An agent-based framework for smart grid balancing exploiting thermal flexibility of residential buildings

Mazzarino, Pietro Rando; Vizia, Claudia De; Macii, Enrico; Patti, Edoardo; Bottaccioli, Lorenzo

doi:10.1109/eeeic/icpseurope51590.2021.9584738

Cited by 2 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The agent could model explicitly any intelligent control and encapsulate an interchangeable physical simulator (which models the physical behaviour as it is) on which to test intelligent strategies. Indeed, several studies implement cosimulation alike environments exploiting MAS tools (Pipattanasomporn et al 2009;Roche et al 2010;Mazzarino et al 2021;Nunna and Doolla 2012;Jung et al 2018). The concept of Agents in MAS applications can easily comply with the definition of SoS covering the needs of a co-simulation framework and, in particular, its required components.…”

Section: Enabling Technologies For Co-simulation Environmentsmentioning

confidence: 99%

“…The analysis is performed by applying a comprehensive benchmark of the possible configurations that each framework could implement. In addition, a similar benchmark is introduced for the integration of the AIOMAS library, presenting both MAS as a simulator and MAS as a co-simulation framework [as in Mazzarino et al (2021)]. In the following sections, the details and peculiarities of these three frameworks are addressed.…”

Section: Enabling Technologies For Co-simulation Environmentsmentioning

confidence: 99%

“…The building simulator is a stateful Simulator that exploits the model presented in Mazzarino et al (2021) to simulate a building equipped with a heat pump system. Within the model, the thermal behaviour of the envelope is treated with a Resistance-Capacitance model (Massano et al 2019) and a fine-grained model of the heat pump with all subsystems (e.g., emission, distribution, and generation) is provided.…”

Section: The Building Simulatormentioning

confidence: 99%

“…ICT, energy, economic and social) (Schloegl et al 2015) Many works have focused on the co-simulation of smart grids by integrating simulators of power grids with ICT communication aspects, so-called Cyber-Physical Energy System (CPES) (Georg et al 2013;Garau et al 2018;Pan et al 2016;. Co-simulation has been widely applied also to integrate several models in order to represent and describe the planning of new RES deployment (Reinbold et al 2019;Steinbrink et al 2019;Schiera et al 2019) or to study the effects of novel control strategies to exploit energy flexibility for demand response applications (Song et al 2017;Bhattarai et al 2016;Abgottspon et al 2018;Mazzarino et al 2021). To ease the coupling of simulators, researchers have started defining standards for co-simulation, such as Functional Mock-up Interface (FMI) (Blochwitz et al 2011), and co-simulation frameworks, such as Mosaik (Schütte et al 2011) and HELICS (Palmintier et al 2017).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem

et al. 2022

Self Cite

View full text Add to dashboard Cite

The transition to a low-carbon society will completely change the structure of energy systems from a standalone hierarchical centralised vision to cooperative and distributed Multi-Energy Systems. The analysis of these complex systems requires the collaboration of researchers from different disciplines in the energy, ICT, social, economic, and political sectors. Combining such disparate disciplines into a single tool for modeling and analyzing such a complex environment as a Multi-Energy System requires tremendous effort. Researchers have overcome this effort by using co-simulation techniques that give the possibility of integrating existing domain-specific simulators in a single environment. Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. In this context, an additional challenge is the different temporal and spatial scales that are involved in the real world and that must be addressed during co-simulation. In particular, the huge number of heterogeneous actors populating the system makes it difficult to represent the system as a whole. In this paper, we propose a comparison of the scalability performance of two major co-simulation frameworks (i.e. HELICS and Mosaik) and a particular implementation of a well-known multi-agent systems library (i.e. AIOMAS). After describing a generic co-simulation framework infrastructure and its related challenges in managing a distributed co-simulation environment, the three selected frameworks are introduced and compared with each other to highlight their principal structure. Then, the scalability problem of co-simulation frameworks is introduced presenting four benchmark configurations to test their ability to scale in terms of a number of running instances. To carry out this comparison, a simplified multi-model energy scenario was used as a common testing environment. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. Experimental results show that a Multi-processing configuration of HELICS reaches the best performance in terms of KPIs defined to assess the scalability among the co-simulation frameworks.

show abstract