Applications of distributed systems in the energy field
Daniel Esteban Morales Bondy
Center for Electric Power and Energy, DTU Elektro With inputs from:
Xue Han
Giuseppe Costanzo Alex Prostejovsky Panos Pediaditis Lasse Orda
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Agenda
Crash course on the power system Cyber-physical systems
SYSLAB Cases:
•
Voltage Control•
Distributed Model Predictive Control•
Web-of-cells•
Distribution Locational Marginal Pricing Future ideasCourse info
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Transmission System Distribution System Generation
Transformer Stations Transmission Customer Residential Customer
What are energy systems?
Takin the electric power system as a reference:
•
Large central generators•
Transmitted by “highways” to the city•
Distributed by “streets” to the houses•
Balance between production and consumption3
Who are the stakeholders?
•
The bulk of the energy is traded day ahead•
Real-time balance is maintained by the Transmission System Operator4
Power Producer
(Balance Responsible Generator)
Transmission System Operator
Ancillary Services Day Ahead
Intra-day/hour Power Markets
Balance Responsible
Consumer Retailer Consumer
Ancillary Services Electricity
Who are the stakeholders?
5
•
The bulk of the energy is traded day ahead•
Real-time balance is maintained by the Transmission System Operator:•
Through a distributed system!There are three control stages
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Transmission System Distribution System Generation
Transformer Stations Transmission Customer Residential Customer
But the system is changing!
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Transmission System Distribution System Generation
Transformer Stations Transmission Customer Residential Customer Distributed Generation Flexible Resources
ICT Infrastructure
kW
0003506
The new power system…
New consumption units:
•
Electric Vehicles•
Heat pumpsFluctuating renewable energy production:
•
Photovoltaic cells•
Wind turbines Battery storageSmart meters
New measurements
8
Transmission System Distribution System Generation
Transformer Stations Transmission Customer Residential Customer Distributed Generation Flexible Resources
ICT Infrastructure
kW
0003506
…with new stakeholders
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Power Producer
(Balance Responsible Generator)
Transmission System Operator
Ancillary Services Day Ahead
Intra-day/hour Power Markets
Balance Responsible
Consumer Retailer Consumer
Ancillary Services Electricity
Distribution
System Operator
Prosumer
Aggregator
Flexibility Services Asset Management
Think about:
Goals of distributed systems:
•
Making resources accessible•
Distribution transparency•
Access, location, migration, relocation, concurrency, failure•
Openness•
Scalability10
The future multi-energy system
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Image: Sandro Bösch/ETH Zürich: https://www.pv-magazine.com/2017/12/19/eth-zurich-demonstrates-decentralized-energy-systems/
The power/multi-energy systems are examples of Cyber-physical systems
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What are Cyber-physical systems?
•
Reactive Computation•
Concurrency•
Feedback Control of the Physical World•
Real-Time Computation•
Safety-Critical Computation13
Distributed Systems vs.
Distributed Control
•
Control is an application that uses distributed systems•
Control can come in a wide variety of distribution14
A distributed system organised as middleware.
From Distributed Systems (Tanenbaum & Van Steen)
Taxonomy for Evaluation of Distributed Control Strategies for Distributed Energy Resources
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SYSLAB:
Intelligent distributed energy system in practice
SYSLAB facilities
• 2 wind turbines (10+11kW)
• 3 PV array (7+10+10kW)
• Diesel genset (48kW)
• Office building (20kW)
• 2 family houses
• Dump load (75kW)
• 3 mobile loads (3x36kW)
• Flow battery (15kW)
• B2B converter (104kW)
• 3 NEVIC EV Charging post
• Machine set (30kW)
• Battery testing bays (300+50+50kVA)
• V2G charging post
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SYSLAB Grid topology
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SYSLAB nodes
•
Every unit is supervised locally by its own controller“node”. Nodes contain a computer, measuring and network equipment, data storage, backup power and field buses “in a box”.
•
Each node can communicate with all other nodes.•
The design does not enforce a central controller. The whole system can be run from anywhere.•
21 SYSLAB nodes +20 helper machines, total ~1000 source files19
Cases
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Case 1: Voltage Controller
•
Background: An increasing fraction of PV in the grid + controllable load in resident house -> Fluctuations of voltage in LV network•
Problem formulation: Regulating active power andreactive power of available components to smooth the voltage profile along the feeder, by minimizing the
overall cost of services and power loss.
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External Grid (100kVA)
Line1 (1050 )m Line2 (725 )m Line3 (375 )m
Length [p.u.]
Voltage 1
+6%
-10%
Case 1: Voltage Controller
•
Experiment setup: Fixed topology of a radial feeder (SYSLAB), contracted services with PVs andresidential loads (10 heaters per house) of certain cost.
