English title

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The annals of history are replete challenges over which feelings have been widely ambivalent. Beyond any shadow of doubt clean and reasonable power production based on environmental problems and lack of fossil fuel sources is one of them. This essay takes an in-depth look at the result of an output simulation of a multi sources hybrid power system includes Wind, PV and Diesel generators to show the effectiveness of the proposed method. In this structure a battery bank used to save the excessive power generation by Wind and PV generators and the Diesel generator plays the role of supporter to enhance the network reliability. All of the shown results have been calculated by Genetic Algorithm-based developed software so called HPSD which abbreviates for Hybrid Power System Designer.

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This paper presents a framework for long term transmission expansion planning in power pool competitive electricity markets. In the proposed approach, maximization of network users’ benefits with satisfying of security constraints is considered for determination of transmission expansion strategy. The proposed model is a complicated non-linear mixed-integer optimization problem. A hybrid genetic-based algorithm and Quadratic Programming (QP) is used for optimization procedure. The discrete decision-making variables of the expansion plan are optimized by genetic algorithm, while QP optimizes the continuous ones. For illustration of the approach capabilities, many test results on IEEE-RTS is presented.

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This paper presents a multi-objective formulation for optimal siting and sizing of distributed generation (DG) resources in distribution systems in order to minimize the cost of power losses and energy not supplied. The implemented technique is based on a genetic algorithm (GA) and weight method that employed to obtain the best compromise between these costs. Simulation results on 33-bus distribution test system are presented to demonstrate the effectiveness of the proposed procedure.

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Utilization of combined heat and power (CHP) systems that simultaneously fulfill the electrical and heating energy
needs is considered as an effective strategy for management of energy demand and consumption. Overall
operational efficiency in excess of 70% is made possible, when these systems are utilized, and it is expected that
needs for both the demand and supply sides are met even under the condition of unsubsidized energy resources. In Iran, where there are energy subsidies for fossil fuel and electricity, utilization of CHP systems requires proper modeling and selection of system components. Due to the large fluctuations in the electrical and heating load during various heating and cooling seasons, it is necessary to investigate the performance of a CHP system so that the optimal operating point is determined. The review of literature shows that the majority of these systems are studies without the means for thermal energy storage. In this study, the objective is the determination of optimal operational point for a CHP System with thermal energy storage tank utilizing evolutionary programming methodology. The findings are compared with the case of a conventional alternative, where the electrical energy is purchased from local utility company and a heating energy is furnished by a conventional boiler. Based on the current utility costs, the results show that the operating cost of a CHP system is nearly 50% lower than that of the conventional alternative. Also, it is shown that the use of absorption chiller reduces dependency on purchased electricity and increases the efficiency of the CHP system.

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In this paper, design of optimal control for the UPFC controllers including power-flow, DC voltage regulator and generator terminals voltage controller are presented. The controllers were designed based on GA optimization method for linearized modified Phillips Heffron model of SMIB in state space form. For nominal operating condition, the eigen-values of the system were obtained which clearly indicated the system was unstable. Therefore a damping controller was designed based on lead-lag compensation to improve the stability. After achieving the required stability, the power-flow, DC voltage regulator and generator terminals voltage controller were designed. Validity of the proposed method was confirmed by time domain simulation results.

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Generation expansion planning (GEP) in the competitive environment of electric industry is an important and complex problem, which is normally performed for about the next 10-30 years horizon. This problem has presented a challenge for both market managers and suppliers regarding the safe provision of loads and acquiring minimum profit for suppliers over the planning interval. In this paper, the aim is to establish dynamic balance between energy supply and demand by adjustment and optimal design of GEP over the horizon of study such that the expected profit to be provided for generating plants. To do so, in this paper the uncertainties of demand and system capacity have been modelled through two stochastic processes. In addition, the market price dynamics and their mutual effects on the GEP are considered. Based on this model, the intention of private sectors and their influences on the obtained results are observable. This nonlinear dynamic problem is solved using an optimization method based on genetic algorithm. The efficiency and ability of the proposed method is examined on a test power system.

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This paper proposes an efficient and novel method for transmission expansion planning in regulated environment of power systems. The method is based on combination of two algorithms such as special genetic algorithm (GA) and constructive heuristic algorithm. The proposed GA has its own special characteristics that make it better than other metahuristic methods in transmission expansion planning problems. The improvement phase is the main characteristic that makes this type of GA more efficient. It means that if after mating procedure (selection, crossover and mutation), the offspring was an infeasible one, by using a suitable constructive heuristic algorithm, and the aforementioned offspring is changed to a feasible one. Also by using a relaxed linear model of TEP and importing cost perturbation, a qualified initial population is obtained and used as an initial population of GA. The simulation results obtained from the proposed method were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time.

