Machine learning for solving charging infrastructure planning problems : a comprehensive review

As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%. View Full-Tex

An efficient chameleon swarm algorithm for economic load dispatch problem

Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16×10−13, 4.16×10−12 and 1.28×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41×10−13 , 8.92×10−13 and 1.68×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work

Electric vehicles charging infrastructure demand and deployment : challenges and solutions

Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper

Performance of gradient-based optimizer on charging station placement problem

The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer

Economic Load Dispatch problem based on Search and Rescue optimization algorithm

The Search and Rescue optimization algorithm (SAR) is a recent metaheuristic inspired by the exploration’s behaviour for humans throughout search and rescue processes. The SAR is applied to solve the Combined Emission and Economic Dispatch (CEED) and Economic Load Dispatch (ELD). The comparative performance of SAR against several metaheuristic methods was performed to assess its reliability. These algorithms include the Earthworm optimization algorithm (EWA), Grey wolf optimizer (GWO), Tunicate Swarm Algorithm (TSA) and Elephant Herding Optimization (EHO) for the same two networks study. Also, the proposed SAR method is compared with other literature algorithms such as Sine Cosine algorithm, Monarch butterfly optimization, Artificial Bee Colony, Chimp Optimization Algorithm, Moth search algorithm. The cases applied in this work are seven cases: three cases of 6-unit for ELD issue, three cases of 6-unit for CEED issue and 10-unit for ELD problem. The evaluation of counterparts is performed for 30 different runs based on measuring the Friedman rank test and robustness curves. Furthermore, the standard deviation, maximum objective function, minimum, mean and values over 30 different runs are applied for a statistical analysis of all used techniques. The obtained results proved the superiority of the SAR in determining the fitness function of ELD and CEED is minimizing the cost of fuel for ELD and emission and fuel costs for CEED

A novel gradient based optimizer for solving unit commitment problem

Secure and economic operation of the power system is one of the prime concerns for the engineers of 21st century. Unit Commitment (UC) represents an enhancement problem for controlling the operating schedule of units in each hour interval with different loads at various technical and environmental constraints. UC is one of the complex optimization tasks performed by power plant engineers for regular planning and operation of power system. Researchers have used a number of metaheuristics (MH) for solving this complex and demanding problem. This work aims to test the Gradient Based Optimizer (GBO) performance for treating with the UC problem. The evaluation of GBO is applied on five cases study, first case is power system network with 4-unit and the second case is power system network with 10-unit, then 20 units, then 40 units, and 100-unit system. Simulation results establish the efficacy and robustness of GBO in solving UC problem as compared to other metaheuristics such as Differential Evolution, Enhanced Genetic Algorithm, Lagrangian Relaxation, Genetic Algorithm, Ionic Bond-direct Particle Swarm Optimization, Bacteria Foraging Algorithm and Grey Wolf Algorithm. The GBO method achieve the lowest average run time than the competitor methods. The best cost function for all systems used in this work is achieved by the GBO technique

Identification of parameters in photovoltaic models through a runge kutta optimizer

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively

An enhanced gradient-based optimizer for parameter estimation of various solar photovoltaic models

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current–voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the Gradient-Based Optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder–Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed; therefore, the proposed algorithm is called a Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E−04, case 2 is 2.43E−04, and the average integral absolute error and relative error values are 1.05E−02 and 3.51E−03, respectively, for all case studies. With Friedman’s rank test values of 2.21 for numerical optimization and 1.66 for parameter estimation optimization, the CCNMGBO stood first among all selected algorithms

An efficient and reliable scheduling algorithm for unit commitment scheme in microgrid systems using enhanced mixed integer particle swarm optimizer considering uncertainties

The use of an electrical energy storage system (EESS) in a microgrid (MG) is widely recognized as a feasible method for mitigating the unpredictability and stochastic nature of sustainable distributed generators and other intermittent energy sources. The battery energy storage (BES) system is the most effective of the several power storage methods available today. The unit commitment (UC) determines the number of dedicated dispatchable distributed generators, respective power, the amount of energy transferred to and absorbed from the microgrid, as well as the power and influence of EESSs, among other factors. The BES deterioration is considered in the UC conceptualization, and an enhanced mixed particle swarm optimizer (EMPSO) is suggested to solve UC in MGs with EESS. Compared to the traditional PSO, the acceleration constants in EMPSO are exponentially adapted, and the inertial weight in EMPSO decreases linearly during each iteration. The proposed EMPSO is a mixed integer optimization algorithm that can handle continuous, binary, and integer variables. A part of the decision variables in EMPSO is transformed into a binary variable by introducing the quadratic transfer function (TF). This paper also considers the uncertainties in renewable power generation, load demand, and electricity market prices. In addition, a case study with a multiobjective optimization function with MG operating cost and BES deterioration defines the additional UC problem discussed in this paper. The transformation of a single-objective model into a multiobjective optimization model is carried out using the weighted sum approach, and the impacts of different weights on the operating cost and lifespan of the BES are also analyzed. The performance of the EMPSO with quadratic TF (EMPSO-Q) is compared with EMPSO with V-shaped TF (EMPSO-V), EMPSO with S-shaped TF (EMPSO-S), and PSO with S-shaped TF (PSO-S). The performance of EMPSO-Q is 15%, 35%, and 45% better than EMPSO-V, EMPSO-S, and PSO-S, respectively. In addition, when uncertainties are considered, the operating cost falls from $8729.87 to$8986.98. Considering BES deterioration, the BES lifespan improves from 350 to 590, and the operating cost increases from $8729.87 to$8917.7. Therefore, the obtained results prove that the EMPSO-Q algorithm could effectively and efficiently handle the UC problem