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Efficient Decision-Making for Space Heating Consumers with Dynamic Pricing

Explore the dynamic decision problem in space heating to optimize costs and living comfort by leveraging time-varying electricity tariffs. Discover the innovative MOHO approach for multi-criteria optimization using interval goal programming.

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Efficient Decision-Making for Space Heating Consumers with Dynamic Pricing

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  1. Goal intervals in dynamic multicriteria problems The case of MOHO Juha Mäntysaari

  2. Decision problem of space heating consumers • Under time varying electricity tariff space heating consumers can save in heating costs by • Storing heat in to the house during low tariff hours • Trading living comfort to costs savings • A dynamic decision problem

  3. Space heating problem • Space heating consumers try to • MIN “Heating costs” • MAX “Living comfort” • subject to • Dynamic price of the electricity • Dynamics of house • Other (physical) constraints

  4. Tout T q d Q Dynamics of the house Q(t)=Q(t-1)+Dtq(t-1)-d(t-1) where d(t) = aDt(T(t) - Tout(t)) Q(t) = T(t)/C, (b = 1/C) Þ T(t) = T(t-1) +bDtq(t-1) -abDt(T(t-1) - Tout(t-1)) Units: [Q] = kWh, [a] = kW/°C, [C] = kWh/°C

  5. Example houses House 2 House 1

  6. Tout p a, b Tmin£T£ Tmax 0 £ q £ qmax d Tref Q Information summary

  7. Goal models 1. Hard constraint (pipe is hard) 2. Soft constraints (pipe is soft)“Interval goal programming” 3. Hard constraint with a goal inside(pipe with a goal)

  8. Hard constraints

  9. Soft constraints

  10. Hard constraints with a goal

  11. Goal models (summary) 1. Hard constraints 2. Soft constraints 3. Hard constraints with a goal

  12. MultiObjective Household heating Optimization (MOHO)

  13. Idea of MOHO • Minimize heating costs using hard lower and upper bounds for indoor temperature • The case of hard constraints • Ask: “How many percents would you like to decrease the heating costs from the current level?” • Solve again trying to achieve the desired decrease in cost by relaxing the indoor temperature upper bound • The e-constraints method (upper bound must be active in order to succeed)

  14. Example: House 2 (1/4) Minimized heating costs:

  15. Example: House 2 (2/4) Decreased costs by 5 %

  16. Example: House 2 (3/4) Decreased again by 5 %

  17. Example: House 2 (4/4) And again by 5 %

  18. Summary • Model and parameters of the house identified • Depending on the definition of the “living comfort” different multicriteria models can be used • Benefits of the simplified approach: • Only bounds of the indoor temperature asked • Comparison and tradeoff only with heating costs

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