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    "INTELLIGENCE DISTRIBUTED IN ELECTRIC VEHICLE" The present invention relates to a system and method for distributed intelligence of energy tracking and energy allocation that may include: receiving data from at least one computer from several identified charging stations and vehicles from customers in locations distributed over a power grid; analyze, with at least one processor from at least one computer, the data regarding the energy available for these locations and the usage history and customer profiles; and send commands, with at least one processor, to reallocate energy in the power grid resources, to deal with anticipated fluctuations or fluctuations in energy demand, based on the analysis. Customer preferences can also be considered in predicting the consequences of energy demand and the need to respond to energy demand. Economic rules can be enforced to encourage customers to follow demand response requirements, where demand is greater than energy supply.
    公开号:BR112014019920B1
    申请号:R112014019920-5
    申请日:2013-02-13
    公开日:2021-02-23
    发明作者:John Z. Dorn；Wade P. Malcolm
    申请人:Accenture Global Services Limited；
    IPC主号:
    专利说明:

    CROSS REFERENCE WITH RELATED PATENT APPLICATIONS
    [001] This patent application claims the priority of U.S. provisional filing application 61 / 598,109, filed on February 13, 2012, which is incorporated by that reference in its entirety. BACKGROUND 1. FIELD OF THE INVENTION
    [002] The present invention relates, in general, to a system and a method to control an industrial network, and, more particularly, to a system and a method to collect data in different sections of the industrial network, and analyze the data collected to control the distribution and load of energy in an electric vehicle. 2. RELATED TECHNIQUE
    [003] A power grid can include any or all of the following: electricity generation; electric power transmission; and electricity distribution. Electricity can be generated through the use of generating stations, such as a coal-fired power plant, a nuclear power plant, etc. For efficiency purposes, the generated electrical energy is increased to a very high voltage (such as 345 kvolts) and transmitted over transmission lines. Transmission lines can transmit energy over long distances, such as state lines or international limits, until it reaches a wholesale customer, which may be a company that owns the local distribution network. The transmission lines can terminate at a transmission substation, which can reduce the very high voltage to an intermediate voltage (such as 138 kvolts). From a transmission substation, smaller transmission lines (such as transmission underlines) transmit the intermediate voltage to distribution substations. In distribution substations, the intermediate voltage can again be reduced to an "average voltage" (such as from 4 kv to 23 kv). One or more supply circuits may originate from the distribution substations. For example, four to dozens of feeder circuits can originate from the distribution substation. The feeder circuit is a three-phase circuit, comprising 4 wires (three wires for each of the 3 phases and one wire for the neutral). The feeder circuits can be routed above the ground (on posts) or under the ground. The voltage in the supply circuits can be derived periodically by using distribution transformers, which reduce the voltage from "average voltage" to the consumption voltage (such as 120 V). The consumer voltage can then be used by the consumer, for example, to charge electric vehicles.
    [004] One or more energy companies can control the energy network, including failure control, maintenance and improvements related to the power network. However, control of the power grid is often inefficient and expensive. For example, a power company, which controls the local distribution network, can control faults, which can occur in the feeder circuits or in circuits, called side circuits, which branch off the feeder circuits. Control of the local distribution network is often based on telephone calls from consumers when an outage occurs, or is based on workers in the field, who analyze the local distribution network.
    [005] Energy companies have tried to improve the energy grid by using digital technology, sometimes called the "smart grid". For example, smarter meters (sometimes called "smart meters") are a type of advanced meter, which identifies consumption in more detail than a conventional meter. The smart meter can then communicate that information, via some network, is returned to the local utility company for monitoring and billing purposes (remote measurement). While these recent advances in improving the power grid are beneficial, further progress is needed. It has been reported that in the United States alone, half of the generation capacity is idle, half of the long-distance transmission network is idle, and two-thirds of its local distribution is idle. Therefore, there is clearly a need to improve control of the power grid.
    [006] A specific example of controlling the power grid refers to the charging of electric vehicles ("EV"). The electric vehicle industry is growing with an increasing number of EV charging stations being incorporated in both commercial and residential locations, to support the growing number of electric vehicles. With the largest number of charging stations, their load absorbed from power grids is increasing, especially at night when people typically plug in their electric vehicles for charging. Network sections may be unable to cope with the increase in load from charging stations, which are able to absorb significant amounts of energy in a short period of time (depending on the type of charging station). Therefore, there is a need to control loading stations efficiently and effectively. BRIEF SUMMARY
    [007] The present invention relates, in general, to a system and a method for controlling an industrial network. The embodiments described in this specification describe a system and method for collecting data in different sections of the industrial network, and analyzing the data collected to control the distribution and load of energy in electric vehicles.
    [008] A system and method for distributed energy tracking and energy allocation intelligence may include: receiving data from at least one computer from several identified charging stations and customer vehicles at locations distributed on a power network; analyze, with at least one processor from at least one computer, the data regarding the energy available for these locations and the usage history and customer profiles; and send commands, with at least one processor, to reallocate energy in the power grid resources, to deal with anticipated fluctuations or fluctuations in energy demand, based on the analysis. The analysis can also consider a time of day and / or a weekday. The analysis can also consider customer preferences within profiles. The analysis can also consider real-time customer inputs related to planned trips or planned loads.
    [009] A system and method for distributed intelligence of energy tracking and energy allocation may also include: receiving, at least one computer, primary parameters relating to the system and the charging infrastructure within the transmission and distribution resources of energy a power grid, and secondary parameters related to customers and customer preferences for electric vehicles (EV); analyze, using at least one processor, the primary parameters, to determine the available energy to charge the stations connected to the resources of the energy grids; analyze, using at least one processor, the primary and secondary parameters to determine whether the EV load, in relation to customer preferences, can be satisfied by the energy available at the charging stations; and, in response to the determination that customer preferences cannot be satisfied by the determined available energy, the processor executes instructions to: perform a demand response on the power network, to compensate for a lack of available energy for the EV load; and enforce established economic rules for EV loading to encourage EV customers to follow the demand response.
