Hydro generating units maintenance scheduling using Benders decomposition

Pravovremeno održavanje proizvodnih postrojenja od iznimne je važnosti za pouzdan i siguran pogon elektroenergetskog sustava. U uvjetima liberaliziranog tržišta električne energije ekonomski aspekti, orijentirani na najveću moguću dobit, moraju biti zadovoljeni. S druge strane, i tehnički aspekti, kojima je cilj održati elektroenergetski sustav iznad željenih granica sigurnosti i pouzdanosti, odnosno koji su protivni ekonomskim ciljevima, također moraju biti zadovoljeni. Stoga se dva suprotna gledišta sukobljavaju u svrhu pronalaska optimalnog rješenja koje će zadovoljiti sve tehničke kriterije i investitora u ekonomskom pogledu. U radu se obrađuje problem pronalaženja optimalnog rasporeda održavanja hidrogeneratora. U tu svrhu se koristi metoda matematičkog programiranja – Bendersova dekompozicija. Nakon kratkog opisa matematičke metode, iznesena je primjena Bendersove dekompozicije na tri hrvatske hidroelektrane u nizu na rijeci Dravi.Maintenance of the power generating facilities in due time is essential for reliable and secure operation of the electric power system. In liberalized electricity market the economical aspects, pointed to the greatest possible revenue, have to be satisfied. On the other hand, technical aspects, which keep the power system above the desired level of safety and reliability, i.e. which are opposed to economical aspects, have to be satisfied as well. Therefore, these two opponent standpoints have to be confronted in order to provide optimal solution which will comply with the strict technical limitations, and which will meet investor\u27s economical requirements. This paper addresses the problem of obtaining the optimal maintenance schedule of hydro generating units. For this purpose, the paper discusses the mathematical programming method – Benders decomposition. After a brief description of the mathematical method, the application of the Benders decomposition on three Croatian hydroelectric power plants in a cascade on the Drava River is carried out

Mixed-Integer Programming Approaches for Hydropower Generator Maintenance Scheduling

