Electric Vehicle Grid Integration: Challenges and Solutions
Transcript of Electric Vehicle Grid Integration: Challenges and Solutions
Jianhui WangAssociate ProfessorDepartment of Electrical and Computer [email protected]
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).
โข 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.
โข 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).
โข 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, โGlobal EV Outlook 2019 Scaling up the transition to electric mobility,โ May. 27, 2019)
Global EV Market 2013-2018
EV Electricity Demand Surges
Note: LDV = light duty vehicles; NPS = New Policies Scenario)
โข In 2018, electricity demand from the global EV fleet is 58 TWh.
โข In 2030, in the New Policies Scenario, electricity demand from the global EV fleet will exceed 640 TWh).
โข In 2030, in the EV30@30 Scenario, electricity demand from the global EV fleet will exceed 1,110 TWh.
โข Our current power distribution networks are not designed for such large scale mobile loads.
(Source: International Energy Agency, โGlobal EV Outlook 2019 Scaling up the transition to electric mobility,โ May. 27, 2019)
EV Charging Scenarios
โข AC level two charger and DCFCcharger with high voltage level and high charging power dominates the public charging facility.
โข Concentrated public charging can cause distribution transformer overloading.
โข 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, โCharging ahead: Electric-vehicle infrastructure demand,โ Aug. 2018
EV Charger by Voltage Level
Source: California Energy Commission, โElectrical Vehicle Charging 101,โ 2019.
(Source: H. K. Trabish, โElectric vehicles can be grid assets or liabilities. How utilities plan will decide,โ 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 โ 6.5 miles/hour 14 โ 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
โข Objective: to model, test, and validate a transformative โenergy spongeโ service that transforms the interdependency between power and transportation systems into extra resilience through sharing electric vehicles (EV).
