Planning of production and utility systems under unit performance degradation and alternative resource-constrained cleaning policies

Abstract

A general optimization framework for the simultaneous operational planning of utility and production systems is presented with the main purpose of reducing the energy needs and material resources utilization of the overall system. The proposed mathematical model focuses mainly on the utility system and considers for the utility units: (i) unit commitment constraints, (ii) performance degradation and recovery, (iii) different types of cleaning tasks (online or offline, and fixed or flexible time-window), (iv) alternative options for cleaning tasks in terms of associated durations, cleaning resources requirements and costs, and (v) constrained availability of resources for cleaning operations. The optimization function includes the operating costs for utility and production systems, cleaning costs for utility systems, and energy consumption costs. Several case studies are presented in order to highlight the applicability and the significant benefits of the proposed approach. In particular, in comparison with the traditional sequential planning approach for production and utility systems, the proposed integrated approach can achieve considerable reductions in startup/shutdown and cleaning costs, and most importantly in utilities purchases, as it is shown in one of the case studies.

A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems

“…Through such frameworks and processes, organisations would possess a platform that supports coherence and repeatability of analysis, irrespective of whom As impressive as the tremendous growth in the recognition of industrial CBM seems, the guidelines of the recently launched asset management standards -ISO 55000 series (initially a publicly available specification published by the British Standards Institution in 2004) in 2014 makes it crystal clear that asset performance and condition data alone are no longer sufficient for making robust MAM engineering decisions in capital intensive sectors such as power [24]. This is based on the premise that the role of MAM is changing from the classical "problem-fixer" to a very important aspect of asset life cycle management through the incorporation of the following key themes [24]: Unfortunately, despite widespread consensus that MAM is a necessity for the cost-effectiveness of any plant, some decision-makers within several organisations still view the maintenance function as a mere cost centre or necessary evil [23,24], which is perhaps why maintenance as a function has struggled to attain the same level of recognition attributed to other vital plant functions such as finance [25][26][27][28][29][30][31][32], production planning [33][34][35][36][37][38][39][40], marketing [41][42][43][44], etc. Searches within several top percentile energy-related journals clearly show a contrariety between the scanty number of academic publications advocating MAM optimisation and the abundant resources on topics such as energy policy and finance.…”

A general optimization framework for the design and planning of energy supply chain networks: Techno-economic and environmental analysis

“…More specifically, Energy Systems Engineering provides a solid methodological scientific framework to arrive at integrated solutions to complex energy systems problems, by adopting a holistic systems-based approach for optimization, simulation and control problems of energy supply chains networks. Energy systems engineering approaches have been presented for subjects related to design and control modeling (Diangelakis and Pistikopoulos, 2017), integrated operational and maintenance planning (Zulkafli and Kopanos, 2016), and low-carbon energy systems (Corbetta et al, 2016).…”

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

“…For instance, Abdollahi et al (2016) developed a mathematical model for the optimal design and operational planning of energy networks based on CHP generators. Zulkafli and Kopanos (2016) presented an optimization framework for the operational and maintenance planning of production and utility systems under unit performance degradation and alternative resource-constrained cleaning policies. Silvente et al (2015) proposed a rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids.…”