2016 Power Systems Computation Conference (PSCC) 2016
DOI: 10.1109/pscc.2016.7540925
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Aggregation of power capabilities of heterogeneous resources for real-time control of power grids

Abstract: Abstract-Aggregation of electric resources is a fundamental function for the operation of power grids at different time scales. In the context of a recently proposed framework for the real-time control of microgrids with explicit power setpoints, we define and formally specify an aggregation method that explicitly accounts for delays and message asynchronism. The method allows to abstract the details of resources using high-level concepts that are device and grid-independent. We demonstrate the application of … Show more

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Cited by 10 publications
(14 citation statements)
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“…However, convex geometric approaches cannot be extended to generate real-time flexibility signals because the approximated sets cannot be decomposed along the time axis. In [11], a belief function of setpoints is introduced for real-time control. However, feasibility can only be guaranteed when each setpoint is in the belief set and this may not be the case for systems with memory.…”
Section: A Contributionsmentioning
confidence: 99%
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“…However, convex geometric approaches cannot be extended to generate real-time flexibility signals because the approximated sets cannot be decomposed along the time axis. In [11], a belief function of setpoints is introduced for real-time control. However, feasibility can only be guaranteed when each setpoint is in the belief set and this may not be the case for systems with memory.…”
Section: A Contributionsmentioning
confidence: 99%
“…To estimate MEF, we need to determine a reward function r(x t , p t ) in (10). We adopt the following reward function that incorporates the constraints and the definition of MEF: r(x t , p t ) =H(p t ) + σ g(x t ; X t , U t ) (11) where the first term is critical and it maximizes the entropy of the probability distribution p t , based the definition of the MEF in Definition III.1; g(x t ) = g(x t , u t ) is a function that rewards the state and action if they satisfy the constraints x t ∈ X t and u t ∈ U t . The reward function is independent of the cost functions, which are synthetic costs in the training stage.…”
Section: B Training Processmentioning
confidence: 99%
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“…e.g., [14], [16]. In this paper, we focus on the disaggregation task, and we consider controllers of the form…”
Section: Pcc Namely Minimizementioning
confidence: 99%
“…Most works focus on aggregation processes for electricity balancing markets in the context of demand-side management and demand response, i.e., aggregation time scales varying from several hours to a few minutes (e.g., [2], [4], [11]- [13]). The aggregation and disaggregation of heterogeneous resources on the second or subsecond timescale was recently proposed in [14]- [16]. Note, however, that the latter papers do not give theoretical guarantees on the tracking properties of the proposed controller.…”
Section: Introductionmentioning
confidence: 99%