Elektrikli araç şarj istasyonlarında sezgisel yöntemler kullanılarak talep cevabı tabanlı optimum enerji yönetimi
Demand response based optimum energy management using heuristic methods in electric vehicle charging stations
Yazar: BİLAL CANOL
Danışman: PROF. DR. BELGİN TÜRKAY
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Elektrik Mühendisliği Ana Bilim Dalı / Elektrik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2023
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering
Dizin: Elektrikli araçlar = Electric vehicles ; Enerji yönetimi = Energy management ; Sezgisel yöntemler = Heuristic methods ; Şarj istasyonları = Charging stations

Özet:
Günümüzde elektrik enerjisi insanoğlunun günlük hayatının her aşamasında sürekli ve kesintisiz olarak kullandığı vazgeçilmez enerji kaynakları arasında bulunmaktadır. Hem dünyanın genel nüfus miktarındaki artışı hem de teknolojik gelişmelerin elektrik enerjisinin kullanımına daha sık yönelmesinden dolayı elektrik enerjisine olan talep son yüzyılda da olduğu gibi gün geçtikçe artış göstermektedir. 
Elektrik enerjisine olan talep miktarının artması Tellegen teoremine göre, elektrik enerjisinin üretimine olan ihtiyacında aynı oranda artış göstermesi gerektiğini belirtmektedir. Ülkelerin elektrik enerjisi üretiminin bir bölümünde ise ısıl değeri yüksek fosil yakıtlar temel hammadde olarak kullanılmaktadır. Fosil yakıt tüketimi ise sera gazı emisyonunu arttırmakta ve bununla birlikte küresel ısınma ve iklim değişikliğiyle sadece insan hayatını değil dünyadaki tüm canlıların yaşamını tehdit etmektedir. 
Fosil yakıt tüketimi sadece enerji üretiminde değil, ulaşım sektörünün de can damarını oluşturmaktadır. Petrol ve türevlerinin gelecekteki belirsiz krizlere yol açabilmesinin yanı sıra tükenebilir bir enerji kaynağı olması, karayolu taşımacılığını geleneksel içten yanmalı motorlu araçlardan farklı tipte araçlara yöneltmektedir. Hem bahsedilen bu sebeplerden dolayı hem de hava ve çevre kirliliği bakımından sera gazı emisyonları azaltmak için ulaşım sektöründe elektrikli araçların kullanımı sıklıkla teşvik edilmektedir.
Son yıllarda elektrikli araç sayısındaki büyük miktardaki artış, farklı talep cevabı programlarını ve elektrikli araçların şarj yönetimi ile araştırmacıların odak noktası haline gelmiştir. Elektrikli araçlar, bulundurdukları bataryayla birlikte farklı güç aktarım tekniklerinin kullanılabildiği, yer değiştirebilen esnek yüklerdir. Bununla birlikte dağıtım sistemine bağlı çok sayıdaki elektrikli araç, güç sisteminde gerilim ve frekans dalgalanmaları, harmonik oluşumlar gibi farklı güç kalitesi sorunlarına ve arz-talep dengesinin bozulması ile yük profilinde puant güç talebinin artmasına neden olabilmektedir.
Bu tez çalışmasında ise, elektrikli araç otoparklarında optimum şarj planlamasını hedefleyen, sezgisel algoritmalardan Genetik Algoritma (GA) ve Parçacık Sürü Optimizasyonu (PSO) tabanlı enerji yönetim modeli önerilmiştir. Önerilen yöntemler, puant yük sınırlamasını temel alarak güç profilinde yük faktörünün azami değere getirilmesini amaçlamaktadır. Elektrikli araçlar ve elektrikli araç şarj istasyonlarıyla ilgili matematiksel modelleme oluşturulmuş ve uygulamadaki kısıtlar ele alınmıştır. Ayrıca araçtan şebekeye enerji aktarımının (V2G) yanı sıra elektrikli araçların otoparka varış zamanı ve vardıkları andaki enerji durumları gibi belirsizlikleri de dikkate alan bir talep cevabı sistemi geliştirilmiştir. 5 farklı durum analizini içeren ilgili enerji yönetim sistemi, kullanılan algoritmalar ve analiz yöntemleri ise MATLAB ortamında gerçekleştirilmiştir. 5 farklı durum analizinin içerisinde çeşitli puant yük sınırlaması ile farklı miktarlarda V2G işlem gücünü içermektedir. Sonuçlar incelendiğinde önerilen enerji yönetim sisteminin kullanıldığı durum analizlerinde yük faktörü değerinin kayda değer miktarda yükseltilebildiği farkedilmiştir. Ayrıca önerilen yöntemlerde analiz sonuçları karşılaştırıldığında, GA ile elde edilen yük faktörü değerlerinin PSO'ya göre daha iyi olduğu görülmüştür.

Abstract:
Today, electric energy is one of the essential sources of energy that human beings use continuously and uninterruptedly at every stage of their daily lives. The demand for electrical energy is increasing day by day, as it has been in the last century, due to both the increase in the world's general population and the technological developments that tend to use electrical energy more frequently.