    [0010] Other systems, processes and characteristics will be, will become, evident to those versed in the technique by examining the figures presented below and the detailed description. It is intended that these systems, processes and additional features are included within that description, are within the scope of the invention, and are protected by the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS
    [0011] Figure 1 is a block diagram of an exemplary system for intelligent control of a distributed network of electric vehicles ("EVs") and EV charging stations, for example, an infrastructure and operational charging center. of EVs.
    [0012] Figure 2 is a block diagram of an advanced version of the system in Figure 1, showing an additional capacity in another embodiment of the EVs load infrastructure control system in Figure 1.
    [0013] Figure 3 is a block diagram of a hierarchy view of the EVs load infrastructure control systems of Figures 1 and 2, showing the energy flow and the types of communication between the different levels of the network and a fleet of electric vehicles and EV charging stations.
    [0014] Figure 4 is an explanatory diagram showing the use of a mobile EV application, which provides real-time data to operators, to help them find and navigate charging stations for use during a trip, the mobile application of EVs integrated in the EVs load infrastructure control system of Figures 1 to 3.
    [0015] Figure 5 is a block diagram of an EVs optimization engine solution architecture, which forms an interface and is a part of the EVs load infrastructure control systems of Figures 1 to 3.
    [0016] Figure 6 is a block diagram of an exemplary load infrastructure control, showing entries in a rules processor.
    [0017] Figure 7 is a flowchart of an exemplary method for distributed energy tracking and energy allocation intelligence, which supports charging for electric vehicles.
    [0018] Figure 8 is a flowchart of another example method for distributed intelligence of energy tracking and energy allocation, which supports charging of electric vehicles.
    [0019] Figure 9 is a generic computerized system, programmable to be a specific computerized system, which can represent any of the computing devices referred to in this specification. DETAILED DESCRIPTION
    [0020] By means of an overview, the exemplary embodiments described below refer to a system for data collection in different sections of the industrial network, and analysis of the data collected, to control vehicle energy consumption and load. electrical. The network may include a fleet of electric vehicles ("EVs") and EV charging stations. Electric vehicles and EV charging stations can contain multiple sensors that communicate wirelessly, over a connected network and / or over power lines, to provide usage data, maintenance and planning needs (to name a few examples) to and from a central control. This data can be used by a cargo infrastructure control system.
    [0021] For example, a single load may not be enough for operators to travel to some locations the next day, requiring a planned stop on the route to carry out an additional load. Other complexities of energy use in electric vehicles have created a need to track their energy use, and for an analysis and forecast of charging, maintenance and similar requirements, with reference to electric vehicles and EV charging stations; and for the disposal of energy loads and / or adjustment of energy supply, based on the load demand on the side of the energy network. The intelligence needed to track and control the energy use requirements of electric vehicles is distributed and dynamic, and presents a particular challenge, which still needs to be addressed in an efficient and sophisticated way.
    [0022] Figure 1 shows an example system 100 for intelligent control of a distributed network of electric vehicles ("EVs") and EV charging stations, for example, an EV charging infrastructure and operational center. Figure 2 shows another exemplary EV load infrastructure control system 200, an alternative version of system 100, having additional capacity, as explained below. In the explanation of cargo infrastructure control systems 100, 200, reference will be made to US patent application serial number 12 / 378,102, filed on February 11, 2009 (published as US patent application No. 2009-0281674 A1) (power of attorney document 10022-1401); U.S. Patent Application Serial No. 12 / 637,672, filed December 14, 2009 (published as U.S. Patent Application No. 2010-0152910 A1) (power of attorney document 10022-1648); U.S. Patent Application Serial No. 12 / 830,053, filed on July 2, 2010 (published as U.S. Patent Application No. 2011-0004446 A1) (power of attorney document 10022-1764); and U.S. provisional patent application serial number 61 / 315,897, filed on March 19, 2010 (proxy document 10022-1709), all of which are incorporated into this specification by reference in their entirety. The incorporated patent applications will be referred to, respectively, as the patent applications '102,' 672, '053 and' 0897.
    [0023] The load infrastructure control systems 100, 200 can be executed in conjunction with and / or using the Business Reference Architecture of Intelligent Network Data (hereinafter referred to as INDE), which can be improved through the use of Information Services. Intelligent Network Data (hereinafter referred to as INDS), both of which are described in patent applications '102,' 672, '053 and' 0897. As will be discussed, some of the components of the INDE and / or the INDS can perform the functions or features of the components or parts of the load infrastructure control systems 100, 200.
    [0024] The cargo infrastructure control system 100 provides a distributed intelligence system, which can be used to track the energy use of electric vehicles by a fleet or several domestic customers. System 100 includes a network 101, through which it communicates, which can be wired or wireless, or a combination of them, and can include the Internet and other communication networks, be it a LAN or WAN variety. Multiple client computers 102 and mobile devices 103 can access network 101 and the services provided by system 100.
    [0025] System 100 also comprises a power network104, which includes substations 105, in which part of the logic and intelligence can reside, to gather data and control the allocation of energy. System 100 further includes multiple charging points or public charging stations 106. Multiple charging points can include standardized charging points (AC) 106a (Type I), fast charging points (DC) 106b (Type II), or both standardized charging points (AC) 106a and fast charging points (DC) 106b, as illustrated in Figure 1. An intelligent meter (SM) can be integrated with charging stations 106 to perform one, some or all of the following: use of screening at times of the day (including generic usage tracking based on all vehicles and / or use of tracking for specific vehicles). System 100 may also include a fleet of 107 electric vehicles with associated EV charging stations 106, where electric vehicles and charging stations may include smart meters (SM). System 100 may also include several domestic customers 108, who drive their respective electric vehicles 109 and charge them at their respective private charging stations 106. Smart meters (SM) can also be integrated with electric vehicles and charging stations. domestic customers. In alternative embodiments, electric vehicles and / or charging points can be the smart devices themselves and capable of communicating with the distributed intelligence system, as described below.
