Automated Distribution Systems within Smart Grid Environment

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1 Faculty of Engineering Tanta University Automated Distribution Systems within Smart Grid Environment

2 Automated Distribution Systems within Smart Grid Environment Prof. Ahmed Mohamed Refaat Azmy Faculty of Engineering - Tanta University Department of Electrical Power and Machines Engineering Director of the automated library project in Tanta University Vice Dean for Community Service and Environment Development azmy@f-eng.tanta.edu.eg - azmy@uni-duisburg.de

3 Electric Power Systems Research Lab Renewable Energy Systems Agenda Research Lab Advanced Distribution Automation System Optimizing Distributed Generation for Residential Application 1

4 Electric Power Systems Research Lab It mainly concerns with producing simulation models for the various components of the power system. In addition, some experimental prototypes are being investigated such as energy storage systems. Also, the lab aims at producing designs, optimal solutions and economic studies for power systems with special concern with the DGs 2

5 Electric Power Systems Research Lab Position Director Prof. Ahmed M. Azmy Name Members Research Assistants Assoc. Prof. Diaa-Eldin Mansour Dr. Doaa M. Yehia Dr. Hosam A. Abdel-Ghany Eng. Mahmoud H. Elkazaz Eng. Mohamed R. Elkadeem Eng. Abd-Elaziz M. Gebriel Eng. Eatmad W. Nahas Eng. Eman K. Bilal 3

6 Electric Power Systems Research Lab Research Areas Economic aspects and dynamic simulation of DG Optimization techniques in power systems especially within smart grid environment Protection-Device coordination in the presence of DG units Demand-side management and dynamic pricing Fault Location Technique of distribution systems containing DG units Thermal Inspections for power system equipment Smart Grids Storage Systems 4

7 Renewable Energy Systems Research Lab Renewable energy systems lab mainly concerns with the research and studies related to the design and performance enhancement of the various components of renewable energy systems. The lab assets include PV modules used for solar energy studies. 5

8 Renewable Energy Systems Research Lab Position Director Members Research Assistants Name Prof. Ahmed M. Azmy Prof. Essam Eddin M. Rashad Assoc. Prof. Diaa-Eldin Mansour Dr. Mohamed K. El-Nemr Dr. Said M. Allam Dr. Doaa M. Yehia Eng. Mohamed G. Mousa Eng. Mahmoud H. Elkazaz Eng. Mahmoud F. Elmorshedy Eng. Rawda Ramadan Eng. Mohamed A. Almozayen 6

9 Renewable Energy Systems Research Lab Research Areas Dynamic modelling and control of renewable energy sources Optimal design and operation of renewable energy systems Improving fault ride through capability of DFIGbased wind turbines Dynamic simulation of stand-alone and hybrid renewable energy units 7

10 Advanced Distribution Automation System (ADAS)

11 Advanced Distribution Automation System Conventional networks do not involved much automation i.e. rely mainly on manual operations, and have not any communication or information exchange (blind system) Advanced distribution automation system (ADAS) A fully controllable and flexible distribution system that will facilities the exchange of electrical energy and information between participants and system components 8

12 Advanced Distribution Automation System Implementation and Evaluation of an ADAS for MV networks would cause: Self-healing grid Improving Reliability level of the distribution network 9

13 Advanced Distribution Automation System Technical and financial issues facing traditional DN, include: Increasing the outages time and outages costs Low level of service reliability Increasing the O&M cost Increasing the applied penalties from regulators Low customer satisfaction min Healthy condition Time for customer trouble call outage report Crew setup/ Travel time Fault Investigation/ Patrol time for FL Manual switching actions for FISR Repair time & Back to normal condition Fault 5-10 min min min min hrs Time required for manual restoration process 10

14 Advanced Distribution Automation System ADAS benefits Applica. Increase service reliability Improve supply quality Increase operation Efficiency Optimize capital cost Reduce O&M costs Customer satisfaction Sell more kwh FLISR VVCO DGRM OFR AMAs AMR DSM FLISR: Fault Location, Isolation and System Restoration 11

15 Advanced Distribution Automation System Detect the fault quickly Locate the fault accurately Isolate only the faulted section Restore the service as quickly as possible < 5 min. Repair time & Back to normal condition Service restoration to healthy customers using DA Crew setup/ Travel time Healthy condition Fault < 5 min min hrs Automated FLISR processes 12

16 Advanced Distribution Automation System Optimal automation level = 64 % 13

17 Advanced Distribution Automation System Reliability assessment Network topology Reliability data - Components failure rate - Switching time - Repairing time - Other reliability data Reliability analysis Algorithm Load point indices r, U, EENS System indices SAIDI, SAIFI, AENSI,... Non-Automated (Conventional) Protection mechanism & operation philosophy Automated 14

18 Optimizing Distributed Generation for Residential Application

19 Optimizing Distributed Generation for Residential Application Thermal power A fuel Cell (or any DG) Electrical grid Natural gas M 1 Electrical power Energy meter (1) Energy meter (2) M 2 Gas burner Thermal load Residential load Electric load M1: gas flow meter for the fuel cell M2: gas flow meter for the residential load Structure of the residential system supplied by a fuel cell 15

20 Optimizing Distributed Generation for Residential Application Thermal load Natural Gas supply NG meter NG meter AC Bus bar PEM FUEL CELL Thermal power Electrical power DC/AC inverter Photovoltaic system DC/AC inverter Electrical load Two way smart meter Electric Grid 16

21 Optimizing Distributed Generation for Residential Application Local control center Home 1 Home 2 Home 3 Home 4 PEMFC 7 kw PEMFC 5 kw PEMFC 3 kw PEMFC 2 kw PV 3 kw PV 2 kw PV 1 kw PV 0.5 kw Elect. Load 1 Therm. Load 1 Elect. Load 2 Therm. Load 2 Elect. Load 3 Therm. Load 3 Elect. Load 4 Therm. Load 4 17

22 Optimizing Distributed Generation for Residential Application Cost n = {C purc,i C sold,i + C NGFC,i i=1 + C NGRL,i + C o&mfc,i +C o&mpv,i + SC i } Cost: Total daily operating cost of the whole System C purc,i : Daily cost of purchased electricity by home i C sold,i : Daily income of the sold electricity by the home i C NGFC,i : Daily cost of NG consumed by the FC at home i C NGRL,i : Daily cost of purchased NG to feed the remaining thermal loads in home i C o&mfc,i, C o&mpv,i : Daily operating and maintenance cost of the FC and PV at home i SC i : Daily start-up cost of the FC in home i n : Number of homes 18

23 Optimizing Distributed Generation for Residential Application Electrical power (kw) Total electric load Total FCs output (Elect) Total PV output Time (h) Thermal power (kw) Total thermal load Total FCs output (Therm) Time (h) 19

24 Optimizing Distributed Generation for Residential Application Isolated mode scenario Electrical power output (kw) 15 Elect. load PV output FC opt. settings Time (h) Thermal power output (kw) Thermal load FC thermal output Time (h) 20

25 Optimizing Distributed Generation for Residential Application Communication in smart grid Multi-layer architecture Home area network (HAN) Neighbourhood or Local area network (NAN/LAN) Wide area network (WAN) 21

26 Optimizing Distributed Generation for Residential Application Communication infrastructure Smart home appliances Thermal load Home display (IHD) ZigBee HOME GATWAY NAN Gas meter ZigBee Two way electric meter FC controller PV controller 22

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