fangshan District, Beijing

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