    [0026] Meters and smart devices can collect energy usage data, including the amount of energy absorbed from the power grid 104, during what periods of the day and according to the vehicle identification. This data can be sent to an operating center 110 for the EVs 100 load infrastructure control system 100. The operating center 100 of the system may include, but is not limited to, an enterprise system 112, EVs core systems 130 and / or an operations controller 150. These aspects of operations center 110 may correlate in some respect with the INDE infrastructure and the '672 and' 053 patent application system. The 112 business system can correlate with the IT business system of the '672 and' 053 patent applications. Core systems of EVs 130 can correlate with the INDE core of patent applications '672 and' 053, and the operations controller 150 can correlate with the operations control center of patent applications '672 and' 053.
    [0027] The business system 112 may include a customer relationship control (CRM) application 113 (such as that made by SAP) for tracking specific domestic customers 108 and their respective electric vehicles and smart charging stations (or smart meters) ), and making decisions related to them. The customer relationship control application 113 can also track and analyze data from anywhere outside the power grid, including public charging points and a fleet of electric vehicles.
    [0028] The business system 112 can also include an application for geospatial intelligence (GIS) 114. The GIS application provides efficient control of critical geospatial data at each stage of a useful life cycle. From capturing geospatial data to processing, integrating and controlling infrastructure, GIS software provides efficient access to critical geospatial data and intelligence information.
    [0029] The business system 112 can also include a master data control (MDM) application 115, a business strategy that treats master data as a corporate resource with a huge impact on the top and bottom lines. Facilitates data consistency across multiple systems for dynamic business processes (operational MDM) and business registration (analytical MDM), while ensuring end-to-end data management and master data control.
    [0030] EVs core systems 130 may include, but are not limited to, a network operations center (NOC) 132, an integration layer 134, an X 136 charge point head end manager, another manager load point head-end 138, and a smart head-end metric 140. NOC 132 can be an OMS-Oracle® Utilities Network Management System (NMS) or some other system. The integration layer 134 can pass and integrate data and analysis to and from: various parts of the power network, such as substations and charging points or charging stations; electric vehicles; and the business system 112. The load point head end managers 136, 138 can control the head ends, subsystems that are responsible for communicating with gauges and smart gauges (such collecting data from them and providing the collected data to the company services). The head end service manager 140 can intelligently consolidate the data, for example, combining load data from distributed load points to the corresponding vehicles identified by a single ID. In this way, the charging activity of the respective vehicles can be tracked and the data combined efficiently for analysis by the EVs core systems 130 and / or the business system 112.
    [0031] Operations controller 150 may include, but is not limited to, a head end or an intelligent network gateway 152 and transmission system operators (TSO) and / or distribution system operators (DSO). The intelligent network gateway 152 may, for example, include an Utilities Intelligent Network Gateway from Oracle®MV90 (for Itron), which is based on the Oracle® Utilities Application Framework (OUAF). The intelligent network gateway 152 provides loading and measurement data processing to adapt data types into useful formats by the rest of the operating center 110.
    [0032] With additional reference to Figure 2, the EVs 200 cargo infrastructure control system may include additional functionality and sophistication. System 200 can track and control parts of the network that absorb energy from the network, such as EV loads, and can track and control parts of the network that incorporate energy into the network, such as renewable energy sources 120. From a consumer perspective, distributed generation is the ability to generate energy in buildings, which can be fed back into the distribution network. The examples of distributed generation are concentrated in renewable energy sources, including solar panels on roofs of buildings, small wind turbines and electric vehicles, for example, electric vehicles having an excessive energy capacity, at the time when generation is needed . Distributed generation provides an improvement in measurement as a whole, in which energy discharges from local energy sources are deducted from measured energy inflows.
    [0033] The business system 112 can also include a relationship center 116, an SAP IS-U 117 system, an Oracle® Management Server (OMS) 118 and a financial control information system (FMIS) 119. The central relationship 116 can receive calls from people who experience or witness consequences with the network or some aspect of the load infrastructure system. An operator can then enter information relating to calls to locate and repair defects or testimony reports and the like, which becomes part of the business data available for access to 112 business system analysis.
    [0034] The SAP IS-U 117 system is an industry-specific solution for the utility industry: an information and sales system that supports utility companies, among others. The SAP IS-U 117 system can help sell and track sales of cargo services to public and private consumers.
    [0035] Oracle® Management Server (OMS) 118 works in the context of an Oracle® Enterprise Manager (OEM) medium. OMS 118 acts as an intermediary liaison agent between "Oracle intelligent agents", which can operate on multiple nodes and through common use of a scheme called DBSNMP, and management consoles, in which database managers see and control their OEM domains.
    [0036] The financial control information system (FMIS) 119 can provide analysis of tracking budgets and expenses. In the context of the present invention, FMIS 119 can help to track and predict the costs associated with charging electric vehicles, including the ability to track rate changes day by day, as it has an impact on the customer's ability to live with a desired budget. FMIS 119, therefore, allows the 200 system to help EV consumers track and control how much they spend on electricity, an alternative to gas and similar costs, but with more sophistication. FMIS 119 can consider the time of day or week, when more innovative rates are available, and conduct a localized economic analysis.
    [0037] The maximum flow core systems 130 may also include an EVs optimization mechanism 142 and a complex event processor (CEP) 144, both of which are discussed in more detail with reference to Figure 5. The flow optimization mechanism maximum 142 and CEP 144 can provide a substantial part of the analyzes available in system 200 for load control, arbitration and maximum flow optimization, as explained below.
    [0038] The operations controller 150 may further include a warehouse control system (WMS) OMS 156, a demand control system (DMS) 158 and a wind head end manager DG 162. The WMS OMS 156 is designed to improve the productivity and efficiency of a warehouse operation, which saves costs and productivity. WMS OMS 156 can be applied to the storage and movement of energy around the network, and from substation to substation and from top to top of post, according to the absorption of parts of the network. The load of electric vehicles will create pockets and peak periods of high demand, for which the WMS OMS 156 is designed to help control.
