HERON DST outcomes in energy scenarios

Transcription

1 14 November 2017 BUILD UP Webinar on HERON An innovative way of incorporating end-users behaviour in EE policy making HERON DST outcomes in energy scenarios Incorporating results, improving scenarios Eleni-Danai MAVRAKI MSc HERON project Scientific expert

2 Structure 1. HERON DST outcomes incorporation A. Scenarios before HERON DST B. HERON DST contribution C. New kind of scenarios 2. Towards preferable scenarios A. From optimum to preferable scenarios B. Further assessment AMS evaluation method 3. General conclusions, comments and observations 2

3 Building scenarios without the barriers caused by end-users behavior is causing deviations from the targets and promotes policy and measures mixtures that in reality are not as efficient as expected, according to the modeling results. HERON DST OUTCOMES INCORPORATION 3

4 Scenarios before HERON DST Buildings 1. Business as usual (BAU) 2. Efficient heating 3. Efficient cooling 4. Building shell improvement 5. Efficient appliances 6. Efficient lighting 7. BEMS application 4

5 HERON case Business as Usual: BAU Energy Efficiency Buildings: EEB0 a. Efficient heating b. Efficient cooling c. Building shell improvement d. Efficient appliances e. Efficient lighting f. BEMS application 5

6 HERON DST contribution Scenarios + HERON DST barriers impact = Scenarios with new policy mixtures 1. Scenarios with different technology mixtures linked with the end-users behaviour barriers. 2. Selection of the scenario with the smallest target deviation as optimum. 6

7 HERON case EE B1 a scenario based on EEB0 that incorporates the impact of ALL the barriers linked with end-users behavior. The existence of barriers prevents the achievement of the target. By using HERON DST, deviation from the target is now quantified in this scenario and reflected in the scenario outcomes. 7

8 New kind of scenarios HERON DST provided a number of suggested technologies combinations that could provide best results / smallest deviation from target. By setting new scenarios and adopting different suggested technologies mixture in them, we have a new selection of options to follow. 8

9 HERON case Scenarios HERON DST suggested technology mixtures Efficient heating Case: Greece, Buildings Building shell improvement Scenarios: Consider the existence of all barriers HERON DST suggests Efficient technology appliances mixtures Efficient lighting Efficient cooling Technology mixtures barriers are per scenario minimized EE B2 EE B3 EE B4 Building Shell Improvement Efficient cooling Efficient Application of BEMS Appliances Building Shell Improvement Efficient heating Efficient Appliances Efficient cooling Efficient heating Efficient Appliances 9

10 Optimum is considered the best scenario; the one that shows the smallest deviation from the target. However, many cases present as Optimum scenarios that do not consider at all the behavioral barriers, hence the high score achieved. In order to prevent conclusions were numbers indicate a scenario and the expert needs to explain why not to adopt it but prefer another, further assessment is required TOWARDS PREFERABLE SCENARIOS BASED ON HERON CASE STUDY GREECE 10

11 BAU: Business as Usual BUILDINGS SECTOR Scenarios: Set EE0: Scenario without barriers Business as Usual (BAU) scenario EE1: Scenario considering all barriers Energy Efficiency (EE B0) scenario EE2: Scenario with minimized barriers mixture #1 Energy Efficiency (EE B1) scenario EE3: Scenario with minimized barriers mixture #2 Energy Efficiency (EE B2) scenario EE4: Scenario with minimized barriers mixture #3 Energy Efficiency (EE B3) scenario TRANSPORT SECTOR Business as Usual (BAU) scenario Energy Efficiency (EE T0) scenario Energy Efficiency (EE Τ1) scenario Energy Efficiency (EE T2) scenario Energy Efficiency (EE T3) scenario Energy Efficiency (EE B4) scenario Energy Efficiency (EE T4) scenario

12 Scenarios: the Optimum After the incorporation of HERON DST outcomes into LEAP developed scenarios, we have results pointing an optimum scenario Case: Greece, Buildings sector Final energy consumption in year 2030, compared to BAU Source is that enough? EE B4 EE B3 EE B2 EE B1 EE B0 BAU 75% 73% 74% 75% 67% 2 But Considers ALL behavioral barriers Doesn t consider ANY behavioral barrier 100% 0% 20% 40% 60% 80% 100%

