Internet-of-Things concepts are evolving the power systems to the Energy Internet paradigm. Microgrids (MGs), as the basic elements in an Energy Internet, are expected to be controlled in a more flexible and corporate manner. This paper proposes a novel distributed control scheme for multi-agent systems (MASs) governed MGs in future Energy Internet. The control objectives are frequency/voltage restoration and proportional power sharing. The proposed control scheme considers both intra and inter MASs interaction, which offers group plug-and-play capability of distributed generators (DGs). The stability and communication delay issues in the control framework are also analysed. A multi-site implementation framework is presented to explain the agent structure as well as data exchange in local area networks and the cloud server. Then a cyber hardware-in-the-loop (C-HiL) experiment is conducted to validate the proposed control methods and multi-site implementation. The experimental results prove the effectiveness and application potentials of the proposed approach.
1 December 2016 in Sustainable Energy, Grids and Networks
Co-simulation is an emerging method for cyber-physical energy system (CPES) assessment and validation. Combining simulators of different domains into a joint experiment, co-simulation provides a holistic framework to consider the whole CPES at system level. In this paper, we present a systematic structuration of co-simulation based on a conceptual point of view. A co-simulation framework is then considered in its conceptual, semantic, syntactic, dynamic and technical layers. Coupling methods are investigated and classified according to these layers. This paper would serve as a solid theoretical base for specification of future applications of co-simulation and selection of coupling methods in CPES assessment and validation.
Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.
The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification.