Denominazione: Management Energy Systems for Smart Islands
Acronimo: MESSI
Tipologia: Progetto PRIN PNRR 2022 finanziato da MUR-EU; Anno di inizio 2023
Responsabile scientifico: Rodolfo Araneo
Ente finanziatore: Missione 4 “Istruzione e Ricerca” - Componente C2 Investimento 1.1
“Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN)” Decreto Direttoriale n. 104 del 02 febbraio 2022 - Avviso pubblico per la presentazione di Progetti di ricerca di Rilevante Interesse Nazionale (PRIN) da finanziare nell’ambito del PNRR
PRIN 2022 2022HMYX2C - CUP MASTER xxxxxxxxx - CUP B53D23002650006
Importo finanziato: 136390,00 €
Partners:
- Dipartimento di Ingegneria Astronautica, Elettrica, Energetica, SAPIENZA Università di Roma
- Politecnico di Milano
Finalità
ESSI project is focused on the study, design, and integration of all the innovative equipment, systems, and algorithms necessary
for the management of microgrids in smart islands.
The microgrid that we consider in the MESSI project will be a strongly automated system integrated with loads, renewable energy
sources (RES), distributed generators (DG), battery energy storage systems (BESS), supercaps, electric vehicle (EV) and electric
boats (EBs) charge stations, desalination units, cold-ironing facilities that will be controlled in a coordinate manner employing
information communication technology (ICT) systems and artificial intelligence (AI) algorithms for their management.
The aim is to meet energy transition needs and to ensure the grid reliability and security of supply as well as reduced numbers of
fossil fuel-fired generation units, which means power flow balancing, grid stability, peak shaving, spinning reserve, supplemental
reserve, system resilience, cybersecurity, and reduced life cycle costing (LCC).
MESSI project will follow a stepwise development of all the processes necessary for building up and managing a modern island
microgrid with a particular reference to the island of Ponza testlab. MESSI’s methodology is not only academic, but also
market/industry-oriented, focusing on the unique combination of commercial software and solutions provided by global market
suppliers, new theoretical developments and algorithms and an innovative prototype of energy management systems (EMS), that
will be built in close collaboration with suppliers with appropriate manufacturing capability.
The final objective is the development of the AI based EMS of the smart islands. It is the core of the energy framework and the key
element for a proper exploitation of all the DERs distributed on the island. It will bring tremendous benefits to the system reliability
and new insights for DSM programs. The EMS shall also include a proper management of power dispatch according to forecasts and demand side management and power quality aspects to account for comprehensive operation of the energy system.
MESSI develops novel and advanced EMS systems for islanded areas and future microgrids to be properly used in the framework of current clean energy transition and enables the creation of decentralized and self-sustainable solutions to be adopted in future
energy communities to enhance and implement energy democracy and independency paradigms. Direct project impact and applications deal with techno-economic, environmental, and societal challenges for the next generation of complex energy systems.
Messi project activities will not cause any significant harm to the environment (DNSH principle) and on the contrary will contribute to follow DNSH regulation, namely Climate Change Mitigation, Protection of Marine Resources in the Ponza island field experimentation and Prevention of Air Pollution.
Risultati attesi
The objectives of MESSI project are manifold
OBJ-1: The first objective of the project is to apply proper design techniques to improve the distribution network proposing new
architecture and technological solutions starting from the starting gaps. The Medium Voltage (MV) distribution grid will be analyzed in terms of the production and consumption of electrical energy, articulation and consistency of the network, and the existing
technologies for metering and automation, thus obtaining all the data and requirements needed to identify the initial or reference
scenario.
The utility grid in Ponza is made entirely with medium-voltage power cables and is operated at 9 kV rated voltage with isolated
neutral. The distribution network has 23 MV/LV substations and seven lines. The most significant lines feeds the entire harbor of
Ponza and the backbone of the island, both topographically and electrically. The main line connects the southern part of the Island
with the northern part (called Le Forna and La Piana), feedings twelve substations and at the moment is the only existing connection infrastructure between two power generation systems.
he electric power generation in the test-bed island is carried out by autonomous diesel-fueled generators (DG) settled in one main
power plant (8.34 MW over five DGs) and a second power plant (2.256 MW over two DGs). Their operation brings several
environmental and economic drawbacks. The annual energy consumption –around 11.8 GWh with a yearly power peak of 4.4 MW
measured during summer months – is supplied at 98.7% by DGs (there are few roof-mount PV plants). The annual diesel
consumption equals 3.131 million liters with an overall average efficiency of the DGs around 35%.
