A Systematic Modeling Approach for Cost-Efficient Operations in Electrical Grids Using Design of Experiments

Abstract

The increasing complexity of modern electrical grids, driven by rising energy demand, renewable energy integration, and distributed generation, has amplified the need for systematic and cost-efficient operational strategies. Traditional approaches to grid management often rely on heuristic methods or reactive adjustments, which fail to fully account for interactions between key operational variables such as load distribution, reactive power control, distributed generation penetration, and energy storage dispatch. This research introduces a comprehensive, data-driven framework employing a Design of Experiments (DoE) methodology to systematically evaluate the impact of these operational factors on total grid operational costs. By integrating mathematical modeling of power systems with factorial experimentation, the proposed approach enables simultaneous assessment of multiple variables and their interactions, providing statistically robust insights into cost drivers and efficiency trade-offs.
The methodology models the electrical network as a combination of nodes and branches, incorporating conventional generation units, distributed renewable resources, storage systems, and load demand profiles. Operational costs are quantified as a composite function of generation, line losses, and ancillary services, including voltage regulation and reactive power compensation. Factorial designs are employed to simulate various operational scenarios, with high- and low-level combinations of control variables analyzed to identify configurations that minimize total cost while maintaining voltage stability and system reliability. Regression and ANOVA analyses are applied to determine the statistical significance of each factor and interaction, following the framework outlined by Vogt et al. (2012).
Simulation results demonstrate that coordinated optimization of load distribution, reactive power settings, and renewable integration can reduce operational costs by up to 14%, while line losses and voltage deviations are simultaneously minimized. Significant interactions between load distribution and reactive power control are identified, highlighting the necessity of a multi-variable optimization approach. Integration of energy storage is shown to enhance peak load management and provide further cost reductions, though uncoordinated deployment of distributed generation can result in ancillary costs if voltage stability is not maintained. The study also reveals that regression-based predictive modeling enables proactive operational decision-making, offering utilities a quantitative tool for balancing cost-efficiency with system reliability.
This research contributes both theoretically and practically by bridging the gap between operational optimization and experimental design in power systems. It provides a structured methodology for assessing complex interactions among operational variables, quantifying cost-saving opportunities, and guiding real-time control strategies. The findings have significant implications for utilities, policymakers, and grid operators,  
particularly in systems with high renewable penetration or variable demand profiles. Future work may extend the framework to incorporate stochastic modeling for renewable intermittency, adaptive real-time control, and integration with smart grid technologies, further enhancing the operational efficiency and economic sustainability of electrical grids.