Jianhui WangAssociate ProfessorDepartment of Electrical and Computer Engineeringjianhui@smu.edu

November 2019

Electric Vehicle Grid Integration: Challenges and Solutions

EV Market Grows Rapidly

IEA analysis based on country submissions, complemented by ACEA (2019); EAFO (2019); EV Volumes (2019); Marklines (2019); OICA (2019).

• In 2018, the global electric car fleet exceeded 5.1 million, up 2 million from the previous year and almost doubling the number of new electric car sales.

• In 2030, in the New Policies Scenario, global electric car sales will reach 23 million and the stock will exceed 130 million vehicles (excluding two/three-wheelers).

• In 2030, in the EV30@30 Scenario, EV sales will reach 43 million and the stock will be more than 250 million (excluding two/three-wheelers).

(Source: International Energy Agency, “Global EV Outlook 2019 Scaling up the transition to electric mobility,” May. 27, 2019)

Global EV Market 2013-2018

EV Electricity Demand Surges

Note: LDV = light duty vehicles; NPS = New Policies Scenario)

• In 2018, electricity demand from the global EV fleet is 58 TWh.

• In 2030, in the New Policies Scenario, electricity demand from the global EV fleet will exceed 640 TWh).

• In 2030, in the EV30@30 Scenario, electricity demand from the global EV fleet will exceed 1,110 TWh.

• Our current power distribution networks are not designed for such large scale mobile loads.

(Source: International Energy Agency, “Global EV Outlook 2019 Scaling up the transition to electric mobility,” May. 27, 2019)

EV Charging Scenarios

• AC level two charger and DCFCcharger with high voltage level and high charging power dominates the public charging facility.

• Concentrated public charging can cause distribution transformer overloading.

• In 2017, the Sacramento Municipal Utility District (SMUD) forecasted that 17% of its distribution transformers need to be replaced due to EV charging.

Energy demand in % of kWh

Source: McKinsey & Company Automotive & Assembly, “Charging ahead: Electric-vehicle infrastructure demand,” Aug. 2018

EV Charger by Voltage Level

Source: California Energy Commission, “Electrical Vehicle Charging 101,” 2019.

(Source: H. K. Trabish, “Electric vehicles can be grid assets or liabilities. How utilities plan will decide,” Utility Dive, May. 17, 2017.)

Voltage Voltage Voltage110V 1-phase AC 240V 1-phase AC 480V 3-phase ACAmps Amps Amps12 - 16 Amps 16 - 40 Amps <200 Amps (Typ. 60 Amps)Charging Speed Charging Speed Charging Speed3.5 – 6.5 miles/hour 14 – 35 miles/hour 178 miles/hour

Challenges and Opportunities

Challenges OpportunitiesUncontrolled charging of large-scaleEVs can affect the safe and economicoperation of power systems in thefollowing aspects: Increased electricity T&D losses

and higher peak load. Increased voltage deviations. Transformers loading. Business models. Increased price volatilities.

If appropriate charging/dischargingstrategies are adopted, EVs cancontribute to power system operationin various ways. Load valley filling. Line congestion management. Demand response. Frequency regulation. Increase renewable penetration. Vehicle-to-grid (V2G).

Harnessing Interdependency for Resilience: Creating an "Energy Sponge" with Cloud Electric Vehicle Sharing

• Objective: to model, test, and validate a transformative “energy sponge” service that transforms the interdependency between power and transportation systems into extra resilience through sharing electric vehicles (EV).

• Funding Source: National Science Foundation

Figure 1 Cloud EV Sharing Service

• Collaborators: University of Wisconsin-Madison, University of South Florida, Arizona State University

• Broader Impact: The outcomes of this research will benefit infrastructure system planning and operation practices, particularly in the context of developing green transportation, smart grid, and green/smart cities.

Charging Management for EV through Tariff Incentive

EV Load Profiles by ScenarioUnconstrained charging: charge as soon as EVs arrivehome.Constrained charging: charging is delayed for 3 hoursafter vehicles arrive home.Smart charging: charge only during the off-peak time.Observations: EV charging is generally more flexible and easier to

manage than shifting multiple residential loads. Varied pricing programs can be designed by utilities

to better manage EV charging-induced spikes inresidential locations.

Smart charging strategies can significantly delay theneeds for new generation capacity.

