Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies.

Cognitive Radio and the emerging Non-Orthogonal Multiple Access (NOMA) techniques hold the potential to fulfill the increasing demands of radio spectrum by 5G-and-B5G wireless systems. It is a usual practice in underlay cognitive radio networks to take assistance from intermediate relays to reach the remote destination. Putting together, NOMA based Cognitive Relay Networks (NCRNs) have recently gained tremendous research attention to improve the spectrum utilization efficiency. This article studies the application of best relay selection (BRS) in downlink scenario of NCRNs in which a base station (BS) being unable to communicate directly with the far user U2 takes assistance from the near user U1 and from the best Decode-and-Forward (DF) relay selected from the potential NCRN operating in underlay environment. Three BRS schemes are proposed selecting a relay which (i) maximizes signal-to-noise ratio (SNR) on relay-U2 link, (ii) minimizes interference on relay-primary user (PU) link, (iii) maximizes the quotient of relay-U2 link’s SNR and relay-PU link’s interference power. Scheme 3 considers the existence of U1 – U2 link as well whereas the other two schemes do not. To characterize the performance of the proposed schemes assuming Rayleigh channel model, closed-form expressions of probability distribution function (PDF) of received SNR at U2, average number of reliable relays, outage probability and bit error rate are obtained. For insight analysis, analytical results are validated through simulations which reveal that relay selection in NCRNs is different from non-cognitive networks and is feasible in low-to-medium SNR regions. The signal from near user further improved the outage performance.

Power scheduling of domestic appliances is a vital preference for bridging the gap between demand and generation of electricity in a microgrid. For a stable microgrid, an acceptable mechanism must reduce the peak to average ratio (PAR) of power demand with supplementary benefits for consumers as reduced electricity charges. Recent studies have focused on PAR and cost reduction for a small consumer population. Furthermore, researchers have mainly considered homogeneous consumer loads. This study focuses on residential power scheduling for electricity cost reduction for consumers and load profile PAR curtailment for a relatively large consumer population with non-homogeneous loads. A sample population of 1000 consumers from various classes of society is considered. The proposed dynamic clustered community home energy management system (DCCHEMS) allows the clustering of appliances based on time overlap criteria. Comparatively flatter power demand is attained by utilizing the clustered appliances in conjunction with particle swarm optimization under the influence of user-defined constraints. Modified inclined block rates with real-time electricity pricing strategies are deployed to minimize the electricity costs. DCCHEMS achieved higher efficiency rates in contrast to the traditional non-clustering and static clustering optimization schemes. An improvement of 21% in peak to average ratio, 4% in cost reduction, and 19% in variance to mean ratio is obtained.

The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.

The rapidly proliferating wireless technology brings an overwhelming increase in wireless applications at a continuous pace. In this framework, a major proportion of overall voice and data traffic originates from indoor users, directing the research world towards the deployment of low‐power, low‐cost, and low‐range femtocell networks in the existing macrocells. However, this development will result in cross‐tier interference as well as interference to the primary or licensed users. Cognitive femtocell networks smartly address this challenge through their intelligent sensing and decision‐making capabilities. This study proposes a joint spectrum sensing technique, which involves individual sensing by multiple cognitive femtocell base stations (CFBSs), each equipped with a uniform linear array (ULA) of passive sensors. The local observations of CFBSs are then evaluated at the fusion centre to make final decision applying majority rule. In addition to the detection of a number of active primary femtocell networks (PFNs), the proposed near‐field source localisation technique provides four‐dimensional parameter estimation of each detected PFN signal, i.e. amplitude, angle‐of‐arrival, frequency, and range. Finally, the proposal is supported by three different implementations, i.e. Hybrid Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony. It also outsmarts an existing single‐ULA based spectrum sensing technique in the literature.

In this article, a multiple relay selection (MRS) scheme for signal-to-noise ratio (SNR) enhancement is proposed for underlay relay-assisted cognitive radio networks (RCRNs). A secondary source-destination pair experiencing deep fading on direct path is assisted by amplify-and-forward (AF) relays in an underlay mode. In this energy-constrained scenario, the aim is to maximize the secondary network's end-to-end SNR through an intelligent power-saving method incorporated with MRS. In contrast to the prior relay selection (RS) schemes, the relay-selection factor is the difference of SNR of the source-relay link and corresponding relay-destination link for each relay along with its corresponding interference channel coefficient. The difference factor aims to achieve the SNR upper bound while performing minimum power amplification, eventually resulting in interference mitigation as well. The proposed algorithm has been implemented using Fuzzy Rule Based System (FRBS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and their performance has been compared through simulations.