ZerOBNL: A framework for distributed and reproducible co-simulation

Puerto, Pablo; Widl, E.; Page, Jessen

doi:10.1109/mscpes.2019.8738787

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…The DAL relies on the generic Simulation Package to implement a persistence scheme for ZerOBNL [52], the co-simulation framework developed for IntegrCiTy's technical framework (cf. Figure 3).…”

Section: Persistence Scheme For Simulation Setupsmentioning

confidence: 99%

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Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level

2021

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Worldwide, cities are nowadays formulating their own sustainability goals, including ambitious targets related to the generation and consumption of energy. In order to support decision makers in reaching these goals, energy experts typically rely on simulation models of urban energy systems, which provide a cheap and efficient way to analyze potential solutions. The availability of high-quality, well-formatted and semantically structured data is a crucial prerequisite for such simulation-based assessments. Unfortunately, best practices for data modelling are rarely utilized in the context of energy-related simulations, so data management and data access often become tedious and cumbersome tasks. However, with the steady progress of digitalization, more and more spatial and semantic city data also become available and accessible. This paper addresses the challenge to represent these data in a way that ensures simulation tools can make use of them in an efficient and user-friendly way. Requirements for an effective linking of semantic 3D city models with domain-specific simulation tools are presented and discussed. Based on these requirements, a software prototype implementing the required functionality has been developed on top of the CityGML standard. This prototype has been applied to a simple yet realistic use case, which combines data from various sources to analyze the operating conditions of a gas network in a city district. The aim of the presented approach is to foster a stronger collaboration between experts for urban data modelling and energy simulations, based on a concrete proof-of-concept implementation that may serve as an inspiration for future developments.

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Section: Persistence Scheme For Simulation Setupsmentioning

confidence: 99%

“…A detailed explanation of the simulation models and the co-simulation configuration [52] is out of the scope of this article. However, the implemented workflow can be considered as a typical workflow for the co-simulation of energy systems.…”

Section: Gas Grid Co-simulation Setupmentioning

confidence: 99%

Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level

2021

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“…The scheme also shows the main heat (red lines), power flows (black lines), and control signals (blue dashed lines). This sub-section briefly highlights that, from a modeling and simulation perspective, a cosimulation method ( [28,30]) was used to assess the interaction between the loading of the electricity distribution grid, the heat supply from HPs and a DH network, and the indoor temperature of residential buildings. Figure 3 sums up the modeling tools that were used for each system involved in the scenarios.…”

Section: Models For the Reference Scenariomentioning

confidence: 99%

“…Given these literature gaps, one objective of this paper was to propose a district-level perspective on potential synergies among the heating, electricity, and transport sectors. To approach this sector-coupling problem, the co-simulations of dedicated models [12,28] and stochastic profiles were combined. Thus, the technical performances of technologies belonging to these different sectors were linked.…”

Section: Introductionmentioning

confidence: 99%

Vehicle-To-Grid for Peak Shaving to Unlock the Integration of Distributed Heat Pumps in a Swedish Neighborhood

2020

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The city of Stockholm is close to hitting the capacity limits of its power grid. As an additional challenge, electricity has been identified as a key resource to help the city to meet its environmental targets. This has pushed citizens to prefer power-based technologies, like heat pumps and electric vehicles, thus endangering the stability of the grid. The focus of this paper is on the district of Hammarby Sjöstad. Here, plans are set to switch from district heating to heat pumps. A previous study verified that this choice will cause overloadings on the electricity distribution grid. The present paper tackles this problem by proposing a new energy storage option. By considering the increasing share of electric vehicles, the potential of using the electricity stored in their batteries to support the grid is explored through technical performance simulations. The objective was to enable a bi-directional flow and use the electric vehicles' (EVs)' discharging to shave the peak demand caused by the heat pumps. It was found that this solution can eliminate overloadings up to 50%, with a 100% EV penetration. To overcome the mismatch between the availability of EVs and the overloadings' occurrence, the minimum state of charge for discharging should be lower than 70%.

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“…• 2019: The Zero OBvious Node Link co-simulator (ZerOBNL) [101] 19 was implemented in Python and used pandapower in a so called partitioned approach, breaking down elements into more manageable parts in larger cosimulations. They employed ZeroMQ [43] [95], a framework that combined pandapower with GNU/Linux based emulators and tools to model asynchronous communication between elements.…”

Section: Power/telecommunications Co-simulation Enginesmentioning

confidence: 99%

Tools for modelling and simulating the Smart Grid

Czekster

2020

Preprint

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The Smart Grid (SG) is a Cyber-Physical System (CPS) considered a critical infrastructure divided into cyber (software) and physical (hardware) counterparts that complement each other. It is responsible for timely power provision wrapped by Information and Communication Technologies (ICT) for handling bi-directional energy flows in electric power grids. Enacting control and performance over the massive infrastructure of the SG requires convenient analysis methods. Modelling and simulation (M&S) is a performance evaluation technique used to study virtually any system by testing designs and artificially creating 'what-if' scenarios for system reasoning and advanced analysis. M&S avoids stressing the actual physical infrastructure and systems in production by addressing the problem in a purely computational perspective. Present work compiles a non-exhaustive list of tools for M&S of interest when tackling SG capabilities. Our contribution is to delineate available options for modellers when considering power systems in combination with ICT. We also show the auxiliary tools and details of most relevant solutions pointing out major features and combinations over the years.

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ZerOBNL: A framework for distributed and reproducible co-simulation

Cited by 4 publications

References 12 publications

Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level

Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level

Vehicle-To-Grid for Peak Shaving to Unlock the Integration of Distributed Heat Pumps in a Swedish Neighborhood

Tools for modelling and simulating the Smart Grid

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