Smart City Demo Aspern Economic evaluation of selected Smart Grid Use Cases

Transcription

1 Smart City Demo Aspern Economic evaluation of selected Smart Grid Use Cases IEWT 2017, Wolfgang Prüggler

2 Agenda Flagship Project Smart City Demo Aspern Research Areas and Testbeds Smart Grid Case Study 1 Smart Grid Case Study 2 Conclusions

3 Flagship project Smart City Demo Aspern Partners and funding This project is funded by the Austrian Climate and Energy fund and implemented within the program Smart Cities Demo 15/02/2017 3

4 Research Areas Smart Building Smart Grid Smart ICT Optimization of energy costs: Increased efficiency of energy utilization Own energy production (PV) Maximization of consumption of self produced energy (based on thermal and electrical storage) Utilization of flexible energy tariffs Load forecasts for market partners Flexibility offerings for market partners Involvement of smart users (Smart User App) DSO as efficient market facilitator Grid transparency down to the customer connection points Data based grid planning and optimization Handling of connection requests Forecasting (loads, grid capacity) Introduction of additional active grid components to increase efficiency and grid reliability Management of grid connected devices and building flexibilities Data provisioning to market partners Smart ICT the foundation for a smart Energy system Creation of a data eco system supporting single or multiple domains of a energy system: Data integration, management and provisioning Simulation and forecasting Digital twins Data analytic

5 Component and Data layers 15/02/2017 5

6 Testbeds Student Dormitory PV (221 kwp) electrical storage (120 kwh) heating elements (2 x 8 kw) in hot water storage Smart HVAC School Campus 2 heat pumps (510 kwth) PV (29 kwp) solar heat (90 kwth) hot water storages with heating elements (2 x 35 kw) Smart HVAC total room automation Apartment Complex 7 different heat pump systems (800 kwth) Solar heat (90 kw) + Hybrid (60 kwpth) PV (20 kwp) + Hybrid (16 kwpel) Soil storage (40 MWhth) Hot water storages (6 X 2000 L) Electrical storage (2 kwh) Smart HVAC Home automation Building data Wiener Netze Meterdata readings transferred daily Transformer Stations 11 Stations with R&D environment Low-Voltage Grid Approx. 500 Smart Meters and 90+ Monitoring Sensors Grid Data ASCR City Data Center ASCR Meter Data Operational Teradata Datawarehouse and Business Analytic environment Weather Prognosis Prognosis 15/02/2017 6

7 Smart Grid Testbed of the ASCR Operative Dashboard Administrative Dashboard Grid State Forecasting Adaptive Assignment Module Switch State Detection Unavailable Data Handling Headquater Wiener Netze Daily transmission of meter data (daily consumption, load profiles) Secondars substations (Aspern) 11 secondary substations fully equipped with R&D infrastructure in operation 4 of these are prepared for the implementation of an intelligent Secondary Substation Node (issn) Meter data ASCR meter data Grid data ASCR Operation Center Continous data capturing recording and validation (LV grid, buildings) Calculation of missing grid measurement values (if possible) Control Center in operation XMPP Ecosystem for WoS / IoT In operation Data integration, app and data analytic environment Semantic Framework Administrative Dashboard Protection with Smart Urban LV Grids non-target within SCDA / inis SCADA (topology ) Smart Buildings (nominal profiles ) CLOUDE Application Manager GridWatchDog FlexOp Low voltage grid (Aspern) Smart Building Testbeds EDGE Application Manager GridLink Storage Module A B App A needs functionality provided by App B GRM BRM 500 Smart Meter and 90 grid sensors transmit their data to the secondary substations 15/02/2017 7

8 Smart Grid Case Study 1 Background Cost / Benefit analysis of grid monitoring solutions and automated Breaker relocation within the the grid area of Seestadt Aspern (Testbed) as well as Vienna on an aggregated basis. 15/02/2017 8

9 Smart Grid Case Study 1 Implemented data General parameters Value Unit Interest rate 6.42 % Inflation 2 % Evaluation time 10 yr Cost parameters Testbed Vienna Unit Notes Amount of monitoring sites # Assumption: 20% penetration level in Vienna area Monitoring equipment cost (CAPEX) /unit Expert estimation, no additional OPEX Amount of automated breaker sites # Assumption: 20% penetration level in Vienna area Automated breaker cost (CAPEX) 3 3 k /unit Expert estimation, no additional OPEX Revenue parameters Testbed Vienna Unit Notes Maximum reduced grid losses MWh/yr Draft value (diploma thesis running) Spot market price /MWh Assumption (average prices) Reduced staff efforts (overtime) h/a Assumption Reduced electricity failure time h/10yr Assumption 15/02/2017 9

