Post on 26-May-2022
Energy Management Systemsfor smart electrical and thermal grids: two case studies
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AGENDA
Energy Management Systems
Energy Management Systems in the electrical grid
H2020 ELSA use case
Energy Management Systems in the thermal grid
H2020 E2D use case
Conclusions2
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ENERGY MANAGEMENT SYSTEMS
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SMART ENERGY SYSTEMS
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Current Energy System
Current energy systems are based on fossil fuels, which make them very reliable and flexible
Energy can be provided “on demand” Energy from Renewable Energy Sources (RES) must be captured
and used immediately
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Smart Energy System (SES)
New technologies and infrastructure to create new form of flexibility
Integration of different source, conversion and storage units, together with demand elements
Combination of electricity and thermal sectors
How to guarantee the flexibility and reliability without fossil fuels?
SES Resources: fuels, solar, wind, geothermal Conversion: from resources to energy Storage: electrical, thermal, fuel Demand: electrical, cooling, heating
SMART GRIDS
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Thermal and electrical grids
Electrical grids connect flexible electricity demands to the intermittent renewable resources
Thermal grids connect the electricity and heating sectors
What makes a grid smart?
Smart grid
A grid that uses modern ICT technologies to gather data and act on grid operation(s)
The physical system(s) must be combined with communication channels
Creation of services on top of the physical components Real-time monitoring, control and decision support
Coordination of each component to improve overall energy efficiency
ENERGY MANAGEMENT SYSTEM
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EMS
Provides tools for the monitoring, control and optimization of energy flows
Enables the intra-collaboration among heterogeneous and distributed energy sources (that can be loads or generators)
Maximizes the system efficiency while satisfying consumers demand and operational/technical constraints
Enables flexibility and energy services to the grid
Domains
In single domain applications, the EMS is in charge of coordinating energy resources that belong to the same energetic domain (i.e., electrical, thermal, etc..)
In multi domain applications, the EMS is in charge of coordinating energy resources that belong to different energetic domains whose contributions concur to the overall energy efficiency
EMS - REQUIREMENTS
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Scalability: the EMS should well scale with respect to the system size
Replicability: the same EMS should be quickly and reliably replicated across different scenarios
Modularity: the EMS should offer different functionalities, each of which can be added or removed in a modular way
Adaptability: the EMS should automatically adapt to the different energy scenarios
EMS - OVERALL CONCEPTReplicable and modular integration of data, tools and services Plug-and-Play
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Meter devices
Consumers load forecast
Renewable forecast
Weather forecasts
Forecast services
Optimization engine
Big
Data
To
ols
Serv
ices
Thermal / ElectricalDemand Response
Flexibility estimator
Renewable model
Storagemodel
Chiller plant / HVACsupervisory control
Data analytics
Fault DetectionDiagnostics
Losses and inefficiencies detector
Thermal gridservices
On
pre
mis
es
Clo
ud
Filteringmethods
Protocols
Predictive maintenance
Energy services
ConsumersThermal/electricalmetering
Renewable metering
Optimization tools Communications
Storage system metering
District/HVAC plant meteringWaste heat
metering
District subsystems
Optimizationbuilder
GUI visualization
District/HVACplantmodel
Waste heat model
Learningmethods
Data Tools
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ELECTRICAL ENERGY MANAGEMENT SYSTEMS
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SMART ELECTRICAL GRIDEvolution of the grid: from passive to active
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Traditional electrical grid Consumers and buildings passive in the grid No distributed and intermittent generation sources No distributed and intermittent loads No distributed operations (monitoring and controls)
Smart electrical grid Consumers and buildings active in the grid Many distributed and intermittent generation sources Many distributed and intermittent loads Many distributed operations (monitoring and
controls)
Coordination of each componentto improve overall energy efficiency
DIGITAL GRID IN THE DIGITAL ERAEvolution of the grid: from active to digital
Big DataInternet of Things
Scott Bedford/Shutterstock6.5TW - 2015 13.5TW - 2040
Flexibility
Available global capacity
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Smart electrical grid Consumers and buildings active in the grid Many distributed and intermittent generation sources Many distributed and intermittent loads Many distributed operations (monitoring and
controls)
SUSTAINABLE AND REPLICABLE SOLUTIONS
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H2020 ELSA – Energy Local Storage Advance system
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Call: H2020, LCE8: Local / small-scale storage Budget: Total: €13.5M Duration: Apr 2015 – Dec 2018 Web-site: www.elsa-h2020.eu
Develop & mature an Electricity Storage System (ESS) based on 2nd life Electric Vehicle (EV) batteries coupled with an Energy Management System (EMS) to deliver smart grid services
DEMONSTRATORS
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Ampere E+ building, Paris, France 250kW max peak load 20kW PV panels 22kWh storage capacity 88kWh upgrade
SASMI, Gateshead College, Sunderland, UK 130kW max peak load 50kW PV panels 33kWh storage capacity
SUSTAINABLE AND REPLICABLE SOLUTIONS
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2nd life batteries in buildings
EV battery: Nissan Leaf 24kWh 2nd life EV battery ~16kWh
SASMI building (UK)Storage system capacity:1st life batteries : 72 kWh2nd life batteries: 48 kWh
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Barriers• Economic uncertainty about 2nd life battery value• Concerns about which entity is responsible for 2nd life
batteries • Remanufacturing costs• Lack of data about battery performance in both 1st
and 2nd life applications
Opportunity
Energy services peak shaving energy arbitrage demand response
INTEGRATED BUILDINGS
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AMPERE E+, Paris, France
La Défense District
Storage Loads
Renewables
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What is missing?
