Generator Maintenance Scheduling Problem Research Articles

Fuel cell preventive maintenance in an electricity market with Hydrogen storage and Scenario-Based risk management

The application of the fuel cell (FC) in improving the clean and energy-efficient cogeneration of heat and power has been broadly justified by researchers. However, the reliable operation of FC units requires periodic maintenance services. In this regard, preventive generation maintenance scheduling (GMS) notably preserves the units’ lifespan, prevents unexpected failures, and restricts imposed repair costs caused by failures. The present work models the GMS problem for FC and highlights its challenges compared to the GMS of conventional generation units. The main challenges, including simultaneous satisfaction of the power-heat dual dependency in FC, the power and heat demand balances, and maintenance considerations, including duration, continuity, and coordination of maintenance, are addressed in this research. The maintenance program is first planned for a year to optimize the maintenance timetable. Afterward, daily generation scheduling is optimized subject to the GMS program obtained for the studied year. Moreover, by using risk management, the economic risk associated with the problem (due to uncertainty in the electricity price) is handled. The obtained results for the impacts of risk management on FC’s maintenance schedule depict an 11.5% improvement in the cost of the worst-case scenarios, namely a cost reduction from M$111.4 to M$98.6.

Optimizing Generation Maintenance Scheduling Considering Emission Factors

Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos.

Data-driven nested robust optimization for generation maintenance scheduling considering temporal correlation

Traditional power grids are gradually transitioning to smart grids with high penetration of renewable energy, which can realize the efficient utilization of power resources and low carbon emissions. However, the uncertainties of renewable energy (e.g., wind power) and load demand pose considerable challenges to secure operation and cost-effective planning in smart grids, such as generation maintenance scheduling (GMS). In this context, conventional methods including stochastic optimization and robust optimization are adopted to cope with the uncertainties and formulate the GMS plan. Unfortunately, these methods fail to consider the temporal information in uncertain variables, which can introduce extra operational costs brought by the uncertainties. To address this issue, we consider the temporal correlation of the uncertain wind power and load demand, and develop a data-driven two-stage nested robust optimization (NRO) approach for GMS to minimize the total costs of power system operation under uncertain scenarios. In our proposed approach, a temporal correlation Dirichlet process mixture model (TCDPMM) is developed to investigate the temporal information in the wind power and load demand datasets. Then, variational Bayesian inference (VBI) is employed to construct the data-driven uncertainty set, in which the temporal information for the uncertain variables and the correlations between the uncertain variables are considered. Subsequently, combined with this uncertainty set, a two-stage GMS problem is converted to a “min–max-max–min” optimization problem which is solved by the parallel Benders’ decomposition algorithm. The effectiveness and superiority of the proposed approach are demonstrated with a six-bus power system and a practical power system in China.

Single Objective Optimization Methods in Electrical Power Systems: A Review

Although the scheduling of maintenance tasks for generators is not a new issue, it has recently attracted new attention due to the significant rise in demand for expanding power system size in modern power systems. Generator Maintenance Scheduling (GMS) is a nonlinear optimization problem, highly dimensional and constrained, and determines when power-producing units must undertake well-planned preventative maintenance. The objective function includes binary variables to indicate whether a generator is undergoing maintenance at a given time and is subject to several restrictions described in this paper. However, the biggest concern of GMS is to produce a precise timetable for preventive maintenance of generating units with low cost and high reliability. Despite that, regrettably, a large volume of research works has accomplished solutions towards a model of GMS with the consideration of either maximizing system reliability or minimizing operation costs as an objective of their research work. This is called Single-Objective Problem (SOP), which involves one objective function that needs to be optimized. SOP is solved by Single-Objective Optimization Method (SOOM). The primary purpose of the research is to present a review of SOOM methods used in solving GMS problems.

Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm

In any metaheuristic, the parameter values strongly affect the efficiency of an algorithm’s search. This research aims to find the optimal parameter values for the Pareto Ant Colony System (PACS) algorithm, which is used to obtain solutions for the generator maintenance scheduling problem. For optimal maintenance scheduling with low cost, high reliability, and low violation, the parameter values of the PACS algorithm were tuned using the Taguchi and Grey Relational Analysis (Taguchi-GRA) method through search-based approach. The new parameter values were tested on two systems. i.e., 26- and 36-unit systems for window with operational hours [3000-5000]. The grey relational grade (GRG) performance metric and the Friedman test were used to evaluate the algorithm’s performance. The Taguchi-GRA method that produced the new values for the algorithm’s parameters was shown to be able to provide a better multi-objective generator maintenance scheduling (GMS) solution. These values can be benchmarked in solving multi-objective GMS problems using the multi-objective PACS algorithm and its variants.

A Discrete Chaotic Jaya algorithm for optimal preventive maintenance scheduling of power systems generators

The main role of the optimal Generator Maintenance Scheduling (GMS) problem in power systems is to develop an optimal preventive maintenance scheduling of the generation part units. An optimal GMS provides power systems with higher operational reliability, extends the generators lifetime and reduces the cost of generators maintenance. The GMS problem is formulated as an optimisation problem. This problem should satisfy both load’s power demand and workforce constraints with ensuring the reliability of power systems at economical operation cost. The GMS problem has been studied for many years when exact mathematical methods have been used in the past to reach exact solutions for small-scale problems. However, these conventional mathematical approaches have many limitations and they suffer from unreasonable computational efforts as system dimension increases. Traditional approximate methods have been adopted to overcome the limitations of exact methods for medium-scale power systems. However, they provide approximate solutions and they require a large computational effort for wide-area systems of big dimensions. Recently, modern methods based on metaheuristics optimisation have taken a long part to solve the GMS problem and to overcome the limitations of approximate methods. In this paper, a proposed Discrete Chaotic Jaya Optimisation (DCJO) algorithm is employed to perform the preventive maintenance scheduling of electric power systems generators. The proposed algorithm is based on a cooperation between the discrete Jaya optimisation algorithm and a proposed move rule based on Chaotic Local Search (CLS) technique to improve both exploration and exploitation phases. The GMS problem is modelled based on the reliability criterion of an objective function of a sum of the squares of the reserves of generation. The optimisation process is performed through minimising an evaluation function of a weighted sum of the objective function and the penalty function for violations of the constraints. The proposed approach has been tested in a 21-unit test system over a planned horizon of 52 weeks in which the peak load is 4739 MW and the maximum generation is 5688 MW and there is a total number of 35 workforce available per week to perform the maintenance tasks. The proposed method has been compared through several statistical tests with recent algorithms of the related works. The obtained results show the effectiveness of the proposed algorithm for solving the GMS problem as compared to other recent algorithms. This approach can be relied at the present upon to solve maintenance scheduling problems of generation units in power systems.

A fast solution approach to solve the generator maintenance scheduling and hydropower production problems simultaneously

The Generator Maintenance Scheduling Problem (GMSP) is a problem that combines a hydropower optimization problem with a scheduling problem. Both problems are known to be hard to solve and combining them leads to an even more challenging mathematical problem. Since the hydropower production functions are nonlinear, hyperplane curve fitting is used to linearize each power production function. The goal of the GMSP is to find an optimal schedule plan to decide when to shut down generators for maintenance. Therefore, one production function needs to be formulated per generator combinations leading to a rather large number of constraints. This paper demonstrates that the complexity of the problems is linked to the number of hyperplanes selected to formulate the power production functions. To accelerate the resolution of the problem, a new heuristic based on the mean square algorithm is presented to reduce the number of hyperplanes required. This heuristic substantially reduces the number of constraints and the solving time is almost ten times faster. Numerical results show that the energy produced and the generated maintenance plannings are similar for both mathematical formulations, more precisely with one hyperplane for each generator combination versus a reduced number of hyperplanes.

Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response

The periodic outage planning of generation units for maintenance services is critical for their economic operation. Ignoring it can lead to premature degradation, high repair costs, and system reliability deterioration due to unexpected outages. Accordingly, generation maintenance scheduling (GMS) can optimally handle this challenge. In a power system with combined heat and power (CHP), although CHP units can efficiently supply power and heat demand, continuous operation of such units can cause an increase in unexpected failure rate and repair costs. Accordingly, this research accomplishes GMS coordination in CHP-integrated energy systems to optimally determine the maintenance plan of units. Firstly, the yearly GMS is planned and then the short-term generation scheduling is optimized. The proposed GMS problem is modeled as a mixed-integer nonlinear programming problem while considering the technical constraints, CHP feasible operation region, and power and heat balance. Moreover, the uncertainty in the market price is considered and the CVaR measure is employed to deal with the risks in the GMS. Furthermore, demand response is also assumed in the short-term generation scheduling while evaluating the impact of the yearly GMS. Numerical simulations with scenario-based optimization are used to evaluate the proposed CHP GMS problem with uncertainty in market price.

Generation maintenance scheduling with renewable sources based on production and reliability costs

This work proposes an efficient method for solving the generation maintenance scheduling (GMS) problem, defined through an interactive process carried out between the independent system operator (ISO) and generation companies (GENCOs). In this problem, starting from a schedule previously informed by GENCOs, it is desired to minimize the expected costs of production and system load shedding for the period of analysis from the ISO point of view. The Evolution Strategy metaheuristic is used to solve the resulting optimization problem. The non-sequential Monte Carlo simulation and Cross-Entropy methods are combined to efficiently assess the maintenance schedules searched during the solving process. Uncertainties related to the behavior of load, unavailability of generation equipment, and variability of renewable energy sources are taken into account in the modeling and solution of the GMS problem. The performance of the proposed method is tested with the IEEE-RTS modified with the inclusion of renewable sources.

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

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.

Generator Maintenance Scheduling Models for Electrical Power Systems: A Review

Interest in Generator Maintenance Scheduling (GMS) has increased due to the advent of demand-related expansion in size for modern power systems. Timely maintenance plays a significant role in minimizing failures and helps in averting cost incurred as a result of production shutdowns. The GMS problem is a complex and nonlinear optimization problem that specifies the schedule for carrying out planned preventive maintenance on power generation units. There is no clear concept to GMS model types and choosing the appropriate maintenance scheduling type. Thus, this paper presented a comprehensive review on GMS models in electrical power systems that covers the maintenance strategies, main elements of GMS models, and optimization methods used in solving GMS models. The list of references comprised related works from the years 2000 until 2020, which were classified into three based on the objectives. A new type of objective function for the GMS models was among the suggestions provided. A numerical example which focuses on a multi-objective GMS model and a proposed multi-objective Pareto ant colony system algorithm are also presented. The results of this review will not only enable researchers to gain a good overview of the existing GMS models for electrical power systems but also provide a source of references in choosing an appropriate maintenance scheduling strategy that is suitable with the type of generating unit and existing operating conditions.

Multiscale Multiresolution Generation Maintenance Scheduling: A Stochastic Affinely Adjustable Robust Approach

Generation maintenance scheduling (GMS), as a medium-term operational planning problem in power system, encounters both midterm and short-term uncertainty sources. This article presents a multiscale multiresolution uncertainty model to characterize midterm and short-term uncertainties distinctively in GMS problem. In the proposed multi-scale multi-resolution GMS (MMGMS) model, the midterm uncertainty of weekly peak loads in the scheduling horizon is characterized via plausible scenarios while short-term uncertainty of hourly loads is addressed through polyhedral uncertainty sets. To make the MMGMS approach tractable, affine policies are incorporated into the proposed model. The resulting MMGMS model, considering midterm as well as short-term uncertainties, is formulated as a stochastic affinely adjustable robust optimization (SAARO) problem. To solve this problem, a new solution methodology including stochastic optimization and probabilistic dual cut is presented. Numerical results on two test systems corroborate the effectiveness of the proposed model and solution approach.

