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

Abstract The continuous increase of (volatile) renewable energy production and the coupling of different energy sectors such as heating, cooling and electricity have significantly increased the complexity of urban energy systems. Such multi-energy systems (MES) can be operated more efficiently with the aid of optimization-based energy management systems (EMS). However, most existing EMS are tailor-made for one specific system or class of systems, i.e. are not generally applicable. Furthermore, only limited information on the actual savings potential of the usage of an EMS under realistic conditions is available. Therefore, this paper presents a novel modular modeling approach for an EMS for urban MES, which also enables the modeling of complex system configurations. To assess the actual savings potential of the proposed EMS, a comprehensive case study was carried out. In the course of this the influence of different user behavior, changing climatic conditions and forecast errors on the savings potential was analyzed by comparing it with a conventional control strategy. The results showed that using the proposed EMS in conjunction with supplementary system components (thermal energy storage and battery) an annual cost savings potential of between 3 and 6% could be achieved.

[1]  Gerald Englmair,et al.  A solar combi-system utilizing stable supercooling of sodium acetate trihydrate for heat storage: Numerical performance investigation , 2019, Applied Energy.

[2]  Edoardo Amaldi,et al.  A detailed MILP optimization model for combined cooling, heat and power system operation planning , 2014 .

[3]  Ognjen Marjanovic,et al.  Generalised control-oriented modelling framework for multi-energy systems , 2019, Applied Energy.

[4]  Alberto Bemporad,et al.  HYSDEL-a tool for generating computational hybrid models for analysis and synthesis problems , 2004, IEEE Transactions on Control Systems Technology.

[5]  Christoph Hochenauer,et al.  Novel validated method for GIS based automated dynamic urban building energy simulations , 2017 .

[6]  Henrik Madsen,et al.  Economic valuation of heat pumps and electric boilers in the Danish energy system , 2016 .

[7]  Barbara Mayer,et al.  Hierarchical Model Predictive Control for Sustainable Building Automation , 2017 .

[8]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[9]  Mohamed Benbouzid,et al.  Microgrids energy management systems: A critical review on methods, solutions, and prospects , 2018, Applied Energy.

[10]  Yan Xu,et al.  Optimal coordinated energy dispatch of a multi-energy microgrid in grid-connected and islanded modes , 2018 .

[11]  Markus Gölles,et al.  A generally applicable, simple and adaptive forecasting method for the short-term heat load of consumers , 2019, Applied Energy.

[12]  C. Streck,et al.  The Paris Agreement: A New Beginning , 2016 .

[13]  Michael Wetter,et al.  Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed , 2011 .

[14]  Christoph H. Glock,et al.  Energy management for stationary electric energy storage systems: A systematic literature review , 2018, Eur. J. Oper. Res..

[15]  Gerald Schweiger,et al.  District energy systems: Modelling paradigms and general-purpose tools , 2018, Energy.

[16]  Pierluigi Mancarella,et al.  Multi-energy systems : An overview of concepts and evaluation models , 2015 .

[17]  Zhang Jianhua,et al.  Energy management system, generation and demand predictors: a review , 2018 .

[18]  Yan Xu,et al.  Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties , 2019, Applied Energy.

[19]  Chris Marnay,et al.  Optimizing Distributed Energy Resources and Building Retrofits with the Strategic DER-CAModel , 2014 .

[20]  Jan-Olof Dalenbäck,et al.  Potential of residential buildings as thermal energy storage in district heating systems – Results from a pilot test , 2015 .

[21]  Mario Kendziorski,et al.  Illustrating the Benefits of Openness: A Large-Scale Spatial Economic Dispatch Model Using the Julia Language , 2019, Energies.

[22]  Gerald Schweiger,et al.  Novel method to simulate large-scale thermal city models , 2018, Energy.

[23]  Manfred Morari,et al.  Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis , 2016, IEEE Transactions on Control Systems Technology.

[24]  Mario Vasak,et al.  Modular energy cost optimization for buildings with integrated microgrid , 2017 .

[25]  Vassilios G. Agelidis,et al.  Control Strategies for Microgrids With Distributed Energy Storage Systems: An Overview , 2018, IEEE Transactions on Smart Grid.

[26]  Francesco Borrelli,et al.  Constrained Optimal Control of Linear and Hybrid Systems , 2003, IEEE Transactions on Automatic Control.

[27]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..