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With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy interval optimization approach to solve the Environmental/ Economic Dispatch (EED) problem with uncertain parameters in the constraints and the objective functions. The objective functions considered are fuel cost and the gaseous emissions of the generating units. Two different types of fuel cost functions are considered in this study, namely, the conventional quadratic function and the augmented quadratic function to introduce more accurate modeling that incorporates the valve loading effects. The latter model presents non-differentiable and nonconvex regions that challenge most gradient-based optimization algorithms. In the proposed approach, objective functions are fuzzified and integrated to represent the fuzzy decision value. On the other hand, load uncertainties are modeled using fuzzy intervals. This fuzzy EED problem formulation provides a modeling flexibility, relaxation in constraints and allows the method to seek a practical solution. The obtained fuzzy multi-objective optimization problem is solved using non-dominated sorting genetic algorithm-II, known for its global searching capabilities, to get the best compromise among all the objectives. The performance of this solution is examined and applied to the standard IEEE 30-bus six-generator test system and the Indian power network of 82-bus by comparing its results with that of the existing methods. Different cases with different complexities have been considered in the study reported in this paper. [ABSTRACT FROM AUTHOR]

Chhana cake, locally termed chhana podo, is a baked traditional dairy product of India. The present study was undertaken for optimization of process parameters pertaining to production of chhana podo. Independent variables, namely, moisture content of feed-mix: 52.5 - 62.5% (wb), baking temperature: 60 - 180°C, baking time: 1 - 9 h and height of feed-mix: 1 - 5 cm were selected heuristically and their effect on dependent variables, namely, hardness, whiteness index, yellowness index, tint of crust and crumb, moisture content and expansion ratio of chhana podo were studied. Although quadratic models fitted to responses exhibited relative deviation percent (Rd) ranging from 1.214 to 5.406%; lack of fit was significant for all responses except crust yellowness index and crust tint. Neural network modeling was adopted (Rd for training = 1.739%, Rd for validation = 1.845%) and relative importance of factors on responses were found. Optimum conditions obtained from genetic algorithm were: moisture content of feed-mix = 57.43% (wb), baking temperature = 151.4°C, baking time = 4.35 h, height of mix = 2.9 cm. [ABSTRACT FROM AUTHOR]

The optimal design of a groundwater remediation system using the pump-and-treat method is a complex task involving modeling physical phenomena such as groundwater flow and contaminant transport and optimizing several goals of concern, while satisfying bounds on certain parameters. Simulation tools such as the finite-element method (FEM) coupled with optimization tools such as the genetic algorithm (GA) has been found to be an efficient and easy to use methodology to solve such arduous problems. In this study, a simulation model using the FEM for groundwater flow and contaminant transport has been developed and coupled with a multiobjective optimization model based on the nondominated sorting genetic algorithm II (NSGA II). The model is used to minimize the cost optimization function as well as the time period for the remediation of the aquifer subject to bounds on pumping rates, groundwater heads, and concentration levels of the contaminant. The coupled FEM-NSGA II model has been applied for the remediation of a real field aquifer near Vadodara, India for a combination of flushing and pumping to demonstrate the effectiveness of the proposed multiobjective technique for achieving the optimal pumping policy. Total dissolved solids are considered to be the main pollutant. Three alternative remediation design scenarios with different pumping well locations have been compared and the best remediation design scenario has been identified to be the one that gives the best Pareto-optimal front. The FEM-NSGA II in general evolves a set of well-spread and consistent Pareto-optimal solutions, which represent the best designs that identify the trade-off between the expected cost and the time period of remediation for all the remediation design scenarios while satisfying the constraints, thus, giving the decision maker a wide set of choices. [ABSTRACT FROM AUTHOR]

In this paper a metaheuristic technique called Ant Colony Optimization (ACO) is proposed to derive operating policies for a multi-purpose reservoir system. Most of the real world problems often involve non-linear optimization in their solution with high dimensionality and large number of equality and inequality constraints. Often the conventional techniques fail to yield global optimal solutions. The recently proposed evolutionary algorithms are also facing problems, while solving large-scale problems. In this study, it is intended to test the usefulness of ACO in solving such type of problems. To formulate the ACO model for reservoir operation, the problem is approached by considering a finite time series of inflows, classifying the reservoir volume into several class intervals, and determining the reservoir release for each period with respect to a predefined optimality criterion. The ACO technique is applied to a case study of Hirakud reservoir, which is a multi-purpose reservoir system located in India. The multiple objectives comprise of minimizing flood risks, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The developed model is applied for monthly operation, and consists of two models viz., for short-time horizon operation and for long-time horizon operation. To evaluate the performance of ACO, the developed models are also solved using real coded Genetic Algorithm (GA). The results of the two models indicate that ACO model performs better, in terms of higher annual power production, while satisfying irrigation demands and flood control restrictions, compared to those obtained by GA. Finally it is found that ACO model outperforms GA model, especially in the case of long-time horizon reservoir operation. [ABSTRACT FROM AUTHOR]

This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multiobjective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem. [ABSTRACT FROM AUTHOR]