13 Evaluation in HERON From OPTIMUM to PREFERABLE 1 st question: What are our initial criteria: 1. Incorporate the end-users behavior 2. Present the most Energy Efficient scenario 2 nd question: How to conclude to a preferable scenario? By selecting an evaluation method with additional criteria 3 rd question: What are the additional criteria, based on the selected evaluation method (AMS)? A Environmental performance B Political acceptability C Feasibility of implementation

14 AMS:The selected evaluation method Integrated multi-criteria analysis method for quantitative evaluation of climate change mitigation policy instruments. Consists of: 1. a set of criteria supported by sub-criteria, all of which describe the complex framework under which these instruments are selected by policy makers and implemented 2. an Analytical Hierarchy Process (AHP) process for defining weight coefficients for criteria and sub-criteria according to the preferences of three stakeholders groups and a Multi-Attribute Theory (MAUT)/Simple Multi-Attribute Ranking Technique (SMART) process for assigning grades to each instrument that is evaluated for its performance under a specific sub-criterion The method was named AMS from the initials of the combined processes and techniques.

15 AMS criteria Weight coefficients of AMS criteria Criteria Weigh coefficients Environmental performance - A 0,168 Political acceptability - B 0,738 Environmental performance - A Political acceptability - B Feasibility of implementation - C 9% 17% Feasibility of implementation - C 0,094 Total 1 74%

16 AMS criterion: ENVIRONMENTAL PERFORMANCE A Environmental performance - A Direct contribution to GHG emission reductions Weight coefficients 0,833 Indirect environmental effects 0,167 Environmental performance subcritera weight coefficients Direct contribution to GHG emission reductions Indirect environmental effects 17% Total 1 83%

17 AMS criterion POLITICAL ACCEPTABILITY B Political acceptability - B Weight coefficients Political acceptability sub-criteria weight coefficients Cost efficiency 0,474 Dynamic cost efficiency 0,183 Competitiveness 0,085 Equity 0,175 Flexibility 0,051 Stringency for noncompliance 0,032 Total 1 9% Cost efficiency POLITICAL ACCEPTABILITY is the Criterion with the highest 5% 3% weight coefficient (74%) among the 3 AMS criteria. Dynamic cost efficiency Competitiveness Cost 19% efficiency is the sub-criterion of Political acceptability with the highest weight 47% Equity coefficient (almost 50%) among the 6 subcriteria. Flexibility The above make Cost efficiency sub-criterion one of the most important in the evaluation 18% process. Stringency for non-compliance

18 AMS criterion FEASIBILITY OF IMPLEMENTATION C Feasibility of implementation - C Implementation network capacity Weight coefficients 0,309 Feasibility implementation subcriteria weight coefficients Implementation network capacity Administrative feasibility Financial feasibility Administrative feasibility 0,581 Financial feasibility 0,11 11% 31% Total 1 58%

19 Case study: What happens when the selected additional criteria are applied on the developed energy efficiency scenarios The difference between the Optimum (best) scenario and the Preferable (highest acceptability) GREECE, BUILDINGS SECTOR

20 Reminder of Optimum Scenario After the incorporation of HERON DST outcomes into LEAP developed scenarios, we have results pointing an optimum scenario Case: Greece, Buildings sector Final energy consumption in year 2030, compared to BAU Source EE B4 EE B3 EE B2 EE B1 EE B0 BAU 75% 73% 74% 75% 67% 100% 0% 20% 40% 60% 80% 100%

21 HERON scenarios evaluation against Environmental performance Sub-criteria performance Scenarios Environmental performance BAU EE B0 EE B1 EE B2 EE B3 EE B4 83,3 70,81 16,8 72,75 74,41 13,91 71,92 14,65 14,98 12,08 16, ,45 14, Direct contribution to GHG emission reductions (0,833) Indirect environmental effects (0,167) BAU EE B0 EE B1 EE B2 EE B3 EE B4 0

