Modeling Electric Vehicle Charging Station Behavior Using Multiagent System

Abstract

Agent-based models(ABMs) are a type of simulation in which a large number of self-sufficient agents interact in a way that combines stochastic and deterministic behavior. Recently, there have been reestablished interests in utilizing multiagent systems (MASs) to get more granular data relating to specific conditions. MESA is an ABM framework for Python. It enables users to quickly develop ABMs with built-in core components, view them with a browser-based interface, and evaluate their findings with Python’s data analysis capabilities. This chapter depicts an ABM of a photovoltaic (PV)-powered electric vehicle (EV) charging station in a university car park modeled using MESA. The goal is to determine the preliminary requirements for PV-powered EV charging stations, which would result in increased PV and cost benefits.

1. Introduction

Agent-based applications are becoming the mainstream in a wide range of domains, including e-commerce, logistics, supply chain management, telecommunications, healthcare, engineering, and manufacturing, as technology advances [1]. Multiagent systems (MASs) emerge as new software technologies that combine a number of artificial intelligence (AI) techniques. It provides a more efficient and natural alternative to building intelligent systems, thereby providing a solution to the current complex real-world problems that must be solved. Autonomy, complexity, adaptability, concurrency, communication, distribution, mobility, security and privacy, and openness are some of the properties of MASs [2, 3]. The concept of MAS is also a trending technology in power engineering applications, such as power system restoration, power system optimization, market simulation/electricity trading, and smart grid control [4]. The MAS’s ability to deal with complex problems through agents, which has been highlighted in various research works, is the basis of using the MAS in many applications.

MAS-based models are used globally to implement demand-side management (DSM) systems, cost optimization, robustness management in microgrids (MGs), and controlling voltage and thermal constraints in distribution networks.

In [5], an MAS-based online voltage monitoring system was proposed. In [4, 6], a dual-layered advanced control and security system based on the MAS, as well as an automated meter reading facility in a smart grid distribution network, was proposed. DSM has been activated from a global research standpoint by maximizing the use of distributed energy sources, environmentally friendly technologies, optimizing algorithms, and the implementation of renewable energy (RE) resources [7, 8, 9, 10]. The use of renewable energy sources in smart grid distribution systems, as well as the multi-MG model, lowers consumer electricity costs [11, 12].

As the number of electric vehicles (EVs) increases, better charging infrastructure is required to provide the necessary energy for mobility with the cost benefits. MASs have seen a surge in popularity in recent years, and they are now widely used in EV-based power system research studies. In [13], a multiagent system (MAS)-based modeling tool has been proposed to assess the effects of EV charging on Singapore’s energy grid. This study looked into the effects of EV temperature (air conditioning) and EV charging load during the charging. In [14], a state of charge (SOC)-based charging algorithm has been suggested, which is divided into two categories: controlled and uncontrolled charging (vehicle to grid—V2G) and grid to vehicle (G2V). This reduces the number of cars that run out of power on their next trip. In [15], a decentralized and intelligent MAS for controlling and managing EV charging in low-voltage (LV) distribution networks was presented. In this context, three case studies were investigated: without EV charging regulation-uncoordinated case or dumb charging; with EV charging regulation and without the voltage charging control; and with EV charging and voltage droop control. The simulation results showed that charging regulation provides significant benefits in terms of voltage control when compared to the other two situations. The proposed solution in [14] has been improved with active demand (AD) program management in [16]. It enabled the incorporation of EVs into the system while mitigating their negative impact on voltage regulation.

Lee et al. [17] discussed the impact of EVs on the electric grid; it was tested using real data from the “My Electric Avenue” initiative with ABM. It looked at how consumers’ use of time-of-use (ToU) tariffs and vehicle range (battery capacity) preferences affect total and peak demand fluctuations at the local substation. In order to depict the complexity of an electric transportation system, an EV implementation based on agents was created [18]. After implementing various charging powers and charging patterns, the results were generated in a qualitative manner. In [19], the effects of influencing variables on EV charging demand, such as driver behavior, charging station location, and electricity pricing, were investigated. An ABM on Net Logo was used in this study to precisely simulate human aggregate behavior and its impact on load demand due to EV charging. In [20], the results of a study that used ABM simulation to simulate alternative charging infrastructure rollout techniques to allow for large-scale EV adoption were presented. The simulation included a variety of user types (residents, visitors, taxis, and sharing), as well as various types of charging infrastructure (level 2, clustered level 2, and fast charging).

