• Energy Research

In the last years, the overall system inertia is decreasing due to the growing amount of energy resources connected to the grid by means of power inverters. As a consequence, reduced levels of inertia can affect the power system stability since slight variations of power generation or load may cause wider frequency deviations and higher rate of change of frequency (RoCoF) values. To mitigate this trouble, end-user distributed energy resources (DERs) interfaced through grid-following inverters, if opportunely controlled, can provide additional inertia. This paper investigated the possibility of improving the control law implemented by a low-cost controller on remotely controllable legacy DERs to provide synthetic inertia (SI) contributions. With this aim, power hardware-in-the-loop simulations were carried out to test the capability of the proposed controller to autonomously measure frequency and RoCoF and provide SI actions by controlling an actual battery energy storage system.

The progressive integration of Distributed Energy Resources (DERs) at distribution level is reshaping the role of distribution networks in the power system operation. Controllable loads, renewable sources, and storage systems can be managed from grid operators to provide services to the electrical grid. Nevertheless, a reliable and effective network control requires coordinated and optimized actions among transmission (TSO) and distribution (DSO) system operators. Assuming a shared balancing responsibility coordination model, the DSO will be called during the daily power system operation to dispatch optimally all control resources at distribution level in order to fulfil power exchange programs scheduled by the TSO. The feasibility of the application of a distribution optimal power flow routine in the assumed TSO-DSO coordination framework is studied through simulations on a detailed representation of an Italian MV urban distribution grid.

6. International Conference on Smart Energy Systems and Technologies, SEST 2023, Mugla, Turkey, 4 Sep 2023 - 6 Sep 2023; Sustainable energy, grids and networks 40, 101501 (2024). doi:10.1016/j.segan.2024.101501 special issue: "Special Issue: 6th International Conference on Smart Energy Systems and Technologies (SEST 2023) / Guest Editors: Dr. Gerardo J. Osório (Prince Henry Portucalense University, Porto,, Portugal); Assoc. Professor Akin Tascikaraoglu (Muğla Sıtkı Koçman University, Muğla,, Turkey); Dr. Andrea Mazza (Polytechnic of Turin Department of Energy, Torino,, Italy); Dr. Alper Ciçek (Trakya University, Edirne,, Turkey)" Published by Elsevier, Amsterdam [u.a.]

Due to the increasing penetration of power electronic interfaced power sources, total system inertia of power systems is decreasing. Significant frequency excursions and high rate of change of frequency (RoCoF) can affect power system security. A contribution to system inertia can be provided by distributed energy resources (DERs) at end-user level. This paper investigates the possibility to develop a low-cost controller that is able to autonomously measure frequency and RoCoF, and implement a synthetic inertia control law on the management system of remotely controllable DERs. The controller was implemented and tested on a real battery storage system through Power Hardware-in-the-Loop experiments.

The authors propose a control architecture aimed at optimizing energy resources for residential prosumers in a demand response framework. The control of resources is performed in two stages. The global control functions, based on the solution of a predictive optimal control problem are ensured by a cloud-based computation platform whereas a closed-loop local controller is responsible for the management of field components and actuators. The methodology is developed for managing residential micro/nano grids comprising PV generation, battery storage and interruptible loads. Simulations are aimed at assessing how much optimality can be affected by prevision errors. Tests are carried out considering a simulated environment characterized by realistic and highly resolved demand and generation curves.

In island distribution grids, the increase of renewable generation sources, whose output is stochastic in nature, implies that large amount of operating reserve must be guaranteed during the grid operation. In this paper, a methodology based on predictive optimal dispatch is proposed to optimize the use of energy resources taking into account operating reserve constraints. Reserve constraints are quantified through a probabilistic approach that takes into account the probabilistic distribution of forecasting errors. The methodology is applied to the distribution model of an actual Italyn small island, supplied by a set of conventional diesel generators together with photovoltaic and storage resources. The performance of the proposed predictive approach is assessed considering the response of the system during different load and generation scenarios. The impacts of reserve provision is also studied considering the effect of an open-loop and a closed-loop control strategy.

The paper presents the results of an on-going collaboration between two Italian universities, Politecnico di Bari (PoliBa) and Politecnico di Torino (PoliTo), for the setting up of a permanent cooperative co-simulation platform, that permits to share real-time laboratory resources for both research and education applications. The two laboratories, LabZERO and G-RTSLab, are located at a geographical distance of about 1,000 km. By sharing their lab resources, PoliBa and PoliTo aim to increase number and extent of the possible methodologies, control apparatus and power devices that can be jointly tested, and to develop specific applications of Remote Power-Hardware-in-the-Loop (R-PHIL) co-simulation in the framework of Transmission-Distribution-Customer coordination. Test results are presented to assess delays in remote communication and to estimate how much sample rate and size of data packets affect such delays. An experimental test of the R-PHIL platform is also presented to demonstrate the stability of the proposed co-simulation architecture.