Sustainable Energy Management Options for a Small Subdivision. Mandy Armstrong SB10 Conference 2010

Transcription

1 Sustainable Energy Management Options for a Small Subdivision Mandy Armstrong SB10 Conference 2010

2 Project Objectives Thesis for Master of Technology in Energy Management Design a decision-support tool for a DG energy system supplying a small sustainable subdivision. What influences the viability of a net-billed gridintegrated system Identify the mix of energy-use options DG = Distributed Generation Net-billing = selling surplus electricity back 2

3 Totara Bank Energy Concept All lots make maximum use of solar energy Coppicing firewood for heating energy. Building Performance Indexes specified by floor area Lot limit of maximum current of 30A & 1 switchboard. Each lot connected to an internal grid, with 1 ICP on a 50 kva transformer connecting site to the network. Reconciliation of energy use will be done through Residents Association rules. 3

4 New Zealand Situation Analysis Retailers: No incentive for DG, cheaper to think big. DG Suppliers: High system prices encourage suppliers while discouraging payers Consumers: Most are unaware or uninterested Councils (some) starting to show leadership Government positions on Building Code, energy strategy and sustainability in general, are weak Internationally, DG projects are a strategic response to increase sustainability awareness, reduce fossil fuel use, & manage network performance 4

5 Community Case Study Insights: NZ Common themes: Eco-type, environmental communities established Off-grid systems common On-grid systems are individually connected Few truly communal net-billed energy facilities Developers not prominent leaders of energy options No Government facilitation Grid integrated communal approach: Totara Valley Innovative residential projects progress when funded privately 5

6 Case Study Insights: International INTENT Demonstration project Grid stability Demand reduction Daytime peak matching Education & awareness (low income) Green Community identity & appearance Range of technologies OUTCOMES Reduced demand (5-60%) = reduced costs NO network stability issues Altered demand pattern Higher property values Higher energy awareness Behaviour linked to tariff type & data display 6

7 Network Interface Community Energy: Features Supply High Grade Import Network & Retailers Supply High Grade On-Site Supply Low Grade Via site to lots Site Energy Demand By house Export surplus Community Energy System Balance Supply, Demand & Export Distribution, Connection & Control Options Manage lot-site network interfaces 7

8 Standard Connection Each house is independent, with own network connection Flow in both directions ICP ICP ICP ICP I-E Import Subdivision with DG I-E Generation area Import Standard subdivision - import only 8

9 Totarabank Connection Options Supply Generation Options: - Whole Site &/or - Individual Lot Exported Electricity Surplus I-E I-E Imported Electricity Supply ICP Internal Electricity Connection & Distribution Loop I-E I-E I-E 9

10 Energy Balance Principles Supply Demand Energy Balance: Managing the system Understanding benefits of import versus on-site generation. 10

11 Demand Profiles Average electricity demand profiles were developed using supplied electrical demand data. Low 3,550 Medium 8,203 High 14,670 NZ average range 8,000 to 10,000 Assumptions on the use of low-grade and high-grade energy, user behaviour, and appliance use. Low : no electric space or water heating, fewer appliances High: HP space heating and cooling, electric water boost 11

12 Results What does the Decision Model provide for residents? What affects the energy decisions for the site and for residents? Based on: Site-wide electricity generation of 3.6 kwp Solar PV + 6kW Proven wind turbine Current available prices where export = import System costs as of August

13 Decision Model: User Outputs Electricity balances by hour, and monthly by year On site electricity utilisation What contribution that electricity makes to site load Long term economics NPV, and the switch price of renewables for Totarabank Average annual costs by house Site infrastructure capacity to manage load Import and export meters 13

14 $ Model : Annual Cost by House Low Demand Standard With RE Value of Saving Value of Export 14

15 $ Model : Annual Cost by House Low vs High Demand Standard With RE Value of Saving Value of Export 15

16 Loop vs Radial Connection No difference in system performance Similar long term economics Loop allows for spreading of capital cost Small differences in capital costs (loop infrastructure) and fixed line costs (radial) Fixed lines charges add an extra $215/yr to a radially connected house (same tariff base) Loop does have islanding built in resilience to power cuts Value of the community approach?? 16

17 In the Loop No significant difference between connections based on house-only, site-only; or a mix of both, in terms of supplied electricity to users. Solar-wind systems had better outcomes for system performance and NPV, compared to solar only (wind is cheaper and blows during low sun periods) Net-billing benefits small unless increase supply, export at peak, or export tariffs change 17

18 Annual Energy Balance (Site) LOW HIGH Zero Net Energy (20 to 90 kw) True Zero Energy would have no import 18

19 Scenario: Load Management System utilisation improved with peak shifting, and daytime occupancy. Increased usage activity during the day used more on-site supply and reduced both import and export. Economics of active load management is driven by pricing : If export tariffs are higher than import, then managing load not as attractive as exporting. But - increased use of electricity on-site should not be at the expense of efficiency. 19

20 Getting the Most from the System Use 100% of the on-site supply especially if export prices are less than standard prices. So, contribution to load should also be as high as possible These are influenced by how much electricity is used at different times of the day. Households need to want to actively manage their demand to get the best from the system and that is not always a priority. Information, convenience, comfort and incentives are all part of the mix! 20

21 Economics of On-site Renewables Differences in NPV increase for systems > 20kW NPV improves as system size decreases, and with increasing demand NPV breakeven (25 yrs) for high and low demand: Capital Cost decrease of 15% to 42% Annual electricity price increase of 6% to 10% An export tariff of 90 c/kwh Gross FiT of 20c/kWh to 40 c/kwh 21

22 What are the benefits? House performance + behaviour = best first step Site renewables = resilience (power cuts, energy prices) Start small and expand as the benefits become obvious The communal approach engaging people Learning from own actions and from each other Learning from access to technology and information Seeing how sustainability works, holistically Reduced demand on the network - fair share 22

23 The Future Create change by doing Learn by doing...then tell the story, warts and all Keep pushing the boundaries Make the sandpit a positive place Add more players that are different to you Collaboration is cool, so overlap the territories Don t wait for a politician to come up with the answers Sustainability is not a banned word, but... Complacency should be. 23

24 Support Slides 24

25 Demand Profiles 25

26 kwh HEEP comparisons winter Winter Profiles -Site Models + HEEP Lower NI Low Medium High Tot. Electric Tot. All Fuels Hour Tot. All Fuels-Solid Fuels 26

27 Stage 1: Demand Profiles Rationale Primary Data Inputs: HEEP Beacon Appliance data Own data How is community electricity demand best estimated? Monitored data, for Feb & July Daily totals for a year Adjust for: Wairarapa Climate, ALF 3.1 Daily load built by end-use & time of use Adjust for: ratios by hr/mth to get an average day/month for a year Adjust for: occupancy, season, family size, baseload, house performance Decision Model Inputs: Low/Medium/High Profiles by hour, per month for Annual Demand; 27

28 Stage 1: Supply Profiles Rationale How much electricity can be generated from available resources to supplement grid supply? Primary Data Inputs: NIWA Cliflo data Performance data PV, wind turbines Hourly Wind & Solar Radiation Data Converted to: Average energy/hr for each month Adjust base curves by number of systems & system size Decision Model Inputs Supply curves by hour, per month for Annual Supply; 28

29 Peak Shift Profiles 29