The SmartGrid concept: a way to increase energy efficiency
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This document discusses challenges and opportunities for smart grids. It describes smart grids as energy networks that can automatically monitor and adjust energy flows based on supply and demand changes. Key challenges discussed include control and protection, seamlessly integrating renewables, and advanced forecasting of generation, load, and prices. The document also outlines some benefits of smart grids like local reliability and reduced emissions. It provides examples of how technologies like wide area monitoring systems, real-time simulators, and forecasting models can help address challenges in developing smart grid systems.
The SmartGrid concept: a way to increase energy efficiency
- 1. European cooperation Network on Energy Transition in Electricity Jorge Bruna CIRCE - Research Centre for Energy Resources and Consumption SESSION 2: SMART GRIDS CHALLENGES: THE VISION OF TECHNOLOGICAL CENTRES WORKSHOP “DEFINING SMART GRIDS: CONDITIONS FOR SUCCESSFUL IMPLEMENTATION” Barcelona, 9th February 2017
- 2. 1. SmartGrid definition 2. Traditional grids vs SmartGrids 3. SmartGrids: PROS and CHALLENGES 4. Challenge: Control and protection 5. Challenge: Renewable seamless integration 6. Challenge: Advanced forecasting 7. Conclusions
- 3. Smart grids are energy networks that can automatically monitor energy flows and adjust to changes in energy supply and demand accordingly. When coupled with smart metering systems, smart grids reach consumers and suppliers by providing information on real-time consumption. Smart grids can also help to better integrate renewable energy. While the sun doesn't shine all the time and the wind doesn't always blow, combining information on energy demand with weather forecasts can allow grid operators to better plan the integration of renewable energy into the grid and balance their networks. Smart grids also open up the possibility for consumers who produce their own energy to respond to prices and sell excess to the grid.
- 5. PROS 1. Local reliability 2. Reduce emissions 3. Reduce power losses of distribution networks CHALLENGES 1. Control and protection 2. Renewable seamless integration 3. Advanced forecasting
- 6. Wide Area Monitoring System technology uses a GPS satellite signal to time- synchronize PMUs or Power Quality Analyzers at important nodes in the power system to send real-time data to a central node. Without ubiquitous, accurate, and reliable real-time sensors, the electric grid will not have the resiliency, reliability, and capacity to manage the unprecedented number of variable renewable energy sources and millions of intelligent devices and systems.
- 8. Power System and PMUs can be modeled in Real-Time Digital Simulator (RTDS) Physical Phasor Data Concentrator Physical PMU C37.118 PROTOCOL Emulated PMUs SYSTEM INTEGRITY PROTECTION SCHEMES
- 9. Purpose: To produce heat and power in order to reduce CO2 emission level and strong dependency from import of gas and oil Main renewables energy sources: • Wind energy • Biomass energy • Solar thermal energy • Photovoltaic energy • Geothermal energy Types of renewable energy sources: • Stochastic • Continuous Main topic to be addressed: • Impact on Power Quality
- 10. Enercon wind turbine Lagerwey wind turbine Vestas wind turbine Solar panels H2 Building SERVER
- 11. 06:00 09:00 12:00 15:00 18:00 21:00 00:00 220 225 230 235 240 400 405 410 415 420 425 430 RMS(V) TIME (hh:mm) Enercon Phase R Vestas Phase R Solar Phase R Foundation Phase R 06:00 09:00 12:00 15:00 18:00 21:00 00:00 220 225 230 235 240 400 405 410 415 420 425 430 Enercon Phase S Vestas Phase S Solar Phase S Foundation Phase S RMS(V) TIME (hh:mm) 06:00 09:00 12:00 15:00 18:00 21:00 00:00 220 225 230 235 240 245 250 400 405 410 415 420 425 430 Enercon Phase T Vestas Phase T Solar Phase T Foundation Phase T RMS(V) TIME (hh:mm) 0 -400 -300 -200 -100 0 100 200 300 400 Voltage(V) TIME -150 -125 -100 -75 -50 -25 0 25 50 75 100 125 150 Current(A)
- 12. Energy forecasting in Smart Grids: • Load • Price • Wind • Solar • Demand response • Congestion • Flexibility
- 13. Measurements Field Devices (P,Q,SoC from EB) EMS inputs External Sources: AEMET OMIE Data Base 24h Forecast Modules Weather (ARX) Generation (ARIMAX) Price (ARIMA) Optimization Module (LP, e.g. CPLEX solver) 24-h Forecasts Set of Restrictions Working Modes Defined by ESCO/ Consumer Set of restrictions (EB configuration by ESCO/Consumer) Updated when needed Updated when new data is available Updated every 15min Minimize energy bill Maximize system service (Flexibility services) Other (future) Updated every 15min Optimization results updated every 15min 24-h program of Control commands EMS outputs Reporting Module Historical/real-time data Alerts Measure & Validation (Energy Saving) Graphics Tables Reports Data processing Demand (ARIMA)
- 14. • Efficient and reliable energy to final customers • Limited primary energy sources • Additional demand supplied by distributed generation • Energy storage to solve intermittent energy sources (e.g. wind and photovoltaic) • New algorithm to forecast: • Generation • Load • WAMS and multi-point deployment of PQ analyzers: • Control and protection • Propagation of PQ phenomena into the grid • Interoperability to develop effective Demand Response strategies