%0 Journal Article
%J IEEE Transactions on Power Systems
%D 2015
%T Capacity Controlled Demand Side Management: A Stochastic Pricing Analysis
%A Margellos, Kostas
%A Shmuel S. Oren
%K CERTS
%K demand-side management
%K reliability and markets
%K RM12-001
%X We consider a novel paradigm for demand side management, assuming that an aggregator communicates with a household only at the meter, imposing a capacity constraint, i.e., a restriction on the total power consumption level within a given time frame. Consumers are then responsible to adjust the set-points of the individual household devices accordingly to meet the imposed constraint. We formulate the problem as a stochastic household energy management program, with stochasticity arising due to local photovoltaic generation. We show how a demand bidding curve for capacity increments can be constructed as a by-product of the developed problem and provide a rigorous pricing analysis that results in a probabilistic “shadow” price envelope. To evaluate the efficacy of the proposed approach, we compare it with an idealized real-time market price set-up and show how our analysis can provide guidelines to consumers when selecting a service contract for load curtailment.
%B IEEE Transactions on Power Systems
%P 1 - 12
%8 03/2015
%! IEEE Trans. Power Syst.
%R 10.1109/TPWRS.2015.2406813
%0 Journal Article
%J IEEE Transactions on Power Systems
%D 2010
%T Co-Optimization of Generation Unit Commitment and Transmission Switching With N-1 Reliability
%A Kory W. Hedman
%A Ferris, Michael C.
%A Richard P. O'Neill
%A Emily Bartholome Fisher
%A Shmuel S. Oren
%K reliability and markets
%K RM08-001
%X Currently, there is a national push for a smarter electric grid, one that is more controllable and flexible. The full control of transmission assets are not currently built into electric network optimization models. Optimal transmission switching is a straightforward way to leverage grid controllability: to make better use of the existing system and meet growing demand with existing infrastructure. Previous papers have shown that optimizing the network topology improves the dispatch of electrical networks. Such optimal topology dispatch can be categorized as a smart grid application where there is a co-optimization of both generators and transmission topology. In this paper we present a co-optimization formulation of the generation unit commitment and transmission switching problem while ensuring N-1 reliability. We show that the optimal topology of the network can vary from hour to hour. We also show that optimizing the topology can change the optimal unit commitment schedule. This problem is large and computationally complex even for medium sized systems. We present decomposition and computational approaches to solving this problem. Results are presented for the IEEE RTS 96 test case.
%B IEEE Transactions on Power Systems
%V 25
%P 1052 - 1063
%8 05/2010
%N 2
%! IEEE Trans. Power Syst.
%R 10.1109/TPWRS.2009.2037232
%0 Conference Paper
%B 2008 IEEE Energy 2030 Conference (Energy)
%D 2008
%T Coupling Wind Generators with Deferrable Loads
%A Anthony Papavasiliou
%A Shmuel S. Oren
%K deferrable loads
%K electricity markets
%K reliability and markets
%K renewables integration
%K RM10-001
%X We explore the possibility of directly coupling deferrable loads with wind generators in order to mitigate the variability and randomness of wind power generation. Loads engage in a contractual agreement of deferring their demand for power by a fixed amount of time and wind generators optimally allocate available wind power with the objective of minimizing the cost of unscheduled and variable supply. We simulate the performance of the proposed coupling in a market environment and we demonstrate its compatibility with existing technology, grid operations and economic incentives. The results indicate that the combination of existing deregulated power markets and demand side flexibility could support large scale integration of wind power without significant impacts on grid operations and without the requirement for prohibitive investments in backup generation.

%B 2008 IEEE Energy 2030 Conference (Energy)
%I IEEE
%C Atlanta, GA, USA
%P 1 - 7
%8 11/2008
%@ 978-1-4244-2850-2
%R 10.1109/ENERGY.2008.4781058