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The simultaneous scheduling of energy and primary reserve is one of most important recent researches. However, in these researches, operation constraints and transmission limitations have been neglected. So it is possible that the power flow results be infeasible since some real constraints are not considered. In this paper congestion constraint in transmission lines, technical capabilities and real constraints of modern generating units are considered in modeling of optimization problem. Congestion constraint is applied using DC power flow. Optimization problem is solved using a novel method based on binary genetic algorithm. Finally, the proposed method has been implemented on IEEE-39 bus case system. The obtained results verify the efficiency of proposed method. In this survey, it is assumed that the market structure is pay as bid.

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Encourage people to investment in small-scale electricity generation and expansion of dispersed generation (DG) may have several advantages such as, reducing the needs for investment in power plants and transmission network developments, improving the competitiveness of electricity market and moderating the costs of electrical energy procurement. In this paper, an encouraging market rule in a pay-as-bid system is proposed to accelerate the appropriate investments in DG units. The proposed rule is viewed and analyzed from the perspective of DG investor and Independent System Operator (ISO) using a novel DG placement and sizing algorithm based on Monte Carlo and Genetic Algorithm (GA). The proposed algorithm is implemented on IEEE 30-bus test system under the encouraging rule and the simulation results are presented and discussed.

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In a daily power market, price and load forecasting is the most important signal for the market participants. In this paper, an accurate feed-forward neural network model with a genetic optimization levenberg-marquardt back propagation (LMBP) training algorithm is employed for short-term nodal congestion price forecasting in different zones of a large-scale power market. The use of genetic algorithms for neural network training optimization has had a remarkable effect on the accuracy of price forecasting in a large-scale power market. The necessary data for neural network training are obtained by solving optimal power flow equations that take into account all effective constraints at any hour of the day in a single month. The structure of the neural network has two input signals of active and reactive powers for every load busbar in every hour of the programming model. These two signals are always available. In this study, an IEEE 118-bus power system is to test the proposed method authenticity. This system is divided into three zones, and a neural network with a genetic algorithm training optimization is employed for every zone. Simulation results show the ability of the proposed method in forecasting the nodal congestion price and its severity in a large-scale power market with a rather low and acceptable error, especially at points of price spikes.  

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Distributed generations that are connected to the network via a converter, employ dq current control method to control their active and reactive power components in grid-connected mode. In this paper a simple lead-lag control strategy is proposed for a distributed generation (DG) unit in island mode. When it is connected to the utility grid, the DG is controlled by a conventional dq-current control strategy for active and reactive power components. Once the islanding occurs, the dq-current controller is disabled and the proposed controller is used in order to for control of the DG unit operating with its local load. The problem of tuning of controller parameters is converted to an optimization problem with a time-domain objective function which is solved by a genetic algorithm. To achieve a robust performance ITAE criterion is used as objective function. The robustness of controller is proved by zero-pole diagram and frequency domain analysis. Simulations results under different operating conditions verify robustness of controller in comparison with a classical d-q based controller. The results reveal that the proposed control strategy has an excellent capability in achieving good robust performance and greatly enhances the dynamic stability of the system against load parameter uncertainties.

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The application of Flexible AC Transmission System (FACTS) devices shows that they can control the power system technical parameters effectively. However, optimal placement and sizing of them are difficult problems and analytical methods cannot be used to solve these problems. To solve such problems, some evolutionary algorithms are employed. The efficiency of these algorithms is related to their objective function. In this paper, two multi objective optimization algorithms, namely: Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) are used to solve the congestion problem in power system. To verify our proposed method, simulations are performed to modify IEEE 14-bus and modified IEEE 30-bus test systems. Firstly, load of the system is increased uniformly until congestion occurs. Then, optimization algorithms are used to find out the optimal location and size of FACTS devices, as well as the optimal amount of changes in active power generation. Two types of FACTS devices namely: Static VAr Compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC) are used for congestion elimination. The obtained results show the effectiveness of the proposed objective functions.

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Reducing the locking time or settling time is one of the major challenges in the design of Delay Locked Loop (DLL) based frequency synthesizer. In this paper a common structure for DLL based frequency synthesizer is considered in which the number of delay cells in the direct path is specified. Then, the designed delay locked loop is optimized using genetic algorithm (GA). GA changes the phase-voltage gain coefficients of the delay cells for achieving the best locking (settling) time.  Typical DLL with a reference frequency of 100 MHz and 8 delay cells is studied. Simulation results is shown the proposed structure is locked in 0.58 mu.

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A hybrid optimization algorithm based on genetic algorithm and choatic hyper spherical search method is proposed. In the proposed method, in order to increase the efficiency of searching the optimal solution, chaos theory along with  genetic operators have been used. This, not only makes the results of the proposed algorithm definite and decreases their standard deviation, but also resolves the weakness of the hyper spherical search optimization algorithm based on chaos theory including the speed of convergence and the weak performance in some benchmark functions. The simulation results on the standard benchmark functions show that the proposed algorithm not only has faster convergence, but also acts as a more accurate search algorithm to find the optimal solution in comparison to the standard hyper spherical search algorithm, chaotic hyper sherical search algorithm, and some other optimization algorithms such as genetic, particle swarm, and harmony search algorithm.

