WO2014018504A2 - Système et procédés pour charge à grande échelle de véhicules électriques - Google Patents

Système et procédés pour charge à grande échelle de véhicules électriques Download PDF

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Publication number
WO2014018504A2
WO2014018504A2 PCT/US2013/051627 US2013051627W WO2014018504A2 WO 2014018504 A2 WO2014018504 A2 WO 2014018504A2 US 2013051627 W US2013051627 W US 2013051627W WO 2014018504 A2 WO2014018504 A2 WO 2014018504A2
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charging
request
electric vehicles
power
service
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PCT/US2013/051627
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English (en)
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WO2014018504A3 (fr
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Lang Tong
Shiyao CHEN
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Cornell University
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Priority to CN201380047532.4A priority Critical patent/CN104981958B/zh
Publication of WO2014018504A2 publication Critical patent/WO2014018504A2/fr
Publication of WO2014018504A3 publication Critical patent/WO2014018504A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the invention relates generally to electric vehicles and more specifically to a system and methods for charging a plurality of electric vehicles simultaneously.
  • a large scale charging infrastructure includes an intelligent energy management system that utilizes a hardware-software architecture for managing simultaneous charging of a plurality of electric vehicles using a variety of energy supply sources.
  • the electrification of the transportation system is a crucial drive toward a clean and sustainable society.
  • Central to the transition toward electric vehicle based transportation is to establish large scale charging infrastructures, i.e., battery charging systems at public parking facilities, work places, and apartment complexes where a large number of electric vehicles get charged simultaneously.
  • Large scale charging infrastructures is extremely appealing for densely populated urban areas, where in-home charging is not an option.
  • Electric vehicle charging imposes significant challenges beyond the traditional gas station commodity-delivery operations, since all aspects of electricity delivery have to fit into the networked power grid.
  • Multiple stakeholders of the electric vehicle industry include government and regulators, vehicle manufactures, utilities companies, infrastructure technology providers and urban area planners. Over the recently years, the stakeholders have been aggressively pushing practical solutions for the imminent roll-out of mass electric vehicles market.
  • One of the current focuses is developing ubiquitous public charging infrastructure.
  • Private equity firms and venture capitalists are also rushing into the business and commitment to moving the entire world away from fossil-fuel vehicles.
  • the involvement of private equity firms and venture capitalists boosts numerous emerging start-ups in the electric vehicle charging business, which again shows the potential of the market.
  • New technologies are emerging, including fast charging and wireless charging equipment. Like businesses triggered by many other new technologies, an appropriate and sustainably profitable business model is crucial for the future of public electric vehicle charging, which includes but is not limited to cost-effective strategies of managing, metering and billing in public electric vehicle charging infrastructure to integrate electric vehicles into the big picture of the networked grid.
  • a variety of modeling and optimization techniques have been proposed for electric vehicle charging scheduling problem.
  • One proposed technique includes an offline decentralized protocol for negotiating day-ahead charging prices and schedules for household electric vehicle charging between the electric vehicle owners and utility, to shift the charging load to fill the overnight demand valley.
  • Another proposed technique casts the household electric vehicle charging scheduling problem into an optimal power flow (OPF) problem in which the solution structure of OPF is leveraged for charging scheduling.
  • OPF optimal power flow
  • Another technique has considered the electric vehicle charging for public garages - with firm energy supply sources - with a heuristic optimization approach.
  • renewable energy for electric vehicle charging along with the need of considering reserves has been investigated; however, without considering the pricing schemes.
  • Other techniques have considered the management of electric vehicle charging with the potential speculation in the provision of additional regulation service required by renewable energy expansion.
  • V2G Vehicle-to-Grid
  • Another technique considers the idea of using a parking facility as an energy exchange station for Vehicle-to-Grid (V2G) applications, which demonstrates the benefits of using electric vehicles as energy storage for demand side management.
  • Yet another technique proposes a decentralized algorithm to coordinate the autonomous electric vehicle charging in a non-cooperative game framework, which converges to a Nash equilibrium that approximately achieves the ideal solution (scheduling electric vehicle load to fill the overnight demand valley).
