Renewable energy sources such as photovoltaic (PV) and wind power are widely used; however, their intermittent nature impairs power supply quality by creating frequency distortions and irregularities ...in voltage. Battery energy storage systems (BESS) are utilized to flatten out and relieve fluctuation issues. To prevent the need for larger storage systems and to prolong their operational life through controlled charging and discharging, a method of control for BESS charging level regulation is necessary. This study presents a solar-wind power and battery state of charge (SoC) control technique using a hydrogen electrolyzer (HE) fuel cell unit. An Intelligent Model Predictive Controller (IMPC) has been developed that utilizes a neural network (NN) plant model for predictive minimization instead of using a mathematical plant model for dynamic management of the HE output, which allows for flattened PV-wind power supply with regulated BESS charging and discharging. The IMPC accepts as inputs the intermittent renewable power and different battery attributes and intelligently manages the HE production while staying within the imposed restrictions. The NN, as opposed to a mathematical plant model, captures the dynamics of the plant with exceptionally high accuracy. Moreover, the NN plant model performance increases as the data gathered from the actual system increases. The neural network model resolves concerns with the MPC model’s mathematical intricacy that occurs as the plant becomes more complicated. According to simulation results, the IMPC greatly reduces PV-wind power fluctuations, for solar power, the IMPC reduces the peak battery SoC by 26.7% and compared to FLC, the peak SoC is reduced by 11.2%. Similarly, for wind power, the peak SoC is reduced by 7.3% and is decreased 3.2% more than the FLC. To demonstrate the power smoothing effectiveness of the IMPC, the peak solar power ramp rate is reduced by 30.3% and the peak wind power ramp rate is reduced by 91.3%. The presented method encourages green hydrogen usage in the electrical sector for supplying firmed solar and wind power.
Solar power is a widely used renewable energy technology that is going to play a key role in the clean energy transition. To address the solar power intermittency problem, battery energy storage ...systems are integrated into the grid such that the excess photovoltaic energy can be stored for later use. However, batteries suffer from energy degradation and therefore storing energy in the form of a chemical fuel helps in overcoming this challenge. Energy storage in the form of hydrogen is an option, but hydrogen is linked with high flammability, poor volumetric density, and high storage costs. Alternatively, ammonia addresses several issues associated with hydrogen. Excess solar energy can be stored in the form of ammonia via the direct electrochemical ammonia synthesizer (EAS). Accordingly, it is crucial to assess the ammonia production requirements as it directly determines the EAS capacity requirements and the overall system costs. Additionally, there is a loss of efficiency with an increase in ammonia production. This study proposes the utilization of power smoothing filters to assess the ammonia production rates of the EAS as well as the nitrogen and hydrogen input requirements. Sliding window filters such as the moving average, moving mean, and moving regression (MR) filters have been utilized to determine the excess solar power available for ammonia production. Simulation results conclude that the power tracking capability of the smoothing filters has a direct impact on the EAS ammonia production rates. Overall, the MR filter has superior power tracking thereby resulting in lower EAS capacity requirements and thus reduced system costs.
The intermittent nature of solar power prevents the large-scale penetration of Photovoltaic (PV) systems in the utility grid as it causes various irregularities such as voltage fluctuations, ...frequency deviations, and reduced overall output power quality. This paper introduces a novel smoothing control methodology for firming of PV power fluctuations. Battery Energy Storage System (BESS) is coupled with solar panel arrangements and included into the grid for solar power smoothing and to stabilize the above-mentioned irregular behaviors. Additionally, smoothing filters such as Low Pass Filters (LPFs) are integrated along with the BESS for optimal functioning and cost reduction. It has been established that the time constant of a LPF directly impacts the degree of solar PV smoothing. Thus, the proposed methodology utilizes the concepts of machine learning and model predictive control to design a control system that intelligently controls the LPF time constant to efficiently rid the PV profile from fluctuations while operating under practical constraints. A high accuracy prediction system is also developed using neural networks. The proposed controller can flatten solar power variations by utilizing the inputs from our prediction system. In addition to the smoothing performance of our controller, the effect on the battery ramp rate and state of charge is also observed. The proposed firming concept has been described theoretically and simulation results have also been demonstrated.
Photovoltaic (PV) power is an extensively used renewable energy resource, but its intermittent nature affects the power supply quality as it results in issues such as frequency aberrations and ...voltage variations. Battery Energy Storage Systems (BESS) are utilized to smooth out and resolve the fluctuation issues. However, a control method is required for BESS charging level regulation to prevent the need for larger storage systems and to extend its operational life through controlled charging/discharging. This paper proposes a novel solar power and battery state of charge (SoC) control technique through the incorporation of hydrogen electrolyzer (HE) fuel cell system and BESS. A machine learning based controller (MLC) is designed for dynamic control of the HE output to allow the dispatching of firmed PV power with controlled battery charging/discharging. The MLC takes the fluctuating power and various battery parameters as inputs and intelligently controls the HE output while obeying the imposed constraints. Results conclude that the MLC greatly reduces the PV power fluctuations and a comparison between the fuzzy logic control for SoC regulation shows that the MLC has better SoC management capability. The proposed methodology promotes the integration of hydrogen into the energy mix as a means for providing controlled solar power.
