A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets

Fusco, Andrea; Gioffrè, Domenico; Castelli, Alessandro Francesco; Bovo, Cristian; Martelli, Emanuele

doi:10.1016/j.apenergy.2023.120739

Cited by 45 publications

(3 citation statements)

References 32 publications

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“…4)The constraints related to flexible loads The constraints related to flexible loads include the minimum transfer period constraint for transferable loads as Equation (11), constraints on maximum and minimum transferable load amounts as Equation (12), constraints on the maximum reducible load amount as Equation ( 13), constraints on the maximum number of reducible load reductions as Equation ( 14), constraints on the maximum reducible load reduction duration as Equation (15), and constraints on the minimum reducible load reduction duration as Equation (16).…”

Section: ) Constraintsmentioning

confidence: 99%

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Dominance Constraints for Risk Control of a VPP’s Optimal Bidding Strategy

Peng,

He,

et al. 2024

IEEE Access

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The Virtual Power Plant (VPP) serves as the primary form of participation for distributed power generation resources in electricity markets. Throughout this market engagement, uncertainties stemming from renewable energy sources and market prices present significant revenue risks for VPPs. Addressing this challenge, the paper proposes a VPP bidding risk management model centered on dominance constraints. In contrast to conventional methods reliant on risk metrics like Conditional Value at Risk, the proposed model offers improved control over downside and tail risks while reducing optimization iterations to just one, thus substantially reducing computational cost associated with risk management. The study also contrasts the efficacy and computational costs of first-order and second-order dominance models. For instance, in a three-scenario benchmark, the first-order dominance model exhibits a 16.7% lower overall risk level compared to the second-order model. Similarly, in a nine-scenario benchmark, the computational time of the first-order model increases by 0.96% due to the introduction of binary variables, and this increase scales with the number of benchmark scenarios. The paper additionally incorporates mean deviation of profits and expected excess returns to develop a bi-objective optimization model. This model not only fulfills risk control requisites but also shapes return distributions to align more closely with VPP decision-makers' profit preferences. The case study is based on a VPP comprising conventional thermal units, wind turbines, energy storage stations, and flexible loads.

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Section: ) Constraintsmentioning

confidence: 99%

“…Optimal bidding strategy models for VPPs are established using various approaches. References [11], [12] introduce models employing a multi-stage stochastic programming approach in the power market. Reference [13] proposes a two-stage robust scheduling model tailored for VPPs with electric vehicles to manage uncertainties effectively.…”

Section: Introductionmentioning

confidence: 99%

Dominance Constraints for Risk Control of a VPP’s Optimal Bidding Strategy

Peng,

He,

et al. 2024

IEEE Access

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“…By implementing measures like optimizing auxiliary combustion and steam-water processes, TPUs can enhance their ramping efficiency and operate below the minimum output of a regular peak regulation (RPR) state, thereby achieving deep peak regulation (DPR) capabilities [6,7]. This enables TPUs to provide a greater regulation capacity, fostering better RES utilization and obtaining additional revenues from auxiliary services [8].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Renewable Energy Accommodation by Coupling Integration Optimization with Flexible Retrofitted Thermal Power Plant

Zou,

Xia,

Chi

et al. 2024

Applied Sciences

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Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs’ downward regulation capabilities by 32%.

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