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.

Load scheduling and peak demand shaving are two critical aspects of the utility grid operation that help both the grid operators as well as end-users. This paper proposes a two-stage community home energy management system for microgrids. The first stage deals with the dynamic clustered community load scheduling scheme. Comparatively flatter power demand was attained using particle swarm optimization (PSO) incorporating user-defined constraints. The new arising or remaining peaks as a consequence of consumer constraints are catered to in the second stage. The second stage proposes a rule-based peak shaving management method for the photovoltaic (PV) systems that are connected to the grid and the battery energy storage systems. The proposed technique determines the dynamic demand and feed-in limits based on the estimations of the upcoming day’s load demand and PV power profiles. Also, the study presents an optimal rule-based management technique for peak shaving of utility grid power that sets the charge/discharge day ahead schedules of the battery. For peak energy minimization, PSO is used to calculate the optimal inputs needed for implementing the appropriate rule-based management strategy. MATLAB is used to test the proposed method for different PV power and load demand patterns, thus, achieving an average improvement of 8.5%.

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.

Home energy management systems (HEMS) in microgrids have played a vital role in revolutionizing the power system. Consumer participation in microgrid load scheduling is a key feature of modern HEMS. Much research has been conducted on minimizing the peak-to-average ratio and cost optimization. This paper proposes a community-based HEMS model incorporating dynamic pricing to achieve these objectives. The loads are dynamically clustered, a process where similar loads are grouped together based on their characteristics and scheduled for one day in 10-minute intervals. The resulting load profile is compared against existing algorithms, demonstrating notable improvements. Moreover, the application of dynamic pricing to the optimized load profile, leveraging resources such as battery-powered electric vehicles, solar PV, battery storage, and the primary grid or main grid, has shown promising results. Priority is given to the renewable and local energy storage to minimize cost. Three case studies, each considering weather-driven variations in appliance usage, demonstrate the potential for significant cost reductions. The surplus energy is returned to the primary grid, while in cases of insufficient microgrid supply, the power is drawn from the primary grid. Net metering with smart meters is used to ensures the accuracy and fairness of the billing process, providing consumers with a transparent and reliable system. The results show a substantial cost reduction of up to 36.2% in the spring and 7.6% in the summer and winter seasons, offering a hopeful outlook for the future of energy management in microgrids.

Nanomaterials, especially carbon-based products, have long been a cutting-edge research topic in the realm of concrete and cement. Among these, graphene along with its related compounds stand out as the most widely studied nano-additives for cementitious materials. Although some of this topic has been covered in the literature, more research is still necessary to completely comprehend the physical properties of cement composites containing graphene oxide (GO). In order to investigate the impact of water-dispersed GO on the paste properties of physical cement, this study compares a typical paste. Using a 7-day curing period, six graphene oxide percentages (0.01%, 0.03%, 0.05%, 0.1%, 0.3%, and 0.5%) were studied. Setting time decreased to 0.05% graphene content, increased up to 0.3%, and plateaued thereafter. Workability reduced due to graphene oxide’s high specific surface, affecting water absorbency. Electrical resistivity increased to 0.05% graphene content, decreasing afterward due to ion diffusion. Optimal Ultrasonic Pulse Velocity was observed at 0.3% graphene oxide. Lower values are attributed to voids and bubbles in graphene oxide-containing mixtures.