โข Funding Source: National Science Foundation
Figure 1 Cloud EV Sharing Service
โข Collaborators: University of Wisconsin-Madison, University of South Florida, Arizona State University
โข 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. Uรงkun, 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, โImpact of plug-in hybrid electric vehicles on power systems with demand response and wind power,โ 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 EVsโ total traveling time.
min๐ฅ๐ฅ,, ๏ฟฝ๐ฃ๐ฃ,๐๐
๏ฟฝ๐๐๏ฟฝ0
๐ฃ๐ฃ๐๐๐ก๐ก๐๐ ๏ฟฝ๐ค๐ค ๐๐ ๏ฟฝ๐ค๐ค +
1๐ผ๐ผ๏ฟฝ๐๐
๏ฟฝ๐ ๐
๏ฟฝ๐๐๐๐๐ ๐ ln ๏ฟฝ๐๐๐๐๐ ๐ โ 1 +1๐ผ๐ผ๏ฟฝ๐๐
๏ฟฝ๐ ๐
(๐พ๐พ๐ฆ๐ฆ๐ ๐ ๐๐๐ ๐ ๐๐๐๐๐ ๐ โ ๐ฝ๐ฝ๐ฆ๐ฆ๐ ๐ โ ๐๐๐ ๐ )๏ฟฝ๐๐๐๐๐ ๐
s.t.
๐ธ๐ธ๐ธ๐ธ ๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก ๐ก๐ก๐๐๐๐๐ค๐ค ๐ก๐ก๐๐๐๐๐๐๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐๐๐ก๐ก๐๐
๐ฃ๐ฃ๐๐ = ๏ฟฝ๐๐๐ ๐ ๏ฟฝ๐ฅ๐ฅ๐๐๐๐๐ ๐ + ๏ฟฝ
๐๐๐ ๐ ๏ฟฝฬ ๏ฟฝ๐ฅ๐๐๐๐๐ ๐ ,โ๐ก๐ก โ ๐ด๐ด
โ๏ฟฝ๐ฅ๐ฅ๐๐๐๐๐ ๐ = ๐ธ๐ธ๐๐๐ ๐ ๏ฟฝ๐๐๐๐๐ ๐ โ๏ฟฝฬ ๏ฟฝ๐ฅ๐๐๐๐๐ ๐ = ๐ธ๐ธ๐๐๐ ๐ ๏ฟฝ๐๐๐๐๐ ๐
โฎ
๐๐๐๐๐ค๐ค๐๐๐ก๐ก ๐ก๐ก๐๐๐๐๐ค๐ค ๐ก๐ก๐๐๐๐๐๐๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐๐๐ก๐ก๐๐
๐๐๐๐,๐ง๐ง = ๐๐๐๐,๐ง๐ง โ๐ข๐ข๐๐,๐๐ ๐๐๐๐,๐ง๐ง
2 + ๐๐๐๐,๐ง๐ง2
๐ธ๐ธ๐๐2โ๏ฟฝ
๐๐,๐ง๐งโฒ โ๐พ๐พ๐พ๐พ,๐ง๐งโฒโ ๐ง๐ง๐๐๐๐,๐ง๐งโฒ โ ๏ฟฝ๐๐๐๐ โ ๏ฟฝ๐๐๐๐ ,โ ๐๐, ๐๐, ๐ง๐ง โ ๐ต๐ต๐ต๐ต
๐๐๐๐,๐ง๐ง = ๐๐๐๐,๐ง๐ง โ๐ค๐ค๐๐,๐๐ ๐๐๐๐,๐ง๐ง
2 + ๐๐๐๐,๐ง๐ง2
๐ธ๐ธ๐๐2โ๏ฟฝ
๐๐,๐ง๐งโฒ โ๐พ๐พ๐พ๐พ,๐ง๐งโฒโ ๐ง๐ง๐๐๐๐,๐ง๐งโฒ โ ๏ฟฝ๐๐๐๐ โ ๏ฟฝ๐๐๐๐ ,โ(๐๐, ๐๐, ๐ง๐ง) โ ๐ต๐ต๐ต๐ต
๐ธ๐ธ๐๐2 = ๐ธ๐ธ๐๐2 โ 2 ๐ข๐ข๐๐,๐๐๐๐๐๐,๐ง๐ง + ๐ค๐ค๐๐,๐๐๐๐๐๐,๐ง๐ง +๐ข๐ข๐๐,๐๐2 + ๐ค๐ค๐๐,๐๐
2 ๐๐๐๐,๐ง๐ง2 + ๐๐๐๐,๐ง๐ง
2
๐ธ๐ธ๐๐2, โ(๐๐, ๐๐, ๐ง๐ง) โ ๐ต๐ต๐ต๐ต
โฎ
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 ownersโ payment for the charging services.
min๐ฅ๐ฅ,๐ฃ๐ฃ, ๏ฟฝ๐๐,๐๐,๐๐,๐๐,๐๐,๐๐,๐๐,๐๐
๐๐๐๐ ๏ฟฝ ๏ฟฝ0,๐ง๐ง โ๐พ๐พ๐พ๐พ
๐๐0,๐ง๐ง โ ๏ฟฝ๐๐โ๐พ๐พ
๏ฟฝ๐๐๐๐ + ๏ฟฝ๐๐๐๐ + ๐๐ ๏ฟฝ ๏ฟฝ๐๐โ๐ด๐ด
๐ก๐ก๐๐(๐ฃ๐ฃ๐๐)๐ฃ๐ฃ๐๐
s.t.๐ธ๐ธ๐ธ๐ธ ๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก ๐ก๐ก๐๐๐๐๐ค๐ค ๐ก๐ก๐๐๐๐๐๐๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐๐๐ก๐ก๐๐๐๐๐๐๐ค๐ค๐๐๐ก๐ก ๐ก๐ก๐๐๐๐๐ค๐ค ๐ก๐ก๐๐๐๐๐๐๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐๐๐ก๐ก๐๐
