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<p>Accurate and traceable measurement of renewable resource data is essential for reliable distributed generation (DG) planning. This study presents a metrology-aware co-optimization framework that simultaneously allocates wind turbine (WT) and photovoltaic (PV) units while scheduling demand response (DR) under quantified measurement uncertainty. Wind speed and solar irradiance are measured using IEC 61400-12-1–compliant anemometers and ISO 9060-classified pyranometers, respectively, with their expanded uncertainties (U95) propagated through the corresponding WT and PV power output models. The planning problem is formulated as a mixed-integer second-order cone programming model, minimizing active power losses in a 33-bus radial distribution network while satisfying network, voltage, and DR constraints. Monte Carlo simulations (1000 trials) reveal that incorporating measurement uncertainty alters the optimal DG siting in 14% of cases and restricts total loss variability to ± 1.8%, thereby confirming the robustness of the proposed scheme. Compared with a benchmark case that excludes DR and metrological considerations, the framework achieves a 29.6% reduction in losses and a 22.4% improvement in renewable utilization. Sensitivity analysis further indicates that higher DR participation mitigates uncertainty impacts and supports deployment of up to three DG units (3.6&#xa0;MW each) before diminishing returns emerge. Overall, the results demonstrate (i) the critical role of DR in strengthening renewable integration and (ii) the necessity of rigorous uncertainty quantification and traceable calibration in measurement-driven power system optimization.</p>

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Metrology-Aware Co-optimization of Wind–Solar Distributed Generation and Demand Response Under Measurement Uncertainty

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Vivek Saxena

摘要

Accurate and traceable measurement of renewable resource data is essential for reliable distributed generation (DG) planning. This study presents a metrology-aware co-optimization framework that simultaneously allocates wind turbine (WT) and photovoltaic (PV) units while scheduling demand response (DR) under quantified measurement uncertainty. Wind speed and solar irradiance are measured using IEC 61400-12-1–compliant anemometers and ISO 9060-classified pyranometers, respectively, with their expanded uncertainties (U95) propagated through the corresponding WT and PV power output models. The planning problem is formulated as a mixed-integer second-order cone programming model, minimizing active power losses in a 33-bus radial distribution network while satisfying network, voltage, and DR constraints. Monte Carlo simulations (1000 trials) reveal that incorporating measurement uncertainty alters the optimal DG siting in 14% of cases and restricts total loss variability to ± 1.8%, thereby confirming the robustness of the proposed scheme. Compared with a benchmark case that excludes DR and metrological considerations, the framework achieves a 29.6% reduction in losses and a 22.4% improvement in renewable utilization. Sensitivity analysis further indicates that higher DR participation mitigates uncertainty impacts and supports deployment of up to three DG units (3.6 MW each) before diminishing returns emerge. Overall, the results demonstrate (i) the critical role of DR in strengthening renewable integration and (ii) the necessity of rigorous uncertainty quantification and traceable calibration in measurement-driven power system optimization.