Safe Integration of Renewables at Transmission and Distribution Level. Alexander Krauss - Digital Grid Software and Solutions

Transcription

1 Safe Integration of Renewables at Transmission and Distribution Level Alexander Krauss - Digital Grid Software and Solutions siemens.com/digitalgrid

2 Overview Table of content The Masterplan - One Platform Active Nework Management Wind Power Managment Page 2

3 Siemens Digital Grid masterplan architecture for a smooth transition to Cloud enabled applications Public cloud Enterprise IT IVR GIS Enterprise Service Bus Grid planning and simulation Smart communication Smart transmission Network planning Smart distribution Asset management CIM WMS/mobile Weather Forecasting Web portals CIS/CRM Billing Grid control applications Smart consumption and microgrids CIM Smart markets Business applications Smart distributed energy systems OT-IT integration, consulting Managed/cloud services Grid cyber security $ CIM Common Information Model (IEC 61970) Page 3

4 Agility in energy: open adaptable manageable, standards-based ecosystem Cloud enabled applications e.g. analytics Enterprise IT IVR GIS Enterprise Service Bus Grid planning and simulation: Smart communication Network planning Asset management Substation: SICAM and SIPROTEC within ENEAS solutions Substation controllers Bay controllers Human machine interfaces Power quality devices Public cloud WMS/mobile Weather Forecasting Web portals CIS/CRM Billing Grid control applications: Spectrum Power PSS Suite CIM CIM Protection devices Third party Remote terminal units Business applications: EnergyIP Field area networks Meter Grid sensors Data concentrator Microgrid controllers Load controls Third party Building/ home energy management system OMNETRIC Group, Smart Grid Compass Managed/cloud services Grid cyber security CIM Common Information Model (IEC 61970) PSS Power System Simulator Page 4

5 Spectrum Power Comprehensive portfolio of applications Basic functions Generic applications Energy market & generation Transmission Distribution Multi-utility/ industry Infrastructure UI (User Centered Design) Data Entry & Data Model (IMM) Archive (HIS) Communication SCADA & Base System Security IT-Interfaces and SOA Forecast Applications Load Shedding Power Applications Resource optimization & scheduling Energy Market Management Transmission Network Applications Training Simulator -Transmission Network Stability Analysis Distribution Network Applications Training Simulator -Distribution Outage Management System GIS-Interface Load Management Electricity, Gas Supply-Management Water Pipeline-Networks Disposal Locking Rota Switching Distribution Energy production Specific Applications Transmission Multi-utility Basic Functions Renewable generation Infrastructure Industry Page 5

6 Spectrum Power 7 Architecture... Meter Data Mgmt Work Force Mgmt Asset Mgmt SAP Customer Inform. System Geographical Inform. System Weather Forecast Network Planning... Enterprise Service Bus Base functionality: data model, UI, SCADA, Archive Systematic outage management for faster and more restoration Distribution load flow calculation, grid optimization and what-if studies Energy resources and production planning; load forecasting for generation and grid operation planning Generation control for more economic and reliable operation HIS IMM GIS I/F OMS CMS TCS DNA TS SA RO FA PA EMM Real-time and day ahead energy market management Spectrum Power High Speed Bus Base SCADA CFE IFS OPC ICCP ELC TNA Base OPF DSA TS LME LMG LMW MERO Communication with substation RTU/SAS and other control centers Transmission load flow calculation, grid optimization and what-if studies Management of infeeds, switchable loads and storages for minimum cost (power, gas, water) Page 6

7 Integrated platform strategy to ensure minimized risk and high cost efficiency Enterprise Service Bus Enterprise IT Spectrum Power Grid control application Transmission & Distribution Network Analysis EnergyIP Smart grid & smart market applications Meter Data Management IVR GIS Digitalization & Automation System stability and system balancing applications Outage and trouble call management Active network management Fleet optimization & scheduling Forecasting and planning applications Energy Market Management CIM Decentralized Energy Resource Management Revenue protection/non-technical Losses Prepaid Energy System Market Transaction Management Energy Engage customer web portal Energy Analytics Network planning Asset management WMS/mobile Center E-car Operation Smart Communication Smart transmission Smart distribution Smart consumption and microgrids Smart markets Smart distributed energy systems Weather Forecasting Web portals $ CIS/CRM Billing CIM Common Information Model (IEC 61970) Page 7

