This paper proposes a continuous-time two-stage stochastic optimization model for multi-fidelity co-optimization of energy and flexibility reserve provided by generating units and energy storage (ES) devices in day-ah...
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This paper proposes a continuous-time two-stage stochastic optimization model for multi-fidelity co-optimization of energy and flexibility reserve provided by generating units and energy storage (ES) devices in day-ahead operation. The flexibility reserve, defined as a single continuous-time trajectory that combines the balancing and ramping reserves, not only supplies the energy deviation but also the ramping requirements of load and renewable generation in power systems operation. The proposed model co-optimizes decision variables with different modeling fidelity, where the energy and flexibility reserve schedules are modeled and optimized by Bernstein polynomials of different degrees to match the flexibility requirements of load and renewable generation in day-ahead and real-time operation stages. Numerical studies, conducted on the IEEE reliability test system with the load and solar data of California ISO, highlight the benefits of the proposed stochastic multi-fidelity model over traditional discrete-time models in efficient utilization of ES flexibility to supply the energy and ramping requirements of the net-load and avoid scarcity events.
This paper defines flexibility reserve trajectory as a single reserve product that not only supplies the energy imbalance in real-time operation but also the resulting ramping requirements by embedding the flexible ra...
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This paper defines flexibility reserve trajectory as a single reserve product that not only supplies the energy imbalance in real-time operation but also the resulting ramping requirements by embedding the flexible ramping trajectories as time derivative of the reserve trajectories. Further, a stochastic optimization model is proposed for multi-fidelity co-optimization of energy and flexibility reserve in day-ahead power systems operation. The proposed model integrates operation decisions with different levels of modeling fidelity in a single stochastic optimization problem. More specifically, day-ahead energy trajectories, day-ahead flexibility reserve capacity trajectories, and real-time flexibility reserve deployment trajectories are modeled by Bernstein polynomials with different degrees as required to match the load and renewable generation with different levels of variability in day-ahead and real-time operations. The proposed model is implemented on the IEEE reliability test system using load and solar generation data of California ISO, where the numerical results demonstrate that the proposed model schedules a reserve capacity that more accurately matches the real-time energy imbalance and ramping requirements of net-load, while reducing the total operation cost of the system.
Wind power curtailment (WPC) occurs because of the non-correlation between wind power generation (WPG) and load, and also due to the fast sub-hourly variations of WPG. Recently, advances in energy storage technologies...
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Wind power curtailment (WPC) occurs because of the non-correlation between wind power generation (WPG) and load, and also due to the fast sub-hourly variations of WPG. Recently, advances in energy storage technologies facilitate the use of bulk energy storage units (ESUs) to provide the ramping required to respond to fast sub-hourly variations of WPGs. To minimize the sub-hourly WPC probability, this paper addresses a generic continuous-time risk-based model for sub-hourly scheduling of energy generating units and bulk ESUs in the day-ahead unitcommitment (UC) problem. Accordingly, the Bernstein polynomials are hosted to model the continuous-time risk-based UC problem with ESU constraints. Also, the proposed continuous-time risk-based model ensures that the generating units and ESUs track the sub-hourly variations of WPG, while the load and generation are balanced in each sub-hourly intervals. Finally, the performance of the proposed model is demonstrated by simulating the IEEE 24-bus Reliability and Modified IEEE 118-bus test systems.
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