Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

Gabaldón, A.; Álvarez, C.; Ruiz-Abellón, María Del Carmen; Guillamón, Antonio; Verdú, S.; Molina, Roque; García-Garre, Ana

doi:10.3390/su10020483

Cited by 9 publications

(11 citation statements)

References 39 publications

(70 reference statements)

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“…The results are shown in Table 1. According to Alcalá, the active, reactive, distortion, apparent power P(W), Q(Var), D(VA), and S(VA) are defined by Equation (2). This definition only considers the fundamental harmonic and pays no attention to the additive property.…”

Section: Validation Of Features Additive Propertymentioning

confidence: 99%

“…Therefore, the harmonic current features have good additive property and they are independent of power network states. Alcalá defined the active power (P), reactive power (Q), apparent power (S) and distortion power (D) in Equation (2) with no consideration of their additive property [23]. The authors calculated the power quantities according to Alcalá's definitions and found that only P and Q have good additive property while D and S do not.…”

Section: Validation Of Features Additive Propertymentioning

confidence: 99%

“…Appliance-level load curves are valuable in demand prediction and scheduling. Prior studies [2][3][4] pointed out that the analysis and sound understanding of demand profile on smaller consumer level could provide reasonably accurate information for predicting peak and average demand and demand shifting. Besides, NILM can also increase the penetration of renewable sources by maintaining the cost and revenue balance in micro-grid [5].…”

Section: Introductionmentioning

confidence: 99%

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A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

2018

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Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart meters. This paper presents a supervised event-based NILM approach for non-linear appliance activities identification. Firstly, the additive properties (stating that, when a certain amount of specific appliances' feature is added to their belonging network, an equal amount of change in the network's feature can be observed) of three features (harmonic feature, voltage-current trajectory feature, and active-reactive-distortion (PQD) power curve features) were investigated through experiments. The results verify the good additive property for the harmonic features and Voltage-Current (U-I) trajectory features. In contrast, PQD power curve features have a poor additive property. Secondly, based on the verified additive property of harmonic current features and the representation of waveforms, a harmonic current features based approach is proposed for NILM, which includes two main processes: event detection and event classification. For event detection, a novel model is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Compared to other event detectors, the proposed event detector not only can detect both event timestamp and two adjacent steady states but also shows high detection accuracy over public dataset with F1-score up to 98.99%. Multi-layer perceptron (MLP) classifiers are then built for multi-class event classification using the harmonic current features and are trained using the data collected from the laboratory and the public dataset. The results show that the MLP classifiers have a good performance in classifying non-linear loads. Finally, the proposed harmonic current features based approach is tested in the laboratory through experiments, in which multiple on-off events of multiple appliances occur. The research indicates that clustering-based event detection algorithms are promising for future works in event-based NILM. Harmonic current features have perfect additive property, and MLP classifier using harmonic current features can accurately identify typical non-linear and resistive loads, which could be integrated with other approaches in the future.

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Section: Validation Of Features Additive Propertymentioning

confidence: 99%

Section: Validation Of Features Additive Propertymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

2018

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“…In this case, the authors evaluate the use of DR and PV as energy resources for power planning aiming at sustainable development. The problem in this work is that customers are considered at an aggregated level through demand price elasticity without the consideration of the different characteristics and flexibilities of customer end-uses [29]. In another past paper [30] the authors propose a shifting optimization algorithm for flattening electricity consumption in residential segments for the so-called advanced tariffs (ToU).…”

Section: Literature Surveymentioning

confidence: 99%

“…The granularity of data is fixed from 10 to 20 s for a better evaluation of end-uses and to achieve a customer database with multiple possibilities (see for example REDD database in a past paper [34]). For example, this database has been tested for Non-Intrusive Load Monitoring Methodologies (NIALM) through Smart Meter data, for different purposes and loads (the use of NIALM for the disaggregation of main loads can be found previously [29] and for small nonlinear loads in a past paper [39]). This pacer, for monitoring purposes, is of considerable interest in the case of some water heaters with fast switching because demand cycling ranges from 10 to 30 s in some periods.…”

Section: Customer Descriptionmentioning

confidence: 99%

Integration of Demand Response and Photovoltaic Resources in Residential Segments

et al. 2018

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The development of renewable sources in residential segments is basic to achieve a sustainable energy scenario in the horizon 2030–2050 because these segments explain around 25% of the final energy consumption. Demand Response and its effective coordination with renewable are additional concerns for residential segments. This paper deals with two problems: the demonstration of cost-effectiveness of renewables in three different scenarios, and the application of the flexibility of demand, performing as energy storage systems, to efficiently manage the generation of renewable sources while improving benefits and avoiding penalties for the customer. A residential customer in Spain has been used as example. The work combines the use of a commercial simulator to obtain photovoltaic generation, the monitoring of customer to obtain demand patterns, and the development of a Physically-Based Model to evaluate the capability of demand to follow self-generation. As a main result, the integration of models (load/generation), neglected in practice in other approaches in the literature, allows customers to improve revenue up to 20% and reach a basic but important knowledge on how they can modify the demand, development of new skills and, in this way, learn how to deal with the characteristics and limitations of both Demand and Generation when a customer becomes a prosumer. This synergy amongst demand and generation physically-based models boosts the possibilities of customers in electricity markets.

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

et al. 2021

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Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

Cited by 9 publications

References 39 publications

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

Integration of Demand Response and Photovoltaic Resources in Residential Segments

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

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