Research pick: The power of load - "Optimisation of operation algorithms based on artificial intelligence in power system control"

As carbon neutrality become an increasingly urgent issue, the challenge of managing increasingly complex and renewable-heavy power systems remains. Work in the International Journal of Reasoning-based Intelligent Systems looks at power system optimization and presents a new approach to reducing both emissions and operating costs using artificial intelligence, AI.

The context for this innovation is the dual carbon goal, a policy framework adopted by many countries aiming to reach carbon neutrality sooner, rather than later. As electricity grids evolve from centralized, fossil-fuel-based structures to more decentralized systems dominated by intermittent renewable sources like wind and solar, they become harder to control in real-time. Grid operators must now balance not only power supply and demand, but also the economic cost of power generation and its environmental impact.

The researchers have used a refined version of a computational technique known as Particle Swarm Optimization (PSO). Originally inspired by the coordinated movement of animals, PSO seeks out optimal solutions from complex systems by simulating swarming behaviour. The researchers have extended PSO with a more advanced variant called Multi-Strategy Adaptive PSO (MAPSO), which incorporates a dynamic “reward mechanism” to improve how the AI searches for optimal outcomes. The researchers have then taken this yet another step forward, with MOMAPSO (Multi-Objective MAPSO). It operates within a dual-objective framework that simultaneously minimizes fuel costs and pollutant emissions, while respecting essential operational constraints such as power balance and generation limits.

Traditional mathematical optimization methods, while theoretically accurate, often struggle under the weight of modern grid complexity. They tend to be computationally demanding and less effective at navigating trade-offs between competing objectives. Heuristic algorithms like PSO and the extended version MAPSO, by contrast, offer a more flexible and efficient alternative.

Yao, L. (2025) ‘Optimisation of operation algorithms based on artificial intelligence in power system control’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 9, pp.34–43.