Dynamic Pricing for Load Balancing in Electric Vehicle Charging Stations: An Integration with Open Charge Point Protocol ^†

Abstract

Given the environmental threats, the adoption of green and clean mobility is crucial for decarbonizing the mobility sector. Green mobility will bring a mass integration of electric vehicle charging stations (EVCSs) to ensure sufficiency for electric vehicle (EVs) users. To achieve this, intelligently distributing the charging load of EVs is essential to prevent stress on local electrical grids. The uneven distribution of EV charging at specific EVCSs leads to load imbalances compared to underutilized stations, necessitating dynamic load-balancing (in real time) mechanisms to optimize grid demands and prevent overloading. To address this challenge, the authors propose an algorithm for balancing EV loads at EVCSs using dynamic charging prices. This algorithm is intended to be integrated into the OCPP. Simulation results indicate that lower pricing at Station A (0.22 $/kWh) attracts more users, reducing congestion at higher-priced Stations B (0.31 $/kWh) and E (0.29 $/kWh). The proposed model encourages users to utilize less crowded stations, achieving a fairer distribution of EV charging demand while providing cost benefits to users selecting those stations.

1. Introduction

In the pursuit of decarbonizing mobility and achieving greenhouse gas emission neutrality, the global community is making concerted efforts to make road transportation eco-friendlier. One of the key strategies being employed is the promotion of electric vehicles (EVs) and hybrid electric vehicles (HEVs). Morocco, among other countries, has embraced this approach and is employing various measures to facilitate the transition to EVs and HEVs. These measures include tax incentives for EV and HEV owners and replacing government vehicles with EVs and HEVs. Given that road transportation contributes to 23% of Morocco’s CO2 emissions, these strategies are anticipated to significantly contribute to the country’s decarburization efforts [1,2]. While these strategies hold the potential to significantly decarbonize mobility, they also pose a unique challenge to electricity distributors. The widespread adoption of EVs and HEVs will inevitably lead to the substantial integration of electric vehicle supply equipment (EVSE) into the grid. This integration will result in an additional, and often unpredictable, load. To address this issue, numerous research studies have focused on predicting the energy consumption of EVSE and identifying its charging patterns through the use of clustering algorithms.

As this works based on prediction and load classification seems to be effective, and the distributors can predict and have mastery over charging patterns, it should have an effective way to load balance inside the grid during peak period, and aim for a balance between energy production and consumption. V2G protocols are the key to this subject; these are protocols between central management systems which manage EV chargers, and also protocols between EVSE, EVs and HEVs. OCPP, OpenADR, Chademo, CCS, GB/T, ISO15118, and IEC61851 are considered the most adopted V2G protocols by CMS, EVSE, EVs and HEVs. In [3], authors proposed a smart system for managing the operations of grid-connected charging stations for electric vehicles (EVs) that use photovoltaic (PV) sources based on the OCPP, for the same objective in [4], where an extension of the OCPP standard was proposed with the aim of including the user in the charging optimization process. For more flexibility, [5] propose a new approach to the design of smart charging systems. They aim to separate the role of the smart charging service provider (SCSP) from the role of the charge point operator (CPO) to provide real flexibility and efficiency. Incorporating the OCPP in an off-grid context, the authors in [6] created a communicative structure that improves the management and functionality of remote EV charging stations. In [7], the authors provide guidelines for charging service providers to set appropriate charging prices and manage electricity, aiming to balance profitability, customer satisfaction, and grid impact despite uncertainties. In [8], the authors propose dynamically adjusting price incentives to guide plug-in electric vehicles (PEVs) towards less popular or underutilized charging stations (CSs). Additionally, in [9], the authors introduce a pricing strategy based on micro-grid dispatching requirements, effectively managing electric vehicle charging loads through price signals.

This paper is divided into sections. Section 1 is about the general introduction of the work, and Section 2 is for the presentation of advantages offered by vehicle-to-grid protocols. Section 3 presents the OCPP protocol and its historical development from its creation until now, then we present in Section 4 the proposed algorithm for dynamic pricing for EVCSs with the aim of load balancing between EVCSs to avoid stress on specific stations and also on the local grid. Finally, we conclude this work by giving insight into the results and perspectives for future research papers.

2. Advantages of Vehicle-to-Grid Protocols

V2G protocols are standards and technologies that enable EVs to interact with the power grid. These protocols facilitate the bidirectional flow of electricity and information between EVs and the grid, allowing vehicles to not only be charged from the grid but also discharge electricity back to it. These protocols have several advantages for the distribution grid.