RÉSUMÉ: Dans les systèmes de production d’électricité, la maintenance régulière des unités de production est essentielle pour éviter des pannes imprévues et coûteuses, pour maintenir l’efficacité du système et pour prolonger la durée de vie de l’équipement. Cependant, l’arrêt des générateurs pour maintenance préventive réduit temporairement la capacité, l’efficacité et la fiabilité du système. Etant donnée une liste des activités de maintenance à réaliser dans un horizon de planification, le problème de planification de maintenance des générateurs (GMSP, pour Generator Maintenance Scheduling Problem) consiste à déterminer un calendrier d’arrêts pour maintenance qui maximise une métrique de performance du système. Le calendrier optimal qui en résulte doit répondre aux exigences opérationnelles de la production d’électricité ainsi qu’aux contraintes de maintenance, telles que les fenêtres de temps des activités de maintenance. Dans les systèmes hydroélectriques, l’ordonnancement de la maintenance des unités de production comporte des défis uniques en raison de la non-linéarité de la production d’hydroélectricité, de l’incertitude des débits d’eau et de l’interdépendance des décisions opérationnelles dans l’espace et le temps. Le GMSP est particulièrement pertinent pour les producteurs d’hydroélectricité parce que l’avancement ou le report des activités de maintenance peut générer des économies significatives en réduisant les déversements d’eau et en améliorant l’efficacité de la production d’hydroélectricité. Nous développons un programme linéaire mixte en nombres entiers (MILP, pour Mixed- Integer Linear Program) pour le GSMP dans les systèmes hydroélectriques, avec hyperplans pour approximer l’effet non-linéaire des rejets d’eau, les niveaux d’eau stockés et le nombre de générateurs actifs sur la production d’hydroélectricité. Nous affinons notre formulation en utilisant des inégalités valides, la désagrégation de variables et de contraintes, et une technique de réduction de modèle basée sur des informations de fenêtres temporelles. Nos tests numériques montrent que la meilleure combinaison de ces techniques peut réduire jusqu’à dix fois le temps de calcul pour obtenir une solution. Pour incorporer l’effet des afflux d’eau incertains, nous étendons notre modèle en un programme linéaire stochastique en deux étapes, et nous implémentons une méthode de décomposition de Benders parallélisée pour sa solution. Nous proposons sept techniques d’accélération, et lors de nos expériences numériques, nous observons qu’une combinaison de cinq de ces techniques permet d’obtenir les meilleures performances, avec une accélération de l’algorithme de Benders quadruplée par rapport à la méthode Benders de base. Nos tests sur une vi grille de calcul avec 200 coeurs pour résoudre le problème avec un grand nombre de scénarios, confirment la supériorité de la méthode Benders parallélisée par rapport à la solution directe avec un solveur général pour MILP. Enfin, nous proposons des extensions de notre formulation, en incluant d’autres contraintes de maintenance pertinentes, des décisions sur la durée des activités et des réserves de production pour anticiper l’incertitude de la charge d’électricité. En outre, nous présentons d’autres stratégies de décomposition pour les GMSP dans les systèmes hydroélectriques et nous discutons des perspectives de recherche, telles que des améliorations à la méthode de décomposition et les applications de notre formulation MILP à des problèmes d’ordonnancement similaires.----------ABSTRACT: In power generation systems, regular maintenance of generating units is essential to prevent costly unplanned outages, to sustain the efficiency of the system, and to extend the lifespan of the equipment. However, shutting down generators for preventive maintenance temporarily reduces the capacity, efficiency, and reliability of the system. Given a list of maintenance activities to be completed within a planning horizon, the Generator Maintenance Scheduling Problem (GMSP) is to determine a calendar of maintenance outages that maximizes a system performance metric. The resulting optimal schedule must meet operational requirements of the electricity production, as well as maintenance constraints, such as time windows of maintenance activities. In hydropower systems, maintenance scheduling of generating units entails unique challenges due to the nonlinearity of the hydroelectricity production, the uncertainty of the water inflows and the interdependence of operational decisions in space and time. The GSMP is particularly relevant for hydropower producers because advancing or postponing maintenance activities can yield significant savings by reducing water spills and improving the efficiency of the hydroelectricity production. We develop a compact Mixed-Integer Linear Program (MILP) for the GSMP in hydropower systems, with hyperplanes for approximating the nonlinear effect of the water discharges, the stored water levels and the number of active generators on the hydroelectricity production. We refine our formulation using valid inequalities, disaggregation of variables and constraints, and a model reduction technique based on time windows information. In computational experiments, we find that the best combination of such tightening techniques can reduce the computational time of the solution by up to one order of magnitude. To incorporate the effect of uncertain water inflows, we extend our model as a two-stage stochastic linear program, and we implement a parallelized Benders decomposition method for its solution. We implement seven acceleration techniques, and through computational experiments, we find that a combination of five of such techniques achieves the best performance with a fourfold speedup of the Benders algorithm. Our tests on a 200-core computer cluster for solving the problem with a large number of inflow scenarios, confirm the superiority of the parallelized Benders method over the direct solution with a general MILP solver. Finally, we outline extensions to our formulation, by including other relevant maintenance constraints, decisions on the duration of activities, and generation reserves to buffer the unviii certainty of the electricity load. Furthermore, we outline alternative decomposition strategies for the GSMP in hydropower systems and we discuss directions of future research, such as enhancements to the decomposition method and applications of our compact MILP formulation to similar scheduling problems

Coordinated generation and transmission maintenance scheduling using mixed integer linear programming

Scheduling of electrical equipment for maintenance tasks is crucial in power system planning as it would affect system operating cost and security. Most existing Mixed Integer Linear Programming (MILP) approaches do not address the interactions between Generation Maintenance Scheduling (GMS), Transmission Maintenance Scheduling (TMS) and Security-Constrained Unit Commitment (SCUC). This research develops a MILP algorithm for the GMS, TMS and SCUC sub-problems to improve the accuracy of coordinated generation and transmission maintenance scheduling. Power flow equation which is based on sensitivity factors is modified to improve the accuracy of transmission maintenance scheduling. To reduce the complexity of the solution procedure as well as to enhance accuracy of the maintenance scheduling model, coupling constraints equations have been formulated to integrate the GMS, TMS and SCUC sub-problems. To further improve the maintenance scheduling ability, a new technique for total operating cost assessment is developed based on an hourly basis to achieve the lowest possible operating cost. Numerical case studies were evaluated on the 6-bus, IEEE 118-bus and utility systems. A comparative study is carried out between the coordinated and individual maintenance scheduling, MILP and Lagrangian Relaxation (LR) approaches, and the maintenance scheduling based on the hourly and day-to-day basis. Simulation results show that coordinated maintenance scheduling is superior to individual maintenance scheduling as it yields lower operating costs. Besides, the proposed MILP outperformed the LR with a cost reduction of up to 5% and lowered the gap tolerance by 0.13%. Moreover, cost saving of nearly 0.14% was achieved using the hourly basis in comparison to the day-to-day basis. From this research, it can be concluded that coordinated maintenance scheduling can provide optimal maintenance schedule which would benefit most of the system planners

