Optimizing Merchant Power Generation for Deregulated Power Markets

Challenge

Our Client, a large Independent Power Producer, wanted to improve the power generation marketing for the plants. This would optimize the future commercial value of the power plants. The challenge is that a typical plant might have 288 different generation points, each has its own revenue and cost. Those 288 daily offers are tied to the operational profile of the equipment and vary with weather and equipment changes. Until now our client was relying on no more than outdated curve on a spreadsheet. This created an opportunity to vastly improve the overall profit from power generation.

Solution

Modern Power Plants are very efficient so are the de-regulated markets they operate in. With all that efficiency, complexity has come along for the ride. In a deregulated power market, most plants can offer their generation capabilities and their costs to a clearing house called Day Ahead Market, DAM.To address the modeling complexity, we created a power generation marketing and planning system for each plant centered around machine learning model representations of the plants.The end result was a robust process that improved the enterprise financial goals through optimized power plant dispatching and generation planning.

Approach

Our approach was to create a two- step solution. 1)Use Machine Learning and Thermodynamics Simulations to create a digital twin representation of each plant capabilities 2) Build an interface for plant operators to forecast future generation capabilities based on local weather forecast. Results are then shared with energy dispatchers to monetize those capabilities.

For most generators this sort of forecasting of the plant capabilities can be out of reach. To make this scalable we designed a turnkey solution that went from plant data collection all the way to dispatch interface and automation of generation planning process . We implemented a monitoring process that made those models adaptive and therefore increased the fidelity of the generation forecasting.

COLLECT

DATA

ORGANIZE

DATA

BUILD

MODELS

CODIFY

MODELS

OPTIMIZE

&

MONITOR


Conclusion

BKO Services designed and built a machine learning based solution for an Independent Power Producer that will enable the IPP to improve profitability by several millions of dollars annually.

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