converters for Smart Energy Grids Phuong H. Nguyen - PowerPoint PPT Presentation

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 times (reliable operation) / Electrical Engineering Department / Electrical Energy Systems Group 25-3-2014 PAGE 1

Smart Energy Grids (SEG) • Need for… Control effort Centralized control Current situation Decentralized control Transmission system Distribution system Distribution system / Electrical Engineering Department / Electrical Energy Systems Group 25-3-2014 PAGE 2

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 25-3-2014 PAGE 3

I – Re-routing Power Flows Universal Smart Energy Framework (USEF) http://ec.europa.eu/energy/gas_electricity/smartgrids/doc/xpert_group3_summary.pdf / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 4

I – Re-routing Power Flows Cell Cell Cell … ... Smart Power Distributed Router routing … ... algorithms … ... Moderator PFC agent Multi-Agent System platform / Electrical Engineering Department / Electrical Energy Systems Group 25-3-2014 PAGE 5

I – Re-routing Power Flows • Distributed and Stochastic Optimal Power Flow • Power system → Directed graph G ( V , E ) • Optimal Power Flow → Minimum Cost Flow 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. / Electrical Engineering Department / Electrical Energy Systems Group 25-3-2014 PAGE 6

I – Re-routing Power Flows Pgen1 Pgen3 Pgen5 Pgen16 Pgen18 Pgen20 1 2 3 4 5 6 7 8 9 10 15 HV MV Power generation [MW] NOP 10 5 11 13 15 19 12 14 16 17 18 20 0 -5 0 5 10 15 20 25 30 35 40 45 50 Simulation time [s] Total generation cost Total operating cost [p.u.] 400 15 Total transmission cost P23 10 P56 Power flows [MW] 300 P1112 5 P1020 200 0 -5 100 -10 -15 0 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 Simulation time [s] Simulation time [s] Event occurs Start power routing / Electrical Engineering Department / Electrical Energy Systems Group 25-3-2014 PAGE 7

I – Re-routing Power Flows Inverter Main system source WT emulator Programmable Fix load load Multi Agent System / Electrical Engineering Department / Electrical Energy Systems Group 25-3-2014 PAGE 8

I – Re-routing Power Flows 3000 Inverter 1 - P, W 2250 1500 750 0 0 20 40 60 80 100 120 time, sec. 500 Inverter 3 - P, W 250 0 -250 -500 0 20 40 60 80 100 120 P.H. Nguyen, W.L. Kling, and P.F. Ribeiro, “Smart power router: a flexible time, sec. agent- based converter interface in active distribution networks,” IEEE 25-3-2014 PAGE 9 Transactions on Smart Grids , 2(3), 487-495, 2012.

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 operators. • Partners: − Mastervolt − TU Eindhoven − Alliander − AmsterdamSmartCity − Greenspread InEnergie / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 10

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 26-3-2014 PAGE 11

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 25-3-2014 PAGE 12

II – Balancing local supply-demand • Smart Grid requires novel control methods: Market- Based Control (MBC) • Rational behavior in the market: Intelligent software agents • PV SiMS: Optimization of residential energy storage coupled with PV generation / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 13

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 26-3-2014 PAGE 14

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 26-3-2014 PAGE 15

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 26-3-2014 PAGE 16

III – Regulating voltage variations • FP7 – INCREASE project: INCreasing the penetration of 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 25-3-2014 PAGE 17

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 26-3-2014 PAGE 18

III – Regulating voltage variations • INCREASE’s proposed solutions / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 19

III – Regulating voltage variations • Different control strategies in INCREASE Type of control Location Response time Local control Inverter terminals ms Fast control Agent Minutes scale Slow control Higher level Agent Hours scale / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 20

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 26-3-2014 PAGE 21

III – Regulating voltage variations • Droop control for voltage variation / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 22

III – Regulating voltage variations • MAS – fast control actions Δ𝑅 = 𝐾1 Δ𝑄 𝐾2 ΔƟ Δ𝑊 𝐾3 𝐾4 − Assuming no reactive power control and Power Factor =1 −1 𝐾 4 [Δ𝑊] Δ𝑄 = 𝐾 2 − 𝐾 1 𝐾 3 −1 −1 𝐾 4 𝑇𝑓𝑜𝑡𝑗𝑢𝑗𝑤𝑗𝑢𝑧 𝑁𝑏𝑢𝑠𝑗𝑦 = 𝐾 2 − 𝐾 1 𝐾 3 / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 23

III – Regulating voltage variations • MAS – fast control actions M A1 A2 An Δ V =0.1 pu Sensitivity factor calculation RFP - Δ P Δ P 1 , cost Δ P 2 , cost Δ P n , cost Dispatching computation Δ P new 1 Δ P new 2 Δ P new n / Electrical Engineering Department / Electrical Energy Systems Group 26-3-2014 PAGE 24