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Applications of AC/DC converters for Smart Energy Grids Phuong H. Nguyen p.nguyen.hong@tue.nl Smart Energy Grids (SEG) Processing burden information Controlling properly at the right moment (real-time control) Balancing supply-demand at all

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Applications of AC/DC converters for Smart Energy Grids

Phuong H. Nguyen p.nguyen.hong@tue.nl

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Smart Energy Grids (SEG)

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 1 25-3-2014

Processing burden information Controlling properly at the right moment (real-time control) Balancing supply-demand at all times (reliable operation)

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Smart Energy Grids (SEG)

  • Need for…

/ Electrical Engineering Department / Electrical Energy Systems Group

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Centralized control Control effort Transmission system Distribution system Distribution system Current situation Decentralized control

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Applications of AC/DC converter

I. Re-routing power flows

EOS – EIT project

II. Balancing local power supply-demand

TKI Switch2SmartGrids – PVSiMS project

  • III. Regulating voltage variations

FP7 – INCREASE project

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 3 25-3-2014

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I – Re-routing Power Flows

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 4 26-3-2014

Universal Smart Energy Framework (USEF)

http://ec.europa.eu/energy/gas_electricity/smartgrids/doc/xpert_group3_summary.pdf

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I – Re-routing Power Flows

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 5 25-3-2014

Moderator agent PFC

Multi-Agent System platform Cell Cell Smart Power Router …... Cell …... …...

Distributed routing algorithms

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I – Re-routing Power Flows

  • Distributed and Stochastic Optimal Power Flow
  • Power system → Directed graph G(V, E)
  • Optimal Power Flow → Minimum Cost Flow

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 6 25-3-2014

P.H. Nguyen, W.L. Kling, and J.M.A. Myrzik, “An application of the successive shortest path algorithm to manage power in multi-agent system based active networks,” European Transactions on Electrical Power, 20(8), 1138-1152, 2010.

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I – Re-routing Power Flows

/ Electrical Engineering Department / Electrical Energy Systems Group

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5 10 15 20 25 30 35 40 45 50

  • 5

5 10 15 Simulation time [s] Power generation [MW] Pgen1 Pgen3 Pgen5 Pgen16 Pgen18 Pgen20 5 10 15 20 25 30 35 40 45 50

  • 15
  • 10
  • 5

5 10 15 Simulation time [s] Power flows [MW] P23 P56 P1112 P1020

HV MV 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 10 20 NOP

5 10 15 20 25 30 35 40 45 50 100 200 300 400 Simulation time [s] Total operating cost [p.u.] Total generation cost Total transmission cost

Event occurs Start power routing

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I – Re-routing Power Flows

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 8 25-3-2014

Main source Multi Agent System Fix load Programmable load WT emulator Inverter system

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I – Re-routing Power Flows

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20 40 60 80 100 120 750 1500 2250 3000 time, sec. Inverter 1 - P, W 20 40 60 80 100 120

  • 500
  • 250

250 500 time, sec. Inverter 3 - P, W

P.H. Nguyen, W.L. Kling, and P.F. Ribeiro, “Smart power router: a flexible agent-based converter interface in active distribution networks,” IEEE Transactions on Smart Grids, 2(3), 487-495, 2012.

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II – Balancing local supply-demand

  • TKI Switch2SmartGrids – PVSiMS project
  • Better matching of supply and demand with:

− New technology for electricity storage and advanced control system − New business relationships between electricity consumers, producers, and grid

  • perators.
  • Partners:

− Mastervolt − TU Eindhoven − Alliander − AmsterdamSmartCity − Greenspread InEnergie

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 10 26-3-2014

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II – Balancing local supply-demand

  • Residential Energy Storage – Hardware
  • Components:

− Li-Ion battery: 5 – 10 kWh − PV Inverter − Combi (Mastervolt):

  • Battery charger
  • Power flow management
  • Monitoring and communication device

/ Electrical Engineering Department / Electrical Energy Systems Group

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II – Balancing local supply-demand

  • Residential Energy Storage – Inverter
  • Interoperable with all the inverters
  • Current Inverter used: Mastervolt ES4.6LT

− Inverter’s Specifications:

  • Battery charger
  • Transormless
  • Nominal Power: 4600VA
  • Grid Voltage 230V +15%/-20%
  • Power factor: > 0.99
  • Reactive power control: -0.90 inductive / +0.90 capacitive
  • Standby power: < 1 W
  • EU efficiency: 97.0 %
  • Max. efficiency: 97.5 %
  • AC connection: Amphenol IP67 connector, suitable for 4-6 mm² cables
  • Efficiency MPP trackers (static/dynamic): 99.9 % / 99.8 %

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 12 25-3-2014

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II – Balancing local supply-demand

  • Smart Grid requires novel

control methods: Market- Based Control (MBC)

/ Electrical Engineering Department / Electrical Energy Systems Group

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  • Rational behavior in the

market: Intelligent software agents

  • PV SiMS: Optimization of

residential energy storage coupled with PV generation

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II – Balancing local supply-demand

  • Coordinating DERs by establishing a local energy

market

  • Local market area definition: geographical area under one

balance responsible party (ETSO-e).

