S. Bracco, F. Delfino, F. Pampararo, M. Robba , M. Rossi

1. A System of Systems Approach for the Control of the University of Genoa Smart Polygeneration Microgrid S. Bracco, F. Delfino, F. Pampararo, M. Robba, M. Rossi stefano.bracco@unige.it, federico.delfino@unige.it, fabio.pampararo@unige.it, michela.robba@unige.it, mansueto.rossi@unige.it DIME Department of mechanical, energy, management and transportation engineering, University of Genova DIBRIS Department of Computer Science, Bioengineering, Robotics and Systems Engineering DITEN Department of Naval, Electrical , Electronic and Communication Engineering, University of Genova

2. Content • Introduction about renewable energies, smart grid, test-bed facilities for research development • Aim of this work • The Savona Campus Smart Polygeneration Microgrid (SPM) • The SPM technologies and different subsystems • A first dynamic decision model for the SPM optimal control • Conclusions and research challenges

3. Introduction • New energy sources with low greenhouse emissions are needed in order to reconcile the huge energy demand with an acceptable climatic impact (Rubbia, 2006) • Renewable resources (wind, solar, biomass, hydro, etc.) can be used to provide energy: they are intermittent and distributed over the territory • Many national and international research programs are aiming at developing innovative technologies and new energy management strategies in order to reach the targets set out by EU in the 20-20-20 Directive • Microgrids integrate different distributed energy sources and energy storage devices, and need intelligent management methods

4. Microgrids research: Experimental tests and demonstration projects • Importance of deriving new methods and tools for the optimal control of smart grids • Lidula and Rajapakse (2011) present a review of existing microgrid test networks around the world (North America, Europe and Asia) and some innovative simulation models present in literature. They review about 20 test research networks around the world • Actually, there are about 90 examples of microgrids around the world. Their number is supposed to increase in the near future

5. Aim of this work • To present the SPM (Smart Polygeneration Microgrid) • To highlight the SPM technologies and sub-systems (like in Phillips (2008)) • To highlight the main research lines to be developed within the SPM • To present a first decision model as an example of the possible approaches that can be used for the SPM optimal control

6. The University of Genova, Savona Campus, SPM: • Born from the “2020 Energy” Project (Italian Ministry of Education, University and Research Funding) at the Savona University Campus • During the year 2011 the preliminary design, the final design and the working plan of the infrastructure have been developed • In the current year 2012 works will start. The expected date for the test-bed completion is June 2013. • The SPM can be used for two main purposes: • demonstration and teaching activities • to test models, methods and tools related to the research challenges of smart grids.

7. 4 Energy Hub Combustion Lab Building «Sustainable Energy» 6 3 2 1 Low/Medium Voltage Electrical devices Control Room 7 4 5 6

8. The SPM technologies • a micro-cogeneration gas turbine fed by natural gas (C65 Capstone model, electrical power output = 65 kWel, thermal power output = 112 kWth, electrical efficiency = 29%, exhaust gas mass flow rate = 0.49 kg/s, exhaust gas exit temperature = 309°C); • a photovoltaic field (nominal power output = 49.9 kWel, 13 parallel arrays, each containing 16 modules, module efficiency = 14.5%, tilt angle = 30°, azimuth angle = -30°); • two cogenerative concentrated solar-powered (CSP) systems, equipped with Stirling engines (each characterized by 1 kWel and 3 kWth power output, electrical efficiency = 13%, thermal efficiency = 40%, solar concentrator diameter = 3.75 m); • two micro wind mills (one HAWT and one VAWT, each characterized by 3 kWel power output); • two absorption chillers (cooling capacity = 32.5 kWth, coefficient of performance = 0.75) equipped with a 3000 l storage tank; • the electrical storage (high voltage Sodium-Nickel batteries having an energy storage capacity of about 100 kWh); • two electric vehicles charging stations; • other electrical devices (inverters and smart metering systems).

9. The SPM’s peculiarity/innovation is due to: • the set of generation units and storage systems for both electrical and thermal energy production that make it a complete test-case; • the possibility of defining and updating a software for the SPM control; • a fast telecommunication network; • and the integration with the research activities of the Engineering Faculty.

