International Journal of Smart Electrical Engineering
Volume:13 Issue: 1, Winter 2024

The household energy management system (HEMS) can optimally schedule home appliances for transferring loads from peak to off-peak times. Consumers of smart houses have HEM, renewable energy sources and storage systems to reduce the bill. In this article, a new HEM model based on the time of usage pricing planning with renewable energy systems is proposed to use the energy more efficiently. The new meta-heuristic whale optimization algorithm (WOA) and the common meta-heuristic of particle swarm optimization (PSO) are used to achieve that. To improve the performance, a mapping chaos theory (CWOA) is proposed. Also, an independent solar energy source is used as a support of the microgrid to achieve a better performance. It is concluded that the energy saving achieved by the proposed algorithm is able to decrease the electricity bill by about 40-50% rather than the WOA and PSO methods. The proposed system is simulated in MATLAB environment.

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (CAES) systems, are essential for maximizing their benefits. This study proposes a novel approach, the Hybrid Artificial Neural Network-Modified Dragonfly Algorithm (HANN-MDA), for determining the optimum capacity of CAES in microgrid planning. The HANN-MDA method combines the learning capabilities of artificial neural networks with the optimization power of the modified dragonfly algorithm. The proposed method aims to minimize the overall cost of microgrid operation while considering the integration of renewable energy sources and the storage capabilities of CAES. Simulation results demonstrate the effectiveness of the HANN-MDA method in accurately determining the optimal CAES capacity, leading to improved microgrid performance and cost savings. The findings highlight the importance of considering CAES in microgrid planning and the potential of the HANN-MDA method for achieving efficient and economically viable microgrid designs.

Visual sensor networks (VSNs) apply directional sensors that can be configured only in one direction and also can be set in one of the possible observing ranges. In this battery-resource-limited environment, battery management and network lifetime expansion are still important challenges. The target coverage problem in such networks, in which all of the specified targets must be continuously observed and monitored by administrators is formulated as an integer linear programming problem (ILP) that is an NP-Hard problem. Although several approaches have been presented in the literature to solve the aforementioned problem, the majority of them suffer from getting stuck in the local trap and low exploration in search space. To address the issue, a discrete cuckoo-search optimization algorithm (DCSA) is extended to solve this combinatorial problem. The discrete operator of the proposed algorithm is designed in such a way that explore search space efficiently and lead to balancing in the local and global search process. The proposed algorithm was examined in different conducted scenarios. The returned results of simulations of numerous scenarios show the dominance of the proposed algorithm in comparison with other existing approaches in terms of network lifetime maximization. In other words, the proposed DCSA has 19.75% and 13.75% improvement in terms of network average lifetime expansion against HMNLAR and GA-based approaches respectively in all scenarios.

The planning and operation of microgrids have become very important challenges in the electricity industry due to the expansion of distributed generation (DG) resources and the development of demand response programs (DRPs). Microgrids generally include renewable DG resources whose generation is random. This leads to uncertainty in system planning. This study discusses microgrid operation management considering DRPs and implementation of conservation voltage reduction (CVR) in the future operation horizon. For this purpose, a stochastic operation planning model for the next day is designed, which is associated with the implementation of DRPs, CVR, and the presence of DG resources to optimize the performance of a smart microgrid to increase reliability and reduce costs. In this study, DRPs are implemented using time-of-use (TOU) and incentive-based programs. Incentive-based programs are used to deal with uncertainty in the commitment of renewable resources, and TOU programs are used to deal with the fluctuation of generation of renewable resources by establishing a relationship between uncertainty and the fluctuation of generation of these resources. Besides, CVR is applied and voltage-dependent load modeling is performed considering innovation in addition to the format of DRPs to further reduce peak loads. The uncertainty of DG resources is modeled using the information-gap decision theory (IGDT) method. This optimization is carried out on a sample microgrid using genetic algorithm (GA). According to the results, the implementation of uncertainty-based DRPs leads to cost reduction and improvement of microgrid reliability.

