The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.

Micro-Grid (MG), a paradigm shift in conventional distribution power systems, facilitates the integration of many Renewable Energy Resources (RERs), storage units, and loads. The key catalyst behind this emerging paradigm is the increased attention to environment-energy sustainability nexus. This novel concept exhibits various attractive features such as sustainability, reliability, resilience, improved power quality, energy security, and liberalization of electric service industries. However, the integration of RER units and load participation into the MG brings various challenges to the stability and operation of the system. These challenges arise as a consequence of the intermittent nature of RERs due to their stochastic behavior and increased level of non-linearity associated with smart load participation. Furthermore, in recent years, the development and deployment of RER in MG networks have demonstrated exponential growth. Therefore, in order to achieve a holistic analysis, a comprehensive review study about various aspects of MG should be investigated. In this regard, this rigorous survey paper presents the meticulous study of various aspects, historical evolution, and key enabling yet transdisciplinary technologies of MG, such as various components, generation resources, load classification, communication infrastructure, energy management, control and optimization, operational modes, and various frameworks, configurations, architectures, and topologies-including the emerging concept of Networked MG with flexible boundaries. This study also reviewed various storage and protection systems in MG, considering the attention to their contributions to the stability of the system. This review also underscores many key issues, challenges, and factors related to the sustainable development of the MG system. Lastly, an all-inclusive cross-sectoral analysis that includes cyber-physical systems, power quality, information and data management, conversion systems, synthetic inertia, and some governance issues has been provided, along with the future directions, progression, and latest development in the field of MG. This survey, therefore, greatly assists and enables researchers to study and analyze the development and prospect of MG technology conveniently.

Grid integration of Solar energy positively affects energy market due to inexhaustible fuel supply and virtually zero emissions. However, inexhaustible renewable fuel supply is punctuated by the problem of intermittency. Intermittency exacerbates the problem of grid operators to bridge the supply and demand gap. Thus, precise output power forecast of grid interfaced Photovoltaic (PV) systems is required for economic dispatch, market regulation, and stable grid operation. This study compares the statistical models of Gaussian Process Regression (GPR) and Support Vector Machine (SVM) for solar power prediction. The models are trained to predict PV system output power against the backdrop of data recorded for Abbottabad City, Pakistan. Both the models have been trained, validated, and compared with each other for varying irradiance and temperature settings. The results depicted that SVM based modeling excel in solar power prediction with Root Mean Square Error (RMSE) lower than GPR based modeling technique. Performance evaluation of models is conducted with error metrics of RMSE, Mean Absolute Error (MAE), and Mean Square Error (MSE). Moreover, prediction quality is qualified based on residual analysis benchmarked by load line analysis of PV system in Simulink.

In Pakistan, the performance of conventional electrical grids is inefficient, resulting in severe energy crises. To overcome the alarming challenges persisting in the energy grids, Pakistan must focus on system protection, grid reliability, distribution and transmission, and power quality. The inefficiencies in grid protection and management signify an overall problematic energy scenario. The solutions to these problems include the improvement of domestic, commercial, and industrial demand-side management and the reduction in distribution network losses. A smart grid (SG) is a critical requirement as it can overcome the shortcomings of the existing grid owing to its promising features, enhanced consumer empowerment, utmost security; efficient and optimized energy flow; and demand-supply management. Thus, the SG is essential to overcome the energy crisis in Pakistan and achieve the standards of other developed nations in the energy sector. This study aims to highlight the significant prospects of SGs within Pakistan with the key objectives of its availability requirements. We compare the energy scenario in Pakistan with that of other countries and recommend various aspects that require improvement through SG implementation. Additionally, we discuss the incorporation of renewable energy resources and present a market analysis regarding SGs to illustrate the SG scenario and its implementation in Pakistan. Moreover, we analyze and evaluate detailed taxonomies of energy generation, energy projects, renewable energy assessment, power market trends in Pakistan, and the basic requirements of SGs. Furthermore, a critical analysis of the energy sector in Pakistan is elaborated, which describes the possibilities, requirements, and strengths pertaining to the transformation of the modern electric grid with respect to the China–Pakistan Economic Corridor. Thus, we believe that our work is more versatile in improving the energy system of Pakistan for the implementation of the SG.

The Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug‐and‐play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus‐based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning‐based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug‐and‐play activities of prosumers. Moreover, an optimization‐based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning‐based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning‐based DAI is thoroughly evaluated using the DRES‐based IEEE 14‐bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics.

A central authority, in a conventional centralized energy trading market, superintends energy and financial transactions. The central authority manages and controls transparent energy trading between producer and consumer, imposes a penalty in case of contract violation, and disburses numerous rewards. However, the management and control through the third party pose a significant threat to the security and privacy of consumers’/producers’ (participants) profiles. The energy transactions between participants involving central authority utilize users’ time, money, and impose a computational burden over the central controlling authority. The Blockchain-based decentralized energy transaction concept, bypassing the central authority, is proposed in Smart Grid (SG) by researchers. Blockchain technology braces the concept of Peer-to-Peer (P2P) energy transactions. This work encompasses the SolarCoin-based digital currency blockchain model for SG incorporating RE. Energy transactions from Prosumer (P) to Prosumer, Energy District to Energy District, and Energy District to SG are thoroughly investigated and analyzed in this work. A robust demand-side optimized model is proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to maximize Prosumer Energy Surplus (PES), Grid revenue (GR), percentage energy transactions accomplished, and decreased Prosumer Energy Cost (PEC). Real-time averaged energy data of Australia are employed, and a piece-wise energy price mechanism is implemented in this work. The graphical analysis and tabular statistics manifest the efficacy of the proposed model.