    [0039] The wind head end manager DG 162 can control where on the grid and when renewable energy sources 120 are provided as extra energy. This can be done during peak hours and in parts of the grid under high energy demand.
    [0040] The DMS 158 can work in conjunction with the analysis of the EVs core systems 130, and possibly the operations controller 150, to control the power distribution of the power grid 104 and substations 105. The DMS 158 can send commands to substations and transformers to move energy from one part of the power grid to another part of the power grid.
    [0041] With continued reference to Figures 1 and 2, one or more third-party co-deployment applications 170 can be integrated with integration layer 134 and receive data and analysis information from operational center 110. Co-deployment applications 170 may include a system and a charge point payment portal 176 and a Web 2.0 application & mobile device 178. These applications can interact with energy providers 172, payment providers 174 and the computers and mobile devices of customers 102 and users mobile 103. The charging point portal and system 176 can act as a link between computers 102 and mobile devices 103 and payment providers 174 (such as credit card companies and / or banks) and 172 energy suppliers (such as utility companies).
    [0042] The Web 2.0 & mobile device application 178 can be run from a server and displayed on mobile devices 103, containing information such as fee information, usage data and billing information associated with, for example, a use of energy by EV cargo customers. Application 178 can make the same information and data available on other computers 102 available on mobile devices 102 via the charging point payment system and portal 176. The Web 2.0 & mobile device application 178 can also, as shown in Figure 4, provide mobile devices 103 with the ability for users to search, find, map and obtain turn-by-turn directions to charging station stations, to determine whether the station is available or in use, and / or to provide information regarding the charge cost at the charge point station. The mobile device user can then directly start and stop a charging session from the mobile device (or other smart handheld), and receive real-time charge status notifications.
    [0043] Figure 3 shows a hierarchy view of the cargo infrastructure control systems of EVs 100, 200 of Figures 1 and 2, showing the energy flow and the types of communication between the different levels of the network and a fleet of 107 vehicles. electric and EV charging stations 106. The different levels of the network include, but are not limited to: (4) company; (3) substation; (2) post top 305; and (1) location. The pole top 305 refers to the transformer level and the location refers to the street level, such as parking lots, charging stations and houses. A home controller 310 can provide a gateway for communication between a home (or home) charging station 108 and network 101.
    [0044] The logic and analyzes discussed with reference to Figure 5 can be tracked and facilitated, depending on the hierarchical level at which the data are being processed or the analyzes made. A 312 load application can make the network or network component shown in each hierarchy level active (or intelligent). Head ends 136, 140, 152 and / or 162 can provide, or at least facilitate, intelligence, data processing and data integration at the company level.
    [0045] The communication of charge and use of energy can flow through several processes, including communication by power line (PLC) through network 104, which can include fiber in addition to power lines. Communication can also be performed over network 101, which can include a wireless aspect and another computerized and networked communication outside the power lines. Electric vehicles 109 can communicate wirelessly and / or in a wired mode, to track charge levels and charge activity by identified electric vehicles and charging stations. The analysis and intelligence can then be traced back to users of electric vehicles for their mobile devices 103, computers 102 and / or electric vehicles 109, for example, to a display panel or a computer in the vehicle.
    [0046] From left to right, a generic flow of intelligence from the EVs 200 load infrastructure control system is shown. At the site or street level, site authentication may be required for devices and smart meters, to communicate with the rest of system 200, including network 104, substations 105 and network 101. At top and pole levels of the substations, the 200 system can carry out batches of collections and transactions, with reference to the distribution of electricity.
    [0047] At the substation level, the demand for grid energy can be predicted in the demand control system 158 at the company level. Furthermore, between the top of the pole and the substation levels, the system 200 can communicate load data to a primary substation 105 of the system 200. Between the local and top of the pole levels, the system 200 can communicate data to the substations of cargo. Between local and company levels, electric vehicles can communicate with the charging point payment system and portal 276 and / or the Web 2.0 & mobile device application 178. Finally, system 200 can provide communications between head ends, at the company level, and the other three levels of the hierarchy: the substation, the pole top and the local levels.
    [0048] Figure 5 is an EVs 500 engine optimization solution architecture that interfaces with, and is part of, the EVs 100, 200 load infrastructure control systems of Figures 1 to 3. The 500 architecture may include the EVs optimization mechanism 142, the complex event processor (CEP) 144, the demand response control system 158, a database of customer profiles 159, other devices 180 that consume and / or generate energy , distributed generation 204, a database of customer profiles 503 and marginal location price data (LMP) 505, which can be stored in a database. All of these aspects of the 500 engine optimization solution architecture can be combined on one or more servers, memory storage devices, and processing devices, and can be implemented as a stand-alone computer or as a distributed system, which communicates over the network. 101. The functions of the optimization engine solution architecture 500 can also be shared with other components and applications, executed at the company level, in the case of the company system 112, the EVs core systems 130 or the operations controller 150 .
    [0049] The EVs 142 optimization mechanism may include, but is not limited to, processors or software modules executable by one or more processors for: 510 load adjustments; 520 smart charge; intermittency mitigation 530; dynamic voltage and capacitance adjustments (VoltVAr) 540; 550 resource management; LMP arbitration (locational marginal pricing) 560; minimizing loss 570; and other 580 rules. CEP 144 may include, but is not limited to, a service calculator cost 584, a load usage / cost tracker 588, and a load demand predictor 590. CEP 144 can be functionally integrated with FMIS 119, or receive analytical data from FMIS 119, with which it performs further analysis and forecasts.
    [0050] Load adjustments 510 can simulate the effective conditions in the power grid, in the case in which a certain amount of energy is transferred from one location to another, such as from one substation to another, or between transformers or other transfers .