22 Cost efficiency (0,474) HERON scenarios evaluation against Political acceptability Dynamic cost Competitiveness efficiency (0,183) (0,085) Equity (0,175) Flexibility (0,051) Stringency for non-compliance (0,032) BAU 5,84 1,09 0,89 0 0,65 0,47 6,6 20,73 Scenarios' Sub-criteria political performance acceptability 17,37 26,75 23,19 19,21 EE B0 5,84 2,74 0,89 17,5 0,65 0,47 EE B1 5,84 2,74 0,89 12,94 0,65 0,47 EE B2 14,67 3,9 2,22 13,68 1,03 0,75 EE B3 9,26 3,9 2,22 14,27 1,03 0,75 EE B4 5,84 3,9 1,4 13,38 1,03 0,47 BAU EE B0 EE B1 EE B2 EE B3 EE B4

23 HERON scenarios evaluation against Feasibility implementation 1,83 Sub-criteria performance Scenarios feasibility implementation 12,84 12,84 1,69 1,69 8,39 8,1 8,1 1,37 1,37 8,1 8,1 1,44 5,29 5,29 5,29 3,31 3,31 2,99 1,2 1,2 1,87 1,87 1,87 Implementation network capacity (0,309) Administrative feasibility (0,581) Financial feasibility (0,110) BAU EE B0 EE B1 EE B2 EE B3 EE B4 BAU EE B0 EE B1 EE B2 EE B3 EE B4

24 Direct contribution to GHG emission reductions (0,833) BAU EE B0 EE B1 EE B2 EE B3 EE B4 0,00 83,30 70,81 72,75 74,41 71,92 Indirect environmental effects 0,00 16,80 12,00 14,45 14,73 0,00 (0,167) Environmental performance 0,00 16,80 13,91 14,65 14,98 12,08 (0,168) - A Cost efficiency (0,474) 5,84 5,84 5,84 14,67 9,26 5,84 Dynamic cost efficiency (0,183) 1,09 2,74 2,74 3,90 3,90 3,90 Competitiveness (0,085) 0,89 0,89 0,89 2,22 2,22 1,40 Equity (0,175) 0,00 17,50 12,94 13,68 14,27 13,38 Flexibility (0,051) 0,65 0,65 0,65 1,03 1,03 1,03 Stringency for non-compliance 0,47 0,47 0,47 0,75 0,75 0,47 (0,032) Political acceptability (0,738) - B 6,60 20,73 17,37 26,75 23,19 19,21 Implementation network capacity 8,39 5,29 5,29 3,31 3,31 5,29 (0,309) Administrative feasibility (0,581) 8,10 8,10 8,10 12,84 12,84 8,10 Financial feasibility (0,110) 2,99 1,20 1,20 1,87 1,87 1,87 Feasibility of implementation (0,094) - C 1,83 1,37 1,37 1,69 1,69 1,44 Total (A+B+C) 8,43 38,90 32,65 43,09 39,86 32,73

25 Case study: Greece, Buildings sector Preferable scenario After the evaluation incorporation of all scenarios of HERON against their DST environmental performance, political acceptability outcomes into LEAP developed and feasibility of implementation, scenarios, due we to have its Cost results Efficiency, pointing as optimum the EE B0 scenario. the Preferable scenario, is EE B2

26 GENERAL CONCLUSIONS COMMENTS AND OBSERVATIONS 26

27 General conclusions 1. Behavioral barriers when taken into consideration present a different deviation from target and a more realistic representation. 2. The incorporation of HERON DST outcomes into developing energy scenarios provide more realistic suggestions of achieving the 2030 target. 3. It is suggested to combine the incorporated HERON DST outcomes into developed scenarios with an evaluation method, in order to conclude to a preferable scenario. 4. The preferable scenarios might not be the most obvious, they are however those with the highest acceptability.

28 Common characteristics of preferable scenarios in HERON 1. Integrate in the greatest extent the endusers behavior 2. Overall performance is more effective compared to other scenarios 3. Contain the policy mixture that best supports the penetration of technologies in the national market

29 HERON developed process Pathway towards preferable scenarios Pathway to facilitate policy makers to include DST outcomes EE Evaluation of EE scenarios Use of incorporation end-users behavioral barriers into EE development HERON DST into energy scenarios EE scenarios EE barriers mapping model results results Assessment to conclude with preferable scenarios

30 HERON project received funding from the European Union s Horizon 2020 Research and Innovation programme under grant agreement No

31 Eleni-Danai MAVRAKI MSc. HERON Project manager Energy Policy and Development Centre (KEPA) National and Kapodistrian University of Athens THANK YOU