Optimal Planning of Microgrid-Integrated Battery Energy Storage

Battery energy storage (BES) is a core component in reliable, resilient, and cost-effective operation of microgrids. When appropriately sized, BES can provide the microgrid with both economic and technical benefits. Besides the BES size, it is found that there are mainly three planning parameters that impact the BES performance, including the BES integration configuration, technology, and depth of discharge. In this dissertation, the impact of each one of these parameters on the microgrid-integrated BES planning problem is investigated. Three microgrid-integrated BES planning models are developed to individually find the optimal values for the aforementioned parameters. These three microgrid-integrated BES planning models are then combined and extended, by including the impact of microgrid islanding incidents on the BES planning solution, to develop a comprehensive planning model that can be used by microgrid planners to simultaneously determine the installed BES optimal size, integration configuration, technology, and maximum depth of discharge. Besides applications in microgrids, this dissertation investigates the integration of BES to provide other types of support in distribution networks such as load management of commercial and industrial customers, distribution network expansion, and solar PV ramp rate control

Lipid nanocarriers overlaid with chitosan for brain delivery of berberine via the nasal route

This research aimed to design, optimize, and evaluate berberine-laden nanostructured lipid carriers overlaid with chitosan (BER-CTS-NLCs) for efficient brain delivery via the intranasal route. The nanostructured lipid carriers containing berberine (BER-NLCs) were formulated via hot homogenization and ultrasonication strategy and optimized for the influence of a variety of causal variables, including the amount of glycerol monostearate (solid lipid), poloxamer 407 (surfactant) concentration, and oleic acid (liquid lipid) amount, on size of the particles, entrapment, and the total drug release after 24 h. The optimal BER-NLCs formulation was then coated with chitosan. Their diameter, in vitro release, surface charge, morphology, ex vivo permeability, pH, histological, and in vivo (pharmacokinetics and brain uptake) parameters were estimated. BER-CTS-NLCs had a size of 180.9 ± 4.3 nm, sustained-release properties, positive surface charge of 36.8 mV, and augmented ex-vivo permeation via nasal mucosa. The histopathological assessment revealed that the BER-CTS-NLCs system is safe for nasal delivery. Pharmacokinetic and brain accumulation experiments showed that animals treated intranasally with BER-CTS-NLCs had substantially greater drug levels in the brain. The ratios of BER brain/blood levels at 30 min, AUCbrain/AUCblood, drug transport percentage, and drug targeting efficiency for BER-CTS-NLCs (IN) were higher compared to BER solution (IN), suggesting enhanced brain targeting. The optimized nanoparticulate system is speculated to be a successful approach for boosting the effect of BER in treating CNS diseases, such as Alzheimer’s disease, through intranasal therapy

Proton Exchange Membrane Fuel Cells Modeling Using Chaos Game Optimization Technique

For the precise simulation performance, the accuracy of fuel cell modeling is important. Therefore, this paper presents a developed optimization method called Chaos Game Optimization Algorithm (CGO). The developed method provides the ability to accurately model the proton exchange membrane fuel cell (PEMFC). The accuracy of the model is tested by comparing the simulation results with the practical measurements of several standard PEMFCs such as Ballard Mark V, AVISTA SR-12.5 kW, and 6 kW of the Nedstack PS6 stacks. The complexity of the studied problem stems from the nonlinearity of the PEMFC polarization curve that leads to a nonlinear optimization problem, which must be solved to determine the seven PEMFC design variables. The objective function is formulated mathematically as the total error squared between the laboratory measured terminal voltage of PEMFC and the estimated terminal voltage yields from the simulation results using the developed model. The CGO is used to find the best way to fulfill the preset requirements of the objective function. The results of the simulation are tested under different temperature and pressure conditions. Moreover, the results of the proposed CGO simulations are compared with alternative optimization methods showing higher accuracy

A Comprehensive Battery Energy Storage Optimal Sizing Model for Microgrid Applications

Microgrids expansion problems with battery energy storage (BES) have gained great attention in recent years. To ensure reliable, resilient, and cost-effective operation of microgrids, the installed BES must be optimally sized. However, critical factors that have a great impact on the accuracy and practicality of the BES sizing results are normally overlooked. These factors include the wide range of characteristics for different technologies, the distributed deployment, the impact of depth of discharge and the number of charging/discharging cycles on the BES degradation, and the coordination of microgrid operation modes. Thus, this paper proposes a comprehensive BES sizing model for microgrid applications, which takes these critical factors into account when solving the microgrid expansion problem and accordingly returns the optimal BES size, technology, number, and maximum depth of discharge. The microgrid expansion problem is formulated using mixed integer linear programming. The nonlinear relationship between the BES depth of discharge and lifecycle is linearized using piecewise linearization technique and implemented to model the BES degradation. The proposed model is validated using a test microgrid. The conducted numerical simulation shows that the proposed model is able to determine the optimal BES size, technology, number, and maximum depth of discharge and further enhances the accuracy and practicality of the BES sizing solutions

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression

An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO–MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kWp rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system

Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components

Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV penetration causing a lot of problems to power system stability, sustainability, reliable electricity production, and power quality, it is critical to forecast SPV power using climatic parameters. The suggested model is built with meteorological conditions as input parameters, and the effects of such variables on predicted SPV power have been studied. The primary goal of this study is to examine the effectiveness of optimization-based SPV power forecasting models based on meteorological conditions using the novel salp swarm algorithm due to its excellent ability for exploration and exploitation. To forecast SPV power, a recently designed approach that is based on the salp swarm algorithm (SSA) is used. The performance of the suggested optimization model is estimated in terms of statistical parameters which include Root Mean Square Error (RMSE), Mean Square Error (MSE), and Training Time (TT). To test the reliability and validity, the proposed algorithm is compared to grey wolf optimization (GWO) and the Levenberg–Marquardt-based artificial neural network algorithm. The values of RMSE and MSE obtained using the proposed SSA algorithm come out as 1.45% and 2.12% which are lesser when compared with other algorithms. Likewise, the TT for SSA is 12.46 s which is less than that of GWO by 8.15 s. The proposed model outperforms other intelligent techniques in terms of performance and robustness. The suggested method is applicable for load management operations in a microgrid environment. Moreover, the proposed study may serve as a road map for the Saudi government’s Vision 2030

State-Of-The-Art in Microgrid-Integrated Distributed Energy Storage Sizing

Distributed energy storage (DES) plays an important role in microgrid operation and control, as it can potentially improve local reliability and resilience, reduce operation cost, and mitigate challenges caused by high penetration renewable generation. However, to ensure an acceptable economic and technical performance, DES must be optimally sized and placed. This paper reviews the existing DES sizing methods for microgrid applications and presents a generic sizing method that enables microgrid planners to efficiently determine the optimal DES size, technology, and location. The proposed method takes into consideration the impact of DES operation on its lifetime to enhance the obtained results accuracy and practicality. The presented model can be used for both grid-tied (considering both grid-connected and islanded modes) and isolated microgrids