Ref.:

M. Biviji, C. Uçkun, G. Bassett, J. Wang and D. Ton, "Patterns ofelectric vehicle charging with time of use rates: Case studies inCalifornia and Portland," ISGT 2014, Washington, DC, 2014, pp.1-5.L. Poch, M. Mahalik, J. Wang and A. Vyas, "Impacts of plug-in hybridelectric vehicles on the electric power system in the western UnitedStates," IEEE PES General Meeting, Providence, RI, 2010, pp. 1-7.

Improve the Wind Penetration through EV and Demand Response

Wind power contains strong variability and uncertainty

Distribution of vehicles by last-trip ending timeWind power is inherently uncertain and variable. High penetration of wind power in the power system threats the security operation of the grid.However: EVs can shift the charging demand from

critical hours to non-critical hours by changing the (1) charging time; (2) charging amount; (3) charging power.

EVs and DR can quickly adjust the end-use consumption.

Batteries in EVs can contribute to load leveling by injecting power to the grid during on-peak hours.

Batteries in EVs can absorb wind power during off-peak times.

Improve the Wind Penetration through EV and Demand Response

Four scenarios investigated in the case studies:1. Unconstrained charging of EVs. 2. 3-hour delayed-constrained

charging of EVs. 3. Smart charging of EVs. 4. DR and smart charging of EVs.

DR programs are assumed to be in place for managing non-EV load. EV hourly charging power under the four scenarios.

Ref.: J. Wang, C. Liu, D. Ton, Y. Zhou, J. Kim, A. Vyas, “Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power,” Energy Policy, vol. 39, no. 7, Jan. 2011, pp. 4016-4021.

Improve the Wind Penetration through EV and Demand Response

EV hourly charging power under 4 scenarios.

Case Study: Running unit commitment program simulations on the four scenarios. The running results for one of the studied weeks are presented.

Result:The cooperation of DR and EV significantly reduces the use of peaking units and achieves the lowest operating costs to the generation units.

Hourly total thermal generation

Start-up cost for each day in scenarios

Charging Load Adjustment through Optimal Pricing

Idea: adjust prices of electricity at public charging stations to influence the spatial distribution of EV charging loads tomitigate their impacts to the grid.

Objective one: minimize the EVs’ total traveling time.

min𝑥𝑥,, �𝑣𝑣,𝑞𝑞

�𝑎𝑎�0

𝑣𝑣𝑎𝑎𝑡𝑡𝑎𝑎 �𝑤𝑤 𝑑𝑑 �𝑤𝑤 +

1𝛼𝛼�𝑟𝑟

�𝑠𝑠

�𝑞𝑞𝑟𝑟𝑠𝑠 ln �𝑞𝑞𝑟𝑟𝑠𝑠 − 1 +1𝛼𝛼�𝑟𝑟

�𝑠𝑠

(𝛾𝛾𝑦𝑦𝑠𝑠𝑝𝑝𝑠𝑠𝑒𝑒𝑟𝑟𝑠𝑠 − 𝛽𝛽𝑦𝑦𝑠𝑠 − 𝜃𝜃𝑠𝑠)�𝑞𝑞𝑟𝑟𝑠𝑠

s.t.

𝐸𝐸𝐸𝐸 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑓𝑓𝑓𝑓𝑤𝑤 𝑡𝑡𝑓𝑓𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑐𝑐𝑡𝑡𝑐𝑐

𝑣𝑣𝑎𝑎 = �𝑟𝑟𝑠𝑠�𝑥𝑥𝑎𝑎𝑟𝑟𝑠𝑠 + �

𝑟𝑟𝑠𝑠�̅�𝑥𝑎𝑎𝑟𝑟𝑠𝑠 ,∀𝑡𝑡 ∈ 𝐴𝐴

∆�𝑥𝑥𝑎𝑎𝑟𝑟𝑠𝑠 = 𝐸𝐸𝑟𝑟𝑠𝑠 �𝑞𝑞𝑟𝑟𝑠𝑠∆�̅�𝑥𝑎𝑎𝑟𝑟𝑠𝑠 = 𝐸𝐸𝑟𝑟𝑠𝑠 �𝑞𝑞𝑟𝑟𝑠𝑠

⋮

𝑃𝑃𝑓𝑓𝑤𝑤𝑒𝑒𝑡𝑡 𝑡𝑡𝑓𝑓𝑓𝑓𝑤𝑤 𝑡𝑡𝑓𝑓𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑐𝑐𝑡𝑡𝑐𝑐

