TY - JOUR
T1 - Security-constrained generation and transmission outage scheduling with uncertainties
AU - Wu, Lei
AU - Shahidehpour, Mohammad
AU - Fu, Yong
PY - 2010/8
Y1 - 2010/8
N2 - This study presents a stochastic model for the independent system operator's (ISO's) optimal coordinated long-term maintenance scheduling of generation units and transmission lines with short-term security-constrained unit commitment (SCUC). Random disturbances of power systems including forced outages of generation units and transmission lines, load forecast errors, and fuel price fluctuations are modeled as scenario trees using the Monte Carlo simulation. Lagrangian relaxation (LR) is applied to separate the coordinated optimization problem into long-term equipment maintenance (LTEM) and stochastic long-term SCUC (LTSCUC) subproblems. For the stochastic LTSCUC subproblem, scenario bundle constraints are relaxed via LR and the optimization problem is decomposed into deterministic LTSCUC problems. LR is applied to each deterministic LTSCUC to relax long-term fuel and emission limits and decompose the problem into short-term SCUC subproblems. The decomposition is further applied to short-term SCUC subproblems for separating hourly unit commitment and transmission network constraints. The unit commitment is formulated as a mixed-integer programming (MIP) problem and solved by the branch-and-cut method using CPLEX. The outcome of this study includes the hourly scheduling of outages of generation units and transmission lines, which corresponds to the optimal generation unit commitment and dispatch, and transmission flows. The hourly schedules minimize the total cost of operation and maintenance and satisfy long-term and short-term constraints of generation units and transmission network with the inclusion of power system uncertainty. A modified IEEE 118-bus system is used to exhibit the effectiveness of the proposed scheduling approach.
AB - This study presents a stochastic model for the independent system operator's (ISO's) optimal coordinated long-term maintenance scheduling of generation units and transmission lines with short-term security-constrained unit commitment (SCUC). Random disturbances of power systems including forced outages of generation units and transmission lines, load forecast errors, and fuel price fluctuations are modeled as scenario trees using the Monte Carlo simulation. Lagrangian relaxation (LR) is applied to separate the coordinated optimization problem into long-term equipment maintenance (LTEM) and stochastic long-term SCUC (LTSCUC) subproblems. For the stochastic LTSCUC subproblem, scenario bundle constraints are relaxed via LR and the optimization problem is decomposed into deterministic LTSCUC problems. LR is applied to each deterministic LTSCUC to relax long-term fuel and emission limits and decompose the problem into short-term SCUC subproblems. The decomposition is further applied to short-term SCUC subproblems for separating hourly unit commitment and transmission network constraints. The unit commitment is formulated as a mixed-integer programming (MIP) problem and solved by the branch-and-cut method using CPLEX. The outcome of this study includes the hourly scheduling of outages of generation units and transmission lines, which corresponds to the optimal generation unit commitment and dispatch, and transmission flows. The hourly schedules minimize the total cost of operation and maintenance and satisfy long-term and short-term constraints of generation units and transmission network with the inclusion of power system uncertainty. A modified IEEE 118-bus system is used to exhibit the effectiveness of the proposed scheduling approach.
KW - Optimal coordination of generation and transmission outage scheduling
KW - security-constrained unit commitment
KW - stochastic modeling of power systems
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U2 - 10.1109/TPWRS.2010.2040124
DO - 10.1109/TPWRS.2010.2040124
M3 - Article
AN - SCOPUS:77954834803
SN - 0885-8950
VL - 25
SP - 1674
EP - 1685
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 3
M1 - 5416324
ER -