• Smart Charging: This concept is about the intelligent management of electric vehicle charging operations. It is about dynamically regulating between the charging speed, charging duration, and power flow based on multiple factors. For instance, during peak electricity demand periods, the charging speed could be decreased to avoid overloading the electricity distribution grid. In contrast, during periods of low power demand or high renewable energy generation, charging could be accelerated so as to maximize profit from renewable energy generation, which is generally not storable. Smart charging can also involve price-responsive charging, where the charging speed is adjusted based on real-time electricity prices.
• Transaction Optimization: This involves the efficient management of charging transactions to maximize the use of charging infrastructure and minimize costs. For example, the system could prioritize charging for vehicles that are near to having 100% in SOC (state of charge) or for those that have a nearer departure time and so will leave soon. It could also involve load balancing between all of the charging stations which belong to the same central management system to avoid overloading a charging station. Meanwhile, any errors or issues that occur during a transaction can be handled gracefully, with clear information provided to the user. With OCPP, operators can collect and store data on charging station usage patterns, such as the time of day and the duration of charging sessions. This data can be used to optimize pricing strategies and tailor pricing plans for specific user groups. Additionally, OCPP supports dynamic pricing, enabling operators to adjust real-time pricing based on factors such as demand, energy costs, and supply constraints [10].
• Device Management: This concerns the remote monitoring, configuration, and control of charging stations. For instance, the central system can remotely update the firmware of a charging station which is under its control, adjust its settings, or diagnose any issues, and perform software maintenance in case of bugs or cyberattacks. This allows for the efficient management of a large network of charging stations, reducing the need for on-site maintenance and enabling quick responses to any problems, and also allows data storage of all actions including electricity consumption in terms of power demand and energy consumption.
• Interoperability: This implicates the ability of different charging stations and central management systems to work together seamlessly. OCPP offers the ability for central management systems and charging stations to be synchronized with each other regardless of the manufacturer or service provider. This is enabled by the use of standard protocols like OCPP 2.0, which present a common language standard for communication between these systems. Interoperability is crucial for creating a flexible and user-friendly charging infrastructure, as it allows an electric vehicle to charge at any station and enables a station to serve any vehicle. It also facilitates the integration of charging infrastructure with other systems, such as energy management systems or grid operators.
• Load balancing: EV-charging load balancing is an advanced energy management system for EV charging; it implies the optimization of the ability to charge multiple EVs simultaneously based on real-time data collected and analyzed from multiple connected chargers and building infrastructure. Effective dynamic load balancing ensures efficient distribution of power across all chargers, preventing overload and maximizing system capacity. As EV charging patterns change during the day, load balancing serves to reduce strain on the building infrastructure by managing the EV’s load. By actively supervising site and charger load in real time, the system ensures that all EVs receive sufficient charging power while maintaining the highest degree of efficiency [11].

Figure 1 illustrates the interaction architecture of electric vehicles with a charging station and between a charging station and central management system and the protocols used in each interaction.

3. Open Charge Point Protocol

3.1. OCPP

The Open Charge Point Protocol is an open communication standard introduced by the OCA Open Charging Alliance. It is mainly used to figure out the difficulties of interactions between charging networks. OCPP supports communication between the charging point and the supplier’s central management system [12]. OCPP is an open standard protocol used to establish communication between the charging infrastructures and central management system using two formats—SOAP/XML and JSON—over Web-Socket [13].

OCPP offers multiple advantages to EV use and electricity suppliers, and also to the supplier’s management system. These advantages are measured in terms of energy management and are also about economic cost.

3.2. OCPP History

• 2009–2010: The protocol was initially developed by E-laad.nl, a foundation established by Dutch grid operators to facilitate the deployment of public charging infrastructure in the Netherlands. The first version, OCPP 0.7, was released in 2009. The aim was to create an open and universal standard for communication between charging stations and central systems, which would allow different brands of charging stations to be managed by the same central system.
• 2010–2012: OCPP 1.0 was released in 2010, followed by OCPP 1.1 in 2011. These versions added new features and improved the protocol’s robustness and reliability.
• 2012–2015: OCPP 1.2 was released in 2012, followed by OCPP 1.5 in 2013 and OCPP 1.6 in 2015. These versions introduced significant new features, such as support for smart charging and the reservation of charging points.
• 2014: The Open Charge Alliance (OCA) was formed in 2014 to take over the development and promotion of the OCPP. The OCA is a global consortium of public and private EV infrastructure leaders.
• 2018–2019: The most recent versions of the protocol include OCPP 2.0, released in 2018, followed by OCPP 2.0.1 in 2019. These versions added further enhancements, including improved security features, support for ISO 15118 (a standard for communication between electric vehicles and charging stations), and new smart charging capabilities [10,14].
• 2024: The future version named OCPP 2.1 is in its developmental phase according to OPEN CHARGE ALLIANCE [15].

Figure 2, as shown below, represents the history of OCPP development [10,11].

4. Dynamic Pricing and Algorithm for Less Busy Charging Stations

To recommend charging stations that are both less busy and cost-effective, this can be done by dynamically adjusting the price per kWh based on the station’s current load. Less occupied stations will offer lower prices, encouraging EV users to choose them and helping to balance the load across all stations.

