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

Abstract

Generation maintenance scheduling (GMS) is an important factor that can improve the reliability of power systems and decrease revenue achieved by generation companies (GenCos). Several GMS models, therefore, are based on these two crucial values. Nonetheless, there is no GMS problem that simultaneously considers unexpected failure of distributed generator (DG), financial return of GenCo, and reserve of system. This paper proposes the GMS model based on a global criterion approach to compromise functions that maximise the GenCo's annual return and probability that no DG fails unexpectedly. The system reserve (SR) is considered as a reliability constraint, while surplus reserve is exchanged with the main grid. To support alternative energy sources that have uncertain outputs and ensure continuous operation of DG, short-term GMS model including power from wind farm, photovoltaic system, energy system storage, and demand response (DR) is also run by adding the SR and inconstant cost of DR. Effectiveness of the proposed model is examined using the IEEE 6 and IEEE 18-bus test systems. Results show that not only the proposed model provides a better GMS solution for the GenCo, resulting in appropriate values of the two objectives, but also alternative energy sources are useful for the short-term GMS. INTRODUCTION Motivation and literature reviewGeneration maintenance scheduling (GMS), in restructured power system, is one of the most important factors used to improve system reliability. It can increase performance and prevent unexpected failure of distributed generator (DG). However, if the GMS model is solved improperly, the financial return of a generation company (GenCo) can be decreased as a result of costs required to support DG maintenance [1, 2], an outage of DG might occur unexpectedly [3], and the system reserve (SR) determined by the independent system operator (ISO) may not be satisfied [4]. Therefore, to ensure the stability of these three crucial values, a multi-objective GMS model should be developed and proposed. Recently, considerable works have focused on improving GMS models by considering various objectives, constraints, energy sources, and mathematical approaches. For the brief This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Optimizing Generation Maintenance Scheduling Considering Emission Factors

“…The mp b,j and D b,t are extracted from [25] and [20,38], respectively. To find the results, there is an assumption that the load demand and electricity price are certain [20,22,23,25,34], while the power loss is not considered. The T is divided into 52 weeks, based on yearly maintenance scheduling, whereas the H is 24 × 7 = 168 h. The δ is assumed to be 0.1 (10%) [23].…”

“…To efficiently obtain the results, as shown in Equations ( 12)-( 14), the natural logarithm is used to linearize the model. The linearized Z is represented by PnF', which should also have a low value, similar to that for Z [22,34].…”

“…To efficiently maintain the GU and secure the power system, the operation, maintenance, and power generation constraints must be satisfied [22,23,25,34].…”

A bidding mechanism for maintenance of generation units considering inter-ISO power exchange

“…In addition, the reserved energy should be above a certain level when some of the generation units are undergoing maintenance, so that the risks of large-scale load loss caused by a reserved energy shortage can be avoided. Thus, the maintenance schedules are required to be properly planned [4][5][6][7]. The ISO manages the planning process of the maintenance considering the costs of maintenance actions for the generation units and the power system reliability.…”

Effect of Deploying Routing and Facility Location Methods on Power Transmission to Increase the Reliability of the Power Network and Minimize the Total Logistics Cost

“…The study also addresses the power generating cost by reducing the team visit to each PP as high power generating PPs cost is high and it affects the other PP module's expected regular service output in the long run (Cai et al, 2020). This problem is addressed by developing an algebraic framework for connecting different cities in the western region to a single loop having a minimum length between maintenance centers and setting up an appropriate number of teams for reliable and stable power supplies (Prukpanit et al 2021).…”