  • Electric Vehicles include both Plug- in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs).
  • Battery charging systems according to the invention can be implemented in parking garages, 2D parking lots, 3D parking lots, and possibly street parking or at any location where a large number of electric vehicles get charged simultaneously, for example, at public parking facilities, work places, and apartment complexes.
  • the infrastructure includes an intelligent energy management system, which is a hardware-software architecture to accesses one or more power sources such as a renewable energy supply source, a local storage source, a power grid source via a set of network switched EV charging ports.
  • the infrastructure according to the invention provides an innovative business model and an efficient way to: integrate multiple power sources, minimize the operating costs by taking advantage of flexible charging schedules and economies of scale, mitigate adverse impacts of unmanaged charging on the power grid, and aggregate demand side response to sell ancillary services to the power grid.
  • a central processing unit (CPU) component interfaces on one side with a large number of networked electric vehicles, and on the other side, with the energy supply sources, including for example, collocated renewable energy sources such as solar panels or wind mills and local storage sources such as installed battery banks or batteries for battery-swap services, and a power grid source.
  • the energy supply sources including for example, collocated renewable energy sources such as solar panels or wind mills and local storage sources such as installed battery banks or batteries for battery-swap services, and a power grid source.
  • the CPU component has three function modules.
  • a power dispatch module delivers an optimal mix of power from renewable energy sources, a power grid, and local storage sources.
  • a scheduling module maximizes profit based on several factors, including the charging requirements specified by owners of the electric vehicles, the availability of renewable power and the state of local storage sources, and the real-time prices for electricity and ancillary services on the power grid. For example, in the situation of a shopping mall parking garage, the charging requirements may specify the desired battery charging level and the deadline around which the owner of the electric vehicle finishes shopping. The owner of the electric vehicle is also referred to as "customer".
  • An admission control module determines the profitability of each arriving electric vehicle charging request and manages the price quoting and billing of customers.
  • the network switch component plays a similar role to a network router on the Internet. Controlled by the scheduling module, the network switch component activates specific charging ports of the network switch to exploit the inherent flexibility of the charging requirements set by individual customers.
  • This architecture can be implemented using level two electric vehicle chargers - e.g., AC energy to the vehicle's on-board charger, 208 - 240 volt, single phase, maximum current 32 amps, maximum continuous input power 7.68 kW - with software layer running on top of the hardware layer to exercise control on the networked switch.
  • the scheduling module for charging electric vehicles controls the power dispatch module and the admission control module.
  • the scheduling module maximizes profits subject to deadline constraints, for example customers' need to charge their electric vehicles to specified battery levels and by the specified times of return.
  • the scheduling module constantly monitors the state of charging of all parked electric vehicles, checks the state of local storage sources and the realtime prices of electricity and ancillary services of the power grid, and projects the availability of renewable energy sources.
  • the scheduling module must have low complexity and run in real-time.
  • the scheduling module Upon the arrival of new customer charging requests, the scheduling module synthesizes the current information of the state of charging of all parked electric vehicles and the energy supply sources, and evaluates profitability of the requests from arriving customers. The scheduling module communicates the estimated profitability with the admission control module to facilitate setting up the price quoted to the incoming customers. Once the quoted price is accepted by the customers, a charging contract is established with specified charging level and deadline. The scheduling module then adjusts the real-time charging schedules of individual electric vehicles accordingly.
  • the scheduling module determines the proper mix of power sources to maintain smooth operations and the desired profit, e.g., electricity may be purchased from the grid to hedge the intermittency of the renewable sources, or the local storage may be discharged.
  • electricity may be purchased from the grid to hedge the intermittency of the renewable sources, or the local storage may be discharged.
  • the scheduling module also communicates with the power dispatch module concerning the charge/discharge decision of the local storage sources, e.g., the charge of the local storage may be performed during the night hours when the electricity price is low, or during the time period with enormous renewable power available.
