SolarPower Webinar. PV Cleaning: Choosing the Optimal Method and Frequency

Transcription

1 SolarPower Webinar PV Cleaning: Choosing the Optimal Method and Frequency 18 October 2017 Partner

2 PV Cleaning: Choosing the Optimal Method and Frequency MODERATOR Michael Schmela Executive Advisor SPEAKER James Kurz Project Manager Partner

3 PV Cleaning: Choosing the Optimal Method and Frequency Solar Power Europe Webinar October 18, 2017 James Kurz, Project Manager

4 Huge PV power plants are being built in desert regions around the world at highly competitive generation cost (increasingly < 3 cents/kwh) Source: Google Maps (Dubai DEWA Sheikh Maktoum Phase 1 & 2 power plants) 2

5 but there is a very dirty elephant in the room: PV module soiling. 3 3

6 Cleaning is the only solution in many regions. 4

7 Urgent question for IPPs: what is the commercially optimal, least-risk way to deal with soiling? 5

8 Apricum The Cleantech Advisory. Apricum at a glance Business Industry focus Founded in 2008, over 200 successful transaction advisory and strategy consulting projects Cleantech. Strong focus on solar, wind, water, energy storage and digital energy Team Clients >40 cleantech experts with decades of industry experience Companies, investors and public institutions Services Locations Transaction advisory Strategy consulting HQ in Berlin, Germany Branch offices: Abu Dhabi and Dubai Representative offices: Argentina, Brazil, Mexico, USA, UK, The Netherlands, Turkey, Saudi Arabia, India, China, Thailand, Indonesia, Philippines, South Korea, Japan 6

9 ILLUSTRATIVE Cleaning is necessary in high and certain moderate soiling regions. Soiling category Temperature [ C], precipitation [mm] Share of global installations 1 Need for cleaning High soiling Constantly arid, precipitation ~0 C mm Regular and/or frequent 0 Jan 0 Dec Moderate soiling 50 Pronounced arid season 100 Infrequent; often only in dry season Jan 0 Dec Low soiling Relatively humid throughout the year Jan Dec Source: Apricum analysis; 1) share of cumulative PV market Cleaning rarely required unless particular circumstances persist (nearby heavy agriculture, bird droppings, pollution, etc.) 7

10 PV projects are nowadays considered highly secure investments with relatively low equity returns. Factors affecting the equity return of PV projects Project revenue PV yield Tariff Project cost Cost of construction O&M cost Financial leverage (x3 6) Equity IRR (6 9%) 1 1) With highly creditworthy counterparties, e.g., in the UAE 8

11 PV module soiling and cleaning affects project returns substantially through multiple drivers. Factors affecting the equity return of PV projects Soiling reduces PV yield, partly mitigated by cleaning Cleaning requires upfront infrastructure Project revenue PV yield Tariff Project cost Cost of construction O&M cost Financial leverage (x3 6) Equity IRR (???) Cleaning incurs running costs, escalating over time 9

12 Developers take substantial risks, underestimating the uncertainty around soiling. Factors affecting the equity return of PV projects Soiling reduces PV yield, partly mitigated by cleaning Cleaning requires upfront infrastructure Cleaning incurs running costs, escalating over time Project revenue PV yield Tariff Project cost Cost of construction O&M cost Financial leverage (x3 6) Residual uncertainty in yield and cost impact, highly leveraged, creates potentially large impact on equity IRR. Little safety margin left! Equity IRR 10

13 Cleaning strategy is a major element in highly competitive PV project tenders in desert locations. Developer wants to bid lowest tariff possible to win the tender On top of stochastic mean of necessary tariff, developer wants as little safety margin as possible: Cleaning strategy needs to be: Return-optimizing Optimal cleaning method Optimum cleaning regime (frequency) Robust Minimum residual yield uncertainty Minimum cost uncertainty 11

14 First and crucial step is to understand climatic and economic parameters of the site. Typical site-specific parameters Climatic Soiling rate under regular conditions Average daily soiling rate Asymptotic soiling rate Sandstorms Distribution and intensity Rain events Length of the wet/dry seasons Average number of rain events in wet/dry seasons Economic Labor cost Inflation rate Water cost Cost for water infrastructure (e.g., large water tanks) Diesel cost (for truck-based) Site geometry Site area Site dimensions Number of access roads through plant in each orientation 12

