Understanding time-of-use pricing and carbon intensity

Understanding hourly energy costs and carbon intensity can help companies optimise their power use, reducing both bills and carbon emissions. Time-of-use pricing varies depending on when it is consumed, with off-peak hours the cheapest and peak hours the most expensive. Carbon intensity refers to actions to reduce grid carbon intensity by shifting from peak to off-peak hours with lower demand. 

There are several different tools and data sources for monitoring intensity, which can help to provide actionable load-shifting and flexibility tactics, making a real contribution to the energy transition. Once an understanding of how each affects the other is established, price and carbon data can be crucial joint signals for smart operations.

How time-of-use tariffs work

To encourage customers to shift away from peak-time energy use, time-of-use tariffs can offer pricing incentives for off-peak usage. 

Static vs. dynamic pricing models

Because renewable energy is a volatile source, such as solar and wind, time-of-use tariffs can help balance energy usage during peak times. Two key types of time-of-use tariffs exist: static pricing, which is more predetermined and therefore more stable, and dynamic pricing, which can change every 30-60 minutes, making it more volatile but more susceptible to lower prices when pricing drops due to its agility.

Half-hourly settlement and cost components

Half-hourly settlements are a more modern way of measuring and billing energy, giving customers a breakdown of when their energy consumption was at the highest price, so they can adjust their consumption habits to lower their energy bills. Added cost components include standing charges, energy consumption charges and specific charges such as electric vehicle costs.

The link between price and carbon intensity

As the renewable mix has increased, the relationship between price and carbon intensity has evolved. 

Correlation: when renewables dominate, prices and carbon diverge

Historically, the cost of energy and carbon intensity were closely linked, as high demand meant more fossil fuels were burned and more carbon was emitted. However, this link is changing as renewable energy is rolled out. This is due to factors such as weather events, for example, high winds, that increase the amount of wind-related renewable energy generated but not the carbon intensity. Including battery storage can further improve this, because energy banked can then be used at a later date when wind energy is less plentiful. 

Visualising daily patterns using intensity maps

One method of tracking emissions levels and the impact energy consumption has on them is to use intensity maps, which can visually isolate location and day (down to the hour) data on energy usage. Analysts can drill down using specialised carbon-intensity maps and APIs.

Identifying optimal load-shifting opportunities

Certain types of machinery use large amounts of energy, and it’s often not possible to reduce the energy required for their everyday operations; therefore, different energy-efficiency approaches are needed. One of these is load shifting, which shifts the operation of this activity from peak to off-peak, reducing grid intensity. The types of high-energy activities that can benefit from load shifting include refrigeration, HVAC, EV charging, and water pumping, which require a high input of energy to operate. 

Since pricing is variable, it can be helpful to estimate savings from shifting consumption windows using tools such as smart meters, which can accurately track the cost of energy shifted to the closest half-hour. 

Technologies that enable flexible consumption

Monitoring and analysing data is a crucial tool for enabling flexible energy consumption.

Smart meters, BMS, IoT sensors, automation platforms

Smart meters allow energy consumers to track when energy pricing and carbon intensity are at their highest, with granularity down to half-hour increments. The Internet of Things (IoT) takes this a step further for the manufacturing industry, deploying numerous tiny sensors across machinery to measure energy consumption in relation to operations. Automation platforms can use this gathered data to automate high-energy activities, so they occur during off-peak times. Having a holistic overview of these factors can help businesses reduce carbon emissions, achieve Environmental, Social and Governance (ESG) goals, meet carbon-reduction targets, and save money. In virtual power plants, aggregated flexibility uses software to create a single power plant from multiple resources, including electric vehicles and distributed energy resources (DERs) such as solar panels and battery storage.

Incorporating time-of-use data into procurement decisions

Data is the linchpin of many facets of the energy transition, and energy procurement is no different. Reporting and performance dashboards can visualise demand and supply, and how their movement affects time-of-use pricing, whilst planning, such as scheduling non-urgent production during off-peak periods, can help bring costs down. Contract structures and operations must be aligned, with tiered pricing structures that reflect peak and off-peak energy pricing. In an evolving world where flexibility is key, particularly in an industry dictated by unpredictable weather patterns, time-of-use data enables procurement to meet operations, making flexibility the new frontier for low-cost, low-carbon energy.