Optimal maintenance scheduling of power producers considering unexpected unit failure

Abstract

A novel unit maintenance scheduling (UMS) problem formulation for a generation producer is presented, to maximise its benefit while thoroughly considering the risk associated with unexpected unit failures. First, the unit failure is characterised by a more practical bathtub-shaped failure behaviour from the modified superposed power law process. Its parameters are estimated from the historical data by solving a nonlinear least-squaresfitting problem via Gauss -Newton iteration method. On the basis of the unit failure analysis, the new UMS formulation is solved by a combination of linear programming and genetic algorithms (GAs), and its impact on the producer's benefit is analysed in detail, including expected profit of selling energy, expected renewal cost of damaged components and maintenance cost. Compared with the current models, the proposed UMS model takes into consideration the influences of market factors as well as unexpected unit failures to strike the right balance between profits and costs related with potential unit failures. Numerical examples on a four-unit producer are utilised to demonstrate the effectiveness of the proposed scheme. NomenclatureThe main mathematical symbols used in this paper are classified as follows:maintenance cost at period t E Profit (t) expected energy-selling profit at period t E Renewal (t) expected renewal cost at period t h( j, t, x j ) failure rate of unit j at period t p m (t) probability of unit operating state m at period t P m ( j, t) generation of unit j in unit operating state m at period t

A Competitive Mechanism of Unit Maintenance Scheduling in a Deregulated Environment

“…Actually, like other machine equipments, unit failure probability characterizes a bathtub-shaped curve [1], [25], [26], and the corresponding failure rate can be formulated as follows [17]: (1) In (1), is the parameter associated with the occasional periods;…”

“…Through the mathematical derivation [17], we can obtain (3) Note that perfect maintenance is assumed in this paper, namely, generating unit is restored to the "as-good-as-new" state after maintenance [27]. In other words, when it is back into operation, its failure rate and reliability will again follow (1) and (3), respectively.…”

“…In other words, its objective is to minimize the load curtailment and optimize the producers' satisfaction as well as ensure the fairness of the coordination process. This is a typical multi-objective optimization issue, which is usually solved by the preemptive goal programming approach with different weights [29] and shown as follows: The objective function in (17) includes three parts: the s and s of generating units and the un-served demand with respect to the producers' will (outage or online) and the system adequacy. In (17), with and large enough that the latter two parts, especially , will have more influence on the objective function, which ensures that the load curtailment is as little as possible within the study periods.…”

“…Firstly, individual producers schedule the maintenance periods by maximizing their benefits [17]- [20] and then submit the outage windows to the ISO. Note that the main focus of UMS at this step is on the producers' self-interest and the obtained outage scheduling should not be carried out without approval of the ISO.…”

Generation maintenance scheduling based on multiple objectives and their relationship analysis

“…There are mainly two types of facility maintenance scheduling in power systems, i.e., the generation MS (GMS) and the transmission line MS, which could be studied independently. This paper focuses on the GMS, which has been studied in much literature (Christiaanse and Palmer, 1972;Kralj and Petrović, 1988;Marwali and Shahidehpour, 1998;Burke and Smith, 2000;Lu et al, 2008;Yare et al, 2008;Feng et al, 2009;Yang and Chang, 2009b;Zhan et al, 2011;Pandzic et al, 2013;Schlünz and van Vuuren, 2013;El-Sharkh, 2014). A comprehensive overview of the GMS in the market environment is provided in Shahidehpour and Marwali (2000).…”