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This paper presents a novel approach for upgrading utility systems by integrating absorption cooling systems using R-curve analysis. The key contributions include a comprehensive methodology for drawing the R-curve, determining priority paths, and optimizing the system without additional capital costs; a step-by-step case study illustrating the process of integrating two absorption chillers and analyzing their impact on cogeneration efficiency and the R-curve; equations for calculating cogeneration efficiency and power efficiency for different turbine paths to prioritize the most efficient paths; and findings revealing that integrating absorption chillers can significantly impact system performance, potentially increasing or decreasing cogeneration efficiency depending on the specific system. The proposed approach enables engineers to optimize utility systems by leveraging existing equipment and integrating absorption cooling efficiently, with the R-curve analysis providing a powerful tool for visualizing and optimizing the system to achieve the best balance of power, cooling, and heating. The findings demonstrate that careful analysis and optimization of the utility system using R-curve techniques can unlock significant energy savings and emissions reductions by prioritizing the most efficient turbine paths and integrating absorption cooling optimally.

Energy-intensive production sites are often supplied with energy by on-site energy systems. Commonly, the scheduling of the systems is performed sequentially, starting with the scheduling of the production system. Often, the on-site energy system is operated by a different company than the production system. In consequence, the production and the energy system schedule their operation towards misaligned objectives leading in general to suboptimal schedules for both systems. To reflect the independent optimization with misaligned objectives, the scheduling problem of the production system can be formulated as a bilevel problem. We formulate the bilevel problem with mixed-integer decision variables in the upper and the lower level, and propose an algorithm to solve this bilevel problem based on the deterministic and global algorithm by Djelassi, Glass and Mitsos (J Glob Optim 75:341–392, 2019. https://doi.org/10.1007/s10898-019-00764-3) for bilevel problems with coupling equality constraints. The algorithm works by discretizing the independent lower-level variables. In the scheduling problem considered herein, the only coupling equality constraints are energy balances in the lower level. Since an intuitive distinction is missing between dependent and independent variables, we specialize the algorithm and add a procedure to identify independent variables to be discretized. Thereby, we preserve convergence guarantees. The performance of the algorithm is demonstrated in two case studies. In the case studies, the production system favors different technologies for the energy supply than the energy system. By solving the bilevel problem, the production system identifies an energy demand, which leads to minimal cost. Additionally, we demonstrate the benefits of solving the bilevel problem instead of solving the common integrated or sequential problem. [ABSTRACT FROM AUTHOR]

The growing need for balancing power combined with the shutdown of conventional power plants requires new balancing-power providers. In this context, industrial energy systems are particularly promising. However, the main task of industrial energy systems is to provide various energy forms. For this purpose, they operate interconnected units to maximize efficiency, but the interconnected operation also increases complexity, limiting flexibility due to the need to supply fixed demands. Energy storage can increase the flexibility of current and future industrial energy systems, thus enhancing the potential for sector coupling within the overall energy system at a low cost. To improve the flexibility of industrial energy systems, we propose a design optimization framework that accounts for investment in energy storage and for the provision of balancing power. Since the request of balancing power is uncertain, we present a stochastic program for the balancing-power market and propose two ways to model storage that both derive feasible storage operations while being computationally efficient. In a case study of a multi-energy system, cost savings between 6% and 17% can be achieved by increasing flexibility for participation in the balancing-power market with investment in heat storage. The sensitivity analysis identifies heat storage as particularly advantageous for heat-driven energy systems. Our method combines long-term investment decisions with short-term operational uncertainties to identify optimal investment decisions, which enhance the energy system’s flexibility for the provision of balancing power.

This paper study of renewable energy sources in electrical output by the appearance of numerous and diverse issues and concerns. Finding smart technologies and algorithms capable of producing efficient solutions is one of the most critical issues. This paper also discusses how the algorithm improved to deal with difficulties linked to renewable energies to obtain the required results. The PSO algorithm was handle numerous issues in non-conventional power source, like optimum solar power, hybrid power, size, and total prices, etc. when the PSO method demonstrated its resolving issues versatility and several scholars continued to explore and refine the PSO algorithm. The proposed method efficiency and correctness are confirmed by develop model, its performance by developed model, its performance investigate the result by using MATLAB/Simulink. Overall, investigations and studies have demonstrated that the technique of PSO algorithm is one of the best renewable energy systems. This is because the technique is simple, efficient, and effective when compared with other optimization techniques and methods.

