International Journal of Smart Electrical Engineering
Year:2015 | Volume:4 | Issue:4|Papers Count:6

This paper, presents a suitable control system to manage energy in distributed power generation system with a Battery Energy Storage Station and fuel cell. First, proper Dynamic Shape Modeling is prepared. Second, control system is proposed which is based on Classic Controller. This model is educated with Genetic Algorithm and particle swarm optimization. The proposed strategy is compared with Classic Controller. The robustness of suggested strategy in the front of nature of wind energy and Environmental conditions led to Parameter changes and load changes. It was investigated and simulated in MATLAB and the result was observed. As a result of simulation, control system has reflected a better behavior rather than load changes. The proposed methods are applied on various situations with actual climate data.

Electricity price predictions have become a major discussion on competitive market under deregulated power system. But, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. In this paper, a new forecast strategy based on the iterative neural network is proposed for Day-ahead price forecasting. For improved accuracy of prediction an intelligent two-stage feature selection is proposed here to remove the irrelevant and redundant inputs. In order to have a fast training the neural network normalization is vital, so in this paper the above technique is used. The proposed approach is examined in the Ontario electricity market and compared with some of the most recently published price forecast methods.

In this paper particle swarm optimization with smart inertia factor (PSO-SIF) algorithm is proposed to solve combined heat and power economic dispatch (CHPED) problem. The CHPED problem is one of the most important problems in power systems and is a challenging non-convex and non-linear optimization problem. The aim of solving CHPED problem is to determine optimal heat and power of generating units with the minimized cost of total system and satisfied constraints of problem. In proposed algorithm inertia coefficients are controlled with respect to cost function in each population. So, each population has unique inertia coefficient and as a result unique velocity in convergent direction for the best group solution. In order to examine the proposed algorithm's capabilities and find optimum solution for CHPED problem, two test systems considering valve-point effect, system power loss and system constraints are optimized. The obtained results demonstrate the superiority of the proposed method in solving non-convex CHPED problem over other new and efficient algorithms.

Since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. They dispatch their power energy generation units in the uncertain environment of power market. As a result, multi-stage stochastic programming has been applied widely by many power generating agents as a suitable tool for dealing with self-scheduling strategies under uncertainty. However, dependence structure between stochastic variables has been almost ignored in the literature. Copula function is a new concept in the probability and statistics field which has the capability to represent the dependence structure among stochastic variables. However, Copula function has recently taken into account in power system studies by some articles. In this article, self-scheduling strategy of a generation utility owning thermal units is investigated while the dependence structure among stochastic load and market price variables is taking into account. We assume that the generation utility is a price-taker agent in a power market, and it also has to meet the load of a specific region as a retailer. The results indicates that as the stochastic dependence structure among load and price variables is considered in modeling load and price scenarios, the output of unit commitment problem changes so that the revenue of generation utility increases.

Gas turbines play an important role in supplying power for the country especially in peak electricity load. The main disadvantages are the turbines, they produce large changes are a result of climate change. However, in times of peak electricity grid and at the same time warm months, can produce gas turbines under the effect of ambient temperature, the amount of reduced considerably. The methods adopted for the turbine compressor inlet air cooling and recovery of their power optimization is necessary. In this paper, different methods of cooling the inlet air to gas turbines for Fars power plants weather conditions and eventually introduced various schemes to install combined cycle gas turbine inlet air cooling system of the plant, good design, Media evaporative air cooler system have been selected according to technical and economic evaluation. Media evaporative air cooler by an external contractor blueprint designed, constructed and installed in the plant. Performance tests are conducted at the site, 11 MW equivalent to 14.5 percent for gas units and 1.2 MW (6.2%) for steam combined cycle unit to the circuit after the Media cooling system in the summer show.

In order to reduce the volume and size of distribution power transformer, precise and extended controlling of transformer output, the conventional transformers are replaced by intelligent universal transformers (IUTs). In this paper, an IUT, typical of Iran national grid, with the voltage ratio of 20kV: 380 V is designed and simulated. Regarding the IUT implementation, a cascaded H-bridge inverter (CHB), and a double active bridge (DAB) inverter, along with a three-phase inverter is used. Finally, the proposed IUT is simulated in various conditions.