An EV powered by an intermittent power source is an excellent way to ensure low-cost, emission-free mobility. An energy storage management hybrid optimization algorithm was presented in [21]. This algorithm flipped between deterministic and rule-based modes of operation depending on the power pricing band allocation. The cost degradation model and the levelized cost of photovoltaic (PV) power were combined in the case of PV-integrated charging stations with on-site energy storage systems. An agent-based charge station model utilizing renewable energy (RE) was proposed in [22]. Charging patterns were determined by scenarios including various RE capacities, policy interventions, limited versus unlimited charging capacity, social charging, and the existence or absence of central control. It was determined that in order to improve sustainable charging, policymakers should employ various incentives for different categories of EV drivers.

To aid comprehension of the state-of-the-art review and to clarify the study’s contributions, Figure 1 is added.

Figure 1.

An overview of this study and its state of the art.

The rest of this chapter is structured as follows. Section 2 presents the modeling of an ABM of a PV-based charging station. Section 3 includes the simulation results and analysis of various scenarios. Section 4 draws the conclusion.

1.1 Solar PV-based EV charging station: Application of MAS

This chapter discusses the development of an agent-based EV charging station on university premises. It is primarily being developed as a solar PV-powered charging system. The objectives of this AB charging system are to optimize the benefits of cost and direct solar supply in a quantitative manner. This study makes two major contributions:

• The MAS simulation model for solar PV charging station for EV is proposed and developed using the motor vehicle database, which is assumed to be as EVs on university premises for the future energy distribution.

• The developed MAS provides a unique ABM simulation platform based on MESA software. It is able to incorporate energy optimization algorithms between interacting agents as SOC-based and TOU-tariff-based scenarios.

A survey was used to collect information about the available motor vehicles in the university car parks. A future EV integration database has been created in accordance with that.

1.1.1 Modeling of the system

The simulation model is built with a variety of agents, including an EV agent, a weather agent, a solar panel agent, a main control agent (MCA), a utility agent, a charging control agent (CCA), and a charge station battery agent. The solar panel agent generates energy based on the weather condition’s temperature and irradiance value, which can be accessed via the weather agent. The university weather broadcasting inverter portal was used to create the weather agent database. Each charging control agent in the developed agent-based system can manage the EVs’ charge while taking into account energy prices and the EV agent’s requirements (EVs’ SOC during arrival, charging option, and arrival time), which is based on one of the following two scenarios: SOC-based or TOU-based tariffs. The EV agent represents the EV owner, who has the option of interacting/charging with the user interface in a range of ways. The charging control agent’s energy scheduling is sent to the main control agent, which evaluates the overall energy supply agents’ (utility agent, solar panel agent, and charge station battery agent) performance using optimizing algorithms. An illustration of the agent-based EV charging station system is presented in Figure 2.

Figure 2.

The system architecture of the multiagent simulation platform.

1.1.2 Agents’ validation

Each of the agents was validated with proper testing results before starting to simulate the system.

1. Solar panel agent: A PV array power calculation system of Homer Pro 3.14 was used to validate the solar panel agent. The results revealed the least amount of variation, which is included in Table 1.

2. EV agent: The EV agent was validated using the New European Driving Cycle (NEDC), Environmental Protection Agency (EPA), and Worldwide harmonized Light Vehicle Test Procedure (WLTP) tests. That was handled in two scenarios: no auxiliary device is used and certain auxiliary devices are turned on (with a power rating of 300 W based on the literature). The Nissan Leaf 2018 model was chosen to simulate testing with a test mass as the curb weight of 1573 kg and an additional payload of 100 kg. The results of the tests within the acceptable range are given in Tables 2 and 3.

3. Charge station battery agent: Lee et al. [23] explored the design and validation of a hardware-in-the-loop lithium-ion battery pack for EPA testing (US06, HW, and USSD/FTP). A two-time constant equivalent circuit battery cell model, as well as a lumped capacitance thermal model, was included in the battery pack model. To test the battery model in an EV as well as the charge station battery, a concurrently operating Li-ion battery from a Nisan Leaf 2012 vehicle power profile has been used. The results are given in Table 4.

The total PV array output Power (one day)	Simulation Model	Homer Model	Error%
	108.60 kWh	108.47 kWh	−0.123%

Table 1.

Total PV array output from the simulation model and Homer software.

