In a significant stride towards optimizing the integration of distributed energy resources (DERs) in unbalanced power networks, a team of researchers led by Laura Sofía Avellaneda-Gómez from the Universidad Distrital Francisco José de Caldas in Bogotá, Colombia, has introduced a novel optimization methodology. Their work, published in the journal *Computation*, focuses on the strategic placement and capacity design of photovoltaic (PV) units and distribution static compensators (D-STATCOMs) within three-phase distribution systems. The goal? To minimize total annual costs, including investment, maintenance, and energy purchases—a critical factor for energy providers and planners.
The research employs a mixed-integer nonlinear programming structure using complex-valued variables, coupled with a leader–follower optimization framework. This approach leverages the Generalized Normal Distribution Optimization (GNDO) algorithm to generate candidate solutions, while a follower stage conducts power flow calculations to assess the objective value. “The GNDO algorithm stands out for its precision and robustness,” explains Avellaneda-Gómez. “It consistently outperforms other metaheuristic algorithms in terms of cost efficiency, solution precision, and statistical dispersion.”
To validate their methodology, the researchers tested it on 25- and 37-node feeders, benchmarking it against three widely used metaheuristic algorithms: the Chu and Beasley Genetic Algorithm, Particle Swarm Optimization, and Vortex Search Algorithm. The results were impressive. For the 25-node system, the cost was reduced from USD 2,715,619.98 to USD 2,221,831.66, an 18.18% decrease. For the 37-node system, the cost dropped from USD 2,927,715.61 to USD 2,385,465.29, an 18.52% reduction. These findings highlight the potential of the GNDO algorithm to significantly cut costs and improve efficiency in distribution system planning.
The commercial implications of this research are substantial. As the energy sector increasingly turns to distributed energy resources to meet growing demand and enhance grid resilience, the ability to optimize the placement and capacity of these resources becomes crucial. “This methodology provides a scalable and reliable tool for energy providers to plan and integrate DERs effectively,” says Avellaneda-Gómez. “It’s a game-changer for the industry, offering a more cost-efficient and precise approach to distribution system planning.”
The research also underscores the importance of advanced optimization techniques in addressing the complexities of unbalanced networks. By adopting a leader–follower framework and utilizing the GNDO algorithm, the researchers have demonstrated a method that not only reduces costs but also ensures robustness and precision. This could pave the way for more sophisticated and efficient energy distribution systems in the future.
As the energy sector continues to evolve, the integration of distributed energy resources will play a pivotal role in shaping the future of power networks. The work of Avellaneda-Gómez and her team offers a promising solution to one of the industry’s most pressing challenges, setting a new standard for cost-efficient and reliable distribution system planning.