Majlesi Journal of Energy Management
Volume:5 Issue: 2, Jun 2016

Demand Response Programs (DRPs) play an important role in price reduction and reliability improvement in the electrical grids. On the other hand optimal and exact modeling of DRPs can be so effective in the load curve estimation with the lowest error. In this paper the linear and non-linear models of the incentive and price based DRPs have been developed. Modeling is based on the price elasticity matrix (PEM) and customer’s benefit function. Also, the effects of implementation of DRPs on the load curve characteristics such as the load factor, peak to valley, and peak compensate have been investigated. Finally based on different independent system operator’s (ISO’s) policies, DRPs are prioritized using strategy success indexes (SSI).

Artificial neural network (ANN) techniques have been recently suggested for short-term electric load forecasting by a large number of researchers, This paper presents an artificial neural network(ANN) approach to electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The ANN load forecasting models are trained on historical data that obtained from a real substation in Mashhad of Iran. Final results indicate average errors of developed models and prove that these models can be applied to the prediction of load in real case.

One of the most significant challenges of energy distribution network companies is producing electricity, heat and cooling demands. One specific solution to overcome the problem is operating different energy networks such as gas and electricity simultaneously. An especial approach to utilization of the networks is “Energy Hubs”. Energy hub entails electricity, gas, heat, and sometimes cooling networks. Energy hub enters gas and electricity as its inputs. Renewable energy resources also can connect to the network. Energy hub function is supplying electricity and heat demands in order that operation costs minimize. CHP is employed as heart of energy hub in which simplifies integration of different energy networks. In this paper, CCHP is employed to operate gas, electricity, heat and cooling energy carriers. Furthermore, solar, energy storages and absorption chiller are integrated to energy hub. The proposed hub is scheduled by the aforementioned technologies in different hot and cold climates in order to minimize the costs. Furthermore, the results demonstrate whether the proposed hub performs better in hot or cold climate.

Load Frequency Control (LFC) is one of the vital parts in power system design, automation, operation and stability. In this paper, we compare two different controllers, the Biogeography-Based Optimization (BBO) based PID controller and Fuzzy Logic Controller (FLC), in LFC problem of two area interconnected hydrothermal power system. The hydro and thermal areas are comprised with an electric governor and reheat turbine, respectively. Also, 1% Step Load Perturbation (SLP) has been considered in any individual area. The mentioned power system with the proposed approach is simulated in MATLAB/SIMULINK and the responses of frequency and tie-line power deviation for these two controllers in each area were shown and compared. The simulation results show that FLC achieves better responses in comparison with BBO based PID controller.

In this paper, at first the modeling of wind turbines equations is studied, then using obtained equations a proportional integral derivative controller (PID) system is designed for system. PID coefficients derived using once SOA algorithm and then CSO algorithm and the results were compared with each other. Simulation of system was performed using MATLAB software.

This paper presents deals with the analysis, modeling, and control of the doubly-fed induction generator (DFIG) for wind turbines. Different rotor current control methods are investigated with the objective of eliminating the influence of the back electromotive force (EMF(, on the rotor current. It is found that the method that utilizes both feed forward of the back EMF and so called “active resistance” manages best to suppress the influence of the back EMF on the rotor current of the investigated methods. This method also has the best stability properties. In addition it is found that this method also has the best robustness to parameter deviations. This paper presents a novel method for state estimation in doubly fed induction generators (DFIGs) using an Extended Kalman Filter. In this work, the conventional nonlinear state space model of a DFIG has been augmented with additional states in order to make rotor position and speed estimates more robust to disturbances. The effectiveness of this method has been tested for various transient cases using MATLAB/Simulink.

The distribution transformer Load forecasting is very essential in the control of future smart grids and an economical interfacing of Distributed Resources (DRs) to distribution networks. A distribution transformer connects DRs to the main grid. Exact distribution transformer load forecasting makes an economical DRs scheduling possible. In this regard, this paper firstly introduces a new Self-Organizing Fuzzy Neural Network (SOFNN). Then, it applies SOFNN to perform a five-minute load forecasting for a real-life distribution transformer in Lorestan Electric Power Distribution Company (LEPDC). Simulation results for active and reactive powers show that the proposed SOFNN outperforms ANFIS.