Efficient planning of energy production and maintenance of large-scale combined heat and power plants

In this study, an efficient optimization framework is presented for the simultaneous planning of energy production and maintenance in combined heat and power plants, and applied in the largest coal-fired cogeneration plant of Kazakhstan. In brief, the proposed optimization model considers: (i) unit commitment constraints for boilers and turbines; (ii) minimum and maximum runtimes as well as minimum idle times for boilers and turbines; (iii) bounds on the operating levels for boilers and turbines within desired operating regions; (iv) extreme operating regions for turbines; (v) energy balances for turbines; (vi) total electricity and heat balances for satisfying the corresponding demands for electricity and heat (for each heat network); and (vii) maintenance tasks for units that must occur within given flexible time-windows. The minimization of the annual total cost of the cogeneration plant constitutes the optimization goal here, and consists of startup and shutdown costs, fixed operating and fuel costs, maintenance costs, and penalties for deviation from heat and electricity demands, and penalties for turbines for operating outside the desired operating regions. An extensive data analysis of historical data has been performed to extract the necessary input data. In comparison to the implemented industrial solution that follows a predefined maintenance policy, the solutions derived by the proposed approach achieve reductions in annual total cost more than 21% and completely avoid turbines operation outside their desired operating regions. Our solutions report substantial reductions in startup/shutdown, fuel and fixed operating costs (about 85%, 15%, and 13%, respectively). The comparative case study clearly demonstrates that the proposed approach is an effective means for generating optimal energy production and maintenance plans, enhancing significantly the resource and energy efficiency of the plant. Importantly, the proposed optimization framework could be readily applied to other cogeneration plants that have a similar plant structure

Optimization of a network of compressors in parallel: Operational and maintenance planning – The air separation plant case

A general mathematical framework for the optimization of compressors operations in air separation plants that considers operating constraints for compressors, several types of maintenance policies and managerial aspects is presented. The proposed approach can be used in a rolling horizon scheme. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments as well as the outlet mass flow rates for compressed air and distillation products are optimized under full demand satisfaction. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. Several case studies of an industrial air separation plant are solved. The results demonstrate that the simultaneous optimization of maintenance and operational tasks of the compressors favor the generation of better solutions in terms of total costs

Preventive maintenance and replacement scheduling : models and algorithms.

Preventive maintenance is a broad term that encompasses a set of activities aimed at improving the overall reliability and availability of a system. Preventive maintenance involves a basic trade-off between the costs of conducting maintenance/replacement activities and the cost savings achieved by reducing the overall rate of occurrence of system failures. Designers of preventive maintenance schedules must weigh these individual costs in an attempt to minimize the overall cost of system operation. They may also be interested in maximizing the system reliability, subject to some sort of budget constraint. In this dissertation, we present a complete discussion about the problem definition and review the literature. We develop new nonlinear mixed-integer optimization models, solve them by standard nonlinear optimization algorithms, and analyze their computational results. In addition, we extend the optimization models by considering engineering economy features and reformulate them as a multi-objective optimization model. We optimize this model by generational and steady state genetic algorithms as well as by a simulated annealing algorithm and demonstrate the computational results obtained by implementation of these algorithms. We perform a sensitivity analysis on the parameters of the optimization models and present a comparison between exact and metaheuristic algorithms in terms of computational efficiency and accuracy. Finally, we present a new mathematical function to model age reduction and improvement factor parameter used in optimization models. In addition, we develop a practical procedure to estimate the effect of maintenance activity on failure rate and effective age of multi component systems

Improving Energy Efficiency in Manufacturing Systems — Literature Review and Analysis of the Impact on the Energy Network of Consolidated Practices and Upcoming Opportunities

