Cuckoo Search Inspired Hybridization of the Nelder-Mead Simplex Algorithm Applied to Optimization of Photovoltaic Cells

A new hybridization of the Cuckoo Search (CS) is developed and applied to optimize multi-cell solar systems; namely multi-junction and split spectrum cells. The new approach consists of combining the CS with the Nelder-Mead method. More precisely, instead of using single solutions as nests for the CS, we use the concept of a simplex which is used in the Nelder-Mead algorithm. This makes it possible to use the flip operation introduces in the Nelder-Mead algorithm instead of the Levy flight which is a standard part of the CS. In this way, the hybridized algorithm becomes more robust and less sensitive to parameter tuning which exists in CS. The goal of our work was to optimize the performance of multi-cell solar systems. Although the underlying problem consists of the minimization of a function of a relatively small number of parameters, the difficulty comes from the fact that the evaluation of the function is complex and only a small number of evaluations is possible. In our test, we show that the new method has a better performance when compared to similar but more compex hybridizations of Nelder-Mead algorithm using genetic algorithms or particle swarm optimization on standard benchmark functions. Finally, we show that the new method outperforms some standard meta-heuristics for the problem of interest

Scheduling electric vehicle charging at park-and-ride facilities to flatten duck curves

In this paper, we explore present a scheduling framework for large-scale electric vehicle charging to flatten duck curves stemming from the imbalance between peak electricity demand and renewable energy production. This situation adds new constraints to power system operations and increases maintenance costs. The focus is on charging systems installed at park-and-ride facilities which are gaining popularity in metropolitan cities. The scheduling problem is modeled as an integer linear problem and various case studies are generated and solved using real-world collected data. The computational experiments show that significant savings can be achieved in reducing power system ramping requirements

An online model for scheduling electric vehicle charging at park-and-ride facilities for flattening solar duck curves

Electrical power systems with high solar generation experience a phenomena called "duck curve" which require conventional power generators to quickly ramp-up their output, thus resulting in financial losses. In this paper, we propose an online model (OLM) for scheduling the charging of electric vehicles (EV) located at park-and-ride facilities for flattening solar "duck curves". This model provides a significant improvement to existing ones for similar systems in the sense that the availability of information is related to the time period for which the optimization is done. In addition, a procedure for finding the schedules for EV charging that significantly decreases the ramping requirements is introduced. Proposed procedure includes a combination of a heuristic function and a neural network (NN) to make a decision on which EVs will be charged at each time period. The training of the NN is done based on optimal solutions for problem instances corresponding to the full information model (FIM). The computational experiments have been performed for instances reflecting different levels of solar generation and EV adoptions and prove highly promising. They show that the OLM manages to find schedules of similar quality as the FIM, while having some more desirable properties

Spectral Method for Solving the Nonlinear Thomas-Fermi Equation Based on Exponential Functions

We present an efficient spectral methods solver for the Thomas-Fermi equation for neutral atoms in a semi-infinite domain. The ordinary differential equation has been solved by applying a spectral method using an exponential basis set. One of the main advantages of this approach, when compared to other relevant applications of spectral methods, is that the underlying integrals can be solved analytically and numerical integration can be avoided. The nonlinear algebraic system of equations that is derived using this method is solved using a minimization approach. The presented method has shown robustness in the sense that it can find high precision solution for a wide range of parameters that define the basis set. In our test, we show that the new approach can achieve a very high rate of convergence using a small number of bases elements. We also present a comparison of recently published results for this problem using spectral methods based on several different basis sets. The comparison shows that our method is highly competitive and in many aspects outperforms the previous work

Improved ACO Algorithm with Pheromone Correction Strategy for the Traveling Salesman Problem

A new, improved ant colony optimization (ACO) algorithm with novel pheromone correction strategy is introduced. It is implemented and tested on the traveling salesman problem (TSP). Algorithm modification is based on undesirability of some elements of the current best found solution. The pheromone values for highly undesirable links are significantly lowered by this a posteriori heuristic. This new hybridized algorithm with the strategy for avoiding stagnation by leaving local optima was tested on standard benchmark problems from the TSPLIB library and superiority of our method to the basic ACO and also to the particle swarm optimization (PSO) is shown. The best found solutions are improved, as well as the mean values for multiple runs. The computation cost increase for our modification is negligible