Existing power grids (PGs) and in-home energy management controllers do not offer its users the choice to maintain comfort and provide a bearable solution in terms of low cost and reduced carbon emission. This work is based on energy usage scheduling and management under electric utility and renewable energy sources i.e., solar energy (SE), controllable heat and power (CHP) and wind energy (WE) together. Efficient integration of renewable energy sources (RES) and battery storage system (BSS) have been suggested to solve the energy management problem, reduce the bill cost, peak-to-average ratio (PAR) and carbon emission. User's electricity bill reduction have been achieved by proposed power usage scheduling method and integrating low cost RESs. PAR minimization have been achieved through shifting the demand in response to real time price from high-peak hours to low-peak hours. In this context, load scheduling and energy storage system management controller (LSEMC) is proposed which is based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization (BFO) and our suggested hybrid of GA, WDO and PSO (HGPDO) algorithm. The performance of the heuristic algorithms and proposed scheme is evaluated numerically. Results demonstrate that our proposed algorithm and the LSEMC reduces the electricity bill, PAR and CO ₂ in Case 1, by 58.69%, 52.78% and 72.40%, in Case 2, by 47.55%, 45.02% and 92.90% and in Case 3, by 33.6%, 54.35% and 91.64%, respectively as compared with unscheduled. Moreover, the user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality and visual improves by 35.55%, 16.66%, 91.64% and 45%, respectively.

A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO ₂ in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user-comfort (UC) improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in aspects of indoor air quality, average delay, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy cost, reduce carbon emission, mitigate peak loads, and improve UC.

The advancement of the smart grids (SGs) is enabling consumers to schedule home appliances to respond to demand response programs (DRs) offered by distribution system operators (DSOs). This way, not only will customers save money on their energy bills and be more comfortable, but the utility company will also be able to regulate peak-hour demand and reduce carbon emissions (CE). Designing an optimization scheme to reduce the electricity bill cost, peak-to-average ratio (PAR), CO2 emission, wait time, and enhance the user comfort in terms of delay, luminance, and thermal comfort is not only the aim of this work but also the need of demand-side management. This research focuses on energy usage, scheduling, and management under the DR program of an electric utility, as well as renewable energy sources integration, i.e., solar energy (SE), thermal, controllable heat and power (CHP), and wind energy (WE). Moreover, the integration of renewable energy sources will reduce electricity bills and also lower the environmental impact of CE. In this context, a smart appliances scheduler and energy management controller (ASEMC) is proposed which is based on heuristic algorithms, i.e., genetic algorithm (GA), wind-driven optimization (WDO), particle swarm optimization (PSO), bacterial foraging optimization (BFO) and our proposed hybrid of GA, PSO, and WDO (HGPDO) algorithm. The performance of the proposed scheme and heuristic algorithms is evaluated via simulations. Results show that in Scenario 1, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 25.7%, 36.39%, and 20.74%, respectively, while in Scenario 2, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 35.25%, 31.72%, and 36.30%, respectively. Furthermore, in Scenario 1, user comfort in terms of cumulative delay, indoor air freshness quality, thermal, and visual comfort improves by 26.77%, 3.28%, 13.33%, and 31.66%, whereas in Scenario 2, user comfort improves by 23.33%, 3.30%, 10%, and 45%, respectively.

Renewable energy communities (RECs) are increasingly recognized as pivotal instruments in the global energy transition, offering decentralized, participatory, and sustainable solutions for energy management, specifically regarding energy production and consumption. The present review provides a comprehensive examination of the REC concept, tracing its regulatory evolution, particularly within the European Union through the renewable energy directives (RED II and RED III) and by analyzing its practical implementation across various countries. This paper explores the diverse technologies integrated into REC projects, such as photovoltaic systems, wind turbines, biogas, hydroelectric, and storage solutions, while also considering the socioeconomic frameworks, management models, and local engagement strategies that underpin their success. Key case studies from Europe, Asia, Africa, and Australia illustrate the various approaches, challenges, and outcomes of REC initiatives in different geographic and policy contexts. The analysis also highlights barriers to implementing RECs, including regulatory uncertainty and market integration issues, and identifies the best practices and policies that support REC scalability. By synthesizing current trends and lessons learned, this review aims to inform policymakers, researchers, and practitioners about the transformative role of RECs in achieving decarbonization goals and accomplishing resilient energy systems.

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2.

The shift towards the integration of and transition to renewable energy has led to an increase in renewable energy communities (RECs) and smart grids (SGs). The significance of these RECs is mainly energy self-sufficiency, energy independence, and energy autonomy. Despite this, in low- and middle-income countries and regions like Pakistan and the Middle East, SGs and RECs are still in their initial stage. However, they have potential for green energy solutions rooted in their unique geographic and climatic conditions. SGs offer energy monitoring, communication infrastructure, and automation features to help these communities build flexible and efficient energy systems. This work provides an overview of Pakistani and Middle Eastern energy policies, goals, and initiatives while aligning with European comparisons. This work also highlights technical, regulatory, and economic challenges in those regions. The main objectives of the research are to ensure that residential service sizes are optimized to maximize the economic and environmental benefits of green energy. Furthermore, in line with SDG 7, affordable and clean energy, the focus in this study is on the development and transformation of energy systems for sustainability and creating synergies with other SDGs. The paper presents insights on the European Directive, including the amended Renewable Energy Directive (RED II and III), to recommend policy enhancements and regulatory changes that could strengthen the growth of RECs in Asian countries, Pakistan, and the Middle East, paving the way for a more inclusive and sustainable energy future. Additionally, it addresses the main causes that hinder the expansion of RECs and SGs, and offers strategic recommendations to support their development in order to reduce dependency on national electric grids. To perform this, a perspective study of Pakistan’s indicative generation capacity by 2031, along with comparisons of energy capacity in the EU, the Middle East, and Asia, is presented. Pakistan’s solar, wind, and hydro potential is also explored in detail. This study is a baseline and informative resource for policy makers, researchers, industry stakeholders, and energy communities’ promoters, who are committed to the task of promoting sustainable renewable energy solutions.