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The sizing of microgrids depends on the type of load and its operational hours. The significance of understanding the load operational characteristics in special purpose islanded microgrids is much needed for economic system sizing. The load operation of special-purpose microgrids often consumes high power for a short duration and remains idle most of the time, thus reducing the load factor. The inclusion of such loads in microgrid sizing causes huge capital costs making islanded microgrids an unfeasible solution. The islanded microgrid under study is an agricultural microgrid in a village having a small Crab Processing Plant (CPP) and a Domestic Sector (DS). The CPP constitutes the major power consumption. The community has a unique load consumption trend that is dependent on the highly uncertain parameter of availability of the crabs. Interestingly, crab availability is an independent parameter and cannot be accurately scheduled. The existing system sizing of the microgrid is performed based on the conventional methods that consider the CPP for full-day operation. However, the microgrid sources, especially the storage system may be reflected as oversized if the crab processing plants do not operate for several days due to the uncertain behavior of CPP causing enormous power wastage. In this paper, an integrated fixed and operational mode strategy for uncertain heavy loads is formulated. The proposed algorithm is based on the optimal sizing methodology aided by the load scheduling control strategy. The Particle Swarm Optimization technique is used for the optimal sizing integrated with the fuzzy logic controller to manage the available load. The membership functions are available excess power and the state of the charge of storage that defines the operational conditions for CPP. Based on input membership functions, the fuzzy controller decides on power dispatch in DS or CPP, keeping considerable SoC available for night hours. The simulation result shows that the time-dependent fuzzy controller approach manages to provide power to both sectors under optimal sizing while reducing the overall cost by 24% less than the existing microgrid. [ABSTRACT FROM AUTHOR]

This paper presents the application of multi-objective genetic algorithms for holistic building design that considers multiple criteria; building energy use, capital cost, daylight distribution and thermal indoor environment. The optimization focus is related to building envelope parameters. To obtain relevant feedback from multi-objective optimizations in early design stages, evaluation speed is a key concern. The paper presents a fast evaluation method fit for the early design stages. It uses a combination of two different quasi-steady-state methods for energy and indoor environment evaluations, a Radiance implementation for daylight simulations and a scripted algorithm for capital cost evaluations. The application of the method is developed around an integrated dynamic model which allows visual design feedback from all evaluations to be an integrated part of the design tool experience. It is concluded, that quasi-steady-state methods implemented as part of integrated dynamic models are fast and flexible enough to support building energy-, indoor environment- and cost-optimization the early design stages. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of distributing empty freight cars in a railway company. We describe and analyze the current planning process, identify the shortcomings of the process, and stress the importance of a reliable distribution process for satisfying customer demand and reducing capital costs. We show how the process can be improved using an optimization model which includes capacity constraints on the trains and adheres explicitly to the arrival and departure times of the trains. The optimization model can be characterized as a multicommodity network flow model with integer requirements. Computational tests show that the model can be solved in acceptable time for real size problems, and indicate that the model generates distribution plans that can improve the quality of the planning process. In this paper we consider the problem of distributing empty freight cars in a railway company. We describe and analyze the current planning process, identify the shortcomings of the process, and stress the importance of a reliable distribution process for satisfying customer demand and reducing capital costs. We show how the process can be improved using an optimization model which includes capacity constraints on the trains and adheres explicitly to the arrival and departure times of the trains. The optimization model can be characterized as a multicommodity network flow model with integer requirements. Computational tests show that the model can be solved in acceptable time for real size problems, and indicate that the model generates distribution plans that can improve the quality of the planning process. [ABSTRACT FROM AUTHOR]

