Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Hughes, Justin T.; Domínguez-García, Alejandro D.; Poolla, Kameshwar

doi:10.24251/hicss.2017.361

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Here, we assume that the future frequency regulation signal can be perfectly predicted within 60 steps (4 minutes). Note, this is a strong assumption that does not hold in practice, since the second-by-second regulation signals are random and are almost impossible to predict [41]. Therefore, the following results indicate the best possible performance of such MPC-based methods.…”

Section: Comparison With Baseline Online Control Algorithmsmentioning

confidence: 96%

“…The first is that most realistic cycle-based degradation functions are not well understood (e.g., they are not known to be convex), making the deterministic version of (2) nontrivial [9]. The second is that in real-time applications such as frequency response, the signal r is inherently random and difficult to forecast [23], [41], while the state of the problem x t is constrained and coupled over time. Therefore, even for relatively simple forms of f (e.g.…”

Section: A Battery Operationsmentioning

confidence: 99%

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Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings

Shi

Tan

et al. 2019

IEEE Trans. Automat. Contr.

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We study the optimal control of battery energy storage under a general "pay-for-performance" setup such as providing frequency regulation and renewable integration. In these settings, batteries need to carefully balance the trade-off between following the instruction signals and their degradation costs in real-time. Existing battery control strategies either do not consider the uncertainty of future signals, or cannot accurately account for battery cycle aging mechanism during operation. In this work, we take a different approach to the optimal battery control problem. Instead of attacking the complexity of battery degradation function or the lack of future information one at a time, we address these two challenges together in a joint fashion. In particular, we present an electrochemically accurate and trackable battery degradation model called the rainflow cyclebased model. We prove the degradation cost is convex. Then we propose an online control policy with a simple threshold structure and show it achieve near-optimal performance with respect to an offline controller that has complete future information. We explicitly characterize the optimality gap and show it is independent to the duration of operation. Simulation results with both synthetic and real regulation traces are conducted to illustrate the theoretical results.

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Section: Comparison With Baseline Online Control Algorithmsmentioning

confidence: 96%

Section: A Battery Operationsmentioning

confidence: 99%

Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings

Shi

Tan

et al. 2019

IEEE Trans. Automat. Contr.

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“…The total regulation that the aggregator provides is the combination of individual regulations of controllable nodes. Therefore, for a specified regulation level x, one would ideally choose the value of g that minimizes the total cost given by (14) respecting the power flow constraints in (5) and the minimum and maximum capacity constraints on each controllable node. Formally, f :…”

Section: Cost Functionmentioning

confidence: 99%

“…[13] provides a dispatch strategy for an aggregate of ON/OFF devices to provide frequency regulation. In [14]- [16], work has been done in the context of microgrids to design mechanisms for optimally allocating a given signal among the DERs within the microgrid. [17] proposes a distributed algorithm to minimize the aggregated cost while satisfying the local constraints and collective demand constraint at the aggregator.…”

Section: Introductionmentioning

confidence: 99%

Participation of Microgrids in Frequency Regulation Markets

Srivastava

Chang

Cortés

2018

2018 Annual American Control Conference (ACC)

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Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this paper, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets. Our approach covers both the identification of bids for the market clearing stage and the mechanisms for the real-time allocation of the regulation signal. The proposed framework is hierarchical, consisting of a top layer and a bottom layer. The top layer consists of the aggregators communicating in a distributed fashion to optimally disaggregate the regulation signal requested by the system operator. The bottom layer consists of the DERs inside each microgrid whose power levels are adjusted so that the tie line power matches the output of the corresponding aggregator in the top layer. The coordination at the top layer requires the knowledge of cost functions, ramp rates and capacity bounds of the aggregators. We develop meaningful abstractions for these quantities respecting the power flow constraints and taking into account the load uncertainties, and propose a provably correct distributed algorithm for optimal disaggregation of regulation signal amongst the microgrids.

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“…On the contrary, in the regulation market, PV farm owners should follow unpredictable regulation signals by alternating quickly between increasing or decreasing outputs. If they do not comply with the signal, they have to pay penalties on the non-compliant amounts [3]. Energy storage systems (ESS)s enable them to alternate quickly between charging or discharging [4].…”

Section: Introductionmentioning

confidence: 99%

MPC-Based Optimal Operation for a PV Farm With Dual ESSs Using Spectral Density Analysis of Market Signals

et al. 2020

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We propose a model predictive control-based optimal offer and operation strategy for a photovoltaic (PV) farm consisting of PV panels and dual energy storage systems (ESS)s to maximize profits in the energy and regulation markets. Although a PV farm owner can better respond to regulation signals with an ESS, it cannot continuously respond to unidirectional regulation signals since the ESS is limited in size. Furthermore, the lifespan of the ESS might be reduced by alternating between charging and discharging because of regulation signals that fluctuate often. To improve the response, we use dual ESSs with separate signals: one small, fast ESS to respond to fluctuations, and one large, slow ESS to respond to unidirectional signals. We decompose the regulation signal into two signals: one for fast and one for slow frequencies based on power spectral density (PSD) analysis. We determine the optimal operation for dual ESSs to maximize the profit through a closed-loop model predictive control (MPC). We can also increase the lifespan of each ESS by limiting their state of charge (SOC) levels through the optimization constraints obtained from the PSD analysis so that the dual ESSs can operate a larger number of cycles. We verify our approach by adjusting the day-ahead (DA) market schedule in the real-time (RT) using the most recently predicted signals and errors between the DA and RT market situations. We show that our strategy incurs lower penalties by tracking the actual regulation signal in the RT market better than conventional approaches based on open-loop controls.

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Coordinating Heterogeneous Distributed Energy Resources for Provision of Frequency Regulation Services

Cited by 6 publications

References 16 publications

Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings

Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings

Participation of Microgrids in Frequency Regulation Markets

MPC-Based Optimal Operation for a PV Farm With Dual ESSs Using Spectral Density Analysis of Market Signals

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