  • the invention amortizes complexity of order O(log n) per electric vehicle, where n is the number of electric vehicles in service. Studies have shown that for the shopping mall garage scenario, the invention improves profit by 20-40% in the light traffic conditions and 100-400% in the heavy traffic conditions compared with unmanaged charging activities. More importantly, the invention provides a flexible framework amenable to include local storage and for scheduling ancillary services.
  • the scheduling module classifies the operating condition of the large scale charging system into uncongested (underloaded) and congested (overloaded).
  • a congested operating condition includes, for example, limited renewable availability, limited local storage or limited parking space (charging ports).
  • Customers arriving to an uncongested charging facility are quoted a price based on the customers' price response curve.
  • Customers arriving to a congested charging facility go through a profitability test, after which the profitable ones are quoted based on the customers' price response curve together with the extra accommodation cost for the previously accepted requests that are forced to be rescheduled due to the new profitable request.
  • the unprofitable ones are instead quoted based on the real-time grid electricity price plus a profit margin. This differentiation according to congestion mitigates the congestion issue and ensures the facility can cover its expense.
  • the price quote based on the customers' price response curve for customers arriving to uncongested charging facility charges a customer who leaves their electric vehicle for a longer period of time in the charging facility a baseline unit price (e.g., per kWh), and charges a customer who needs their electric vehicle charged immediately an urgency premium above the baseline unit price for requesting charging with strict timeliness.
  • the rationale includes the customers' price response and the potential congestion imposed by the charging request. Specifically, a customer who demands their electric vehicle charged immediately are willing to pay higher price, and customers who leave their electric vehicle for a longer period of time allow the scheduling module to optimize the charging time, and thus the profit realized. It is also contemplated that customers may also pay an additional parking charge.
  • the quoted prices are competitive because the scheduling module takes full advantage of available collocated renewable energy sources with negligible marginal generation cost, local storage sources, and most importantly, the flexibility of charging times for the parked electric vehicles. Meanwhile, the quoted price also takes into account the intermittency of the renewable sources and randomness of the real-time electricity prices, and protects the profitability of the service provider from the unfavorable randomness and fluctuation.
  • the pricing scheme according to the invention is coupled with a deadline scheduling algorithm that exploits the available charging capacity and the customer's flexible schedule.
  • the pricing of a charging request is a function of not only the amount of required charging but also the deadline of completion.
  • a job with a relaxed deadline offers the scheduling flexibility to accommodate more profitable requests.
  • Such requests also give the owner of the electric vehicle the opportunities to avoid power surge and exploit future pricing advantages. Therefore, customers should be given price incentives to offer their flexibilities.
  • economies of scale also open up demand response participation for the service provider.
  • Each large scale charging facility may be treated as a single wholesale customer by their utility company and could sell ancillary services, such as frequency regulation, to the power grid. Such services further reduce the overall net payments to the power grid.
  • ancillary services such as frequency regulation
  • charging ports are intelligently activated from the electric vehicle side of the networked switch. Specifically, charging schedules are assigned and re-assigned based on a threshold test on profitability. In one embodiment, the invention follows a greedy procedure to adjust the tentative schedule for the parked electric vehicles at all times.
  • the service provider of the system maintains a tentative schedule for each currently available charging port and makes reserve dispatch and admission and scheduling decisions upon a variety of events, for example, when a new request releases or when an energy source becomes available or unavailable.
  • the infrastructure has access to three types of energy sources: (1) renewable energy sources, which are stochastic and fluctuate across time. The forecasted level may be available, which can be exploited for scheduling; (2) on-site local storage sources, for example, the infrastructure may maintain a battery bank to/from which charge and discharge can be made - electricity drawn from the battery bank may be used to charge electric vehicles; (3) a power grid source from which power can be purchased based on contracted or real-time pricing.
  • a competitive service provider may have an on-site renewable energy source at reasonable scale, for which the marginal cost of energy is small. Due to the intermittency of the renewable source, the service provider may choose to purchase power from the power grid from time to time to cover the down time of the renewable source.