Utility systems provide thermal energy and power for industrial processes while emitting significant amounts of carbon dioxide. This study investigates the integration of Combined Heat and Power (CHP) units and renewable energy sources into utility systems in the context of diverse energy demands and low-carbon operations. A two-stage stochastic programming model (TSSP) is proposed for sustainable utility systems. The main objective of the model is to minimize the annual total cost and the other two objectives are to maximize renewable energy penetration rate and minimize grid net interaction level. The model tackles renewable energy sources' uncertainty and evaluates environmental metrics by computing carbon emission costs. In TSSP, the first stage involves new equipment capacity planning. The second stage involves considering uncertainties related to wind speed and solar radiation. Monte Carlo simulations and probability density functions were used to generate a variety of scenarios for the optimization of operational scheduling decisions. Finally, a case study was conducted to validate the effectiveness of the proposed model. Compared to traditional deterministic optimization in utility systems, the two-stage stochastic programming of sustainable utility systems can reduce carbon emissions by 5.6 percent annually and overall costs by 8,907,733 CNY. It provides decision-makers with a range of reference options by balancing multiple objectives, including economic factors, renewable energy penetration rate, and grid interaction level. [ABSTRACT FROM AUTHOR]

Offshore plants have many inherent constraints and risks compared to onshore plants, and it is crucial to optimize the operation of the offshore plant by adhering to constraints. Previous studies proposed a framework of utility systems that operate under various conditions. However, few studies have addressed the optimization of utility systems during the operating life cycle. To fill in this gap, we propose a new methodology for the design and optimization of the utility systems in the offshore plants. The utility systems are designed and optimized in Aspen Utilities Planner, considering the full life cycle of the oil and gas wells. For this, we first developed steady-state models of the topside processes based on every feasible operating scenario throughout the full life cycle. Then the utility consumption data was extracted from the simulation results and analyzed. The power system was designed using Aspen Utilities Planner (AUP), and it was optimized to maximize the thermal efficiency of the utility system, satisfying power demand for all the scenarios. The optimized results illustrate the significant saving of the operating cost and reduction of CO2 emission rate. Our studies suggest a generic framework to design utility systems in offshore plants. [ABSTRACT FROM AUTHOR]

The IAPWS-IF97 (Wagner et al. (2000) J Eng Gas Turbines Power 122:150) is the state-of-the-art model for the thermodynamic properties of water and steam for industrial applications and is routinely used for simulations of steam power cycles and utility systems. Its use in optimization-based design, however, has been limited because of its complexity. In particular, deterministic global optimization of problems with the IAPWS-IF97 is challenging because general-purpose methods lead to rather weak convex and concave relaxations, thus resulting in slow convergence. Furthermore, the original domains of many functions from the IAPWS-IF97 are nonconvex, while common global solvers construct relaxations over rectangular domains. Outside the original domains, however, many of the functions take very large values that lead to even weaker relaxations. Therefore, we develop tighter relaxations of relevant functions from the IAPWS-IF97 on the basis of an analysis of their monotonicity and convexity properties. We modify the functions outside their original domains to enable tighter relaxations, while we keep them unchanged on their original domains where they have physical meaning. We discuss the benefit of the relaxations for three case studies on the design of bottoming cycles of combined cycle power plants using our open-source deterministic global solver MAiNGO. The derived relaxations result in drastic reductions in computational time compared with McCormick relaxations and can make design problems tractable for global optimization. [ABSTRACT FROM AUTHOR]

