An improved algorithm for single-unit commitment with ramping limits

Wuijts, Rogier Hans; Akker, Marjan van den; Broek, Machteld van den

doi:10.1016/j.epsr.2020.106720

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…This algorithm is based on a recurrence relation on functions that represents for each generator state at each time step the value of the optimal generator schedule that ends in that state. The RFF+ algorithm is very efficient because it does not need to compute the optimal economic dispatch for each possible 'on-interval' of a generator and it can identify superfluous functions which reduces the computation time significantly (see Wuijts et al [2021a] for more details).…”

Section: Generator Subproblem 1ucmentioning

confidence: 99%

“…Our algorithm is the first Langrangian algorithm to force convergence and feasibility for the UC including ramping limits without requiring an ad-hoc repair heuristic. Secondly, we solve the subproblems very efficiently by applying our 1UC algorithm Wuijts et al [2021a], which strongly outperforms earlier algorithms for 1UC especially on longer time horizons, and also our newly created algorithm to solve transmission sub problems. We performed computational experiments on all known benchmark instances.…”

Section: Introductionmentioning

confidence: 99%

“…The time complexity of our 1UC algorithm was experimental established as linearWuijts et al [2021a] in the length of the time steps but also linear in the length of the minimum up and downtime. This is speculative but the superlinearity of FERC923 could be explained by the fact that this instance has multiple units with minimum up and downtime of 168 hours.…”

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confidence: 99%

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Effect of modelling choices in the unit commitment problem

2023

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In power system studies the unit commitment problem (UC) is solved to support market decisions and assess system adequacy. Simplifications are made to solve the UC faster, but they are made without considering the consequences on solution quality. In this study we thoroughly investigated the impacts of simplifications on solution quality and computation time on a benchmark set consisting of almost all the available instances in the literature. We found that omitting the minimum up- and downtime and simplifying the startup cost resulted in a significant quality loss without reducing the computation time. Omitting reserve requirements, ramping limits and transmission limits reduced the computation time, but degraded the solution significantly. However, the linear relaxation resulted in less quality loss with a significant speed-up and resulted in no difference when unserved energy was minimized. Finally, we found that the average and maximum capacity factor difference is large for all model variants.

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Section: Generator Subproblem 1ucmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of modelling choices in the unit commitment problem

2023

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“…These inequalities strengthen the linear-programming relaxation and speed up the calculation time. Dynamic programming algorithms that keep track of a set of functions that represent the overall cost of generator schedules until each time step is presented in [9]. The results of the case study show linear scaling characteristics that can speed up the state-of-the-art for piece-wise linear and quadratic generation cost curve.…”

Section: A Background and Contribution On Thermal Generator Bidding Using Robust Optimizationmentioning

confidence: 99%

Individual Thermal Generator and Battery Storage Bidding Strategies Based on Robust Optimization

2021

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“…According to the above decomposition methods, it can be found that the computational efficiency of 1UC is crucial since the 1UC serves as a subproblem of the original large-scale UC problems and is widely used for single-unit self-scheduling/ bidding problems [14].…”

Section: Introductionmentioning

confidence: 99%

Convex Hull Model for a Single-Unit Commitment Problem With Pumped Hydro Storage Unit

Ding

Sun

et al. 2023

IEEE Trans. Power Syst.

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The single-unit commitment (1UC) maximizes the revenue within a time series of given electricity prices while respecting generator-specific constraints, leading to a mixedinteger program (MIP). To reduce the computational complexity of the 1UC with a pump storage unit (PSU-1UC), we construct the convex hull of PSU-1UC in this paper. Focusing on the possible combination of alternating generation and pumping time intervals, we first establish a dynamic programming (DP) model that can be solved in polynomial time. Then, we reformulate the DP model into a linear programming (LP) model in a higherdimensional space, which provides the convex hull of the PSU-1UC. It helps to speed up the large-scale unit commitment problems solved by decomposition algorithms from the perspective of formulation transformation. Numerical experiments demonstrate the effectiveness and efficiency of the proposed model.

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An improved algorithm for single-unit commitment with ramping limits

Cited by 6 publications

References 11 publications

Effect of modelling choices in the unit commitment problem

Effect of modelling choices in the unit commitment problem

Individual Thermal Generator and Battery Storage Bidding Strategies Based on Robust Optimization

Convex Hull Model for a Single-Unit Commitment Problem With Pumped Hydro Storage Unit

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