Business models for Energy Storage Systems

Recent commitments to reduce greenhouse gas emissions in the electricity industry associated with the electrification of segments of heat and transport sectors pose significant challenges of unprecedented proportions . The unique features of Energy Storage Systems (ESS) coupled with the flexibility of providing services to multiple sectors of the electricity industry, make it a key technology to tackle current and upcoming challenges in the electricity industry. Although ESS have the potential to support future system integration with large amounts of renewable generation, the potential value that ESS brings to stakeholder s and its associated economics are not well understood to date. In addition, further research is needed on its business model in various markets and system conditions, in particular in the value associated with each service or set of services. In this context, the conducted research has addressed ESS operational aspects when considering a multiple services portfolio provided to various stakeholders and sensitive to market and system conditions. New ESS operational frameworks together with a computationally efficient modelling framework are proposed for a better understanding of ESS business models. The novelty introduced with this work is associated with a multiple service business model for ESS which considers services to distribution network operators, system operators, low capacity value generation and participation in the energy market. In addition, the economic aspects of ESS considering various operating policies for maximum revenue is also investigated and enhances the understanding of ESS to develop appropriate market mechanisms and allow efficient deployment of ESS in the electricity industry.Open Acces

Exploiting Dynamic Programming in Optimizing Reliability-Centered Maintenance: Case Study of Medium-Sized Aluminum Manufacturing Plant

Maintenance Scheduling (MS) is one of the most persistent issues that might arise in a manufacturing facility. It is crucial to do routine maintenance on machinery in order to avoid unplanned breakdowns. This type of failure could cause costly manufacturing process interruptions. Numerous strategies have been developed in an effort to deal with MS. But because each system has specific requirements and limitations, this is a particularly challenging problem. The scheduling of maintenance for machine units at a manufacturing facility that produces aluminum in moderate amounts is explored in this study using a dynamic programming approach. The Maintenance Scheduling model is put into practice using a Reliability-Centered Maintenance (RCM) strategy after an investigation of the architecture and infrastructure of the plant. By maintaining machines at acceptable dependability values and minimizing maintenance costs, this method is created to optimize the maintenance schedule. Here, factors like reliability and failure rate that affect the MS problem are discussed and investigated. Applying the model to a situation that represents the aluminum manufacturing allows it to be tested in a variety of different circumstances. The results of applying the model to the test cases are given, followed by a discussion of the results. The results obtained are reasonable and show that the dynamic programming strategy is a successful way to fix the MS issue that the manufacturing plant's machines are experiencing

A Risk-Based Approach to Automate Preventive Maintenance Tasks Generation by Exploiting Autonomous Robot Inspections in Wind Farms

In this paper, we dealt with some problems of operation and maintenance in wind farms. We focused on the main critical aspects of any maintenance strategy, which must include the identification of the plant elements to inspect as well as the planning of the possible actions aimed at minimizing production losses. At the same time, any maintenance strategy must take into account the possible costs. In fact, those decisions can be made based on risk-based methods. We designed a risk-based maintenance approach to plan inspection tasks to be assigned to service robots in wind power plants. A supervisory control and data acquisition (SCADA) system is employed to collect and manage suitable data (power, wind velocity, and related machine events), and the risk is evaluated on a daily basis over the data collected. The evaluation of the risk is strictly related to the healthiness of the power plant itself. Then, the tasks are created and scheduled based on a certain priority, which is strictly correlated with the evaluated risk. For the analysis of our approach, we used the real data collected on a wind power plant in Greece over 396 days. The power plant is capable to produce an overall power of 7.2 MW, and it is composed of eight wind turbines of 900 KW per each. We observed that, out of 396 days, 50 days presented machine events leading to a related risk evaluation for which our approach will produce 258 inspection tasks. From this analysis, we conclude that the application of the risk-based methodology paired with the exploitation of permanent robots on the field could result in a 225-MWh reduction of the plant's lost production, in other words, an increase of production of 45.6%

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems