Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies

Kohlhepp, Peter; Harb, Hassan; Wolisz, Henryk; Waczowicz, Simon; Müller, Dirk; Hagenmeyer, Veit

doi:10.1016/j.rser.2018.09.045

Cited by 148 publications

(62 citation statements)

References 93 publications

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“…One definition, introduced in [119], is the possibility of the appliance getting involved in DR programs, taking into account not only the appliance characteristics but also the usage preferences from the user. Note that HVAC and power heaters are highly flexible loads, thanks to the inertia of an associated thermal storage and the need to fulfil some quality constraints [120].…”

Section: Use Of Nilm In Hemsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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Section: Use Of Nilm In Hemsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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“…For the 4th term, two hours of past demand values are used (eight-time steps in the past). Equations (9) and (10) represent the two-step L-ARX regression model as follows:…”

Section: Linear Arx Regression Modelsmentioning

confidence: 99%

“…In fact, studies related to DSM and its impact on the power sector cannot be just summarized in a few lines. However, various review articles have deeply discussed and surveyed these issues [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

Sheha

Powell

2019

Processes

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With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

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“…However, its potential for flexibility remains virtually unknown. [35] presents the recent projects that are facilitating the transition from research to development. In general terms, and due to their inherent characteristics, there are two types of TCL, with different operating principles.…”

Section: Smart Appliances Overviewmentioning

confidence: 99%

A Novel Direct Load Control Testbed for Smart Appliances

et al. 2019

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The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. To this end, this paper proposes a novel Direct Load Control (DLC) testbed, aiming to conveniently support the research community, as well as analyzing and comparing their designs in a laboratory environment. Based on the LabVIEW computing platform, this original testbed enables access to knowledge of major components such as online weather forecasting information, distributed energy resources (e.g., energy storage, solar photovoltaic), dynamic electricity tariff from utilities and demand response (DR) providers together with different mathematical optimization features given by General Algebraic Modelling System (GAMS). This intercommunication is possible thanks to the different applications programming interfaces (API) incorporated into the system and to intermediate agents specially developed for this case. Different basic case studies have been presented to envision the possibilities of this system in the future and more complex scenarios, to actively support the DLC strategies. These measures will offer enough flexibility to minimize the impact on user comfort combined with support for multiple DR programs. Thus, given the successful results, this platform can lead to a solution towards more efficient use of energy in the residential environment.Energies 2019, 12, 3336 2 of 16 since 2010 [3]. Although the electricity demand for major appliances has slightly decreased since 2007, mainly due to improvements in their energy efficiency, the rapid proliferation of small appliances and brown goods has absorbed these savings. The energy consumption due to these small loads has grown twice as fast as that of large appliances in the last decade. In addition, only one-third of domestic appliances consumption is under regulatory protection, particularly in emerging markets. This may become even more relevant in the near future as the demand for electricity in buildings increases due to the impact of the charging infrastructure for electric vehicles. While it is true that there is a need to increase the rigor of existing policies by extending regulatory coverage to a broader range of devices, on the other hand, user awareness may be the key factor. However, to achieve this, consumers should be rewarded to some extent when changing their behavior. The availability of information and communication technologies (ICT) on SG can be decisive in meeting this commitment through the widespread adoption of DR strategies.In other areas, such as power electronics, it is common to find a complete chain of modeling, development, testing, optimization, virtual validation, and rapid prototyping commercial tools that integrate seamlessly into a convenient testing and development environment such as these tools of Typhoon (Typhon, Somerville, USA) [4] and dSPACE (dSPACE, Paderborn, Germany) [5]. It is possible to find testbed...

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Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies

Cited by 148 publications

References 93 publications

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

A Novel Direct Load Control Testbed for Smart Appliances

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