Carbon savings in the UK demand side response programmes

Lau, Eng Tseng; Yang, Qingping; Stokes, Lee; Taylor, Gareth; Forbes, A B; Clarkson, P.; Wright, P.S.; Livina, Valerie N.

doi:10.1016/j.apenergy.2015.08.123

Cited by 24 publications

(11 citation statements)

References 17 publications

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“…Subsequent to a review of programs for utilities, the Electric Power Research Institute estimated that in the US, DR reduced the peak demand by as much as 45 000 MW [36]. Nevertheless, DR uptake is still relatively low due to inadequate marketing strategies and low awareness-raising in the promotion of energy and cost savings [37].…”

Section: Participants and Designmentioning

confidence: 99%

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: Participants and Designmentioning

confidence: 99%

Overview of Demand-Response Services: A Review

2022

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“…Flexible demand can act as a form of reserve [20], and loads can be scheduled in order to minimise cost by following a Real Time Pricing (RTP) tariff based on SMP or to provide ancillary services. By reducing the need for polluting generators at peak times, DSM can also be used to reduce greenhouse gas emissions [21]. A detailed summary of DSM and demand response schemes can be found in [22] and [23].…”

Section: Introductionmentioning

confidence: 99%

Quantification of wind curtailment on a medium-sized power system and mitigation using municipal water pumping load

Davison-Kernan

Liu²,

McLoone³

et al. 2019

Renewable and Sustainable Energy Reviews

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The issues of climate change and dependence on fuel imports as well as a good local wind resource have led to a significant expansion of wind generation on the power system of Ireland in recent years, with further development planned. As it operates on a relatively small and isolated system, Irish wind generation must be carefully managed to mitigate its variable nature. This could lead to significant curtailment of wind power for system stability reasons, undermining the economic case for wind generation and reducing its effectiveness at displacing traditional, fossil-fired generation.This work quantifies the potential curtailment that might be seen on the future Irish and other power systems under several different scenarios, and presents Demand Side Management (DSM) as a tool to reduce curtailment. DSM of a water supply system is investigated to this end, as well as the inputs that would be necessary for such a system in order to benefit both the water system operator and the power system as a whole.

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“…This was a small scale, technology-specific study but results showed that DSR programmes with heat-pumps could cause significant reductions in CO 2 emissions. Lau et al considered the carbon savings in a number of DSR initiatives, comparing the business-as-usual case with various smart DSR intervention programmes [4]. Smith et al explored how DSR, capacity planning and carbon emissions will interact in the future.…”

Section: Introductionmentioning

confidence: 99%

Smart Scheduling of Household Appliances to Decarbonise Domestic Energy Consumption

Stacpoole

Sun

Jiang

2019

2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)

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2019) 'Smart scheduling of household appliances to decarbonise domestic energy consumption.any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.Abstract-Demand side response (DSR) and the interconnectivity of smart technologies will be essential to transform and revolutionize the way consumers engage with the energy industry. The carbon intensity of electricity varies throughout the day as a result of emissions released during generation. These fluctuations in carbon intensity are predicted to increase due to increased penetration of variable generation sources. This paper proposes a novel insight into how reductions in domestic emissions can be achieved, through the scheduling of certain wet appliances to optimally manage low carbon electricity. An appliance detecting and scheduling algorithm is presented and results are generated using real demand data, electricity generation and carbon intensity values. Reductions were achieved from the variations in grid carbon intensity and the availability of solar generation from a household photovoltaic (PV) supply.

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Carbon savings in the UK demand side response programmes

Cited by 24 publications

References 17 publications

Overview of Demand-Response Services: A Review

Overview of Demand-Response Services: A Review

Quantification of wind curtailment on a medium-sized power system and mitigation using municipal water pumping load

Smart Scheduling of Household Appliances to Decarbonise Domestic Energy Consumption

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