Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles

Abstract

The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared.

How Smart Metering and Smart Charging may Help a Local Energy Community in Collective Self-Consumption in Presence of Electric Vehicles

“…Coordinating the charging of many electric vehicles via V1G can also reduce the total costs of ownership [46]. For a single user, demand charge management via V1G can synchronize the charging to the over-generation of the roof-mounted photovoltaic plant so to maximize self-consumption [47]; similarly, V1G can apply a time-of-use tariff in order to reduce the electricity bill [48]. Similarly, demand charge management via V1G can coordinate the charging of electric vehicles in a car park [49,50] or in a narrow geographical border [51], applying machine learning methods [52,53], taking into account the users' preferences [54] or the batteries' state of health [55], thus limiting the demand during peak hours and, in general, providing valuable grid services to network operators.…”

Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization

“…However, the dependence of the charging profile on the charging rate (C-rate) is not considered. The reference [33] shows the implementation of the smart charging in an aggregate residential building, which aims to improve the self-consumption by the integration of photovoltaic and EVs. On the other hand, [34] presents an optimization strategy of power flows among the PV system, the grid, and EVs in a workplace.…”

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

“…EV charging peak load limitations have been studied from different viewpoints, e.g. in cases of non‐residential building [19], apartment building energy community [20], and residential real estate [21]. In [19], a real‐time valley‐filling algorithm to reduce peak demand in commercial and industrial buildings is proposed.…”

“…The peak power limit for a new month is estimated based on historical data, and then, if necessary, the limit is adjusted to a new level based on real‐time measurements. In [21], the EV charging scheduling objective is to flatten the net load profile of a residential real estate with a photovoltaic (PV) generator. The charging scheduling problems were formulated and solved with quadratic programming approaches.…”

“…The algorithms presented in these studies limit peak loading, but they share common shortcomings when it comes to real‐life solutions. EVs are often assumed to use the charging current set by the EV supply equipment (EVSE) as in [19–21], but the EVSE can only set the maximum charging current, and the EV on‐board charger (OBC) decides the charging current below this limit. Therefore, an EV might not utilise the whole charging capacity allocated to it.…”