𝑃𝑃𝑘𝑘,𝑧𝑧 = 𝑃𝑃𝑚𝑚,𝑧𝑧 −𝑢𝑢𝑚𝑚,𝑘𝑘 𝑃𝑃𝑚𝑚,𝑧𝑧

2 + 𝑄𝑄𝑚𝑚,𝑧𝑧2

𝐸𝐸𝑚𝑚2−�

𝑘𝑘,𝑧𝑧′ ∈𝐾𝐾𝐾𝐾,𝑧𝑧′≠𝑧𝑧𝑃𝑃𝑘𝑘,𝑧𝑧′ − �𝑃𝑃𝑘𝑘 − �𝑃𝑃𝑘𝑘 ,∀ 𝑚𝑚, 𝑘𝑘, 𝑧𝑧 ∈ 𝐵𝐵𝐵𝐵

𝑄𝑄𝑘𝑘,𝑧𝑧 = 𝑄𝑄𝑚𝑚,𝑧𝑧 −𝑤𝑤𝑚𝑚,𝑘𝑘 𝑃𝑃𝑚𝑚,𝑧𝑧

2 + 𝑄𝑄𝑚𝑚,𝑧𝑧2

𝐸𝐸𝑚𝑚2−�

𝑘𝑘,𝑧𝑧′ ∈𝐾𝐾𝐾𝐾,𝑧𝑧′≠𝑧𝑧𝑄𝑄𝑘𝑘,𝑧𝑧′ − �𝑄𝑄𝑘𝑘 − �𝑄𝑄𝑘𝑘 ,∀(𝑚𝑚, 𝑘𝑘, 𝑧𝑧) ∈ 𝐵𝐵𝐵𝐵

𝐸𝐸𝑘𝑘2 = 𝐸𝐸𝑚𝑚2 − 2 𝑢𝑢𝑚𝑚,𝑘𝑘𝑃𝑃𝑚𝑚,𝑧𝑧 + 𝑤𝑤𝑚𝑚,𝑘𝑘𝑄𝑄𝑚𝑚,𝑧𝑧 +𝑢𝑢𝑚𝑚,𝑘𝑘2 + 𝑤𝑤𝑚𝑚,𝑘𝑘

2 𝑃𝑃𝑚𝑚,𝑧𝑧2 + 𝑄𝑄𝑚𝑚,𝑧𝑧

2

𝐸𝐸𝑚𝑚2, ∀(𝑚𝑚, 𝑘𝑘, 𝑧𝑧) ∈ 𝐵𝐵𝐵𝐵

⋮

Traveling time of EVs to their destinations

link flow distributions of PEVs

link flow distributions of regular vehicles

Charging Load Adjustment through Optimal Pricing

Objective two: minimize the EV owners’ payment for the charging services.

min𝑥𝑥,𝑣𝑣, �𝑞𝑞,𝑝𝑝,𝑃𝑃,𝑄𝑄,𝑉𝑉,𝜌𝜌,𝜏𝜏,𝜆𝜆

𝑝𝑝𝑚𝑚 � �0,𝑧𝑧 ∈𝐾𝐾𝐾𝐾

𝑃𝑃0,𝑧𝑧 − �𝑘𝑘∈𝐾𝐾

�𝑃𝑃𝑘𝑘 + �𝑃𝑃𝑘𝑘 + 𝜋𝜋 � �𝑎𝑎∈𝐴𝐴

𝑡𝑡𝑎𝑎(𝑣𝑣𝑎𝑎)𝑣𝑣𝑎𝑎

s.t.𝐸𝐸𝐸𝐸 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑓𝑓𝑓𝑓𝑤𝑤 𝑡𝑡𝑓𝑓𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑐𝑐𝑡𝑡𝑐𝑐𝑃𝑃𝑓𝑓𝑤𝑤𝑒𝑒𝑡𝑡 𝑡𝑡𝑓𝑓𝑓𝑓𝑤𝑤 𝑡𝑡𝑓𝑓𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑐𝑐𝑡𝑡𝑐𝑐

