Building Smart Grid with µems CEZ Spring Conference, 16 th -17 th April 2014 Igor Dremelj, VP Smart Grid Solutions EMEA 1
TOSHIBA Group and its Business segments Company name: TOSHIBA CORPORATION Headquarters : Tokyo Founded: Common Stock: Net Sales: July 1875 439,901 million (~ 3,2 billion) 5,800 billion (~ 43 billion) TOSHIIBA Group President & CEO Mr. Hisao TANAKA Total assets: 6,106,732 million (~ 45,2 billion) Number of Employees: 206,087 Energy & Infrastructure Power Systems Infrastructure Systems Community Solutions Community Solutions Clouds & Solutions Cloud Services Healthcare Medical systems Healthcare IT Services Electronic Devices Semiconductor & Storage Products Consumer & Lifestyles Digital Products Home Appliances T&D, BESS, µems, AMM, Elevators, Lightning, HVAC, HEMS, BEMS, VPP, * New organization as of Oct. 1, 2013 Results for FY2012. EUR conversion at the rate of 1 = 134.96, for convenience only. 2
Networks with conventional Generation & Loads. History? Unidirectional flows from transmission to end consumer Predictable, low DG penetration if any Low monitoring and control on MV/LV if any ( passive control ) Centralized control on conventional generation and transmission Fit and forget approach most issues solved in network planning stage * DG Distributed Generation
Networks of Today and Tomorrow? Distributed and intermittent generation Active network system management Coordinated centralized and distributed control? Investments in ICT New Rules & Responsibilities DNO + System services = DSO? Ensuring Security of the system and Quality of service
New challenges = New task for Energy Market Roles? Supply response and portfolio optimization Grid challenges set new requirements for the market roles and interaction Integration of renewable energy resources: wind, biogas, photovoltaic Generation Revenue assurance, Outage prevention and reduction of downtime Infrastructure/asset protection and optimization Balancing power, Energy efficiency and optimization Energy value Sales Aggregation Prosumer Balancing Transmission Distribution Asset value Dynamic capacity management - Peak shaving instead of peak generation 5
Example: Quality of electric power supply Continuity of power supply in grids SAIDI, SAIFI, ENS, AIT... Planned vs non-planned (fault) outages Fault location and restoration time Weather conditions Ratio of overhead lines and cable Ability of power distributor or producer to respond to applicable requirements of end customers (commercial quality) Not directly related to the physical operation of systems Incentive/penalty mechanisms Motivational quality control SAIDI, SAIFI, ENS as reliability parameter Penalties as percentage of annual network fee, average value of ENS, SAIDI - System Average Interruption Duration Index ENS - Energy not supply Copyright 2013 Toshiba Corporation and Landis+Gyr. All rights reserved. 6 SAIFI - System Average Interruption Frequency Index AIT - Average Interruption Time
Example: Finnish market statistics 2011 DNO companies in Finland 85 Network lenght km 382 740 Consumers pcs 3 348 730 Connection points pcs 1 758 642 Primary Substations 1 097 Amount of Substations Distr. Substations 132 368 Outages per consumer, year Amount of nonplanned outages Duration, hour 6.20 Volume, pcs 9.31 LV network 23 319 MV network 54 703 Customers entitled to outage compensation (2011) Defected consumers: 371 630 Compensation (avr./consumer): ~126 >> BIG IMPACT OF UNFAVORABLE WEATHER CONDITIONS! >> LARGE SHARE OF OVERHEAD LINES IN RURAL! Copyright 2013 Toshiba Corporation and Landis+Gyr. All rights reserved. 7
Example: Slovenia Feb 2014 Copyright 2013 Toshiba Corporation and Landis+Gyr. All rights reserved. 8
Towards Smart Grid Advanced Metering Infrastructure (AMI) Exact information on consumer level Transparency to network status at endconsumers Historical data for planning & predicting the future Network Digitalization & Active Network Management Awareness of MV and LV status Fault locating Voltage regulation Renewables management Balance demand and supply Right- and real-time information will be the key for a successful transformation! 9
Dynamic Environment will require New Tools Forecast demand Forecast renewable energy output Schedule energy supply Forecast & planning Control fluctuation Smoothing of PV & WT output (real time) Control supply and demand in real-time Regulate voltage levels Manage demand Network optimization Real-time monitoring & controlling Right-time scheduling 10
Awareness enables assets optimization and efficiency Digitalization of network and substations creates awareness on MV/LV network facilitating Active Network Management! TSO DNO 100% Available Information 0 SCADA RTU µems Smart Distribution Substation Automation Smart Meter HV MV LV 11
Example: Active voltage regulation in distribution network Controlling of the complex MV/LV network environment requires digitalization of network and transformers! Substation for distribution Load Load Load Load Load Load LV/MV Flow TVR BESS Voltage rise STATCOM Allowable voltage range Upper voltage Lower TVR - Thyristor Voltage Regulator BESS Battery Energy Storage System STATCOM - Static Synchronous Compensator µems micro Energy Management System Within Voltage Range Rise