INTEGRATED BUILDINGS
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AMPERE E+, Paris, France
Renewables
La Défense District
Storage Loads
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Building EnergyManagement System
BUILDING EMS FOR SASMI DEMONSTRATION
GUI monitor
Renewable metering
Renewable model
Learningmethods
Renewable forecast
Buildingelectricalmetering
Learningmethods
Buildingload forecast
Storage system metering
Storage model
Optimization builder Optimization engine
Flexibility estimator
Grid Services- Peak Shaving
Optimal Storage Performance
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BEMS RESULTS – AMPERE E+ BUILDINGPeak shaving
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BEMS RESULTS – AMPERE E+ BUILDINGPeak shaving
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BEMS RESULTS – AMPERE E+ BUILDINGEnergy arbitrage
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BEMS RESULTS – AMPERE E+ BUILDINGDemand Response
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BEMS RESULTS – AMPERE E+ BUILDINGDemand Response
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THERMAL ENERGY MANAGEMENT SYSTEMS
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SMART THERMAL GRIDSThe next-generation of more integrated and coordinated thermal grids
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Next-Gen of DHC Systems Low-temperature networks and low-energy buildings
Unlock multiple resources
Increased RES penetration
Demand Response and Demand-side Management, including prosumers
Integration of thermal and electrical grid
Coordination of each componentto improve overall energy efficiency
SMART THERMAL GRIDSEnablers for the Next-Gen DHC system: monitoring, control and decision support
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Challenges Case-by-case approach
Control & optimization design (limited system equipment coordination)
Management and synchronization of distributed and concurrent production
Coordination of generation (different sources) with demand (different characteristics)
E2DISTRICTEnergy Efficient Optimized District Heating and Cooling
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Call: H2020, EE13-2015 Budget: Total: €2M Duration: Feb 2016 - Jun 2019 Web-site: www.e2district.eu
Develop and demonstrated an innovative ICT-basedmanagement platform for real-time monitoring,intelligent control and decision support for energyefficient district heating & cooling.
ENERGY EFFICIENT OPTIMIZED DISTRICTSUsing advanced control and data analytics tools
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Goals Optimize plant-wide and district performances (energy
efficiency, costs) Satisfy operational and technical constraints Guarantee consumer demand and system safety Enable flexible district
How
Intelligent coordination between different DHC plantgeneration equipment, the distribution network and thedemand (end-user)
Prediction of customers thermal demand Adaptive behaviour to demand, weather and operational
variations Modular approach, easy to extend to different district
architectures in a plug-and-play fashion
DISTRICT EMS FOR CIT DEMONSTRATIONDown-selection for CIT demo-site
Storage system metering
Consumersthermalmetering
District plant metering
Learningmethods
Learningmethods
Learningmethods
Consumersload forecast
Optimizationbuilder
Storagemodel
Districtplantmodel
Optimization engine
Thermal gridservices
Big
Data
To
ols
Serv
ices
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CIT Campus
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CIT TEST-BED
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DH Demo-site @ CIT Campus, Cork University campus, 30.000 m2 of useful floor Two gas-fired boilers, 1800 kW each A gas-fired 400 kW CHP Thermal storage
CIT - BASELINE
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CIT – PEAK SHAVING
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CIT – LOAD SHIFTING
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CONCLUSIONS
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CONCLUSIONS
An EMS framework has been presented
Cross-domain capabilities through the coordination of thermal and electrical resources
Demonstrations in real-demo sites and in simulations were performed showing the effectiveness of the approach
ELSA Next: Demand Response controller will be merged with HVAC controller
E2D Next: real demonstration in CIT and proof-of-concept validation in simulated cooling and low-temperature districts
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THANK YOU FOR YOUR ATTENTION
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mangang@utrc.utc.com
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