massimo la scala - hevsmulti-energy system, smart energy system or briefly smart energy grids the...
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Massimo La Scala
Politecnico di Bari – Italy
Overview: the story of a transition
Smart grid & Smart cities applications
Optimization issues
Tangible multiple energy carrier grids, focus on grids (long term investments)
Experience gained by our research group
Implemented projects
Lessons learned & future work
Recent projects (power system group at Politecnico di Bari)
Smart Grids for multi-utilities: Poliba, AMGAS SpA, AMET SpA, 1.3
M€ 2009-2013.
“SOLAR”, Università del Salento & Poliba 14 M€, 2011-2015
METERGLOB – 2.2 M€, 2011-2015.
RES NOVAE – PON Smart Cities con ENEL, GE, IBM, Università di
Cosenza, ENEA 23,4 M€ 2012-(2015).
Companies Cluster (9) 2014 -2017, “Energy Routers and cloud
computing for smart grids”, 2.5 M€.
Lab Innovative conversion processes PrInCE 12.4 M€ 2012-2015
Lab ZERO (Zero Emission Lab)”, Politecnico di Bari e ENEA, 2.5 M€,
2014-(2017).
… others
Smart Grids Functionality
Adaptive relays (DG frequency relays ) Anti – Islanding Fast faulted line identification Logic selectivity (interruption < 1’’ ) Voltage Regulation (DG partecipation to VVO)
Puglia 2600 MW PV, 2400 MW wind 170 M€ project (2014-2018) N. 202 primary substation/satellite Centers N. 1.400 MT Lines N. 7.900 controlled secondary substations N. 30.000 Smart Info N. 130 Charging infrastructure
Puglia Active Network a regional Smart Grid
Big Companies Role
Electrical Distribution Systems
In the path towards smart grids, distribution systems face the greatest challenge
traditionally passive networks
built with a straightforward radial (or multi-radial) configuration
minimal ability of monitoring and controlling power flows
Luckily, distribution systems undergo profound modifications due to:
distributed energy resources (DERs),
smart metering (in Italy 2nd massive deployment) ,
storage/PHEVs
AMET MV Distribution in Trani Trani : 56000 inhabitants
900 buses, 1000 lines, 500 loads nodes,
100 remotely controllable disconnectors,
35 MW load peak 60 MW PV requested
Upgrading AMET Distribution Control center
SCA
DA
°°
ODPF
TP
SE
ONR
VVO
FPFS
CVP
SC
AR
SM
ADMS
• CVR: Conservative Voltage Regulation
• SE: State Estimator
• VVO: Voltage – Var Optimization
• SMS: Storage Management System
• ODPF: Optimal Distribution Power Flow
• EDA: Environmental Data Acquisition
• ONR: Network Reconf iguration
• MMS: Maintanance Monitoring System
• AR: Adaptive Relaying
• CA: Contingencies Analysis
• SM: Switch Management
• FPFS: directional Fault and Power
Failure System
• SDF: Supply and Demand Forecast
• TP: Topology Processor
• SC: Short Circuit analysis
• CVP: Capacitor/Voltage regulator
Placement
• OTS: Operator Training Simulator
DISTRIBUTION NETWORK
• ULTC: UnderLoad Tap Changer
• RCS: Remote Controlled Switches
• DG: Distributed Generation
• SCs: Switching Capacitors
• SF: Storage Facilities
AMI
• MDI: Meter Data Integration
• AMR: Automatic Meter Reading
SDF
CA
SMS
CVR
EDA
OTS
Off-line
Real time concentrator
AMR
AMR
AMR
concentrator
SERVER
MDI
GIS INTERFACE
MMS
EnvironmentalMonitoring
Stations
CONTROL CENTER
ADMS
Signals from RTUs
AMI
DISTRIBUTION NETWORK
RCS
SCs
ULTC
DG
DGSF
A basic piece of SW: The Three Phase Optimal Power Flow
Optimizes active and reactive control resources in the presence of unbalanced conditions
Controls both three-phase loads/generation and single-phase ones
Unbalanced conditions
Object-oriented environment, improved representation of loads and other components
Include new control variables and devices
Load curtailment / active power control
Volt-Var Optimation (VVO) ( Controlling DG and tap changers )
Conservative Voltage Regulation (CVR)
Another piece of SW: ONR formulation
Optimal Network Reconfiguration code evaluates the necessary switching maneuvers for implementing the best grid configuration which minimizes losses or generation curtailment during congestion management (or increases Host capacity)
ONR is a MINLP formulated as
),,(Cmin obj,
uxVux
subjected to
Cobj is the objective function to be minimized
V is the set of nodal voltage
x is the set of continuous control variables (for example generated active and reactive power)
u is the set of discrete control variables (i.e. the open/closed status of each switch)
0),,(f uxV 0),,(g uxV
ONR algorithm
The problem is solved by
decomposing the MINLP
into two problems
A simulated annealing
module searches and
selects radial network
configurations, whereas a
nested code performs
Distribution Three-Phase
Load Flow (DTLF) or solves
a more complex optimization
problem (for example a
TDOPF)
select or detect initial
configuration
k=0 u=u0
evaluate objective function C
through DTLF or TDOPF
k=k+1
close a random switch
search all switches that can be opened; open one
randomly and set new configuration u=uk
is uk a new
configuration
?
