December 19, 2011
Logic holds – it is better to have an approximate answer to the right question rather than a precise answer to the wrong question. Combined with the better-known rule – you can’t manage what you don’t measure – and the complex challenges and opportunities facing our power and finance industries today are better revealed.
At the crossroads of energy policy and project finance decision-making is the ever-present analysis framework, LCOE – the levelized cost of energy. Hidden within this robust and evolving mathematical solution is a trove of unexplored potential uniquely favoring solar power and distributed generation over conventional power and centralized generation. Data outputs from LCOE are trending toward provable economic and societal biases in support of distributed solar, and reveal an observable frequency of more preferred and precise answers to defensibly more useful questions. As these claims are shown by levelized analysis to be mathematical fact and deployable at scale, the LCOE tool can help us pursue our solar energy future more confidently at a quickening pace.
Levelizing the Field – as turning a new phrase and proposing a thesis goes – would challenge our national leaders and applied practitioners to carefully re-evaluate the framework for defining both input and output measurements available in the analytical framework commonly known as LCOE. More plainly said, we need to do the math, and do so logically. When we do, we run headlong into some very compelling conclusions about solar energy versus conventional alternatives:
1) Solar LCOE produces the most precise answers to ever more reliable questions supported by increasingly more accurate measurements. In other words, solar levelized analysis is more reliable and bankable (certain) than any other power generation source analysis over its useful life and investment cycle – and across multiple inputs.
2) As and when solar energy policies align with more reliable questions supported by economic proofs, legacy energy policies are necessarily transformed by levelized analysis. In other words, when solar energy investments are recognized to improve public and private balance sheets like no other generation source can, policymakers will adjust accordingly and incumbent policies will be forever changed in advancing support for solar.
3) L(x)OE methods will begin to overtake the existing LCOE-centric framework, as solving to their variable answers begin to predominate in the most sophisticated power finance models. In other words, using LCOE to solve to cost alone will prove to be an inferior business intelligence tool compared to models that solve to multiple, levelized variables – answering more questions and enabling better decision-making across a broader range of factors.
Levelizing the Field implicitly holds that we are not asking the right questions, but that when we begin to ask them more clearly, better approximate and precise answers will follow:
1) Is the useful life for a solar plant facility much longer than LCOE analysis currently assumes?
2) How do you price risk if one source provides an almost fixed lifetime LCOE while another guarantees only a volatile and unknowable LCOE?
3) If solar achieves something close to $1/watt installed within 10 years, and provides “free fuel” at a radically lower LCOE, will we continue to support conventional power, and does this eventually render even in-service power obsolete?
4) Will consumers ever be able to negotiate a 5+ year discounted, fixed rate schedule with their utility, and pay virtually nothing for electricity thereafter?
5) Does forward-looking LCOE defy conventional wisdom? Will we spend more for less energy per capita, or will we spend far less to use much more, and cleanly?
6) What does L(x)OE tell us about on-site solar combining economically with on-site storage?
7) Will evolving net-metering policies and a smart transmission grid further unlock LCOE advantages?
8) As technologies continuously increase solar output, do energy and utility business models fundamentally change?
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