15 Next the plant layout and mounting structure architecture need to be accounted for. Layout specific parameters Mounting architecture Fixed tilt Tracker with distributed drive system Tracker with centralized drive system Module orientation Portrait Landscape Rows of modules across table Columns of modules per table Other Possibility to connect tables to form long rows Ability to stow tracker systems at high angles Gaps between tables in a row Gaps within tables for drive systems In-table obstructions/ protrusions 13

16 There are a wide range of cleaning methods and solution providers both wet and dry. Manual cleaning Semi-automated truckmounted cleaning Semi- and fully-automated robotic cleaning Source: Apricum analysis; 14

17 A large variety of cleaning technology vendors are vying for business, but only few solutions are effective and bankable. Cleaning technology vendor summary Manual devices 1 Truck-mounted Semi-automatic robots Fully automatic robots Companies Subsegments Dust broom Brush trolley Manual water brushes Hydraulic arm (water, steam or compressed air) Pressurize water tank and hose Battery powered Cabled Rail mounted Frame mounted Free moving across surface 2 Sprinkler 2 Source: Apricum analysis based on research and interviews; 1) Not exhaustive, many players; 2) Typically used only for rooftop 15

18 Certain site or plant design attributes significantly affect competitiveness for each type of cleaning method. Factors affecting competitiveness by cleaning method Manual Truckmounted Semi-auto robot Full-auto robotic + Low labor cost + Infrequent cleaning needs + Large labor pool available High inflation rate Sensitivity to module damage Inaccessible or expensive water for wet cleaning + Cheap, available water + Flexible cleaning requirements + Labor can work in cooled vehicles Unavailable water Narrow rows Difficult terrain Tracker drive line Sensitivity to module damage Source: Apricum analysis; 1) For robots requiring water + Flexible cleaning requirements + Compatible with tracker systems + Upfront cost sensitivity Hot environments (labor exposed) Inaccessible water 1 Obstructions within tables + High soiling rates + Water unavailable + High labor rates + High predictability required + Large site No tracker products (yet) Sensitivity to upfront cost Obstructions within tables 16

19 Manual, truck-based and semi-automatic robotic cleaning methods require careful tuning of cleaning frequency. Revenues NPV 1x Impact of yield improvement 365x Ever less marginal returns from increasing cleaning frequency Net project NPV Optimum Cleaning frequency Cost NPV 1x Impact of O&M cost 365x Very high cleaning frequency generates high OPEX impact 26x Cleaning frequency Cleaning frequency 17

20 Robotic cleaning is much more robust: marginal cost of cleaning is negligible high frequency cleaning is optimal. Revenues NPV 1x Impact of yield improvement 365x Cleaning frequency Ever less marginal returns from increasing cleaning frequency Net project NPV Optimum Cost NPV Impact of O&M cost Each cleaning cycle has negligible additional cost 365x Cleaning frequency 1x 365x Cleaning frequency 18

21 In regions where labor cost is relatively high, soiling very high and/or water cost is high, robotic cleaning wins. Example case: Relative NPV of PV project by cleaning solution [USD cents/wp] Dubai Manual dust broom (baseline) Manual brush trolley Water truck Robotic (dry) Semi-auto robot Frequency [days] Source: Apricum analysis; assumes fixed tilt system that robotic systems are compatible with 19

22 Conclusion: cleaning can be a make or break factor for competitive PV projects. Cleaning is becoming a crucial topic for PV developers and IPPs, as projects are increasingly built in dirty regions and margins for error shrink There is no one size fits all solution; methodology and cleaning regime need to be optimized per site and project layout Knowledge of climatic parameters and careful planning is key! Dry robotic technologies promise to be the most robust and scalable technology going forward Bankability issues still underestimated Key challenge for fully-automated robotic solution providers will be to offer an efficient tracker solution 20

23 Thank you for your attention James Kurz Project Manager T

24 PV Cleaning: Choosing the Optimal Method and Frequency MODERATOR Michael Schmela Executive Advisor SPEAKER James Kurz Project Manager Partner

25 SolarPower Webinar Thank you for attending Download this presentation under PAST WEBINAR on Partner SolarPower Europe (European Photovoltaic Industry Association) Rue d Arlon 69-71, 1040 Brussels, Belgium T / F info@solarpowereurope.org /