North America's electricity systems are often administered by an independent body (an administrative and legal entity) which oversees the price setting and price discovery process, and facilitates market's tendency towards an equilibrium (for the sake of system's stability). Thus, fluctuating prices, act as signals to producers. In other words upward sloping demand should motivate higher prices, which in turn should motivate more producers to enter the market and meet the imminent market demand.
Ontario's electricity market is an example. Ontario's Independent Electricity System Operator - IESO for short - records Ontario's power data, and is committed to keeping this data available for download for the public (on ieso.ca website).
Price variability is a feature of North America's electricity markets. As demand fluctuates, prices adjust in order to provide adequate incentive for various electricity producers to enter and leave the market. This continuous adjustment takes place in order to maintain supply and demand in the province fairly close to equilibrium.During peak hours demand rises sharply, and prices adjust accordingly. Price spikes, however, are short lived and rare occurrences that are essentially different from normal price fluctuations during peak hours. Weather abnormalities and extreme temperatures are thought to be one of many factors which can increase the likelihood of price spikes. Forecasting price spikes followed by demand management (although difficult) can help with stabilizing the system. The benefits associated with this kind of demand management are reduced electricity bill for consumers, as well as reduced pressure on the system and reduced likelihood of outages.
Technological advences in the realm of micro-controllers have made this form of demand management readily available. Modern Micro-controller Units (MCUs) can easily connect to the necessary information infrastructure (Internet of Things or IoT) and perform impressive tasks. Utilizing Machine Learning algorithms in order to learn and predict price spikes should also be viable.
The right combination of MCU, ML model, and adequate IoT infrastructure can facilitate demand management. Couple this cocktail of equipment and information technology with an energy storage unit, and it would be possible to further reduce stress on the electricity system. Consumers equiped with this cocktail of technology and equipment can but electricity when there is surplus in the system and disconnect from the system when there is deficit. This form of active demand management would have seemed impossible prior to IoT era. In other words, absence of information and communication technology (ICT) infrastructure meant there was no means of managing the information transmition mechanism. Modern MCUs and IoT have been crucial developments in this realm.
Readily available MCUs - such as ESP32 or Raspberry Pi - which could "read" real-time prices from ieso website and execute ML models in real-time could act as information transmission mechansims between the market and end users of energy.