Evaluating the impact of energy efficiency programs on generation maintenance scheduling

This paper presents a novel approach to generation maintenance scheduling (GMS) problem. The GMS problem is one of the main issues in the restructured power system, due to its effect on the security risks and the profit of the generation company (Genco). In order to consider the risk of GMS for producers, the GMS problem from the perspective of producers is modeled using non-cooperative game theory, and to determine the Genco's optimal strategy profile, the Nash equilibrium is used. In this paper, the coordinating procedure to satisfy reliability criterion and greenhouse gases (GHGs) reduction is done by the independent system operator (ISO). In recent years, extensive researches have been fulfilled on the implementation of energy efficiency programs in the restructured electricity industry. Energy efficiency programs play a significant role in meeting our energy requirements, energy security risks, and GHGs problems. In this paper, an economic-environmental model of efficiency power plants (EPP) has been applied to analyze impacts of the energy efficiency programs in the generation maintenance scheduling (GMSEPP). Also, the impact of energy efficiency on emission production in GMSEPP is considered. The expansion of the EPP has been facilitated by regulatory support schemes. In this work, the energy efficiency feed-in tariff is considered as a regulatory support system to encourage the investors. The Implementation of the energy efficiency programs on generation maintenance scheduling can lead to the improvement in the reliability and reduction in GHGs in the modified IEEE RTS 24 bus.

Risk-based stochastic short-term maintenance scheduling of GenCos in an oligopolistic electricity market considering the long-term plan

Maintenance of the generation units is critically important due to reliability of power grid. However, the operation of the grid is directly impacted by the generation maintenance scheduling (GMS). In order to avoid a decrease in the required reserve power, it is necessary to coordinate the planned outage of power plants by the independent system operator (ISO) in a restructured environment. In this study, the risk-constrained short-term GMS problem in an oligopolistic electricity market is accomplished to decrease the risk of reserve power shortage. Decision-making under uncertainty of price and behavior of rivals in an electricity market are investigated. Moreover, the possibility of offering some proposals for maintenance intervals by the generation companies (GenCos) is also considered. Although all the proposals are considered by the ISO, there is no guarantee for their approval, due to the security constraints. In the case that the priorities for a typical GenCo are unacceptable, the offers for the unaccepted intervals are recognized and minimum reserve level is decreased by the ISO. Moreover, the long-term maintenance scheduling is established for provision of the rivals’ behavior of individual GenCos. IEEE reliability test system is employed to assess the effectiveness of the proposed GMS.

Tradeoffs between levelling the reserve margin and minimising production cost in generator maintenance scheduling for regulated power systems

One of the key focus areas for a power utility is the planned preventative maintenance of the power generating units in its power system. The well-known generator maintenance scheduling (GMS) problem involves finding a schedule for the planned maintenance outages of generating units in a power system. A novel bi-objective model is proposed for the GMS problem in which demand reliability is maximised, by minimising the sum of squared reserves (SSR), and electricity production cost (predominantly fuel cost) is minimised. A novel production planning module is proposed to estimate the production cost associated with an energy generation plan, using a linear programming (LP) model to solve the economic dispatch (ED) problem, which precedes application of a simple unit commitment (UC) algorithm. A dominance-based multi-objective simulated annealing approach is then adopted to determine trade-off solutions to the model. Parallel computing is also utilised to increase the efficiency of approximating the Pareto front. The modelling approach is demonstrated in the context of a case study involving the 32-unit IEEE Reliability Test System. The results are compared to the best known single-objective solution in the literature, which only minimises the SSR, and the conflicting relationship between the two model objectives is investigated. It is found that more non-dominated trade-off solutions result if the load demand increases (i.e.the gap between installed capacity and load demand decreases). Therefore, if the installed capacity is sufficiently high, the reliability objective of minimising the SSR produces sufficiently small production cost solutions. Fuel cost savings of 0.41% are achieved in respect of a most “reliable” solution in the literature, but considerable cost savings are possible (up to 7.11%) if the maintenance duration and crew constraints are relaxed.

Mathematical approach assisted differential evolution for generator maintenance scheduling

Maintenance scheduling of power generating units is very essential for the economical and reliable operation of a power system. The objective of Generator Maintenance Scheduling (GMS) problem is to find the exact time interval for preventive maintenance of power generating units in order to minimize the operating cost, maximize the system reliability and to extend the life time of the generating units. In this paper, the problem of scheduling of generating units for maintenance is formulated as a mixed integer optimization problem by considering minimizing the operating cost. Since generator maintenance scheduling is a mixed integer problem, differential evolution algorithm is suitably modified to handle the integer variables. The control variables in differential evolution algorithm are integers which denote the starting period of each generating unit for carrying out maintenance work. The lambda iteration method is used to determine the optimal generation schedule of committed generating units. This paper presents a mathematical approach assisted differential evolution (MADE) to solve maintenance scheduling problem in a power system. The performance of the proposed algorithm is validated by considering two test systems. The result obtained by the proposed MADE method is compared with mathematical approach assisted particle swarm optimization. The test results reveal the capability of the proposed MADE algorithm in finding optimal maintenance schedule for the GMS problem.

