Sharif Digital Repository

Long-term electricity load and price forecasts have become critical inputs to energy service provider (ESP) decision makings in restructured environments. This paper presents a three-stage hierarchical approach for long-term electricity load forecasting. These stages are called yearly trend model (YTM), weekly trend model (WTM), and daily trend model (DTM). The first stage fits an appropriate function to data and extracts its yearly trend. The weekly and daily trends are then extracted using the Box-Jenkins method in WTM and DTM, respectively. For doing so, candidate trends are identified using auto correlation function (ACF) and partial auto correlation function (PACF) plots. Then, Akaike... 

In the restructured power markets, price of electricity has been the key of all activities in the power market. Accurately and efficiently forecasting electricity price becomes more and more important. Therefore in this paper, an Artificial Neural Network (ANN) model is designed for short term price forecasting of electricity in the environment of restructured power market. The proposed ANN model is a four-layered perceptron neural network, which consists of, input layer, two hidden layers and output layer. Instead of conventional back propagation (BP) method, Levenberg- Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. Matlab is used for... 

Computational intelligence (CI) as an offshoot of artificial intelligence (AI), is becoming more and more widespread nowadays for solving different engineering problems. Especially by embracing Swarm Intelligence techniques such as ant colony optimization (ACO), CI is known as a good alternative to classical AI for dealing with practical problems which are not easy to solve by traditional methods. Besides, electricity load forecasting is one of the most important concerns of power systems, consequently; developing intelligent methods in order to perform accurate forecasts is vital for such systems. This study presents a hybrid CI methodology (called ACO-GA) by integration of ant colony... 

Demand forecasting plays an important role as a decision support tool in power system management, especially in smart grid and liberalized power market. In this paper, a demand forecasting method is presented by using support vector regression (SVR). The proposed method is applied to practical hourly data of the Greater Tehran Electricity Distribution Company. The SVR parameters are selected by using a grid optimization process and an investigation on different kernel functions. Moreover, correlation analysis is used to find exogenous variables. Acceptable accuracy of load prediction is shown by comparing the result of SVR model to that of the artificial neural networks and the actual data,... 

This paper describes how Enexis, one of the largest distribution network operators in the Netherlands, has adapted its load forecasting method for HV/MVtransformers to incorporate the influence of distributed generation. This new method involves the making of separate forecasts for demand and generation and determining the resulting transformer loading, based on the known correlation between demand and generation  

High penetration of constant-power loads (CPL) in dc microgrids may cause a destabilizing effect on the system that can lead to severe voltage oscillations. This paper addresses stability problems of the CPLs and proposes a simple active damping technique to damp the oscillations caused by CPLs. The particle swarm optimization algorithm has been used to find the best values of the parameters of the proposed active damper to achieve maximum damping of the oscillations. The effectiveness of the proposed approach is verified by simulations  

This paper presents a new method in order to predict the monthly electricity peak load of a country based on the prediction of Discrete Fourier Transform (DFT) of monthly peak electricity demand variation using the ARIMA methodology. For validation, the result of this method was used to predict monthly peak load variation of the recent two years in Iranian national grid. The primary goal of this article is to show the application and implementation of Discrete Fourier Transform to predict monthly variation of electricity peak load in national electric power systems. Furthermore, it is elaborated to demonstrate the benefits and shortcomings of DFT approach comparing to the commonly used... 

This paper studies the economic impact of using inaccurate price forecasts on self-scheduling of generation companies (GenCos) in a competitive electricity market. Four alternative sets of price forecasts are used in this study which have different levels of accuracy. The economic impact of price forecast inaccuracies is calculated by comparing the economic benefits of the GenCos in two self-scheduling scenarios. In the first scenario, electricity market price forecasts are used to optimally schedule the GenCos' next day operation. In the second scenario, perfect price forecasts, i.e., actual market prices, are used for self-scheduling of the GenCos. Two indices are utilized to quantify the... 

In this paper, a new bidding strategy for pay-as-bid market suppliers is introduced. This method is based on a systematic analysis of interactions of market with the suppliers via several market indices as well as forecasting important indices by artificial neural networks. Besides, the proposed method considers the practical limitations in the system and deals with incomplete information handling, closely. Next, a strategic bidding approach is proposed for optimal bidding by the suppliers. In these investigations, the paper focus is on the experimental situation of Iran electricity market as a pay-as-bid market and a sample generating company with several generating units from this market... 

