Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings

Lesage‐Landry, Antoine; Callaway, Duncan S.

doi:10.1109/lcsys.2020.2989110

Cited by 26 publications

(16 citation statements)

References 39 publications

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“…Theoretical work focuses on the control and optimization of flexible consumption; the objective can focus on carbon, electricity costs, or grid stability (or some combination, as in [24]) without significant difference in the theoretical treatment. Example of theoretical frameworks for flexible load include control methods [25] using linear time-invariant control systems [26], a Markov decision process with deferrable loads [27], decentralized control [28], and distributed dynamic online convex optimization [29]. These methodologies are applied to a variety of load types, including electric vehicle charging [30], grid-connected battery storage [31], [32], and thermostats [33]- [35].…”

Section: A Related Researchmentioning

confidence: 99%

Carbon-Aware Computing for Datacenters

Radovanović¹,

Koningstein²,

Schneider³

et al. 2021

Preprint

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The amount of CO2 emitted per kilowatt-hour on an electricity grid varies by time of day and substantially varies by location due to the types of generation. Networked collections of warehouse scale computers, sometimes called Hyperscale Computing, emit more carbon than needed if operated without regard to these variations in carbon intensity. This paper introduces Google's system for Carbon-Intelligent Compute Management, which actively minimizes electricity-based carbon footprint and power infrastructure costs by delaying temporally flexible workloads. The core component of the system is a suite of analytical pipelines used to gather the next day's carbon intensity forecasts, train day-ahead demand prediction models, and use risk-aware optimization to generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all datacenter clusters across Google's fleet. VCCs impose hourly limits on resources available to temporally flexible workloads while preserving overall daily capacity, enabling all such workloads to complete within a day. Data from operation shows that VCCs effectively limit hourly capacity when the grid's energy supply mix is carbon intensive and delay the execution of temporally flexible workloads to "greener" times.

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Section: A Related Researchmentioning

confidence: 99%

Carbon-Aware Computing for Datacenters

Radovanović¹,

Koningstein²,

Schneider³

et al. 2021

Preprint

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“…Example 1 (Power grids). In the context of power grids, (2) may capture a demand response task [13], [14] or a realtime optimal power flow problem [11]. In this case, x in are the power setpoints of distributed energy resources, w t is a vector of powers consumed by uncontrollable loads, and y t may represent the total power as y t = 1 x in + 1 w t and/or voltage magnitudes (one can also compute the matrix A t based on a linearized AC model).…”

Section: Problem Statementmentioning

confidence: 99%

“…The theoretical and algorithmic endeavors are motivated by a number of problems arising in power systems [11], [13], [14], autonomous vehicles [15], [16], charging of electric vehicles [17], and human-aware robot systems [18], just to name a few. While Section II will explain some examples, we stress here that an accurate model of the comfort/satisfaction level of users is typically unknown, and it may vary not only across individuals, but also for the same individual [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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Time-Varying Optimization of Networked Systems with Human Preferences

Ospina¹,

Simonetto²,

Dall’Anese³

2021

Preprint

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This paper considers a time-varying optimization problem associated with a network of systems, with each of the systems shared by (and affecting) a number of individuals. The objective is to minimize cost functions associated with the individuals' preferences, which are unknown, subject to timevarying constraints that capture physical or operational limits of the network. To this end, the paper develops a distributed online optimization algorithm with concurrent learning of the cost functions. The cost functions are learned on-the-fly based on the users' feedback (provided at irregular intervals) by leveraging tools from shape-constrained Gaussian Processes. The online algorithm is based on a primal-dual method, and acts effectively in a closed-loop fashion where: i) users' feedback is utilized to estimate the cost, and ii) measurements from the network are utilized in the algorithmic steps to bypass the need for sensing of (unknown) exogenous inputs of the network. The performance of the algorithm is analyzed in terms of dynamic network regret and constraint violation. Numerical examples are presented in the context of real-time optimization of distributed energy resources.

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“…Distributed convex optimization has received a great deal of interest in recent years due to its widespread practical applications in many fields [1–8]. Compared with centralized optimization, distributed optimization has an essential difference in that the full knowledge of the overall problem structure need not to be known in advance.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive online learning for convex optimization with weight decay

Shen

Fang

et al. 2020

Asian Journal of Control

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This paper investigates an adaptive gradient‐based online convex optimization problem over decentralized networks. The nodes of a network aim to track the minimizer of a global time‐varying convex function, and the communication pattern among nodes is captured as a connected undirected graph. To tackle such optimization problems in a collaborative and distributed manner, a weight decay distributed adaptive online gradient algorithm, called WDDAOG, is firstly proposed, which incorporates distributed optimization methods with adaptive strategies. Then, our theoretical analysis clearly illustrates the difference between weight decay and L2 regularization for distributed adaptive gradient algorithms. The dynamic regret bound for the proposed algorithm is further analyzed. It is shown that the dynamic regret bound for convex functions grows with order of scriptOfalse(nfalse(1+logTfalse)+nTfalse), where T and n represent the time horizon and the number of nodes associated with the network, respectively. Numerical experiments demonstrate that WDDAOG works well in practice and compares favorably to existing distributed online optimization schemes.

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Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings

Cited by 26 publications

References 39 publications

Carbon-Aware Computing for Datacenters

Carbon-Aware Computing for Datacenters

Time-Varying Optimization of Networked Systems with Human Preferences

Distributed adaptive online learning for convex optimization with weight decay

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