Accelerated solution of the transmission maintenance schedule problem:a Bayesian optimization approach

To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on-site maintenance operations.Considering the computational complexity of the mixed integer programming(MIP)problem,a machine learning(ML)approach is presented to solve the transmission maintenance scheduling model efficiently.The value of the branching score factor value is optimized by Bayesian optimization(BO)in the proposed algorithm,which plays an important role in the size of the branch-and-bound search tree in the solution process.The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.

Security-constrained Transmission Maintenance Optimization Considering Generation and Operational Risk Costs

With the large-scale integration of renewable energy,the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy generation.Simultaneously,in a market-oriented operation mode,the power dispatching control center aims to reduce the overall power purchase cost while ensuring the security of the power system.Therefore,a security-constrained transmission maintenance optimization model considering generation and operational risk costs is proposed herein.This model is built on double-layer optimization framework,where the upper-layer model is used for maintenance and generation planning,and the lowerlayer model is primarily used to address the operational security risk arising from the random prediction error and N-1 transmission failure.Correspondingly,a generation-maintenance iterative algorithm based on a defined cost feedback is included to increase solution efficiency.Generation cost is determined using long-term security-constrained unit commitment,and the operational risk cost is obtained using a double-layer N-1 risk assessment model.An electrical correlation coupling coefficient is proposed for the solution process to avoid maintenance of associated equipment simultaneously,thereby improving model convergence efficiency.The IEEE 118-bus system is used as a test case for illustration,and test results suggest that the proposed model and algorithm can reduce the total cost of transmission maintenance and system operation while effectively improving the solution efficiency of the joint optimization model.

Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind power.Meanwhile,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power systems.This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices.The problem is formulated as a two-stage stochastic mixed-integer programming model.The first stage arranges the maintenance periods of transmission lines.The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios.The proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both stages.Therefore,the progressive hedging algorithm is applied.The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.