Global energy context has become more and more complex in the last decades: raising prices of depleting fossil fuels, together with economic crisis and new international environmental and energy policies, are forcing companies (and manufacturing industry in particular, which is responsible for 90% of industry energy consumptions, in turn making up the 51% of global energy usage, as listed on EIA, the Energy International Agency, website, last accessed on the 5th of October 2014) to cut energy wastes and inefficiencies, and to control their consumptions. Besides the existing analysis of the above mentioned regulatory and economic concerns, Energy Efficiency criticality for manufacturing systems has recently been investigated and proved also by the analysis of its connection with Productivity Efficiency [1-4], which resulted to be strong and mutual, and of the numerous non-energy benefits achieved while performing energy efficiency measures [5], such as the improvement of corporate image and the environmental impact reduction. Over most recent years, Energy Efficiency has therefore become a critical factor for industrial plants’ competitiveness, and is now definitely considered as a key driver to economic development and sustainability. But, despite it all, it is often still difficult for many companies to understand its effectiveness, in good part because of the difficulties met in focusing its technical and economic benefits, as Laitner [6] highlights: “Energy Efficiency has been an invisible resource. Unlike a new power plant or a new oil well, we do not see energy efficiency at work. (...) energy efficiency may be thought of as the cost-effective investments in the energy we do not use either to produce a certain amount of goods and services within the economy.” As a matter of fact, Energy Efficiency still represents a challenging goal for most companies. As above mentioned, numerous problems are yet to overcome in quantifying its benefits and evaluating the cost-effectiveness of related investments, and most of all the huge variety, complexity and changeability of fields, technologies and methodologies involved in its improvement in production systems are responsible for the slowing down of their resolution and of the spread of Energy Efficiency measures and culture. In fact, in order to individuate and prioritize suitable improvement interventions and Energy Efficiency opportunities, and to design and customize the Energy Management System or the Monitoring and Control System according to a particular company’s needs, a deep and complete knowledge of many different subjects and disciplines (ranging from physics and thermodynamics to economy and project management) is needed, besides a good ability and practical sensibility to direct one’s efforts in the right way. Considering that Energy Efficiency isn’t obviously the core business of manufacturing industry, such effort might sometimes be very laborious, and in recent years many companies have decided to demand Energy Management activities to specialized external companies, the so-called Energy Service Companies (ESCos). ESCos generally own the know-how required to individuate Energy Efficiency measures and are also able to fund Energy Efficiency investments (see [7] for a specific literature review); what they usually do not own is a deep understanding of the company’s dynamics, situations and needs, as well as the capability to draw a long-term development path towards the achievement of a diffused Energy Efficiency culture within the company, which shall be consistent with the company’s vision and policies and is essential in order to consolidate and continuously upgrade improvements in such sector. It is then crucial for companies to have at least a general consciousness of all intervention areas and of all possible improvements, both managerial (and/or behavioural) and technological, that could be pursued and achieved, in order to be able to lead their own way towards their sustainable development, and also to capitalize ESCOs’ assistance and services. In order to overcome part of these difficulties, and in particular to make it easier for companies to address their efforts and catch best efficiency opportunities, a logical and systemic approach is necessary: it would help not to overlook any possible area of improvement, to easily classify and understand those areas, but also to identify the most suitable and cost-effective, and eventually to prioritize them. In the light of this, some studies have already been conducted in order to find out methods and tools to assess the current level of maturity of a company in the Energy Management field [8], and to help individuating a possible development path. However, although they point out some possible development scenarios, they do not provide a complete and organic categorization of all possible areas of intervention, so as to make it easier for practitioners to address their efforts into the right way. In this chapter, a new conceptual scheme to organize and classify Energy Efficiency measures is defined, leading from the definition of Energy Cost per Product Unit and further breaking it up in order to identify and define all possible areas of intervention, providing for each of them a brief overview of possible measures and opportunities and a specific literature review. All scientific papers, books and technical papers considered for the literature review of each area (chosen on the basis of a wide literature research and on authors’ on-field experience) are recalled and systematized in Table 1, so that the reader is guided through their examination and rapidly addressed to their consultation. In addition, a qualitative evaluation of the impact of some possible Energy Efficiency measures from each area on the energy network is given, in order to give both practitioners and researchers a first input to further focus on this additional feasibility evaluation criteria for Energy Efficiency measures, which enables to evaluate them on a national or international level rather than considering the benefits or concerns belonging to a single company

Integrated condition-based planning of production and utility systems under uncertainty