𝑃𝑃𝑓𝑓𝑤𝑤𝑒𝑒𝑡𝑡 𝑚𝑚𝑡𝑡𝑡𝑡𝑘𝑘𝑒𝑒𝑡𝑡 𝑡𝑡𝑐𝑐𝑑𝑑 𝐸𝐸𝐸𝐸 𝑡𝑡𝑓𝑓𝑓𝑓𝑓𝑓 𝑡𝑡𝑓𝑓𝑐𝑐𝑡𝑡 𝑡𝑡𝑓𝑓𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑐𝑐𝑡𝑡𝑐𝑐 �𝑡𝑡𝑎𝑎 − �𝜌𝜌𝑖𝑖𝑟𝑟𝑠𝑠 + �𝜌𝜌𝑗𝑗𝑟𝑟𝑠𝑠 � �𝑥𝑥𝑎𝑎𝑟𝑟𝑠𝑠 = 0,∀𝑡𝑡 = 𝑡𝑡, 𝑗𝑗 ∈ 𝐴𝐴, 𝑡𝑡 ∈ 𝑅𝑅, 𝑐𝑐 ∈ 𝑆𝑆𝑡𝑡𝑎𝑎 − �𝜌𝜌𝑖𝑖𝑟𝑟𝑠𝑠 + �𝜌𝜌𝑗𝑗𝑟𝑟𝑠𝑠 ≥ 0,∀𝑡𝑡 = 𝑡𝑡, 𝑗𝑗 ∈ 𝐴𝐴, 𝑡𝑡 ∈ 𝑅𝑅, 𝑐𝑐 ∈ 𝑆𝑆

⋮

Reformulate the objective one and two as an equivalent bi-level programming model:

𝑈𝑈𝑝𝑝𝑝𝑝𝑒𝑒𝑡𝑡 𝑓𝑓𝑒𝑒𝑣𝑣𝑒𝑒𝑓𝑓 → min(𝑣𝑣, �𝑞𝑞,𝑝𝑝,𝐺𝐺)

𝑝𝑝𝑚𝑚 � ∑ 0,𝑧𝑧 ∈𝐾𝐾𝐾𝐾 𝑃𝑃0,𝑧𝑧 − ∑𝑘𝑘∈𝐾𝐾 �𝑃𝑃𝑘𝑘 + �𝑃𝑃𝑘𝑘 + 𝜋𝜋 � ∑𝑎𝑎∈𝐴𝐴 𝑡𝑡𝑎𝑎 𝑣𝑣𝑎𝑎 𝑣𝑣𝑎𝑎 + Γ � max�0,∑ 0,𝑧𝑧 ∈𝐾𝐾𝐾𝐾 𝑃𝑃0,𝑧𝑧 � 𝑝𝑝𝑚𝑚 −

Charging energy costs

Vehicle traveling toll costs

Charging Load Adjustment through Optimal Pricing

Solution framework

Result：The travel cost is reduced by 2.6%The real power losses are reduced by 4.5%

Conclusion:Electricity pricing can be an effective tool for addressing the challenges that EV charging loads impose on power distribution grids, and integrated pricing of electricity and roads has potential for better managing and operating thecoupled transportation and power networks.

Ref.: F. He, Y. Yin, J. Wang, Y. Yang, “Sustainability SI: optimal Prices of electricity at public charging stations for plug-in electric vehicles,” Networks and Spatial Economics, vol. 16, no. 1, pp. 131-154.

Reduce Energy Loss through a Prediction-based Power DispatchChallenges: The mobile EV charging load causes extrapower loss due to imperfect power dispatch planning.

Idea: we propose a model predictive control (MPC)-basedpower dispatch approach. The proposed objectivefunctions minimize the operational cost whileaccommodating the EV charging uncertainty.

𝑀𝑀𝑡𝑡𝑐𝑐:𝐹𝐹 =∑𝑡𝑡 ∑𝑗𝑗 𝑡𝑡𝑗𝑗 𝑃𝑃𝑗𝑗 𝑡𝑡 +∑𝑡𝑡 ∑𝑘𝑘 𝑡𝑡𝑘𝑘 𝑃𝑃𝑘𝑘 𝑡𝑡 +∑𝑡𝑡 𝑡𝑡𝑔𝑔𝑟𝑟𝑖𝑖𝑔𝑔(𝑃𝑃𝑔𝑔𝑟𝑟𝑖𝑖𝑔𝑔(𝑡𝑡)) +∑𝑡𝑡 ∑𝑗𝑗 𝑡𝑡𝑗𝑗𝑈𝑈 𝑡𝑡