Summary: We propose an inventory‐based optimization model for a system of electricity supply chain. The system involves two parties, including a power plant and a transmission station. In the investigated system, power plant performs the production activities from raw material. Thus, the power plant is responsible for the raw material purchasing, electricity production to fulfill both in‐house and consumer demand, and for electricity distribution to the transmission station. In‐house demand for electricity, which further be called by the utility system demand, is used to perform the production process. Electricity distribution to end consumers is carried out by the transmission station. Here, we consider a stochastic demand, blackout cost, and purchasing decision to order raw material. The model aims to minimize the expected joint total cost for the supply chain system. Several decisions are determined including electricity consumption of utility system and transmission station, power plant distribution factor, emergency backup factor, and raw material ordering factor to supplier. We propose an iterative procedure to solve the model and provide a numerical analysis to get better insight on the related problem. [ABSTRACT FROM AUTHOR]

The issue of energy has emerged as one of the greatest challenges facing mankind. In an industrial perspective, the development of site utility systems (generally combined heat and power (CHP) systems) for the generation and management of utilities provides a great potential source for energy savings. However, in most industrial sites, a master–slave relationship usually governs this kind of system and limits the potential operating capacity of CHP. To improve the decision-making process, Agha et al. (2010. Integrated production and utility system approach for optimising industrial unit operation. Energy, 35, 611–627) have proposed an integrated approach that carries out simultaneous and consistent scheduling of batch production plants and site utility systems. The modelling of the problem relies on a mixed integer linear programming (MILP) formulation. Nevertheless, although it is a powerful mathematical tool, it still remains difficult to use for non-expert engineers. In this framework, a graphical formalism based on existing representations (STN, RTN) has been developed: the extended resource task network (ERTN). Combined with an efficient and generic MILP formulation, it permits various kinds of industrial problems, including production and consumption of utility flows to be modelled homogenously. This paper focuses on the semantic elements of the ERTN formalism and illustrates their use through representative examples. [ABSTRACT FROM PUBLISHER]

• Method for simultaneous design of batch production and utility system. • Integrated MILP superstructure. • Balancing the cost trade-off between production and utility system. • Benefit of simultaneous design increases for higher share of energy cost. • Large cost savings in utility system overcome slight increase in production cost. Production systems are commonly designed without considering the required utility system. Only subsequently, the utility system is designed for the given production system. This sequential treatment typically results in suboptimal solutions, in particular, for batch systems where the energy demand varies with time. Thus, we propose a method for the optimal design of integrated batch production and utility systems, which covers decisions on the system structure, component sizing and scheduling of both production and utility system. The method integrates superstructure MILP models of a utility system and a production system. For two case studies from literature, the simultaneous design and scheduling of the integrated system is beneficial and shown to reduce cost and increase profit by more than 5.4%. Our analysis indicates that the benefits of the proposed method increase for industry sectors with high share of energy costs. [ABSTRACT FROM AUTHOR]

Abstract Operation of the production system and of the utility system is commonly optimized separately without feedback between them. This separated planning in general increases total costs. Conceptually, a joint optimization of production and utility system is desirable, but often not possible in practice. We overcome such practical limitations by proposing a repeated Stackelberg game. In the proposed method, the production schedule is determined such that costs for both production and utility system are reduced. For this purpose, price information are passed from the utility system to the production system leading to fast convergence of the repeated Stackelberg game. The proposed repeated Stackelberg game is applied to two literature-based case studies. The proposed method reduces the total costs in one repetition by 9% and 5%. The suggested method provides an effective and practical approach to operate systems with reduced minimal total costs in practice. Highlights • Method to coordinate scheduling of production and utility systems. •Solution of integrated complex scheduling problem is avoided. •Stackelberg game between production and utility system. •Load- and time-dependent energy prices as exchanged information. •Proposed method reduces cost significantly. [ABSTRACT FROM AUTHOR]

There were 1,288 sewer and 235 other utility manholes baited to control Norway rat (Rattus norvegicus) populations in downtown Boston using pulsed-baiting methods. About 15% of all sewer, 18% of phone, and 26% of electric manholes had rat activity. Sewer populations were most associated with residential areas with low flow, small diameter (

