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In this paper, a new prediction model for aggregated loads of buildings has been propose. Due to high correlation of prediction performance with related horizons and aggregated more customers, a new strategy is developed to provide a forecasting model based on high accuracy. While, consumption shape of a single users normally has low structure to be exploited and vice versa. So, combining different users develops the relative prediction performance up to special point. Outside this point, no more enhancement in relative performance can be found. This model is useful in optimal power system operation and planning in micro-grids. In this paper, besides the aggregated loads of building, the electric vehicle (EV) impact on network has been considered. While, by considering the load growth prediction and impact of EV adaption on load curves this problem can be an important issue for the power grid operation and management. So, an accurate prediction model is presented in this work which is composed of new feature selection and enhanced support vector machine (ESVM) based forecast engine. Effectiveness of the proposed model is carried out to the part of Budapest city through comparing with other prediction models. Obtained results demonstrate the validity of proposed method. [ABSTRACT FROM AUTHOR]

Enterprises and business systems are introducing modern information and communication technologies into their organizations to improve their business processes and gain a competitive advantage. Theoretical research, as well as research carried out in practice, unambiguously indicate that companies take this step without the necessary professional analysis of business processes and insufficient knowledge of the project management methodology. Also, in this area there is a lack of adequate literature, especially in the field of information technology (IT) project management. This paper provides a new approach for research in the field of project management throughout a studious analysis of risk management in IT projects. To confirm hypotheses, this paper examines and outlines concrete IT management solutions in business systems with emphasis on education organizations, which leads to business improvement and achievement of positive business results. • Enterprises and business systems on introducing modern information and communication technologies. • To improve their business processes and gain a competitive advantage. • Project management through a studious analysis of risk management in IT projects. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) is pioneer nanotechnology that has been gained attention in the research community for proposing low power, low area, and highly efficient digital circuits in next-generation computer designs. Memory is utilized as one of the primary parts in different circuits design, hence the design and optimization of random access memory (RAM) cells have become one of the most attractive research areas in QCA technology. In this paper, a new D-latch-based nano-scale RAM cell is proposed which is suitable for the implementation of efficient single-layer QCA-based circuits. In addition, by employing this memory cell, 1 × 4 RAM is also presented. These circuits are designed using QCADesigner and functional correctness of simulation results is also proved. According to the collected results, the introduced nano-scale structure involves low latency and a small number of cells in contrast to its previous counterparts. [ABSTRACT FROM AUTHOR]

In the current close-to-smart power systems, renewable energies are the most significant elements that need to be carefully addressed in power system studies. In this regard, microgrids (MGs) have been introduced recently to activate the large penetration of renewables. However, integration of such generation technologies that are associated with severe uncertainty in their power output would significantly impact the scheduling of energy resources in MGs. Thus, efficient energy management systems are required to be employed in MGs. Therefore, this paper presents a day-ahead scheduling framework for an MG equipped with a solar photovoltaic (PV) unit. In this respect, different climate conditions and their impacts on the power output of the PV unit and the optimal scheduling of the MG have been investigated in this paper. To this end, four different days from the four seasons have been used to extract the data of solar irradiance. The scheduling problem has been formulated in a single-objective optimization framework, where the objective function is defined as minimizing the total operating cost over the scheduling period. An effective optimization algorithm named "modified krill herd (MKH)" algorithm is proposed to solve the mentioned day-ahead scheduling problem, while there are renewable and non-renewable generating units, besides an energy storage system. Furthermore, a comprehensive comparison has been made between the MKH algorithm and some well-known optimization algorithms to verify the superior performance of the suggested method. [ABSTRACT FROM AUTHOR]

Market players face electricity market price uncertainty as a challenging issue in restructured electricity markets. To overcome this problem, taking optimal bidding and offering strategies is very important. This paper proposes a new mathematical model as a hybrid robust-stochastic method in order to maximize the expected profit of a compressed air energy system. Also, this study considers the uncertainty of market price with a set of scenarios via stochastic method while it models the uncertainty of maximum capacity of cavern via robust optimization approach. Furthermore, the proposed model formulates mixed-integer linear programming and obtains optimal offering and bidding curves of a compressed air energy system, which are robust against the uncertainty associated with market price and cavern uncertainty. Obtained results show that total profit, without considering cavern's uncertainty, is equal to $9585 while this amount for the most robust case obtained as $8753. This means that being robust against the maximum capacity of caver's uncertainty reduces total profit by about 8.68%. • Optimal scheduling of compressed air energy storage (CAES) is studied. • Charge/discharge of CAES system is managed in the presence of uncertainties. • A hybrid robust-stochastic approach is proposed to model the uncertainties. • Optimal bidding and offering strategies of CAES is obtained based on proposed method. • Robust mixed-integer linear programming is modeled to obtain global optimal results. [ABSTRACT FROM AUTHOR]

In this paper, the energy procurement problem for a large electricity consumer is solved under various resources. In this problem, the uncertainty of pool market price is a big challenge to achieve optimal result. In this paper, the information gap decision theory has been proposed to handle the pool market price uncertainty. The results of information gap decision theory are presented in three different strategies for the large consumer. These three strategies include risk-averse, risk-neutral and risk-taker strategies which examine the large consumer risk at various prices in pool market. In addition, the results in all strategies point out the importance of demand response program in reducing the large consumer’s costs. In the risk-neutral strategy, the large consumer cost with and without demand response program is $36,945 and $40,253 respectively. Therefore, the positive impact of demand response program has reduced large consumer cost about 8.2%. Large consumer resistance is 72.5% higher than the without use of demand response program mode in the risk-averse strategy. Finally, large consumer cost is 8% less than the without use of demand response program mode in the risk-taker strategy. [ABSTRACT FROM AUTHOR]

Retraction notice to "Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory" [Sol. [Extracted from the article]