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Plug-in Electric Vehicles (PEVs) will play significant role in the future smart grids. In this regard, the increasing appearance of PEVs can create new challenges in the optimal operation of these devices. In this way, this paper suggests a new method for optimal coordination of PEVs for reducing the total cost of the system during the day. The proposed method makes use of the idea of vehicle-to-grid (V2G) for shifting the energy demand in the grid. The problem is then formulated in an intelligent framework based on bat algorithm (BA) and Monte Carlo method to be solved optimally. Meanwhile, we suggest a new modification method for BA to improve its search ability for optimal coordination of PEVs. The proposed problem is examined on the IEEE test system with five PEV fleets. [ABSTRACT FROM AUTHOR]

Optimal Economic Dispatch (ED) is a precious and useful method for minimizing the total cost of power generation of thermal units. Since the optimal ED problem is a non-convex nonlinear constraint optimization problem, several methods have been proposed in the last years to solve it effectively. In this way, this paper suggests a new intelligent method based on Social Spider Optimization (SSO) algorithm to solve the ED problem globally. The SSO algorithm is constructed based on the cooperative behavior of social spiders in a colony and can solve both continuous and discontinuous problems. In order to improve the search ability of this algorithm, we also suggest the levy version of this algorithm called levy SSO or shortly LSSO. For making the problem more practical, valve-loading effect, prohibited zone, multi-fuel option and spinning reserve are considered as the constraints of the problem. The superiority and high stability of the proposed LSSO is demonstrated using the 6-bus, 10-bus and 40 bus IEEE test systems. [ABSTRACT FROM AUTHOR]

Investigating the impacts of ever-increasing wind power generation on power grids is an inseparable part of the modern power system analysis. Development of better wind-thermal coordination economic dispatch is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. Apart from intrinsic random nature of realistic power grids intermittent nature of wind energy has added more uncertainties in studying the power systems. In this way, finding efficient probabilistic methods to inspect these uncertainties can play an important role to comfort studies. Therefore, this paper presents a novel optimization method based on Shuffled Frog Leaping Algorithm (SFLA) to cope with large non-convex Economic Dispatch (ED) problems while there are uncertainties in the network due to wind turbines. The proposed Probabilistic ED (PED) formulation includes ramp rate limits, Prohibited Operating Zones (POZs), system spinning reserve, valve loading effects, and multiple fuel options to provide a realistic model. The PED is solved based on an approach using Point Estimate Method (PEM) to model the uncertainty in wind speed. The proposed algorithm is successfully examined on different test systems and compared with several of the most recently published ED techniques. [ABSTRACT FROM AUTHOR]

In this paper, a novel two-stage modification firefly Optimization algorithm (MFA) is proposed for the placement of Proton Exchange Membrane Fuel Cell Power Plants (PEM-FCPP) in distribution systems. FCPPs, as Distributed Generation (DG) units, can be taken into account as combined sources of Heat, Power, and Hydrogen (CHPH). The overall system efficiency can be improved by CHPH operation of FCPPs. Also, it will produce hydrogen which can be stored for the future use of FCPPs or can be sold to make a profit. The objective functions under investigation consist of the operating costs of electrical energy generation of distribution substations and FCPPs, the voltage deviation and the total emission. In this paper, only the placement of CHPH FCPPs is considered while investment cost of devices is not considered. Owing to the different, non-commensurable and nonlinear objectives, using conventional single-objective approaches makes difficulties to solve the problem. Other than that, the placement of FCPP in distribution systems is a mixed integer problem. Therefore, in order to overcome these problems, the firefly algorithm is utilized. In addition, a new two-stage modification is employed to search the problem area globally. Due to the multi-objective characteristic of the optimization, implementing the proposed MFA leads to obtain a set of solution in the problem instead of only one. The proposed algorithm is tested on a 69-bus distribution system to validate the effectiveness and impressiveness of the approach. [ABSTRACT FROM AUTHOR]

This paper deals with the stochastic placement and sizing of the distribution static compensator in the distribution systems considering uncertainty. The proposed stochastic framework utilises the point estimate method to consider the forecasting uncertainty of the active and reactive loads in the load flow equations. The objective functions to be investigated are total active power losses and the voltage profile simultaneously. In order to reach a proper compromise to satisfy all the objective functions, an interactive fuzzy satisfying approach is employed. Also, since the problem investigated is a type of discrete, nonlinear, multi-objective optimisation problem, a new modified optimisation technique is proposed to escape from the local optima as well as the premature convergence. Finally, the satisfying performance of the proposed method is examined on the 86-bus IEEE distribution system. [ABSTRACT FROM PUBLISHER]

This paper develops a new framework to solve the practical non-convex economic dispatch problem with regard to the constraints including ramp rate limits, valve loading effect, prohibited operating zone, spinning reserve and multi-fuel option. In the proposed framework, bat algorithm, as a new optimization algorithm, is employed to explore the problem search space globally. In addition, a newly introduced adaptive modification method is developed to improve the variety of the bat population when increasing the convergence speed simultaneously. This modification is constructed based on the roulette wheel mechanism in conjunction with the crossover and mutation operators from the genetic algorithm. At the end of paper, the superiority of the proposed framework is examined using the IEEE 10-unit and 40-unit test systems. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic structure is described to model the uncertainty effect of the active and reactive loads in the DSTATCOM allocation and sizing problem. The proposed technique has 2m+1 point estimate method (PEM) to trap the uncertainty associated with the expected error of the loads. The objectives are minimization of the total active power losses which diminish the voltage deviation of the buses. Other than that, a new optimization algorithm based on the bat algorithm (BA) is presented to search the problem space globally. Also, the idea of interactive fuzzy satisfying method is applied in the multi-objective formulation to provide a proper balance between the optimization of the objective functions. In the end, the proposed method is tested on the 69-bus IEEE distribution system to validate its feasibility and effective performance. [ABSTRACT FROM AUTHOR]

Optimal capacitor placement problem is a significant and precious strategy which can be supposed as an always appropriate solution to reduce the operating costs in the power systems. Nevertheless, as the result of nonlinear and discrete characteristics of this problem, traditional optimization methods can not solve it effectively. Therefore, this paper proposes a novel optimization framework based on shuffled frog leaping algorithm (SFLA) to solve the optimal capacitor placement and sizing problem properly. In this regard, a new modification approach based on the mutation and crossover operators is suggested to improve the local search ability of this algorithm. The effectiveness and satisfying performance of the proposed method is assessed on two IEEE test systems. Comparative results sow the superiority of the proposed optimization method than the other popular methods in the area. [ABSTRACT FROM AUTHOR]

In the new competitive electricity markets, the necessity of appropriate load forecasting tools for accurate scheduling is completely evident. The model which is utilised for the forecasting purposes determines how much the forecasted results would be dependable. In this regard, this paper proposes a new hybrid forecasting method based on the wavelet transform, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) for short-term load forecasting. In the proposed model, the autocorrelation function and the partial autocorrelation function are utilised to see the stationary or non-stationary behaviour of the load time series. Then, by the use of Akaike information criterion, the appropriate order of the ARIMA model is found. Now, the ARIMA model would capture the linear component of the load time series and the residuals would contain only the nonlinear components. The nonlinear part would be decomposed by the discrete wavelet transform into its sub-frequencies. Several ANNs are applied to the details and approximation components of the residuals signal to predict the future load sample. Finally, the outputs of the ARIMA and ANNs are summed. The empirical results show that the proposed hybrid method can improve the load forecasting accuracy suitably. [ABSTRACT FROM PUBLISHER]