In process industries, the heating gap in heat exchanger networks (HENs) is normally compensated by the steam generated from a utility system, thus these two mutually influencing systems should be designed as a whole through establishing structural interrelationships. In this work, an improved stage-wise superstructure of HENs is proposed to integrate with a Rankine cycle-based utility system. Inner- and inter-stage heaters are considered in the new structure. Furthermore, the selection of steam in different levels is also investigated, extending the possibilities of steam utilization in HENs and generation in utility systems. The presented methodology is able to realize the optimal design of HENs by considering the supply and utilization of steam. Heaters’ allocations, matches of streams, steam distribution and utilization are optimized accompanying with the trade-off amongst equipment investment, fuel consumption and power generation in objective, which is highly related to the final structure of the system. The optimization problem is formulated into a mixed-integer non-linear programming (MINLP) model and solved towards the lowest total annual cost (TAC) of the entire system. Finally, a case study with two scenarios is studied. The detailed results are given and analyzed to demonstrate the benefit from structural improvement.

Highlights • Integrated condition-based planning of production and utility systems under uncertainty. • Operational and cleaning plans are simultaneously optimized for both systems. • A rolling horizon two-stage scenario-based stochastic programming model is presented. • Improved performance degradation and recovery models for units are introduced. • Cumulative time of operation and operating level deviation affect the unit performance. Abstract 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 modeling representation is introduced; resulting in a hybrid reactive-proactive 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. [ABSTRACT FROM AUTHOR]

Natural gas industrial parks are composed of raw natural gas refining plants, urea plants and power plants. The processes of natural gas refining and utilization in industrial parks are energy-intensive and involve complicated mass and energy networks. This study concentrates on heat and material stream integration for sustainable energy utilization in natural gas industrial parks. Energy integration of industrial parks is reviewed and a mixed integer non-linear programming framework is presented to minimize the total annual cost for the retrofitting of energy integration across plants in natural gas industrial parks. The mathematical framework combines the optimization models of energy-intensive processes, power plant operation and a new heat exchanger network design using some key variables. Total site analysis is proposed for a natural gas industrial park to obtain energy-intensive processes with energy saving potential and determine the process streams for energy integration across plants. The mathematical framework is then applied to the natural gas industrial park and can be solved using the deterministic global optimization solver in a reasonable solution time. The results demonstrate that the static investment payback period is less than two months and the total hot utility decreases by 30.5% through the energy retrofit of the entire natural gas industrial park. [ABSTRACT FROM AUTHOR]

It is important to target the cogeneration potential of utility systems in the process of designing or retrofitting Total Site utility systems. Simulation software has been widely used in process design and analysis. However, it is rarely employed in estimating of the cogeneration potential of a utility system. A new method is presented in this paper for targeting the cogeneration potential of Total Site utility systems with a commercial software. The simulator is used to calculate the temperatures of steam mains, steam flowrates and shaft power rates. Shaft power generation by steam turbines in expansion zones are computed with the built-in simulation module. Compared to the existing approaches, the new method needs neither establishing iterative calculation, nor programming procedure, and it requires fewer parameters. For the illustrated case studies, the results obtained by this method are comparable to those obtained in the literature. It is shown that the calculation procedure proposed in this paper is simple, and the results obtained are accurate. In addition, the calculation of the steam turbine capital cost and environmental footprints are considered in the targeting procedure to provide decision makers with a more complete picture of the designed sites. This should allow them to efficiently screen and compare the major design options on the basis of both economic and environmental performance. [ABSTRACT FROM AUTHOR]

CO 2 emissions from bitumen upgrading represent a major source of greenhouse gas emission in the oil sands industry of Canada. In this paper, optimal design of bitumen upgrading plant was studied with the aim of CO 2 reduction. Various CO 2 capture techniques including oxyfuel combustion, pre–combustion and post–combustion are modeled and incorporated into an integrated optimization model for simultaneous design of bitumen upgrading plant and the associated utility plant. To solve the resulting large-scale mixed integer nonlinear programming (MINLP) problem, augmented Lagrangian decomposition method is used. Optimal configurations under different scenarios (including plant capacity, natural gas, electricity and crude oil prices, and carbon tax) are discussed. The key findings of this study include: (i) hydrocracking for bitumen upgrading is favored, (ii) producing extra power and high–quality duty with a gas turbine burning natural gas is beneficial, and (iii) carbon capture with pre–combustion technology is suggested. [ABSTRACT FROM AUTHOR]