By means of specific software, we will investigate several structural modifications and improvements to enhance energy efficiency
and network resilience:
a) Creation of a new MV connections to upgrade the port network and prepare it for the future cold ironing (or shore-to-ship power) of the ferry coming from other islands.
b) Construction of a new MV connection between one power station and substation to create a first grid mesh. This is an essential
step for the smartization of the network: it allows modifying the arrangement of the grid according to the loads and faults, improving robustness and resiliency.
c) Creation of MV connection to allow the powering of two 350 kW rated-power seawater desalination skids, which will be able to
operate together with their tanks as a controllable load in the event of overproduction from RES.
A model of the network will be implemented, and it will be used to carry out various simulations, with the aim of evaluating the
system’s performance under both normal working condition and under fault conditions, also for future scenarios. We will conduct
load flow, fault, and stability analysis.
Additionally, we will touch hot novel theoretical aspects concerning the definition and measurability of resilience with respect to
reliability and robustness [16]. Therefore, the system is put through increasingly adverse scenarios and evaluated for adequate
performance and disaster recovery. Appropriate figures of merit (FOM) [17], [18] (see Figure 4) will be developed and applied since
there is not yet a consensus in literature.
OBJ-2: once the future arrangement and requirements of the future grid has been defined, we will investigate how to step forward a real microgrid, addressing system integration and metering infrastructure technologies, with all the following research aspects:
a) Optimal integration of renewables-based DG systems, e.g., utility-scale photovoltaic (PV) systems, hybrid vertical axis wind plants (also in collaboration with industrial partner, Ropatec S.r.l. ) around one urban area, roof-mounted PV systems. Appropriate software will be developed to define best locations and size. Environmental impact aspects and potential social acceptances will be addressed as well.
b) Integration of the cold ironing in the Harbor to supply shoreside electrical power to a ship at berth when its engines are turned off.
Cold-ironing is really challenging in a small island [19] and call for an electric system conceived and designed for that, because the
energy must be supplied form RES and not just from DGs, in order to reduce the emission of large amounts of greenhouse gases into the atmosphere [20] in the context of future green islands.
c) Integration of EVs and EBs charging stations, especially in the center of the two main “cities” of the island. Understanding and
quantifying the value of such services would require detailed technical and economic modelling of the network and the private/public transportation system, including present and future business planning. They involve stakeholders, municipality, local operators, retailer and individual users. We will apply advanced optimization models (e.g., teaching learning based optimization, particle swarm optimization, random forest swarm optimization, game theory, etc) to search for the best suitable solution.
d) Integration of a centralized BESS in one power station or/and distributed BESSs in secondary distribution substations, to provide: daily and weekly storage capability for peak shaving and energy-shifting, spinning reserve, and the energy necessary by the onshore power supply for vessels at berth (also in collaboration with industrial partners, ABB S.p.A and Varta S.p.A.).
e) Integration of centralized or distributed supercapacitors to shave the fast fluctuations originated by the RESs and the DERs (also
in collaboration with industrial partners, DimacRed S.p.A.).
f) Smartization of the substations through the implementation of a metering, monitoring, diagnostic and control system. The system involves the installation of specific hardware (e.g., smart meters and control systems) and the supply of a specifically tailored
software. Particular attention will be paid to the metering systems, as well as to the storage and management of the data in a
remote cloud system. To this end, we will work with one academic start-up (DREAM S.r.l. [21]) that is particularly active in the
energy field. DREAM designed the metering platform ENERGY HOLTER, a smart metering solution with best-in-class scalability that is able to support any commercial device present today on the market. ENERGY HOLTER delivers actionable information to help you measure, monitor, optimize operations, improve efficiency, and boost business intelligence. Furthermore, it improves traditional advanced metering infrastructures and meter data management functionality with robust data analytics and operational data
storage and management capabilities [22]. The supervision system will acquire a series of signals distributed in different parts of the network by means of wired signals (fiber optic network) or via GSM. Everything will be managed through a clous-based SCADA
system equipped with a digital-twin with disaster-recovery functions and protection against cyber-attacks. The planned archite ture considers possible and future integrations.
g) Conceive the equivalent circuit models (ECMs) of all the main components and necessary power electronic converters, that we will conduct using different computational environments. The theoretical aspects involved in the research will be manifold and
groundbreaking. MESSI project will address the equivalent circuit of supercap and BESS through different techniques. It is a crucial
step to find a balance between complexity and precision and provide reliable results in real time. State-of-the art adaptive systems
suffer computational complexity and their robustness in extrapolation is poor, making them weakly predictive and of little practical
use. We will use a combination of concentrated physical and/or electrochemical models, address temperature dependence and
time-varying behavior of the parameters, apply order reduction techniques to reduce the large number of unknown parameters, with the final goal to obtain a reliable multiphysics ECM. We will compare ECMs using data obtained from experimental campaigns.
