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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]

This paper deals with the optimal operation management of the distribution feeder reconfiguration (DFR) considering the uncertainty effects. In contrast to the conventional objective functions, this paper considers the System Average Interruption Frequency Index (SAIFI) as a reliability index. Meanwhile, the total active power losses and the voltage deviation objective functions are considered as the other targets too. In order to make the analysis more practical, the uncertainty associated with the active and reactive load forecast errors are modeled in a stochastic framework based on 2 m Point Estimate Method (PEM). In the proposed stochastic optimization framework, an external memory called repository is defined to store the non-dominated solutions which are found during the optimization process. Also, a fuzzy based clustering technique is defined to keep the size of the repository within the predefined limits. Since the proposed problem is a nonlinear, discrete complex optimization problem, this paper proposes an intelligent self adaptive modified optimization algorithm based on θ-firefly algorithm to solve the optimal multi-objective stochastic DFR problem suitably. The proposed self-adaptive modification method consists of three sub-modification techniques which let each firefly choose the sub-modification method that best suits its situation adaptively. The feasibility and superiority of the proposed method is tested on a standard IEEE test system. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new probabilistic framework based on 2m Point Estimate Method (2m PEM) to consider the uncertainties in the optimal energy management of the Micro Girds (MGs) including different renewable power sources like Photovoltaics (PVs), Wind Turbine (WT), Micro Turbine (MT), Fuel Cell (FC) as well as storage devices. The proposed probabilistic framework requires 2m runs of the deterministic framework to consider the uncertainty of m uncertain variables in the terms of the first three moments of the relevant probability density functions. Therefore, the uncertainty regarding the load demand forecasting error, grid bid changes and WT and PV output power variations are considered concurrently. Investigating the MG problem with uncertainty in a 24 h time interval with several equality and inequality constraints requires a powerful optimization technique which could escape from the local optima as well as premature convergence. Consequently, a novel self adaptive optimization algorithm based on θ-Particle Swarm Optimization (θ-PSO) algorithm is proposed to explore the total search space globally. The θ-PSO algorithm uses the phase angle vectors to update the velocity/position of particles such that faster and more stable convergence is achieved. In addition, the proposed self adaptive modification method consists of three sub-modification methods which will let the particles choosel the modification method which best fits their current situation. The feasibility and satisfying performance of the proposed method is tested on a typical grid-connected MG as the case study. [Copyright &y& Elsevier]

Technically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes. [ABSTRACT FROM AUTHOR]

This article proposes a novel stochastic framework based on point estimate method (PEM) and firefly algorithm (FA) for the optimal operation of plug-in electric vehicles (PVs) in the distribution systems. The stochastic problem will consider the uncertainties of active and reactive loads, the number of PVs in the fleets and the departure and arrival time at charging locations. According to the high complexity and severity of the problem, a new version of FA called θ-FA is used to search the solutions in the polar space instead of the traditional Cartesian space. The objective function is to minimize the total cost of the system considering the operation and security constraints. The feasibility and high performance of the propose problem are examined on a standard IEEE test system. [ABSTRACT FROM AUTHOR]

This paper suggests a new self-adaptive modification method using firefly algorithm (FA) to investigate the multi-objective probabilistic distribution feeder reconfiguration problem. In this regard, the idea of phase angle vector is employed to replace the traditional Cartesian framework in the FA and thus called θ-FA. Also, a new modification method based on an adaptive mechanism is suggested that will allow each firefly to choose the appropriate modification technique during the optimization suitably. As regards the objective functions, the main focus of this paper is to assess the effect of the reconfiguration on the reliability indices including active power losses, voltage deviation, and system average interruption frequency index. In order to handle the uncertainty effects, a sufficient framework based on 2 m + 1 point estimate method is proposed too. The satisfying performance of the proposed method is checked using IEEE 32-bus radial distribution system. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive modification approach based on harmony search algorithm (HS) to solve the multi-objective environmental economic dispatch problem. The proposed algorithm makes use of adaptive formulations to update the adjusting parameters of HS including the pitch adjusting and bandwidth parameters and the harmony memory consideration rate during the optimization process. Meanwhile, a useful modification is proposed to improve the variety of the harmony population effectively. In order to handle both the cost and emission objective functions, the ideas of trapezoidal fuzzy membership function and weighting factor are employed. The satisfying performance of the proposed method is examined through the IEEE standard test system. [ABSTRACT FROM AUTHOR]

Abstract: This paper assesses the operation and management of electrical energy, hydrogen production and thermal load supplement by the Fuel Cell Power Plants (FCPP) in the distribution systems with regard to the uncertainties which exist in the load demand as well as the price of buying natural gas for FCPPs, fuel cost for residential loads, tariff for purchasing electricity, tariff for selling electricity, hydrogen selling price, operation and maintenance cost and the price of purchasing power from the grid. Therefore, a new modified multi-objective optimization algorithm called Teacher-Learning Algorithm (TLA) is proposed to integrate the optimal operation management of Proton Exchange Membrane FCPPs (PEM-FCPPs) and the optimal configuration of the system through an economic model of the PEM-FCPP. In order to improve the total ability of TLA for global search and exploration, a new modification process is suggested such that the algorithm will search the total search space globally. Also, regarding the uncertainties of the new complicated power systems, in this paper for the first time, the DFR problem is investigated in a stochastic environment by the use of probabilistic load flow technique based on Point Estimate Method (PEM). In order to see the feasibility and the superiority of the proposed method, a standard test system is investigated as the case study. The simulation results are investigated in four different scenarios to show the effect of hydrogen production and thermal recovery more evidently. [Copyright &y& Elsevier]

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]

According to the significance of power load demand forecasting, this paper suggests a new hybrid method to reach more accurate model with fast response. The proposed model consists of two algorithms: Self Adaptive Modified Bat Algorithm (SAMBA) and Artificial Neural Network (ANN). In recent years, SAMBA has been used as a powerful tool in the optimization problems. On the other hand among the most popular methods, ANN has shown powerful performance in load prediction as the result of its ability to detect nonlinear mappings among different variables. In addition, the special ability of SAMBA in fast convergence, its low dependency to setting parameters and simple implementation make this algorithm more premiere than the other optimization algorithms. Therefore, in this paper for the first time we use SAMBA to regulate the weight matrix of ANN and optimize the degree of uncertainty which exist in load demand prediction. [ABSTRACT FROM AUTHOR]