Multicriteria decision support for planning district heating

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1 Department of Energy Technology Multicriteria decision support for planning district heating Haichao Wang Postdoctoral researcher Department of Energy Technology Aalto University School of Engineering +358 (0)

2 Outline Background and Motivation Objective and Scope Decision Support Framework and Application Sub-models for decision support Multicriteria aggregation Application Conclusions Current and Future Work

3 Background and Motivation 1 Energy conservation 2 Environmental issue 3 Economy concern 4 Technology promotion DH = District Heating

4 Background and Motivation Combined district heating Peak load shaving in monthly horizon Heat load Heat load Peak heating period Peak heating period Q' Qload,b Peak shaving heat production Q' Qload,b Peak shaving heat production Basic district heat production Basic district heat production 0 t1 Operation time of basic heat producer t2 n d Time 0 Operation time of basic heat producer n Duration time (a) heat load curve in chronological sequence (b) heat load duration curve Heat load for domestic hot water is not included in this analysis.

5 Background and Motivation Why combined district heating? Why multicriteria decision support? Energy efficiency Reliability Eco-sustainability Economy Motivation Better criteria system Conflicting & incommensurable Qualitative & quantitative Uncertain & imprecise

6 Objective and Scope Research objective combined district heating system with gas-fired boilers in secondary side Definition of basic heat load ratio: Q Q Q load, b load, p load Q loadb, Q load Q load, p Q load 1 Q loadb, Q load Basic heat load ratio Q load, p Q load (1 ) Peak-shaving ratio

7 Decision Framework Development and Application Sub-models Decision support framework Equivalent Electricity (EE) Mean spatial Distribution (MSD); CO2 Net heating cost (NHC) Reliability Energy Environment Economy Technology

8 Decision Framework Development and Application Multicriteria Aggregation What is SMAA? SMAA is a family of methods developed and improved for aiding multicriteria decision making in problems with uncertain, imprecise, or partially missing information. How does SMAA work? x x? Optimal? Optimal M(x,w) w w? M(x,w) (a) traditional decision making (DM) (b) DM based on SMAA

Complementary judgment matrix (CJM) w2 Plane: w 1 +w 2 +w 3 =1 min w3 w3 w2 w1 w1 min

9 Decision Framework Development and Application Multicriteria Aggregation Weight determination n n Weight space: W w R : wj 0, w 1 j 1 j Feasible weight space Complementary judgment matrix (CJM) w2 Plane: w 1 +w 2 +w 3 =1 min w3 w3 w2 w1 w1 min min w2 w2 A max w3 w3 W max w1 w1 max w2 w2 w1 w1 w3 (a) a weight vector w3 (b) a feasible weight space

10 Decision Framework Development and Application Application Case study a combined district heating system in Daqing city of China has been adopted to demonstrate the decision support framework. can be found on School of Engineering newsletter: Item Value Unit Heat load 616 MW Substations 50 Design outdoor temperature 26 C Design indoor temperature 18 C Heating period 181 d

11 Conclusions CHP is playing an indispensible role especially in DH sector Peak load should be better satisfied in a DH system Combined district heating system can better supply the peak load and thus improve the energy and environmental efficiencies The multicriteria decision framework is needed for planning or retrofitting DH systems and other energy supply systems Complementary judgment matrix (CJM), feasible weight space and SMAA help improve the accuracy and reliability of multicriteria decision making with conflicting, incommensurable, uncertain and imprecise information.

12 Current and Future work Current work RICES Developing an LP model to optimize the district heating system with CHPs, heat only boilers (HOBs), a solar thermal plant and a thermal energy storage (TES). One week model is solved, one month and one year model will be built and solved soon. One-day (or two-day) ahead model is proposed to guide the real-life operation of the system. Weather Elec. Price Heat Demand Optimal Operation One day ahead Next day

13 Current and Future work Result and conclusion Comparison of our LP model with EnergyPro program One week result selected from one year model by EnergyPro

14 Heat load ,00E+01 2,50E+01 2,00E+01 1,50E+01 1,00E+01 5,00E+00 0,00E+00 5,00E+02 4,00E+02 3,00E+02 2,00E+02 1,00E+02 0,00E+00 MW Tolkkinen NEW CHP Tolkkinen OLD CHP Harabacka CHP Solar heat Heat demand Hour Power Storage capacity Storage level

15 Current and Future work Comparison of our LP model with EnergyPLAN program Conclusion: We cannot rely only on the commercial energy analysis tools. Future work Improve our LP model Further comparison between our LP model and other Energy analysis program Modelling and optimization of CHP based district heating systems with renewable energy source (RES) and energy storage Smart Thermal Grid (STG) study.

District heating investment planning

Energiatekniikan laitos District heating investment planning Risto Lahdelma Professor, Energy technology for communities Email: risto.lahdelma@aalto.fi Tel: +358 40 503 1030 Energy technology, School of