The increase in energy demand needs to be met with environmentally friendly resources to lower the production of greenhouse gases. Solar power is a popular choice as it is available in abundance and ...is relatively cheap. However, the large-scale penetration of intermittent solar Photovoltaic (PV) power causes multiple instabilities in the grid such as frequency issues and voltage deviations. To counteract these instabilities, Battery Energy Storage System (BESS) is integrated in the grid as it reduces the PV fluctuations and promotes optimal operation. However, storage systems are expensive and thus smoothing filters are also coupled with the BESS for cost reduction and power smoothing. Traditional filters such as Low Pass Filters (LPF) and Moving Average (MA) filters are capable of solar power smoothing but have poor power tracking capabilities. To compensate for the delayed power tracking, larger energy storage systems are required which in turn adds to the operational costs. This paper proposes a locally weighted filter for solar PV smoothing with BESS. The proposed filter has significantly better power tracking capabilities than both the LPF and MA filters. Thus, as compared to the conventional LPF and MA filters, the proposed filter can achieve better solar power smoothing with reduced time delay and optimum battery storage capacity.
Wind power is a widely utilized renewable energy resource, but the irregular nature of wind affects the resul-tant power supply quality and results in fluctuating power. Large scale penetration of ...variable power causes issues such as frequency variations and voltage alterations. Battery Energy Storage Systems (BESS) are effectively used to smooth out the power variations and to resolve the fluctuations caused issues. However, a suitable control strategy is required to regulate the wind power output and battery state of charge (SoC) levels as it otherwise leads to the requirement of larger storage systems. This paper proposes a novel feedforward neural network controller (FNNC) for wind power and SoC control through the regulation of the hydrogen electrolyzer (HE) fuel cell (FC) system output based on the battery parameters feedback. The proposed FNNC takes the fluctuating wind power and battery parameters such as SoC and charging/discharging power as inputs and intelligently controls the HE output while complying with the imposed controller constraints. Simulation results conclude that the FNNC considerably lessens the wind power fluctuations and a comparison study between the widely used fuzzy logic control for SoC management demonstrates that the FNNC has better SoC reduction capabilities. The proposed controller also significantly reduces the ramp rate and promotes the incorporation of green hydrogen into the energy mix via the HE-FC system as a way for delivering controlled wind power into the grid with battery life enhancement through SoC management.
Growing Unmanned Aerial Vehicle (UAV) market trends and interest in potential uses such as monitoring, visual inspection, object detection, and path planning have shown promising results using ...machine learning techniques. However, UAV adoption faces several challenges in real-life scenarios as lowaccuracy sensors are involved in the identification, tracking, and localization of UAVs. In order to overcome the aforementioned challenges, this paper proposes an intelligent machine learningbased system coupled with computer vision (CV) to detect objects and localize UAVs equipped with just a monocular camera. The experimental results using the Telo DJI drone demonstrate that the proposed methodology can detect, track objects, and localize the drone with high accuracy. The system's ability for automated monitoring in real environments can lend its uses for urban traffic, logistics, and security applications.
Wind power generation is an attractive renewable energy technology that promotes the reduction of greenhouse gases. Nevertheless, the inherent alternating nature of wind power affects the stability ...of the grid as it results in frequency variations, voltage deviations, and increased ramp rates. Battery Energy Storage Systems (BESS) are incorporated in the microgrid to alleviate the aforementioned issues and to promote optimal operation by reducing the power fluctuations. Additionally, power firming filters and algorithms are also combined with the batteries for ramp rate curtailment, power flattening, and cost reduction. Widely used filters such as Low Pass Filters (LPF) and Moving Average (MA) filters are capable filters for fluctuating power control but have poor power tracking capabilities. To account for the resultant power lag, bigger batteries are needed which increases the operating costs. This paper presents a Hodrick Prescott Decomposition filter for wind power firming and enhanced power tracking. Simulation results conclude that the proposed methodology has significantly better power flattening and tracking capability than both the LPF and MA filters. As compared to the traditional filters, the proposed filter leads to decreased battery charging/discharging and appropriate state of charge control which in turn reduces the size of the batteries required for optimal operation.
As smart grids are expected to revolutionize the current electrical systems, short-term load forecasting (STLF) has emerged as a critical issue that must be solved before the smart grid's ...applications can be fulfilled. Recently, the rise of big data combined with machine learning has made neural network a viable solution for STLF. However, the impact of weather parameters for residential loads forecast is rarely investigated, despite the fact that it is an essential element that impacts power consumption patterns. In this paper, a Nonlinear Autoregressive with Exogenous (NARX) input recurrent neural network is designed for STLF while considering the effects of different weather features. Analysis based on correlation heatmaps has been carried out to determine the relations of the weather features with the load power consumption as well as with the other weather features. Accordingly, based on the correlation heatmaps, two weather features namely dew point and wind speed have been chosen for accurate predictions. Moreover, a prediction comparison has also been performed to demonstrate how high correlation among the weather features can lead to overfitting, noisy, and less accurate power consumption predictions. The proposed models' load power consumption performance is tested on 108 residential loads using the UMass Smart* Dataset.
A key factor driving the development of autonomous vehicles (AVs) is safety. AVs should be able to navigate autonomously by generating appropriate waypoints and following them instantaneously. Thus, ...an effective path planning and trajectory tracking control system should be developed. Predicting the AV's future path is an important issue due to its importance in trajectory tracking approaches. This paper proposes a novel neural network (NN) model for AV trajectory forecasting based on the applied steering angle. Predicting future lateral position and yaw angle before applying the steering angle guarantees that the optimal steering angle is used to minimize error. The NN model has been tested on different driving scenarios and simulation results validate the suggested NN model's effectiveness.
