In electricity markets, supply and demand have to be balanced perfectly in real time. A major task of the Independent Systems Operator (ISO)2 on the wholesale (transmission) level and of the Distribution Utility (DU) on the regional (distribution) level is to monitor the grid and to maintain balance while keeping voltage, frequency, and power factor within very tight bounds. This task gets more challenging as more renewable energy sources, such as solar and wind, are connected to the grid [2]. Many of these sources (e.g. wind) are only partially predictable. The grid balancing problem has been studied on various levels (wholesale vs. retail) and with different approaches [1]. We study the problem of how to determine the price that energy brokers (retailers) need to pay in case their profile of consumption and production of electricity is imbalanced in the current time slot.3 In a retail electricity market, brokers have the possibility to control some portion of customer production and consumption, such as by offering price concessions in exchange for the ability to remotely interrupt loads or sources for limited periods of time. Examples of such controllable loads are remotely controlled CHPs (Combined Heat and Power - gas turbines that supply both heat and power, and allow some control of production) and domestic water heaters that can be remotely turned off to temporarily reduce consumption. We present two different scenarios and the related market mechanisms to balance supply and demand: I) without controllable loads, and II) with controllable loads for the current time slot. Our main mechanisms for a regional real-time balancing market are efficient, and create an incentive to resolve imbalances in the day-ahead market. An advantage of our methods is that they are compatible with existing energy market structures (keeping the day-ahead market as a given), and therefore the required restructuring would be less disruptive than with other methods currently suggested.
23rd Benelux Conference on Artificial Intelligence, BNAIC 2011
Erasmus University Rotterdam

de Weerdt, M., Ketter, W., & Collins, J. (2011). Pricing mechanism for real-time balancing in regional electricity markets. Presented at the 23rd Benelux Conference on Artificial Intelligence, BNAIC 2011. Retrieved from