Tools for Network digitalization and Active network management EMS energy management solution Smart distribution substation automation SCiB energy storage solution Extended information with Gridstream AMI 13
Network digitalization & Active network management with µems Use cases Equipment in MV network Equipment in LV network Power System Visualization Fault Locating Voltage Regulation Renewable Energy Source Control Energy Optimization µems server Smart distribution substation Smart distribution substation RDT / TVR SVC / STATCOM Grid storage µems controller Smart inverter (Photovoltaic / Wind) Grid storage Smart distribution substation Smart metering Smart distribution substation Smart metering TVR Prosumer storage µems controller Smart inverter (Photovoltaic / Wind) Heat pump Smart metering Prosumer storage 14
Our offering for Smart Grid SCADA DMS 15
Smart Energy Management Solution - µems Scheduling Generation Scheduling Battery Scheduling µems is used to maintain the stability of a power system to which renewable energy sources are connected Forecasting Renewable Generation Forecasting Demand Forecasting Monitoring & Controlling Power Flow Stabilization Fluctuation Reduction for Renewable Energy Load Frequency Control Application functions of Micro Energy Management System (µems) Forecasting supply and demand Scheduling generation and storage Smoothing fluctuation of renewable energy sources Real-time supply and demand control Balancing the impact of unpredictable loads, e.g. EV Voltage regulation in MV and LV networks Frequency control in micro grids Demand response 16
Smart battery solutions for energy storage Energy storage for Prosumers: Small to Medium scale BESS Increase utilization of self-generated power Short to medium-term emergency power supply at power outages Smart Battery technology - SCiB - High input and output - Low temperature operation - Rapid charging - Long lifetime Grid storage: Medium to Large scale BESS Renewables integration on MV level Reducing transmission peak loads Frequency/Voltage regulation Bridging short-term power outages SCiB Super Chargable ion Battery (http://www.scib.jp/en/product/index.htm) 17
Smart distribution substation automation Functionality of Smart Distribution Substation Automation (SDSA) Transformer and station monitoring and control Power, Current, Voltage and energy flow supervision Delivery Quality supervision MV failure indication (outage, earth and short circuit) LV network management LV outage indication Remote Control of MV Switch / Recloser Remote control and monitoring the local energy resources Reference metering for Network loss analyzes 18
Extended information with Gridstream AMI AMI provides exact information on consumer level On-time consumption and historical profile information Micro generation reverse power measurements On-time power quality information Voltage quality log (EN50160) Outage statistics Remote controlling of residential loads Analysis of dynamic consumption trends 19
Interaction of Smart Grid and Smart Market Assigning value to free capacity in the distribution networks I/O Loads 15 or 60 min consumption data Collecting near real time consumption data (<1min) AMI Market data Billing: ISU, EDM Virtual Power Plant Distributed Resource Management DR/DLC signals Capacity DER signals DR Programs Energy trading Near real time data Smart Market entities DR/DLC Demand Response/Direct Load Control DER Distributed Energy Resources 20
Interaction of Smart Grid and Smart Market Energy Price vs. Network Capacity Congestion Management Smart Market operates with fictive price as control signal strives for power balance (demand/supply) Smart Grid uses physical measurements for controls strives for grid usage optimization Measure, Monitor, React Congestion management has priority, results in grid measures first and endconsumer measures last; it must be transparent, objective & non-discriminatory! 21
22 Summary Decentralized µems Solution for optimal energy system management in challenging new dynamic distribution network environment Forecasting supply and demand Scheduling generation and storage Real-time demand control & Congestion management Smoothing fluctuation of renewable energy sources Balancing the impact of unpredictable loads, e.g. EV Voltage regulation in MV and LV networks Frequency control in micro grids & secondary power resources
Thank you. Copyright 2013 Toshiba Corporation and Landis+Gyr. All rights reserved.
Appendix Applying µems - Use cases and examples Copyright 2013 Toshiba Corporation and Landis+Gyr. All rights reserved. 24
Forecasting demand Power (MW) Time 0:00 12:00 24:00 Demand Forecast
Forecasting supply Power (MW) PV Time 0:00 12:00 24:00 PV Output Forecast
Scheduling generation and storage Power (MW) Generator PV Time 0:00 12:00 24:00 Generator Schedule
Balancing supply: surplus power Power (MW) PV Generator Time 0:00 12:00 24:00 Generator Schedule
Balancing supply: lack power PV Generator Time 0:00 12:00 24:00 Generator Schedule
Smoothing fluctuation of renewable energy sources Grid 10:50 11:00 11:10 Time
Balancing EV load impact in the network 16:00 18:00 20:00 Time