๐๐๐๐๐ค๐ค๐๐๐ก๐ก ๐๐๐ก๐ก๐ก๐ก๐๐๐๐๐ก๐ก ๐ก๐ก๐๐๐๐ ๐ธ๐ธ๐ธ๐ธ ๐ก๐ก๐๐๐๐๐๐ ๐ก๐ก๐๐๐๐๐ก๐ก ๐ก๐ก๐๐๐๐๐๐๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐๐๐ก๐ก๐๐ ๏ฟฝ๐ก๐ก๐๐ โ ๏ฟฝ๐๐๐๐๐๐๐ ๐ + ๏ฟฝ๐๐๐๐๐๐๐ ๐ ๏ฟฝ ๏ฟฝ๐ฅ๐ฅ๐๐๐๐๐ ๐ = 0,โ๐ก๐ก = ๐ก๐ก, ๐๐ โ ๐ด๐ด, ๐ก๐ก โ ๐ ๐ , ๐๐ โ ๐๐๐ก๐ก๐๐ โ ๏ฟฝ๐๐๐๐๐๐๐ ๐ + ๏ฟฝ๐๐๐๐๐๐๐ ๐ โฅ 0,โ๐ก๐ก = ๐ก๐ก, ๐๐ โ ๐ด๐ด, ๐ก๐ก โ ๐ ๐ , ๐๐ โ ๐๐
โฎ
Reformulate the objective one and two as an equivalent bi-level programming model:
๐๐๐๐๐๐๐๐๐ก๐ก ๐๐๐๐๐ฃ๐ฃ๐๐๐๐ โ min(๐ฃ๐ฃ, ๏ฟฝ๐๐,๐๐,๐บ๐บ)
๐๐๐๐ ๏ฟฝ โ 0,๐ง๐ง โ๐พ๐พ๐พ๐พ ๐๐0,๐ง๐ง โ โ๐๐โ๐พ๐พ ๏ฟฝ๐๐๐๐ + ๏ฟฝ๐๐๐๐ + ๐๐ ๏ฟฝ โ๐๐โ๐ด๐ด ๐ก๐ก๐๐ ๐ฃ๐ฃ๐๐ ๐ฃ๐ฃ๐๐ + ฮ ๏ฟฝ max๏ฟฝ0,โ 0,๐ง๐ง โ๐พ๐พ๐พ๐พ ๐๐0,๐ง๐ง ๏ฟฝ ๐๐๐๐ โ
Charging energy costs
Vehicle traveling toll costs
Charging Load Adjustment through Optimal Pricing
Solution framework
Result๏ผThe 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, โSustainability SI: optimal Prices of electricity at public charging stations for plug-in electric vehicles,โ 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.
๐๐๐ก๐ก๐๐:๐น๐น =โ๐ก๐ก โ๐๐ ๐ก๐ก๐๐ ๐๐๐๐ ๐ก๐ก +โ๐ก๐ก โ๐๐ ๐ก๐ก๐๐ ๐๐๐๐ ๐ก๐ก +โ๐ก๐ก ๐ก๐ก๐๐๐๐๐๐๐๐(๐๐๐๐๐๐๐๐๐๐(๐ก๐ก)) +โ๐ก๐ก โ๐๐ ๐ก๐ก๐๐๐๐ ๐ก๐ก
Subject to:โ๐๐๐๐๐๐ ๐ก๐ก + โ๐๐ ๐๐๐๐ ๐ก๐ก + โ๐๐๐๐๐๐ ๐ก๐ก + โ๐๐๐๐๐๐ ๐ก๐ก +๐๐๐๐๐๐๐๐๐๐ ๐ก๐ก = ๐๐๐ฟ๐ฟ๐ฟ๐ฟ๐ ๐ ๐ ๐ ๐ก๐ก + ๐ท๐ท๐๐๐๐๐ ๐ ๐๐ ๐ก๐ก + ๐ท๐ท๐ธ๐ธ๐ธ๐ธ ๐ก๐ก ,โ๐ก๐ก
๐๐๐๐,๐๐๐๐๐๐ โค ๐๐๐๐ ๐ก๐ก โค ๐๐๐๐,๐๐๐๐๐ฅ๐ฅ ,โ๐๐
๐๐๐๐,๐๐๐๐๐๐ โค ๐๐๐๐ ๐ก๐ก โค ๐๐๐๐,๐๐๐๐๐ฅ๐ฅ ,โ๐๐
0 โค ๐๐๐พ๐พ ๐ก๐ก โค ๐๐๐๐๐๐๐ฃ๐ฃ๐๐๐๐ ,โ๐ก๐ก๐ธ๐ธ๐๐๐๐๐๐ โค ๐ธ๐ธ๐ฆ๐ฆ โค ๐ธ๐ธ๐๐๐๐๐ฅ๐ฅ ,โ๐ฆ๐ฆ
Procedure of the proposed MPC-based power dispatch method
Ref.: W. Su, J. Wang, K. Zhang, A. Q. Huang, โModel predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty,โ 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
max๐น๐น ๐ธ๐ธ๐๐๐ธ๐ธ๐๐,๐๐๐ถ๐ถ๐ถ๐ถ ,๐๐๐ท๐ท๐ถ๐ถ๐ถ๐ถ = โ๐ก๐ก=1๐๐ (๐๐๐ถ๐ถ๐ถ๐ถ๐ถ๐ถ๐๐๐ธ๐ธ๐๐,๐ก๐ก โ ๐พ๐พ๐ก๐ก๐๐๐ธ๐ธ๐๐,๐ก๐ก โ ๐๐๐ต๐ต๐ท๐ท๐๐๐ท๐ท๐ถ๐ถ๐ถ๐ถ,๐ก๐ก)
Payments from EV owners Energy costs battery degradation costs
๐๐. ๐ก๐ก.
