Risk-Based Coordination of Maintenance Scheduling and Unit Commitment in Power Systems

Abstract

The line random failure caused by multiple uncertainties has become a non-negligible factor which influences the safe operation of power systems. In this paper, a risk-based coordination model of maintenance scheduling and unit commitment is established and the objective is to minimize the total operation cost consisting of the maintenance cost, generation cost and risk cost. A random generation method is proposed to obtain line failure scenarios based on arbitrary failure rate functions. In the base case, security constraints must be satisfied while the potential overflow caused by line outages is calculated in contingency scenarios. In order to handle this mixed integer quadratic programming, a solving strategy based on three-layer iterations is applied. In the exterior iterative layer, Lagrangian relaxation technology is used to coordinate the maintenance scheduling and the risk-based unit commitment until the maintenance schedule is available for the optimized unit commitment plan. In other two iterative layers, Benders decomposition method is adopted to the further problem decomposition. In the middle layer, iterations between the unit commitment sub-problem and the risk-based dispatch sub-problem are used to guarantee the optimality of risk-based unit commitment. In interior iterations, the feasibility of unit commitment plan is checked by an optimal load shedding model. Case studies are performed on a 6-bus test system and a 118-bus test system. Results show that the total operation cost decreases by coordination while the system operating risk can be effectively controlled.

Optimal generation maintenance scheduling considering financial return and unexpected failure of distributed generation

“…The remaining power of ESS is demonstrated by Equation (26). Limitations of charging/discharging are determined by Equations ( 27) and (28).…”

“…The problem is run by stochastic affinely adjustable robust technique. In [26], costs of transmission and generation maintenance, and risk are considered in the GMS model. The risk cost is based on the punishment coefficient for the overflow of the line.…”

A practical optimization model for maintenance scheduling in power grid

“…The literature [12] uses a relaxation-induced algorithm to solve the mixed-integer programming problem encountered in the maintenance schedule optimization, which effectively reduces the computational burden and improves the computational speed of the model. In terms of constraints, literature [13] considers the stochastic faults caused by multiple uncertainties on the line, established a risk-based coordination model for maintenance schedule and unit input, and proposes a solution strategy based on a three-level model iteration.…”

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

“…An approach to such an integrated GS and MS formulation that considers N−1 contingency constraints was proposed in [18], [19]; however, it ignored the impacts of the GC and the operation constraints of the units on maintenance determination. Furthermore, in the medium-and long-term time domains, forecasting uncertainty typically has an impact on decision-making in a power system [20]- [22]. In [20], both the MC and minimum expected power shortage based on risk assessment were considered.…”

“…Furthermore, in the medium-and long-term time domains, forecasting uncertainty typically has an impact on decision-making in a power system [20]- [22]. In [20], both the MC and minimum expected power shortage based on risk assessment were considered. A multi-objective optimization model of MS was established, providing a reference technical guide for MS considering multiple objectives.…”