The fixed capacitor-magnetically controlled reactor (FC-MCR) is a type of static var compensator (SVC) that can greatly contribute to the availability and stability of power systems. This paper proposes a comprehensive reliability model for the FC-MCR using the Markov process approach. The modeling process adheres to actual operational principles and divides the MCR structure into two sections: electro-magnetic section and core magnetization section. Subsequently, the Markov models proposed for these sections are integrated with the Markov model of the fixed capacitor bank to derive the FC-MCR reliability model. Recognizing the impact of environmental conditions on electrical equipment failure rates, the proposed reliability model takes into account temperature variations and assesses their influence on the probabilities of the FC-MCR operating state. By examining the simulation results and conducting sensitivity analysis, it was found that the availability of the FC-MCR is influenced by various components and environmental conditions, which necessitates different reliability enhancement measures. Moreover, a comparison between the reliability indices of the FC-MCR and its counterpart (FC-TCR) in diverse environmental conditions revealed that the FC-MCR is less affected by temperature variations compared to the FC-TCR.

This paper introduces an innovative and creative systematic thinking method based on TRIZ thinking technique which can improve the performance of unified power flow controllers (UPFC). This new device is called distributed power flow controller (DPFC). The difference between UPFC and DPFC lies in exchanging three-phase series converter with single-phase converters that are distributed along the transmission line. This is based on one of the 40 principles of TRIZ innovative thinking technique, called segmentation. The basic changes, occurring in the DPFC, compared to UPFC are eliminating common DC-link between the series and shunt converters, and replacing three-phase series converters with single-phase series converter. These changes, lead to more economic and more reliable system. Similar to UPFC, the DPFC adjusts line impedance, transmission angle and bus voltage simultaneously. The DPFC design procedure based on differences with UPFC is described and DPFC advantages in mitigation of transmission line voltage sag and fluctuation are shown; while in some cases, simulation results of using UPFCs, DSTATCOM, TCSC and SVC indicate voltage sags. Finally, this paper introduceس one innovatively DPFC called fuzzy self-organized DPFC (FSO-DPFC) to solve the most important problem of DPFC. DPFC series converter with and without FSO controller (FSO-DPFC) are simulated. The simulation results of using FSO-DPFC, compared to traditional DPFC, shows more system ability to mitigate disturbances in the central controller signals.

Iran is a vast country rich with energy resources. Perhaps, such unlimited energy resources were the reason to keep us in a dream of abundance, and as a result, neglecting justifiable energy conservation. Buildings are one of the main energy consumption and waste sources, and regretfully, still many of them are constructed violating modern engineering rules and solely through experimental and traditional methods. Air conditioning systems are still calculated and designed using estimation. However, we have to know such methods are obsolete in developed countries since ages ago, and we shall commence with a new determination and effort right away if we would like to reach the Global trend. Fortunately, many of the ways have been taken before us, and have certain instructions. In late 20th century and upon development of smart technologies, development of communication and internet networks, sensor networks and sensors, extensive efforts and studies started to use such group of technologies to present solutions to improve humans lives. Using IOT to control the house smartly has been one of the study fields during recent years. The suggested method tries to improve the house smart control for energy conservation. The used sensors are heat and humidity sensors with the duty of monitoring smart home. In this article we plan to suggest optimized smart method to control smart sustainable common home relays smartly. The sensors data is entered into the sustainable common home smart control system and is on/off considering the house relays smart systems algorithm.

Electromechanical oscillations in power systems usually exist due to incompatible conditions and disturbances in the network. Meta-heuristics using search strategy are used to find near-optimal solutions. Typically, the implementation of this approach involves the utilization of a fitness function to assess the candidate solutions. In nature-inspired metaheuristics optimization algorithms, an analogy from nature is used to generate approximate solutions for practical optimization problems. This work presents a comprehensive investigation into various nature-inspired optimization algorithms, including ant colony optimization, genetic algorithm, and bat algorithm. The primary focus of this paper is to explore their efficacy in the coordinated design of Power System Stabilizers (PSS) and Flexible Alternating Current Transmission Systems (FACTS). The objective of this coordinated design is to improve energy system stability and mitigate power system oscillations. Finally, new directions are provided to researchers who work in the field of applications of nature-inspired optimization algorithms and coordination configuration of PSS and FACTS regulators.