    [0051] Smart charge 520 can track individual smart meters (SM) within charging stations 106 and within identified electric vehicles 109, so that planning can be coordinated and / or suggested to EV customers in order to optimize the use of energy by diffusing it on typical days and on a typical week.
    [0052] Intermittent 530 mitigation can endeavor to deal with intermittent power supplies, which are not unpredictable. This includes renewable energy sources, such as wind.
    [0053] Electric utility companies today are constantly striving to find a balance between sufficient power generation, to satisfy customers' dynamic load requirements, and minimize their investment and operating costs. They spend a great deal of time and effort trying to optimize each element of their generation, transmission and distribution systems to achieve their physical and economic goals. In many cases, "real" generators lose valuable resources - waste that, if not efficiently controlled, can go directly to the bottom line. The energy companies, therefore, decide the concept of a "virtual generator", or a virtual source of energy, which can be activated when necessary, very attractive. Although, generally, only representing a small percentage of the global generation capacity of utility companies, virtual generators are quick to activate, provide, cost effective and represent a form of "green energy" that can help companies utilities to meet their carbon emission standards.
    [0054] The virtual generators use forms of dynamic feedback and capacitance (Volt / Var) 540 adjustments, which are controlled by detection, analysis and automation. The method as a whole involves first leveling or limiting voltage profiles by adding additional voltage regulators to the distribution system. Then, by moving the voltage profile up or down, within the operating voltage limits, utility companies can obtain significant benefits. Because voltage adjustments influence VArs, utility companies must also adjust both capacitor placement and control.
    [0055] Resource management 550 can control energy flow requirements to and from resources in the power grid, such as electric vehicles, charging stations, renewable energy sources, substations and transformers.
    [0056] LMP (marginal location pricing) arbitration 560 can be performed to allow customers to take advantage of a price difference between two or more markets, confronting a combination of comparative parts that are capitalized by imbalance, the profit being the difference between market prices.
    [0057] Loss minimization 570 can be performed to reduce the energy losses inherent in the lines and loads of the power network, which is also addressed in the '530 patent application.
    [0058] Complex event processor (CEP) 144 can perform complex event processing, which was also addressed as CEP processing in patent application '053. Complex event processing refers to processing states, state variations exceeding a defined level threshold, such as energy, time, or incremental value of a count such as the event. It requires the respective monitoring of events, the recording of events, the recording of events and the filtering of events. An event can be observed as a change of state with any physical or logical condition, or one discriminated differently from and in an economic system, all state information with an attached time stamp, defining the order of occurrence, and a topology, defining the place of occurrence.
    [0059] CEP 144 can include event correlation mechanisms (event correlators), which analyze a mass of events, pinpointing the most significant ones, and qualifications. Although CEP 144 can generally refer to high level events with low level events, CEP 144 can also generate inferred events using rules 180 and other types of artificial intelligence.
    [0060] The EVs 142 optimization mechanism can work together with CEP 144, to analyze the data and correlate and / or produce events that can optimize energy use and the costs of using it within the energy network. Data may include, but is not limited to, energy usage history (and other consumption data) received from electric vehicles 109, EV charging stations 106, public fleet charging events 107, domestic event charging 108, distributed generation 204, renewable energy sources 120 and other 180 devices, which can be connected to the grid. Consumption data can include a vehicle profile, a price the customer is willing to pay, travel and cargo habits, etc. The data can be provided over a wireless interface, so that the system 200 does not necessarily have to wait for the electric vehicle to be parked at a charging station and can continuously gather and track data. Today, most vehicles include a sensor in the fender, which can provide a dedicated data route back to operational center 110.
    [0061] The EVs 142 optimization mechanism can send analytical results and suggested control measures to the DMS 158 system, which can then send commands in real time to electric vehicles, EV charging stations, substations, pole top transformers or block and the like, to control energy flow, load synchronization that affects pricing and availability, and rules relating to load, energy flow control and other aspects of energy use optimization. CEP 144 can calculate service cost, load usage and cost tracking over time and over different periods. CEP 144 can also forecast the demand for energy use and the associated costs related to that demand in the future.
    [0062] The analytical results can be presented to customers 'electrical vehicles, computers and / or mobile devices on graphical user interfaces (GUIs) or customers' web portal (such as an application or browser, or similar, as shown in Figure 4 ), so that customers can understand and make use of and introduce decisions based on them. GUIs can receive customer selections to allow planning of load times, locations, durations, or according to established budgets based on estimated costs for a proposed load schedule.
    [0063] As an example, a customer will go to the city center and anticipate that he will be there for a certain period of time. Based on the projected location, system 200 can provide the customer with possible locations and charge rates. If the customer indicates a planned stop for loading at one of these locations, system 200 (for example, the DMS 158) can alert a substation, which controls power to the charging stations at that location, as part of a demand forecast. This substation can then transfer additional energy at the scheduled time, to ensure that the utility company can satisfy an increase in demand at those times and locations. When the customer arrives, the charging station can send a notification that the identified electric vehicle has been plugged into the charge, and system 200 (for example, FMIS 119) can charge the customer at current rates.
    [0064] The utility company can provide a discount to the customer, when the customer plans, ahead of time, an hour and a place to charge an electric vehicle, to encourage customers to alert the system 200 of their future security needs. demand, making it easier for the 200 system to program the expected load distribution.
    [0065] As another example, a customer can submit preferences regarding when and where the customer will normally charge an electric vehicle and submit other consumption data, which can be used to make a profile for the customer. If the customer then decides to charge an electric vehicle outside of preferred hours and locations, the 200 system will give the customer a premium above and beyond the normal rate.
    [0066] The distributed intelligence of the present invention can occur in different degrees at the different levels discussed with reference to Figure 3, as well as at the company, substation, pole top and street levels. Part of the 200 system function, at the various levels may be more than gathering data and passing command, but at least part of the data analysis can be done at the top pole and / or substation levels - closer to street level rather than at the company level - which can allow the 200 system to react more quickly to varying energy needs, based on EV load and anticipated energy needs, and the costs for them. In addition, decision trees can be built as part of the analysis, which can help CEP 144 build demand forecasts.
