• Energy Research

The large−scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The microgrid concept has been proposed to locally control and manage a cluster of local distributed energy resources (DERs) and loads. If the net load power can be accurately predicted, it is possible to schedule/optimize the operation of battery energy storage systems (BESSs) through economic dispatch to cover intermittent renewables. However, the load curve of the microgrid is highly affected by various external factors, resulting in large fluctuations, which makes the prediction problematic. This paper predicts the net electric load of the microgrid using a deep neural network to realize a reliable power supply as well as reduce the cost of power generation. Considering that the backpropagation (BP) neural network has a good approximation effect as well as a strong adaptation ability, the load prediction model of the BP deep neural network is established. However, there are some defects in the BP neural network, such as the prediction effect, which is not precise enough and easily falls into a locally optimal solution. Hence, a genetic algorithm (GA)−reinforced deep neural network is introduced. By optimizing the weight and threshold of the BP network, the deficiency of the BP neural network algorithm is improved so that the prediction effect is realized and optimized. The results reveal that the error reduction in the mean square error (MSE) of the GA–BP neural network prediction is 2.0221, which is significantly smaller than the 30.3493 of the BP neural network prediction. Additionally, the error reduction is 93.3%. The error reductions of the root mean square error (RMSE) and mean absolute error (MAE) are 74.18% and 51.2%, respectively.

Microgrids mainly rely on distributed energy resources (DER) unable to generate electricity at the expected voltage and frequency. This necessitates the usage of inverters acting as a conditioning interface between the DER and microgrid, hence the name inverter-interfaced distributed generators (IIDG). On the other hand, the fast response of the primary control of inverters causes unconventional behavior of IIDGs under fault conditions, which can severely affect all parts of relaying, that is, fault sensing and polarization and faulted phase selection. This issue becomes more pronounced when an inverter-based microgrid operates in autonomous mode. This paper analyzes the root causes of such unconventional responses that challenge the traditional protection schemes. At first, the inverter control strategies including current limiting are briefly discussed. Then, the paper is continued by analyzing the response of an IIDG feeding its local load to balanced and unbalanced faults, where MATLAB/SIMULINK is used for simulation studies. It is shown how the constraints set by the control strategy itself and current limiter affect the response of IIDGs to fault conditions and consequently, their equivalent models under fault conditions. The findings presented in the paper clearly show that protective functions face difficulties in coping with fault conditions in IIDG-based microgrids due to their different equivalent models during fault period. These studies in turn help modify existing protection schemes or devise new ones applicable to this concept.

Abstract The rising world-wide trend toward developing clean energy resources has caused dispersed installation of renewable energy resources (RESs) in distribution grids. Microgrid (MG) concept is proposed as a key factor in optimal and secure integration of, mostly converter-based, RESs into power systems. One of the major challenges related to MG control is ineffectiveness of droop control in accurate power sharing which is affected by the feeder impedance. In this paper, a fuzzy-based consensus control protocol is developed to address this issue in multi-bus MGs (MBMGs). Consensus signals are inserted into the conventional droop controller as complementary part to overcome the drawback of the droop control in power sharing in MBMGs. Dynamic fuzzy coefficients of consensus signals are designed to model X/R ratio of the grid impedance in the control system. In addition, a novel small signal model of MBMG is developed, by considering the conventional droop control, MBMG power network and power lines impedance to design and assess performance of the control system. Consensus control is also incorporated into the proposed control system of MBMG to analyze the stability. Simulation results are presented to assess effectiveness of the control strategy in MATLAB\Simulink.

Increasing power demand, aging distribution systems and concerns towards greenhouse gas emissions have resulted in the increased occurrence of distributed generation (DG) within distribution networks. The conventional protection methods designed for passive radial distribution networks may become redundant with large bidirectional power flow and dynamic network topology. Additionally, the anti-islanding protection currently employed limits the benefits of wide-scale DG installation and autonomous operation. The microgrid concept can solve these problems, but several challenges must be overcome before practical implementation. Besides bi-directional power flow, the vast variance between the fault current in grid-connected and autonomous mode and the arbitrary output impedance of the inverter-interfaced DG units in fault conditions and current limiting mode pose a challenge to the protection schemes that use traditional overcurrent protection devices. Many researchers have proposed various techniques, but a robust protection scheme capable of protecting microgrids against different faults for both modes of operation under dynamic network topologies and being financially viable is still to be developed. Hence, the main objective of this paper is to critically review various AC microgrid protection methods proposed in the literature, focusing on analysing the recent protection approaches using modern intelligent techniques. Open research problems and future research trends in AC microgrid protection are also presented in this research. ; No Full Text

Solar energy is considered the major source of clean and ubiquitous renewable energy available on various scales in electric grids. In addition, solar energy is harnessed in various electronic devices to charge the batteries and power electronic equipment. Due to its ubiquitous nature, the corresponding market for solar-charged small-scale batteries is growing fast. The most important part to make the technology feasible is a portable battery charger and the associated controllers to automate battery charging. The charger should consider the case of charging to be convenient for the user and minimize battery degradation. However, the issue of slow charging and premature battery life loss plagues current industry standards or innovative battery technologies. In this paper, a new pulse charging technique is proposed that obviates battery deterioration and minimizes the overall charging loss. The solar-powered battery charger is prototyped and executed as a practical, versatile, and compact photovoltaic charge controller at cut rates. With the aid of sensor fusion, the charge controller is disconnected and reconnects the battery during battery overcharging and deep discharging conditions using sensors with relays. The laboratory model is tested using a less expensive PV panel, battery, and digital signal processor (DSP) controller. The charging behavior of the solar-powered PWM charge controller is studied compared with that of the constant voltage–constant current (CV–CC) method. The proposed method is pertinent for minimizing energy issues in impoverished places at a reasonable price.

The penetration of intermittent wind turbines in power systems imposes challenges to frequency stability. In this light, a new control method is presented in this paper by proposing a modified fractional order proportional integral derivative (FOPID) controller. This method focuses on the coordinated control of the load-frequency control (LFC) and superconducting magnetic energy storage (SMES) using a cascaded FOPD–FOPID controller. To improve the performance of the FOPD–FOPID controller, the developed owl search algorithm (DOSA) is used to optimize its parameters. The proposed control method is compared with several other methods, including LFC and SMES based on the robust controller, LFC and SMES based on the Moth swarm algorithm (MSA)–PID controller, LFC based on the MSA–PID controller with SMES, and LFC based on the MSA–PID controller without SMES in four scenarios. The results demonstrate the superior performance of the proposed method compared to the other mentioned methods. The proposed method is robust against load disturbances, disturbances caused by wind turbines, and system parameter uncertainties. The method suggested is characterized by its resilience in addressing the challenges posed by load disturbances, disruptions arising from wind turbines, and uncertainties surrounding system parameters.

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.