10. External Net The SPM and the Campus sub-systems SPM electricaldemand The SPM electrical sub-system The CAMPUS electrical sub-system Electrical power production, storage, net - Campus - SPM exchanges Co-generation plants (microturbine) CSP The SPM control sub-system Campus demand Thermal production, thermal storage SPM thermaldemand The SPM thermal sub-system The CAMPUS thermal sub-system

11. The SPM electrical sub-system It includes: a cogeneration microturbine, two parabolic solar concentrators, two wind turbines, a photovoltaic, electrical vehicle charging stations, smart meters, inverters, storage batteries, a dedicated grid connected to the existing Campus grid and to the public distribution network

12. The net is essentially characterized by a number of bus-bars (nodes) connected by a ring distribution network. • At each node, a power balance for incoming and outcoming power flows could be considered. • Different levels of detail could be used to model this sub-system • If a detailed representation of the units’ dynamic behaviour were necessary, a quite complex electromechanical model, taking into account gas turbines and inverters dynamic models, together with their governors, controllers, etc., should be adopted. • In the proposed application, given the relatively short connections between bus-bars and, most important, the simulation step of 15 minutes, far larger than the time required by the electrical sub-system to reach the steady state, this subsystem can be modeled as a single bus-bar, considering the active power balance only.

13. The SPM thermal sub-system Heat losses in the district heating network are neglected as well as the dynamics due to the heat storage system. The following generation units have been considered: the microturbines and the Campus boiler.

14. The SPM control sub-system It includes local controllers, a communication network, and a central controller. The control network uses both communication protocols Modbus and RS485, and the protocol IEC 61850 that, in future years, should become a reference standard for communications and control architectures in the low-voltage smart grid sector. ICT

15. The software for the overall system management guarantees the SPM operations, monitoring and alarms management. Moreover, it allows adding new components, models and tools useful for the optimization and control. Local controllers sub-system: they include the interfaces with the field, composed by those devices that directly interact with the electrical network (RTUs-Remote Terminal Units) with actions on measurement of relevant parameters (i.e., current, voltage, temperature, etc.) and on the different actuators (e.g., switches)

16. Dynamicoptimizationfor the SPM optimalcontrol

17. State of the art and innovation • The increase of renewable energies (intermittent, distributed) and of the concept of distributed generation has opened new challenges for the definition of decision models, decision support systems, and controllers that may help in the planning and management of the overall electrical grid. • Key issues from a system engineering point of view: lack of a unified mathematical framework with robust tools for modeling, simulation, control and optimization of time critical operations in complex multicomponent and multiscaled networks (Amin, 2011). • Other issues related to smart grids are: stochastic models for demands, prices, resources availability estimation; state estimation and robustness, fault diagnosis, algorithms to deal with complex systems and models • Recent reviews about the research needs in control of microgrids with storage (Zamora and Srivastava, 2010): centralized and decentralized architectures. • Moreover, in literature, despite many contributions related to planning decision problems, there are few articles in the field of real time optimal control of a mix of renewable power plants integrated in an electrical network • In this work, as an example, a first dynamic optimization problem is presented, with specific reference to a portion of the SPM.

18. Decision Support Systems (DSSs) may help for planning and control purposes, taking into account the different objectives and/or sub-systems Modules of a DSS that should be used for different spatial scales: local to national/international level Modules of a DSS that should be used for different decision problems at different time scales: strategic planning, tactical planning, and operational management

19. Aim of the presented decision model • Determining the optimal values over time of the microturbines and boiler electrical and thermal power output, of the storage injection/withdrawal, and of the electrical power exchange with the external grid, according to the time-varying thermal and electrical loads, fuel and electricity prices, available energy forecasts • Different performance indexes can be formalized (Delfino et al., 2010), for example: costs related to purchasing of electricity and natural gas; benefits due to electricity sale and incentives for local consume of produced energy; the carbon footprint of the overall system . • In this work, operating costs and benefits are considered as objectives, while the carbon footprint is evaluated for the optimal solution. The formalized decision model includes only a sub-set (decribed by the previous equations) of the SPM system

20. State and control variables State variables are represented by the storage state of charge SOCt Decision variables Primary: PPE,k,t, PPE,B,t, PNET,t and PS,t Secondary (Pel,k,t, Pth,k,t, Pth,B,t) Binary control variable, δk,t: it is set to 1 if the k-th microturbine works in time interval (t, t+1), and 0 otherwise.

