Optimalno punjenje flote električnih vozila zasnovano na modelskom prediktivnom upravljanju

master's thesis

• Title (english): Optimal electric vehicle fleet charging based on model predictive control
• Author: Luka Grden
• Mentor: Branimir Škugor (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2024-03-28, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mechanical Engineering

Abstract

Električna vozila (EV) igraju ključnu ulogu u transformaciji transportnog sektora prema održivom, čistom i energetski učinkovitom modelu. Njihovim usvajanjem postiže se smanjenje onečišćenja zraka i buke te stvara infrastrukturni okvir koji podržava ekološki prihvatljiv način prijevoza. Ipak, s rastućim brojem EV-a, javljaju se izazovi u vezi s potrebom za razvojem i poboljšanjem elektroenergetske infrastrukture. Osim mogućeg nedostatka dostupnih punionica, rastuća primjena EV također može predstavljati problem sa stanovišta eksploatacije postojećeg energetskog sustava, gdje pri punjenju sve većeg broja vozila dolazi do razmjernog povećanja opterećenja na električnoj mreži. Uzimajući u obzir dinamičke potrebe korisnika te promjenjive uvjete ponude i potražnje, upravljanje punjenjem ključno je pitanje kako bi se svim korisnicima osigurala pouzdana i učinkovita isporuka električne energije. U ovom diplomskom radu istražena su rješenja za upravljanje punjenjem flote EV s ciljem optimalne potrošnje energije i smanjenih troškova punjenja. Shodno tome, predstavljeni su agregatni i distribuirani model flote dostavnih EV, zajedno s njihovim parametrima i ulaznim razdiobama. Budući da usvojeni modeli flote EV imaju linearna svojstva, u literaturi prepoznata MILP metoda optimizacije korištena je u svrhu optimiranja van realnog vremena, ali i za postavljanje optimalnih upravljačkih strategija. Upravljačke strategije punjenja temeljene su na algoritmu modelskog prediktivnog upravljanja (eng. Model Predictive Control, MPC), pri čemu su razmatrana dva različita pristupa za punjenje u realnom vremenu. Pri tome, hijerarhijski pristup temelji se na optimizaciji punjenja na agregatnom modelu, nakon čega se optimalna vrijednost agregatne snage heurističkim algoritmom raspoređuje na pojedina EV. S druge strane, distribuirani pristup koristi optimizaciju direktno na razini individualnih EV. Uspoređujući s tupim punjenjem, gdje se vozila uvijek pune maksimalnom dostupnom snagom, konačni rezultati pokazuju kako se na oba načina mogu dobiti značajne uštede. Pri tome, hijerarhijski pristup uz smanjenu računalnu kompleksnost daje nešto lošija rješenja, dok distribuirani pristup daje rješenje koje je ekvivalentno globalnom optimumu dobivenom optimiranjem van realnog vremena na distribuiranom modelu. Također, razmatrani su slučajevi gdje se trošak punjenja u zadanom periodu optimira preko unaprijed poznatog profila cijene električne energije posebno, ali i u kombinaciji s ekonomski isplativom energijom iz obnovljivih izvora.

Abstract (english)

Electric vehicles (EV) play a crucial role in transforming the transportation sector towards a sustainable, clean, and energy-efficient model. Their adoption leads to a reduction in air and noise pollution and creates an infrastructure framework that supports environmentally friendly transportation. However, with the growing number of EVs, challenges arise regarding the need for development and improvement of the electric infrastructure. In addition to the potential lack of available charging stations, the increasing use of EVs can also pose a problem from the perspective of exploiting the existing electric energy system, where charging a growing number of EVs leads to a proportional increase in the load on the electrical grid. Taking into account the dynamic needs of users and variable supply and demand conditions, charging management is a crucial issue to ensure reliable and efficient delivery of electrical energy to all users. This master's thesis explores solutions for managing EV fleet charging with the aim of optimizing energy consumption to reduce charging costs. Accordingly, aggregate and distributed models of delivery EV fleets are presented, along with their parameters and input distributions. Since the adopted EV fleet models have linear properties, the mixed integer linear programming (MILP) optimization method recognized in the literature was used for offline optimization, as well as for setting the optimal control strategies. Control strategies for EV charging are based on the model predictive control (MPC) algorithm, with two different approaches considered. In this regard, the hierarchical approach is based on optimizing charging at the aggregate model level, after which the optimal value of aggregate power is heuristically allocated to individual EVs. On the other hand, the distributed approach uses optimization directly at the level of individual EVs. Compared with unmanaged charging, where vehicles are always charged with the maximum available power, the final results show that significant savings can be obtained in both ways. In this regard, the hierarchical approach provides slightly worse solutions with reduced computational complexity, while the distributed approach provides a solution equivalent to the global optimum obtained by the offline optimization on the distributed model. Also, scenarios were considered where the charging cost in a given period is optimized over a known profile of electricity price separately, and in combination with economically viable energy from renewable sources.

• Keywords: električna vozila; optimiranje punjenja; modelsko prediktivno upravljanje; mješovito cjelobrojno linearno programiranje; flota vozila
• Keywords (english): electric vehicles; charging optimization; model predictive control; mixed integer linear programming; vehicle fleet
• Language: croatian
• URN:NBN: urn:nbn:hr:235:435800
• Study programme: Title: Mechanical Engineering; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access; Embargo expiration date: 2025-03-21
• Created on: 2024-03-22 14:21:38