Knowing when to act: an optimal stopping method for smart grid demand response

Iwayemi, Abiodun; Yi, Peizhong; Dong, Xihua; Zhou, Chi

doi:10.1109/mnet.2011.6033035

Cited by 41 publications

(20 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of these studies have recognised that some appliances, such as those for cooking, are not suitable for remote management because of the potentially adverse impact on customers (Pina, Silva, and Ferrao 2012;Soares, Gomes, and Antunes 2014). From economic disciplines, there has been work on improving models of domestic energy usage (Iwayemi et al 2011;Richardson et al 2010). Trials of dynamic pricing have been conducted with householders (see Darby and McKenna 2012;Faruqui and Sergici 2010 for reviews).…”

Section: Introductionmentioning

confidence: 99%

A qualitative study of perspectives on household and societal impacts of demand response

Murtagh

Gatersleben

Uzzell

2014

Technology Analysis & Strategic Management

View full text Add to dashboard Cite

Despite the importance of demand response (DR), there has been little exploration of its potential impact on the individual or society. To address this gap, semi-structured interviews were conducted with 21 households in the south of England, in which two DR vignettes were presented: peak pricing and remote demand control during critical peaks. Peak pricing was seen as inequitable, burdening the less affluent, the less healthy, families and working mothers. Adverse societal outcomes may result from peak pricing, with potential for disruption of time-dependent household routines including the socially vital ritual of family mealtimes. Householders perceived their peak-time consumption to be determined by society's temporal patterns and not within their control to change. Third-party control in demand-side management was perceived to contravene householders' rights of control inside their homes. Alternative approaches to shifting peak demand, which combine technological, economic and socio-psychological insights, are considered.Keywords: demand response; demand-side management; dynamic pricing; peak energy consumption; qualitative; inequity IntroductionDemand response (DR) has been positioned as essential to energy policy in Europe (EU 2011). In response to the threat of global warming, which greenhouse gas emissions from human activities are exacerbating (IPCC 2013), policy-makers are seeking to reduce emissions through improved energy efficiency. Smart grids are seen as crucial facilitators of this objective, but it is DR which is expected to facilitate greatly increased levels of energy efficiency (Faruqui, Harris, and Hledik 2010). To date, there has been extensive research on the economic and engineering aspects of DR but little exploration of its potential impact on the individual or society. The current paper aims to address this gap.Traditionally, energy systems were designed and planned from the perspective of energy generation (Bilton et al. 2008). However, seasonal variation and overcapacity to deal with peaks * Corresponding author. Email: n.murtagh@ucl.ac.uk mean that significant inefficiency has been designed into existing power systems. In the UK, total consumption can be under 55% of capacity (Strbac 2008). In the smart grid, the electricity infrastructure will be augmented with a two-way digital communications network between enduse premises, distribution and transmission nodes and generating centres. The smart grid will reach end-user premises through smart meters. Already underway in several European countries, national rollout of smart meters in the UK is planned from 2015 to 2020, and an accompanying communications campaign emphasises that this will enable consumers to 'take control' (Smart Energy GB, 2014). With smart metering, there is the potential not only to predict demand to a much finer degree of granularity but also to influence demand. This capability is known as DR and is critical to realising the anticipated benefits of the smart grid (EU 2011).Definitions in the literature of DR o...

show abstract

Section: Introductionmentioning

confidence: 99%

A qualitative study of perspectives on household and societal impacts of demand response

Murtagh

Gatersleben

Uzzell

2014

Technology Analysis & Strategic Management

View full text Add to dashboard Cite

show abstract

“…This type of remote access will be for controlling non-critical appliance usage at peak times (demand response). A method for scheduling the starting and stopping of domestic appliances with feedback to/from the smart meter is described by Iwayemi et al [51]. The objective is to schedule particular appliances to run at low-tariff times (e.g.…”

Section: Smart Meter Data Managementmentioning

confidence: 99%

Context-awareness and the smart grid: Requirements and challenges

2015

View full text Add to dashboard Cite

New intelligent power grids (smart grids) will be an essential way of improving efficiency in power supply and power consumption, facilitating the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The delivery of efficiencies and advanced services in a smart grid will require both a comprehensive overlay communications network and flexible software platforms that can process data from a variety of sources, especially electronic sensor networks. Parallel developments in autonomic systems, pervasive computing and context-awareness (relating in particular to data fusion, context modelling, and semantic data) could provide key elements in the development of scalable smart grid data management systems and applications that utilise a multi-technology communications network. This paper describes: 1) the communications and data management requirements of the emerging smart grid, 2) state-of-the-art techniques and systems for context-awareness and 3) a future direction towards devising a context-aware middleware platform for the smart grid, as well as associated requirements and challenges.Apart from these policy drivers, power production and distribution will also have to operate in an increasingly deregulated and competitive market environment. Consequently the existing infrastructure needs to be upgraded to meet the projected challenges and demands. This upgrade will include the introduction of computational intelligence in all parts of the power network. It is this intelligence that will ultimately make the grid 'smart'. The principal smart grid trends and systems are surveyed in detail by Fang et al. [5]. Their survey distinguishes between (a) smart infrastructure for energy, information and communications; (b) smart management and control and (c) smart protection system for reliability analysis, failure protection, security and privacy. For each of these main headings they describe challenges in deployment, the range of technologies that might be used, the requirement for new standards, interoperability issues and the need for further research. It is clear from the range of technologies and standards available that there will be no unique smart grid infrastructure or architecture. However any smart grid infrastructure will require that electronic sensors be deployed not only in the power grid but also in consumer premises (homes, buildings, factories etc.). The sensors will monitor all aspects of power production, distribution and consumption.A multi-technology communications network will link these sensors with higher level management and control systems. The management and control systems will have access to actuators (switches, relays, circuit breakers etc.), to distributed generation sources and to consumer appliances in order to maintain end-to-end power production and delivery. Gao et al. [6] summarise the communications systems that will be deployed in different segments of the smart grid. A more detailed survey of...

show abstract

“…This architecture enables the aggregation of houses as intelligent networked collaborations, instead of seeing them as isolated passive units in the energy grid. One of the approaches being used to reduce the peak demand and improve the system reliability is demand response (DR), in which the end users modify their electricity consumption patterns in response to price variations or incentives provided by the utility [5]. In this paper architecture for the integration of smart metering technology into the current system for taking the advantage of lower real time prices is discussed.…”

Section: Introductionmentioning

confidence: 99%

Development of Infrastructue for Residential Load to Reduce Peak Demand and Cost of Energy in Smart Grid

I.¹,

Choksi²

2015

International Journal of Innovative Research in Electrical, Ele

View full text Add to dashboard Cite

The renewable energy sources like solar and wind power will be economical in future due to upward pressure in fossil fuel prices, progress in renewable technologies as well as economy scale resulting from large scale production of renewable energy sources. As the usage of renewable energy sources will increase, decentralized supply and demand side management will be used instead of conventional centralized supply side management in future smart grids. Smart meters offer solution to this by allowing more sophisticated measurement of consumption and the implementation of real time pricing. Using power scheduling of appliances we not only reduce the peak demand but operate the appliances when price is low. In this paper real time price based demand response management for residential appliances using intelligent metering and billing system using smart meter is described. Next generation meters will use Automated Meter Reading (AMR) and provide more sophisticated services to the customers. Here architecture of smart grid that is capable of automated reading, alert management (for various events like exceeding peak demand, exceeding monthly target bill etc.) and web dash board to view and control the consumption of various appliances is introduced.

show abstract

Knowing when to act: an optimal stopping method for smart grid demand response

Cited by 41 publications

References 6 publications

A qualitative study of perspectives on household and societal impacts of demand response

A qualitative study of perspectives on household and societal impacts of demand response

Context-awareness and the smart grid: Requirements and challenges

Development of Infrastructue for Residential Load to Reduce Peak Demand and Cost of Energy in Smart Grid

Contact Info

Product

Resources

About