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A genetic algorithm (GA) is a stochastic direct search strategy that mimics the process of genetic evolution. The GA applied herein works on a population of structural designs at any one time, and uses a structured information exchange based on the principles of natural selection and wurvival of the fittest to recombine the most desirable features of the designs over a sequence of generations until the process converges to a "maximum fitness" design. Principles of genetics are adapted into a search procedure for structural optimization. The methods consist of three genetics operations mainly named selection, cross- over and mutation. In this study, a method of finding the optimum topology of truss/beam structure is pro- posed by using the GA. In order to use GA in the optimum topology problem, chromosomes to FEM elements are assigned, and a penalty function is used to include constraints into fitness function. The results show that the GA has the potential to be an effective tool for the optimal design of structures accounting for sizing, geometrical and topological variables.variables.

Power system under heavily loaded conditions is at high risks of consequent voltage instability problem. Voltage deviation minimization is the reliable indicators of voltage security of power networks. The aim is to minimize the voltage deviation and installation cost under increasing load condition in power system network. GA is used to solve the optimization problem in this paper which is one of the heuristic methods. Genetic Algorithm optimizes the location and size of the SVC. The effectiveness of the proposed work is tested in IEEE-30 Bus test system. It has also been observed that the proposed algorithm can be applied to larger systems and do not suffer with computational difficulties. The obtained results show that the allocation of SVC device with the proposed method considerably enhanced the voltage stability and reduce installation cost.

Flexible Alternating Current Transmission Systems (FACTS) represents a vast development in the area of power system operation and control. As we know that under heavily loaded conditions our power system is at high risks of consequent voltage instability problem. This paper gives an overview about application of series connected Flexible alternating current transmission system (FACTS) for improvement of power system performance like transfer stability, secure voltage profile and reduce the system losses etc. FACTS devices require huge capital investment. Therefore, heuristic techniques are used for optimal location and sizing of series FACTS controllers like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc. These techniques are used to solve the optimization problem. This paper gives details of optimal placement and sizing of FACTS devices based on different evolutionary techniques which is used for minimization of transmission loss, enhancement of stability of power system. In this study one of the FACTS devices is used as a scheme for enhancement of power system stability.Proper installation of FACTS devices also results in significant reduction of transmission loss. In this review,TCSC is selected as the compensation device.

Since solar, wind and tidal energies mainly depend upon statistical parameters with respect to changing climate and environment, focus on hybrid generation system design increases the availability of the power generation system. The hybrid system also reduces the dependence on one environment parameter thus providing the consumer with reliable and cheap electricity. Therefore optimal combination and sizing design of hybrid generation system considering the battery has a very important role in the use of renewable energy effectively and economically. Several approaches were developed to achieve the optimal configurations of the hybrid systems such as the least square method, the trade-off method and the probabilistic approach method. This study presents a method to design the optimal combination and unit sizing for new and renewable hybrid systems. The method is to find the optimal configuration and sizing among sets of system components, that meets the desired system requirements, with the lowest value of the energy cost. The availability of the methodology is demonstrated with the field data acquired from sets of experiments.

Suitable location and optimal sizing are impact on voltage stability margin of the distributed system. It is important to accurately simulate the random output active power of Distributed Generation (DG). In order to model uncertainties of intermittent distributed generation and load, this paper proposes a multi-scenario tree model of wind-photovoltaic-load using multiple scenarios technique based on the Wasserstein distance metrics, which generates asymptotically optimal scenario. And in this paper, a multi-objective optimizes control model with scenario tree is presented, which including objectives that are the total active power losses and the voltage deviations of the bus. Moreover, a new hybrid Honey Bee Mating Optimization and Particle Swarm Optimization (HBMO-PSO) algorithm is proposed to solved the problems. In the HBMO-PSO algorithm, the mating process is corrected, which the PSO algorithm is combined with the HBMO algorithm to improve the performance of HBMO. Finally, a typical IEEE 33-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method. Simulation results illustrate the correctness and adaptability of the proposed model and the improved algorithm.

This paper presents an approach for optimal sizing and sitting of distribution generation units in smart microgrid under pool electricity market to reduce total cost and power loss of whole smart microgrid. The costs comprise capital cost, replacement cost, operation and maintenance cost, fuel cost, reliability cost, power loss cost and selling and buying electricity cost. The new idea of this paper is the investigation of pool electricity market aspects in optimization of smart microgrid. On the other hand, cost minimization of smart microgrid is related to their bidding strategies. Therefore two different optimization tools are considered. First, a game-theoretical (GT) model has been used for bidding strategy of smart microgrid as a price-maker, in a long-term electricity market. Secondly, a particle swarm optimization (PSO) algorithm is employed to obtain the best cost value of smart microgrids construction. This study was performed for the Ekbatan residential complex in Tehran, Iran. It has three smart microgrids consist of renewable energy resources. They participate in a long-term electricity market as a price maker. The results show that the proposed method is more effective and has lower cost in finding optimum size and location of distribution generation in smart microgrids.

