A Risk Based Method for Energy Management of Smart EV Parking Lot Equipped with Renewable Energies

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

Hamid Helmi ¹ , taher abedinzade ² , جمال بیضاء ³ , Sima Shahmohammadi ⁴ , Ali Daghigh ⁵

1 - Department of Electrical Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
2 -
3 -
4 - Department of Electrical Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
5 -

Received: 2024-06-25 Accepted : 2024-08-21 Published : 2025-01-23

Keywords: Energy Management System, Distributed Energy Resources, Electric vehicles, Renewable Energy, Uncertainty, Peer to Peer, Organic Photovoltaics,

Abstract :

This paper presents an innovative method for operational planning of microgrids, focusing on maximizing profitability. The approach addresses key uncertainties, including the probabilistic charging/discharging behavior of EVs and the integration of renewable energy sources like wind and solar. A major challenge with renewables is energy wastage due to storage limitations and grid congestion. EVs offer a solution through Vehicle-to-Grid (V2G) technology, which enables them to supply electricity back to the grid, improving renewable energy utilization. This paper introduces two Energy Management System (EMS) models, with a key innovation being a Coordinated EMS that facilitates peer-to-peer (P2P) power trading between stations and prosumers. The model, evaluated across five stations under ten uncertainty scenarios, is formulated using Mixed-Integer Linear Programming (MILP) and implemented in GAMS/CPLEX. By integrating P2P transactions and organic photovoltaics (OPV) technology, it enables off-grid EV charging and utilizes excess solar energy in remote areas. Results indicate that the Coordinated EMS with P2P trading improves profitability by up to 1.17 times. The findings of this research align with efforts to reduce peak load in distribution grids by reducing reliance on centralized infrastructure, demonstrating the potential benefits of coordinated energy management strategies in microgrids.

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Full-Text:



Journal of Artificial Intelligence in Electrical Engineering, Vol. 12, No. 47 , November 2023

A Risk Based Method for Energy Management of Smart EV Parking Lot Equipped with Renewable Energies

Hamid Helmi1, TaherAbedinzadeh2*, Jamal Beiza3, Sima Shahmohammadi4, and Ali Daghigh5

1,2,3,4,5Department of Electrical Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

Email:[e-mail](correspondig auther)

Receive Date: 25June 2024 Accept Date:21August 2024

Abstract

This paper presents an innovative method for operational planning of microgrids, focusing on maximizing profitability. The approach addresses key uncertainties, including the probabilistic charging/discharging behavior of EVs and the integration of renewable energy sources like wind and solar. A major challenge with renewables is energy wastage due to storage limitations and grid congestion. EVs offer a solution through Vehicle-to-Grid (V2G) technology, which enables them to supply electricity back to the grid, improving renewable energy utilization. This paper introduces two Energy Management System (EMS) models, with a key innovation being a Coordinated EMS that facilitates peer-to-peer (P2P) power trading between stations and prosumers. The model, evaluated across five stations under ten uncertainty scenarios, is formulated using Mixed-Integer Linear Programming (MILP) and implemented in GAMS/CPLEX. By integrating P2P transactions and organic photovoltaics (OPV) technology, it enables off-grid EV charging and utilizes excess solar energy in remote areas. Results indicate that the Coordinated EMS with P2P trading improves profitability by up to 1.17 times. The findings of this research align with efforts to reduce peak load in distribution grids by reducing reliance on centralized infrastructure, demonstrating the potential benefits of coordinated energy management strategies in microgrids.

Keywords: Energy Management System, Distributed Energy Resources, Electric vehicles, Renewable Energy, Uncertainty, Peer to Peer, Organic Photovoltaics

 

 

