Grid Mind: Prolog-Based Simulation Environment for Future Energy Grids

Fundamental changes in the current energy grids, towards the so called smart grids, initiated a range of projects involving extensive deployment of metering and control devices into the grid infrastructure. Since in many countries, the choice of supportive information and communication technologies (ICT) for the grid devices still remains an open question, benchmarking tools aimed at predicting their behavior in the deployed solution play an essential role in the decision-making process. This paper presents a Prolog-based simulation environment, named Grid Mind, primarily intended for the very purpose. The tool was successfully used to generate simulation scenarios in several smart-grid related projects and became a self-standing simulation tool for the evaluation of information and communication technologies used to deliver lowvoltage metering and monitoring data. The tool is continuously evolving, aimed to become an integral part of the future energy grid design in the Czech Republic and beyond

Loosed coupled simulation of smart grid control systems

Smart grids rely on the integration of distributed energy resources towards an intelligent and distributed manner to organize the electrical power grid enabled by a bidirectional flow of information to improve reliability and robustness, fault detection and system operation, and plug-and-playability of energy devices. The integration of information and communication technologies (ICT), one of the key enablers of smart grids, will ease the deployment of intelligent and distributed systems implementing the automation functions. In this context, there is a need to assess how these systems, developed using these emergent technologies, e.g., multi-agent systems, data analytics and machine learning, will behave and affect the working conditions of the power grid. This paper aims to explore the development of a transparent and loose-coupled interface between the behavioral control system and the physical or simulated power system environment, in a coupled simulation perspective, aiming to assess and improve the development of such systems during the design phaseinfo:eu-repo/semantics/publishedVersio

Towards generalized co-simulation of urban energy systems

Maximizing energy conservation, improving energy efficiency and integration and control of renewable energy sources arecritical in order to achieve a low carbon future. An integrated modelling system is needed to evaluate and improve energyperformance of urban energy systems’ design and operation, from both financial and environmental perspectives.To this end, this paper presents an urban energy co-simulation framework. It is based on co-simulation standard FunctionalMock-up Interface (FMI) and CityGML-based semantic 3D city model and utilized programing packages, like PyFMI,FMILibrary, and mosaik, which is capable of orchestrating the execution of dynamic simulation models supporting the for cosimulation.To demonstrate the proof of concept, two simulation tools are coupled in the first instance: EnergyPlus and No-MASS. Based on the two use cases, the principles and workflow of the framework and results from its application are described.Results from use cases show that synchronization and interaction between our urban energy co-simulation framework andcoupled co-simulation components works as intended. The paper concludes by discussing strategies to tackle more complex andmultiscale energy systems

A Hardware-in-Loop Simulation of DC Microgrid using Multi-Agent Systems

Smart-grid is a complex system that incorporates distributed control, communication, optimization, and management functions in addition to the legacy functions such as generation, storage, and control. The design and test of new smart-grid algorithms require an efficient simulator. Agent-based simulation platforms are the most popular tools that work well in the control and monitoring functionalities of the power electric network such as the microgrid. Most existing simulation tools necessitate either simulated or static data. In this paper, we propose a hardware-in-loop simulator for de-microgrid. The simulator reads the power generated by the PV panels and the battery SoC using Raspberry PI. A physical agent that runs on Raspberry PI sends the real-time data to a de-microgrid simulator that runs on a PC. As a proof of concept, we implemented a load-shedding algorithm using the proposed system

A modular co-simulation approach for urban energy systems

Cities are the main site of energy consumption, which result in approximately 71% of global CO2 emissions. Therefore, energy planning in cities can play a critical role in climate change mitigation by improving the efficiency of urban energy usage. The energy characteristics of cities are complex as they involve interactions of multiple domains, such as energy resources, distribution networks, storage and demands from various consumers. Such complexity makes urban energy planning a challenging task, which requires an accurate simulation of the interactions and flows between different urban energy subsystems. Co-simulation has been adopted by a number of researchers to simulate dynamic interactions between subsystems. However, the research has been domain specific and could only be used in limited areas. There was no generic approach to tackle the interoperability challenge of a comprehensive simulation for urban energy systems. To address such a gap, the aim of this thesis is to develop a generic and scalable urban energy co-simulation approach to comprehensively model the dynamic, complex and interactive nature of urban energy systems. This was achieved through the development of a generic and scalable urban energy co-simulation architecture and approach for the integration and orchestration of urban energy simulation tools, also called simulators, from different domains. Nine requirements were identified through a literature review of co-simulation, its approaches, standards, middleware and simulation tools. A conceptual co-simulation architecture was proposed that can address the requirements. The architecture has a modular design with four layers. The simulator layer wraps the simulation tools; the interconnection layer enables the communication between tools programmed in different programming languages; the interoperability layer provides a mechanism for the tool composition and orchestration; and the control layer controls the overall simulation sequence and how data is exchanged. Based on the architecture, a Co-simulation Platform for Ecological-urban (COPE) was developed. Suitable co-simulation software libraries were adopted and mapped together to fulfil the requirements of each layer of COPE to achieve the research objectives. For different simulation purposes, subsystem simulation tools from different domains could be selected and integrated into the platform. A master algorithm could then be developed to orchestrate and synchronise the tools by controlling how the tools are run and how data are exchanged among the tools. In order to evaluate COPE’s fundamental functionality and demonstrate its application, two case studies are presented in the thesis: simulating multiple application domains for a single building and multiple (interacting) buildings respectively. From the case studies, it was observed that COPE can successfully synchronise and manage interactions between the co-simulation platform and integrated simulation tools. The simulation results are validated by comparing the results obtained from the direct coupling approach. The applicability of COPE is demonstrated by simulating energy flows in urban energy systems in a neighbourhood context. Computing performance diagnostics also showed that this functionality is achieved with modest overhead. The layered modular co-simulation approach and COPE presented in this thesis provide a generic and scalable approach to simulating urban energy systems. It could be used for decision making to improve urban energy efficiency

On Statistical QoS Provisioning for Smart Grid

Current power system is in the transition from traditional power grid to Smart Grid. A key advantage of Smart Grid is its integration of advanced communication technologies, which can provide real-time system-wide two-way information links. Since the communication system and power system are deeply coupled within the Smart Grid system, it makes Quality of Service (QoS) performance analysis much more complex than that in either system alone. In order to address this challenge, the effective rate theory is studied and extended in this thesis, where a new H transform based framework is proposed. Various scenarios are investigated using the new proposed effective rate framework, including both independent and correlated fading channels. With the effective rate as a connection between the communication system and the power system, an analysis of the power grid observability under communication constraints is performed. Case studies show that the effective rate provides a cross layer analytical framework within the communication system, while its statistical characterisation of the communication delay has the potential to be applied as a general coupling point between the communication system and the power system, especially when real-time applications are considered. Besides the theoretical QoS performance analysis within Smart Grid, a new Software Defined Smart Grid testbed is proposed in this thesis. This testbed provides a versatile evaluation and development environment for Smart Grid QoS performance studies. It exploits the Real Time Digital Simulator (RTDS) to emulate different power grid configurations and the Software Defined Radio (SDR) environment to implement the communication system. A data acquisition and actuator module is developed, which provides an emulation of various Intelligent Electronic Devices (IEDs). The implemented prototype demonstrates that the proposed testbed has the potential to evaluate real time Smart Grid applications such as real time voltage stability control