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During recent years, increment of incentives for deliberate subversive activities against power systems along with the restrictions on the allocation of financial resources for protection of these infrastructures have absorbed significant attentions of researchers toward the necessity of optimal allocation of these finances; and such the way, to minimize damages and consequently energy not supplied in case of future events. Planners of subversive activities as fully strategic actors target those low probability events which are no considered in protection schemes to maximize quantity of damages to power system. In this study, a new game theory based scheme is proposed in form of a zero sum playoff in order to provide an optimal allocation strategy. In this paper, two budget allocation algorithms have been presented to protect transmission lines and substations against deliberate threats. In the first algorithm, allocation of a certain amount of annual funding to transmission lines and substations is formulated with the aim of attaining the best possible condition of power grid in term of system reliability; while, required budget and procedure of allocation of this budget to targeted system utilities against undefined strategy of malicious individuals are calculated in second algorithm, in order to reach a predefined level of system reliability. Proposed model is implemented employing MATLAB (i.e. to execute single-objective Cuckoo optimization algorithm as well as multi-objective non-dominated sorting genetic algorithm II) and GAMS (i.e. to determine reliability of grid by use of power system load flow) software. Totally, optimized game strategy of protectors to acquire the best condition of system reliability has been obtained. Results validate effectiveness and applicability of proposed method in cases of optimality of allocation technique and subsequently increase of reliability indices.

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In this paper, the minimax optimization method has been proposed to design the shaped dual reflector antenna for being used in the geostationary satellite (GEO). For this purpose, the distortion on the reflector surface is considered with an expansion of B-spline functions and the coefficients of expansion are obtained using the minimax optimization method. In comparison with other optimization methods, which are based on evolutionary algorithms that prolong the antenna design, this is a gradient-based method and significantly accelerates the antenna design. In the minimax optimization method, there is a need for calculating the derivatives of the optimization fitness function compared to the problem variables. Thus, in this paper, the derivative of the fields calculated by physical optic have been analytically calculated compared to the reflector surface variables. A comparison of the simulation results and implementation time of two minimax methods with the genetic algorithm indicates the high efficiency and rate of minimax method.

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Sparse matrices appear in a number of problems related to science and engineering. The performance of algorithms designed for solving such problems depends significantly on the bandwidth of the problem matrix.  The bandwidth of a sparse symmetric matrix is the distance from the main diagonal beyond which all elements of the matrix are zero. Minimizing the bandwidth of a matrix is an NP-complete problem. Considering the importance of this problem, numerous algorithms have so far been presented for its solution among which metaheuristic algorithms have performed much better than other algorithms. The issue with using metaheuristic algorithms for addressing this problem is that the bandwidth size, which has been employed for comparing the quality of solutions produced by these algorithms in almost all previous studies, is not an appropriate measure, and therefore cannot direct the search process towards high-quality solutions. In this research, the above-mentioned issue is investigated, and a new approach is presented for dealing with it.  

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In this paper, a new method for locating fast electric charging stations with a regional perspective and taking into account the coefficient of interchange between regions is proposed. First, the city must be divided into specific areas, then, using the parameters of electric vehicle density, the required earth and social visibility of the region, the initial candidate points in each area are determined. Given land price and distance from the network substation as the main parameters, the location of the construction of the fast charging station is defined as an optimization problem and then solved by the genetic algorithm. In the proposed method, in addition to the location of the fast charging stations, the number of fast charging units (capacities) at each station is also determined. In the proposed algorithm, by defining the coefficient of exchange between adjacent regions, it is possible to relocate charging units so that the number of charge units can be flexible in proportion to the price of land in each area. The proposed method has been implemented for Mashhad metropolitan area with consideration of nine areas in accordance with the Mashhad power distribution company and the results of simulation for different scenarios are presented.
 

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In this paper, the economic load dispatch is investigated in order to minimize the operation cost in DC Microgrids. The operation cost includes the DG production cost and the grid electricity, while other parameters like component’s efficiency and demand response are considered in this study. In order to model the DC Microgrids, an electrical grid with wind and photovoltaic units, fuel cell and energy storage systems are considered connected to the main grid. This study is done in MATLAB and GAMS software, and the results are analyzed and compared in both cases. The results prove the suitability of both algorithms especially at peak hours. The voltage profile is within the limits for all cases. The economic load dispatch shows that energy storage systems are more applied in the optimization in MATLAB software; however, DGs are more applied in the optimization in GAMS software, while Microgrid is transferring more electricity with main grid.
 

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The process of empowering muscles in order to make them to a normal and common value is an expensive and prolonged work. There are some commercial exercise machines used for  this purpose called rehabilitation systems. However, these machines have limited use because of some reasons. In this paper, an algorithm and an improved rule are presented for controlling a rehabilitation system of lower limbs which is implemented on a 3-Degree Of Freedom  planar robot. Estimation and optimization of control parameters will be done by artificial neural networks and genetic algorithms, respectively (intelligent strategy). MATLAB Simulink is used for simulations. It shows that proposed algorithm in comparation with other similar methods is a low-cost method and needs to less energy and force with high accuracy.