Model Predictive Control for Market-Based Demand Response Participation

Qureshi, Faran A.; Gorecki, Tomasz T.; Jones, Colin N.

doi:10.3182/20140824-6-za-1003.02395

Cited by 20 publications

(14 citation statements)

References 7 publications

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“…Paulus and Borggrefe (2011) establish that demand side management bears considerable monetary potential in energy intensive industries. Qureshi et al (2014) develop a model to investigate economic potential of demand side management in office buildings. Ashok and Banerjee (2000) pioneer the field of industrial demand side management.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive modelling framework for demand side flexibility in smart grids

et al. 2017

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The increasing share of renewable energy generation in the electricity system comes with significant challenges, such as the volatility of renewable energy sources. To tackle those challenges, demand side management is a frequently mentioned remedy. However, measures of demand side management need a high level of flexibility to be successful. Although extensive research exists that describes, models and optimises various processes with flexible electrical demands, there is no unified notation. Additionally, most descriptions are very process-specific and cannot be generalised.In this paper, we develop a comprehensive modelling framework to mathematically describe demand side flexibility in smart grids while integrating a majority of constraints from different existing models. We provide a universally applicable modelling framework for demand side flexibility and evaluate its practicality by looking at how well Mixed-Integer Linear Program (MIP) solvers are able to optimise the resulting models, if applied to artificially generated instances. From the evaluation, we derive that our model improves the performance of previous models while integrating additional flexibility characteristics.

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Section: Related Workmentioning

confidence: 99%

A comprehensive modelling framework for demand side flexibility in smart grids

et al. 2017

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“…Objectives such as minimum energy use and minimum cost are usual. It can prove particularly efficient in the presence of time-of-use electricity tariff schemes, and can enable the control of buildings for participation in complicated demand response programs [2]. An environment is provided in the toolbox which facilitates MPC controller design.…”

Section: Controllermentioning

confidence: 99%

“…Step 2: The building data is used to automatically generate a linear state-space model of the form of equation (2). At this point the inputs to the model are heat fluxes to each zone.…”

Section: B Control Of Thermal Storage Through Mpcmentioning

confidence: 99%

“…OpenBuild allows one, for instance, to easily simulate the addition of batteries, heat storage or photovoltaic panels attached to the building in MATLAB. The potential of this tool has been demonstrated by performing advanced simulations of Demand Response participations [2].…”

Section: Introductionmentioning

confidence: 99%

“…Its advantages include the ability to explicitly deal with constraints, incorporate predictions and optimize complex economic objectives. Research on MPC for building control is moving towards more complex applications such as demand response potential assessment [2] where the advantages of using optimal controller become even more apparent. One of the main difficulties of MPC design for buildings is to create and validate models capturing the behavior of the building and the Heating, Ventilation and Air Conditioning System (HVAC) and are appropriate for controller design.…”

Section: Introductionmentioning

confidence: 99%

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OpenBuild : An integrated simulation environment for building control

Gorecki

Qureshi

Jones

2015

2015 IEEE Conference on Control Applications (CCA)

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Abstract-This paper introduces the OpenBuild toolbox for MATLAB. OpenBuild is a toolbox for advanced controller design for buildings heating ventilation and air conditioning systems, with emphasis on Model Predictive Control. It provides researchers in the control community the ability to test algorithms on a wide range of realistic simulation scenarios, by providing most of the data needed to perform simulation and optimization. It combines the convenience of controller design in MATLAB with the simulation capabilities of the building simulation software EnergyPlus. It includes a building modeling tool to construct linear state-space models of building thermodynamics based on building description data, making it useful for design of optimal controllers requiring a good prediction model, as well as providing the input data necessary for simulation such as weather, occupancy and internal gains data. The ability to co-simulate the building between MATLAB and EnergyPlus enables fast prototyping and validation of the models and controllers. This paper presents the working principles and functionality of OpenBuild.

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Provoking Residential Demand Response Through Variable Electricity Tariffs - A Model-Based Assessment for Municipal Energy Utilities

Scheller

Krone

Kühne

et al. 2018

Technol Econ Smart Grids Sustain Energy

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With the suitable infrastructure of information and communication technologies in place, customers are able to perform demand response (DR), meaning that they can decrease or increase their electricity consumption in response to changes in their electricity tariff. In this research, different variable electricity tariffs are designed taking both customer and utility preferences into account. Subsequently, a model-based analysis on the basis of optimization model IRPopt (Integrated Resource Planning and Optimization) is carried out. Electricity customers are exposed to the designed tariffs in order to find out whether variable electricity tariffs are a suitable instrument for municipal energy utilities to exploit the potential laying in residential DR. Loads considered for DR in this work are those of selected electric household appliances and the loads of electric heat pumps. One major contribution of this work is that the assessment differentiates between different types of energy utilities, whose specific generation profiles are taken into account in the design of the variable tariffs. The results show that variable electricity tariffs have a small economic potential. However, customers only benefit if the design of the business model includes a proper compensation mechanism. In this context, successful business models require the direct cooperation of different municipal energy market actors. Furthermore, taking the specific generation profiles of municipal energy utilities into account in the design of variable electricity tariffs helps to increase the energy autonomy of municipalities.

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Model Predictive Control for Market-Based Demand Response Participation

Cited by 20 publications

References 7 publications

A comprehensive modelling framework for demand side flexibility in smart grids

A comprehensive modelling framework for demand side flexibility in smart grids

OpenBuild : An integrated simulation environment for building control

Provoking Residential Demand Response Through Variable Electricity Tariffs - A Model-Based Assessment for Municipal Energy Utilities

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