  • the scheduling module includes an assignment policy that decides whether to assign a newly arrived electric vehicle customer to project future renewable energy source, or to purchase from the power grid to fulfill the electric vehicle charging required. The decision between the two different choices is necessary since the service provider has to assign electric vehicles according to the projected renewable energy availability, to avoid assigning electric vehicles beyond the future availability.
  • the service provider always activates the chargers for the electric vehicles with earlier deadlines at any time instant with the already determined mixture of renewable, local storage and grid power.
  • the local storage source is charged when there is surplus of renewable power, or the electricity price from the power grid is below a certain threshold, e.g., during the night hours when the electricity price is lower than the day time.
  • the scheduling module achieves the optimal competitive ratio over the set of all online algorithms for constant renewable availability and urgency-independent pricing.
  • Competitive ratio measures the worst case performance of an online algorithm versus the optimal offline algorithm, which has perfect information of the electric vehicle charging requests ahead of time.
  • the average performance demonstrates a significant gain over the unmanaged charging and earliest deadline first charging without threshold assignment.
  • the effect of the pricing scheme with congestion-urgency-differentiation also demonstrates another boost in profitability due to capturing different customer needs.
  • Advantages of the invention include a networked switch system architecture that enables controllability and avoids unmanaged charging, a framework of congestion-urgency-differentiated pricing to explore best practices rate structure strategies, and a large scale electric vehicle charging scheduling and power dispatch, which manages the impact of electric vehicle charging on overall load profiles.
  • FIG, 1 illustrates one embodiment of a system for charging a plurality of electric vehicles according to the invention.
  • FIG. 2 illustrates a flow chart of one embodiment of operation of an algorithm according to the invention.
  • FIG. 3 illustrates a flow chart of one embodiment of operation of an algorithm according to the invention.
  • FIG. 4 illustrates a flow chart of one embodiment of operation of an algorithm according to the invention.
  • FIG. 5 illustrates an example of one embodiment of pseudo code according to the invention.
  • the invention is directed to a large scale charging infrastructure including an intelligent energy management system that utilizes hardware-software architecture for managing simultaneous charging of a plurality of electric vehicles using a variety of energy supply sources including renewable energy supply sources, local storage sources, and power grid sources. Operating costs are minimized by taking advantage of flexible charging schedules and economies of scale and adverse impacts of unmanaged charging are mitigated. Specifically, the invention considers the sporadic arrival of customers to the facility and customer requirements relating to deadlines and battery charging level. The pricing, admission and scheduling aspects of the large scale charging facility operations are investigated, and a utility based pricing scheme explores the customers' time flexibility, together with an online admission and scheduling algorithm with worst case competitive ratio guarantee for a linear utility function.
  • the admission and scheduling tend to be easy for the service provider of the facility if the overall charging load from the customer requests is well below the facility capacity. However, if overwhelmingly many charging requests arrive in a short period of time, e.g., during rush hours or due to events like sports games, the admission and scheduling is performed by one or more online algorithms which demonstrates satisfactory performance for both underloaded and overloaded scenarios.
  • the system 100 for charging a plurality of electric vehicles according to the invention is shown in FIG. 1.
  • the system 100 includes a plurality of charging ports 102, a network switch component 104 in communication with a Central Processing Unit (CPU) component 106 and one or more energy supply sources 108.
  • the one or more energy supply sources 108 include a renewable energy supply source 110, a local storage source 112, and a power grid source 114.
  • a network switch component 104 is connected to each charging port 102 and the network switch component activates one or more charging ports 102.
  • the central processing unit component includes a an admission control module 200, a scheduling module 300 and a power dispatch module 400.
  • the admission control module 200 includes an admission algorithm to determine profitability of each charging port. Based on the profitability determined for each charging port 102, the admission control module 200 determines pricing and billing. Specifically, the admission control module 200 determines the price quote and final price of the service provided to an electric vehicle.
  • the scheduling module 300 includes an algorithm to maximize profit of the system 100.
  • the power dispatch module 400 manages the impact of electric vehicle charging on overall load profiles. Specifically, the power dispatch module 400 includes an algorithm that determines the energy sources 108 to obtain power and further, the power dispatch module 400 delivers power from the one or more energy supply sources 108 to the one or more charging ports 102 through the network switch component 104.