	NEDC testing				WLTP testing
	Simulation energy consumption (kWh/100 km)	Standard energy consumption (kWh/100 km)	Error	Total distance (km)	Simulation energy consumption (kWh/100 km)	Standard energy consumption (kWh/100 km)	Total distance (km)	Error
Without auxiliary	13.63	14.5	−6%	11.022	17.75	19.4	23.26	−8.46%
With auxiliary	14.52	14.5	0.138%		18.4	19.4		−5.15%

Table 2.

The NEDC and WLTP testing results of the ABM simulation.

		City_FC	HW_FC	Combined_FC
Standard energy consumption (kWh/100 km)		17.2	21.2	19.1
Standard fuel economy (km/Wh)		0.006424	0.005212	0.005879
Without auxiliary (simulation)	Fuel economy (km/ Wh)	0.0056755	0.005286	0.005500
	Error $\left(\frac{\mathrm{STD}-\text{Simulation}}{\mathrm{STD}}\right)\%$	−15.348	−0.778	−9.542
With auxiliary (simulation)	Fuel economy (km/ Wh)	0.00543755	0.005172	0.005318
	Error $\left(\frac{\mathrm{STD}-\text{Simulation}}{\mathrm{STD}}\right)\%$	−11.652	1.422	−6.439

Table 3.

The EPA testing results of the simulation.

Drive cycle	RMS current (A)	Test/simulation (SIM)	Error $\left(\frac{\text{Test}-\text{Simulation}}{\mathrm{STD}}\right)\%$
US06	71.12	Test	1.38
	72.1	SIM
HW	36.18	Test	0.06
	36.2	SIM
USSD/FTP	23.85	Test	1.68
	24.25	SIM

Table 4.

The EPA testing results for EV battery simulation.

2. ABM with MESA

2.1 MESA simulation software

The ABM of the EV charging station is modeled based on a multigrid scenario in MESA. MESA, a platform that provides a Python environment for agent behavior, is used to implement the agent-based control system. It contains the model (model, agent, schedule, and space), analysis (data collector and batch runner), and visualization (visualization server and visualization browser page) [24]. The portion of the Python-based MESA simulation source code is depicted in Figure 3.

Figure 3.

The portion of source code—model.py.

The interaction of agents begins with the EV agent, which sends SOC information and charging requirements to its charging control agent (CCA). The request is then sent to the main control agent (MCA) by each CCA. The MCA sends requests to each energy resource, including the solar agent, charge station battery agent, and utility grid agent. The MCA then calculates the optimal charging schedule based on the charging scenarios (SOC-based and TOU-tariff-based). The MCA coordinates each step with each energy resource until the charging process is complete. Each iteration does its internal operations, such as calculations for energy management in MESA. Each agent is responsible for its own tasks.

Solar agent: It generates solar energy as per temperature and irradiance data, which is accessed from the weather agent.

Utility agent: In this ABM, there is no power limit for the utility agent. It serves as a backup supply, allowing PV sources to sell excess energy and EVs to obtain power, depending on the power management strategy.

Main control agent: It serves as a coordinator, receives requests for EV charging from each charge pole agent, and performs internal calculations in accordance with management scenarios.

The ABM’s activity diagram is depicted in Figure 4. It shows the information flows as well as agent internal operations.

Figure 4.

The activity diagram of the ABM.

2.2 Knowledge representation of agents

ABM with AI provides a real-time application that is extremely beneficial to all industries. Its popularity stems from its adaptability in different subfields (reasoning, knowledge representation, machine learning (ML), planning, coordination, communication, and so on). First, consider a few advantages of this combined technique: ABM drives emergent phenomena, precisely defines a natural system, and is flexible [2, 25].

Most complex real-world systems are only partially decomposable, and one solution would be to give the components the ability to decide on the nature and scope of their interactions at run time. Still, when combined with ML, ABM has the potential to create a new type of computing based on agents—by learning agents’ behavioral patterns.

Many ABMs can easily incorporate various ML techniques such as genetic algorithms (GAs), neural networks (NNs), and Bayesian classifiers. It has two interlocked cycles for examining input, making decisions, and producing output. The ML algorithm uses the ABM as an environment for this framework, while the ABM uses the ML algorithm to maintain the agents’ internal models [26]. The framework has described how ML techniques are used in ABM in Figure 5.

Figure 5.

The integrated cycles of ABM and ML [26].

