Advanced Primary Controllers for Inverter Based Power Sources: Microgrids and Wind Power Plants

The aim of this doctoral thesis is to present the research activity fulfilled during the Ph.D. studies. The research project of the candidate was focused on two main cores. The first core is centred in the microgrid area; in particular in islanded microgrid modelling and control. Firstly, the model was compared with experimental results collected in some facilities available at University of Genoa. Then traditional controllers for islanded microgrid are analysed and explored, proposing a new stability estimation procedure for droop controlled microgrid. Finally, a new control strategy based on Model Predictive Control (MPC) is proposed in order to collect many functionalities in just one control layer. MPC is widely used in MG environment, but just for power and energy management at tertiary level; instead here it is here proposed with an inedited use. Some experimental validations about this new methodology are obtained during a research period in Serbia and Denmark. The second core is related with synthetic inertia for wind turbine connected to the main grid, i.e. frequency support during under-frequency transients. This aspect is very important today because it represents a way to increase grid stability in low inertia power systems. The importance of this feature is shared by all the most important Transmitter System Operators (TSO) all over the world

Network and System Management for the Security Monitoring of Microgrids using IEC 62351-7

Interest in adding renewable energy sources to the power grid has risen substantially in recent years. As a response to this growing interest, the deployment of microgrids capable of integrating renewable energy has become more widespread. Microgrids are independent power systems that deliver power from different kinds of Distributed Energy Resources (DERs) to local energy consumers more efficiently than the conventional power grid. The microgrid leverages advanced information and communication technologies for vital protection, monitoring, and control operations as well as for energy management. With the use of information technology comes the need to protect the microgrid information layer from cyberattacks that can impact critical microgrid power operations. In this research, a security monitoring system to detect cyberattacks against the microgrid, in near-real time, is designed and implemented. To achieve this, the system applies Network and System Management (NSM) for microgrid security monitoring, as specified by the IEC 62351-7 security standard for power systems. The specific contributions of this research are (i) an investigation on the suitability of NSM for microgrid security monitoring; (ii) the design and implementation of an NSM platform; (iii) the design and implementation of a security analytics framework for NSM based on deep learning models; (iv) the elaboration of a comprehensive microgrid simulation model deployed on a Hardware in the Loop (HIL) co-simulation framework; and (v) an experimental evaluation on the effectiveness and scalability of the NSM security monitoring platform for detection against microgrid attack scenarios, with a methodology being used to systematically generate the scenarios. The experimental results validate the usefulness of NSM in detecting attacks against the microgrid