The paper proposes an optimal sizing method of a customer's Battery Energy Storage System(BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost(energy cost). The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost reduction rate(hereinafter reduction rate) curves via capacity of BESS. Case study uses real data from industry customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.

In a hybrid energy system, different energy sources (photovoltaic (PV), wind, diesel, etc.) as well as energy storage devices are connected together to supply the electrical load. Since the produced power of PV and wind turbine (WT) is dependent on the variation of the resources (sun and wind) and the load demand fluctuates, the main attribute of such hybrid systems is the ability of satisfying the load at any time and storing the excess energy for the later use in deficit conditions. This paper presents a methodology to size and to optimize a stand-alone hybrid PV/Wind/Diesel/Battery bank minimizing the Total annual cost and Loss of Power Supply Probability (LPSP) using a GA and PSO based optimization algorithm respectively. The effectiveness of the proposed method was verified by Matlab software.In this paper, first the mathematical model of various parts of hybrid system is presented. Then, the proposed algorithm is used. Finally, simulation results (number of PV panels, number of wind turbines, number of battery storages, system total cost,power diagram of hybrid power system components) for solar-wind -diesel systems is presented.The simulation results of the proposed approach show that the use of PSO can be more efficient than GA in the size optimization of hybrid energy system.

This paper deals with the joint decisions on pricing and ordering for a monopolistic retailer who sells perishable goods with a fixed lifetime or demand period. The newsvendor-typed problem is formulated as a two-period inventory system where the first period represents the inventory of fresh or new-arrival items and the second period represents the inventory of items that are older but still usable. Demand may be for either fresh items or for somewhat older items that exhibit physical decay or deterioration. The retailer is allowed to adjust the selling price of the deteriorated items in the second period, which stimulates demand and reduces excess season-end or stale inventory. This paper develops a stochastic dynamic programming model that solves the problem of preseason decisions on ordering-pricing and a within-season decision on markdown pricing. We also develop a fixed-price model as a benchmark against the dual-price dynamic model. To illustrate the effect of the dual-price policy on expected profit, we conduct a comparative study between the two models. Extension to a generalized multi-period model is also discussed.

This study aims at examining both internal and external effects of an imperfect-quality production system along with a pricing decision in practice. The problem investigated involves a production system that follows a non-deteriorating stable process, generating binomial yields, thereby passing defective items to customers and negatively affecting demand along with a firm’s pricing decision. Then customers return those defectives for exchange or refund, and the firm disposes them through rework, salvage, or scrap. For this previously unexplored problem of practical significance, we develop a nonlinear profit-maximization model of jointly determining production lot size, rework frequency, defective proportion and selling price. In solution, due to the mathematical complexity of the model in finding optimal closed-form solutions, we first reduce the objective function using differential calculus and then develop a nonlinear programming model to obtain global optimal solutions. We present a numerical example and conduct extensive sensitivity analyses to illustrate critical behaviors of model variables, in order to provide important managerial insights into practice in making comprehensive marketing and production decisions.

In this work, Self-adaptive Differential Evolutionary (SaDE) algorithm is proposed to solve Optimal Location and Size of Capacitor (OLSC) problem in radial distribution networks. To obtain the SaDE algorithm, two improvements have been applied on control parameters of mutation and crossover operators. To expand the study, three load conditions have been considered, i.e., constant, varying and effective loads. Objective function is introduced for the load conditions. The annual cost is fitness of problem, in addition to this cost, CPU time, voltage profile, active power loss and total installed capacitor banks and their related costs have been used for comparisons. To confirm the ability of each improvements of SaDE, the improvements are studied both in separate and simultaneous conditions. To verify the effectiveness of the proposed algorithm, it is tested on IEEE 10-bus and 34-bus radial distribution networks and compared with other approaches.

The presence of responsive loads in the promising active distribution networks (ADNs) would definitely affect the power system problems such as distributed generations (DGs) studies. Hence, an optimal procedure is proposed herein which takes into account the simultaneous placement of DGs and smart meters (SMs) in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Seeking to power loss minimization, the optimization procedure is tackled with genetic algorithm (GA) and tested thoroughly on 69-bus distribution test system. Different scenarios including variations in the number of DG units, adaptive power factor (APF) mode for DGs to support reactive power, and individual or simultaneous placing of DGs and SMs have been established and interrogated in depth. The obtained results certify the considerable effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the lowest value of power losses as well.

