The Federal Energy Regulatory Commission, defines Demand Response (DR) as: ‘Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized’. In this sense DR can be a more cost-effective alternative than adding generation capabilities to meet the peak and or occasional demand spikes: “The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. The goal is to reflect supply expectations through consumer price signals or controls and enable dynamic changes in consumption relative to price”.
Liberalization and deregulation of power markets coupled with smart grid technology (including, sensors, smart meters, control and automation systems and software – the IoT in its power distribution systems manifestation) have allowed a proliferation of DR Services, the best example being when a service provider aggregates loads from numerous participants (industry, commercial or residential) and offers to reduce or shut down these loads for a specified time period in response to some signal (including price signals) from the power utility or distribution system operator. This is done for a fee which is shared between the service provider and the owner of the load and helps the system avoid adding capacity to cope with peak demand or emergencies, while increasing reliability and stability of the grid. Hence an expansion of the “Negawatt” concept.
Over the last 10 years DR services have increased in scope and sophistication, as big data analytics have enabled more accurate prognosis of demand and consumption patterns and better demand / capacity balancing through better fine-tuned price signals, while taking also into account capacity constraints, reserves, power asset condition and, importantly, integrating intermittent renewable resources, such as wind and solar.
Due to the large potential benefits and in addition to commercial applications already available in the market, mainly in US, but also in western Europe and Canada, significant investment funds are going into DR programs to scale and fully automate the solutions. Companies such as Smart Grid Energy, Energy Pool, Energy Aware Technology, EnergyConnect (now part of Johnson Controls), NRG Business, Comverge, EnerNOC, and GridPoint are leading developments in this field.
Start-ups however are also active. This is how Dr Ashkan Rahimi-Kian, CEO of one start-up – i-EMS Group – from Toronto, Canada describes the DR field:
“Benefits of effective Demand Response (DR) programs can be summarized as follows:
- Lowering the bulk power consumers’ energy bill (up to 25% or even more),
- Allowing power utilities to control the market clearing price (MCP) in day-ahead, hour-ahead, real-time energy and reserve markets (as active market participants who respond inversely to high energy prices),
- Increase power system reliability (e.g. line congestion avoidance) and voltage/frequency security (e.g. preventing blackout) when in effect during peak demand hours and combined with photo-voltaic (PV) & wind farms daily/hourly scheduling -as a virtual power plant or VPP,
- Delay or avert costs of distribution network expansion, and extend the life cycles of the power system apparatus (e.g., lines, cables, transformers, and switches).
Nowadays, communications & computer networks (IT/ICT networks) are becoming inseparable parts of modern/smart power grids; and huge amounts of measurement data (from advanced metering infrastructure and sensors like AMI, RTU, PMU) are communicated, stored and managed through the IT/ICT networks, data management systems and software applications. Intelligent DR programs/services are among service applications for bulk power consumers (in residential, commercial and industrial sectors), power utilities and power system operators to accurately and effectively monitor end users energy usages, determine optimal energy usage strategies for them (with respect to time of use (TOU) & real-time (RT) price tariffs and power system loading & contingency conditions) on a daily, weekly or yearly basis. Power utilities could determine optimal demand bids (demand functions) to actively participate in the day-ahead, hour-ahead energy and reserve markets (based on consumers’ committed hourly/daily energy savings) to control the market clearing price, and increase the power system reliability, and voltage/frequency security.
The i-EMS Group are developing an intelligent energy management software/service application (to be used via smart phones/tablets/laptops) for residential, commercial or industrial consumers as a means of effective, user friendly and affordable DR Service to save on consumers’ energy bills; it could easily be integrated to the demand bid software/service (for local power utilities to enter the day-ahead, hour-ahead energy and reserve markets), and the PV/wind farms’ volatility risk management process as well”.
Given that VPP/DR programs are a very interesting application within the broader IoTS space we will be following developments here more closely in the future.
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