Algorithm

This is the proposed algorithm shows in Algorithm 1:

Algorithm 1: Dynamic pricing proposed algorithm.

1: Initialize an empty list stations 2: for each ChargStationId in ChargStations do 3:   Filter Active Bookings: 4:    chargStationBookings ← EMPTY_LIST() 5:    for each booking in Bookings do 6:      if booking. StationId = ChargStationId and booking. Time > CurrentTime then 7:        APPEND booking TO chargStationBookings 8:    end if 9:    end for 10:   Retrieve Station Capacity: 11:    TotalSlots ← DATABASE_LOOKUP(ChargStationId) 12:   Calculate Load & Occupancy Rate: 13:    Load ← LENGTH(chargStationBookings) 14:    OccupancyRate ← Load/TotalSlots 15:   Retrieve Base Price: 16:    BasePrice ← BasePrices[ChargStationId] 17:   Apply Dynamic Pricing: 18:    DynamicPrice ← BasePrice * (1 + DynamicPricingFactor * OccupancyRate) 19:   Store Station Data: 20:    APPEND (ChargStationId, Load, OccupancyRate, DynamicPrice) TO stations 21: end for 22: Sort Charging Stations by Price: 23: stations ← SORT(stations) BY DynamicPrice (ascending order) 24: Return Recommended Stations: 25: return stations

This algorithm concerns the orientation of EV users in relation to charging stations that are both less busy and cost-effective by dynamically adjusting prices considering each station’s current load. By setting higher prices for busier stations and lower prices for less occupied ones, the algorithm will incentivize users to choose less crowded stations, balancing power demand and reducing congestion. The algorithm is constructed to give a list of charging stations sorted by dynamic price, prioritizing stations with lower occupancy.

In the first step, the algorithm starts by initializing an empty list, (stations), which will store data about each charging station, including its current occupancy rate and dynamically adjusted price. This list will later be sorted and used to orient EV users to the most suitable charging stations based on current availability and cost.

Next, the algorithm iterates through each charging station in the provided list, (ChargStations). For each station, it filters the (Bookings) data to find active or upcoming bookings based on the current time, (CurrentTime). This step is about the identification of how busy each station currently is, providing a basis for calculating its occupancy rate and load. By using this information, the algorithm can define which stations are busier than others, setting the stage for dynamic pricing.

Once the list of active bookings for a station is defined, the algorithm will calculate its load, defined as the count of active bookings, and retrieve the station’s (TotalSlots) (total number of available charging slots). Using these values, it computes the (OccupancyRate) as the ratio of active bookings (Load) to the total slots (TotalSlots). This occupancy rate represents how full the station is at the current moment, which will directly influence the price adjustment.

The algorithm then retrieves the base price per kWh from the (BasePrices) dictionary. This base price will be modified according to the station’s occupancy rate. The dynamic price is calculated by multiplying the base price by a factor based on the occupancy rate: (DynamicPrice) = (BasePrice) * [1 + (DynamicPricingFactor) * (OccupancyRate)]. Here, (DynamicPricingFactor) is a constant that controls how sensitive the price is to changes in occupancy. The higher the occupancy rate, the higher the dynamic price, making busy stations more expensive and encouraging users to consider less crowded ones.

After calculating the dynamic price, the algorithm stores the station’s ID, load, occupancy rate, and adjusted price as a tuple in the (stations) list. Once all stations are processed, the algorithm sorts the (stations) list in ascending order by (DynamicPrice). This sorted list, (RecommendedStations), highlights stations with lower prices (and thus likely lower occupancy), making them the most attractive options for users (Appendix A).

Finally, the algorithm returns the sorted list, (RecommendedStations), as the output. This list guides EV users to stations that are both less busy and better priced, serving to balance the charging load across the distribution grid and enhancing user experience by reducing wait times and crowding. Through the concept of dynamic pricing and prioritization based on load, this algorithm effectively leverages price incentives to manage station demand.

5. Conclusions

The paper concludes that the proposed algorithm for balancing electric vehicle loads at charging stations using dynamic pricing is a viable solution to address the challenges of uneven distribution and load imbalances in the electric vehicle charging infrastructure. Simulation results demonstrate that reduced pricing at Station A (0.22 $/kWh) effectively draws more users, thereby decreasing congestion at the higher-priced Stations B (0.31 $/kWh) and E (0.29 $/kWh). The implemented model successfully motivates users to choose less crowded stations, resulting in a more equitable distribution of electric vehicle (EV) charging demand. This approach also offers cost advantages for users who opt for these less congested stations. By integrating this algorithm into the OCPP, the authors aim to optimize grid demands and prevent overloading, thereby supporting the mass integration of electric vehicle charging stations necessary for green mobility and decarbonizing the mobility sector.