The demand for natural gas is increasing in the energy market because of its lower emissions and sustainable development. This increasing demand for natural gas promotes the capacity expansion of raw natural gas refining systems (RNGRSs), resulting in parallel refining processes in a RNGRS. Optimizing the material stream network between these refining processes is very challenging because of the complex thermodynamics, unit operations and utility configurations. An optimization framework is presented for the retrofit of the material stream network between these refining processes to improve the economic performance. The retrofit framework integrates raw natural gas supply, refining processes, utility subsystems and product delivery and is formulated as a mixed-integer nonlinear programming (MINLP) optimization model to obtain an optimal material stream network to increase profit. The model presented here is applied to a Chinese industrial RNGRS and results in an optimal retrofit. A comparison before and after the retrofit demonstrates a significant increase in profit. [ABSTRACT FROM AUTHOR]

Decarbonizing complex industrial energy systems is an important step to mitigate climate change. Designing the transition of such sector-coupled industrial energy systems to low-carbon designs is challenging since both cost-efficient operation and the reduction of environmental impacts over the whole life cycle need to be considered in the system design. Optimal system designs can be identified using software: Recently, the open-source framework SecMOD was introduced for the linear optimization of multi-energy system models, considering environmental impacts by fully integrating life-cycle assessment. In this work, we extend SecMOD to allow mixed-integer decisions that are vital to model industrial energy systems. Thereby, we provide the first open-source mixed-integer linear program framework with full integration of life-cycle assessment. We use SecMOD to investigate the benefits of a pumped-thermal energy storage system in a sector-coupled industrial energy system and identify trade-offs regarding the system design by comparing the economic and climate optimum. • Integration of MILP optimization and life-cycle assessment. • Open-source framework for optimization of industrial energy systems. • Utilization of sector-coupling for future-proof energy system design. • Case study considering pumped-thermal energy storage. • Assessment of cost-optimal and climate-optimal energy system designs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A heat exchanger network (HEN) is an important part in processing plants used to recover heat from process streams. A utility system supplies heating and cooling utilities and introduces additional hot and cold streams for the processes. The HEN and utility system (e.g., Rankine cycle-based cogeneration system) are closely interconnected primarily through steam, steam condensate leaving the turbines, and process surplus heat. The recovery of the sensible heat from the steam condensate and process surplus heat through an integration technique may contribute significantly to the reduction of the heating and cooling utility consumption in the heat exchanger network as well as in the primary energy consumption in the utility system. In this paper, a systematic methodology for the simultaneous synthesis and design of a utility system and HEN is proposed. The heat recovery from the steam condensate and boiler feedwater preheating are integrated into the HEN synthesis together with the design optimization of a Rankine cycle-based utility system. In addition to the simultaneous design of the utility and heat-recovery systems, the optimization variables include the steam condensate target temperature, the steam level for process heating, the energy demand for the utility system, the returning temperature of the steam condensate, and the final temperature of the boiler feed water. The total site HEN is composed of several interlinked sub-HENs. A model for the new hot utility-process cold stream HEN is formulated together with the hot -cold process streams of the HEN. The linking constraints between sub-HENs and the utility system are formulated. Several case studies are elaborated to demonstrate the effectiveness and applicability of the proposed methodology. Compared with the former design methods without integrating steam condensate sensible heat and boiler feedwater preheating, meaningful economic benefits can be achieved by applying the proposed framework. [ABSTRACT FROM AUTHOR]

