A comparison between mixed-integer linear programming and dynamic programming with state prediction as novelty for solving unit commitment

Putz, Dominik; Schwabeneder, Daniel; Auer, Hans; Fina, Bernadette

doi:10.1016/j.ijepes.2020.106426

Cited by 19 publications

(3 citation statements)

References 35 publications

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“…The notion of a stage manifests in the multistage aspect of DP when it is structured based on temporal or spatial considerations. The term "state" pertains to the variable values and characteristics associated with a particular stage (Putz et al, 2021). Consider GEP DP, where the stage represents the decision-making timeframe, the number of newly constructed units delineates the path, and the cumulative unit count signifies the state.…”

Section: Nonlinear Programmingmentioning

confidence: 99%

A review of optimization modeling and solution methods in renewable energy systems

Yu,

You,

Zhou

2023

Front. Eng. Manag.

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The advancement of renewable energy (RE) represents a pivotal strategy in mitigating climate change and advancing energy transition efforts. A current of research pertains to strategies for fostering RE growth. Among the frequently proposed approaches, employing optimization models to facilitate decision-making stands out prominently. Drawing from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems (RES) from 1990 to 2023 within the Web of Science database, this study reviews the decision-making optimization problems, models, and solution methods thereof throughout the renewable energy development and utilization chain (REDUC) process. This review also endeavors to structure and assess the contextual landscape of RES optimization modeling research. As evidenced by the literature review, optimization modeling effectively resolves decision-making predicaments spanning RE investment, construction, operation and maintenance, and scheduling. Predominantly, a hybrid model that combines prediction, optimization, simulation, and assessment methodologies emerges as the favored approach for optimizing RES-related decisions. The primary framework prevalent in extant research solutions entails the dissection and linearization of established models, in combination with hybrid analytical strategies and artificial intelligence algorithms. Noteworthy advancements within modeling encompass domains such as uncertainty, multienergy carrier considerations, and the refinement of spatiotemporal resolution. In the realm of algorithmic solutions for RES optimization models, a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven optimization. Furthermore, this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps, expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors.

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Section: Nonlinear Programmingmentioning

confidence: 99%

A review of optimization modeling and solution methods in renewable energy systems

Yu,

You,

Zhou

2023

Front. Eng. Manag.

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“…The linear programming, however, has been well known for its capability in dealing with large-scale problems, making it preferable in the multireservoir operation. Furthermore, its ability in tackling integer variables allows it to represent the nonlinear and nonconvex objective function and constraints much better (Becker & Yeh, 1974;Putz et al, 2021). Although the problem size and computing burden will increase observably when introducing more integer variables, the LP is still very reliable, with readily available and low-cost MILP solvers (Needham et al, 2000;Trezos, 1991).…”

Section: Introductionmentioning

confidence: 99%

Optimal multireservoir operation for flood control under constrained operational rules

Yang

Zheng

Feng

et al. 2022

J Flood Risk Management

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This paper presents a multireservoir flood control model that incorporates operating rules to alleviate the risk that results from traditional flood control models. The model is accurately reformulated into one that can be solved with the mixed integer linear programming (MILP) and approximated with a two‐stage linear programming (TSLP) to speed up the solution by excluding all the binary variables. A scroll decision‐making strategy is proposed by assuming only a few days of future inflows being predicted with certainty. With 10 historical and 30 designed floods in different magnitudes, the models and methods are applied to the Yangtze River basin, which includes 11 cascade reservoirs. The results on average show the MILP reduces the rule violation by 17%, while the TSLP is more than 150 times faster in CPU time. Excluding one of the reservoirs from the simulation reveals that the Three Gorges (TG) contributes the most in flood detention, accounting for 76% on average for once‐in‐a‐century floods. Interestingly, with the scroll decision‐making strategy implemented, the change from 3 to 7 days of certain forecasting has the most significant reduction in both the rule violation and flood detention, indicating that the forecasting accuracy of more recent inflows is more valuable for flood control.

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“…The most popular solution technique for this large-scale, mixed-integer, combinatorial and nonlinear programming problem is mixed-integer linear programming (MILP) [20,21]. The efficient optimized commercial solvers and various toolboxes developed for this technique lead to its flexible and convenient application [22].…”

Section: Introductionmentioning

confidence: 99%

On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming

Zhang

2021

Sustainability

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China has declared ambitious carbon emission reduction targets and will integrate increasing shares of variable renewables for the next decades. The implementation for flexibility modification of thermal power units and deep peak regulation ancillary service market alleviates the contradiction between rapid capacity growth and limited system flexibility. This paper establishes three flexibility modification schemes and two price rules for simulation and proposes an analysis framework for unit commitment problem based on mixed-integer linear programming to evaluate the policy mix effects. Results confirm the promoting effects of flexibility modification on integrating variable renewables and illustrate diverse scheme selections under different renewables curtailment. Particularly, there is no need for selecting expensive schemes which contain more modified units and more developed flexibility, unless the curtailment decrement is compulsorily stipulated or worth for added modification cost. Similarly, results also prove the revenue loss compensation effect of deep peak regulation ancillary service market and illustrate diverse price rule selections under different curtailment intervals. Price rule with more subdivided load intervals and bigger price differences among them is more effective, especially under the higher requirement for curtailment rate. Therefore, the government should further enlarge flexibility modification via but not limited to more targeted compensation price, while generators should further consider a demand-based investment.

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A comparison between mixed-integer linear programming and dynamic programming with state prediction as novelty for solving unit commitment

Cited by 19 publications

References 35 publications

A review of optimization modeling and solution methods in renewable energy systems

A review of optimization modeling and solution methods in renewable energy systems

Optimal multireservoir operation for flood control under constrained operational rules

On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming

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