The weather agent in our solar-powered EV charging station model is updated with ML techniques to update its temperature and irradiance value. Excel is also used to generate the observed dataset from the faculty weather portal for an ML tool. Excel can be a valuable addition to your ML toolkit. This can aid in the visualization and analysis of smaller datasets.

2.3 Simulation and results

The simulations are evaluated in terms of direct solar benefit (DSB) and cost-benefit analysis. DSB defines the solar PV contribution to requested EV demand, which is calculated from Eq. (1).

$$\text{Total}\mathrm{DSB}\left(\%\right)=\frac{\text{Direct solar supply for}\mathrm{EV}\text{charging}}{\text{Total}\mathrm{EV}\text{demand}}\times 100\%$$E1

where

$$\text{Direct solar supply for}\mathrm{EV}\text{charging}=\begin{array}{l}\text{Available solar generation}\\ -\text{Remaining solar energy.}\end{array}$$

The system is simulated in 5-min intervals. This is simulated in two ways: SOC-based charging with a flat tariff and TOU-tariff-based charging.

The ABM of EV charging stations initially investigated using an SOC-based flat charging scenario. For EVs, three charging algorithms have been developed: uncontrolled, vehicle to grid (V2G), and grid to vehicle (G2V). TOU-tariff-based charging is simulated with slow, average, and fast charging options. The simulations have yielded numerical results for DSB and cost benefits.

2.3.1 SOC-based charging with flat tariff

Uncontrolled charging: This implies that when the EV is connected to the university charging station, the battery is charged until it reaches maximum SOC or disconnection.

Vehicle to grid (V2G): It converts EVs into energy storage systems, allowing any excess energy stored in the EV’s battery to be injected back into the grid, which is enabled by the SOC values of EV. When the EV’s battery pack SOC falls below 50%, it begins charging by fast charging (30 kW); otherwise, it charges by average charging (6.6 kW) until the EV reaches 80%. If the EV’s SOC is greater than 80%, the energy in the EV battery can be pushed back into the electrical grid.

Grid to vehicle (G2V): It allows the EVs to charge with controlled charging while parked in the university car park. If the EV’s SOC falls below 50%, it immediately begins charging either through fast charging or through average charging.

Figures 6–8 show the simulation of the above three charging scenarios. GE—grid energy, BSE—battery storage energy, and SE—solar energy.

Figure 6.

Grid, station battery, and solar energy distribution for uncontrolled charging.

Figure 7.

Grid, station battery, and solar energy distribution for V2G charging.

Figure 8.

Grid, station battery, and solar energy distribution for G2V charging.

When the PV energy supply is insufficient to fully charge the EVs, the stationary storage charges the EV, and the energy is supplied by the public grid. The main drawback is that PV energy supply does not completely benefit EV charging and that reliance on the public grid increases when charging is uncontrolled.

This ABM is modeled to serve as a university charging station (workplace charging). A large number of EVs are likely to be parked for a longer duration. To increase the direct solar benefit of EV charging, slow charging (2.3 kW) is combined with G2V charging.

Figure 9.

Grid, station battery, and solar energy distribution for G2V combined with slow charging.

Flat cost charging is used to simulate SOC-based charging; the three charging modes have the same unit cost. Figures 6–8 show how uncontrolled charging and G2V result in the highest demand peak from the grid. The V2G has a lower peak because it allows only a few EVs to charge. According to our database, the majority of EVs are not far from our faculty. We also include simulation assumptions, such as when the EVs begin their journey with 100% SOC each day. When EVs arrive at the faculty, they have an average of more than 80% since they are not far away from the faculty. It causes them to inject excess energy into the grid as an energy source until it reaches 80% of SOC. It helps in reducing the grid’s need for additional energy generation and the demand for power supply resources. Figure 9 shows the simulation observation of G2V slow charging (Figure 9).

Figure 10.

DSB percentage for SOC-based charging scenarios.

However, V2G and G2V permit obtaining energy consumption from energy resources and charging the vehicle when implementing controlled charging. When the SOC exceeds 50%, fast charging begins to preserve the lifetime of the battery from the full depth of discharge.

G2V controlled charging is combined with slow charging to improve DSB. It reduces grid dependability while improving DSB, as illustrated in Figure 9.

Figure 10 shows that DSB is calculated for all charging strategies based on SOC charging. EVs charge in average charging (6.6 kW) mode when uncontrolled charging and G2V charging mode are enabled. Both have almost the same DSB. In V2G, few cars need to be charged by the charging station, resulting in a moderate DSB value. When G2V is paired with slow charging, the DSB rises.

