Robust Energy-Constrained Frequency Reserves From Aggregations of Commercial Buildings

Vrettos, Evangelos; Oldewurtel, Frauke; Andersson, Göran

doi:10.1109/tpwrs.2015.2511541

Cited by 108 publications

(103 citation statements)

References 42 publications

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“…Therefore, the existence of the affine state feedback policy guarantees the existence of a feedback policy (13) with the uncertainty approximation (11) and (12). Hence, the RMPC-AU (14), (6)- (8), which is subject to the feedback policy (13), is recursively feasible.…”

Section: B Terminal Set Approximationmentioning

confidence: 99%

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Robust Model Predictive Control with Adjustable Uncertainty Sets

Kim

Zhang

Guanetti

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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In this paper, we present Robust Model Predictive Control (MPC) problems with adjustable uncertainty sets. In contrast to standard Robust MPC problems with known uncertainty sets, we treat the uncertainty sets in our problems as additional decision variables. In particular, given a metric for adjusting the uncertainty sets, we address the question of determining the optimal size and shape of those uncertainty sets, while ensuring robust constraint satisfaction. The focus of this paper is on ensuring constraint satisfaction over an infinite horizon, also known as persistent feasibility. We show that, similar as in standard Robust MPC, persistent feasibility can be guaranteed if the terminal set is an invariant set with respect to both the state of the system and the adjustable uncertainty set. We also present an algorithm for computing such invariant sets, and illustrate the effectiveness of our approach in a cooperative adaptive cruise control application.

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Section: B Terminal Set Approximationmentioning

confidence: 99%

“…Remark 1. If the uncertainty set (11) is assumed to always include {0}, then Theorem 1 can guarantee recursive feasibility of the reformulated RMPC-AU (14),(6)- (8), at least with W k|t = {0} for k = 0, . .…”

Section: B Terminal Set Approximationmentioning

confidence: 99%

Robust Model Predictive Control with Adjustable Uncertainty Sets

Kim

Zhang

Guanetti

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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“…We utilize "Normalized Dynamic and Traditional Regulation Signals" from PJM for the period January [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]2013. This data is available at [23], and includes two regulation signals-RegD, a fast response signal, and RegA, a filtered version of RegD for slower ramping generators; both signals are updated every 2 s. Figure 1 shows a representative segment of the aforementioned data.…”

Section: Datasetmentioning

confidence: 99%

“…The authors of [15] and [16] propose an ambitious multi-level control framework for coordinating aggregations of commercial buildings. The nicely integrated result considers the interaction between the aggregator and the system operator/reserves market as well as the internal control of a single type of DER.…”

mentioning

confidence: 99%

Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Hughes

Domínguez-García

Poolla

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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We discuss a framework for coordinating the response of distributed energy resources (DERs) connected to electric power distribution networks to provide frequency regulation services. These resources include plug-in electric vehicles, thermostatically controlled loads, and microturbines. In this framework, we consider an aggregator that participates in the real-time market by submitting an offer to provide frequency regulation services. If the offer is accepted, the aggregator needs to coordinate the response of a set of DERs. The DERs are compensated through bilateral contracts, the terms of which are negotiated in advance. The DER coordination problem the aggregator is faced with is cast as an optimal control problem, and we propose a bilayer framework to obtain a sub-optimal solution. In the first layer, we utilize model-predictive control techniques driven by regulation signal forecasts and parameter estimates to obtain a reference control signal for the DERs. A second control layer provides closed-loop regulation around the reference computed by the top layer, which minimizes the error that arises due to forecast error, plant-model mismatch, and the slower speed of the optimal control.

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“…In [12] [13], robust optimization models are developed for estimating and optimally scheduling the aggregate reserve capacities considering the uncertainties of regulation signals and forecast errors.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Power Flexibility in Unbalanced Distribution Systems

Chen

Dall’Anese

Zhao

et al. 2020

IEEE Trans. Smart Grid

113

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With a large-scale integration of distributed energy resources (DERs), distribution systems are expected to be capable of providing capacity support for the transmission grid. To effectively harness the collective flexibility from massive DER devices, this paper studies distribution-level power aggregation strategies for transmission-distribution interaction. In particular, this paper proposes a method to model and quantify the aggregate power flexibility, i.e., the net power injection achievable at the substation, in unbalanced distribution systems over time. Incorporating the network constraints and multi-phase unbalanced modeling, the proposed method obtains an effective approximate feasible region of the net power injection. For any aggregate power trajectory within this region, it is proved that there exists a feasible disaggregation solution. In addition, a distributed model predictive control (MPC) framework is developed for practical implementation of the transmission-distribution interaction. At last, we demonstrate the performances of the proposed method via numerical tests on a real-world distribution feeder with 126 multi-phase nodes.Index Terms-Power aggregation, distributed energy resources, unbalanced optimal power flow, power flexibility, distributed optimization. NOMENCLATUREA. Parameters N Number of buses (except the substation). N Y , N ∆ Number of buses with wye-, delta-connection. L Number of distribution lines. v, v Upper, lower limits of the three-phase nodal voltage magnitudes for all buses. i L , i L Upper, lower limits of the three-phase line current magnitudes for all distribution lines. P g,ψ i,t , P g,ψ i,t Upper, lower limits of active PV power generation in phase ψ of bus i at time t. S g,ψ i,t Apparent power capacity of PV units in phase ψ of bus i at time t. P e,ψ i,t , P e,ψ i,t Upper, lower limits of active power output of ES devices in phase ψ of bus i at time t. S e,ψ i,t Apparent power capacity of ES devices in phase ψ of bus i at time t. E i , E i Upper, lower limits for state of charge of ES devices at bus i. X. Chen and N. Li are with the School of Engineering and Applied Sciences, Harvard University, USA. d,ψ i,t , P d,ψ i,t Upper, lower limits for controllable active loads in phase ψ of bus i at time t. F out i,t Outside temperature for HVAC systems at bus i at time t. F i , F i Upper, lower limits of comfortable temperature zone for HVAC systems at bus i. ∆t Length of each time slot under discretized time horizon. B. Variables p Y , q Y ∈ R 3N Y Column vector of the three-phase active, reactive power injection via wye-connection. p ∆ , q ∆ ∈ R 3N∆ Column vector of the three-phase active, reactive power injection via delta-connection. v ∈ R 3N Column vector collecting the three-phase nodal voltage magnitudes for all buses. i L ∈ R 3L Column vector collecting the three-phase line current magnitudes for all distribution lines. p 0 ∈ R 3 Column vector of the three-phase net active power injection at the substation. P g,ψ i,t , Q g,ψ i,t Active, reactive PV power generation ...

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Robust Energy-Constrained Frequency Reserves From Aggregations of Commercial Buildings

Cited by 108 publications

References 42 publications

Robust Model Predictive Control with Adjustable Uncertainty Sets

Robust Model Predictive Control with Adjustable Uncertainty Sets

Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Aggregate Power Flexibility in Unbalanced Distribution Systems

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