    [0067] For example, at the levels of the pole or block transformers, the rules may relate to the control of the resources of the transformers, based on load profiles considered and determined, such as peak energy use during the day, during the hottest or coldest times of the day, etc. The rules can support the design of load profiles, which can be tracked by CEP 144 and stored in the 503 customer profile database. The DMS 158 system can then perform demand response to maintain loads on transformers, according to the project load profiles. CEP 144 can track and modify maintenance intervals to maintain energy storage at street level, based on frequency of use.
    [0068] With energy usage data, which includes usage history, the load demand predictor 590 can use consumer profiles to determine and release predicted loads in a way that optimizes usage without depleting energy. Through thousands of feeders and thousands of transformers, this can be a challenging task and one that is, by nature, very widespread.
    [0069] Each substation 105 contains several transformers, which can be interconnected to a bus (Figure 3). System 200 can track loads of block-mounted transformers from the field to perform common sense checks on usage history, and also aggregate loads from multiple feeders to later apply rules to those aggregate loads. System 200 can also examine hot spots in a substation 105 within this data. In this way, the system 200 can more accurately track the loads and control the loads on the substation transformers.
    [0070] The rules executed by the EVs 142 optimization mechanism, or some equivalent, at the substation level, such as the load application 312, can be switching rules, in which the loads can be shared between the substations. The loads can be controlled at the company level or at the substation level. In addition, an operator can optionally confirm or intervene at the substation level, to ensure that loads are properly distributed across transformers. System 200 may be able to extract monitoring from more complex utility companies in substations. Sensors can be placed on transformers that communicate by fiber, wirelessly or through power lines, to pass their data to speed sensor controllers, coupled with load applications, and to corporate communication systems (Figure 3).
    [0071] System 200 may also include intelligence at the top of the block or in block transformers, such as current and voltage control. The system 200 can examine the instantaneous values and the spectral content, if there are consequences of power quality. The load usage tracker 588 and / or the load application 312 can gather the detected current and voltage measurements, check the quality and reliability of a transformer, and check the phase connectivity in power lines and transformers. Depending on the parameters entered in the load usage tracker 588 and / or in the load application 312, the system 200 can increase or decrease the energy load on the respective transformers.
    [0072] If all electric vehicle owners try to charge their electric vehicles at the same time, for example, starting at five or six o'clock at night, the power grid will be excessively charged so that it cannot handle the entire load. This is particularly true in urban areas, where there is a greater concentration of electric vehicles. Consequently, the EVs 142 optimization mechanism can use its 580 rules and other logic to optimize energy flows to transformers and charging stations, in an equitable manner, which also facilitates charging electric vehicles in a reasonable period of time. Some charging stations 106a can be standardized charging stations (Type 1) and provide a slow charge to charge electric vehicles at a lower speed, while other charging stations 106a can be high speed chargers (Type II), and thus , create greater drainage in the power grid 104. The power can be redistributed to the substations and connected transformers, to provide additional energy, during peak periods and in places containing high-speed charging stations 106b, for example.
    [0073] Figure 6 is a block diagram of an exemplary load infrastructure control 600, showing data inputs on a 612 rules processor. The 612 rules processor can comprise a processor running one or more re- gras discussed in this specification. The rules processor 612 can be included within the EVs optimization mechanism 142, complex event processor (CEP) 144 and / or within the load application 312, depending on what data is analyzed to generate that result. The 612 rules processor can receive data inputs, analyze data inputs, and generate, among other outputs, any combination of the following: an indicator, a recommendation, or another message (for example, to a mobile device or electric vehicle). user), and / or a command to control a portion of the network infrastructure to move energy allocations.
    [0074] Data entries in the 612 rules processor may include, but are not limited to, system parameters 614, load infrastructure parameters 616, business rule parameters 618, customer parameters and your preferences 620, other parameters 622, and economic rules parameters 624.
    [0075] The parameters of 614 systems may include, but are not limited to, availability of local resources, such as whether the transmission and distribution components, close to the EV infrastructure, are energized and in an available state. The system parameters can also indicate the level of use of feeders, so that an energy feeder is capable of supporting vehicle load, and, therefore, what level or levels of load. System parameters can also indicate whether on-site generation is available (such as whether solar energy is available or is supported by vehicle load on the grid or vehicle in vehicle). The system parameters can also indicate if there is energy storage in place, and if it is sufficient to support an increase in load. The parameters can further indicate whether there are any operational restrictions at the site, whether any abnormal events are detected, what operational mode an EV station is in, and whether ancillary services are provided.
    [0076] Infrastructure parameters 616 may include, but are not limited to, indicators relating to whether the EV infrastructure is functioning, whether a charging station output is reserved by a surface, and what level of infrastructure capacity is supported - structure (such as level 1, 2, 3 or vehicle in the network - V2G).
    [0077] The parameters of commercial rules 618 may include, but are not limited to, whether the vehicle having access to the EV infrastructure: is a new customer or an existing one; whether the vehicle is borrowed or owned by the driver; and how the payment method will be.
    [0078] The parameters of customers and their preferences 620 may include, but are not limited to: preferred speed of loading; transaction cost (buy and / or sell energy); purchase preference for source generation (for example, "green"); purchase benefits for associated generation (for example, "green"), where the associated generation represents any generating source connected to a charging station, which can be controlled on site and used to support the station's function; reservation and reservation time; customer account information; and incentives, credits and penalties. Some of these preferences have already been discussed and introduced in determining the intelligence of the 200 system. The customer parameters and preferences can also be extracted from customer profiles.
    [0079] The other parameters 622 may include, but are not limited to, if significant data or events, which may occur in the power grid 104, which may affect the ability to provide energy for a load, which is requested or expected, based on in the parameters listed above.
