Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models

Gabaldón, A.; García-Garre, Ana; Ruiz-Abellón, María Carmen; Guillamón, Antonio; Álvarez-Bel, Carlos; Fernández-Jiménez, L. Alfredo

doi:10.1016/j.jup.2021.101213

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…Gina María Pipoli Azambuja, et al further investigated the relationship between passive and active customer engagement on customer well-being [10]. Lee, L. et al used an experience sampling method to explore the impact on customer-directed behavior using a customer-centered approach [11], while A. Gabaldón et al devised a customer demand baseline that can be used to assess response [12]. Kostromitina, M. et al applied keyword analysis to analyze the method of customer rating, and the analysis found that when customers make an assessment, they pay more attention to the service itself [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Yan,

Su,

et al. 2023

Applied Mathematics and Nonlinear Sciences

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In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of EG 11 is 0.1395, the importance of EG 1 is 0.1116, and the importance of EG 5 is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.

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Section: Literature Reviewmentioning

confidence: 99%

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Yan,

Su,

et al. 2023

Applied Mathematics and Nonlinear Sciences

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“…There are also other models proposed in the literature for calculating the baseline. An example is the model proposed in [121], which takes into account simple adjustment factors through physically based models. According to the authors, gaming responses are detected before and after DR.…”

Section: Novel Tools For Estimationmentioning

confidence: 99%

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

et al. 2022

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Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.

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“…Another issue regarding the operation of microgrids is heavy peak loads. In order to overcome the severe peak loads safely and without load shedding, the demand response program (DRP) will play a critical and vital role [5,6]. By definition, DRP is the mitigation in the electrical load demand for reducing the peak demand or avoiding the system to enter the emergency status from the normal status [7].…”

Section: Introductionmentioning

confidence: 99%

Effective demand response and GANs for optimal constraint unit commitment in solar‐tidal based microgrids

Mobtahej

Esapour

Tajalli

et al. 2021

IET Renewable Power Gen

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A new approach for optimal demand response program (DRP) in the microgrid considering the high penetration of the solar energy and tidal units as significant and popular renewable sources in the system is proposed here. The proposed method makes use of a multi-objective problem (MOP) to not only minimize the total operation cost of the scheduling problem but also mitigate the high risk of the interruption in power delivery due to the components failure rate and long repairing rates. Considering the high complexity and nonlinearity of the formulation, a novel heuristic method based on the firefly algorithm is introduced to solve the problem without any assumption or killing the accuracy. In addition, a dynamic three-phase correction (DPC) formulation is proposed which can help to increase the global search characteristics of the method when boosting the convergence capability of the model. Due to the hard predictability nature of the solar irradiance, a deep learning model based on generative adversarial networks (GAN) is presented to predict the output power of the solar and tidal units properly. The high performance and feasibility of the proposed multi-layer problem are assessed on an IEEE test system.

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Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models

Cited by 10 publications

References 22 publications

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

Effective demand response and GANs for optimal constraint unit commitment in solar‐tidal based microgrids

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