Generation bidding game with potentially false attestation of flexible demand

With the onset of large numbers of energy-flexible appliances, in particular plug-in electric and hybrid-electric vehicles, a significant portion of electricity demand will be somewhat flexible and accordingly may be responsive to changes in electricity prices. In the future, this increased degree o...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2015-03, Vol.2015 (1), p.1-11, Article 29
Main Authors: Shan, Yuquan, Raghuram, Jayaram, Kesidis, George, Miller, David J, Scaglione, Anna, Rowe, Jeff, Levitt, Karl
Format: Article
Language:English
Subjects:
Advanced signal processing techniques and telecommunications network infrastructures for Smart Grid analysis
Demand
Electricity
Electricity consumption
Electricity pricing
Engineering
Generators
Hybrid vehicles
Markets
Mathematical models
monitoring and management
Quantum Information Technology
Revenues
Signal,Image and Speech Processing
Spintronics
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Summary:With the onset of large numbers of energy-flexible appliances, in particular plug-in electric and hybrid-electric vehicles, a significant portion of electricity demand will be somewhat flexible and accordingly may be responsive to changes in electricity prices. In the future, this increased degree of demand flexibility (and the onset of only short-term predictable intermittent renewable supply) will considerably exceed present level of uncertainty in day-ahead prediction of assumed inelastic demand. For such a responsive demand idealized, we consider a deregulated wholesale day-ahead electricity marketplace wherein bids by generators (or energy traders) are determined through a Nash equilibrium via a common clearing price (i.e., no location marginality). This model assumes the independent system operator (ISO) helps the generators to understand how to change their bids to improve their net revenue based on a model of demand-response. The model of demand-response (equivalently, demand-side bidding day ahead) is based on information from load-serving entities regarding their price-flexible demand. We numerically explore how collusion between generators and loads can manipulate this market. The objective is to learn how to deter such collusion, e.g., how to set penalties for significant differences between stated and actual demand, resulting in higher energy prices that benefit certain generators.
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-015-0212-3