evaluate objective function Ck
through DTLF or TDOPF
Ck< Ck-1?
yes
no
yes
accept new configuration uk
and reduce temperature T
no select a random number R and
set probability P(T)
R< P(T)?yes
T< Tmin?
yes
STOP
no discard new configuration and
restore old one
u=uk-1
no
Congestion management
32 MW produced by PV causing line congestions
Generation curtailment might be necessary
In this case, ONR finds an optimal solution with no generation curtailment
0 100 200 300 400 500 600 700 800 9000
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
# line
I / Im
ax [p.u
.]
0 5 10 15 20 25 300
0.5
1
1.5
2
2.5
iteration #
C [p
.u.]
0 100 200 300 400 500 600 700 800 9000
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
# line
I / Im
ax [p.u
.]
befo
re O
NR
aft
er
ON
R
ENEL Distribuzione, IBM, GE, ENEA , Poliba, UniCal,
SMEs, 23.4 M€
LV Grid: a Grey area of the grid
Most of challenges: DER, PV, EVs, residential DR, ….
Voltage and currents profiles are not known and almost never monitored, except for aggregated measurements at MV/LV interface
Few measurements are used to represent the state of hundreds of nodes and branches that are connected to the main MV feeder.
Load and generation imbalance is in general undetermined at LV level.
The exact location of single-phase objects (loads and generators) is not known at central level and, sometimes, not known at all.
LV active/reactive control (District Level -Bari)
case num. active
resources
num. reactive
resources
active
control
[ΔkW]
reactive
Control
[ΔkVAr]
D1 48 21 82.12 7.02
D2 48 0 83.33 0.00
D3 48 63 82.10 27.51
case num. active
resources
num. reactive
resources
active
control
[ΔkW]
reactive
control
[ΔkVAr]
losses
[%]
E1 0 63 0.00 144.41 6.39
E2 48 0 132.89 0.00 6.24
E3 48 63 99.21 150.29 6.15
Load curtailment for congestion relief (Street Disconnectors cabinets remotely controlled)
Active/reactive control for power losses
LV SCADA ? Smart meters available (in Italy 2nd deployment, Open Fiber, Fiber to
the Home, 1Gbps, Bari almost completed)
Automated Measurement Infrastructure (AMI)
Relevant number of non synchronized measurements from SMs Robust State estimation and large use of pseudomeasurements
New codes should deal with complexity of multi-phase unbalanced models
Few Low Voltage Distribution State Estimation tested on small systems
First trials of auto-detected inventory of LV grids; tests carried out to identify where a smart meter is physically located (both phase and line) [J. Varela et. al. 2015]
Smart charging EV stations
EVs can be a problem for urban grids
But the new infrastructure may become a monitoring and controlling resource on the LV grid
A car is parked for the 96% of the day, EV-charging power can be considered at least as a flexible load to be dispatched at occurrence along a wide time interval.
or in some cases (V2G) can be employed as a source
creation of innovative ancillary services, for ex. power balancing in the presence of RES
Optimal amount of EV-charging power in the presence of line and transformer congestions, voltage drops, power counterflows, phase imbalance, etc.
Charging Infrastructure Remote Management Enabling market players in providing services to the customers
Optimal charging Vision
More LV components Can we use more power electronics in LV ? Easy?
Custom power for PQ ( DVR, Dstatcom, UPQC,…)
New LV distribution topologies ( more meshes, cellular, microgrids)
Power flow control (active and reactive) in LV (B2B, UPFC between feeders)
Utility-Microgrid Load Sharing by B2B
Power electronics Voltage regulators
New smart charging stations: more active pedestals (reactive control, dispersed storage, pollution control, IoT, etc.)