1.       Introduction

1.1       Motivation

Over the past few centuries, renewable energy has been increasingly recognized as a means to alleviate energy shortages [1]. According to the planning by the International Renewable Energy Agency (IRENA), by the year 2050, over two-thirds of energy production will be derived from renewable sources, with contributions from renewable sources such as wind and solar energy reaching 60% [2]. However, both wind and solar energy face significant waste due to energy storage challenges. As the world’s largest producer of wind and solar power, China experienced an average wind curtailment rate of 3.2% and a discarded wind power quantity of approximately 6 billion kilowatt-hours in the first quarter of 2022. The solar curtailment rate was2.8%, with a discarded solar power quantity of around 2.4 billion kilowatt-hours[3]. Countries worldwide are also grappling to varying extents with energy storage issues leading to wastage of green energy. The prevailing viewpoint suggests that managing the surplus of wind and solar power is more challenging than addressing their deficiencies [4]. This is attributed to the intricate nature of storing wind and solar energy, where surplus electricity can result in an increased burden on the power grid. Therefore, optimizing electrical energy storage and promptly integrating excess electricity into the grid are crucial measures to enhance the utilization of green energy and achieve sustainable development. EVs are considered a key solution to address energy storage challenges. V2G power technology is one of several storage technologies, enabling vehicles to feed electricity into the grid. Through unified demand control in the power system, V2G can better utilize fluctuating renewable energy. For power companies, V2G offers benefits such as backup power, load balancing, peak load reduction [5, 6], and reduced uncertainty in daily and hourly power load forecasts [7].

Importantly, numerous studies suggest that V2G can effectively enhance the energy efficiency of wind and solar power [3, 8]. Conventional EVs (battery EVs and plug-in hybrid EVs) can contribute to peak shaving by charging in an orderly manner at night, but they cannot feed power back to the grid during the day, offering only limited peak load reduction for fluctuating grids [9].  In contrast, V2G EVs not only contribute to peak shaving at night but can also provide power back to the grid during peak demand hours in the daytime [10], making their advantages more apparent in terms of green energy utilization [11]. 

Expanding access to reliable, low-cost sustainable energy, such as solar power, can help reduce poverty, inequality, and climate change impacts. The Charge Around EVs involves driving an EV powered by portable OPV solar panels, demonstrating the potential of printed solar technology for off-grid charging. These lightweight OPV panels generate renewable energy in remote areas, addressing EV range anxiety and showcasing the feasibility of off-grid solar charging [12].

 

1.2       Literature Review

 Mohamed et al.in [13] designed a fuzzy controller to manage the charging processes of EVs to reduce the overall daily cost and mitigate their impact on the power grid. Tushar et al. in [14] proposed a classification scheme of EVs, such that the PV driven charging station can trade with different energy entities to reduce its total energy cost. Under the Time of Use (TOU) price, Liang et al. in [15] studied the charging/discharging scheme in Vehicle-to-Grid (V2G) system and obtained a state-dependent policy to minimize the charging cost for individual EVs. Considering the battery characteristic and TOU price, Wei et al. in [16] designed an intelligent charging management mechanism to maximize the interests of both the customers and the charging operator. 

Considering unpredictable EVs patterns and EV various charging preferences, Wang et al. in [17] designed a Hybrid Centralized-Decentralized (HCD) charging control scheme for EVs to coordinate the EV charging processes, such that the revenues of the whole charging system can be maximized. Kim et al. in [18] developed an algorithm to find the optimal charging scheduling, service pricing and energy storage scheme, such that the profit of charging stations can be maximized. Jin et al. in [19] presented a Lyapunov optimization for EV charging scheduling problems to maximize the utilization of renewable energy and reduce total charging cost. These works typically assumed that the EV charging requirements or the renewable energy can be estimated and do not consider the real time EV charging requirements and renewable energy.

Zhou et al.in [20] achieved the Demand Side Management (DSM) by scheduling intelligent EV charging to relieve the power grid pressure. Wang et al. in [21] designed a novel Two-stage EV charging mechanism to determine the energy generation and charging strategy dynamically, such that the peak-to-average ratio (PAR) and the energy cost can be reduced. Liu et al. in [22]. proposed a leader-follower game model between the EV owners and the distribution service provider, and then designed an optimal pricing based EV charging scheduling scheme to avoid system peak load.

