|Title||Can Energy Bids from Aggregators Manage Deferrable Demand Efficiently?|
|Publication Type||Conference Paper|
|Year of Publication||2015|
|Authors||Hao Lu, Wooyoung Jeon, Timothy D Mount, Alberto J Lamadrid|
|Conference Name||48th Annual Hawaii International Conference on System Sciences (HICSS)|
|Conference Location||Kauai, HI|
|Keywords||CERTS, deferrable demand, RM07-002|
Our previous research has shown that distributed storage capacity at load centers (e.g. Deferrable demand) controlled by a system operator can lower total system costs by smoothing out and flattening the daily dispatch profile of conventional generating units and providing ramping services. Since it is in reality impractical for system operators to control large numbers of customers with deferrable demand directly, aggregators will in all likelihood be responsible for managing the individual sources of deferrable demand using instructions provided by a system operator. The objective of this paper is to compare the performance of deferrable demand when 1) the aggregators act as clients to the system operator and receive physical charge/discharge instructions for managing deferrable demand (i.e. Centralized control), with 2) the aggregators follow their own interests and submit bids for purchasing energy into the wholesale auction using projected prices provided by the system operator (i.e. Hierarchical control). The analysis uses a stochastic form of multi-period Security Constrained Optimal Power Flow (SCOPF) in a simulation using a reduction of the Northeast Power Coordinating Council (NPCC) network for representative days. This model treats potential wind generation and load as stochastic inputs and determines the optimum daily profiles of dispatch and demand for different realizations of hourly wind generation and load. Ramping capacity is acquired to ensure that transitions from the realizations in one hour to the next hour, as well as contingencies, can be supported. The results show that if aggregators receive stochastic forecasts of energy prices for the next 24 hours, their optimum strategy for minimizing the expected cost of their purchases from the grid is to determine a high threshold price for discharging and a low threshold price for charging, and as a result, they provide ramping services as well as benefitting from day/night price arbitrage. However,- the results are sensitive to the form of the price forecasts.