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.
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.
During a period of transition towards decarbonised energy networks, maintaining a reliable and secure energy supply whilst increasing efficiency and reducing cost will be key aims for all energy supply chain (ESC) networks. Renewable energy sources, such as biomass, will play an important role in future ESC's as climate change mitigation becomes an increasingly important priority. This paper seeks to address these requirements by presenting an optimization model for the design and planning of biomass integration into the ESC networks. A supply chain model was derived and the governing equations were solved using the General Algebraic Modelling System software (GAMS) to achieve an optimal solution. The results of the study indicate that a reduction in the emissions cost of up to 4.32% is achievable on integration of 5-8% of biomass into the ESC network. However, a 4.57% increase in the total cost of the ESC network was recorded at the biomass fraction in the mixed fuel of 7.9%, with the fixed assets cost having the largest impact on the total cost of the ESC network. It has been shown that the cost increment in the assets and operational costs of a biomass and coal co-fired combined heat and power plant can be offset by the cost reductions obtained from reduced carbon dioxide emissions. Economic arguments for dual-fuel plants, therefore, require the introduction of effective carbon pricing legislation. It is concluded that such policy implementations can be effective at mitigating the effects of climate change and would assist in achieving a global carbon neutral economy.
Highlights • A unified modeling representation (E-STN) for material and energy supply chains. • General optimization model for the design/planning of material and energy supply chains. • Optimization of capacity expansion, energy mix, techno-economic & environmental aspects. • Emissions caps are more effective measures for emissions reduction than emissions costs. • Cost versus emissions study via sensitivity analysis and multi-objective optimization.
The increase in energy consumption, environmental pollution issues, and low-carbon agenda has grown the research area of demand side management (DSM). DSM programs provide feasible solutions and significantly enhance the efficiency and sustainability of electrical distribution systems. This paper classifies and discusses the broad definition of DSM based on the comprehensive literature study considering demand response and energy efficiency. The implementation of Artificial Intelligence algorithms in DSM applications has been employed in many studies to help researchers make optimal decisions and achieve predictions by analyzing the massive amount of historical data. Owing to its simplicity and consistent performance in fast convergence time, Particle Swarm Optimization (PSO) is widely used as a part of the swarm AI algorithm and has become a prominent technique in the optimization process to exploit the full benefit of the demand-side program. The variants of PSO have been developed to overcome the limitations of the original PSO and solve the high complexity and uncertainty in the DSM optimization process. The proposed PSO-based algorithm can optimize consumers' consumption curves, reducing the peak demand and hence minimizing the electricity cost when integrated with the DR programs or EE measures. The research works of the PSO algorithm in DSM have seen an increasing trend in the past decade. Therefore, this paper reviewed the application of the PSO-based algorithm in DSM fields with some constraints and discussed the challenges from the previous work. The potential for new opportunities is identified so that PSO methods can be developed for future research.INDEX TERMS Demand side management (DSM), demand response (DR), energy efficiency (EE), metaheuristic algorithms, particle swarm optimization (PSO), swarm intelligence.
This study focuses on the operational and resource-constrained condition-based cleaning planning problem of integrated production and utility systems under uncertainty. For the problem under consideration, a two-stage scenario-based stochastic programming model that follows a rolling horizon modelling representation is introduced; resulting in a hybrid reactiveproactive planning approach. In the stochastic programming model, all the binary variables related to the operational status (i.e., startup, operating, shutdown, under online or offline cleaning) of the production and utility units are considered as first-stage variables (i.e., scenario independent), and most of the remaining continuous variables are second-stage variables (i.e., scenario dependent). In addition, enhanced unit performance degradation and recovery models due to the cumulative operating level deviation and cumulative operating times are presented. Terminal constraints for minimum inventory levels for utilities and products as well as maximum unit performance degradation levels are also introduced. Two case studies are presented to highlight the applicability and the particular features of the proposed approach as an effective means of dealing with the sophisticated integrated planning problem considered in highly dynamic environments.
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