8 Overview Table of content The Masterplan - One Platform Active Nework Management Wind Power Managment Page 8

9 New challenges for grid management due to growing need for integration of renewable generation Increasing installation of renewable energies No clear direction of power flow Violation of voltage limits Overload situations Observability improvement Volt-/VAR management Capacity management Page 9

10 Spectrum Power Active Network Management Releasing hidden capacity by Active Network Management Network state Problem detection Decision making Set-point command Active Network Management System Energy storage Voltage control device Controllable generation Controllable loads Real time thermal rating Page 10

11 Spectrum Power TM ANM Active Network Management, Use case Use case description Decentralized power generation and distributed infeed causes voltage band violations on high, medium and low voltage level overload of grid components (transformers, lines, etc.) power quality problems due to inverters Active network management monitors the network state to detect network volatilities, suggests counter measures and implements the counter measures in closed loop Page 11

12 Spectrum Power Active Network Management Active Network Management based on real time state estimation Estimate Control Topology change Significant measurement change Voltage Violations Overloads Configured Cycle Page 12

13 Data Concentrator Local Coordination & Prioritization Front-End SCADA Spectrum Power Active Network Management System components of an hierarchical solution Topological Coloring Central Capacity Control State Estimation Management (Siemens Spectrum Power Active Network Management) Control Volt / Var Optimization Voltage Management Optimization Control Center & Assets Battery Capacity Management Thermal Modelling RDC Secondary OLTC AVC Management Capacitor Control Local Control Thermal (Siemens Autonomous Management Substation Controller) Local Voltage Management RDC Primary Page 13

14 Spectrum Power TM ANM References Northern Power - Grid Grand Unified Scheme The Grand Unified Scheme (GUS) brings together battery storage, enhanced voltage control, demand response and real-time thermal rating in closed loop for optimal grid operation. Energy Northwest - Smart Street project Voltage management at the HV level to reduce network losses and conservation voltage reduction (CVR) at LV level to reduce energy demand, and run LV meshed networks to release network capacity. IREN2 Microgid with renewable generation as substitute of conventional generating units. Migrogid providing flexibilities for the distribution grid operation Page 14

15 Electricity North West, Smart Street Current network situation +6% Limits on DG at HV High Voltage leads to poor Energy Efficiency for Customers +10% Loss Reduction in 11/6.6kV by increasing the voltage level to an optimal level. -6% Low voltage leads to unnecessary losses in DNO network -6% Limits on LCTs such as heat pumps The coordination of the capacitor banks will optimize the voltage levels on HV network for overall loss minimization. The loss reduction will be achieved by increasing the voltage level to an optimal level. Optimized network situation Page kv 11 / 6.6 kv LV +6% -6% Increased scope for DG Optimised voltage for overall loss minimization 33 kv 11 / 6.6 kv LV Increases Energy Efficiency for Customers +10% Increased scope for LCTs such as heat pumps -6% Energy Efficiency by reducing the voltage close to the lower limit Energy efficiency will be achieved by reducing the voltage close to the lower limit. A meshed network will be utilized using LV circuit breakers along with capacitor banks to flatten the voltage profile thus permitting ENW to drop the voltage across both feeders (using OLTC transformers) to facilitate reduction in losses and energy consumption.

