A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis

Möser, Andreas; Muschick, Daniel; Gölles, Markus; Nageler, Peter Josef; Schranzhofer, Hermann; Mach, Thomas; Tugores, Carles Ribas; Leusbrock, Ingo; Stark, Sanja; Lackner, Fabian; Hofer, Anton

doi:10.1016/j.apenergy.2019.114342

Cited by 63 publications

(27 citation statements)

References 25 publications

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“…The general multi-energy management system presented in [18] employs hybrid system modelling to coordinate urban energy systems using MPC. The framework is highly versatile and suited to development of modular controller models for relatively small systems.…”

Section: A Related Literaturementioning

confidence: 99%

Model Predictive Control of Smart Districts With Fifth Generation Heating and Cooling Networks

Taylor

Long

Marjanović

et al. 2021

IEEE Trans. Energy Convers.

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Fifth Generation District Heating and Cooling (5GDHC) networks, in which low temperature water is distributed to water-source heat pumps (WSHPs) in order to meet thermal demands, are expected to have a significant impact on the decarbonisation of energy supply. Thermal storage installed in these networks offers operational flexibility that can be leveraged to integrate renewable electrical and thermal energy sources. Thus, when considered as part of a smart multi-energy district, 5GDHC substation devices (e.g. WSHPs, storage) may be optimally operated using Model Predictive Control (MPC) in order to match demand with low-cost supply of electricity. However, the application of MPC requires the ability to model 5GDHC networks within the context of a multi-energy system. Hence, this paper extends an existing, generalised control-oriented modelling framework for multi-energy systems to accommodate 5GDHC networks. Additions include the ability to represent hydraulic pumps, thermodynamic cycle devices (such as WSHPs) and multienergy networks within the framework. Furthermore, an economic MPC (eMPC) scheme is proposed for energy management of 5GDHC-based smart districts. Finally, a case study is presented in which the proposed eMPC controller is compared with rulebased control for economic operation of a smart district. Index Terms-model predictive control, smart districts, district heating and cooling, multi-energy systems.

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Section: A Related Literaturementioning

confidence: 99%

Model Predictive Control of Smart Districts With Fifth Generation Heating and Cooling Networks

Taylor

Long

Marjanović

et al. 2021

IEEE Trans. Energy Convers.

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“…To do so additional Boolean or integer variables need to be added to the models, leading to mixed-integer linear programmes (MILP), which are currently the most common approach for optimization-based predictive supervisory control of hybrid energy systems in both industry and academia, see e.g. [2], [3], [4]. Many years of research and development on the application of MILP (not only) for the predictive supervisory control of hybrid energy systems have devised fast algorithms like branch-and-bound or branch-and-cut as well as efficient heuristics that are able to derive proven optimal solutions for such MILPs under tight time constraints, see e.g.…”

Section: Task 55 Towards the Integration Of Large Shc Systems Into Dhc Networkmentioning

confidence: 99%

Supervisory control of large-scale solar thermal systems

Gölles¹,

Unterberger²,

Kaisermayer³

et al. 2021

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“…Since u on/off,t is a binary variable, any terms in (2) involving multiplication with it and another term can be conveniently modelled with a set of four constraints, see e.g. [4,32]. Therefore, these multiplications are not in violation of the chosen MILP formalism.…”

Section: Combined Heat and Power (Chp) Plantmentioning

confidence: 99%

“…Energy systems that span multiple energy domains are often referred to as multi energy systems (MES), see e.g. [18,32]. In order to control such complex energy systems, a supervisory control approach is needed.…”

Section: Introductionmentioning

confidence: 99%

Progressive hedging for stochastic energy management systems

et al. 2020

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Energy systems have increased in complexity in the past years due to the everincreasing integration of intermittent renewable energy sources such as solar thermal or wind power. Modern energy systems comprise different energy domains such as electrical power, heating and cooling which renders their control even more challenging. Employing supervisory controllers, so-called energy management systems (EMSs), can help to handle this complexity and to ensure the energy-efficient and cost-efficient operation of the energy system. One promising approach are

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A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis

Cited by 63 publications

References 25 publications

Model Predictive Control of Smart Districts With Fifth Generation Heating and Cooling Networks

Model Predictive Control of Smart Districts With Fifth Generation Heating and Cooling Networks

Supervisory control of large-scale solar thermal systems

Progressive hedging for stochastic energy management systems

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