  • FIG. 2 illustrates a flow chart of one embodiment of operation of an algorithm related to admission decisions according to the invention.
  • the request for service is represented by the arrival (release) time r, charging (processing) time p and deadline d for completion of the service.
  • arrival (release) time r For example, a customer who lives in an apartment in a high-rise building without overnight charging equipment may arrive at a charging facility near his office building around 8 am on the way to work. The customer may intend to catch a flight for a conference at 2 pm and plan to leave for the airport at 12 pm.
  • the current battery level may be 10 miles and in order to make the round trip to the airport the desired battery level after charging is 50 miles.
  • the release time is 8 am
  • the deadline is 12 pm
  • the processing time is determined by the 40 miles desired battery level as well as the charging speed of the charging port.
  • the charging (processing) time is based on an amount of power required by the electric vehicle.
  • the service provider When a customer request is received by the system at step 202 and the facility is running well below capacity, the service provider offers a price quote at step 204. After the service provider is given the charging request parameters r, p, and d the service provider offers a price v for the charging request.
  • the objective of the service provider is to maximize the revenue, whereas the objective of the customer is to obtain battery charging at a reasonable price.
  • the system allows the service provider to decline a customer request, for example because the facility is currently busy serving more profitable requests, to protect the utility of the service provider and thus indirectly expands the collective customer utility by allocating the time and charging infrastructure to the requests with better individual utility.
  • the service provider prices the customer requests according to the individual utility and conducts the admission control and scheduling in a revenue -seeking fashion for his own benefit.
  • the individual utility function is not known or known with uncertainty to the service provider, the impact of using a pricing function that deviates from the true individual utility function may be considered.
  • the customer is also allowed to evaluate the price quote and decide to seek charging elsewhere.
  • the interaction between the customers and the service provider of the facility is summarized in the price quote offered by the service provider at step
  • the request for service is terminated.
  • a contract is entered at step 208 between the service provider and the customer.
  • the service provider should immediately admit the customer, dispatch the request to one of the lightly loaded charging ports and append the request at the end of the current schedule. Otherwise, the charging infrastructure would be left idle and potential profit would be lost. In this easy-to- accommodate situation, the service provider essentially takes a greedy approach and notices that admitting the request will bring more revenue for now.
  • step 210 it is determined whether or not the service is complete by the deadline d. If the accepted charging request is completed by its deadline as promised, the final price is submitted by the system to the customer at step 212. Payment is then received by the system from the customer at step 214. If the accepted charging request is not completed by its deadline as promised at step 210, the service provider may have to pay a penalty depending on the amount of unfinished charging level. At step 216, the penalty is calculated. Specifically, the p
  • non-completion penalty is equal to V P ' where V denotes the unfinished charging level.
  • the non-completion penalty is the fraction in the quoted price that corresponds to the unfinished charging level.
  • the profit obtained by the service provider is the total value of all completed charging requests before their deadlines, less all penalties paid for the admitted requests that miss their deadlines.
  • the pricing, admission and the scheduling has to be conducted in an online fashion, i.e., the service provider knows the parameters of request 7 ⁇ only at the release time r,-.
  • the system strives to design an online management scheme with satisfactory performance in both underloaded and overloaded regimes.
  • One algorithm according to the invention makes the scheduling decision in a greedy manner with minimum backtrack in updating the schedule after admitting the newly released request. Specifically, if the service provider decides to admit the request and dispatch power to a charging port, the schedule of charging ports is updated within the CPU 106 (see FIG. 1) by tight-scheduling the newly released request in the interval [d - p, d] where p and d are the processing time and deadline of the newly released request, respectively.
  • the service provider decides to admit the newly released request, the request is profitable once accepted but difficult to accommodate into the current schedule. Therefore in order to accommodate the newly released profitable request, the service provider sacrifices the jobs in the current schedule in the time interval [d - p, d], some of which may have deadlines far into the future, but still have potential in being completed.
