Demand Response for Ancillary Services

Ma, Ookie; Alkadi, Nasr; Cappers, Peter; Denholm, Paul; Dudley, Junqiao Han; Goli, Sasank; Hummon, M.; Kiliccote, Sila; MacDonald, Jason; Matson, Nance E.; Olsen, Daniel; Rose, Cody; Sohn, Michael D.; Starke, Michael; Kirby, Brendan; O’Malley, Mark

doi:10.1109/tsg.2013.2258049

Cited by 277 publications

(142 citation statements)

References 6 publications

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“…Examples of such research are presented in Refs. [4][5][6][7][8]. Also simulations on a detailed level have been made to assess the technical potential of flexibility in households [9][10][11][12].…”

Section: Smart Grids: a Brief Reviewmentioning

confidence: 99%

Identifying the Factors for Ensuring Customers Actively Engaged in Smart Grids

Hull¹,

Bjørnstad²,

Boerakker³

et al. 2015

JEPE

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Smart grids are expected to become an essential component of the future energy system. The technical potential of smart grids is far reaching and increasingly well understood, and smart grids are now in the early phases of market deployment in several regions, particularly, in Europe and the US. Less understood than the technical aspects is how and to what degree end users (i.e. the customers) are willing and able to embrace smart grid technologies and the changes in mindset associated with this transition. This article reports the main findings from an IEA (International Energy Agency)-DSM (demand side management) project addressing the role of customers in a smart grid deployment scheme, specifically how customer behavior may restrict the technical potential of smart grids from being realized. With a model of household energy behavior as the theoretical point of departure, the research builds on experiences from various smart grid pilot studies, together with consumer research within similar domains, to identify behavioral challenges that are likely to hamper adoption of "smart grid behaviors". Based on this insight, a set of recommendations to minimize customer resistance to smart grid deployment is suggested.

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Section: Smart Grids: a Brief Reviewmentioning

confidence: 99%

Identifying the Factors for Ensuring Customers Actively Engaged in Smart Grids

Hull¹,

Bjørnstad²,

Boerakker³

et al. 2015

JEPE

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“…DR can also benefit consumers [14], ancillary services [15], and even all involved market parties [16]. In this field, deterministic analysis methods for DR behavior have been widely studied [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load

Shuzi

Zeng²,

Ding

et al. 2016

Energies

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Demand response (DR) programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL), plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization model with the combined uncertainties from the wind power output and the demand response scenario is proposed. The proposed model analyzes the demand response behavior of PEL by maximizing the electricity consumption satisfaction and interaction benefit satisfaction of PEL. Finally, a case simulation on the provincial power grid with a 151-bus system verifies the effectiveness and feasibility of the proposed mechanism and models.

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“…2 The most appropriate type of loads to provide ancillary services are thermostatically controlled loads (TCLs). The potential of TCLs has been broadly discussed in the scientific [3], [4] and industrial circles [5]; while controllability of electrical loads is a current research topic [6].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the response in the voltage will be similarly small. However, it may be enough to provide ancillary services that require only about 1% of flexibility of typical power consumption levels [2].…”

Section: Introductionmentioning

confidence: 99%

Voltage control-based ancillary service using thermostatically controlled loads

Bogodorova

Vanfretti

Turitsyn

2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

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Abstract-This paper discusses the possibility of providing voltage control-based ancillary services using thermostatically controlled loads (TCLs). The idea is to change the voltage at the point of common coupling in order to control power consumption of the TCLs through time. This allows to utilize the most common type of loads in the grid to provide ancillary services. These services differ from usual demand response strategies that operate on short to medium time scales. In this paper the authors propose a controller that regulate the power consumption by minimizing the power consumption error signal and by taking physical restrictions into account. Voltage restrictions arise due to the small window of voltage variability about the nominal value of the controlled voltage. In addition, capacitor bank switching interactions with motor and thermostatically controlled loads is discussed.

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Demand Response for Ancillary Services

Cited by 277 publications

References 6 publications

Identifying the Factors for Ensuring Customers Actively Engaged in Smart Grids

Identifying the Factors for Ensuring Customers Actively Engaged in Smart Grids

Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load

Voltage control-based ancillary service using thermostatically controlled loads

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