Complex turbine (i.e., steam turbine with multiple controlled and uncontrolled extractions) is one of the most important components in industrial utility system. The function of complex turbine is to generate power electricity, provide steam for the processes, and heat boiler feedwater using uncontrolled extraction steam. Although many studies have focused on the modeling and optimization of utility systems of different scales, few works have been reported on the modeling and operation optimization for utility system containing multiple complex steam turbines. In this paper, a superstructure of utility network consists of multiple interconnected various types of complex turbines (ICSTUN) is constructed. The superstructure demonstrates not only an external utility network that supplies utility energy for the processes but also an internal utility network that supplies boiler feedwater. A systematic modeling approach is developed for robust simulation, validation and operation optimization of ICSTUN. A mixed-integer nonlinear programming (MINLP) model based on an improved modeling principle of complex turbine is formulated for the operation optimization of ICSTUN. The MINLP model is able to simultaneously optimize the external and internal utility networks. A solving strategy incorporating model decomposition, model relaxation and case initialization is proposed to find a feasible solution. A large utility network in a petrochemical complex is studied to test the accuracy of the complex turbine model and the effectiveness of the proposed operation optimization methodology. The validation results of the complex turbine model show that the mean relative error between field operation data and simulation data is less than 1.18% and is acceptable in engineering application. The optimization of a single utility plant yields a maximum coal reduction rate of 4.59%, and the optimization of the total utility system consisting of two utility plants yields a maximum coal reduction rate of 6.01%. The optimization contribution is discussed in detail, and several conclusions and suggestions are presented. [ABSTRACT FROM AUTHOR]

The research presented here is focused on improving energy management in a building complex through analytical and empirical modelling of its utilities. First, we introduce current European policy on energy savings in buildings. The modelling starts with a literature review and a thorough study on a heating and cooling system of a particular building complex-the National Theatre in Prague, Czech Republic. Standard building automation and control systems cannot optimize the building's operations to the fullest and thus do not provide the best cost savings possible. A mathematical model of the energy system and its integration into a building control system is an essential prerequisite for any optimization here. The development of a model which can be integrated into a control system during real-time operation of the building is a very complicated task. Our paper presents a procedure to develop such a model and methods to apply it in a real-life operation. First, the mathematical model is implemented in a simulation tool, which enables an efficiency evaluation of the system. This simulation tool offers especially important support for building automation and control systems when deciding the most effective operation of heat or cold utilities. The model greatly helps in monitoring and optimizing daily offtake limits for natural gas, which is highly appreciated by the building's technical management. Our practical applications of the model show new possibilities for simulation and optimization calculations which are completely unique in building management systems so far. [ABSTRACT FROM AUTHOR]

TSA (total site analysis) has shown to be an efficient tool for identifying heat integration measures in industrial clusters, leading to the optimal design of utility systems and energy bill reduction. In order to justify investments, any proposed utility system must be shown to be able to operate in all configurations that an industrial cluster can encounter, especially those relating to varying heat demand. Previous TSAs have generally been carried out using yearly means of heat exchange loads or using scenarios corresponding to specific operation modes of the sites. While these have been useful for designing systems under normal conditions, they are not fit for evaluating minima and peaks in utility demand. Carrying out a TSA on each possible configuration of a cluster is not feasible from a computational and results analysis point of view. A method is therefore proposed to represent the variability of data over long periods in a reduced form in order to carry out engineering studies. A methodology is proposed to identify typical operating periods of an industrial cluster made up of several production units. This algorithm exploits a multi-objective optimisation to identify n periods that delimit typical operating modes or multiple profiles. A TSA was previously carried out on the Stenungsund petrochemical cluster in Sweden, leading to the design of a utility system to significantly reduce the overall energy consumption of the cluster. The solution proposes that a common utility system would decrease the hot utility demand from 124 MW th to 70 MW th . The multi-period analysis methodology is demonstrated by application to this case study in order to identify the resilience of the proposed solution when faced with variations in heat production and consumption. The multi-period analysis of the proposed utility system leads to the identification of a peak utility demand of 88 MW th rather than the previously identified 70 MW th . A Total Site Sensitivity Analysis leads to a better understanding of the contribution of each of the clusters units and feasibility of investments. [ABSTRACT FROM AUTHOR]