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Planning and Control in Smart Grids

This study explores innovative planning and control methods for managing heat pumps in smart grids. The focus is on optimizing their operation based on dynamic factors such as electricity pricing, local demand, and heat storage levels. Utilizing a home controller, the approach emphasizes when to run heat pumps to maximize efficiency and sustainability. It delves into the necessity of flexible predictive models and offers insights into stakeholder cooperation for effective grid management. This work underscores the importance of adaptable strategies in enhancing the performance of distributed energy resources.

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Planning and Control in Smart Grids

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  1. Planning and Control in Smart Grids Johann Hurink Department of appliedmathematics

  2. Uses electricity (2-5 kVA) Produces heat (aCOP ≥ 3.8) Combined with heat buffer Example Heat Pumps

  3. Example: Heat Pumps Use of a home controller • When to run the heat pump? • If needed • If prize is low • If locally electricity is produced • If no other (large) appliances are running • Based on the content of the heat store • Based on the content of a battery • Based on the heat demand in the (near) future • Based on agreed profiles (e.g. day ahead market) • …. Math inside! Flexible prediction, planning and control methods are needed

  4. Example: Heat Pumps Is local control in homes sufficient? • Home Controllers optimize based on local objectives! • ? •  Smart Grids

  5. Example: Heats Pumps use case 100 Heat pumps Price signals may even worsen the situation!

  6. Example: Heats Pumps use case Heat pumps: Grid dimensioning • Toersche, H.A., Bakker, V., Molderink, A., Nykamp, S., Hurink, J.L., Smit, G.J.M. (2012) Controlling the heating mode of heat pumps with the TRIANA three step methodology. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1-7 • Nykamp, S.,Molderink, A., Bakker,V., Toersche, H.A., Hurink, J.L., Smit, G.J.M. (2012) Integration of Heat Pumps in Distribution Grids: Economic Motivation for Grid Control, accepted for IEEE ISGT Europe, Berlin.

  7. Example: storage Competition or cooperation of stakeholders • storage asset • Power: 2 MW • Capacity: 8 MWh

  8. Example: storage Competition or cooperation of stakeholders • Scenario’s: • No storage • Optimization for grid purposes • Peak shaving • Optimization for arbitrage purposes • Day ahead market • Optimization for arbitrage purposes • Intraday market

  9. Example: storage Competition or cooperation of stakeholders

  10. Example: storage Competition or cooperation of stakeholders • Combine grid and arbitrage purpose • To realize these potentials we need: Math inside! prediction, planning and control methods • S. Nykamp, M.G.C. Bosman, A. Molderink, J.L. Hurink, G.J.M. Smit: (2012) Value of distributed storage - competition or cooperation of stakeholders?, submitted to IEEE Transaction s on Smart Grid

  11. Lessons learned • Combination of locally good solutions ≠ globally good solution • Different stakeholders lead to different solutions • Different levels have to interact • Devise • Houses • Micro grids • Macro grids • …. • Within a level cooperation is needed • Flexible planning and control methods are needed

  12. TRIANA DISTRIBUTED CONTROL methodologyof the university of Twente Levelled Approach Exploit distributed computational power & communication limitations Solve as locally as possible ... ...

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