Data aggregation is a technique that is used to decrease extra and repetitive data in cluster based routing protocols. As we know SPEED routing algorithm is based on service quality and dose not perform data aggregation. In this article, we try to add an data aggregation technique to the available techniques without interfering with the functions of previous ones. The idea involves virtual configuration of sensors and specification of an individual ID to the created data by the sensors in each region, then data aggregation in relay node is done by this ID, resulting in less energy consumption, lower traffic and repeated data, an increase in network lifetime and better quality of service  

In the United States electricity market standard, when several transmission service requests (TSRs) are made for a specific pair of power injection/extraction points, the independent system operator (ISO) must allocate the available transmission capability (ATC) associated with the pair to the requests. An ATC allocation must determine the amount of accepted requests as well as the priority of the accepted requests during emergencies. The requests could have different types (recallable/non-recallable), tariffs, and time frames and these factors must be properly considered in ATC allocation. This paper proposes a method for optimal ATC allocation in the planning horizon which incorporates the... 

SPEED routing algorithm is a service quality based algorithm in which data aggregation dose not happen. In this article, data aggregation has been added to the conventional technique in SPEED algorithm. The idea involves virtual configuration of sensors and specification of an individual ID to the created data by the sensors in each region, then data aggregation in relay node is done by this ID, resulting in less energy consumption, lower traffic and repeated data, an increase in network lifetime and better quality of service. © 2009 IEEE  

Genco's offering strategies could change SCUC convergence; as a result, generation plants profit is adjusted. The minimum profit alteration with production changeless, defined as discrete strategies. Here, different discrete strategy base on marginal cost was tested and compare with other strategies. Revenue was calculated with game theory method to find Nash equilibrium as mix game; then, market power indices was estimated. These models help ISO to simulate power market and analyze it before markets occurred. Because of uncertainty in producer's strategy, model was implemented in probabilistic pivotal strategy and finally checked simulation on IEEE 30 buses test case system and market power... 

There are two strategies for deadlock handling in routing algorithms in NoC: deadlock avoidance and deadlock recovery. Some deadlock recovery routing algorithms are re-injection based, such as: Compressionless (CR), Software-Based (SW-TFAR) and AFBAR. In spite of the performance comparison, none of existing researches have focused on the energy consumption of various routing algorithms. We evaluate these routing algorithms according to their energy consumption and latency. Our experimental results show the better performance and worse energy consumption of deadlock recovery routing algorithms compared to deadlock avoidance routing algorithms. In addition, the best and worst energy... 

The focus of the current research is to develop an intelligent design process that uses existing data as a tool for the designers, one that fully utilizes the ability of the computer to interpolate and extrapolate the scattered data. Surface pressure measurements were conducted for a pitch oscillation wing in a subsonic closed circuit wind tunnel. Experimental results have been used to train a multilayer perceptron network. This work indicates that neural networks can reliably predict aerodynamic coefficients and forecast the effects of reduced frequencies on the wind turbine blade performance. ©2007 IEEE  

This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO... 

In recent years, subsidy payment has been made a main challenge for governments due to growth of population and energy price in global markets. Targeted subsidy plan has been implemented in Iran since last year, and this article discusses its effects on costs and revenues of Tehran Electricity Company as one of the major subdivisions of Iran's electricity industry. In the new system, structural changes have been made in electricity tariffs, so it is not possible to predict the future trends in electricity consumption only based on the past consumptions. In order to model the decision making structure in the recent conditions, a system dynamics method is applied. In addition to modeling the... 

In this paper, a recurrent neural network model is developed in order to forecast the energy consumption as a complex nonlinear function of gross domestic product (GDP) and population in Iran. This intelligent model is trained by total energy consumption data as output and the population and GDP as inputs during 1976-2001, while 5 annual data points of the following years (2002-2006) are used to validate the model. It can describe time dependencies efficiently and the convergence rate is much faster. This model forecasts the trend of energy consumption annually. Simulation results show that this model can predict energy consumption in Iran with acceptable accuracy. It is expected that this... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects... 

With recent deregulation in electricity industry, price forecasting has become the basis for this competitive market. The precision of this forecasting is essential in bidding strategies. So far, the artificial neural networks which can find an accurate relation between the historical data and the price have been used for this purpose. One major problem is that, they usually need a large number of training data and neurons either for complex function approximation and data fitting or classification and pattern recognition. As a result, the network topology has a significant impact on the network computational time and ability to learn and also to generate unseen data from training data. To...