16 Active Network Management Experiences from ENW Smart Street Goal to Target Three key steps... Coordinated voltage control, using transformers with on load tap changers and capacitors, across HV and LV networks Interconnecting traditionally radial HV and LV circuits Real time coordinated configuration and separate voltage optimization targets of HV and LV networks and expected targets Release capacity up to four times faster and 40% cheaper than traditional reinforcement techniques for low carbon technology clusters. Deliver conservation voltage reduction to improve the energy efficiency of customers electrical appliances reducing energy up to 3.5% - 4% per annum, and lowering network losses by up to % per annum across HV and LV networks. This will deliver recurring financial savings for customers, without degradation to the quality of customers supplies. Page 16

17 Active Network Management - ANM Solution Overview Increasing installation of renewable energies Improves situational awareness No clear direction of power flow Reduces voltage band violations and equipment overloads Violation of voltage limits Network state Problem detection Decision making Set-point command Minimizes distribution losses Overload situations Supports various network components Observability improvement Volt-/VAR management Active Network Management System Optimizes in full closedloop operation Capacity management Energy storage Voltage control device Controllable generation Controllable loads Real time thermal rating Scalable and flexible Page 17

18 Spectrum Power ANM Key benefits 1 High grid stability supports operator in detecting network volatility early enough to react in time 3 Reliable voltage regulation Generic on-line optimization that can be easily extended to cover additional network areas and controllable resources 2 Efficient peak load management provides comprehensive information for taking decisions to avoid problems such as voltage violations and overloads 4 Reduced costs Helps to avoid expensive network extensions while operating the existing network in an optimized way Page 18

19 Overview Table of content The Masterplan - One Platform Active Nework Management Wind Power Managment Page 19

20 Wind Power Management Introduction Wind power generation mainly influenced by Weather effects, Wind Farm Structure, Region Experience from countries in the sector shows that advanced tools are needed to forecast wind power generation Principal actors: power producers, transmission system operators and independent power generators Uncertainty of wind hard to predict Accurate and reliable forecasting systems of wind power production increases wind penetration Page 20

21 Input Data 4 types of input data: Wind Forecasts RTM WPE WPF Wind forecasts (speed and direction) in a distributed set of geographical coordinates and close to the location of the wind farms Real time measurements (SCADA) are received every 15/20/30 or 60 min and give the power production of each telemetered wind farm Wind Power Estimations are received each hour: estimation of last 24 hours of total wind power produced (telemetered and non telemetered) Total Wind Power Forecasts are received each hour: forecast of total wind power produced by the system WPF and WPE files are given by external forecasters and are optional Page 21 21

22 Prediction Algorithm Calculates predictions based on real time measurements and meteorological forecasts (wind speed and wind direction) up to 48 hours Ensemble forecast: 8 forecasts generated and combined internally 8 prediction models Parametric models Less historical data is needed to obtain initial estimates Nonparametric models More historical data are needed to be more accurate M1: Persistence Model M2: Autoregressive Model M3: Autoregressive and Linear Speed Model M4: Autoregressive and Quadratic Speed Model M5: Autoregressive, Linear Speed, and Wind Direction Model M6: Autoregressive, quadratic speed and wind direction model M7: Autoregressive and Non-parametric Prediction Based on Speed and Direction M8: Non-parametric Prediction Based on Speed and Direction Page 22 22

23 Prediction Algorithm The final forecast is obtained by combining the predictions generated by 8 different models RTM Wind Forecasts Model 1 (accuracy 1) Model 2 (accuracy 2) Model 8 (accuracy 8) Combinations Final Prediction Page 23 23

24 Benfits and Key features Total / regional wind power forecast 1h up to 10 days Voltage 1 Pre- and Post contingencies 2 7 Locational Marginal Price for Intraday and Day ahead Statistical and combined forecasting method 3 Key Features 6 Disaggregation of the result up to 48 hours After the Fact Error Analysis /20/30/60 minutes time grid Page 24 24

25 How does it look like? STWPF sample display: wind farms capacities per location Page 25 25

26 How does it look like? STWPF sample display: wind direction Page 26 26

27 Questions? Alexander Krauss Sr Business Developer EM DG SWS S MI Humboldtstr Nürnberg Germany Phone: alexander.krauss@siemens.com siemens.com/digitalgrid Page 27