Ant colony optimization for power plant maintenance scheduling optimization—a five-station hydropower system

Environmental Modelling & Software

Kapelan

Kasprzyk

et al. 2014

521

317

In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Environmental Modelling & Software

Kapelan

Kasprzyk

et al. 2014

521

317

“…The formulation of some common constraints, including the allowable maintenance window, availability of resources, load, continuity, completion, precedence and reliability are presented in [16], and repeated in this paper (Equations (2) to (6)) for the sake of completeness. In addition, a minimum maintenance duration constraint (Equation (7)) is specified as a result of the incorporation of the 'maintenance duration shortening' and 'deferral of tasks' options in the refined definition of the PPMSO problem presented here.…”

Section: Objectives and Constraintsmentioning

confidence: 99%

“…The probability that a particular start day is chosen is a function of the associated pheromone trail and heuristic value. The heuristic formulation for selection of the maintenance start day is given by Equations (11) to (16). As mentioned above, the heuristic formulation in Equation (11) .…”

Section: Loadmentioning

confidence: 99%

“…The ACO-PPMSO formulation was tested on a problem instance and found to outperform various metaheuristics adopted for the same problem instance in other studies [15]. The formulation was later used to solve a 5-station hydropower maintenance scheduling optimization problem [16], which demonstrated the capabilities of the ACO-PPMSO formulation when compared with traditional methods based on engineering judgement.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a new local search strategy that is capable of improving the solutions obtained by the ACO metaheuristic is introduced. In order to examine the utility of the improved ACO-PPMSO formulation and the usefulness of the new local search strategy, the 5-station hydropower case study investigated by [16] is adopted.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Power plant maintenance scheduling using ant colony optimization: an improved formulation

Foong

Engineering Optimization

Simpson

2008

Self Cite

It is common practice in the hydropower industry to either shorten the maintenance duration or to postpone maintenance tasks in a hydropower system when there is expected unserved energy based on current water storage levels and forecast storage inflows. Therefore, it is essential that a maintenance scheduling optimizer can incorporate the options of shortening maintenance duration and/or deferring maintenance tasks in the search for practical maintenance schedules. In this paper, an improved Ant Colony Optimization -Power Plant Maintenance Scheduling Optimization (ACO-PPMSO) formulation that considers such options in the optimization process is introduced. As a result, not only the optimum commencement time, but also the optimum outage duration, is determined for each of the maintenance tasks that needs to be scheduled. In addition, a local search strategy is developed to boost the robustness of the algorithm. When tested on a 5-station hydropower system problem, the improved formulation is shown to be capable of allowing shortening of maintenance duration in the event of expected demand shortfalls. In addition, the new local search strategy is also shown to have significantly improved the optimization ability of the ACO-PPMSO algorithm.

An adaptive ant colony optimization framework for scheduling environmental flow management alternatives under varied environmental water availability conditions

Szemis

Water Resources Research

Dandy

2014

Human water use is increasing and, as such, water for the environment is limited and needs to be managed efficiently. One method for achieving this is the scheduling of environmental flow management alternatives (EFMAs) (e.g., releases, wetland regulators), with these schedules generally developed over a number of years. However, the availability of environmental water changes annually as a result of natural variability (e.g., drought, wet years). To incorporate this variation and schedule EFMAs in a operational setting, a previously formulated multiobjective optimization approach for EFMA schedule development used for long-term planning has been modified and incorporated into an adaptive framework. As part of this approach, optimal schedules are updated at regular intervals during the planning horizon based on environmental water allocation forecasts, which are obtained using artificial neural networks. In addition, the changes between current and updated schedules can be minimized to reduce any disruptions to longterm planning. The utility of the approach is assessed by applying it to an 89km section of the River Murray in South Australia. Results indicate that the approach is beneficial under a range of hydrological conditions and an improved ecological response is obtained in a operational setting compared with previous longterm approaches. Also, it successfully produces trade-offs between the number of disruptions to schedules and the ecological response, with results suggesting that ecological response increases with minimal alterations required to existing schedules. Overall, the results indicate that the information obtained using the proposed approach potentially aides managers in the efficient management of environmental water.