Social welfare maximization based optimal energy and reactive power dispatch using ant lion optimization algorithm

In this paper an optimal energy and reactive power dispatch problem is solved by using the ant lion optimization (ALO) algorithm by considering the total cost minimization and social welfare maximization (SWM) objectives. Two different market models are proposed in this work, i.e., conventional/sequential market clearing and the proposed/simultaneous market clearing. In each market model, two objectives, i.e., total cost minimization and SWM are considered. The conventional social welfare (SW) consists the benefit function of consumers and the cost function of active power generation. In this paper, the conventional SW is modified by including the reactive power cost function. The reactive power cost calculation is exactly same as that in the conventional practice. The most important difference is that instead of doing cost calculation in post-facto manner as in conventional practice, simultaneous approach is proposed in this work. The scientificity and suitability of the proposed simultaneous active and reactive power methodology has been examined on standard IEEE 30 bus test system

PREDICTION OF THE COPPER PRODUCTION IN THE FRAMEWORK OF ELECTRICAL ENERGY CONSUMPTION USING ARTIFICIAL NEURAL NETWORK

The metallurgical process of the copper production is a very complex process and requires the consumption of electrical energy in large quantities. One of the challenges of today is to reduce the use of electrical energy by increasing the energy efficiency of the system. This challenge can be solved by developing energy management in mining companies. In order to approach the development of energy management, it is necessary to create models for predicting the volume of copper production by investigating electricity consumption in the main production stages. In this paper, the consumption of electricity required in the process of copper production is analyzed on the example of a local mining company. Data on electricity consumption were collected for a period longer than one year and the parameters were divided according to the main phases of the metallurgical process. Two models for predicting copper production using artificial neural network were created and the most influential parameters were identified. The significance of the models is reflected in the efficient forecasting of the copper production and therefore the demand for electrical energy. Another advantage of the models is the increased possibility for rationalization of electricity consumption on the basis of the influential parameters. The models are recognized as flexible and can find their application in related companies

An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting

© 2018 Elsevier B.V. Accurate and stable annual electricity consumption forecasting play vital role in modern social and economic development through providing effective planning and guaranteeing a reliable supply of sustainable electricity. However, establishing a robust method to improve prediction accuracy and stability simultaneously of electricity consumption forecasting has been proven to be a highly challenging task. Most previous researches only pay more attention to enhance prediction accuracy, which usually ignore the significant of forecasting stability, despite its importance to the effectiveness of forecasting models. Considering the characteristics of annual power consumption data as well as one criterion i.e. accuracy or stability is insufficient, in this study a novel hybrid forecasting model based on an improved grey forecasting mode optimized by multi-objective ant lion optimization algorithm is successfully developed, which can not only be utilized to dynamic choose the best input training sets, but also obtain satisfactory forecasting results with high accuracy and strong ability. Case studies of annual power consumption datasets from several regions in China are utilized as illustrative examples to estimate the effectiveness and efficiency of the proposed hybrid forecasting model. Finally, experimental results indicated that the proposed forecasting model is superior to the comparison models