Figure 11.

Grid, station battery, and solar energy distribution for slow charging.

Figure 12.

Grid, station battery, and solar energy distribution for average charging.

2.3.2 TOU-tariff-based charging

The TOU-based charging scenario simulates three different charging options. It includes slow, average, and fast charging. The charging powers are 2.3 kW, 6.6 kW, and 30 kW, respectively. In TOU-tariff-based charging, two major streams are considered: cost and direct solar benefit. The charging station’s various start times are simulated to determine the most advantageous point of solar PV-based charging. The simulation observations are depicted in Figures 11–13.

Figure 13.

Grid, station battery, and solar energy distribution for fast charging.

This system is simulated with different station charging start times for the EV to see how PV energy affects EV charging. Figure 14 summarizes the simulation results.

Figure 14.

The DSB results for EVs under TOU tariff.

According to the findings, charging that begins at 11 a.m. has a higher DSB. However, our database shows that the few cars left the faculty before 11 a.m. Fast charging is assumed to have a 30-kW unit charge power excess for a few EVs in this simulation. As a result, the total demand to be achieved is reduced. Aside from that, the charging start time of 10 a.m. has a higher DSB. The slow and average charging have better DSB than the fast charging. The proportion of PV charging has increased, while the reliance on the public grid has decreased.

Besides that, the stationary storage lasts longer, preventing rapid discharge. It does not exceed its minimum SOC (20%). The stationary storage energy decreases as it approaches its capacity limit with minimum SOC, and it is then supplied by the public grid during off-peak hours. The charge station battery SOC pattern is shown in Figures 15–17 for slow, average, and fast charging, respectively.

Figure 15.

Station battery SOC pattern for slow charging at 10 a.m.

Figure 16.

Station battery SOC pattern for average charging at 10 a.m.

Figure 17.

Station battery SOC pattern for fast charging at 10 a.m.

As per the results, in slow and average charging modes, PV and stationary storage share more power. DSB is also increased when the charge station charging time is set to 10 a.m. in the university charging unit.

TOU tariff charging is expected to be cost-effective in PV-powered EV charging stations when compared with SOC-based charging. This creates a win-win situation for both charging station operators and EV owners. Figures 18–20 show the graphical representation of the total and profit costs for both EV users and station owners.

Figure 18.

Total cost based on CEB unit cost and simulation unit cost.

Figure 19.

Total profit for the EV owner in LKR (Sri Lankan Rupees).

Figure 20.

Total profit for the charge station owner in LKR (Sri Lankan Rupees).

The cost benefit was calculated compared to the Ceylon Electricity Board’s (CEB’s) current EV charging price. As previously discussed, fast charging did not meet the total EV demand. Aside from that, slow and average charging has lower costs for both charge station operators and EV owners, which is more beneficial at 10 a.m. charging time. Fast charging also provides a higher grid-injected price benefit at the 10 a.m. start time to charge station operators than the other two start times.

3. Conclusion

There have been many exciting developments in ABM recently, and the use of truly adaptive agents within ABM is one promising guarantee that is still being explored. ABM is more than a simulation tool; it also aids in the reduction of operational risk and the development of new strategies for the organization. The incorporation of ML techniques into ABM should enable the creation of new and unique models.

Simulation model discussed focuses on the preliminary requirements and cost-effective model for charging in a university car park. Two main strategies were presented, SOC-based and TOU-tariff-based, which demonstrated improvements in terms of DSB and cost benefits. When compared to an uncontrolled charging strategy, SOC-based charging is safe and has many benefits. Uncontrolled EV charging causes a significant increase in power demand, which may cause power congestion or voltage issues in the power system. On the contrary, G2V charging with a slow charging mode has the advantage of distributing the charging load over time by limiting the peak power demand.

It is shown that the proposed system can effectively improve the DSB as well as cost benefits by implementing TOU-tariff-based charging. It is cost-effective for both charge station operators and EV owners. Two charging modes are advantageous for the requirements and feasibility conditions in the university car park: slow and average charging.

Our ABM charging station can communicate and collaborate with each agent to achieve the required system behavior. The MAS must be coordinated with its characteristics in order to attain the purpose [2, 3]. Scalability is an essential aspect to consider when creating practical MASs. The simulation model will expand the interaction between the agents without hesitation or delay as the number of EVs in the model expands.