•
Aggregation: flexible active power and reactive power•
Goal: voltage within the limit band & efficient power delivery22
house
Supervisory Controller
Local
Controller Local
Controller Local
Controller
PV house house
PV house ? PV house
Case 2: Distributed Model Predictive Control
•
Background: Increased electric consumption andproduction in households creates load congestion and reverse flows in the distribution system
•
Problem formulation:The goal of coordinating units is to constrain the aggregated consumption/production of a cluster to a fixed value provided by the DSO, while minimising the electricity bill•
Experiment setup: 1 house, 2 Electric vehicles, 1 battery storage, 1 PV, all in fixed configuration.23
MPC
PCC
Blackboard
PCC
MPC MPC MPC
MPC
Case 3: Web-of-cells
•
Background: EU project exploring new control concepts in the power system•
Problem formulation: How can the power system be controlled such that it is composed of smaller self-balancing areas (cells), i.e. produce and consume locally
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Case 3: Web-of-cells
•
Background: EU project exploring new control concepts in the power system•
Problem formulation: How can the power system be controlled such that it is composed of smaller self-balancing areas (cells), i.e. produce and consume locally
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Case 3: Web-of-cells
•
Experiment setup: 3 cells with diverse production and consumption units in SYSLAB•
Goal: Maintain a frequency close to the nominal26
Case 4: Distribution Locational Marginal Prices
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01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18
23 24 25 19
20 21 22
26 27 28 29 30 31 32 33
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18
23 24 25 19
20 21 22
26 27 28 29 30 31 32 33
EV 1 EV 2 EV 3 EV 4
EV 5 EV 6 EV 7
EV 8 EV 2
EV 1
EV 3 EV 4 EV 5 EV 6
EV 7 PV 1 EV 8
PV 2 PV 3
PV 4 PV 5 PV 6
PV 3 PV 1
PV 4 PV 5 PV 6
PV 2
PV 7
PV 8
Aggregator 1 Aggregator 2
EV i PV i
EV i PV i
Now… In the near future
Case 4: Distribution Locational Marginal Prices
•
Calculate an optimal local price for power given the system constraints28
https://www.eia.gov/todayinenergy/detail.php?id=3150
Case 4: Distribution Locational Marginal Prices
Problem formulation:•
Objective function:•
Cost of serving consuming DERs, like Electric Vehicles (EVs)•
Profit from generated from producing DERs, like Wind Power (WP) and photovoltaics (PVs).•
Constraints:•
Max. and min. charging power of EVs•
Max. and min. generated power from WP and PV•
Energy needs of EVs•
Line limits•
Voltage level limitsOnly the DSO has to consider the last two
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Case 4: Distribution Locational Marginal Prices
Method:•
Use location marginal pricing to represent the congestion cost, as seen by the DSO•
Formulate two types of optimisation problems:•
one for the DSO and•
another for each Aggregator•
Introduce congestion cost into the Aggregator’s problem to...•
make two problems equivalent, according to the dual decomposition method•
Use an iterative method to find the congestion cost in a distributed way30
Case 4: Distribution Locational Marginal Prices
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Uncontrollable DERs With the DLMP method
Future Ideas:
Energy Communities
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Power Plant
Control center / SCADA
Substation
Distributed Power Generation
Electric Vehicle
Wind Turbine DER PV DER
Electric Vehicle
Wind Turbine DER PV DER
Aggregator
The Cloud
Electric Vehicle Wind Turbine DER
PV DER
Electric Vehicle
PV DER
PV DER
Electric Vehicle Electric Vehicle
Electric Vehicle
Control center / SCADA
Overlay Networks
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Overlay Network P2P Protocol
Application
Peer to Peer
Network
Overlay Networks
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Centralized Overlay Network Unstructured Overlay Network Structured Overlay Network
Overlay Networks
Today Overlay Networks can be used, but a centralized data hub approach is still preferable due to:
•
Fixed Contract Relationships•
Performance is good•
Security•
Easier to implement for DSO/TSOTomorrow an Overlay Network is needed because:
•
Changing Contract Relationships•
Highly volatile networks (mobile, residential internet)•
Performance can be guaranteed•
Decentralization•
Autonomy35
Propaganda: Special Course on Distributed Control
Course to be carried out in Lyngby and Risø Learning objectives:
1. Describe relevant applications of distributed control systems in smart grid and energy management context;
2. Explain why smart grid system need to be validated and what elements a validation test needs to define;
3. Recognize a characteristic properties of a distributed control system and classify according to DTU taxonomy
4. Implement a distributed control system based on concrete specifications
5. Examine requirements of a given distributed control system and translate these into quantifiable test criteria
6. Develop and execute an experiment using distributed control in smart grid context within a distributed systems testbed
7. Quantify and evaluate the performance of a distributed control system based on experiment results and test specification
8. Create and communicate a reproducible experiment or validation test
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Point of common coupling
A typical DTU course…
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… in industry practice…
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… in Distributed Control Systems
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Course content
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If you’re interested:
•
Send me an email: bondy@elektro.dtu.dk•
Answer our student questionnaire:•
https://goo.gl/forms/SD1uSclVK24eJDOc241
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