Subject to:∑𝑚𝑚𝑃𝑃𝑚𝑚 𝑡𝑡 + ∑𝑛𝑛 𝑃𝑃𝑛𝑛 𝑡𝑡 + ∑𝑚𝑚𝑃𝑃𝑚𝑚 𝑡𝑡 + ∑𝑚𝑚𝑃𝑃𝑚𝑚 𝑡𝑡 +𝑃𝑃𝑔𝑔𝑟𝑟𝑖𝑖𝑔𝑔 𝑡𝑡 = 𝑃𝑃𝐿𝐿𝐿𝐿𝑠𝑠𝑠𝑠 𝑡𝑡 + 𝐷𝐷𝑏𝑏𝑎𝑎𝑠𝑠𝑏𝑏 𝑡𝑡 + 𝐷𝐷𝐸𝐸𝐸𝐸 𝑡𝑡 ,∀𝑡𝑡

𝑃𝑃𝑗𝑗,𝑚𝑚𝑖𝑖𝑛𝑛 ≤ 𝑃𝑃𝑗𝑗 𝑡𝑡 ≤ 𝑃𝑃𝑗𝑗,𝑚𝑚𝑎𝑎𝑥𝑥 ,∀𝑗𝑗

𝑃𝑃𝑘𝑘,𝑚𝑚𝑖𝑖𝑛𝑛 ≤ 𝑃𝑃𝑘𝑘 𝑡𝑡 ≤ 𝑃𝑃𝑘𝑘,𝑚𝑚𝑎𝑎𝑥𝑥 ,∀𝑘𝑘

0 ≤ 𝑃𝑃𝐾𝐾 𝑡𝑡 ≤ 𝑃𝑃𝑙𝑙𝑏𝑏𝑣𝑣𝑏𝑏𝑙𝑙 ,∀𝑡𝑡𝐸𝐸𝑚𝑚𝑖𝑖𝑛𝑛 ≤ 𝐸𝐸𝑦𝑦 ≤ 𝐸𝐸𝑚𝑚𝑎𝑎𝑥𝑥 ,∀𝑦𝑦

Procedure of the proposed MPC-based power dispatch method

Ref.: W. Su, J. Wang, K. Zhang, A. Q. Huang, “Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty,” Electric Power Systems Research, Vol. 106, Jan. 2014, pp 29-35.

DG generation costs

Energy storage discharging costs

Costs for energy from the grid

DG start up/shut down costs

Power balance

Generation limits

Power transfer limits

Voltage deviation limits

Reduce Energy Loss through a Prediction-based Power Dispatch

System Setup: A modified IEEE 37-node distribution test feeder. DG, wind generator, solar generator, DES, and PEV are connected with nodes 701, 722, 730, and 737, respectively.

Case Study:Scenario 1: Non-MPC based Day-aheadScenario 2: MPCScenario 3: Perfect Forecasting

Compared with the non-MPC based day-ahead approach the MPC method reduces the charging costs by 13.44% and power loss by 15.78%.

Risk Management for EV Aggregators

Challenge: the information gap between the forecasted and actual electricity prices posts revenue risks for the EVaggregators.

Strategy: manage the revenue risk of the EV aggregator through an information gap decision theory (IGDT)-based approach.

Goal: guarantee the predefined profit for risk-averse decision-makers or pursue the windfall return for risk-seeking decision-makers.

Framework of EV aggregator participating in electricity markets

Managing the Profit Risk of the EV Aggregator Using Information Gap Decision Theory

Objective: maximize the total profit of EV aggregators

max𝐹𝐹 𝐸𝐸𝑃𝑃𝐸𝐸𝑉𝑉,𝑃𝑃𝐶𝐶𝐶𝐶 ,𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶 = ∑𝑡𝑡=1𝑇𝑇 (𝜆𝜆𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐸𝐸𝑉𝑉,𝑡𝑡 − 𝛾𝛾𝑡𝑡𝑃𝑃𝐸𝐸𝑉𝑉,𝑡𝑡 − 𝜆𝜆𝐵𝐵𝐷𝐷𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶,𝑡𝑡)

Payments from EV owners Energy costs battery degradation costs

𝑐𝑐. 𝑡𝑡.

