Multi‐site European framework for real‐time co‐simulation of power systems

Stevic, Marija; Estebsari, Abouzar; Vogel, Steffen; Pons, Enrico; Bompard, Ettore; Masera, Marcelo; Monti, Antonello

doi:10.1049/iet-gtd.2016.1576

Cited by 56 publications

(64 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also becomes more crucial to predict such demands as any dramatic changes in environmental conditions like solar irradiation, weather, temperature, and wind speed would change the net demand profile very fast, which may not be captured promptly by system control and management systems. This could result in power imbalance [11], low power quality, or voltage violations. If a similar scenario occurs in neighboring distribution networks, the aggregation of effects would even initiate some frequency deviation in the upstream transmission systems.…”

Section: Renewable Energy Integrationmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

Self Cite

View full text Add to dashboard Cite

The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

show abstract

Section: Renewable Energy Integrationmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…It allows combining the three simulators in a simple manner as explained in Section 5.4. VILLASnode, a software project for coupling real-time simulations in LANs [22,23], is a suitable alternative for mosaik in the case of synchronizations with very short intervals.…”

Section: Synchronizationmentioning

confidence: 99%

A Cosimulation Architecture for Power System, Communication, and Market in the Smart Grid

et al. 2018

Self Cite

View full text Add to dashboard Cite

Smart grids evolve rapidly towards a system that includes components from different domains, which makes interdisciplinary modelling and analysis indispensable. In this paper, we present a cosimulation architecture for smart grids together with a comprehensive data model for the holistic representation of the power system, the communication network, and the energy market. Cosimulation is preferred over a monolithic approach since it allows leveraging the capabilities of existing, well-established domainspecific software. The challenges that arise in a multidomain smart grid cosimulation are identified for typical use cases through a discussion of the recent literature. Based on the identified requirements and use cases, a joint representation of the smart grid ecosystem is facilitated by a comprehensive data model. The proposed data model is then integrated in a software architecture, where the domain-specific simulators for the power grid, the communication network, and the market mechanisms are combined in a cosimulation framework. The details of the software architecture and its implementation are presented. Finally, the implemented framework is used for the cosimulation of a virtual power plant, where battery storages are controlled by a novel peak-shaving algorithm, and the battery storages and the market entity are interfaced through a communication network.

show abstract

“…cosimulation to investigate the interconnection of domains from a global point of view [3]- [5], hardware-in-the-loop (HIL) where a real hardware is integrated to the validation framework to consider the realistic behaviour of such hardware and its impact to the system [6], [7], hybridsimulation [8], integration of HIL to co-simulation [9]. These approaches can be implemented either in a single platform or by coupling several complementary platforms in a crossinfrastructure manner [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…In [12], a trans-oceanic integration of electrical equipment, controller and grid emulator is implemented. In [10] and [11], two frameworks (VILLAS and JanDER) connecting multiple hardware, real-time simulator and network simulator in the scale of Europe were presented. In a smaller scale, an integration of two experimental platforms PREDIS-PRISMES via hybrid cloud architecture is introduced in France [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Integration of SCADA services in cross-infrastructure holistic tests of cyber-physical energy systems

Nguyen

Tran

Bésanger

et al. 2019

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Syst

View full text Add to dashboard Cite

Cyber-Physical Energy System, due to its multidomain nature, requires a holistic validation methodology, which may involve the integration of assets and expertise from various research infrastructures. In this paper, the integration of Supervisory Control and Data Acquisition services to crossinfrastructure experiment is proposed. The method requires a high degree of interoperability among the participating partners and can be applied to extend the capacity as well as the degree of realism of advanced validation method such as co-simulation, remote hardware-in-the-loop or hybrid simulation. The proposed method is applied to a case study of multi-agent system based control for islanded microgrid where real devices from one platform is integrated to real-time simulation and control platform in a distanced infrastructure, in a holistic experimental implementation.

show abstract

Multi‐site European framework for real‐time co‐simulation of power systems

Cited by 56 publications

References 22 publications

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

A Cosimulation Architecture for Power System, Communication, and Market in the Smart Grid

Integration of SCADA services in cross-infrastructure holistic tests of cyber-physical energy systems

Contact Info

Product

Resources

About