    [0080] The economic rules parameters 624 may include, but are not limited to: rate in force at the time of loading at a charging station; current demand response rates, such as critical peak pricing or other rates related to demand response; localized rates in force, such as whether the rates are governed by the use of local resources; if any penalties are accounted for, such as opting out of a demand response event, which will decrease or interrupt the energy available for charging at a desired time; and applicable fees or charges for a transaction during the demand response.
    [0081] Rules can be formulated from any one or a combination of the parameters listed above, which form the data entries for the 612 rules processor. Some of these rules have already been discussed with reference to Figures 1 - 5. Another example rule can dictate that if a user waits for an hour to charge his electric vehicle, then the user will save a certain amount (like a dollar discount). Another example rule may dictate that a premium cost is charged for using local generation or attached storage, when a demand response event is underway, which is trying to reduce energy consumption at the desired charging station.
    [0082] Figure 7 is a flowchart of an exemplary method for distributed intelligence of energy tracking and energy allocation in a power network, which supports charging of electric vehicles. In block 710, at least one computer receives data from several identified charging stations and electric vehicles from customers at locations distributed on a power grid. In block 720, at least one processor from at least one computer analyzes the data with respect to the energy available for those locations and customer usage history and profiles. In block 730, at least one processor sends commands to reallocate energy to the power grid resources, to handle fluctuations or programmed fluctuations in energy demand, based on analysis.
    [0083] In addition to the analysis step of block 720, in block 740, the at least one processor may also consider one or a combination of time of day and day of the week. In addition to the analysis step of block 720, in block 750, at least one processor can also consider customer preferences, which are included in customer profiles. In addition to the analysis step of block 720, in block 740, at least one processor can also consider customer inputs in real time for planned trips or planned loads.
    [0084] Figure 8 is a flowchart of another exemplary method for distributed intelligence of energy tracking and energy allocation in a power network, which supports charging of electric vehicles. In block 810, at least one computer receives primary parameters relating to a system and a charging infrastructure within the power transmission and distribution resources of a power network. In block 820, at least one computer receives secondary parameters related to electric vehicle customers (EVs) and customer preferences. In block 830, at least one processor from at least one computer analyzes the primary parameters, to determine the available energy for the charging stations connected to the resources of the power grid. In block 840, at least one processor analyzes the primary and secondary parameters to determine whether the EV load, in relation to customer preferences, can be satisfied by the energy available at the charging stations.
    [0085] In block 850, at least one processor transmits a decision regarding whether the EV load, in relation to customer preferences, can be satisfied by the energy available at the charging stations. If the decision is yes, then the method is repeated from block 810. If the decision is no, then at block 860, the at least one processor executes instructions to execute a demand response on the network, to compensate for a power failure available to charge EVs; and, in block 870, it executes economic rules attributed to the EV load, to encourage EV customers to follow the demand response.
    [0086] Figure 8 illustrates a general computerized system 900, programmable to be a specific computerized system 900, which can represent any server, computer or component (or its groups) of the load infrastructure control systems 100, 200. The system computerized 900 may include an ordered listing of instruction set 902, which may be executed to cause the computerized system 900 to perform any one or more of the computer-based processes or functions described in this specification. The computerized system 900 can operate as a standalone device or can be connected, for example, using the 101 network, to other computerized systems or peripheral devices.
    [0087] In a networked arrangement, the computerized system900 may operate at the capacity of a server or with a client user computer on a server-client network physical medium, or as a peer computerized system on a physical medium of point-to-point (or distributed) network. The computerized system 900 can also be implemented as, or incorporated into, various devices, such as a personal computer or a mobile computing device, capable of executing a set of instructions 902, which specifies actions that will be conducted by that machine, including and not limited to, access to the Internet or the Web by any form of browser. Furthermore, all the systems described can include any group of subsystems, which execute, individually or jointly, a set, or multiple sets, of instructions, to execute one or more computer functions.
    [0088] The computerized system 900 may include a memory904 in a 920 bus, for communication of information. The operable code, to make the computerized system perform any of the acts or operations described in this specification, can be stored in memory 904. Memory 904 can be a random access memory, an exclusive read memory, a programmable memory, a hard disk drive or any other type of volatile or non-volatile memory or storage device.
    [0089] The computerized system 900 may include a processor 908, such as a central processing unit (CPU) and / or a graphics processing unit (GPU). The 908 processor may include one or more generic processors, digital signal processors, specific integrated circuits for specific applications, field programmable circuit arrays, digital circuits, optical circuits, analog circuits, their combinations, or other devices currently known or going be further developed for data analysis and processing. Processor 908 can implement instruction set 902, or another software program, such as manually programmed or computer generated code, to implement logic functions. The logic function or any described system element can, among other functions, process and / or convert an analog data source, such as an analog electrical, audio or video signal, or a combination of them, to a digital data source for audiovisual or other digital processing purposes, such as compatibility for computer processing.
    [0090] The computerized system 900 may also include a disc or an optical disc drive 915. The optical disc drive 915 may include a computer-readable medium 940, in which one or more sets of instructions 902, for example, software, may be embedded. Furthermore, instructions 902 can perform one or more of the operations as described in this specification. Instructions 902 can reside completely, or at least partially, inside memory 904 and / or inside processor 908, during execution by the computerized system 900. Consequently, databases 503 and 505 above in Figure 5 can be stored in memory 904 and / or on the 915 disk drive.
    [0091] Memory 904 and processor 908 may also include computer-readable media, as discussed above. A "computer-readable medium", a "computer-readable storage medium", a "machine-readable medium", a "propagated signal medium" and / or a "signal-carrying medium" may include any device, which includes , stores, communicates, propagates or transports software, for use by or in conjunction with a system, device or device executable by instructions. The machine-readable medium may be selectively, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor propagating system, apparatus, device or medium.
    [0092] Additionally, the computerized system 900 can include an input device 925, such as a keyboard or mouse, configured for a user to interact with any of the components of the system 900. It can also include a display 970, such as a display of liquid crystal (LCD), a cathode ray tube (CRT), or any other display suitable for carrying information. The display 970 can act as an interface for the user to see the operation of the 908 processor, or specifically as an interface to the software stored in memory 904 or on the 915 disk drive.
