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As an important application in the power system operation and electricity market clearing, the network-constrained unit commitment (NCUC) problem is usually executed by Independent System Operators (ISO) in an open-looped predict-then-optimize (O-PO) process, in which an upstream prediction (e.g., on renewable energy sources (RES) and loads) and a downstream NCUC are executed in a queue. However, in the O-PO framework, a statistically more accurate prediction may not necessarily lead to a higher NCUC economics against actual RES and load realizations. To this end, this paper presents a closed-loop predict-and-optimize (C-PO) framework for improving the NCUC economics. Specifically, the C-PO leverages structures (i.e., constraints and objective) of the NCUC model and relevant feature data to train a cost-oriented RES prediction model, in which the prediction quality is evaluated via the induced NCUC cost instead of the statistical forecast errors. Therefore, the loop between the prediction and the optimization is closed to deliver a cost-oriented RES power prediction for NCUC optimization. Lagrangian relaxation is adopted to accelerate the training process, making the C-PO applicable for real-world systems. Case studies on an IEEE RTS 24-bus system and an ISO-scale 5655-bus system with real-world data show that the proposed C-PO can effectively improve the NCUC economics as compared to the traditional O-PO. [ABSTRACT FROM AUTHOR]

Due to their fast response ability and high ramping rates, pumped-storage hydropower (PSH) plants are playing an increasingly critical role in mitigating power generation fluctuations induced by the growing proliferation of renewable energy resources. However, because they are energy-limited resources, actual reserve deployment from PSHs would cause the deviation of state-of-charge (SOC) from the scheduled value, which, once accumulated over time, could induce potential violations of SOC limits and cause non-dispatchability of PSHs. In recognizing this potential issue, it can become important to securing the reserve schedule of PSHs and other energy limited resources preventively in the day-ahead market clearing. To ensure reserve dispatchability of PSHs despite SOC deviations, this paper proposes the reserve secure constraints, which are cast as a distributionally robust chance-constrained formulation by judiciously introducing random parameters to describe deployment ratios of scheduled regulation reserve from PSHs. These constraints are converted into tractable second-order cone constraints, and further approximated as linear constraints to be compatible with the current ISO day-ahead market clearing model. A modified IEEE 118-bus system with multiple PSHs is adopted to verify the proposed approach. [ABSTRACT FROM AUTHOR]

In Independent System Operator's (ISO) energy market clearing process, sub-transmission/primary distribution systems are usually modeled as simplified fixed or flexible loads. This simplification, however, may not be proper for emerging distribution systems with an increasing penetration of distributed energy resources (DER). As a matter of fact, scheduling results from the simplified model could induce power flow violations of distribution lines, while also misrepresenting contributions of DERs on economic operations of the entire grid. To this end, this paper proposes a multi-port power exchange model and associated bidding strategies, in order to accurately and compactly formulate the contribution of highly DER-penetrated sub-transmission/distribution systems in the ISO energy market while resolving computational complexity. The proposed models can effectively capture physical and economic impacts of sub-transmission/distribution systems in the ISO market clearing, while respecting their internal physical limits. Numerical studies show that, with proper settings, the proposed approach can accurately reflect impacts of DER-penetrated distribution networks on market clearing results and power flows, while presenting high computational performance. [ABSTRACT FROM AUTHOR]

In recognizing significant advantages in achieving carbon emission reduction and mitigating variability of renewables, an increasing installation of combined-cycle (CC) units has been observed recently. This new shift in the generation portfolio has urged Independent System Operators (ISO) to continuously enhance CC unit models in their unit commitment (UC) tools, in order to derive physically feasible and operationally reliable unit commitment and generation dispatch schedules of CC units by accurately capturing their operation features. To this end, this paper accurately simulates startup and shutdown processes of individual turbines of a CC unit during the configuration transition procedure. A dynamic ramping model and the corresponding spinning reserve model for individual turbines are also proposed to further enhance the modeling accuracy. A modified IEEE 118-bus system with multiple CC units is adopted to evaluate the proposed models. [ABSTRACT FROM AUTHOR]

The unit commitment problem is fundamental to the optimal operation of power systems, which is solved by Independent System Operators routinely in day-ahead markets to guarantee operational security and economics. Continuously enhancing the unit commitment model to improve modeling accuracy in response to future market challenges and participants' requests has always been one of Independent System Operators' main tasks, for deriving physically feasible and operationally reliable unit commitment and dispatch schedules that can render better economic efficiency. Thermal units account for 68% of the total installed electricity generation capacity and supply 63% of total electricity consumption in U.S.. Startup time of thermal units could vary from hours to days, and the corresponding startup cost could be 100 times different and as high as $500,000 per startup. To this end, this paper accurately accesses detailed startup and shutdown procedures of thermal units by simulating dynamic unit temperature evolution and modeling sequential unit operation stages. In addition, dynamic ramping and forbidden zones are also considered to further enhance the modeling accuracy. A modified IEEE 118-bus system is adopted to validate and evaluate the proposed models. • Accurate heating time and startup cost calculation of thermal units via dynamic temperature evolution model. • Rigorous model on dynamic startup/shutdown procedures of generators. • Integration dynamic ramping, spinning reserve, and forbidden zone models of generators. [ABSTRACT FROM AUTHOR]