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This paper addresses essential aspects of decision‐making and management in energy resources. To achieve this, a tri‐objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants' output in the proposed multi‐objective optimization problem as a scenario‐based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load‐shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug‐in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83‐bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi‐objective problem defined for each scenario is solved by the augmented epsilon‐constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision‐making method. [ABSTRACT FROM AUTHOR]

Nowadays, nonlinear loads are widely employed within the industrial applications that cause harmonic distortions in the network. Passive harmonic filters stand out as the preeminent and cost‐efficient remedy for mitigating the impact of harmonic distortions. Any changes in power system conditions such as load variability and photovoltaic resources uncertainty have a significant impact on the harmonic conditions which can affect the quantity and positioning of passive harmonic filters. Hence, in this paper, a method including an innovative optimization problem is proposed for probabilistic planning of passive harmonic filters considering system conditions variability, which is solved by heuristic methods and multi‐criteria decision‐making techniques. By doing this, a solution is obtained where grid losses and the passive harmonic filter costs are minimized and the power factor and frequency response are improved on selected buses. Also, in this paper, the index that gauges the severity of total harmonic distortion is proposed to determine the worst case of the network in terms of harmonic distortions which is so vital for system operators, especially the systematic approach to designing passive harmonic filters. To indicate the efficiency of the proposed approach, the electrical system of an actual Iranian copper mine of significant scale is employed, featuring the integration of non‐linear loads. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a planning model for a multi‐energy microgrid (MEM) that supplies the electricity, heating, and cooling loads. This controls flexible demands and provides continuous control in the presence of smart and comprehensive programming of electricity, heat, ice, compressed air, and hydrogen energy storage. The features of the MEM are considering the losses and amortization costs of electricity, heating and cooling energy storage, and the operating area of the combined heat and power (CHP) units in the planning procedure. Besides, the principle of convexity in the CHP unit operation area is attended. The proposed formulation is applicable in days of summer and winter seasons for a variety of planning studies. The outcomes represent that utilizing the proposed MEM planning results in significant advantages for the power network and the consumer. The cost savings are respectively obtained equal to 33% and 34% in the winter and summer seasons. The cost savings can be considered besides the investiture cost of MEM elements to specifying the MEM profits made during the designing step. Another valuable result is the cost‐saving of PHEV smart charge about 1.38 times of PHEV smart discharge for summer and about 1.05 for winter. It also makes the curve of demand optimal utilization from the demand response program and energy storage plan. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) is combining different types of generations and interruptible loads to be able to contribute to the market as a power plant with a significant output. In other words, different power generations are combined in a complementary manner to form a defined generation and a demand profile. Because the most important elements of these operators are distributed generation (DG) units and demand response loads (DRLs), the determination of optimal location, capacity, type, as well as the installation time of DGs, DRLs participation size, and place in the distribution system are the most important challenges of operators. In this paper, the formulation of optimal placement of DG resources and DRLs is presented and solved simultaneously in a distribution system to determine the optimal VPP. For this purpose, the concepts of commercial virtual power plant and technical virtual power plant are introduced first, and then the optimal VPP, which can send its energy bids as a short-term and long-term power scheduling to the power market, is determined. Here, commercial virtual power plant and technical virtual power plant will act jointly as commercial-technical virtual power plant to extract the results of the proposed optimization procedure. In this paper, the binary particle swarm optimization algorithm is used to solve the optimization problem in the distribution system. The proposed method is applied to the IEEE 33-bus distribution test network, and the results confirm the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for fundamental power calculation based on a wavelet transform with preprocessing by the Hilbert transform. The proposed method increases the frequencies of all harmonics by half of the main frequencies and locates both odd and even harmonics in the centres of the extracted frequency bands. Unlike the conventional method, there is no frequency interference and the fundamental power calculation is enhanced. In order to examine the proposed method, the steel electric arc furnace (EAF) of the Mobarakeh Steel Company, Esfahan, Iran is simulated in MATLAB software at the early stage of charging. Then the fundamental active, reactive and apparent power of the EAF are calculated using simulated and real data. Results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]