Yang Cao

Meteorologically sensitive loads (MSLs) are emerging as important demand-side flexibility resources in low-carbon power systems, yet their deliverable flexibility is highly dependent on the quality and timeliness of meteorological information. In particular, multi-source weather forecast uncertainty may shift the load baseline, distort regulation margins, and eventually lead to a mismatch between day-ahead flexibility commitments and intraday delivery capability. To address this issue, this paper proposes a weather-aware achievable-flexibility assessment framework for MSLs under meteorological uncertainty. A weather-coupled thermal model is first developed to explicitly link outdoor temperature uncertainty with baseline power and flexibility boundaries. Dempster--Shafer theory (DST) is then introduced to fuse heterogeneous meteorological forecasts by considering source reliability and inter-source conflict, thereby generating time-varying probability bounds for temperature forecast errors. Based on this uncertainty representation, a two-stage assessment framework is established to characterize the progressive contraction from technical flexibility to market-committed economic flexibility and then to intraday achievable flexibility. In the intraday stage, an hourly-updated DST-based distributionally robust optimization model is formulated to quantify the deliverable portion of day-ahead commitments under updated meteorological uncertainty. A case study based on Finnish meteorological and balancing-market data is used to demonstrate that ignoring intraday weather updates can systematically overestimate achievable flexibility and underestimate delivery shortfall. Compared with conventional updated distributionally robust optimization (DRO), the proposed DST-DRO provides a closer agreement with the actual-weather benchmark in terms of achievable flexibility, shortfall, and penalty cost. Sensitivity analysis further reveals that the sustained-duration requirement substantially affects the trade-off between flexibility utilization and delivery reliability. These results confirm the necessity of integrating multi-source meteorological uncertainty and rolling weather updates into the achievable-flexibility assessment of MSLs.

As the persistent proliferation of renewable energy sources (RESs) will have a significant impact on the prediction of power system inertia, a statistically accurate prediction model for the system inertia, like the traditional prediction-then-optimize (PTO) model, might not necessarily lead to higher economics and security in the day-ahead scheduling. This paper proposes a predict-and-optimize (PAO) model for the day-ahead inertia prediction using distributionally robust unit commitment (DRUC). First, the probabilistic prediction of day-ahead inertia is obtained by the kernel density estimation (KDE), and the confidence set is used for the uncertainty quantization of DRUC. Then, the inertia prediction method is embedded into DRUC, combining with the relevant feature and the closed-loop feedback of DRUC cost increment. The cost-based day-ahead inertia prediction method is trained, in which the prediction quality is evaluated by inducing the DRUC cost increment rather than the statistical prediction error. Furthermore, the proposed PAO is trained by rolling updates to efficiently capture the time series characteristics of inertia, reducing the computational burden and enhancing the solution quality of DRUC. The case studies are carried out on the Finnish power system with a public dataset. The results show that the proposed PAO model offers a better prediction accuracy and incurs smaller re-dispatch cost increments as compared with those of the traditional PTO model, providing a higher security and economically efficient tool for transmission system operators (TSOs) in the day-ahead UC scheduling.

The interdependence of electricity and natural gas networks (ENGNs) has led to increased complexity and competing interests among stakeholders. In the event of a leakage failure in a natural gas pipeline, the changes in pipeline parameters pose significant challenges to the optimization and dispatch of natural gas and power grid supply systems. Hence, it would be difficult to simultaneously coordinate the respective interests and reduce the loss of load in the power grid. This paper first presents a natural gas pipeline flow model that accounts for pipeline leakage and linepack, followed by a two-stage dispatch to optimize the benefits for both electricity and natural gas suppliers, where the optimal dispatch with the worst-case scenario is linearized through mixed-integer programming (MIP). Then, a joint solution model for leakage rate and linepack is established based on the macropore leakage model and the dispatch of natural gas suppliers, considering the impact of natural gas flow rate changes on leakage parameters. The actual natural gas supply and the two-stage optimal dispatch are obtained by using the column-and constraint generation (C&CG) method. Simulation results demonstrate that the proposed two-stage optimal dispatch improves the power supply reliability and offers promising economic prospects for ENGNs, where the accuracy of power grid dispatch is significantly improved considering the natural gas pipeline leakage and linepack.