A suitable energy management scheme and integrating renewable energy resources (RERs) can significantly increase energy efficiency and the stability of future grids operation. This work modeled a household energy management comprising a microgrid (MG) system and demand response programs (DRPs). Residential loads with price-based tariffs are introduced to reduce peak load demands and energy costs. For incorporating the uncertainties in RERs, their stochastic nature is modeled with a probabilistic method. This paper proposes a joint optimization approach for the optimal planning and operation of grid-connected residential, rural MG integrated into renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated into MG by including photovoltaic (PV), wind turbine (WT), fuel cell (FC), and diesel engines (DEs). A multi-objective optimization problem has been formulated to minimize the operating cost, pollutant treatment cost, and carbon emissions cost defined as C1, C2, and C3, respectively. The load demand has been rescheduled because of three DRPs, i.e., critical peak pricing (CPP), real-time electricity pricing (RTEP), and time of use (TOU). Further, the EV load has also been analyzed in autonomous and coordinated charging strategies. Using a judgement matrix, the proposed multi-objective problem is transformed into a single-objective problem. The results of an artificial bee colony (ABC) algorithm are compared with the particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished by employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different numbers of EVs and load data to reduce different costs. To analyze the impact of DRPs, the residential, rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs, respectively. The simulation results with different test cases are formulated while analyzing the tradeoff between ABC and PSO algorithms. The simulation analysis shows that multiple DRPs, EVs, and RERs offered a substantial trade-off.

Lithium-ion batteries have become the most appropriate batteries to use in modern electric vehicles due to their high-power density, long lifecycle, and low self-discharge rate. The precise estimation of the state of charge (SOC) in lithium-ion batteries is essential to assure their safe use, increase the battery lifespan, and achieve better management. Various methods of SOC estimation for lithium-ion batteries have been used. Among these methods, the model-based estimation method is the most practical and reliable. The accuracy of the utilized model is a crucial factor in realizing better SOC estimation in the model-based method. In this paper, an enhanced battery model is proposed to estimate the SOC precisely via an optimized extended Kalman filter. The model considers the most influencing factors on the estimation accuracy, such as temperature, aging, and self-discharge. The parameterization of the model has defined the dependency of sensitive parameters on state estimation. As a fundamental step before estimating the SOC, the capacity degradation is evaluated using a straightforward approach. Later, a particle swarm optimization algorithm is utilized to optimize the vector of process noise covariance to enhance the state estimation. The performance of the proposed method is compared to recent techniques in the literature. The results indicate the effectiveness of the proposed approach in terms of both accuracy and computational simplicity.

The combination of droop control and communication-based secondary control is eligible to maintain steady voltage and frequency at the point of common coupling (PCC) together with the property of power-sharing between distributed generators. However, the performance of this combination is threatened by slow dynamic and communication failure. In this context, an improved droop control is proposed to address the issue of slow dynamic and enhance the power-sharing accuracy in the autonomous microgrid. The droop coefficients are determined through a comprehensive analysis to assess the eigenvalues and plot the loci of the dominant poles. A robust secondary control is subsequently proposed to restore the voltage and frequency to their nominal values with a novel feature that ensures the performance continuity during communication failure. The key idea of the proposed secondary control relies on utilizing the predictive values of PCC voltage in the dq-rotating frame using two dynamic lookup tables during communication loss. The proposed control is evidenced by the simulation results in MATLAB environment.

Along with the great benefits of utilizing renewable energy (e.g., wind energy) in the power system, there are also some issues, such as increasing the uncertainty and reducing the system inertia. Communication-based centralized control has started to play a significant role in reacting to the aforementioned issues, especially for relatively small systems, such as microgrids. In this context, in this paper, an enhanced communication-based hierarchical control for a dual mode wind energy conversion system-based microgrid is modeled and investigated. The primary stage utilized the P-V/Q-f droop method, which is the preferred droop method to be used in microgrids when the line impedance is mainly resistive. The secondary stage relied on an enhanced methodology for compensating the deviations of voltage and frequency and improving the performance of the microgrid during small and large signal disturbances. Moreover, as this microgrid operates in a dual mode, the mode transition cases from grid-tied mode to autonomous mode and vice versa have been addressed. Thereafter, an improved control scheme for the unplanned outage transition and a modified control scheme for the pre-synchronization and reconnection transition were proposed. Finally, the proposed work was evaluated by the simulation results in MATLAB environment.

Usually, rural areas can be electrified via three-phase distribution transformers with relatively large capacities. In such areas, low voltage lines are used for long distances, which cause power losses and voltage drop for different types of consumers. Reducing losses and improving voltage profiles in rural distribution networks are significant challenges for electricity distribution companies. However different solutions were proposed in the literature to overcome these challenges, most of them face difficulties when applied in the conventional distribution network. To address the above issues, an applicable solution is proposed in this paper by installing a number of small-capacity distribution transformers instead of every single large-capacity transformer in rural areas. The proposed approach is implemented in the branch network of Al-Hoqool village, which belongs to the Nineveh distribution network. The network has been inspected on-site, drawn, and analyzed using the electrical systems analysis program (ETAP). The analysis showed that using the single-phase pole-mounted transformers can improve the voltage in the network’s end by 29% and enhance the voltage profile for all consumers. The analysis has also demonstrated that the modification can reduce the total power losses by 78% compared to the existing network. Concerning the economic aspect, the payback period for the proposed network is assigned to be 20 months.

Li-ion batteries can be charged with different techniques according to the charging time and required capacity usage. Most charging techniques face difficulties when implemented in PV systems due to the intermittent and unpredictable nature of the power supply. This paper addresses the issue of determining the appropriate charging technique for Li-ion batteries in a PV system. We have developed a mode-selective control approach that determines the optimal charging mode according to the given SOC and solar irradiation, aiming to maximize the utilization of the generated PV power. The developed control approach has been implemented using a dual-switched buck converter in the MATLAB/Simulink environment. The key control algorithm focused on regulating current, with different references being used based on the selected charging mode. Three references for charging current were set: the maximum current, the required current assigned based on the given SOC, and the pulsed current. The pulsed current reference was employed during a stage of the charging process to accelerate charging and prevent dissipation of PV power. Furthermore, a gain-scheduled controller with carefully picked control parameters was used to ensure stable operation across different modes. The results proved the effectiveness of the proposed control in reducing charging time and minimizing PV power dissipation without resorting to the use of harmful charging currents.