Central Model Predictive Control of a Group of Domestic Heat Pumps - Case Study for a Small District

Leeuwen, R.P. van; Fink, Jiří; Smit, Gerard J. M.

doi:10.5220/0005434301360147

Cited by 4 publications

(13 citation statements)

References 16 publications

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“…We also define schedules for occupancy-related thermal gains and losses, ventilation and domestic hot water consumption. The parameters, types of houses and schedules are explained in more detail in [3]. As we use the same models and input data for the model predictive control part and for the on-line simulation, we assume perfect predictions.…”

Section: Demand Datamentioning

confidence: 99%

“…However, in [2], we demonstrate the efficiency of a method called time scale linear programming, which involves prediction and planning, which is more accurate the closer the planning horizon is to the present time and less accurate for a planning horizon further into the future. In [3], we apply this method to a model predictive control simulation for a group of 100 heat pumps and investigate the quality of the obtained solutions. The algorithms show excellent results for the thermal comfort, which is subject to several constraints during the optimization.…”

Section: Related Workmentioning

confidence: 99%

“…In [2,3], we presented how MILP can be applied to minimize peak electricity demand. This method is also used in this paper in the MILP control presented for comparison purposes.…”

Section: Chp Modelmentioning

confidence: 99%

“…In order to model the heating demand of the house, we have developed in [3] a model predictive control approach based on an equivalent electrical circuit model representation of each house, consisting of two resistances and two capacitors. Model parameters are estimated by using data created by simulation in TRNSYS, which we explain in [15].…”

Section: House Modelmentioning

confidence: 99%

“…In [2], we investigate two control methods, one called global MILP (Mixed Integer Linear Program), the other called time-scale MILP, which solves the same problem with far less computational effort, but with equal quality of the solution. However, in [3], we further demonstrate how the control works within a model predictive control framework. With this framework, we demonstrate satisfactory thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

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Earliest Deadline Control of a Group of Heat Pumps with a Single Energy Source

Fink

Leeuwen

2016

Energies

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Abstract:In this paper, we develop and investigate the optimal control of a group of 104 heat pumps and a central Combined Heat and Power unit (CHP). The heat pumps supply space heating and domestic hot water to households. Each house has a buffer for domestic hot water and a floor heating system for space heating. Electricity for the heat pumps is generated by a central CHP unit, which also provides thermal energy to a district heating system. The paper reviews recent smart grid control approaches for central and distributed levels. An online algorithm is described based on the earliest deadline first theory that can be used on the aggregator level to control the CHP and to give signals to the heat pump controllers if they should start or should wait. The central controller requires only a limited amount of privacy-insensitive information from the heat pump controllers about their deadlines, which the heat pump controllers calculate for themselves by model predictions. In this way, a robust heat pump and CHP control is obtained, which is able to minimize energy demand and results in the desired thermal comfort for the households. The simulations demonstrate fast computation times due to minor computational and communication overheads.

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Section: Demand Datamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In [2,3], we presented how MILP can be applied to minimize peak electricity demand. This method is also used in this paper in the MILP control presented for comparison purposes.…”

Section: Chp Modelmentioning

confidence: 99%

Section: House Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Earliest Deadline Control of a Group of Heat Pumps with a Single Energy Source

Fink

Leeuwen

2016

Energies

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Peak load reduction of multiple water heaters: Respecting consumer comfort and money savings

Belov

Vasenev

Kartak

et al. 2016

2016 IEEE Online Conference on Green Communications (OnlineGreenComm)

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Demand Response programs can allow residential electricity consumers to cut their energy bills. However, in case of contingencies in the energy system when the guaranteed peak load reduction is needed, comfort of consumers can be significantly deteriorated and they can choose to opt out. This paper investigates the possibility of peak load reduction and yet highly respecting consumers' comfort by coordinating a group of electric tank water heaters.The proposed peak shaving mechanism accounts for interests of both utility companies and their customers. It employs two optimization models tailored to the needs of both sides to optimally schedule individual water heaters. The suggested Simulation results show the potential of the proposed mechanism to provide the guaranteed peak load reduction thus contributing to the stability of the electrical grid, while transparent balancing between comfort-money and comfort-energy incorporated in the control scheme is of interest and use to green consumers.

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Batteries in Smart Microgrids

Homan¹

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To counter the effects of global climate change, attributed to the CO 2 emissions resulting from burning fossil fuels for generating electricity and heat, global efforts are being made to achieve an energy transition. This includes that the share of energy generated using sustainable sources (e.g. solar, wind, hydro) should be increased, while the share of energy generated using fossil sources (e.g. natural gas, coal, oil) should be decreased, ultimately phasing out the usage of fossil fuels altogether. How this energy transition should be achieved, or even when the energy transition should be completed is subject of heated debates in political arenas, courts of law and the society as a whole. Whether or not future electricity demands can be met by sustainable sources is a particular important part of this debate. On the one hand, fossil fuels are (for the moment) cheap and readily available, by using fossil fuels it is always possible to generate the appropriate amount of electricity to match the demand. On the other hand, generating electricity from sunlight or wind can only be done when enough sunlight is available or the wind-speed is in an appropriate bandwidth. However, as electricity is also used during the night, and on cloudy, windless or stormy days, using sustainable energy sources as the primary supply for electricity generation can lead to a significant mismatch between supply and demand. This can imply that sometimes the electricity generated by solar parks during the day has to be curtailed because there is no demand for it, while during the night electricity still has to be generated using fossil fuels to meet the demand.A solution to this problem seems obvious: store the electricity. This allows to generate electricity using sustainable sources when available, and to store a sufficient amount to be able to meet the demand at all times. Although, this solution sounds simple, still many questions remain. Which type of storage should be used?, Where should the storage be located?, What should be the capacity of the storage?, How should the storage be used?, etc. In this thesis these types of questions are addressed for a specific type of storage: batteries. To answer these questions, and to support the important decisions necessary to complete the energy transition, three contributions are made:The first contribution is the development of the diffusion buffer model (DiBumodel) for battery state of charge (SoC) prediction. This model is specifically designed to be used in simulation tools for energy management in (smart) grids. Hence, this model should be a consolidation of broad applicability, accuracy and simplicity. The broad applicability of the DiBu-model is demonstrated by accurate predictions of the SoC of Lead-acid, Lithium-ion Polymer and Lithium Tot slot, en eigenlijk als voornaamste, bedank ik mijn familie. Pap en mam, jullie staan altijd voor me klaar, met woord en daad en vol belangstelling voor mijn werk. Ook als ik in het weekend naar Sassem kom, om tijd door te brengen bij Tjarda, of ...

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Central Model Predictive Control of a Group of Domestic Heat Pumps - Case Study for a Small District

Cited by 4 publications

References 16 publications

Earliest Deadline Control of a Group of Heat Pumps with a Single Energy Source

Earliest Deadline Control of a Group of Heat Pumps with a Single Energy Source

Peak load reduction of multiple water heaters: Respecting consumer comfort and money savings

Batteries in Smart Microgrids

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