A general rolling horizon optimization framework for the integrated condition-based operational and maintenance planning of production and utility systems in process industries is presented. In brief, the proposed optimization framework considers for the production and utility units: (i) improved unit performance degradation and recovery models that depend on both the cumulative time of operation and the unit operating levels deviation of units; (ii) modified operating capacities under online cleaning periods; (iii) different types of cleaning tasks (flexible time-window and online or offline condition-based); (iv) alternative options for offline cleaning tasks; (v) limited availability of cleaning resources; (vi) the initial state of the overall system at the beginning of each planning horizon; and (vii) terminal constraints for the rolling horizon problem. Total cost constitutes the objective function of the resulting problem and includes unit operating costs, cleaning costs, energy consumption costs and resource purchases costs. The case studies solved show that when compared to solutions obtained by sequential approaches the proposed integrated approach provides significantly better solutions in terms of total costs (reduction from 5%-32%), and especially in cost terms related to utility units operation, energy consumption, cleaning and startup/shutdown operations. Unnecessary cleanings and purchases of resources can be avoided by the proposed integrated approach. Overall, the significant reduction in total costs is a direct result of the enhanced energy efficiency of the overall system through the efficient generation and use of energy, the improved utilization of energy and material resources resulting in a more sustainable and cleaner production practices

Operational and maintenance planning of production and utility systems in process industries.

Major process industries have installed onsite the utility systems that can generate several types of utilities for meeting the utility requirements of the main production systems. A traditional sequential approach is typically used for the planning of production and utility systems. However, this approach provides suboptimal solutions because the interconnected production and utility systems are not optimised simultaneously. In this research, a general optimisation framework for the simultaneous operational and maintenance planning of utility and production systems is presented with the main purpose of reducing the energy needs and resources utilisation of the overall system. A number of industrial-inspired case studies solved show that the solutions of the proposed integrated approach provides better solutions than the solutions obtained by the sequential approach. The results reported a reduction in total costs from 5% to 32%. The reduction in total costs demonstrate that the proposed integrated approach can result in efficient operation of utility systems by avoiding unnecessary purchases of utility resources and improved utilisation of energy and material resources. In addition, the proposed integrated optimisation-based model was further improved with the presence of process uncertainty in order to address dynamic production environment in process industries. However, integrated planning problems of production and utility systems results to large mixed integer programming (MIP) model that is difficult to solve to optimality and computationally expensive. With this regards, three-stage MIP-based decomposition strategy is proposed. The computational experiments showed that the solutions of the proposed MIP-based decomposition strategy can achieve optimal or near-optimal solutions at further reduced computational time by an average magnitude of 4. Overall, the proposed optimisation framework could be used to integrate production and utility systems for effective planning management in the realistic industrial scenarios.PhD in Energy and Powe

Agent-based technology applied to power systems reliability

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling

Existing multi-objective Generator Maintenance Scheduling (GMS) models have considered unit commitment problem together with unit maintenance problem based on a periodic maintenance strategy. These models are inefficient because unit commitment does not undergo maintenance and periodic strategy cannot be applied on different types of generators. Present graph models cannot generate schedule for the multi-objective GMS models while existing Pareto Ant Colony System (PACS) algorithms were not able to consider the two problems separately. A multi-objective PACS algorithm based on sequential strategy which considers unit commitment and GMS problem separately is proposed to obtain solution for a proposed GMS model. A graph model is developed to generate the units’ maintenance schedule. The Taguchi and Grey Relational Analysis methods are proposed to tune the PACS’s parameters. The IEEE RTS 26, 32 and 36-unit dataset systems were used in the performance evaluation of the PACS algorithm. The performance of PACS algorithm was compared against four benchmark multi-objective algorithms including the Nondominated Sorting Genetic, Strength Pareto Evolutionary, Simulated Annealing, and Particle Swarm Optimization using the metrics grey relational grade (GRG), coverage, distance to Pareto front, Pareto spread, and number of non-dominated solutions. Friedman test was performed to determine the significance of the results. The multiobjective GMS model is superior than the benchmark model in producing the GMS schedule in terms of reliability, and violation objective functions with an average improvement between 2.68% and 92.44%. Friedman test using GRG metric shows significant better performance (p-values<0.05) for PACS algorithm compared to benchmark algorithms. The proposed models and algorithm can be used to solve the multi-objective GMS problem while the new parameters’ values can be used to obtain optimal or near optimal maintenance scheduling of generators. The proposed models and algorithm can be applied on different types of generating units to minimize the interruptions of energy and extend their lifespan