Zhang et al. in [23] proposed a Markov Decision Process (MDP) based charging scheduling scheme to minimize the mean waiting time for EVs. Wang et al. in [24] proposed a mobility-aware coordinated charging strategy for EVs in VANET-Enhanced Smart Grid, which can improve the overall energy utilization, avoid power system overloading, and can address the range anxieties of individual EVs. Farzin et al. in [25] developed a novel framework based on the non-sequential Monte Carlo simulation method to quantify the potential contribution of parking lots to the reliability of PV–Grid charging systems. Yang et al. in [26] proposed a risk-aware day-ahead scheduling and real time dispatch algorithm to minimize the EV charging cost and the risk of the load mismatch. Lee et al. in [27] took into account the competition of neighbouring EV charging stations with renewable energy sources using game theory, and proved that there exists a unique pure Nash equilibrium for best response algorithms with arbitrary initial policy. These works mainly focused on operational efficiency of charging systems and the utilization of renewable energy in long-term, rather than the real time benefit of the parking lot. Also, they lack the quick response abilities to the real time changing information. Sheykhloei et al. optimized the operation of renewable energy resources and a natural gas network to reduce electrical load costs and improve system reliability in in [28] where a 24-bus power system with PV, wind turbines, battery storage, and a 7-node gas network is analyzed over 24 hours to determine optimal resource placement and capacity. This work uses join units combining atural-gas-fired distributed generators and Power to Gas units. By utilizing MILP power and gas fluctuations are managed effectively.

 

1.3       Contributions

In this paper, an EMS model has been developed based on [29] for the EV station equipped with renewables and storage. An aggregator for the EV charge/discharge station is established in a way that applies the aggregated EMS model and P2P model to reduce the Energy cost of EVs station. Numerical studies with and without aggregators as well as P2P transactions have explained profit increase, especially from a balancing market point of view. By utilizing OPV-based portable solar panels, this study explores the feasibility of off-grid EV charging in remote areas, reducing dependence on traditional charging infrastructure while harvesting and utilizing excess solar energy. This approach not only mitigates range anxiety in long-distance EV travel but also helps address renewable energy wastage by enabling efficient energy use in locations without centralized grid access. 

The innovations in model are as follows:

·       Application of EMS for multi-EVs-stations system in order to guarantee the procumer benefits in coordinated structure and integration of OPV to provide additional renewable energy

·       Development of P2P power transaction between EVs stations for uncertainty and variability management of load and renewables 

 

In the remainder of this paper, section 2 expresses the proposed model. The numerical studies are provided in section 3. Section 4 represents the conclusions.

2.       Proposed Model

2.1.                      Mathematical Model of Individual EMS

This paper introduces an EMS model for the EVs station based on [29].

 

2.2       System Modelling

The mathematical formulation includes several key equations defining system operations and constraints. The objective function (OF) is represented by Equation (1). 

 

Sets:
	Scenarios
	Time
I	Controllable EVs
Parameters and Variables:
	Aging coefficient of battery duo to cyclic charge and discharge
	Probability of scenarios
	Price of electricity
	Incentive paid for demand curtailment
	Penalty applied to demand who refuse DR adjustment
	Load inelasticity
	Nominal power of controllable EVs
	Power of organic photovoltaics EVs
	Minimum State of charge battery
	Minimum State of charge EV
	Maximum State of charge battery
	Maximum State of charge EV
	Charge rate of battery
	Charge rate of EV
	Discharge rate of battery
	Discharge rate of EV
	Capacity of battery
	Capacity of EV
	Maximum charge rate of battery
	Maximum charge rate of EV
	Maximum discharge rate of battery
	Maximum discharge rate of EV
	Initial power of Controllable EV
	Power of station to grid
	Power of grid to station
	Battery Aging Cost
	EV Aging Cost
	Initial power of grid to station
	Power of station to grid before DR application
	Critical demand of EVs at station
	Dissatisfaction of EV consumers
	Rate of charge
	Rate of discharge
	Power of Controllable EV
	Power of station to vehicle
	Initial power of station to Vehicle
	Power of vehicle to station
	Power of wind to station
	Power of solar to station
	Power of battery to station
	Power of station to battery
	Interruptible curtailable EVs
	State of charge / discharge for battery
	State of charge / discharge for EV
	Binary variable if battery charge set 1
	Binary variable if battery discharge
	Binary variable if EV charge set 1
	Binary variable if EV discharge set 1
	Binary variable if EV is ON

The first term accounts for the income generated from selling and purchasing power between the grid and the station. The second term represents the costs associated with battery and EV operations. The third term includes incentive income based on the Demand Response Program (DRP), while the fourth term corresponds to the penalty costs incurred for participation in the DRP. Finally, the fifth term models EV owners' dissatisfaction due to deviations from their initial consumption plans.

Equation (2) represents the battery costs related to battery and EV wear, considering the additional cycling nature of the batteries in the given modes.

(1)

 

Equation (3) models EV owners' dissatisfaction, while Equation (4) ensures demand balance within the system.

(2)

 

(3)

Equation (5) enforces the constraint that a station battery and EVs cannot charge and discharge simultaneously.

(4)

Equation (6) defines the controllable portion of station demand, which corresponds to the total consumption of controllable EVs, assuming each EV's consumption is equal to its nominal power. The operation of individual EVs is controlled by determining their ON/OFF states, ​. Additionally, EV operations are scenario-dependent, allowing both EVs and station batteries to compensate for renewable energy uncertainties.

(5)

Equation (7) ensures that the daily consumption of each controllable EV is limited to its required consumption, while Equations (8) and (9) guarantee that all controllable EVs operate continuously within their designated usage periods

(6)

 

(7)

 

(8)

Equation (10) describes variations in station and EV battery levels, while Equation (11) defines the charging and discharging limits of both station and EV batteries.

(9)

 

(10)

Equations (12) to (15) specify the constraints on charging and discharging rates for station and EV batteries

(11)

Equation (16) calculates the station's power balance, considering contributions from wind and solar power, as well as power transfers from the station battery and EVs.

	(12)
	(13)
	(14)
	(15)

Power transactions between the grid and the station are limited by Equation (17), and Equation (18) ensures that the station can only transmit power in one direction at any given time.

2.3       Modelling The Effect Of Uncertainty

One of the well-known indicators to measure the financial risk is conditional value at risk (CVaR). CVaR is a risk assessment technique defined as follows:

         (19)

 

	(16)
	(17)
	(18)

Decision variables of CVaR,  and are as follows:

(20)

In this study, the OF represents a maximization problem, where α and β denote confidence levels ranging between 0 and 1, determined by the decision maker. The positive variable ​ measures the positive deviation between the value at risk (VaR) and the objective function obtained in each scenario.

Compared to VaR, CVaR offers several advantages that enhance its applicability in risk management. While VaR is only continuous for normal probability distributions, CVaR remains continuous across all probability distributions, addressing this limitation. Additionally, CVaR extends beyond VaR by controlling losses in extreme scenarios, effectively capturing the risk that exceeds the VaR threshold. At the same confidence level, CVaR is considered more conservative than VaR, as it accounts for risks beyond the VaR threshold. However, due to its conservative nature, CVaR may not be a suitable risk measure for highly risk-averse decision-makers. In this paper, CVaR is integrated into the objective function alongside the mean value, ensuring a comprehensive risk-aware optimization approach. This integration enhances the robustness of the model by considering extreme losses while maximizing system profitability.

2.4        Coordinated EMS with P2P Power Trading between Stations

 

The proposed EM model in the previous section can be aggregated to extend its application across multiple procumers. In this approach, the profitability of each procumer must be ensured within the coordinated model, considering the inherent uncertainties in renewable energy generation and load demand. By implementing a coordinated EM strategy for multipe stations, it is highly probable that the overall profitability of each station will increase due to optimized energy management. The schematic of coordinated model for EM is depicted in Fig.1.

	(21)
	(22)

Fig .1. Coordinated EM model for procurers

Coordinated EMS model is same as the introduced model with adding equations (23) to (25) as follows:

In the above equations, S represents the number of stations, while the constraints are equation (1)-(22) and  denotes the maximum objective variable for each station individually.

To further enhance the EMS, P2P facilities are incorporated, allowing energy exchange between stations. The traded power between stations follows the approach outline in [21], while from perspective of each station, other stations are treated as a black box in the optimization process.

	(23)
	(24)
	(25)

The power output and input summation of stations other than jth station, is determined by Equation (26) and Equation (27).

	(26)
	(27)

Equation (28) and Equation (29) state that these summations are equal to the corresponding station (jth one) input/output, respectively.

	(28)
	(29)

 

Equation (30) provides the power balance of each station after implementation of P2P transactions between microgrid and integration of OPV.

 

 

3.       Numerical Studies

To evaluate the proposed model, a station in Italy is considered as the case study. All relevant data for this study is available in [25].  Fig.2 depicts the correlation expected cost and risk level sensitivity. It can be concluded that increased risk sensitivity results in higher financial impacts, highlighting heightened volatility and financial risk.

