“Cost Optimal” solutions don’t always provide the best mix for power generation, researchers say

Cost concerns can be heavily heavy on decision-making as industry, utilities and regulators are considering the best ways to meet the growing needs of power generation.

However, new research shows that choosing the cheapest option is not necessarily the best solution, indicating that even a slightly wavy room at cost can provide more social, environmental and politically consistent results.

The recently published paper in the Jar Journal relies on the work of Neha Patankar, an assistant professor at Binghamton University. NehaPatankar uses a technique called modeling to generate alternatives (MGAs) for systematically mapping economically and technically viable planning strategies and their trade-offs.

Collaborators for the new paper include researchers from the Delft Institute of Technology (Netherlands), UIT (Norwegian Arctic University, Denmark Institute of Technology, Princeton University, Technisch University Berlin and the University of Oslo.

With the growing number of power generation options, from fossil fuels and nuclear to solar, wind and hydroelectric power, determining the right combination of technologies is a complex challenge, not just choosing the lowest-cost option.

“Even a small mitigation of the total system cost of just 2% can lead to a fundamentally different technology portfolio to meet the increased electricity demand,” says Patanker, a faculty member of the Faculty of Systems Science and Industrial Engineering in Thomas J. Watson Engineering and Applied Sciences. “It emphasizes that so-called ‘cost-optimized’ solutions are very sensitive to uncertainty assumptions and can only provide false certainty. ”

The most stringent price-oriented models driven by artificial intelligence and machine learning are not recommended based on variables that are more important in the long run, such as ecological, social, political, or environmental impacts.

“Using MGAs to view options that are nearly optimal costs can reveal strategies that are consistent with the objectives of the model, such as social viability, resilience to sudden supply disruptions, or hedging against policy changes,” Patanker said. “Stakeholders can see a virtually viable consensus solution hidden in asserting cost optimality.”

As climate change accelerates the shift towards renewable energy, researchers like Patanker and her collaborators are working to develop effective strategies to navigate the complex trade-offs of energy transitions.

“Our main conclusion is that MGA is currently accessible and versatile enough to be a standard for improving the reliability and usefulness of the analyses that shape globally urgent energy transition decisions,” said Francesco Lombardi, assistant professor at Tu Delft and lead author of the new paper.

“Many organizations that use energy planning models directly in their strategies can quickly take our recommendations to improve the quality of their analysis and provide reliable, virtually viable advice.”