Optimal technology choice and investment timing: A stochastic model of industrial cogeneration vs. heat-only production

Wickart, Marcel; Madlener, Reinhard

doi:10.1016/j.eneco.2006.12.003

Cited by 70 publications

(36 citation statements)

References 26 publications

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“…The electrical conversion efficiency (E e ) of the microturbines is 35%, and the CHP's heat-recovery rate (E e · E h ) is 52.5%, while the boiler's conversion efficiency (E b ) is 90%. These parameters are in line with [7], [8], and [19]. Finally, we consider on-site generation at 300 kW e , 600 kW e , and 900 kW e capacity levels.…”

Section: Technology Datamentioning

confidence: 96%

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Optimal operation of combined heat and power under uncertainty and risk aversion

Maurovich-Horvat

Rocha

Siddiqui

2016

Energy and Buildings

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Despite the proven benefits of combined heat and power (CHP) and recently introduced subsidies to support it, CHP adoption has not met its targets. One of the possible reasons for this is risk from uncertain electricity and gas prices. To gain insights into the risk management of a CHP unit, we develop a multi-stage stochastic mean-risk optimisation model for the medium-term management of a distributed generation system with a gas-fired microturbine with heat recovery and a boiler. The model adopts the perspective of a large consumer that procures gas (for on-site generation) and electricity (for consumption) on the spot and futures markets. The consumer's risk aversion is incorporated into the model through the conditional value-at-risk (CVaR) measure. We show that CHP not only decreases the consumer's expected cost and risk exposure by 10% each but also improves expected energy efficiency by 4 percentage points and decreases expected CO 2 emissions by 16%. The risk exposure can be further mitigated through the use of financial contracts.

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Section: Technology Datamentioning

confidence: 96%

“…Using real options valuations, [19] find that, under higher price volatility levels, it is more profitable to invest in a CHP system than in conventional generation. Similarly, [20] observe that high electricity price volatility relative to that of natural gas generation cost increases the value of a CHP investment.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal operation of combined heat and power under uncertainty and risk aversion

Maurovich-Horvat

Rocha

Siddiqui

2016

Energy and Buildings

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“…One of such possible risks is uncertainty of electricity and gas prices [29]. Wickart and Madlener [30] found that, under higher price volatility levels, a CHP system offers more profit than a conventional generation plant.…”

Section: Effect Of Variation In Electricity and Fuel Pricesmentioning

confidence: 99%

The Significance of a Building’s Energy Consumption Profiles for the Optimum Sizing of a Combined Heat and Power (CHP) System—A Case Study for a Student Residence Hall

et al. 2018

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University buildings, such as student residence halls with year-round consistent energy demands, offer strong opportunities for Combined Heat and Power (CHP) systems. The economic and environmental feasibility of a CHP project is strongly linked with its optimum sizing. This study aims to undertake such an assessment for a CHP system for a student residence hall located in London, the United Kingdom (UK). The study also aims to undertake a sensitivity analysis to investigate the effect of different parameters on the project's economics. Necessary data are collected via interviews with the University's Energy Manager. Modeling of the CHP system is performed using the London South Bank University (LSBU, London, the UK) CHP model. Results demonstrate that optimum sizing of CHP is crucial for achieving higher economic and environmental benefits and strongly depends on the authenticity of the energy consumption data, based on which the CHP is being sized. Use of incorrect energy data could result in an undersized or oversized CHP system, where an oversized system will result in higher negative results compared to an undersized system. Finally, Monto Carlo statistical analysis shows that electricity price is the significant factor that could affect the project's economics. With an increasing spark gap, the payback period decreases, and vice versa.

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“…Laurikka (2006) shows that, within an emissions trading scheme, a traditional investment decision rule concerning investments in IGCC (Integrated Gasification and Combined Cycle) technology may lead to biased results compared to an approach where uncertainties and real options are accounted for. Wickart and Madlener (2007) develop a model for the choice between combined heat and power production and heat-only production for an industrial firm. The model accounts for uncertainties in energy prices and determines the best technology choice as well as the timing of the investment.…”

Section: Related Workmentioning

confidence: 99%

An optimization methodology for identifying robust process integration investments under uncertainty

et al. 2009

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An optimization methodology for identifying robust process integration investments under uncertainty AbstractUncertainties in future energy prices and policies strongly affect decisions on investments in process integration measures in industry. In this paper, we present a fivestep methodology for the identification of robust investment alternatives incorporating explicitly such uncertainties in the optimization model. Methods for optimization under uncertainty (or, stochastic programming) are thus combined with a deep understanding of process integration and process technology in order to achieve a framework for decision-making concerning the investment planning of process integration measures under uncertainty. The proposed methodology enables the optimization of investments in energy efficiency with respect to their net present value or an environmental objective. In particular, as a result of the optimization approach, complex investment alternatives, allowing for combinations of energy efficiency measures, can be analyzed. Uncertainties as well as time-dependent parameters, such as energy prices and policies, are modelled using a scenario-based approach, enabling the identification of robust investment solutions. The methodology is primarily an aid for decision-makers in industry, but it will also provide insight for policy-makers into how uncertainties regarding future price levels and policy instruments affect the decisions on investments in energy efficiency measures.

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Optimal technology choice and investment timing: A stochastic model of industrial cogeneration vs. heat-only production

Cited by 70 publications

References 26 publications

Optimal operation of combined heat and power under uncertainty and risk aversion

Optimal operation of combined heat and power under uncertainty and risk aversion

The Significance of a Building’s Energy Consumption Profiles for the Optimum Sizing of a Combined Heat and Power (CHP) System—A Case Study for a Student Residence Hall

An optimization methodology for identifying robust process integration investments under uncertainty

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