  • the pricing function v (r, d, p) is tied to the individual utility of the customer request, since this provides an incentive for the customers to consider their flexibility and submit charging requests with relaxed deadlines whenever possible.
  • the pricing scheme has two effects for the system operation.
  • the pricing scheme determines customer response, i.e., shaping the fraction of customers that accept a certain price v (r, d, p) offered for requests with release time r, deadline d and processing time p.
  • customer response i.e., shaping the fraction of customers that accept a certain price v (r, d, p) offered for requests with release time r, deadline d and processing time p.
  • v (r, d, p) i.e., shaping the fraction of customers that accept a certain price v (r, d, p) offered for requests with release time r, deadline d and processing time p.
  • the pricing scheme may affect the specific admission and scheduling decision since different prices may tag different priorities to the charging requests in the view of the service provider. It is sensible for the service provider to devote more resource and time on the customers who accepted more rewarding quoted prices.
  • the first effect of the pricing scheme leads to the traditional method of pricing a standard product. Specifically, with the knowledge of the customer response curve f(v; r, d, p), where (v; r, d, p) gives the fraction of customers with release time r, deadline d and processing time p that are willing to accept the price v, the service provider maximizes vf (v; r, d, p). This method balances the quoted price with the customer response curve; in both extremes of v the revenue function / (v; r, d, p) cannot assume the maximum since either v orf (v; r, d, p) is too small.
  • the customer response curve / (v; r, d, p) is difficult to obtain or approximate due to the three additional parameters ;-, d, and p.
  • the contention for charging infrastructure and time is explicit. With the limited peak power injection from the electricity network when there are overwhelming requests for charging in a short period of time, the service provider simply cannot fulfill all the requests, even at the expense of more operational cost. Therefore, the pricing scheme according to the invention optimally allocates charging infrastructure and time among charging requests.
  • the utility u u(p, ⁇ ) is an increasing function of the processing time p when ⁇ is fixed, since the electricity consumed is proportional to the charging level requirement p.
  • the decreasing trend of u u(p, ⁇ ) in ⁇ can also be interpreted with the interaction among the charging requests that come close in time.
  • a charging request with relative deadline factor very close to 0 cannot afford to be moved around or delayed in the time axis. Therefore, stricter commitment in time and charging infrastructure is necessary to fulfill the request which may potentially block or delay other requests.
  • the decreasing trend in the price represents the commitment premium.
  • FIG. 3 illustrates a flow chart of one embodiment of operation of an algorithm related to scheduling according to the invention.
  • the central processing unit receives a request for service from an electric vehicle at step 302.
  • the potential profit associated with accepting the request and the potential loss associated with declining the request is determined at step 304.
  • the service provider enumerates the potential charging ports.
  • the admitting option is evaluated by considering the quoted price as well as the incurred potential non-completion penalty; the declining option is evaluated by recognizing the potential value of the requests that would have been affected upon admitting the new request.
  • the previous requests may be affected and failed, or affected and forced to resort to the reserve energy.
  • the ratio of the profit associated with admitting and the loss associated with declining is computed for each potential charging port and all compared at step 306 to obtain the maximum ratio at step 308. Only if the maximum ratio meets or exceeds a predetermined threshold at step 310, the service provider will admit this request. The algorithm assigns or dispatches the request to the charging port with the maximum ratio at step 312.
  • the system maintains a tentative schedule for each charging port at all times; when a customer request is released, the system checks whether it is possible to append the new request at the end of the current tentative schedule of one of the charging ports while meeting its deadline. If the deadline can be met, then the request is admitted and appended at the end of the current tentative schedule of that charging port. Otherwise, the system determines whether to admit the request based on the profits of the options of accepting and declining. If the profit associated with accepting is not sufficiently large, then the request is simply declined service.
  • the request is scheduled on the charging port with the maximum profit ratio in the time interval [d, - p ⁇ 3 ⁇ 4; the previous schedule after time dj - p t is then moved to start at time dj, or the end of the current schedule, whichever comes later in time, and the system further checks whether there are any moved requests that already missed their deadlines after the moving, deletes them and moves the requests accordingly to fill the gap left by the requests deleted.
