Stochastic control of cooling appliances under disturbances for primary frequency reserves

Borsche, Theodor; Santiago, Juan de; Andersson, Göran

doi:10.1016/j.segan.2016.06.001

Cited by 11 publications

(5 citation statements)

References 29 publications

(57 reference statements)

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“…The increased reliance on renewable generation [15] and unreliabilities resulting from a rapid drive towards a smart grid [16] are increasing the demand for PFR, which has traditionally been provided by fossil fuel-based solutions [17]. There is a growing volume of research on solutions for providing PFR with distributed intelligent energy resources such as electric vehicles [18,19], domestic loads [20,21] and industrial processes [22]. The increased penetration of renewables is driving investments to battery-provided PFR [23].…”

Section: Batteries In Primary Frequency Reservesmentioning

confidence: 99%

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

et al. 2021

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Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances. In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets.

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Section: Batteries In Primary Frequency Reservesmentioning

confidence: 99%

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

et al. 2021

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“…Here, the authors use an online recursive identification algorithm that captures the predominant dynamics and disturbance patterns of the refrigerator based on that proposed in [21]. The model (1) is widely used in the refrigerator control literature, including [8][9][10][11]18,[22][23][24]:…”

Section: Real-time Identification Of Refrigerator Dynamicsmentioning

confidence: 99%

“…Following one of the first studies on modelling and control of TCLs in the 1980s [6,7], a number of more recent investigations have set out to model refrigerator populations, with authors developing 2 of 20 models for large aggregated networks of TCLs and the impact of cooling appliances on the grid frequency. In reference [8], the thermal storage of domestic refrigerators is used to facilitate improved power balancing, whilst [9][10][11][12] propose a decentralized stochastic controller for the aggregated use of refrigerators to respond to mains frequency fluctuations. The use of food retailing refrigeration systems for a large supermarket chain to contribute to firm frequency response (FFR) and demand side response (DSR) is presented in [13].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators

Sabegh

Bingham

2019

Energies

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The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to schedule the operation of widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, the paper proposes the real-time estimation of the thermal mass of each refrigerator in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands commensurate with accommodating food safety standards. A custom model predictive control (MPC) scheme is devised using binary quadratic programming to realize the scheduling methodology which is implemented through IoT hardware (based on a NodeMCU). Benefits afforded by the proposed scheme are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can i) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e., peak load levelling), ii) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and iii) contribute to a widely distributed aggregated load shed for demand side response purposes in order to aid grid stability. Importantly, the number of compressor starts per hour for each refrigerator is also bounded as an inherent design feature of the algorithm so as not to operationally overstress the compressors and reduce their lifetime. Experimental trials show that such co-ordinated operation of refrigerators can reduce energy consumption by ~30% whilst also providing peak load levelling, thereby affording benefits to both individual consumers as well as electrical network suppliers.

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“…Apart from undesirable frequency control activation TCLs can advance further instability and system failure. Nevertheless, the utility of TCLs continues to be the focus of research when analysing the potential contribution for short-term modulation in decentralised control flexible demand response scenarios [23,[32][33][34].…”

Section: State-of-the-artmentioning

confidence: 99%

On the use of thermal inertia in building stock to leverage decentralised demand side frequency regulation services

Williams

Short

Crosbie

2018

Applied Thermal Engineering

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Most governments are applying financial instruments and other polices to encourage distributed renewable electricity generation (DREG). DREG is less predictable and more volatile than traditional forms of energy generation. Closure of larger fossil-fuelled power plants and rising share of DREG is reducing system inertia on energy networks such that new methods of demand response are required. Usually participation in non-dynamic frequency response is reactive, affecting the duty cycle of thermostatically controlled loads. However, this can adversely affect building thermal efficiency. The research presented takes a proactive approach to demand response employing heat transfer dynamics. Here, thermal dynamics exhibit a significantly larger inertia than electrical power consumption. Thus, short-term fluctuations in energy use should have less effect on temperature regulation and user comfort in buildings than existing balancing services. A prototype frequency sensor and control unit for proactive demand response in building stock is developed. The paper reports on hardware-in-the-loop simulations, testing real thermal loads within a simulated power network. The instrumented approach adopted enables accurate real-time electrical frequency measurement, while the control method offers effective demand response, which suggest the feasibility of using decentralised frequency control regulation as a novel approach to existing demand response mechanisms. Keywords Frequency regulation; decentralised control; demand response. Nomenclature transport delay, s load damping constant, s Δ frequency deviation, Hz inertia time constant, s TCL controller integral gain ALFC secondary loop gain, p.u. MW/Hz s ℎ thermal load gain TCL controller proportional gain Δ step change in power demand, p.u. Δ change in hydraulic amplifier output Δ ℎ electrical power deviation, p.u. Δ change in turbine power output regulator, Hz/p.u. MW Δ temperature deviation, p.u. governor time constant, s ℎ thermal load time constant, s turbine time constant, s ̅ sample mean number of entries sample standard deviation standard error standardized test statistic

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Stochastic control of cooling appliances under disturbances for primary frequency reserves

Cited by 11 publications

References 29 publications

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators

On the use of thermal inertia in building stock to leverage decentralised demand side frequency regulation services

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