21. The electrical sub-system Microturbine k: Power output [kW] depends on environmental conditions Efficiency depends on microturbine power level and ambient temperature θt : ambient temperature Power that corresponds to primary energy ηel,k,t and Pel,k,t being respectively the actual values in time interval (t,t+1) of electrical efficiency and power output The primary energy flow is PPE,k,t (natural gas flow rate multiplied by its lower heating value)

22. The storage (Brekken, 2011): As regards the photovoltaic system, its power output PPV,t is an input of the model. Finally, there is the variable PNET,t which indicates the power exchanged with the external grid (withdrawn if positive, injected if negative). The formalized decision model includes only a sub-set (decribed by the previous equations) of the SPM system

23. The thermal sub-system Microturbine k: Pth_nom,k and Pel_nom,k being respectively the gas turbine nominal thermal and electrical power output (evaluated at ISO conditions). Boiler:

24. The objective function CTOT=operating costs over the time optimization horizon CB=boiler costs CK =microturbine costs CNET, BNET=costs and benefits related to the electricity exchange with the net

25. NGpp and NGppwf being the natural gas purchasing price with and without fee (0.7 and 0.427 €/m3). TESpp : the thermal energy service purchasing price of the boiler (0.0853 €/kWhPE) LHV :the natural gas lower heating value (9.7 kWhPE/m3) CNET,t is the electricity purchasing price expressed in €/kWhe CNET is a medium price of the electricity sold to the external grid; CNG is the gas unitary cost (€/m3) for cogeneration gas turbines

26. The constraints Allequationspreviouslyformalized Del,t and Dth,t : respectively, the electrical and the thermal power demand Each microturbine is characterized by a “technical minimum power” (Pmin,el,k), below which the machine is shut down in order to avoid high CO emissions; Battery state of charge

27. The carbon footprint assessment fe ,fo : the emission (56 tCO2/TJPE) and the oxidation factor (0.995) of the natural gas μ1 ,μ2 : conversion factors fe,NET : the emission factor of the national electrical mix (0.465kgCO2/kWhe)

28. Results Time horizon: 24 hours Time discretization:15 Mixed non linear optimization problem The optimal value of the daily operating cost is equal to 475 €, and CO2 emissions are equal to 1.38 t. Receding Horizon control scheme Horizon: 3 hours Local optima runtime: 10 seconds Global optimum runtime (Global Solver): 4 minutes Number of variables: 108

29. Results Scenario b, the control variable PS,tfor the storage has been set equal to zero. The daily operating cost is of 479 € and CO2 emissions are equal to 1.43 t.

30. Conclusions and research challenges • A test-bed facility at the Savona University Campus has been shown • The SPM sub-systems have been highlighted • A first dynamic decision model for the SPM optimal control has been investigated • A lot of research efforts can be applied to the SPM in the future: modelling/simulation of the whole SPM, centralized/decentralized control schemes, algorithm for reduction of complexity, multi-objective decision problems, etc.

31. Possible impact of the SPM on the Savona City

32. Challenges for the control subsystem • Dynamic decision models for real time optimal control that take into account multiple objectives (economic, environmental, technical) • Dynamic decision models that take into account the switching possibilities (i.e., on/off of the different operation modes of generators, storages, electrical system, demands) of the overall system. This challenge can be exported to the building context • Complexity reduction • Decision problems for microgrids based on hierarchical, distributed, multilevel architectures, taking into account the different information flows • Models and methods for wider smart grids or for networked microgrids • Models for resources, prices, demands estimation/forecasting • Risk Assessment, reliability, fault diagnosis

33. Thankyou

34. References R. Zamora, A.K. Srivastava, “Controls for microgrids with storage: Review, challenges, and research needs.” Renewable and Sustainable Energy Reviews, Vol. 14, pp. 2009-2018, 2010. W.A. Lidula, A.D. Rajapakse, “Microgrids research: A review of experimental microgrids and test systems.” Renewable and Sustainable Energy Reviews, Vol. 15 pp. 186-202, 2011. L.R. Phillips, “The microgrid as a system of systems”, Systems of Systems Engineering – Principles and Applications, Taylor & Francis Publishers, London, UK, 2008. T.K. Brekken, , A. Yokochi, A. von Jouanne, Z.Yen, H.M. Hapke, D.A. Halamay, “Optimal Energy Storage Sizing and Control for Wind Power Applications”, IEEE Transactions on Sustainable Energy, Vol. 2, pp. 69-77, 2011. Delfino, F., Denegri, G.B., Invernizzi, M., Amann, G, Bessède, J.L., Luxa, A., Monizza, G. A Methodology to Quantify the Impact of a Renewed T&D Infrastructure on EU 2020 Goals. IEEE Power and Energy Society General Meeting, 2010.