Dividing wall columns (DWCs) are able to reduce operating costs and save capital costs for distillation columns, which verifies that DWC is a useful strategy in terms of distillation process intensification. DWCs are better choices than the corresponding conventional distillation sequences from both economical and environmental point of views. As DWC saves energy considerably and reduces CO 2 emissions, it proves to be a breakthrough towards sustainable distilling. The four-product Kaibel DWC can further intensify the distillation process, and save energy cost about 40%. However, due to the complicated structures and strong interactions, the optimal design of the four-product Kaibel DWC is challenging to solve through conventional optimization methods. Therefore, this paper investigates the applicability of probabilistic global optimization algorithms, including standard particle swarm optimization (PSO), chaotic PSO, cellular PSO, and quantum-behaved PSO algorithms. This paper aims to explore the most appropriate improved PSO algorithm for the Kaibel DWC. The standard PSO and chaotic PSO show the premature convergence in the optimization process, while the cellular PSO and quantum-behaved PSO can prevent premature convergence. The chaotic PSO algorithm is more appropriate for the optimization of the continuous function, and it is less suitable for the optimization of the Kaibel DWC. The quantum-behaved PSO algorithm is relatively not appropriate for the optimization of the Kaibel DWC because of the long calculation time for rigorous simulation. The cellular PSO algorithm, based on the concept of cellular neighborhood, is the most appropriate algorithm for the optimization of the Kaibel DWC. • The applicability of improved particle swarm optimization (PSO) methods are studied. • The standard PSO, chaotic PSO, cellular PSO, and quantum-behaved PSO are compared. • The cellular PSO is the most suitable method for the Kaibel dividing wall column. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a thermoeconomic analysis and optimization of micro gas turbines (MGT) up to 500kWe is presented. This analysis is strongly related to the need of minimizing specific capital cost, still high for MGT large market penetration, and optimizing MGT size to match market needs. The analysis was carried out for both existing regenerative MGT cycles and new inter-cooled regenerative cycles, using the Web-based ThermoEconomic Modular Program by the University of Genoa. The attention is mainly focused on the basis of thermodynamic, geometric and capital cost parameters of the main MGT devices (such as recuperator size, material and effectiveness, turbine inlet temperature, and compressor pressure ratio) and on economic scenario (fuel cost, cost of electricity, etc.) for different MGT size in the range 25–500kWe. [Copyright &y& Elsevier]

Abstract: In order to gain an extra increment of efficiency to compensate for capital costs, one of the main issues in the design of advanced supercritical power plants is the reduction of boiler exit gas temperature below typical values of conventional, subcritical units. Currently, the use of heat exchange surfaces made of plastic has become feasible, thereby avoiding corrosion and fouling problems derived from cold-end acid condensate. In this manner, flue gas temperature can be reduced down to typically 90°C, which obviously leads to an increase of boiler efficiency. Besides, there is an additional energy available for heating the main condensate flow of the power cycle. If modification of air–gas rotary heaters is also considered, a manifold of possibilities opens up for plant optimization and integration of components. The objective of this paper is to analyze this class of schemes for increasing power output and net efficiency of a reference supercritical plant. A complete simulation of the steam cycle is assembled using Aspen Plus and different plant configurations are examined under reduced exit gas temperatures. Several uses of flue gas energy are considered, taking into account limits of temperature and realistic efficiencies of heat exchangers. Mass flow rates, point of extraction of condensate, pressures and temperatures are selected heuristically to optimize performance. Finally, required exchange areas are estimated, and a cost analysis is carried out in order to economically assess the new configurations and estimate the additional profit for the plant. [Copyright &y& Elsevier]

Water distribution network is a costly infrastructure and plays a crucial role in supplying water for the consumers especially for those who are living in the urban areas. The importance and huge capital cost of the system leads to considerable attention on seeking the optimal cost design. The necessity for such a sound research attention arises from the complexity associated with the problem. In the recent years, stochastic optimization algorithms like genetic algorithm, simulated annealing, ant colony optimization etc. are found to be successful in exploring the optimal combination of pipe diameters that can satisfy the hydraulic-head requirements with least cost. In this paper, the details on the optimal water distribution network design with a novel technique called honey-bee mating optimization and its validation with two benchmark water distribution networks are presented. From the results, it is observed that the proposed algorithm identifies the optimal solution with relatively less number of evaluations than the other well-established stochastic optimization algorithms. [ABSTRACT FROM AUTHOR]