A model predictive control approach for demand side management of residential power to heat technologies

Krämer, M.; Jambagi, Akhila; Cheng, Vicky

doi:10.1109/energycon.2016.7513903

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…The heating system is a good candidate to allow the load management [34,35,36], because of its ability to use the walls as storage, thanks to their inertia. In this study, the building is considered as a controllable load, in other case studies, it could be modelled as loads or storage with heat losses.…”

Section: State-space Of the Controllable Loadsmentioning

confidence: 99%

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Blaud

Haurant

Claveau

et al. 2020

International Journal of Electrical Power & Energy Systems

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The present study deals with Multi-Energy Systems (MES) modelling and advanced control with Economic Model Predictive Control (EMPC). MES provide energy flexibility, efficiency, and adaptability thanks to several energy carriers. MES are identified as a lever for integrating renewable energy. A MES novel formulation technique called Multi-Prosumer Node (MPN) is developed in this paper. MPN makes possible the modeling of MES, considering MES dynamics, several energy carriers, converters, on-grid, and off-grid. In addition, this MES modeling approach is compatible with predictive control strategies like the EMPC. In fact, EMPC is able to take into account loads, weather, renewable power and energy grid cost predictions to minimise economic costs. A real case study is implemented to examine MPN capabilities, which it is composed of renewable generators, loads, storages from two-energy carriers. Two real scenarios have been developed in order to represent realistic winter and summer cases. Simulation results, thanks to modelling with MPN and EMPC advanced control, demonstrate that the node is optimally controlled, devices dynamics are considered on a minute scale, and energy conversion from one carrier to another one is taken into account while economic cost minimisation is performed. The gained results indicate that the presented MPN modelling and optimisation approach reduces economic cost by 8.21% in winter case and 84.24% in summer case compared to the benchmarks which are composed of rule-based control.

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Section: State-space Of the Controllable Loadsmentioning

confidence: 99%

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Blaud

Haurant

Claveau

et al. 2020

International Journal of Electrical Power & Energy Systems

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“…The zone temperature 𝑇 𝑧 must stay within the range between 19 °C and 21 °C, which is represented by an inequality constraint. The complete mathematical formulation is detailed in Kramer et al (2016).…”

Section: Building Parameter Set and State Space Modelmentioning

confidence: 99%

Bottom-up Modeling of Residential Heating Systems for Demand Side Management in District Energy System Analysis and Distribution Grid Planning

Krämer¹,

Jambagi²,

Cheng³

2017

Building Simulation Conference Proceedings

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The growing share of intermittent renewable energy sources together with the upcoming electrification of heating and cooling in European countries results in an increasing demand for operational flexibility in the energy system. Demand side management (DSM) of electric loads offers operational strategies to enable the provision of flexibility in demand of previously fixed loads. Residential electro-thermal heating systems are promising candidates to better utilize excess electricity generation from renewable energy sources, but may also add additional stress to existing grid infrastructure during peak load times. This paper introduces a bottom-up model to represent residential buildings in a district energy system. The operational behaviour of a model predictive control strategy is demonstrated, and its impact on the aggregate load curve is shown. The focus is on electrothermal heating systems like heat pumps in Germany.

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“…In recent years, the application of MPC in the field of power systems and smart grids is increasing as it allows problem‐specific formulation of objective functions and combination of conflicting objectives, continuous update of system constraints, the consideration of forecasts, and the loopback principle, which results in high performance and functionality of the control method. Several concepts for centralized, decentralized, and distributed MPC have been presented in literature . The advantage of centralized methods is the performance in terms of optimality.…”

Section: Introductionmentioning

confidence: 99%

Distributed model predictive control strategies for coordination of electro‐thermal devices in a cooperative energy management concept

Stoyanova

Gümrükcü

Aragón

et al. 2019

Optim Control Appl Methods

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Summary This work discusses three promising strategies for the compensation of deviations within the online phase of a cooperative energy management concept. Whereas in the planning phase, hourly schedules for the active electro‐thermal devices are negotiated on a daily basis, the second phase tracks the schedule, including forecast updates, and compensates the deviations. The latter online compensation is realized with a distributed and hierarchical‐distributed model predictive control strategies, which are compared with continuous rescheduling with receding horizon and evaluated in terms of computational and optimization performance and scalability. The optimization is implemented and deployed using an open‐source optimization framework, which automatically manages optimization control, integration of renewable generation and load forecasts, multi‐instantiation and communication over an internet of things communication protocol. We address the case of significant deviations from the day‐ahead forecast and the tracking of an obsolete schedule and suggest a combined method, which combines the advantages of rescheduling and a distributed MPC strategy. The method applies a dynamic threshold to evaluate the deviation trend over time and to decide if compensation is still feasible or if a rescheduling should be triggered. The methods are evaluated based on several performance indicators such as residual deviation, number of switching events and computation and communication requirements and scalability, and offer a methodology for the control design of systems of different dimensions.

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A model predictive control approach for demand side management of residential power to heat technologies

Cited by 10 publications

References 16 publications

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Bottom-up Modeling of Residential Heating Systems for Demand Side Management in District Energy System Analysis and Distribution Grid Planning

Distributed model predictive control strategies for coordination of electro‐thermal devices in a cooperative energy management concept

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