A practical resource scheduling with OPF constraints

Abdul-Rahman, K.H.; Shahidehpour, Mohammad; Aganagic, M.; Mokhtari, Soroush

doi:10.1109/pica.1995.515170

Cited by 20 publications

(12 citation statements)

References 9 publications

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“…The startup cost and the last term in (15) are dropped since the minimization is with respect to t i P . When the units' fuel cost functions are represented as polynomial functions as in (10), the minimum of (16) can be found by taking its first derivative.…”

Section: A the Dual Problem Optimizationmentioning

confidence: 99%

“…Different initial values may also lead LR to different solutions. In [15], the initial multiplier λ t was set to the hourly system marginal cost of the schedule to satisfy the power balance constraint and the initial multiplier μ t was set to zero, leading to an infeasible initial solution. Alternatively, the initial multiplier λ t was set to the hourly system marginal cost of the schedule to satisfy both the power balance and spinning reserve constraint, whereas the initial multiplier μ t was set to zero which was generally lower than the optimal value [16].…”

Section: B a New Initial Scheduling Of Ucmentioning

confidence: 99%

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Thermal unit commitment solution using an improved Lagrangian Relaxation

Benhamida¹,

Abdallah²,

Rashed³

2007

REPQJ

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Abstract. An improved Lagrangian relaxation (LR) solutionto the thermal unit commitment problem (UCP) is proposed in this paper. The algorithm is characterized by: (1) a new Matlab function to determine the optimal path of the dual problem, (2) new initialization procedure of Lagrangian multipliers, based on both unit and time interval classification, (3) a flexible adjustment of Lagrangian multipliers, and (4) a dynamic search for uncertain stage scheduling, using a Lagrangian relaxation -dynamic programming method (LR-DP). After the LR best feasible solution is reached, and when identical or similar units exist, a unit decommitment is used to adjust the solution. The proposed algorithm is tested and compared to conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA), and genetic algorithm based on unit characteristic classification (GAUC) on systems with the number of generating units in the range of 10 to 100. The total system production cost of the proposed algorithm is less than the others especially for the larger number of generating units. Computational time was found to increase almost linearly with system size, which is favorable for large-scale implementation. Key WordsUnit commitment, Generation Scheduling, Lagrangian Relaxation, Unit classification J, Q: primal and dual solution of the LR based UC algorithm; ε : Pre-specified tolerance.

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Section: A the Dual Problem Optimizationmentioning

confidence: 99%

Section: B a New Initial Scheduling Of Ucmentioning

confidence: 99%

Thermal unit commitment solution using an improved Lagrangian Relaxation

Benhamida¹,

Abdallah²,

Rashed³

2007

REPQJ

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“…Step 3: Maximization problem (6) to (9) is solved, and the cumulative decommitment merit is found for every generator.…”

Section: Algorithm 1: Determining Udrmentioning

confidence: 99%

A practical algorithm for unit commitment based on unit decommitment ranking

Tanabe

Kurita

Tada

et al. 2001

Electrical Engineering Japan

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This paper presents a practical algorithm for solving Unit Commitment (UC) problems with network constraints represented in Optimal Power Flow (OPF). The proposed algorithm is based on Successive Approximation Dynamic Programming (SADP) according to a Unit Decommitment Ranking (UDR) which is a priority list for unit decommitment procedure. The UDR can be determined by Dynamic Programming (DP) according to the information of OPF consisting of a set of Lagrange multipliers (dual variables). Since the set of Lagrange multipliers represents the relationship between the optimal value of objective function and OPF constraints, the proposed algorithm can determine power system resource scheduling in consideration of network constraints, such as line flow and voltage constraints. The feasibility and effectiveness of the proposed algorithm are demonstrated on a modified IEEE Reliability Test System. © 2001 Scripta Technica, Electr Eng Jpn, 138(2): 1–13, 2002

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“…Single unit dynamic programming is used to solve PBUCP and Lagrangian multipliers are updated using sub-gradient method [2]. Unit commitment schedule is obtained by satisfying the constraints such as ramp rate limits, fuel constraints, multiple emission requirements, as well as minimum up and the down time limits, over a set of time periods [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%