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Optimal operation of energy storage in buildings: The use of hot water system Emma Johansson

Optimal operation of energy storage in buildings: The use of hot water system Emma Johansson Supervisors: Sigurd Skogestad and Vinicius de Oliveira. Agenda. Project description Work done Model validation Further work. Project description.

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Optimal operation of energy storage in buildings: The use of hot water system Emma Johansson

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  1. Optimal operationof energy storage in buildings: The use of hot water system Emma Johansson Supervisors: Sigurd Skogestad and Vinicius de Oliveira

  2. Agenda • Project description • Work done • Model validation • Furtherwork

  3. Project description • Optimal operation of energy storage in buildings with focus on the optimization of an electrical water heating system. • Objective is to minimize the energy cost of heating the water • Main complications: Electricity price and future demand • Goal: To propose, implement and compare different simple policies that result in near-optimal operation of the system.

  4. Proposed policies • Should be robust in some to-be-defines sense (e.g. must be feasible for at least 95% of the cases) • Should result in significant savings compared to trivial solution • Should be simple to implement in practice.

  5. Process flow scheme Dynamic model:

  6. Model assumptions • qhw and Thws controlled directly by the consumer • Perfect control when feasible Perfect control: else

  7. Model equation Definition of the state, input and disturbance vectors.

  8. Model validation

  9. PID controller

  10. Demand profile • Randomly generated demand profiles from MATLAB script, qhw.

  11. Electricity Price • On-off peak price • Time varying price

  12. Implementing a switch

  13. Price threshold, pB • Defining set-points for the temperature at the switch

  14. Results • Switching between set-points as the price is higher or lower than the price threshold PB.

  15. Weekly average • PB average from previous week

  16. Average from previous day

  17. Average current day • Comparing the total cost with different boundaries, also assuming the electricity price for the current day is known, and the average of this day can be used.

  18. No boundary? • The lowest price threshold resulted in the lowest cos, what are the result with no boundary?

  19. No boundary? • Tstart = 90 °C, low total cost. • Tstart = 65 °C, higher total cost.

  20. Cost function • Original cost function: • Implementing a penalty into the cost function:

  21. Further work • Optimization problem: min J(PB,Tbuffer) • Decision variables: PB and Tbuffer Finding the optimal PB and Tbuffer which provides the lowest total cost. Simulate for longer periodes and generalizing the simulation Find near optimal policies

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