Combined Heat and Power in Commercial Buildings: Investment and Risk Analysis

Abstract

Combined heat and power (CHP) systems can generate electricity locally while they recover heat to satisfy heating loads in buildings, which means they provide efficient energy. On-site generators may reduce both the expected energy costs and cost risk exposure for developers. With volatile energy prices, a deterministic modeling framework will not yield a fair value of CHP systems because flexibility in the operational response to price changes is not taken into account. In this paper, we present a Monte Carlo simulation model that is used to find the CHP value under uncertain future wholesale electricity and natural gas prices. When considering investing in a CHP system on should consider both return and risk. Clearly, both investment return and risk depend on local energy tariffs and energy loads. We highlight an example where CHP is marginally profitable and the investment decision is not straightforward. Interestingly, CHP systems were found particularly attractive with volatile electricity prices because their ability to respond to high prices provides efficient hedges to energy cost risk. Therefore, developers should not be discouraged but rather embrace onsite generation in markets with volatile prices. From the analysis, it can also be concluded that sizing of CHP systems can be related to the energy tariff structure and cost risk preferences as well as to energy loads.

Keywords

Combined heat and power (CHP)risk analysis investment appraisal real option valuation Monte Carlo

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References

Awerbuch

(2000). “Investing in Photovoltaics: Risk, Accounting and the Value of New Technology.” Energy Policy 28:1023-1035

Clewlow

Strickland

(2000). Energy Derivatives: Pricing and Risk Management. London: Lacima Publications

Deng

S-J

Xia

Z.D.

(2006). “A Real Options Approach for Pricing Electricity Tolling Agreements.” International Journal of Information Technology & Decision Making 5 (3): 421-436.

Dixit

Pindyck

R. S.

(1994). Investment under Uncertainty. New Jersey: Princeton University Press

LBNL (2006). Tariff Analysis Project Database. Ernest Orlando Lawrence Berkeley National Laboratory. Available online at: http://tariffs.lbl.gov/

Firestone

Marnay

(2005). The Effects of Electricity Tariff Structure on Distributed Generation Adoption in New York State. Ernest Orlando Lawrence Berkeley National Laboratory. Paper-LBNL-57942. Available online at: http://repositories.cdlib.org/lbnl/LBNL-57942

Firestone

(2004). Distributed Energy Resource Customer Adoption Model - Technology Data. Report available online at: http://der.lbl.gov/data/DERCAMTechDataOnline.pdf

Hoff

T. E.

(1997). “Using Distributed Resources to Manage Risk Caused by Demand Uncertainty.” The Energy Journal. Special Issue: Distributed resources: Towards a new paradigm of the electricity business;(18): 63-83.

IEA (2002). Distributed Generation in Liberalised Electricity Markets. International Energy Agency. Paris: OECD

10.

Lovins

A. B.

Datta

K. E.

Feiler

Rabago

K. R.

Swisher

J. N.

Lehmann

Wicker

(2002). Small is Profitable: The Hidden Economic Benefits of Making Electrical Resources the Right Size. Rocky Mountain Institute, Snowmass, CO.

11.

Schwartz

E. S.

Smith

J. E.

(2000). “Short-Term Variations and Long-Term Dynamics in Commodity Prices.” Management Science 2000; 46 (7): 893-911.

12.

Siddiqui

A. S.

Marnay

Edwards

J. L.

Firestone

Ghosh

Stadler

(2005). “Effects of Carbon Tax on Microgrid Combined Heat and Power Adoption.” Journal of Energy Engineering 131(1):2-25.