A System Architecture for Autonomous Demand Side Load Management in Smart Buildings

Costanzo, Giuseppe Tommaso; Zhu, Guchuan; Anjos, Miguel F.; Savard, Gilles

doi:10.1109/tsg.2012.2217358

Cited by 225 publications

(107 citation statements)

References 18 publications

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“…In end user electric installations one rarely meets loads with regulated power, hence power limitation processes are implemented mostly by turning off selected devices or by temporary blocking of their work (time shift). Loads occurring in end user installations on the basis of their operating characteristics can be divided into three basic types [25]:…”

Section: Classification Of Loadsmentioning

confidence: 99%

Distributed active demand response system for peak power reduction through load shifting

Benysek¹,

Jarnut²,

Werminski³

et al. 2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The large variability in power consumption in electrical power systems (EPS) influences not only growth balance losses and technical losses, but also in some cases reduces energy security. Delayed restoration of power generation, combined with unpredictable weather events leading to the loss of generating power can lead to a situation in which to save the stability of the power system there must be introduced in the system a load power limit or even disconnection of end-user in a given area, which will significantly reduce the comfort of use of energy. This situation can be prevented through either the building of new intervention power units or the aggregated use of new energy technologies, such as distributed network resources (DER), which are part of an intelligent Smart Grid network. Such resources bring together virtual power plants (VPP) and demand side management (DSM). The article presents an alternative decentralized active demand response (DADR) system, that by acting on selected groups of loads reduces peak loads with minimized loss of comfort of energy in use for the end-user. The system operates without any communication. The effectiveness of the proposed solution has been confirmed, outlined in test results obtained by the authors from a developed analytical model, which also contains stochastic algorithms to decrease the negative impact of such DSM systems on the power system (power overshoot and oscillation).

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Section: Classification Of Loadsmentioning

confidence: 99%

Distributed active demand response system for peak power reduction through load shifting

Benysek¹,

Jarnut²,

Werminski³

et al. 2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…In [28] it is proposed that communication architecture is composed of different layers, considering a layer to implement the interface between the HMS, and the system operator energy management system. The loads management was implemented in another layer using a mixed-integer programming in order to minimize the operation costs considering the demand response opportunities.…”

Section: Management Of Demand Response Events In House Management Sysmentioning

confidence: 99%

Dynamic load management in a smart home to participate in demand response events

Fernandes

Morais

Vale

et al. 2014

Energy and Buildings

121

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In future power systems, in the smart grid and microgrids operation paradigms, consumers can be seen as an energy resource with decentralized and autonomous decisions in the energy management. It is expected that each consumer will manage not only the loads, but also small generation units, heat-ing systems, storage systems, and electric vehicles. Each consumer can participate in different demand response events promoted by system operators or aggregation entities. This paper proposes an innovative method to manage the appliances on a house during a demand response event. The main contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources' management in a demand response event, mainly the ones of long duration, by changing the priorities of loads during the event. A case study with two scenarios is presented considering a demand response with 30 min duration, and another with 240 min (4 h). In both simulations, the demand response event proposes the power consumption reduction during the event. A total of 18 loads are used, including real and virtual ones, controlled by the presented house management system.

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“…Сущность его, в разра-батывании моделей графиков нагрузки электро-приемников, их синтеза на вводах гражданских объектов. Первичная информация для моделиро-вания базируется на предварительно полученной измерением параметров графиков нагрузки ана-логичных функционирующих систем электро-снабжения Ключевые слова: графоаналитический метод, жилищно-коммунальное хозяйство, электриче-ская нагрузка, моделирование графиков, погреш-ность вычисления that are capable of integrating various sources of energy [3]. The algorithms are constructed for the regulation of electric energy of residential, municipal facilities based on statistical data regarding the forecast for demand in load considering self-adjusting electric receivers [4].…”

Section: Introductionunclassified

Development of the graphical-analytical method for calculating electric load at civilian objects

Bondarchuk

2017

EEJET

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objects acquires a fundamental importance for the selection and protection of the elements of electrical supply system of the country as a whole.The relevance of the chosen topic of research is in the development of a new approach to solve the problem of accurate determining of the electric load at civilian objects. Literature review and problem statementAn analysis of the scientific publications testifies to the fact that the first results regarding the study and substantiation of electrical loads and their prediction appeared in the beginning of the 20th century. However, in terms of determining the loading of civilian objects, detailed coverage of its features has been carried out only in the last decades. Thus, the simulation is examined of daily schedules in power consumption by residential and municipal facilities in order to respond to its demand and to lower the load by using smart networks [1]. The modes of electrical consumption by residential areas with a 15-minute interval have been modeled [2]. Systems architectures are being developed to control the load on intelligent buildings by smart networks

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A System Architecture for Autonomous Demand Side Load Management in Smart Buildings

Cited by 225 publications

References 18 publications

Distributed active demand response system for peak power reduction through load shifting

Distributed active demand response system for peak power reduction through load shifting

Dynamic load management in a smart home to participate in demand response events

Development of the graphical-analytical method for calculating electric load at civilian objects

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