Power Cogeneration Utilities Study

Help ensure uninterrupted service from your cogeneration plant while optimizing energy efficiency and costs.

Cogeneration facilities are like fingerprints; each one is unique, though all are designed with the common goal of keeping service uninterrupted while optimizing energy efficiency and controlling costs. The Worldwide Power Cogeneration Performance Analysis (Power Cogeneration Study) analyzes cogeneration power blocks by separating the unit into the components that produce steam from those that produce electricity, while comparing Key Performance Indicators (KPIs) 0f each with industry peers.

Since there are a limited standard set of parameters for describing cogeneration performance. Each cogeneration component is measured against Solomon’s developed metrics to identify where each power block component stands against the competition.

 

  • Thermal Efficiency – The Cogen block’s overall energy conversion efficiency from fuel inputs to energy outputs, including all the electric generators and thermal production units within the Cogen block boundaries.
  • Thermal Efficiency CTG – The electric energy conversion efficiency of a Combustion Turbine Generator.
  • EII® RatioThe actual thermal efficiency to predicted thermal efficiency of your power block.
  • Plant-Manageable Expenditures/EGC – Cogen block expenditures, normalized for complexity and cost comparisons to similar and diverse Cogen peers.
  • EUFth – The unavailability of equipment related only to thermal production.
  • NCFelec – Utilization of equipment related only to electricity production during the reporting period.
  • NOFelec – An electric generator unit’s average load when in operation.
  • EFORelec – The time the electric generator unit was forced out of service caused by issues or equipment repairs, and those periods when the unit was in service but could not reach full capability due to forced
  • Cogeneration Redundancy Factor – Although not technically a KPI, this reveals how your level of redundancy compares to your peers and the costs associated with redundancy, component, or equipment repairs.


The Cogeneration Utilities cohort of the Power Cogeneration Study provides a clear view of how your cogeneration facility is performing compared to your peers, no matter what type of process or fuels you are using. The analysis helps you properly allocate your limited resources to keep reliability high and minimize risks.

Cogeneration Equivalent Forced Outage Rate Example

Congenators pay for reliability (of supply of thermal and electrical energy to their industrial host facility) in three possible ways:

  1. Initially building in more redundant equipment and production capacity (and subsequently paying for maintenance on that redundant equipment)
  2. Keeping extra capacity ‘at the ready’ or in ‘hot standby’ service in case of a disruption
  3. Taking a very conservative approach to maintenance activities


With increasing costs across the industry, data-driven insight is needed to create a robust cost-management strategy. The study delivers the quality and detailed data that you need to reduce operational unreliability, potentially your largest business cost.

  • Increase your reliability – Unreliability is not acceptable and potentially the biggest business cost. As a result, you need to understand the contributing causes and unreliability in financial terms.
  • Keep redundancy at the designed level – Solomon’s Cogeneration Redundancy Factor reveals how your levels compare to peers and how costs are driven by having that redundancy.
  • Rely on data rather than opinion to manage asset risk – Use objective data as a starting point for making business decisions and to determine the best allocation of resources (both human and financial), creating the most opportunity for improved profit while managing risk.
  • Minimize cost – As costs are increasing across the industry, you need data to determine what the best in the industry have been able to accomplish to create a solid cost-management strategy.


Study is supported by industry experts with decades of experience managing power generation and cogeneration facilities. Receive valuable and practical insight to support sustained performance improvement.

Frequently Asked Questions (FAQ)

A: The Power Cogeneration Study is a complete and thorough data-driven analysis of cogeneration power assets, including relative performance of Cogen peers, which support risk management, asset management, and asset optimization processes and activities.

A: Optimization implies that a complete, comprehensive set of data is being monitored and analyzed to achieve asset performance goals, by managing inputs (such as financial and human resources), to optimize the production or outputs of the assets in the most efficient manner. The data analyses consider risk, resource utilization, and integrated programs that connect strategic and tactical activities that support getting the most from an asset.

A: No, but Solomon’s Senior Power Advisors understand the technical, financial, and commercial aspects of cogeneration asset performance over a variety of performance metrics to best interpret the data. The Study goal is to deliver insight and quantify opportunities for improvement.

