Scheduling of domestic water heater power demand for maximizing PV self-consumption using model predictive control

Sossan, Fabrizio; Kosek, Anna Magdalena; Martinenas, Sergejus; Marinelli, Mattia; Bindner, Henrik W.

doi:10.1109/isgteurope.2013.6695317

Cited by 42 publications

(28 citation statements)

References 10 publications

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“…Performance degradation patterns do not have a well identifiable trend. 6 C. Method C The parameters of Method C are input time series sampling time and piecewise constant segment length c (in number of samples). Their influence on the nRMSE is shown in Fig.…”

Section: B Methods Bmentioning

confidence: 99%

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Sossan

Nespoli²,

Medici³

et al. 2018

IEEE Trans. Ind. Inf.

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We consider the problem of estimating the unobserved amount of photovoltaic (PV) generation and demand in a power distribution network starting from measurements of the aggregated power flow at the point of common coupling (PCC) and local global horizontal irradiance (GHI). The estimation principle relies on modeling the PV generation as a function of the measured GHI, enabling the identification of PV production patterns in the aggregated power flow measurements. Four estimation algorithms are proposed: the first assumes that variability in the aggregated PV generation is given by variations of PV generation, the next two use a model of the demand to improve estimation performance, and the fourth assumes that, in a certain frequency range, the aggregated power flow is dominated by PV generation dynamics. These algorithms leverage irradiance transposition models to explore several azimuth/tilt configurations and explain PV generation patterns from multiple plants with non-uniform installation characteristics. Their estimation performance is compared and validated with measurements from a real-life setup including 4 houses with rooftop PV installations and battery systems for PV self-consumption.

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Section: B Methods Bmentioning

confidence: 99%

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Sossan

Nespoli²,

Medici³

et al. 2018

IEEE Trans. Ind. Inf.

Self Cite

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“…Perhaps the most researched control paradigm applied to demand response are model-based approaches, such as Model Predictive Control (MPC) [7], [9], [8]. Most MPC strategies use a gray-box model, based on general expert knowledge of the underlying system dynamics, requiring a system identification step.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice

Ruelens

Claessens

Quaiyum

et al. 2018

IEEE Trans. Smart Grid

128

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Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residential demand response. However, the stochastic and nonlinear dynamics of electric water heaters, makes it challenging to harness their flexibility. Driven by this challenge, this paper formulates the underlying sequential decision-making problem as a Markov decision process and uses techniques from reinforcement learning. Specifically, we apply an auto-encoder network to find a compact feature representation of the sensor measurements, which helps to mitigate the curse of dimensionality. A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation. In a simulation-based experiment using an electric water heater with 50 temperature sensors, the proposed method was able to achieve good policies much faster than when using the full state information. In a lab experiment, we apply fitted Q-iteration to an electric water heater with eight temperature sensors. Further reducing the state vector did not improve the results of fitted Q-iteration. The results of the lab experiment, spanning 40 days, indicate that compared to a thermostat controller, the presented approach was able to reduce the total cost of energy consumption of the electric water heater by 15%.

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“…Electric heating systems, water heaters and refrigeration units are all loads that, although with different levels of flexibility, can be controlled to temporarily defer the consumption thanks to the their thermal mass. Among several algorithms for shifting the consumption of flexible demand, MPC comes to prominence as a method to achieve the non disruptive controllability of individual DSRs through a consumption incentive signal, like for example a dynamic electricity price [21,22] or according to the availability of renewable production, as done in [23,24]. MPC consists in determining the electrical power consumption trajectory of a DSR that minimizes a given penalty function (like the total cost of the operation) while obeying to consumer comfort and operational constraints by implementing a DSR prediction model.…”

Section: Introduction and Objectivementioning

confidence: 99%

Grey-box modelling of a household refrigeration unit using time series data in application to demand side management

Sossan¹,

Lakshmanan²,

Costanzo³

et al. 2016

Sustainable Energy, Grids and Networks

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This paper describes the application of stochastic grey-box modeling to identify electrical power consumption-to-temperature models of a domestic freezer using experimental measurements. The models are formulated using stochastic differential equations (SDEs), estimated by maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model over-fitting. A nonlinear model based on the reversed Carnot cycle is also presented and included in the modeling performance analysis. As an application of the models, we apply model predictive control (MPC) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR).

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Scheduling of domestic water heater power demand for maximizing PV self-consumption using model predictive control

Cited by 42 publications

References 10 publications

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice

Grey-box modelling of a household refrigeration unit using time series data in application to demand side management

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