𝐸𝐸𝑡𝑡 = 𝐸𝐸𝑡𝑡−1 + 𝐸𝐸𝑎𝑎𝑟𝑟𝑟𝑟,𝑡𝑡 − 𝐸𝐸𝑔𝑔𝑏𝑏𝑝𝑝,𝑡𝑡 + 𝜂𝜂𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝐶𝐶,𝑡𝑡 −𝑃𝑃𝐷𝐷𝐷𝐷𝐷𝐷,𝑡𝑡𝜂𝜂𝐷𝐷𝐷𝐷𝐷𝐷

,∀𝑡𝑡

𝐸𝐸0 = 𝐸𝐸𝑇𝑇𝑃𝑃𝐸𝐸𝑉𝑉,𝑡𝑡 = 𝑃𝑃𝐶𝐶𝐶𝐶,𝑡𝑡 − 𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶,𝑡𝑡 ,∀𝑡𝑡

⋮

Uncertainty Model for the electricity price:Γ 𝛼𝛼, �̅�𝛾 = 𝛾𝛾: (𝛾𝛾 − �̅�𝛾)𝑇𝑇 𝐶𝐶−1(𝛾𝛾 − �̅�𝛾) ≤ 𝛼𝛼2 ，𝛼𝛼 ≥ 0

Robustness function:�𝛼𝛼 𝐵𝐵,𝑡𝑡𝑟𝑟 = max

𝐾𝐾,𝛾𝛾𝛼𝛼: min

𝛾𝛾∈𝑈𝑈(𝛼𝛼,�𝛾𝛾)𝐹𝐹(𝐵𝐵, 𝛾𝛾) ≥ 𝑡𝑡𝑟𝑟

Opportunity function:

�̂�𝛽 𝐵𝐵, 𝑡𝑡𝐿𝐿 = min𝐾𝐾,𝛾𝛾

𝛼𝛼: max𝛾𝛾∈𝑈𝑈(𝛼𝛼,�𝛾𝛾)

𝐹𝐹(𝐵𝐵, 𝛾𝛾) ≥ 𝑡𝑡𝐿𝐿

Ref.: J. Zhao, C. Wan, Z. Xu and J. Wang, "Risk-Based Day-Ahead Scheduling of Electric Vehicle Aggregator Using Information Gap Decision Theory," IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1609-1618, July 2017.

Managing the Profit Risk of the EV Aggregator

Ref.: J. Zhao, C. Wan, Z. Xu and J. Wang, "Risk-Based Day-Ahead Scheduling of Electric Vehicle Aggregator Using Information Gap Decision Theory," IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1609-1618, July 2017.

Case studyThe numerical solution to the optimization problem: max𝐹𝐹 𝐸𝐸𝑃𝑃𝐸𝐸𝑉𝑉 ,𝑃𝑃𝐶𝐶𝐶𝐶,𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶 is a neutral risk profit as shown as point O.

A risk-averse decision maker can choose a robust profit target that is below the max𝐹𝐹 𝐸𝐸𝑃𝑃𝐸𝐸𝑉𝑉 ,𝑃𝑃𝐶𝐶𝐶𝐶 ,𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶 . A decision maker with positive attitudes towards risk can choose a opportunity target profit.

ConclusionThis IGDT-based scheduling methodology allows EV aggregators to consider different risk attitudes towards the uncertainty of electricity market prices. The proposed IGDT-based model provides an effective way for EV aggregators to pursue a predefined profit target.

Robustness price curve for profit target 𝑡𝑡𝑟𝑟 = $3900 and opportunityprice curve for profit target 𝑡𝑡𝐿𝐿= $8100 compared with nominal price.

Electric vehicle charging and discharging power by robust scheduling strategy for profit target 𝑡𝑡𝑟𝑟 = $2700 compared with neutral risk scheduling.

Electric vehicle charging and discharging power by opportunisticscheduling strategy for profit target 𝑡𝑡𝐿𝐿 = $9900 compared with neutral risk scheduling.

Robustness price curve for profit target 𝑡𝑡𝑟𝑟 = $3900 and opportunityprice curve for profit target 𝑡𝑡𝐿𝐿= $8100 compared with nominal price.

Neutral risk

- The development of EVs and their challenges to the grid • EV Technology and Markets• EV Charging Demand and its Impacts to the Grid

- Tackling the EV’s Challenges to the Grid• Charging Management for EVs through Tariff Incentive• Charging Load Adjustment through Optimal Pricing• Reducing Energy Loss through a Prediction-based Power Dispatch• Managing the Profit Risk of an EV Aggregator

- The contribution of EVs to the Grid• Improve the Wind Penetration through EV and Demand Response• Valley Filling through EV Fleets• Fast Frequency Regulation through Battery Swapping Stations

Conclusions