Active Coordination of Thermal Household Appliances for Load Management Purposes

Koch, Stephan; Zima, Marek; Andersson, Göran

doi:10.3182/20090705-4-sf-2005.00028

Cited by 39 publications

(38 citation statements)

References 8 publications

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“…Active demand-side management system [15] Central hub controller [16] Controller [17][18][19] Demand-side control unit [20] Demand-side management system [21] Energy box [22,23] Energy consumption control unit [24] Energy consumption scheduler [25,26] Energy management controller [27] Energy management system [5,[28][29][30][31][32][33] Energy scheduler [12] ESTIA [34] Expert and predictive system [35] Gateway System [7] Home automation system [36,37] Home energy controller [38] Home energy management device [39,40] Home energy management system [41][42][43][44] Home energy management unit [45] Home gateway [46] Load manager household [47][48][49] MavHome [50,51] Optimized energy management system [52] Power scheduler [53] Residential energy management system [14,[54][55]…”

Section: Name Referencesmentioning

confidence: 99%

Home energy management systems: A review of modelling and complexity

Beaudin

Zareipour

2015

Renewable and Sustainable Energy Reviews

388

206

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a b s t r a c tThe increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and production schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles, and consumer comfort.The existing literature has presented several methods, such as mathematical optimization, model predictive control, and heuristic control, for creating efficient operation schedules and for making good consumption and production decisions. However, the effectiveness of the methods in the existing literature can be difficult to compare due to diversity in modelling parameters, such as appliance models, timing parameters, and objectives.The present paper provides a comparative analysis of the literature on HEMS, with a focus on modelling approaches and their impact on HEMS operations and outcomes. In particular, we discuss a set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations, and modelling consumer well-being.The presented work is organized to allow a reader to understand and compare the important considerations, approaches, nomenclature, and results in prominent and new literary works without delving deeply into each one.

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Section: Name Referencesmentioning

confidence: 99%

Home energy management systems: A review of modelling and complexity

Beaudin

Zareipour

2015

Renewable and Sustainable Energy Reviews

388

206

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“…Attention towards the actuation of TCLs has grown as the penetration of intermittent renewable energy increases with innovations being made in theoretical frameworks, controlled environment pilot tests, development of new technologies, and field deployments [12][13] [14]. In this section, we review theoretical approaches to demand side grid flexibility, also known as demand response, review existing solutions to the actuation of thermostatically controlled loads to provide power grid services, and briefly discuss field deployments available in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…The theoretical approach to demand response has motivated a wide body of work that seeks to show that large aggregations of TCLs can be used both to bid into grid related ancillary service markets for profit as well as to maintain reliable power system operations [18][19] [20][21] [22]. Detailed end-use models explore associated uncertainties in aggregating TCLs, algorithmic bidding approaches toggle load switch controllers for managing wind forecast error and reducing external balancing penalties, and control and differential equation approaches for modeling the effect of broadcasting signals for TCL set point adjustments [17][23] [24] [25]. In general, room temperature, inside temperature (of a room, or inside a refrigerator, for example), power consumption, and TCL characteristics (resistance, capacitance, and wall thickness, for example) are all used for the design of a smart controller [20].…”

Section: A Theoretical Approaches To Demand Responsementioning

confidence: 99%

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

Baridó

Suffian

Rosa

et al. 2017

Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

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Abstract-The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development.Index Terms-Internet of Things (IoT), wireless sensor networks, energy management, energy reporting, electric grid flexibility.

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“…In addition to reversible energy storage in the form of pumped hydro, batteries, flywheels etc., a very important form is heat storage. Methods to increase the controllability of loads with inherent storage are emerging, such as control strategies for household appliances with thermal inertia and for prospectively large amounts of electric vehicles connected to the power system [7], [8]. Ubiquitous controllable energy storage is likely to have positive effects on system operation, ranging from security-relevant power reserves to loss reduction on the distribution system level [9]- [11].…”

Section: B Energy Storage In Power Systemsmentioning

confidence: 99%

Unified System-Level Modeling of Intermittent Renewable Energy Sources and Energy Storage for Power System Operation

Heussen

Koch

Ulbig

et al. 2012

IEEE Systems Journal

Self Cite

133

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Abstract-The system-level consideration of intermittent renewable energy sources and small-scale energy storage in power systems remains a challenge as either type is incompatible with traditional operation concepts. Non-controllability and energy-constraints are still considered contingent cases in market-based operation. The design of operation strategies for up to 100 % renewable energy systems requires an explicit consideration of non-dispatchable generation and storage capacities, as well as the evaluation of operational performance in terms of energy efficiency, reliability, environmental impact and cost. By abstracting from technology-dependent and physical unit properties, the modeling framework presented and extended in this paper allows the modeling of a technologically diverse unit portfolio with a unified approach, whilst establishing the feasibility of energy-storage consideration in power system operation. After introducing the modeling approach, a case study is presented for illustration.

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Active Coordination of Thermal Household Appliances for Load Management Purposes

Cited by 39 publications

References 8 publications

Home energy management systems: A review of modelling and complexity

Home energy management systems: A review of modelling and complexity

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

Unified System-Level Modeling of Intermittent Renewable Energy Sources and Energy Storage for Power System Operation

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