Master Thesis Jonathan Wood and Stefan Funk EnergyLab

Transcription

1 CAN DEMAND RESPONSE HELP REDUCE FUTURE DISTRIBUTION GRID INVESTMENTS? K AN ECONOMIC STUDY OF PEAK SHAVING IN THE NORWEGIAN DISTRIBUTION GRID: SEMIAH PILOT IN ENGENE, SØRLANDET Master Thesis Jonathan Wood and Stefan Funk EnergyLab

2 What is demand response? Active Passive Supply Grid Demand Demand signals

3 What is demand response? Active Active Supply Grid Demand Demand signals Congestion signals Price signals

4 Why is this useful to the Distribution Grid? Transmission Distribution Flexible demand High load Predictable Intermittency stress

5 Peak shaving in the Distribution Grid 1. Align with renewables 2. Avoid overloading

6 Our Project: SEMIAH 3 year EU funded project March 2014 to March 2017 Two pilots each of 100 households in Norway and Switzerland Norway controlling of boilers and electric heating panels Switzerland - boilers, heat pumps and optimization of roof-top PV Our work optimisation of boilers for peak shaving in Norway

7 The Problem: Engene Transformer Max load: 25MW Data source: Agder Energi Nett

8 K l a r a S j o k l a r a. s j n h h. n o Norwegian Pilot Installation Source: Agder Energi Nett 8

9 Typical Household Data Data Source: SEMIAH Pilot

10 Boiler Behaviour Data Source: SEMIAH Pilot

11 Boiler Behaviour Data Source: SEMIAH Pilot

12 Aggregated loads Data Source: SEMIAH Pilot

13 Aggregated loads Data Source: SEMIAH Pilot

14 Boiler Consumption - A Closer Look Data Source: SEMIAH Pilot

15 V Optimisation A: DSO Peak Shaving

16 Optimisation B: Aggregator as a supplier Assume perfect balancing No liquidity

17 Methodology Define boiler load constraints Define objective function Run optimisation against prices Marginal costs = Second optimisation Initial optimisation Re-run optimisation Add additional constraint or penalty Different boundaries for boiler load constraints (sensitivity analysis) Objective function always procurement cost of energy Additional peak constraints and penalties for the different models

18 Results: Aggregator as a supplier Cost Optimisation Day-ahead: cost reduction of up to 4.3% low price volatility Reserve option market: negligible boiler incompatibility Aggregator Peak Shaving Total peak reduction of 8.3% Sustained boiler peak reduction of up to 54%

19 Results: Aggregator as a supplier

20 Results: DSO Peak Shaving Using the day-ahead prices to reveal marginal costs Test 1: Reduce boiler load to zero during Engene peak periods Can be achieved at zero marginal cost Test 2: Reduce boiler load to zero during pilot peak periods Can be achieved at zero marginal cost Test 3: Reduce boiler load to zero during 8-10am and 5-7pm Significant reduction can be achieved at negligible marginal cost

21 Results: DSO Peak Shaving

22 Limitations of our approach Data for only 22 households available quality issues Boiler water temperature could not be modelled Ex-post optimisation using observed consumption and prices Only boiler appliance considered Intraday market could not be modelled

23 Conclusions DSO only results encouraging but installation costs too high Aggregator could bare installation costs and charge DSO a fee Revenues from day-ahead and reserve option market optimization limited for aggregator with only boilers Aggregator should charge DSO fee for peak shaving Peak shaving, including more than only boiler (e.g. EVs), interesting and feasible Regulatory framework to set incentives and define roles is missing Future price volatility and level are crucial for the value of flexibility

24 DEMAND RESPONSE NOW

25 Electrification EVs, Heat pumps, solar, digitalisation

26 Every house is unique Existing ones are the issue

27 Large buildings are easy Universities, apartment blocks, hospitals, offices

28 Self-consumption optimisation Consume when you produce

29 29

30 K l a r a S j o k l a r a. s j n h h. n o Thank you! stefan.funk@misurio.ch jonathan.rk.wood@gmail.com 30

31 Appendix

32 Load Hours of the day Data source: SEMIAH Pilot

33 Load Hours of the day Data source: SEMIAH Pilot