Transmission-constrained generation maintenance scheduling considering market-based demand response

Summary Introduction of smart grid has changed different paradigms in power industry. System-wide demand participation in power system operational planning is one of such influenced paradigms. To comply with these influenced paradigms, generation maintenance scheduling (GMS), as one of the operation planning processes, should be modified to adapt to the new changes. This paper proposes a new model to consider demand response (DR) in the GMS problem. The proposed GMS problem is modelled as a mixed integer linear programming problem and solved for the two known test cases, namely IEEE reliability test system and IEEE 118-bus test system. The results show that considering DR leads to more dispersed and uniformed generation unit maintenance schedules and lower overall system operation cost in comparison with the case in which DR is not considered. Copyright © 2016 John Wiley & Sons, Ltd.

Reliability based Generator Maintenance Scheduling using Integer Coded Differential Evolution Algorithm

In this paper, Generator Maintenance Scheduling (GMS) in a vertically integrated power system is considered. The objective of the GMS problem is to find the particular time interval for maintenance of power generating units with an intention of maximizing the security of the power system. In this paper, scheduling of generating units for planned preventive maintenance is formulated as a mixed integer optimization problem by considering maximizing the average value of reliability index subject to a set of nonlinear constraints. Integer Coded Differential Evolution (ICDE) algorithm is developed to solve the GMS problem. The Lagrange Multiplier method is used to find the overall production cost for the maintenance schedule that is obtained using ICDE algorithm. To demonstrate the effectiveness of the proposed approach, two test systems are considered and are validated by comparing results obtained with that of Integer Coded Particle Swarm Optimization. The test results reveal the capability of the proposed ICDE algorithm in finding optimal maintenance schedule for the generator maintenance scheduling problem.

Optimal Generator Maintenance Scheduling Using a Hybrid Metaheuristic Approach

This paper presents an optimal approach for solving the generator maintenance scheduling (GMS) problem using a hybrid metaheuristic-based method. The GMS problem is formulated mathematically as a minimization problem of an aggregated objective function that handles both the reliability performance and the constraints violations. A new optimization approach, based on simulated annealing (SA) and ant colony optimization (ACO) is proposed to solve this problem. The optimization results for a 21-unit test system show that the proposed hybrid approach is more efficient than the standard SA and ACO methods and also genetic algorithms (GAs). It is also shown that the proposed approach is less sensitive to the variations of some control parameters and is more reliable than the other approaches.

A new approach for maintenance scheduling of generating units in electrical power systems based on their operational hours

Generator maintenance scheduling (GMS) determines the outage periods of generating units in a one-year or two-year planning horizon for regular safety inspection. This paper introduces a new practical GMS for centralized electrical power systems in which, in contrary to previous studies, the outage periods are scheduled based on operational hours of units. Predefined minimum and maximum operating hours of units after a maintenance outage define the beginning and ending of their maintenance windows, respectively. In addition, unit commitment (UC) as a short-term planning is considered in the GMS with hourly time scale. Therefore, this problem becomes more complex than periodic maintenance scheduling. In this paper, a novel mixed-integer linear programming (MILP) model for the problem is developed. However, due to the problem intractability, two different solution algorithms on the basis of ant colony optimization (ACO) and simulated annealing (SA) are extended for the GMS problem. Both algorithms use some developed heuristics and feasibility rules, namely UC heuristic, for solving the UC problem. Numerical results indicate that the solution algorithms perform well compared to the exact solution of the MILP model obtained using CPLEX solver. Also, the solution algorithms as well as the UC heuristic are examined carefully. To demonstrate the performance of the proposed algorithms, two test systems containing 26 and 36 generating units are investigated.