10 Smart Grid Case Study 1 Results Testbed NPV of cost and revenues in [ ] % 20% 30% 40% 50% 60% 70% 80% 90% 100% Applicable revenues in % (cost steady) Staff Losses Total revenues Cost GM and automated Breaker 15/02/

11 Smart Grid Case Study 1 Results Vienna NPV of cost and revenues in [ ] % 20% 30% 40% 50% 60% 70% 80% 90% 100% Applicable revenues in % (cost staedy) Staff Losses VOLL Vienna Total revenues Cost GM and automated Breaker VOLL Vienna 1h: k - Source: Project BlackÖ1 15/02/

12 Smart Grid Case Study 2 Background Cost / Benefit analysis regarding grid effects of future building optimisation deriving upper cost limits of a future Flexibility-Operator within the grid area of Testbed as well as Vienna on an aggregated basis (based on CoOpt Project model) 15/02/

13 Smart Grid Case Study 2 Grid Simulation Future development scenarios for Vienna were estimated based on the CoOpt project (simulations performed by AIT; general assumption 2% load increase/yr; 4 PV-plants/LV-Grid-segment in 2030) Scenario 1: Average heat pump power = 1 kw / household Scenario 2: Average heat pump power = 2 kw / household Scenario 3: Average heat pump power = 2.5 kw / household Comparing the following operational strategies: 20% of buildings with heat pumps (100% operation at 13:00; e.g. market signal reaction) 20% of buildings with heat pumps (random operation between 11:00 and 15:00 Flex-OP operation) Results derived for Vienna on an aggregated basis (based on CoOpt Model) Downscaling of results for Testbed 15/02/

14 Smart Grid Case Study 2 Grid Simulation Results Worst case day (12 th July) was simulated as the maximum load of grid assets is given (high PV generation as well as load situation) Scenario 1 Scenario 2 Scenario 3 Operation Strategy Number of grids Year Load factor [%] 1,35 1,35 1,35 1,35 1,35 1,35 PV per grid (at H0-nodes ); P=4.6 kw Share of flats with heat pumps [%] Max. load transformer [%] 30,8 29,57 43,07 34,45 49,21 37,89 Max.load cables [%] 63,48 69,5 68,72 69,5 79,12 69,5 p.u. max. 1,038 1,039 1,036 1,038 1,036 1,037 p.u. min. 0,948 0,949 0,93 0,939 0,921 0,934 Flex-Op effect at transformers (operation strategy 2 vs 1) -1,2% -8,6% -11,3% Flex-Op effect at cables +6,0% +0,8% -9,6% 15/02/

15 Smart Grid Case Study 2 Implemented economic data General parameters Value Unit Interest rate 6.42 % Inflation 2 % Evaluation time 10 yr Testbed Vienna (20%) Unit Cable lenght kv km Cable installation cost kv /m Cable lenght 1 kv 9, km Cable installation cost 1 kv /m Number of transformers # Average Cost of transformer stations (incl. transformers) k /station 15/02/

16 Smart Grid Case Study 2 Results Vienna NPV of grid cost in [k ] % -4% -3% -2% -1% 0% 1% 2% 3% 4% 5% Vienna: Consump on of grid reservers in % Cable Transformers and sta ons Possible FleOP cost 15/02/

17 Smart Grid Case Study 2 Results Vienna 15/02/

18 Smart Grid Case Study 2 Results Testbed 15/02/

19 Conclusions Case Study 1 Savings in grid losses and staff hours hardly would pay off grid monitoring and automated breaker relocation actions Thus, additional effects e.g. reductions in electricity failure times are necessary to pay off investments Cost reductions are expected if less monitoring sites are necessary (will be updated in the last project phase) Updates regarding achievable grid losses will be made Case Study 2 Grid design is robust in Vienna, thus there is currently no need for a FlexOp implementation Even more, FlexOp scheduling is strongly dependent on local grid conditions and grid progonsis quality Thus, avoid unintended negative grid effects However, if FlexOp operation is needed in the future to free grid capacity (e.g. to enable simultaneous market participation of buildings or high capacity EV-charging), the system design should offer high expandability to achieve economy of scale effects 15/02/