A: The Solomon Power database consists of over 10,000 unit-years of operational, maintenance, and performance data. The database includes a representative sampling of what is installed across the entire Power Sector, including a representative population of cogeneration assets, both in industrial and district heating service. Rankine, Brayton, and Combined thermodynamic cycles are all represented within the database. The database for cogeneration assets includes a variety of conventional fossil fuels, others include off-gases, off liquids from other industrial processes, alternative and renewable fuels, and fuel blends.

A: Solomon uses standard industry performance metrics, along with additional proprietary metrics for more robust assessments and employs patented methods to compare any unique cogeneration asset to others. With over 25 years of Power Comparative Analysis experience, assigned Solomon advisors have worked in the Power and Utilities sector for decades, ensuring an accurate, and ‘real-world’ comparison.

Solomon has developed several patented normalization models, including ‘first principal characteristics’ of comparison including fundamental drivers of cost performance, energy efficiency performance, and personnel utilization performance.

A: Standard power industry technical indicators and cost indicators are the first level of comparison data used. Solomon also developed its own proprietary indicators that give a broader perspective on performance comparisons. Finally, the patented Solomon normalization methods and performance indicators quantify important cost-drivers in operations and maintenance spending, efficiency-drivers, and human resource utilization drivers, to manage cogeneration asset comparisons.

Solomon has developed several patented normalization models, including ‘first principal characteristics’ of comparison including fundamental drivers of cost performance, energy efficiency performance, and personnel utilization performance.

A: Solomon uses comparisons from its power and cogeneration database to look for similar manufacturer and model peer groups to compare LTSA (Long-Term Services Agreement) or CSA (Contract Services Agreement) for major maintenance activities, considering the utilization levels of the equipment, technical, and financial cost comparisons, relative to similar peer assets. The comparison costs can be evaluated on ‘per MW capacity’, ‘per MWh production’, ‘per Equivalent Generation Complexity’ (patented, normalized modeling that compares Actual to Predicted cost performance), ‘per Fired Hour’, ‘per Start’ or ‘per Equivalent Start’, or ‘per Equivalent Operating Hour’.

Learn How You Can Participate

Obtain a clear picture of your competitive position with data-driven, actionable insight.

Download The Study Invitation

Join other industry members that leverage the Power Study to analyze and compare their cogeneration assets against peers with their power block’s production losses, thermal efficiency, spending, and more. Start your journey to optimized energy efficiency, reliability, and costs.

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Access the largest database of performance database for the power industry, with data

on 10,000

power blocks/generating unit-years of data, 

over 220

gas-fired, oil-fired, Rankine and Brayton Cycle units, both with cogeneration blocks,

and more than 400

combustion turbines.

eBook: Power Generation Insight for Leaders, Technicians & Analysts

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Why Participate?

  • Examine spending costs and unavailability performance by component, unit, or plant to quantify risk to operations
  • Realize the implied future availability and future reliability performance risk from current levels of maintenance effort
  • Determine whether assets are under- or over-funded for desired performance levels
  • Expose unmitigated risks and identify opportunities for cost savings
  • Review lost market opportunity (off-site sales revenue in regional electric market) to help make the best decisions regarding offsetting Cogen cost of production for industrial hosts
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Metrics that Support Risk Management & Asset Optimization

Energy companies need to deliver top asset performance. There is no substitute in asset management systems for high-quality consistent data to measure and identify gap closure opportunities since managing a power plant requires careful consideration of many variables and trade-offs. How does one go about optimizing their assets?

  • * EGC® = Equivalent Generation Complexity – a patented Solomon indicator for normalizing installed equipment impacts, age-related impacts, utilization-related impacts, etc., all of which are cost-drivers between various Cogen Block configurations
  • * EII® = Energy Intensity Index – a patented Solomon indicator for normalizing energy efficiency drivers to isolate controllable versus uncontrollable losses

Study Deliverables

Study recipients receive the following:

  • Executive Results summary presentation with KPIs relative to peers and actionable insight supported by study data
  • Analyses including validated input, study peer group data, client tables, master events “lost revenue opportunity,” start reliability analysis, and trend data
  • Workshop covering how to review and use the data analyses delivered

Data Quality, Benchmarking Methodology

A Foundation for Effective Comparative Performance Analysis and Decision Support

We prioritize the integrity and confidentiality of participant-submitted data and rigorously review that data before benchmarking begins. Then, we employ our normalization process and benchmarking methodology to provide valuable and trusted peer group comparisons that deliver meaningful KPIs. Finally, our staff of senior consultants apply their deep industry experience to develop practical insight and recommendations to enable your success.

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