Stochastic optimization framework for online scheduling of an EV charging station in a residential place with photovoltaics and energy storage system

Abstract

House and building energy management systems (HEMS) are becoming key when it comes to assure grid stability and to offer flexibility. At the same time, energy systems technology has evolved to enable energy storage systems and electric vehicles to be managed together with local generated energy taking into consideration the preferences of the household owner. Contributing to this tendency, this work presents a stochastic optimization platform (SOFW) for optimal control using dynamic programming and stochastic optimization models. A stochastic optimization model involving a household composed of photovoltaics, energy storage system and an electric vehicle is designed and tested within SOFW. The uncertainties of the plugin time and state of charge of the battery of the electric vehicle are modeled using a Markovian process and a Monte-Carlo simulation. The results showed that the proposed stochastic optimization model can be solved using dynamic programming and deployed as a continuous optimal control within SOFW. The system will be deployed shortly in Italy within one use case of the Storage4Grid (S4G) project.

Cooperative Control of Charging Stations for an EV Park with Stochastic Dynamic Programming

“…This requirement was established during the development of the project, enabling edge computing as the pilar of the site's control. For this reason, the platform Raspberry Pi 3 with Raspbian (Debianbased) as operating system was chosen as the alternative for reading the inputs of smart meters and for containing the software of the stochastic optimization framework (SOFW) presented in [2] and discussed in the next sections.…”

“…The architecture of SOFW was already presented in a previous work [2]. The main objective of the framework is to help users to deploy stochastic optimization models into realtime applications such as the use case presented in this paper.…”

“…As a second novelty, our approach takes advantage of real-time information, of a stochastic optimization model as well as of PV prediction. It uses our own developed Stochastic Optimization Framework Software (SOFW) [2], which links real-time information from the system with the stochastic optimization model and returns the optimization results as setpoints for drivers controlling the charging stations. SOFW deploys a Model Predictive Control (MPC) system and applies stochastic dynamic programming to solve the stochastic optimization problem.…”

Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

“…Several studies in the literature, such as [12–15], have optimised EV charging in residential real estates. These studies optimise charging cost under TOU electricity pricing [12] or RT pricing [13–15]. However, these studies did not consider modern peak power‐based tariffs.…”