Demand Response and AI: Real Time Energy Optimisation
Get the lowdown on how AI can make Demand Response smarter. Cut costs and carbon emissions while improving grid stability.
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Demand Response and AI: Real Time Energy Optimisation
Get the lowdown on how AI can make Demand Response smarter. Cut costs and carbon emissions while improving grid stability.
Loading reading time...
Demand Response and AI: Real Time Energy Optimisation
Get the lowdown on how AI can make Demand Response smarter. Cut costs and carbon emissions while improving grid stability.
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Demand response and AI: optimising energy consumption in real-time

What is demand response?

Demand response is the consumer’s response to periods of high or low electricity demand at the national electricity provider by adjusting its energy consumption.

Demand response encourages consumers to use energy during off-peak demand periods by offering low electricity prices during those periods.

Low price periods (off-peak periods) result from high-level generation of renewable energy at time slots that are not high-demand periods due to the uncontrolled nature of renewable energy resources.

Demand response programmes can reduce utilities’ peak demand by an average of 10%, according to the American Council for an Energy-Efficient Economy (ACEEE).

Why do we need a demand response?

Demand response implementation is necessary for three reasons:

  1. Saving money
  2. Reducing carbon use
  3. Improving stability

Saving money for consumers

  • Cost avoidance in high-priced zones
  • Notifications when negative prices are announced

Saving carbon for the environment

  • Efficient use of renewable energy generation by encouraging consumers to shift their loads to high-generation time slots from renewable energy
  • Reduce the need for fossil fuel-based power plants by eliminating the need to switch them on in times of grid stress.
  • Reduce the number of fossil fuel power plants in the plans.

Improving stability in the grid

  • Matching between supply and demand keeps the grid stable.
  • Removing stress slots improves grid reliability.
  • Fewer blackouts
Solar Duck Curve Explained: What it Means in Western Australia
Figure 1: Typical consumption ‘duck shape’ curve (Source: 
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 Figure 2 Solar and wind production curves vs demand curve, circles in orange show the peak load time (Source: Farmer, Warren & Rix, Arnold. (2019). Optimising Power System Frequency Stability using Virtual Inertia from Inverter-based Renewable Energy Generation. 10.13140/RG.2.2.16471.91043.source:)

From Figure 2, we can see that the solar system’s generation peaks at noon. This peak doesn’t match the demand peak in the early evening at 6:00 PM when everybody comes home and starts to switch on their devices. 

Another peak is at 6:00 AM, when everybody wakes up and gets ready to start their day.

Implementation of demand response

Demand response is changing the way we use energy from the grid by

  • Shifting energy use time, either by days or hours
  • Avoiding energy demand peaks in the morning and afternoon
  • Planning device operations during the low-priced periods

What applications are the best for demand response?

Demand response can be used for

  • Irrigation, aquatic centres, and all water pumping applications
  • Flexible operations in the manufacturing process
  • Cold storage
  • Commercial buildings
  • Air-conditioning

Demand response and green energy

The utility has to produce more power from fuel-based stations to cover these high-demand periods. This leads to an increase in carbon emissions or requires large-scale storage systems, which are very expensive.

The most efficient and less costly solution encourages consumers to use flexible loads in these high-generation time slots. 

In this way, demand response techniques combined with green energy generation from solar and wind make a perfect match for a stable, efficient, and cleaner energy system.

Demand response and smart energy markets

The energy price should be dynamic to trace these high-generation time slots and encourage consumers to consume power at a suitable time on the grid. 

This dynamic price is a whole new set for the energy market because setting the energy price will require data from different resources, such as weather, generators, and consumption data. 

We also need to know the historical, real-time and predicted data from the previous resources. 

Here comes the need for a smart energy market that uses AI technologies to deal with historical and real data to predict the grid status both in the short and long term and thus set the energy price dynamically to respond to real-time changes in the grid.

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Figure 3: Smart energy market

How can demand respond?

There are different types of demand responses, depending on the type of load.

Automatically controlled loads

Some loads can be controlled automatically to work within the consumer’s comfort zone, but also in a manner that matches the dynamic energy price for minimum bills and stable grid optimisation. 

This controlling mechanism can be implemented through IoT techniques by adding sensors and switches for the devices under control when needed. Fortunately, many devices already have smart interfaces, especially for high-power consumers like air conditioners, industrial machines, and water pumps.

Manually controlled loads

Some loads cannot be automatically controlled because they depend on consumer behaviour and time, like TVs, washing machines, and other household and industrial devices that are simply attached to the person’s time and behaviour. 

This load can be controlled manually by giving the consumer real-time energy costs with alarms and notifications when their consumption reaches a certain point or when the electricity market offers low-priced energy as the generation from renewable energy resources increases.

For both types of control, AI techniques can play a significant role in communication with the energy market and controlling consumer loads automatically or by sending notifications to the consumer. 

This role can enhance energy system efficiency by applying AI optimisation tools on the market and consumer sides and using real-time data from both sides to update the price and consumption schedule continually.

Responding to the dynamic energy price and controlling the devices can be much more accessible by implementing AI applications to control the whole operation and communicate.  

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Figure 4: AI system for load control

AI systems to support demand response operations

Consumer-side AI platform

  • Monitoring consumption patterns on a daily and seasonal basis.
  • Generate the optimum schedule to match consumption and energy prices.
  • Generate control algorithms for automatically controlled devices using IoT techniques.
  • Communicate with the consumer to schedule the uncontrollable loads.
  • Communicate with the grid-side platform to update the consumer-side data for further matching algorithms among consumers.    

Energy market AI platform

  • Collect real-time data from the consumer, grid, and weather to generate the energy price.
  • Forecast data from the different resources to predict the energy price for long-term and short-term plans.

Grid-side AI platform

  • Control the operation of utility power plants for better supply-demand optimisation.
  • Communicate with the consumer side and market platforms for monitoring and regulations.

Bottom line

In summary, we can use AI technology for demand-side response in the following functions:

  1. Create consumption plans and schedules from demand and supply data both at the utility and single consumer levels.
  2. Generate a dynamic tariff, an on-peak, and an off-peak seasonal tariff.
  3. Use IoT to follow the optimum consumption schedule automatically.
  4. Organised scheduling between consumers for a better demand response optimisation plan 

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Dr Samah Hashim
Dr Samah Hashim is a solar energy teacher and researcher. She shares her knowledge by writing articles and producing YouTube videos.

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