On a demand response pilot demonstration in the technical university of Cluj-Napoca

Micu, Dan D.; Bargauan, Bogdan; Ceclan, Andrei; Şteţ, Denisa; Czumbil, Levente; Catinean, Andrei; Ploycarpou, Alexis

doi:10.1109/icepe.2016.7781445

Cited by 8 publications

(11 citation statements)

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“…The second category comprises DR systems classified by incentives into t of instruments, so-called "reward programs", namely, price-based DR (PBDR centive-based DR (IBDR) [7,30,39,[41][42][43][44][45][46]. The interrelation is demonstrated in Fi The first category comprises DR systems divided into centralized and distributed by their control mechanism.…”

Section: Classification Of Demand Response Programsmentioning

confidence: 99%

“…The second category comprises DR systems classified by incentives into two types of instruments, so-called "reward programs", namely, price-based DR (PBDR) and incentivebased DR (IBDR) [7,30,39,[41][42][43][44][45][46]. The interrelation is demonstrated in Figure 6.…”

Section: Classification Of Demand Response Programsmentioning

confidence: 99%

“…1. Price-based DR (PBDR) provides customers with a time-varying (dynamic) electricity rate [41,42,45,46]. Price-based programs comprise:…”

Section: Directmentioning

confidence: 99%

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Overview of Demand-Response Services: A Review

2022

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It is essential for the electricity sector to analyze and determine the distribution capacity throughput and apply new methods aimed at increasing the capacity of the transmission system. Consequently, the transition to modern electricity networks is two-sided, i.e., involving technological and social modifications. The demand response (DR) redistributes consumption away from peak times when grid load and costs are the highest. It incentivizes customers to use electricity when supply is high and inexpensive due to various market mechanisms. The present DR policy proposals stress the importance of fostering behavioral change through competitive pricing and customer participation in reducing carbon emissions and implementing smart energy solutions (including monitoring tools, such as smart meters and applications). The internet of things (IoT) has been applied to ensure adaptive monitoring of energy consumption and cost-effective and adequate demand-side management (DSM). The article is based on the research of the most recent sources of DR implementation methods applied at the power distribution level. It explains the main concepts, classifications, and entities implementing DSM programs, and suggests new visions and prospects for DSM and DR. Moreover, it discusses the application of blockchain technology potential for the internet of energy.

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Section: Classification Of Demand Response Programsmentioning

confidence: 99%

Section: Classification Of Demand Response Programsmentioning

confidence: 99%

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Overview of Demand-Response Services: A Review

2022

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“…7) For the real-time implementation of AC-based DR, data collection methods at the appliance level should be im- Shenzhen, China [182] China [262] India [10] Singapore [263] Sweden [264] Romania [265], [266] Portugal [267], [268] Finland [268] Czech Republic [269] Italy, Spain, and France [270], [271] Latvia [272] Greece [273] Turkey [274] Flanders, Belgium [275] Spain [269] South Australia [29] Australia [12] Sydney, NSW [276] Western Sydney, NSW [258] Jerrabomberra and Queanbeyan, NSW [258] USA [277] Austin, USA [29] California, USA [12] Texas, USA [259] Austin, USA [278] California, USA [258] USA [279] Control proved. 8) End-users do not intentionally agree to engage in DR programs.…”

Section: Future Trends and Major Challengesmentioning

confidence: 99%

Technologies and Practical Implementations of Air-conditioner Based Demand Response

Waseem

Lin

Ding

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand of end-users. Demand response (DR) has acquired considerable attention due to uncertain generation outputs from intermittent renewable energy sources and advancements of smart grid technologies. The percentage of the air-conditioner (AC) load over the total load demand in a building is usually very high. Therefore, controlling the power demand of ACs is one of significant measures for implementing DR. In this paper, the increasing development of ACs, and their impacts on power demand are firstly introduced, with an overview of possible DR programs. Then, a comprehensive review and discussion on control techniques and DR programs for ACs to manage electricity utilization in residential and commercial energy sectors are carried out. Next, comparative analysis among various programs and projects utilized in different countries for optimizing electricity consumption by ACs is presented. Finally, the conclusions along with future recommendations and challenges for optimal employment of ACs are presented in the perspective of power systems.

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“…Local energy manager (LEM) [ 53 ] may take over a range of information (energy consumption, energy production, energy storage) directly from energy consumers or, indirectly through monitoring systems or building management system/building energy management system (BMS/BEMS) implemented at each pilot site. Local energy manager (LEM) has also the role of performing the necessary calculations in order to quantify the results obtained by implementing demand response events, through base line forecasting algorithms and key performance indicators (KPIs) calculation.…”

Section: Overview Of the Implemented Demand Response Systemmentioning

confidence: 99%

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

Jurj

Czumbil

Bargauan

et al. 2021

Sensors

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The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.

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On a demand response pilot demonstration in the technical university of Cluj-Napoca

Cited by 8 publications

References 1 publication

Overview of Demand-Response Services: A Review

Overview of Demand-Response Services: A Review

Technologies and Practical Implementations of Air-conditioner Based Demand Response

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

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