Stochastic Model Predictive Control for Multi-Energy Systems with High Penetration of Electric Vehicles

  • Anita Aliu

Student thesis: Phd


The growing adoption of electric vehicles presents an opportunity to explore the numerous benefits to network operators. For example, aggregated electric vehicles can replace peaking power plants traditionally used to satisfy peak energy demands. However, unlike other distributed energy resources, electric vehicles do not have fixed locations within single- or multi-energy systems as they can be connected to any charging station. This makes optimally coordinating their charge/discharge operations challenging, compounded when uncertainties related to electric vehicles' characteristics, availability, and charging preferences are considered. Presented is a generalised mobile storage model representing successive electric vehicles' charge/discharge operations that will utilise a charging station. The model is not restricted to a fixed number of electric vehicles and can be used to analyse the different ways charging stations are utilised in residential, commercial, and public areas. The mobile storage model extends a generalised modelling framework primarily developed for predictive control applications. Modifications are made to the entire framework for application in stochastic predictive control. The effectiveness of the modified framework is demonstrated with three representative case studies. One illustrates how the model is incorporated into the generalised framework and implemented within a deterministic energy management scheme. Results show significant cost savings when exploiting successive electric vehicles utilising a charging station. The other is used to demonstrate how uncertainties are incorporated within the generalised framework and implemented within a stochastic energy management scheme. Results show significant cost savings compared to the deterministic scheme. Finally, the last case study has a varying number of charging stations. It is used to analyse the performance of a stochastic scheme whose optimisation problem is designed to consider charge/discharge power smoothing applications. This is done to prevent damage when electric vehicles are used for ancillary services such as peak demand management. Analyses presented show the challenges in implementing the stochastic scheme in different areas.
Date of Award1 Aug 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorOgnjen Marjanovic (Supervisor) & Alessandra Parisio (Supervisor)


  • Mixed Integer Programming
  • Energy Management Scheme
  • Bi-Directional Power Flow
  • Stochastic Optimisation
  • Electric Vehicles
  • Model Predictive Control
  • Distributed Energy Resources
  • Multi-Energy Systems
  • Demand-Side Management

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