The increase in demand for electric power indicates that, according to the Tellegen theorem, the need for electric power generation is expected to increase at the same pace. Fossil fuels with high calorific value are used as the basic raw material in a significant share of the electrical energy production of the countries. Fossil fuel consumption, on the other hand, increases greenhouse gas emissions, and with it, global warming and climate change threaten not only human life but all living things in the world. The consumption of fossil fuels is the vital element not only in energy production, but also in the transport sector. The amount of carbon dioxide emissions in the transportation sector constitutes about a quarter of the global energy-related carbon dioxide emissions. Due to problem of the depletion of these fossil fuels, which have a significant share in electrical energy production, the trend towards modern electricity networks has increased. The fact that petroleum and its derivatives can cause uncertain crises in the future, as well as being an exhaustible energy source, directs road transport to different types of vehicles from traditional internal combustion engine vehicles. The use of electric vehicles in transport is frequently encouraged both for these reasons and in order to reduce greenhouse gas emissions in terms of air pollution and the environment. 
The massive increase in the number of electric vehicles over the last few years has become the focus of researchers with various demand response programs and charging load management of electric vehicles. Electric vehicles are flexible loads that can be moved, where different power transmission techniques can be used together with the battery that they contain. However, a large number of electric vehicles connected to the distribution system can cause different power quality problems such as voltage and frequency fluctuations, harmonic formations in the power system, and an increase in peak power demand in the load profile with the deterioration of the supply-demand balance. When electric vehicles are considered as moving load and energy storage units, they provide flexibility in grid management in terms of electrical power systems thanks to demand-side management. For this reason, many studies are carried out all over the world on the charging management of electric vehicles at electric vehicle parking lots. According to recent studies in the literature, heuristic algorithms used to optimize charging management in electric vehicle charging stations have been the focus of researchers and have been used in many studies. Today, amongst heuristic algorithms, genetic algorithms and particle swarm algorithms are widely used for optimisation problems. It is one of the reasons to prefer that heuristic algorithms can be applied to complex problems that are difficult to solve, together with linear or nonlinear equations and constraints. Generally, in studies on electric vehicle charging stations, profit maximization was aimed in the literature and the load factor value was ignored.
In computer science, genetic algorithm is the most popular type of evolutionary algorithms, which can process natural evolution processes in the computer environment in the most precise way. The GA was originally proposed by John Holland and his students in 1970 for adaptive search and system design. Today, it is often used in solving optimization problems, machine learning, and pattern recognition issues. The genetic algorithm first initiates the first generation with an initial population. The initial population is created with a few chromosomes. Chromosomes consist of a series of genes. After the initial population is created, crossover and mutation operators are applied for the selection process and the fitness value is determined for each individual in the chromosome. At the end of each generation created, the genetic algorithm performs elitism, comparing newly created populations with previous populations. These operators and their parameters have an impact on the genetic algorithm's performance. Operators of natural selection, crossover and mutation continue to a fixed number of generations or until a termination condition is met, typically for optimization problems.
Particle swarm optimization is a stochastic population-based heuristic optimization method, first put forward by J. Kennedy and R. C. Eberhart in 1995, inspired by the movement of animal herds in nature, that tries to iteratively improve a candidate solution. Particle swarm optimization takes into account the particles and swarms that constitute the solution to a problem. Compared with other evolutionary algorithms, the concept of individual and population in the genetic algorithm matches the terms of particles and swarm, respectively, in particle swarm optimization. Each of the particles is trying to move towards the best position according to the previous experience of both itself and other particles. In order for the particles to move in the research space, the next iteration is carried out by updating the velocity and position vectors of the particles. Following the update of the position and velocity vectors, for each iteration, the best particle value since the beginning is called the best regional solution and the best solution of the swarm is called the global best solution. In this way, optimum solutions are tried to be obtained.
In this thesis, a demand response based energy management model is proposed, which aims at optimum charging planning based on day ahead planning in electric vehicle parking lots. In the solution method of the proposed model, the genetic algorithm and particle swarm optimization, which are heuristic algorithms, are employed. The proposed methods aim to maximize the load factor in the power profile based on the peak load limitation. The load serving entity or the system operator is ordered to postpone the desired amount of loads from the electric vehicle charging station to a future time and to transfer energy from the vehicle to the grid within certain time intervals. Mathematical modeling of electric vehicles and electric vehicle charging stations has been made and the limitations of the application have been taken into account. The mathematical model includes that the load serving entity or system operator is instructed to postpone the desired amount of load from the electric vehicle charging station to a future time and transfer energy from the vehicle to the grid at certain time intervals. In addition, a demand response system has been developed that takes into account uncertainties such as the arrival time of electric vehicles at the electric vehicle parking lot and the energy status at the time of arrival. The determination of the battery energy level of electric vehicles at the moment they arrive at the electric vehicle parking lot is obtained using the normal distribution. The two-parameter Weibull distribution is used to determine the arrival time of electric vehicles in the electric vehicle parking lot. In this way, it is aimed to obtain more realistic results by using the data parameters of a real car park.
The associated energy management system, consisting of five different case studies, as well as the algorithms and analytical methods used were performed in the MATLAB environment. Five different case studies involve varying amounts of vehicle-to-grid energy with various peak load limitations. When the results are examined, it is observed that the load factor value can be increased significantly in the case analyzes where the proposed energy management system is used. In addition, when the analysis results are compared in the proposed methods, it is seen that the load factor values obtained with GA were better than PSO.