Electric vehicles (EVs) are treated as controllable EVs, with a waiting capacity of three hours in the morning and four hours in the evening. The verification of the model has been conducted through the following analyses:

·       Risk Analysis

·       Comparison between Coordinated EMS and Individual EMS

Two scenarios have been examined for a system consisting of five EV stations:

·       Senario 1: Implementation of Individual EMS

·       Senario2: Implementation of Coordinated EMS

 

 

Fig .2. Expected cost changes by β

 

The income for each station and aggregated income of all stations are shown in Fig.3.

 

Fig .3. Comparative chart of individual station   profits and coordinated profits

 

The income distribution among these stations, highlights a noticeable improvement in total earnings when a coordinated EMS is implemented. Moreover, Table.1 further quantifies this enhancement, demonstrating the specific financial gains achieved by each station under EMS.

Based on Table 1, the total difference between EM and EMS provides a profit enhancement amounting to $95,044.87, which reflects a 1.17-fold increase in overall profitability.

It can be concluded that coordinating energy management through the integration of P2P transactions leads to a more profitable EMS for consumers.

(30)

Table (1). Profit in coordinated model

Station Individual (EM) ($) Coordinated (EMS) ($) 1 111024.472 127316.193 2 111145.897 128589.355 3 111186.712 129875.249 4 111320.274 131174.001 5 111430.973 132485.741 Total 554395.666 649440.540

The incorporation of OPV systems further enhances this profitability by enabling decentralized and flexible energy generation. Consequently, the participation of EV stations in demand response (DR) programs can be increased, as OPV technology facilitates more sustainable and efficient energy trading, encouraging greater engagement from EV owners. 

The total wind and solar generation across the stations is illustrated in Fig.4.

 

 

(a)

(b)

(c)

Fig.4. (a)Wind, (b)Solar, (c)Total Renewable Energy Generation (Appendix, Table I)

 

Fig.5 presents a comparison of load profiles under three different conditions: without EMS, with individual EMS, and with coordinated EMS. The results demonstrate that the DPR effectively reduces energy purchases during peak price periods, leading to cost savings. Furthermore, the coordinated EMS outperforms the individual EM, providing greater efficiency in load shifting and energy cost reduction.

Fig.5.  Total station load (a)without use of EMS, (b)with individual EMS and (c)coordinated EMS (Appendix, Table II)

 

Fig.6depicts the difference in EV loads in scenario 2 and 3. During the 6-hour peak period, the load in scenario 3 is higher, primarily due to the enhanced integration of solar, wind, and OPV energy for EV charging. In contrast, during off-peak periods, scenario 3 shows a lower load, as renewable energy generation does not sufficiently meet the demand, leading to greater reliance on conventional power sources or stored energy. The inclusion of OPV further supports the system by providing additional renewable energy, thus facilitating improved load management and balancing.

 

Fig.6. Difference of EV loads between scenarios 3 and 2

Fig.7illustrates the comparison of station income under two different scenarios.

Fig.7. Income of stations (a) Individual and (b) Coordinated two scenarios (Appendix, Table III)

The results indicate that the coordinated model proves to be more profitable not only for individual entities but also for the grid operator. This is primarily due to its ability to effectively manage and shift loads, resulting in improved energy utilization and economic benefits compared to the individual EMS application. Additionally, when incorporating OPV, the coordinated model's efficiency and profitability are further enhanced, as OPV contributes to renewable energy generation, lowering overall energy costs and maximizing grid stability.

 

4.       Conclusion

In this paper, the energy management of the system is analyzed to maximize network profitability by integrating renewable energy sources (wind, solar) and controllable EVs, while incorporating certainty with the conditional risk criterion in two different modes. The results show that the total profit has increased in all stations compared to the other mode. Additionally, both individual station profits and the overall network profit have improved. By utilizing OPV technology, the profitability of both individual stations and the overall network has increased. On a larger scale, this concept can also contribute to reducing peak loads in distribution grids.

5.       The use of bi-level optimization could ensure profitability at both upper and lower levels, providing a balanced and efficient approach to energy management. Additionally, integrating V2G technology into EMS could further increase station profits while lowering EV charging costs. Moreover, efforts to develop adoption models for multi-mode transportation patterns could be explored. For future research, hub energy systems could be considered as a test model to enhance cooperation between various energy resources.

 

 

 

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Appendix

 

Table I. Solar, Wind and Total Energy Generation

1