2016 Optimization Days

HEC Montréal, Québec, Canada, May 2 — 4, 2016

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TA8 Energy Storage and Demand Response for Smart Grids

May 3, 2016 10:30 AM – 12:10 PM

Location: St-Hubert

Chaired by Miguel F. Anjos

4 Presentations

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    10:30 AM - 10:55 AM

    Unsupervised learning based on Markov chain modeling of hot water demand processes

    • Shu Fan, presenter, Polytechnique Montréal
    • Roland Malhamé, GERAD - Polytechnique Montréal
    • Vahid Partovi Nia, Polytechnique Montréal

    Electric water heating loads constitute natural targets within demand side management programs. Adequately clustered hot water stochastic demand models across the power system are needed to develop control policies meeting diversified customer needs. Unsupervised learning is explored on measurement based two state Markov chain models of hot water extraction processes.

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    10:55 AM - 11:20 AM

    Power capacity profile estimation for smart buildings

    • Juan Gomez, presenter, Polytechnique Montréal
    • Miguel F. Anjos, GERAD, Polytechnique Montréal

    This approach builds power capacity profiles for residential units in a smart grid. It uses the concepts of total demand, met demand and level of service in a demand response context. A combination of a least-squares problem and heuristic methods is used to compute the expected required power capacity profile.

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    11:20 AM - 11:45 AM

    Logic constrained equilibria with applications to transportation and power systems with storage

    • Franklin Djeumou Fomeni, presenter, GERAD, Polytechnique Montréal

    In this research we present a study of the equilibria in traffic networks and power system networks with storage in the presence of logic constraints. The latter constraints are modelled as binary variables and are added to standard equilibrium models.

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    11:45 AM - 12:10 PM

    Handling dynamic constraints in power system optimization

    • François Gilbert, presenter, Argonne

    The inclusion of dynamic stability constraints is the nominal objective of many optimization-based power systems analyses. In current practice, this is typically done off-line. We present an approach for the on-line inclusion of dynamic constraints in power grid optimization problems. The approach is based on an encapsulation that allows for a loose coupling between the optimization and the numerical simulations. We demonstrate the benefits of the approach on a 118 bus systems, for which we solve an economic dispatch with transient constraints.

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