Moreover, MESSI project will address the microgrid behavior under steady-state and dynamic conditions via an accurate modelling of the loads, DERs and DG systems, since the main purpose is to assess the system architecture and to have a reliable model for
validation of distributed management and control algorithms, and of demand response programs.
OBJ-3: The final objective is the development of an advanced EMS for smart islands. It is the core of the energy framework and the
key element for a proper exploitation of all the DERs distributed on the island. It will bring tremendous benefits to the system
reliability and new insights for DSM programs. The EMS must balance the power generation and demand by means of the energy
storage, supercapacitors, the dispatchable generators, and demand management when possible. At the same time, the EMS should optimize the system efficiency and minimize the operational cost. Its design is the most challenging task because there is no national grid to rely on.
An advanced EMS architecture shall be designed in the MESSI project to effectively communicate with all the DERs and controllable loads and to incorporate the information provided by the monitoring systems. The EMS shall also include a proper management of power dispatch according to forecasts and demand side management and power quality aspects to account for comprehensive operation of the energy system. MESSI project will take advantages from MG2lab of Politecnico di Milano in developing and testing EMS functionalities [23].
For forecasting and management purpose, we will try to move a step beyond the cutting edge solutions in distributed AI algorithms.
Each DER will be assigned a neural system able to deal with the measured parameters and the relevant EMS information on loads
and power sources. We will consider a full deep learning approach based on recurrent models, which will be stacked with other
layers suited to advanced filtering techniques and attention mechanisms for data aggregation and multivariate prediction. Each local model will be learnt by using its own local data and by exchanging some “high-level” information, mostly related to model
parameters rather than to raw data, with its topological neighbors only. We will investigate distributed learning on both shallow and deep neural models through the custom adaptation of consensus-based synchronous solutions over directed and undirected graphs, such as Distributed Average Consensus, DeGroot learning, Bayesian probabilistic models, etc.
OBJ-4: The EMS proof of concept will be implemented at M2GLab and also on the Energy Holter web-based platform with a multi-layer structure. The software support will be developed on three distinct layers: IoT Gateway, IoT Backend and Human-System
Interface Platform.
The first layer will deal with the acquisition of data, the attribution of time labels, their aggregation into mega data and their correlation according to mathematical logical rules and the detection of instantaneous events (e.g., anomalous conditions, failures,
alarms, drifts of parameters).
The second layer will consist of a database, capable of operating at low latency with a large amount of data, and a set of applications that reside on a cloud server to generate the digital twin of the network. It shall include a complete CMMS (Computerized
Maintenance Management System) system that allows you to associate specific events with the relative tickets for the management and resolution of events, with the relative document management system. Moreover, it will allow the early detection of failures thanks to predictive tools based on historical data, integrity factors, statistical inference methods and engineering approaches.
The third layer will consist of the application platform, which makes the services and functions of the system available to the user
through a responsive web design (RW) (in collaboration with XRIT S.r.l.). The layer allows the operator to have a complete view of
the state of the microgrid, a real-time representation of the sensor statuses, and data collection. The task will touch the hot topic of the predictive maintenance PdM [20], [24] which can be based on three different maintenance technique: existing sensor-based,
test-sensor-based, and test-signal-based. We will particularly focus our attention on the third type that is an active maintenance:
suitable signals will be injected into the equipment or microgrid to test them (see Figure 6) and will investigate data-driven
methodologies.
Risultati raggiunti
At the present date the project is under development. The following WP has been partially concluded:
WP-1 Improvement of the network design: proposal and adoption of best-in-class solutions:
T1.1 Network Analysis
T1.2 Improved Network solutions
WP-2: Multi-components modelling and integration
T2.1 DER and load modelling
T2.2 System integration and operation
T2.3 Metering infrastructure technologies
T3.1 Medium-term EMS functionality
T3.2 Short-term EMS functionality