  • FIG. 4 illustrates a flow chart of one embodiment of operation of an algorithm related to energy dispatch according to the invention.
  • the algorithm of the system reviews the energy supply sources to determine availability of power of each of the energy supply sources.
  • the availability of power of each of the energy supply sources is compared with a plurality of requests for service and each request of the plurality of requests is redistributed based on the availability of power of each of the energy supply sources.
  • the algorithm of the system determines if power is available from a renewable energy source at step 404. If power is available at step 404, power from the renewable energy source is distributed to the request at step 406. If power is not available from the renewable energy source at step 404, the algorithm of the system determines if power is available from a local storage source at step 408. If power is available at step 408, power from the local storage source is distributed to the request at step 410. If power is not available from the local storage source at step 408, the algorithm of the system determines if power is available from the power grid source at step 412. If power is available at step 412, power from the power grid source is distributed to the request at step 414. If power is not available from the power grid source at step 412, the request cannot be fulfilled.
  • the service provider has to decide whether the profit from the fulfilled requests is worth the electricity bill incurred.
  • the power or energy from the energy sources may be dispatched when there is excessive arrival of profitable requests.
  • the already admitted requests become more risky in terms of fulfillment.
  • the redistribution of already admitted requests is due to the variability of the renewable energy source over time. Specifically, when a renewable energy source becomes available, the system redistributes the requests that were previously arranged to complete with power from local storage sources or from the power grid to the newly available renewable energy source to cut the reserve energy bill and non-completion liability.
  • the system collects the requests that are still within their deadlines and were previously expected to fail or incur reserve energy supply. All collected requests go through admission and scheduling with the now available additional renewable energy source. Similarly, when energy from a renewable energy source becomes unavailable, the requests currently in its tentative schedule are at risk. System redistributes the risky requests to the remaining renewable energy sources, and the system may find it necessary to incur additional reserve chargers.
  • the system may decide not to spend renewable energy on the request. However, the request may still be fulfilled with energy from a local storage source if profitable. The request price and the energy cost are compared to decide the profitability to incur additional power from the local storage source for the newly released request.
  • FIG. 5 illustrates an example of one embodiment of pseudo code according to the invention.
  • T arr gets admitted and appended to the current schedule on Processor i, which represents a charging port, if it is appendable on Processor as shown on line 4 in FIG. 5. Otherwise, if T arr is not appendable to any of the m processors (charging ports), the profits Profit /)accep? and Profit /i£f e c//sky e associated with admitting and declining T arr respectively get compared, where i indicates the processor (charging port) index.
  • T arr For the charging port that maximizes the profit ratio (Processor i), if admitting T arr assumes better profit as shown on line 7, then T arr is admitted and appended at the end by d an . (i.e., scheduled in the time interval ⁇ d arr -p am d a/r ]), and the current schedule after d arr - p arr is moved and modified accordingly as shown by line 8 and line 9 in FIG. 5. Otherwise, if admitting T arr does not have better profit, T arr is declined service as shown in line 11.
  • the threshold ⁇ as shown in line 7 represents the tradeoff between the current revenue versus the newly arrived requests.

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Abstract

L'invention porte sur une infrastructure de charge à grande échelle, laquelle infrastructure comprend un système de gestion d'énergie intelligent qui utilise une architecture matériel-logiciel pour gérer la charge simultanée d'une pluralité de véhicules électriques à l'aide d'une variété de sources d'alimentation en énergie comprenant des sources d'énergie d'alimentation en énergie renouvelable, des sources de stockage locales et des sources de réseau du secteur d'énergie. Les coûts de fonctionnement sont minimisés par le fait de tirer parti de programmations de charge souples et d'économies d'échelle, et les impacts défavorables d'une charge sans intervention humaine sont atténués.
PCT/US2013/051627 2012-07-23 2013-07-23 Système et procédés pour charge à grande échelle de véhicules électriques WO2014018504A2 (fr)

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