Optimizing local energy for economic and sustainable power supply

In the shift toward decentralized energy systems, ensuring economic and efficient dispatch of power from distributed energy sources like solar photovoltaics, wind turbines, and microgrids is no longer optional but it’s essential. This comprehensive study, led by Prof. Mohan Lal Kolhe of the University of Agder, Norway, reviews how optimization techniques can support this critical energy transition while aligning with global sustainability goals.

“As we move toward smarter and more sustainable grids, the ability to economically dispatch distributed energy becomes central to reducing operational costs, improving reliability, and ensuring equitable energy access,” says Prof. Kolhe. “Our review bridges foundational models like linear and quadratic programming with emerging solutions like artificial intelligence, hybrid algorithms, and real-time multi-agent systems.”

The study found that classical techniques such as Linear Programming and Quadratic Programming remain effective for structured energy networks. However, complex, non-linear scenarios with high renewable penetration, heuristic and metaheuristic techniques; such as Genetic Algorithms, Particle Swarm Optimization, and Fuzzy Logic; offer more adaptable solutions. These are particularly relevant in systems with uncertain demand, varying renewable supply, and high interconnectivity.

What sets this review apart is its attention to real-world application, including voltage and frequency stability, network loss reduction, emission minimization, and even predictive control in microgrids. In doing so, the work supports multiple UN Sustainable Development Goals; ensuring clean energy (SDG 7), sustainable cities and infrastructure (SDG 11), climate action (SDG 13), and fostering international research cooperation (SDG 17).

One striking insight was the emergence of hybrid techniques that blend mathematical rigor with learning-based adaptability, showing promising results in balancing energy cost, carbon footprint, and technical resilience in distributed grids. The authors conclude that while optimization is a mathematical pursuit, its real-world impact is social and environmental. Better dispatch strategies not only lower energy costs but also build smarter, more resilient energy systems for future generations.