Active distribution networks increasingly rely on battery energy storage systems to balance variable renewable generation and manage peak demand. However, operators face a persistent challenge: developing scheduling strategies that reduce near-term operating costs without accelerating battery aging or violating network constraints. Researchers have now proposed a solution using an enhanced optimization algorithm designed specifically for this problem.
The methodology centers on an adapted JAYA algorithm (AJAYA) that simultaneously optimizes active and reactive power dispatch from distributed BESS units. Unlike conventional approaches that treat storage scheduling as a purely economic problem, this framework explicitly incorporates battery degradation costs into the optimization objective. This prevents operators from pursuing short-term savings that trigger excessive charge-discharge cycling, ultimately extending asset life.
The algorithm coordinates multiple technical requirements: maintaining power balance across the network, respecting voltage and current limits, honoring converter capacity ratings, and managing state-of-charge constraints. Solutions are validated using AC power flow analysis with successive approximations, ensuring electrical feasibility rather than relying on simplified linear models.
Validation occurred on modified 33- and 69-node distribution networks representative of Colombian grid conditions, tested under both deterministic and uncertain scenarios reflecting real weather and demand variability. Compared against five competing optimization algorithms—genetic algorithms, multiverse optimization, salp swarm, grey wolf, and vortex search methods—AJAYA delivered superior cost reductions with processing times below 0.05 hours per 24-hour scheduling cycle.
The robustness of results, reflected in standard deviations below 0.06% across multiple runs, suggests the algorithm reliably converges to high-quality solutions. This consistency matters for operators deploying the tool across varying grid conditions and seasons. The framework serves dual purposes: advancing academic understanding of multi-objective energy management and providing practical decision-support for utilities and BESS owners balancing immediate cost control against long-term reliability and equipment preservation in modernizing distribution networks.
