4.7 Article

Demand-Side Management for Regulation Service Provisioning Through Internal Pricing

Journal

IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 27, Issue 3, Pages 1531-1539

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2012.2183007

Keywords

Dynamic programming; electricity demand response; electricity markets; electricity regulation service; smart-grid; pricing; welfare maximization

Funding

  1. NSF [EFRI-0735974, EFRI-1038230]
  2. DOE [DE-FG52-06NA27490]
  3. ARO [W911NF-11-1-0227]
  4. ODDR&E MURI10 program [N00014-10-1-0952]
  5. Div Of Electrical, Commun & Cyber Sys
  6. Directorate For Engineering [1239021] Funding Source: National Science Foundation
  7. Div Of Information & Intelligent Systems
  8. Direct For Computer & Info Scie & Enginr [1237022] Funding Source: National Science Foundation

Ask authors/readers for more resources

We develop a market-based mechanism that enables a building smart microgrid operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market independent system operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e. g., a one-hour-long time-scale). During this period the ISO follows shorter time-scale stochastic dynamics to repeatedly request from the SMO to decrease or increase its consumption. We model the operational task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated nonlinear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is shown to be tight and the static policy provides an efficient approximation of the dynamic pricing policy. Equally importantly, this framework allows us to optimize the long time-scale decision of determining the optimal regulation service reserve quantity. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available