V2G
DC grids ? Smart Transformer
«Food distribution, energy provision, water supply, waste removal, information technology and susceptibility to pandemics are all the Achilles heels of cities»
( World Bank 2010)
Power Smart Grids show the promise to integrate electricity generated by renewables into the energy mix, and allow a feasible e-mobility, to provide efficient and reliable widespread demand response, to utilize storage for peak reduction and shifting, etc.
1/3 of final energy use is electricity in towns
Why Smart Cities ?
Smart Energy Grids & Smart Cities
The expansion of the Smart Grid term is usually addressed as Multi-Energy System, Smart Energy System or briefly Smart Energy Grids
The multiple energy carrier approach takes into account all relevant energy carriers ( electricity, natural gas, liquid fuels) and services such as (heating, cooling, transportation, etc.) and multiple storage options (electric, thermal, hydraulic head, hydrogen,…)
This approach can provide more flexibility in the optimization and planning tools for increased energy efficiency and sustainability
A hybrid energy system (district)
gas turbine
turbine PV
batterystoragesystem
pump
water reservoir
electricloads
thermalloads
gas boiler
grid supply / market
natural gas distribution
grid
Proposed control architecture (I)
system state
(power, storage
and fuel
availability) DB parameters
time varying
trajectories
formulation and solution of a
discrete optimal control problem
set-points of
all controlled
devices
field data
real time
control
Mathematical formulation (I)
The optimization problem aims to minimize operative
costs along a selected time window T,
with px being the instant injected or demanded power, and
subject to energy balancing equality constraints
and technical minimum and maximum constraints
T
tx
xx ttpcmin0
d)(p
t
tpkt
tpke
xxx
xxx
0)(
0)(
x,tptpp xxx maxmin )(
Mathematical formulation (II)
The introduction of storage units the following differential
equations and constraints related to the generic qs quantity
of energy stored must be added to the formulation
with
and
Inequality constraints take into account the limitations on
storing capability
The minimum state of charge (SOC) can be constrained
stqtfq sss ))(),(( p
0(0) ss Qq
s,tqtqq sss maxmin )(
Predictive Dispatch Across Time of Hybrid Isolated Power Systems
Developed within the project “GEI5 – Green Energy Island” with two industrial partners
Project aimed at designing a pre-fab stand-alone micro grid based on a hybrid energy system configuration
Optimization of a complex system composed by many competing generation and storage technologies
Applications: dispersed generation, civil protection, remote installations (oil wells), armed forces, etc.
This methodology was applied on energy hubs and multi energy carrier systems (including heat and cool balancing equations)
Isolated Hybrid energy systems
Urban energy optimization
Urban Energy hub or grids of energy hubs
The components within the hub can
establish redundant connections between
inputs and outputs.
Side effects: Optimal Gas Flow
SCADA implementation
OGF equivalent to OPF in power
Some simulation tools
Infrastructure DER presence
Simulation of Control
Simulation time step
MESCOS Electrical-Thermal
Yes Yes
(complex) Second
CITYSIM Thermal Yes No Hourly
ENERGYPLUS Thermal Yes No Minute
ENERGYPLAN
Electrical-Thermal-Transport
Yes Yes (simple) Hourly
HYBRID2 Electrical Yes Yes
(complex) 5
minutes
GRID-LAB Electrical (detailed)
Yes Yes
(complex) Sub-
seconds
RETSCREEN Electrical-Thermal
Yes Yes (simple) Hourly
RAPSIM Electrical (detailed)
Yes No Minutes
Need for more inter-related infrastructural issues (how the operation or the demand on one infrastructure affects the others)
Concurrent optimization of more services: ex. Mini-hydro, gas turbo-expanders and compressors, e-mobility, gas-resiliency (buried, alternative to electricity), etc.
Buildings modelling, urban microclimate
Side effects: Suburban District Regeneration
“San Paolo” district (Bari, Italy)
Inspiration from the “Appleseed project”: reduce energy costs to create business opportunities
District optimization & design : integrating short term operation for long term planning
The overall problem: trigeneration + on-site hydrogen production
District optimization & design : integrating short term operation for long term planning
uzz
,max ROI
N
k
kk RCmin1
),(-),( uzuzu
0uzh ,
0uzg ,
subject to
Decomposition of the overall problem into a two-stage optimization
Barriers
Insufficient knowledge and skills for interdisciplinary collaboration (synthesis of multiple disciplines), training in conducting interdisciplinary research
Limited funding available for interdisciplinary research, specialization is favored
Funding criteria not fit for measuring interdisciplinary research
Publication process, academic promotion favors mono-disciplinary research and specific bibliometric indices
University systems not fully able to cultivate interdisciplinary researchers
Early and mid career energy researchers suffer most when choosing interdisciplinary studies
Interdiscplinary power system research
Power people can give a contribution? (rhetoric question…)
Power system realm has always been involved in, at least, a multidisciplinary approach (additive juxtaposition of disciplines)
Power industry has always paid a special attention to social issues (authorization processes, national energy policies, privacy issues, etc.)
