Women as the emergent economic power influence the shape of the global economy. For the prosperity of the nation, it is important that women should be involved in the job creation activities rather than providing with the job opportunities, as businesses are more time flexible than being an employee especially for women; where they are playing multiple roles at a time. In order to work on the efficiency and efficacy of the entire women, this paper studied the relationship between successful antecedent such as motivation, personality trait, creativity, access to finance, family factors and success of women entrepreneurial endeavors was being assessed. For this purpose data were collected from women entrepreneurs through a purposive sampling technique by using five likert scale questionnaire. Analysis of the study revealed that antecedents plays a positive role in the success of women entrepreneurial endeavors, however family support also appears to have a strong mediation effect on the success of women entrepreneurial endeavors but a negative value of beta indicates that women in under-developed society generally are not warmly encouraged by the family to opt for entrepreneurial endeavors. So, there is a need to work on this aspect in order to encourage other female participation in the economic growth.

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An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.

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Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

Abstract:Recently women entrepreneur literature in developed countries has been assessed through their life course approach due to the criticism on the absence of study from the perspective of structural pattern, in which the women are embedded through means of qualitative study. This reveals so many clarifications to the existence themes. Still the Pakistani entrepreneurial literature neglecting the women entrepreneur life course experiences due to social-cultural context of Pakistan. The present study focused on the ten Pakistani women entrepreneur from Islamabad and Peshawar region by the lens of life course approach through semistructured interview. The qualitative analysis of the life course approach among Pakistani women entrepreneur exposed some additional factor to life course approach and contrary findings as compared to the developed countries. The Pakistani women's rich stories are affected through life course approach but the socialcultural context made it little worst means bringing into business with less flexible approach.

Using a statistical method which is based on random matrix theory, the results for the nearest-neighbor energy spacing distributions E(S) obtained from experimental as well as from computational data have been selected for review study. The obtained results confirm that the energy spacing correlation between secondary charged particles depends upon the charged particles multiplicity and central collisions are also associated with charged particles multiplicity.