Social welfare maximization in a double-sided auction market is performed by implementing an aggregation-based particle swarm optimization (CAPSO) algorithm for optimal placement and sizing of one Static Synchronous Series Compensator (SSSC) device. Dallied simulation results (without/with line flow constraints and without/with SSSC) are generated to demonstrate the impact of SSSC on the congestion levels of the modified IEEE 14-bus test system. The proposed CAPSO algorithm employs conventional quadratic smooth and augmented quadratic nonsmooth generator cost curves with sine components to improve the accurate of the model by incorporating the valve loading effects. CAPSO also employs quadratic smooth consumer benefit functions. The proposed approach relies on particle swarm optimization to capture the near-optimal GenCos and DisCos, as well as the location and rating of SSSC while the Newton based load flow solution minimizes the mismatch equations. Simulation results of the proposed CAPSO algorithm are compared to solutions obtained by sequential quadratic programming (SQP) and a recently implemented Fuzzy based genetic algorithm (Fuzzy-GA). The main contributions are inclusion of customer benefit in the congestion management objective function, consideration of nonsmooth generator characteristics and the utilization of a coordinated aggregation-based PSO for locating/sizing of SSSC.

The problem of jointly determining a robust optimal bundle of price and order quantity for a retailer in a single-retailer, single supplier, single-product supply chain is considered. Demand is modeled as a decreasing power function of product price, and unit purchasing cost is modeled as a decreasing power function of order quantity and demand. Parameters defining the two power functions are uncertain but their possible values are characterized by ellipsoids. We extend a previous study in two ways; the purchasing cost function is generalized to take into account the economies of scale realized by higher product demand in addition to larger order quantity, and an exact transformation into an equivalent convex optimization program is developed instead of a geometric programming approximation scheme proposed in the previous study.

To address the increasing demand for clean electrical energy, renewable energy technologies that generate power for microgrids have become a promising solution. The number of distributed generators connected to microgrids have been dramatically increased. Microgrids were first proposed as a solution for islanded and isolated systems. However, microgrids have the ability to operate autonomously, unlike conventional power grids. In this paper, a control system is proposed for microgrids that use a photovoltaic (PV) energy source and batteries, which are responsible for energy storage. An energy management strategy (EMS) is provided to stabilize the direct current (DC) bus voltage in the case of photovoltaic power fluctuation or load variation. This paper also proposes the sizing of PV panels and batteries to give the number of batteries and photovoltaic panels connected in series and in parallel. This proposed sizing system is based on different parameters, such as the losses from the installation of batteries as well as the maximum and minimum state of charge. Simulation results demonstrates that the proposed EMS and sizing of photovoltaic panels and batteries is able to respond to load demands. A comparison of different configurations is also made to determinate the optimum technology in terms of low cost and small PV field surface.

As the reduction of greenhouse gases(GHGs) emission has become a global issue, the microgrid markets are growing rapidly. With the sudden changes in the market, Korean government suggested a new business model called 'Self-Sufficient Energy Islands'. Its main concern is a stand-alone microgrid composed of Distributed Energy Resources(DERs) such as Renewable Energy Sources(RESs), Energy Storage System(ESS) and Fuel Cell, in order to minimize the emission of GHGs. According to these trend, this paper is written to propose an optimal sizing method of DERs in a stand-alone microgrid by using Genetic Algorithm(GA), one of the representative stochastic methods. It is to minimize the net present cost with the variables, size of RESs and ESS. In the process for optimization, the sunless days are considered as additional constraints. Through the case study analysis, the size of DERs installed in a microgrid system has been computed using the proposed method in MATLAB. And the result of MATLAB is compared with that of HOMER(Hybrid Optimization of Multiple Energy Resources), a well-known energy modeling software.

Polymer electrolyte membrane (PEM) fuel cell stacks are constructed by stacking several to hundreds of unit cells depending on their power outputs required. Fuel and oxidant are distributed to each cell of a stack through so-called manifolds during its operation. In designing a stack, if the manifold sizes are too small, the fuel and oxidant would be maldistributed among the cells. On the contrary, the volume of the stack would be too large if the manifolds are oversized. In this study, we present a three-dimensional computational fluid dynamics (CFD) model with a geometrically simplified flow-field to optimize the size of the manifolds of a stack. The flow-field of the stack was simplified as a straight channel filled with porous media to reduce the number of computational meshes required for CFD simulations. Using the CFD model, we determined the size of the oxidant manifold of a 30 kW-class PEM fuel cell stack that comprises 99 cells. The stack with the optimal manifold size showed a quite uniform distribution of the cell voltages across the entire cells.