مجله کیفیت و بهره وری صنعت برق ایران
سال سوم شماره 2 (پیاپی 6، پاییز و زمستان 1393)

Social psychological perspective is one of the most important standpoints that have been used to encourage optimal electricity consumption patterns among families through awareness raising and attitude change. However، critics contend that there are de facto variables that thwart the realization of people’s altered attitudes. In order to empirically examine these two opposing views، the researcher conducted a survey on electricity consumption in Isfahan households. The results supported the ideas held by the critics of social psychological perspective. In this regard، the results revealed that attitudinal variables did not correlate with household electricity consumption so that they could only affect electricity consumption through influencing action-related variables.

Reduce the use of electric vehicles in addition to environmental concerns, can reduce the peak and fill the valley daily load characteristic of network. In other words, in the context of smart grids, electric vehicles battery can charge and discharge planning process to improve the load characteristics. with the emergence of smart grids and using advanced metering infrastructure (AMI), customers are instantaneously aware of prices therefore it is expected that the demand side customers change their consumption patterns according to the forecasted prices by interrupting, shifting or even locally generating the load. This response pattern is causing massive changes in network load curve. In this article, a multistage model using neural networks and ANFIS to forecast the day-ahead load of price-responsive smart grid environments have been provided. Then, smart charge and discharge planning of electric vehicles in the parking according to the forecasted load curve of next day In considering smart charge and discharge operation strategy, a Complete probabilistic model of the car parking area is provided. The probabilistic model is based on a new hybrid optimization algorithm consists of sequential Monte Carlo simulation and imperialist competitive algorithm. Finally, the proposed model applied to four sample days data from the years 2014-2013 of the NSW electricity market in Australia and determined smart charge and discharge planning of electric vehicles in the parking in price responsive of smart grid environment.

Recently optimal energy management in active distribution networks becomes more important challenges because of concerning about fossil energy resources and environmental issues. On the other hand, this problem is critically complex considering non-dispachable wind generation and plug-in electric` vehicles (PEVs) with high penetration. In this paper, an optimal procedure is presented for active distribution network operation including wind generation, battery units and PEVs. The operation process is considered as an optimization problem which is solved using Tabu Search (TS) algorithm. The simulation results show that the operation and reliability costs of distribution network decrease efficiently and increase the reliability of system using the proposed methodology.

In restructured electricity markets, accurate price forecasting plays an important role for all market participants. Due to the complexity and distinct nature of the electricity price, a single forecast engine cannot capture and model all different patterns in price signals. As a result, to improve forecast accuracies, this paper proposes a hybrid method to use advantages of several forecast engines simultaneously. In the proposed method, three primary engines, artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and autoregressive moving average (ARMA), provides three independent forecasts of the price. Then, a new fusion algorithm combines these three forecasts to obtain a unified single price forecast. The proposed method obtains feedback from previous error of the primary forecast engines to adjust their effect on the final forecast. The proposed method is evaluated using price data of Spanish electricity market. Results indicate that the proposed method outperform each primary forecasting engine.

Capacity market is one of the most successful mechanisms to ensure power system adequacy where generation units sell their capacity in a competitive manner. High investment costs, long construction periods and lack of competition between generation units in some sections of the grid are factors which may justify resources based on demand side management deployment in capacity market. Hence this paper aims at reducing reliability costs and generations markets power using mentioned resources deployment in capacity market. The reliability cost is calculated as some of costs paid to the generators proportionate to their share. Furthermore, market power of players is evaluated using MRCI index. The proposed method is tested in IEEE 57 bus network and results are reported.and results are reported.

In this paper a robust self scheduling model for a price taker GenCo, when participating in a day ahead power market, is introduced. For this purpose a new uncertainty set is developed to tackle with the uncertain coefficients. The proposed model helps in avoiding the profit loss corresponding to the uncorrelated values of coefficients. In addition a robust counterpart based on the proposed uncertainty set for a self scheduling problem is developed. The results of the study using a simulation model for thermal units reveals that the proposed model can be considered as an effective model for all GenCos specially for risk taker producers.