Author: David Manning

April 14, 2020

Advanced Analytics and Long-Term Planning

Rapid change in the power sector adds to the complexity of evaluating investment decisions. Implementing advanced analytics and improving optionality can help companies make informed investment decisions during rapid sector changes. The following Q&A was an interview conducted by UtilityDive with our Energy Analyst, David Manning. Below you will see David’s comments on advanced utility investments. 

What are long-term planning challenges utilities face in determining what assets to invest in and how to time those investments?

Broadly, the changing power mix, increased generator intermittency, more distribution-connected generation, flat or declining load, and uncertainty around the pace of vehicle electrification are all contributing to planning challenges. These sources of uncertainty create challenges in determining what generator attributes will be optimal in the medium and long term. 

For example, is it preferable to invest in a generator that can produce power at a low marginal cost or a generator that has a higher cost/MWh, but is very flexible and can provide ancillary services at a low cost? The answer will likely be a moving target for the foreseeable future. In light of these uncertainties, strategies that allow flexibility in decision-making may provide utilities and IPP’s an edge in managing risk.

One approach to increasing flexibility is to deploy modular investments that can be constructed quickly. For example, deploying a small natural gas plant or PV+Storage project to meet an increase in load may be a lower risk approach than building a large combined cycle plant that may not be heavily utilized. Given uncertainty, bigger is not necessarily better.

Lastly, another approach to increasing investment return is to foster optionality. For example, when siting a new wind farm, making sure that there is space and line capacity to add a storage system provides flexibility which may increase long term project utilization.

Can you list what analytic tools will be important for evaluating long-term investment planning in the current market climate?

Because of the increased complexity of power market dynamics, more sophisticated analytical tools are critical for effective investment decision making.

Constrained optimization that comprehensively captures a unit’s operational constraints (e.g. ramp up and ramp down limits, minimum generational level) is critical for evaluating how a potential asset will perform under changing market dynamics. 

Increased market complexity also requires additional sophistication in effectively modeling power price dynamics and market volatility. Analytical tools will need to model prices at hourly and sub-hourly granularity, co-simulate asset performance in energy and ancillary service markets, and model locational price dynamics.

Analytical tools will also need to be able to perform stochastic scenario analysis that evaluates a range of possible scenarios, such as different forward curves or daily price shapes. Thankfully, the need for more complex analytical modeling is being met by improvements in both cloud computing power and analytic software tools. 

How can enhanced analytics improve utility planning?

Robust analytics can improve the evaluation of investments in renewable, storage or flexible natural gas generators, where granular market dynamics are critical to understanding asset performance. Analytics can also support detailed risk modeling of a full portfolio of assets, and new asset investment decisions can be evaluated based on how they fit into a larger utility portfolio.


In light of a rapidly-changing power sector, more sophisticated analytics can help evaluate how potential investments perform in an uncertain future. Effectively modeling the uncertainty of future scenarios, at both hourly and full portfolio level, will help companies more effectively manage investment risk.

Are you ready to improve your energy analytics? Request your free demo today.


March 8, 2020

A Quick Review of Outage Cost Analysis

Outages can pose a significant risk to generation asset managers. Some outages are under managers’ control, such as routine planned maintenance, while other outages are unexpected, including forced generator outages due to mechanical failure or transmission outages from severe weather events. Outages add to the complexity of energy risk management – risk managers may have a strategy for effectively hedging market risk but could be significantly over-hedged during an unexpected outage. cQuant’s Outage Cost Analysis (OCA) model gives risk managers a powerful tool to improve scheduled maintenance planning and more effectively manage outage events.


The OCA model is smoothly integrated within the cQuant Analytics Platform, allowing it to leverage cQuant’s stochastic Monte Carlo price simulation and asset dispatch models to evaluate outage risk across a range of simulated future price scenarios. This facilitates a robust analysis of outage costs, allowing risk managers to model the expected cost of an outage as well as the distribution of possible outage costs around the mean. OCA has the flexibility to model the costs of both partial (e.g. one turbine of a combined cycle plant) and full plant outages, and users can easily specify different outage durations to investigate, ranging from just a few hours to a full month.

Let’s Review Some Outage Cost Analysis Use-Cases:

Scheduled Maintenance Optimization: Generators need to be taken offline for planned routine maintenance to ensure they continue to operate reliably. Depending on when a generator is taken offline, the asset could incur significant lost revenue. OCA can help asset managers assess the optimal timing for scheduled maintenance to minimize expected lost revenue.

Unscheduled Outage Risk Assessment: These outages can occur due to a range of unexpected events, from technical or mechanical issues at a plant to extreme weather events or natural disasters, such as PG&E’s recent outages due to California wildfires. OCA provides a simulated range of costs from future outages to help understand and manage outage risk.

Outage Insurance Valuation: Some plant managers may consider procuring outage insurance to cover their downside risk due to unplanned outages. While this insurance can provide a tangible benefit in the case of an outage, it can be costly and the benefits can be difficult to value. By assessing outage costs stochastically using market-specific price dynamics, OCA can help price outage insurance and indicate the average monetary benefit it could be expected to provide.

Outage Cost Analysis Conclusion

The Outage Cost Analysis model can easily be integrated into cQuant Analytics Platform modeling workflows. It provides a flexible tool for risk managers to use cQuant’s simulation-based methodology to evaluate outage risk. OCA can be flexibly deployed to serve a number of applications and use cases including maintenance schedule optimization, unplanned outage cost assessment, and complex outage insurance product valuation.

About is an industry-leading provider of cloud-based energy analytics solutions. cQuant’s advanced analytics platform provides sophisticated energy-focused models across a wide range of industry verticals, from renewable energy project development and contract structuring to thermal generation asset analysis, hedge optimization, and exotic derivative valuation. Visit for more information.


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