๐ธ๐ธ๐ก๐ก = ๐ธ๐ธ๐ก๐กโ1 + ๐ธ๐ธ๐๐๐๐๐๐,๐ก๐ก โ ๐ธ๐ธ๐๐๐๐๐๐,๐ก๐ก + ๐๐๐ถ๐ถ๐ถ๐ถ๐๐๐ถ๐ถ๐ถ๐ถ,๐ก๐ก โ๐๐๐ท๐ท๐ท๐ท๐ท๐ท,๐ก๐ก๐๐๐ท๐ท๐ท๐ท๐ท๐ท
,โ๐ก๐ก
๐ธ๐ธ0 = ๐ธ๐ธ๐๐๐๐๐ธ๐ธ๐๐,๐ก๐ก = ๐๐๐ถ๐ถ๐ถ๐ถ,๐ก๐ก โ ๐๐๐ท๐ท๐ถ๐ถ๐ถ๐ถ,๐ก๐ก ,โ๐ก๐ก
โฎ
Uncertainty Model for the electricity price:ฮ ๐ผ๐ผ, ๏ฟฝฬ ๏ฟฝ๐พ = ๐พ๐พ: (๐พ๐พ โ ๏ฟฝฬ ๏ฟฝ๐พ)๐๐ ๐ถ๐ถโ1(๐พ๐พ โ ๏ฟฝฬ ๏ฟฝ๐พ) โค ๐ผ๐ผ2 ๏ผ๐ผ๐ผ โฅ 0
Robustness function:๏ฟฝ๐ผ๐ผ ๐ต๐ต,๐ก๐ก๐๐ = max
๐พ๐พ,๐พ๐พ๐ผ๐ผ: min
๐พ๐พโ๐๐(๐ผ๐ผ,๏ฟฝ๐พ๐พ)๐น๐น(๐ต๐ต, ๐พ๐พ) โฅ ๐ก๐ก๐๐
Opportunity function:
๏ฟฝฬ๏ฟฝ๐ฝ ๐ต๐ต, ๐ก๐ก๐ฟ๐ฟ = min๐พ๐พ,๐พ๐พ
๐ผ๐ผ: max๐พ๐พโ๐๐(๐ผ๐ผ,๏ฟฝ๐พ๐พ)
๐น๐น(๐ต๐ต, ๐พ๐พ) โฅ ๐ก๐ก๐ฟ๐ฟ
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: max๐น๐น ๐ธ๐ธ๐๐๐ธ๐ธ๐๐ ,๐๐๐ถ๐ถ๐ถ๐ถ,๐๐๐ท๐ท๐ถ๐ถ๐ถ๐ถ 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 max๐น๐น ๐ธ๐ธ๐๐๐ธ๐ธ๐๐ ,๐๐๐ถ๐ถ๐ถ๐ถ ,๐๐๐ท๐ท๐ถ๐ถ๐ถ๐ถ . 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 ๐ก๐ก๐๐ = $3900 and opportunityprice curve for profit target ๐ก๐ก๐ฟ๐ฟ= $8100 compared with nominal price.
Electric vehicle charging and discharging power by robust scheduling strategy for profit target ๐ก๐ก๐๐ = $2700 compared with neutral risk scheduling.
Electric vehicle charging and discharging power by opportunisticscheduling strategy for profit target ๐ก๐ก๐ฟ๐ฟ = $9900 compared with neutral risk scheduling.
Robustness price curve for profit target ๐ก๐ก๐๐ = $3900 and opportunityprice curve for profit target ๐ก๐ก๐ฟ๐ฟ= $8100 compared with nominal price.
Neutral risk
- The development of EVs and their challenges to the grid โข EV Technology and Marketsโข EV Charging Demand and its Impacts to the Grid
- Tackling the EVโs Challenges to the Gridโข Charging Management for EVs through Tariff Incentiveโข Charging Load Adjustment through Optimal Pricingโข Reducing Energy Loss through a Prediction-based Power Dispatchโข Managing the Profit Risk of an EV Aggregator
- The contribution of EVs to the Gridโข Improve the Wind Penetration through EV and Demand Responseโข Valley Filling through EV Fleetsโข Fast Frequency Regulation through Battery Swapping Stations
Conclusions