REAL TIME BALANCING OF SUPPLY AND DEMAND IN SMART GRID BY USING STORAGE, CONTROLLABLE LOADS AND SMART GENERATIONS Abdulfetah Shobole, Dr. Arif Karakaş Yildiz Technical University Department of Electrical Engineering Yildiz Technical University, Department of Electrical Engineering Outline 1. Why to balance between generation and supply? 2. What are the couses of mismatch? 3. How to balance the mismatch? 4. The proposed method. 5. Modeling and Simulation. 6. Results and Conclusion. Why to balance between generation and supply? – To make the system stable – For maintaining frequency – Prevent black outs due to cascading outages BALANCE What are the couses of mismatch? – Consumption change with time. – Intermittent Energy Sources – Contingencies How to balance the mismatch? • In Traditional Power System. – Deterministic ahead of time dispatching – Through telephone communication and paper. – The balancing is done by controlling the conventional generations with reserve. How to balance the mismatch? • In Smart Grid. – The operation component in SG model is concerned with managing the energy flow in Smart grid. – Balancing demand and supply in real time is one of the characteritics of Smart Grid. – Demand response and Storages in addition to conventional generations. from NERC The Proposed Method Data are automatically read from power system – Smart communication technologies are involved. – AMI for the loads – WASA • Generations • Storage • Metrological data • Contingencies The Proposed Method Make decisions in Real Time Optimize the decisions by considering the situations DGs are considered as VPP by aggregating their output. Use all the available apportunities Demand response Storage Conventional generations Distributed generations Take your share and pass to the next algorithim is used. Start Update Load data Generation data Storage From AMI, WASA, Smart grid data servers, market data servers, etc. Yes Is the balance achieved? No Yes Adjust the storage to reset the mismatch. Is the available storage enough for frequecy control? No Calculate the left share for the next step and set the available storage. Yes Adjust the loads to reset the mismatch. Are the available loads enough for frequency control? No Calculate the left share for the next step and Adjust the available Load. Adjust conventional Generations. Yes Is adjusting DGs feasable? No Adjust Distributed Generations. The Modeling and Simulation DigSilentPowerFactory It is Commercial has the ability to simulate load flow, RMS fluctuations and transient events in the same software environment It has programming feature (DigSilent Programming Language) The Modeling and Simulation The DigSilent Network model to test the proposed algorithm. Simulation Results and Conclusion Generation from DGs, Generation from Conventional Power Power Plants, Total Generation The storage system tracks the variation from the DGs and resetting the mismatch Simulation Results and Conclusion Generation from DGs, Generation from Conventional Power Power Plants, Total Generation Storage, Controllable load and smart generation are involved in adjusting mismatch Simulation Results and Conclusion The smart grid enables the power system to be more efficient and stable, especially when renewable energy systems which are intermittent resources. In smart grid it is possible to integrate renewable energy systems and handle the mismatch between demand and supply by controlling the system in real time. This requires the access to data from generations, loads, storage systems, energy markets, etc. This is possible in smart grid due to communication, information and sensor infrastructures laid throughout the electricity network. The system mismatch can be handled even if the ratio of the renewable energy resources in the system is very high. The proposed method is also applicable for the variation of the load or any other contingency conditions that disturb the balance between demand and supply. The order of choice of the controller whether to use storage, controllable loads, smart generations or load shading depends on the factors like available capacity, environmental data, market data, location of the resources, etc.