Methodologies developed in the power area can be effectively utilized in other realms.
Different energy infrastructures share the same needs for more automation, optimization in operations, better performances and efficiency.
Large use of optimization tools.
Living labs are based on the:
participation and involvement of final users early on in the innovation process;
experimentation and demonstration of scientific and technological outcomings into the real world;
experimentation of new governance models for including users in the innovation process.
These principles constitute the pillars of the LabZERO organization.
• Since the proposal, LabZERO is based on a close cooperation with thirty initial partners including large companies and SMEs (small medium enterprises) as industrial developers, public territorial bodies, public administrations and Municipalities as potential users of know-how and demonstrators, industrial associations for the dissemination of results.
ELECTRICITY RTDS & Fast Prototyping
Smart Mobility, Storage & Smart Grids;
Grid Analysis and Testing Microgrid Test Facility
MECHANICS Eco-friendly refrigeration;
Wind turbines prototyping; Biomass Plants Testing
ENEA Solar heating & cooling;
Nanocomposites and nanostructured materials
BUILDINGS Mechanical and
thermohygrometric characterization of materials
and structures; Non destructive testings
Smart Grids, MV/LV Distribution Automation, Protections The test facility consists of a microgrid which can be operated in both stand-alone and grid-connected mode. The microgrid in LabZERO consists of the following components: • SCADA system; • photovoltaic generator; • interchangeable mini-wind turbines; • cluster of fans dispatchable load through inverter
control; • controllable loads; • Battery Energy Storage System based on Li Fe
PO4 technology equipped with a local controller for PQ and PF control;
• V2G recharging pedestal for EVs; • small size biomass-fired combined-cycle
cogenerator The microgrid is interfaced with the OPAL RTDS to execute PHIL (Power Hardware in the Loop) experimentations, so it is possible to connect to the test facility any (simulated) power component.
More details in: • S. Bruno, S. Lamonaca, G. Rotondo, U. Stecchi, M. La Scala, “Unbalanced Three-phase
Optimal Power Flow for Smart Grids”, IEEE Trans. On Industrial Electronics Vol. 58, n. 10, October, pp. 4504-4513.
• M. Bronzini, S.Bruno, M. La Scala, R. Sbrizzai, “Coordination of Active and Reactive Distributed Resources in a Smart Grid” , PowerTech 2011, Trondheim, 19-23 June, 2011.
• S. Bruno, M. La Scala, U. Stecchi, “Monitoring and Control of a Smart Distribution Network in Extended Real-Time DMS Framework”, Cigré International Symposium - Bologna, Italy, September 13-15, 2011.
• S. Bruno, S. Lamonaca, M. La Scala, U. Stecchi, "Integration of Optimal Reconfiguration Tools in Advanced Distribution Management System", IEEE PES Innovative Smart Grid Technologies Europe 2012, October 14 -- 17, 2012, Berlin, Germany.
• M. La Scala, A. Vaccaro, A.F. Zobaa, “ A Goal Programming Methodology for Multiobjective Optimization of Distributed Energy Hubs Operation” Applied Thermal Energy, vol. 71, p. 658-666, ISSN: 1359-4311.
• S. Bruno, M. Dassisti, M. La Scala, M. Chimienti, C. Cignali, E. Palmisani, “Predictive Dispatch Across Time of Hybrid Isolated Power Systems”, IEEE Transaction on Sustainable Energy, Vol. 5, No. 3, pp. 738-746, July 2014.
• S. Bruno, M. Dassisti, M. La Scala, M. Chimienti, G. Stigliano, E. Palmisani, “Managing Networked Hybrid-Energy Systems: A Predictive Dispatch Approach”, 19th World Congress of the International Federation of Automatic Control (IFAC 2014), August 24-29, 2014, Cape Town, South Africa.
• M. La Scala, Editor, From Smart Grids to Smart Cities. ISBN 978-1-84821-749-2, 54 pp., Wiley-ISTE, 2017.