International Journal of Smart Electrical Engineering
Volume:2 Issue: 1, Winter 2013

Design and expansion of distribution systems seems inevitable in view of the need to satisfy the rise in energy consumption in a technical and economical way. Optimal location, sizing and determining the service area of substations is one of the principle problems in expansion of distribution systems. Also uncertainty is one of the important factors that increase risk of exact decision makings. This paper presents a fuzzy multi-objective model for HV/MV substations planning so that uncertainties are modeled using fuzzy numbers (trapezoidal form). The proposed fuzzy model is based on the risk of economic and technical objectives as well as fuzzy values of investment, operation and loss cost of the substations and primary feeders. This model determines the optimal time, location and size of substations using a multi-objective genetic algorithm (NSGA-II). The proposed model is applied on a typical distribution system to assess the efficiency of the approach.

Many real time processes have complex, uncertain and nonlinear dynamics. Boilers are nonlinear, time varying, multi-input multi-output (MIMO) systems, whose states generally vary with operating conditions. The major problem in controlling that system is that its drum water pressure and steam flow dynamics include an integrator that results a critically stable behavior. Conventional controller previously used have a set of limitations, e.g. empirical tuning of their parameters when the operating conditions of the controlled process are changed. The application of fuzzy control scheme which is compounded with classic controller (PID) may provide more effective and flexible control of boilers in power stations. This research employing fuzzy logic systems due to their transparency and nonlinear features for controlling dynamics, uncertain and highly nonlinear boiler systems and PID controller due to its fast response

Real-time pricing schemes make the customers to feel the energy price volatility and improve their load profiles. However, these schemes have no significant effect on demand-side uncertainty reduction. In this paper, considering smart grid infrastructures and smart building Energy Management System (EMS), a new real-time pricing scheme is presented to reduce the uncertainty of demand-side. In the proposed method, EMS announces its electric demand during each period of next day to the retailer. The price of energy for the pre-specified amounts is day-ahead price, but any deviation from this amount is settled through spot market price will be determined several minutes before the corresponding period by retailer. Numerical results of an illustrative example are implemented to demonstrate how this scheme makes motivation in customers to reduce their demand uncertainties

In the deregulated power systems, transmission congestion is one of the significant and main problems of the electrical networks which can cause incremental cost in the energy. This problem has resulted to new challenging issues in different parts of power systems which there was not in the traditional systems or at least had very little importance. Transmission congestion happens when the maximum available power transmission capacity is lower than the consumption side. As congestion happens, the system power losses are increased which can cause problem in the voltage constraints. Therefore, this paper proposes a new method to handle the optimal management and control of congestion problem by the use of distributed generations (DGs). In this regard, the optimal size and location of DGs are investigated using the powerful bacteria foraging algorithm (BFA) as a new intelligence-based optimization technique to solve the congestion problem on the three IEEE 14-bus, 30-bus and 57-bus test systems. The simulation results show the high speed, fast convergence and accurate performance of the proposed algorithm to solve the congestion problem in the system

The Networked Control System in modern control widely uses to decrease the implementation cost and increasing the performance. NCS in addition to its advantages is inevitable. Nevertheless they suffer of some limitations and deficiencies. Packet loss is one of the main limitations which affect the control system in different conditions and finally may lead to system instability. For this reason, it is important to model the system properly. In this paper, a new model has been proposed that is very simple and independent from the main system. This model based on Robust Theory structure. Robust theory is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. Robust control methods are designed to function properly so long as uncertain parameters or disturbances are within some (typically compact) set.

In this paper, we present a novel algorithm for VANET using cloud computing. We accomplish processing, routing and traffic control in a centralized and parallel way by adding one or more server to the network. Each car or node is considered a Client, in such a manner that routing, traffic control, getting information from client and data processing and storing are performed by one or more server in different bases. The procedure is about each client that receives its situation by GPS system and sends it online to the concerned server via GPRS networks. In order to perform routing and displaying data, each client sends a request to server. All processes and operations will be executable by the software which is inside the server. Finally, we propose an algorithm to work in server and make decision on route selection on the basis of three priorities of traffic, safety and shortest route. This algorithm is represented for the first time. The results indicate that by using this method VANET network is improved. By using this algorithm, we can communicate generally, not regionally, and improvement of processing and accurate statistics is achieved. In fact, the processing in the server is Fuzzy. It means that these priorities are Fuzzy.

In this paper smart operation of micro energy hub is presented. Energy hub system consists of certain energy hubs and interconnectors that are coordinated by energy hub system operator. An energy hub contains several converters and storages to serve demanded services in most efficient manner from available energy carriers. In this paper, the flexibility of energy delivery point is enhanced using micro energy hub concept. Smart micro energy hub is operated in minimum cost considering the price of input energy carriers, the amount of forecasted output energy carriers and the available facilities included in the hub. Regarding this matter, two micro energy hubs with different characteristics are modelled which are equipped with CHP unit, warmer, boiler and heat storage. The effectiveness of the proposed system is validated by running numerical study on a test system.

Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optimization uses a similar mechanism to solve the optimization problem. Usually the main difficulties of evolutionary algorithm for solving the optimization problem are: early convergence, loss of population diversity, and placing in a local minimum .Therefore, it needs the way that preserves the variation and tries to avoid trapping in local minimum. In this paper by combining ant colony algorithm and mutation hybrid algorithms that leads to the better solution for optimization of FPGA (Field Programmable Gate Array) placement problem is made. They are different types of swarm intelligence algorithm. After designing the algorithm, its parameters tuning have been done by solving several problems, and then the proposed methods have been compared with the other approaches. The results show that in most problems, the proposed hybrid method is able to obtain better solutions and makes fewer errors.