  • Main benefits:

− Local consumption of local renewable energy production. − Market-based techniques achieve good system-wide properties despite of self-interested participants − Bids act as abstraction of technical characteristics of components. − Solving local knowledge problem.

/ Electrical Engineering Department / Electrical Energy Systems Group

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II – Balancing local supply-demand

  • Benefits of S-PV units in the context of a local

energy market

  • DSO: mitigation of intermittent nature of PV generation through

storage, market integration of distributed energy resources, possibility of using districts as VPPs for e.g. ancillary services.

  • Aggregator: Storage offers flexibility for realizing VPP business

cases.

  • Prosumer: Maximization of self-consumption of PV generated

electricity, minimizing electricity bill, participate in the reduction of CO2 emissions.

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 15 26-3-2014

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II – Balancing local supply-demand

  • Research areas for PV SiMS project:
  • Local energy market design for the presence of S-PV units.
  • Machine learning techniques for consumption and generation

forecasting.

  • Individual device agent design.
  • Multi-agent system design.

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 16 26-3-2014

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III – Regulating voltage variations

  • FP7 – INCREASE project: INCreasing the penetration
  • f Renewable Energy sources in the distribution grid by

developing control strategies and using Ancillary SErvices

  • 13 partners – 4 different coutries
  • Request EC budget: 3 m€
  • Duration: Sept. 2013 – Dec. 2016

/ Electrical Engineering Department / Electrical Energy Systems Group

PAGE 17 25-3-2014

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III – Regulating voltage variations

  • Main role of TU/e
  • Development of voltage mitigation algorithm
  • Agent based coordinative control
  • Validation

− Simulation − Lab test − Field Trials

/ Electrical Engineering Department / Electrical Energy Systems Group

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III – Regulating voltage variations

  • INCREASE’s proposed solutions

/ Electrical Engineering Department / Electrical Energy Systems Group

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III – Regulating voltage variations

  • Different control strategies in INCREASE

/ Electrical Engineering Department / Electrical Energy Systems Group

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Type of control Location Response time Local control Inverter terminals ms Fast control Agent Minutes scale Slow control Higher level Agent Hours scale

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III – Regulating voltage variations

  • Voltage unbalance
  • The voltage unbalance

can be solved using a combination of:

− Three-phase damping control strategy for three- phase grid connected DRES − Single-phase DRES with included droop properties and controlled by MAS

/ Electrical Engineering Department / Electrical Energy Systems Group

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III – Regulating voltage variations

  • Droop control for voltage variation

/ Electrical Engineering Department / Electrical Energy Systems Group

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III – Regulating voltage variations

  • MAS – fast control actions

Δ𝑄 Δ𝑅 = 𝐾1 𝐾2 𝐾3 𝐾4 ΔƟ Δ𝑊

− Assuming no reactive power control and Power Factor =1 Δ𝑄 = 𝐾2 − 𝐾1 𝐾3

−1𝐾4 [Δ𝑊]

𝑇𝑓𝑜𝑡𝑗𝑢𝑗𝑤𝑗𝑢𝑧 𝑁𝑏𝑢𝑠𝑗𝑦 = 𝐾2 − 𝐾1 𝐾3

−1𝐾4 −1

/ Electrical Engineering Department / Electrical Energy Systems Group

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III – Regulating voltage variations

  • MAS – fast control actions

/ Electrical Engineering Department / Electrical Energy Systems Group

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M A1 A2 An

ΔV =0.1 pu RFP - ΔP Sensitivity factor calculation ΔP1 , cost ΔP2 , cost ΔPn , cost Dispatching computation ΔPnew 1 ΔPnew 2 ΔPnew n

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III – Regulating voltage variations

/ Electrical Engineering Department / Electrical Energy Systems Group

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Vmax = 1.05 p.u =241.5 V ; Ideal inverter

10 20 30 40 50 60 70 80 90 215 220 225 230 235 240 245 250

Time (15 mins block) Volatage ph-N (V)

Original Voltage Bus 2-OVB 2 OVB 3 OVB 4 OVB 5 OVB 6 Controlled Voltage Bus 2-CVB 2 CVB 3 CVB 4 CVB 5 CVB 6

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Conclusions

  • Smart Energy Grids – High uncertainty
  • Needs of
  • Smart power electronic interfaces
  • Distributed intelligence
  • Smart integration framework

/ Electrical Engineering Department / Electrical Energy Systems Group

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Thank you!

p.nguyen.hong@tue.nl