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

DIBRISDepartment of Computer Science, Bioengineering, Robotics and Systems Engineering

DIMEDepartment of mechanical, energy, management and transportation engineering, University of Genova

DITEN Department of Naval, Electrical , Electronic and Communication Engineering,University of Genova

ContentIntroduction about renewable energies,

smart grid, test-bed facilities for research development

Aim of this workThe Savona Campus Smart Polygeneration

Microgrid (SPM)The SPM technologies and different

subsystemsA first dynamic decision model for the SPM

optimal controlConclusions and research challenges

IntroductionNew 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

Microgrids research: Experimental tests and demonstration projects Importance of deriving new methods and tools

for the optimal control of smart gridsLidula 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

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

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.

1 2 3

4

5 6

7

4

6

Energy Hub

Combustion Lab

Low/Medium VoltageElectrical devices

Building «Sustainable Energy»

Control Room

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).

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.

The SPM and the Campus sub-systems

The CAMPUS thermal sub-system

The CAMPUS electrical sub-system

External Net

Campus demand

SPM electrical demand

SPM thermal demand

Co-generation plants (microturbine)

CSP

The SPM control sub-system

The SPM electrical sub-system

The SPM thermal sub-system

Electrical power production, storage, net - Campus - SPM exchanges

Thermal production, thermal storage

The SPM electrical sub-systemIt 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

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.

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.

The SPM control sub-systemIt 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.

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)

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.

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.

Decision Support Systems (DSSs) may help for planning and control purposes, taking into account the different objectives

and/or sub-systems

The Local Subsystem

The Zonal Subsystem

The National Subsystem

Building Sub-District District

Electr

ical M

arket

Monit

oring

Su

b-S

ystem

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

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

State and control variablesState 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.

The electrical sub-systemMicroturbine k:

_ max, min,

_ , ,

min,

Pel k t k

el full k t Pk t t k

P ifP

f if

)( *,,

*,, tkelktkel ph

tkfullel

tkeltkel

,,_

,,*,,

tkfullel

tkeltkel P

Pp

,,_

,,*,,

tkel

tkeltkPE

PP

,,

,,,,

θt : ambient temperature

_ max, min,

_ , ,

min,

el k t k

el full k t

k t t k

if

g if

Efficiency depends on microturbine power level and ambient temperature

Power output [kW] depends on environmental conditions

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)

The storage (Brekken, 2011):

,1

S tt t t

PSOC SOC t

CAP

The formalized decision model includes only a sub-set (decribed by the previous equations) of the SPM system

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).

tkelktkth PP ,,,,

knomel

knomthk

P

P

,_

,_

tBPEBtBth PP ,,,,

Pth_nom,k and Pel_nom,k being respectively the gas turbine nominal thermal and electrical power output (evaluated at ISO conditions).

Microturbine k:

Boiler:

The thermal sub-system

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

NETNETKBTOT BCCCC minmin

ppppppwftkelt NGNGNGLHVCNG 12.025.0 ,,

NGpp and NGppwf being the natural gas purchasing price with and without fee (0.7 and 0.427 €/m3).

tTESPC pptBPE

T

tB

,,

1

0

K

k

T

tt

tkel

tkelK

kkK CNG

LHV

tPCC

1

1

0 ,,

,,

1

tPCC tNETtNET

T

tNET

)0,max( ,,

1

0

tPCB tNETNET

T

tNET

)0,min( ,

1

0

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

The constraintsDel,t and Dth,t :

respectively, the electrical and the thermal power demand

tStPVtkel

K

ktNETtel PPPPD ,,,,

1,,

tBthtkth

K

ktth PPD ,,,,

1,

tkkeltkel PP ,,min,,,

0,,, tktkel MP 10 tSOC

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

All equations previously formalized

The carbon footprint assessment

NETCOKCOBCOCO EEEE ,,, 2222

tffPE oetBPE

T

tBCO

1,,

1

0,2

K

koetkPE

T

tKCO tffPE

11,,

1

0,

2

tfPE NETetNET

T

tNETCO

,0max 2,,

1

0,2

fe ,fo : the emission (56 tCO2/TJPE) and the oxidation factor (0.995) of the natural gas μ1 ,μ2 : conversion factorsfe,NET : the emission factor of the national electrical mix (0.465kgCO2/kWhe)

ResultsTime 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 schemeHorizon: 3 hoursLocal optima runtime: 10 secondsGlobal optimum runtime (Global Solver): 4 minutes Number of variables: 108

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

Conclusions and research challengesA test-bed facility at the Savona University

Campus has been shownThe SPM sub-systems have been highlightedA first dynamic decision model for the SPM

optimal control has been investigatedA 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.

Possible impact of the SPM on the Savona City

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

ReferencesR. 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, Bessede, 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.