    [0093] Computerized system 900 may include a communication interface 936, which provides communications over communications network 101. Network 101 may include wired networks, wireless networks or combinations thereof. The 936 communication interface network can provide communications by any number of communication standards, such as 802.11. 802.17, 802.20, WiMax, 802.15.4, cell phone standards or other communication standards. Just because one of these standards is listed does not mean that any one is preferred, as any number of these standards may never be adopted in a commercial product.
    [0094] Consequently, the method and system can be realized in hardware, software or a combination of hardware and software. The method and system can be realized in a centralized manner in at least one computerized system, or in a distributed mode in which different elements are spread over several interconnected computer systems. Any type of computerized system or other device, adapted to conduct the processes described in this specification, is adequate. A typical combination of hardware and software can be a generic computerized system with a computer program, which, when loaded and executed, controls the computerized system in such a way that it conducts the processes described in this specification. This programmed computer can be considered a special use computer.
    [0095] The method and system can also be embedded in a computer program product, which includes all aspects that enable the implementation of the operations described in this specification and, which, when loaded in a computerized system, is capable of conducting these operations . The computer program means, in the present context, any expression, in any language, code or notation, of a set of instructions intended to make a system, having an information processing capacity, perform a particular function, directly or after one or more of the following: a) conversion to another language, code or notation; and b) reproduction in a different material form.
    [0096] The matter described above should be considered illustrative, not limiting, and the appended claims are intended to cover all of these modifications, which fit within the true spirit and scope of the present invention. Thus, to the maximum extent permitted by law, the scope of the present embodiments will be determined by the broadest possible interpretation of the claims presented below and their equivalents, and should not be restricted or limited by the foregoing detailed description. Although several embodiments have been described, it will be evident to those skilled in the art that many more embodiments and implementations are possible within the scope of the detailed description presented above. Consequently, the embodiments should not be restricted except in light of the appended claims and their equivalents.
    权利要求:
    Claims (13)
    [0001]
    1. Method for distributed intelligence of energy tracking and energy allocation, the method being executable by at least one computer including at least one processor and memory, including the steps of: receiving (710) data, by at least one computer, the from several identified charging stations (106) and electric vehicles (109) from customers in locations distributed on a power grid (104), where the data received includes selections from customers to allow for charging time planning, planning charge location, and charge duration planning for charging electric vehicles (109); receiving a plurality of load infrastructure and system parameters of the energy network resources (104); analyze (720), with at least one processor, data related to the energy available for these locations and the history of use and customer profiles, in which analysis additionally includes determining, from the parameters, if there is sufficient energy available within the power network (104) to support the charging of electric vehicles (109) at charging station locations (106) within the power network (104); and send (730) commands, with at least one processor, to reallocate energy in the energy network resources (104), to deal with expected fluctuations or fluctuations in energy demand, based on the analysis, in which each network resource energy (104) is any one or more of: an electric vehicle (109); a charging station (106); a renewable energy source; a substation; a transformer; and characterized by the fact that the method additionally comprises simulating real conditions in the power grid (104), tracking individual smart meters within charging stations (106) and within identified electric vehicles (109) so that planning can be coordinated or suggested to customers, deal with intermittent power supplies; control via virtual automation, analysis and detection generators using dynamic voltage and capacitance adjustments, in which voltage profiles are leveled or limited by adding additional voltage regulators to the distribution system, and the voltage profile is moved up or down within the operational voltage limits, manage energy flow requirements to and from resources in the power grid (104), and perform loss minimization to reduce power losses inherent in the grid lines and loads. energy (104).
    [0002]
    2. Method, according to claim 1, characterized by the fact that the analysis also considers one or a combination of an hour of the day and a day of the week; and or where the analysis also considers customer preferences, which are included in customer profiles; and / or where the analysis also considers real-time customer inputs related to planned trips or planned loads.
    [0003]
    3. Method, according to claim 1, characterized by the fact that it additionally comprises: receiving, by at least one computer, the primary parameters related to the load system and infrastructure within the power transmission and distribution resources of a network of energy (104); and secondary parameters related to customers and customer preferences for electric vehicles (EVs); analyze, using at least one processor, the primary parameters to determine the available energy for the power charging stations (106) connected to the power network resources (104); analyze, using at least one processor, the primary and secondary parameters to determine whether the EV load, relative to customer preferences, can be satisfied by the energy available at the charging stations (106); and in response to the determination that customer preferences cannot be satisfied by the determined available power, the processor executes instructions to: perform a demand response on the power grid (104) to compensate for a lack of available power to charge EVs; and enforce established economic rules for EV loading, to encourage EV customers to follow the demand response.
    [0004]
    4. Method, according to claim 3, characterized by the fact that executing the demand response comprises reallocating energy to and from several of the energy network resources (104), to deal with expected fluctuations or fluctuations in demand for energy, based on planning, that affect energy demand at charging stations (106); and / or where the parameters relating to the system and the charging infrastructure include whether there are energy storage resources in place and whether they are sufficient to support the EV load.
    [0005]
    5. Method, according to claim 3, characterized by the fact that it also comprises executing commercial rules in addition to economic rules, in which the parameters related to the commercial rules include whether an electric vehicle (109) is a new one or an existing customer. , and how the payment will be made by the customer.
    [0006]
    6. Method, according to claim 3, characterized by the fact that the parameters related to economic rules include: a rate in force at the time of loading at a loading station (106); demand response rates in effect at peak pricing; and localized rates; and / or in which the parameters related to economic rules include penalties for the option of excluding a demand response, which would decrease or interrupt the energy available for loading at a desired time; and applicable fees or charges for a transaction during the demand response.
    [0007]
    7. System (100) for distributed energy tracking and energy allocation intelligence comprising: at least one computer including at least one processor and memory, at least one computer configured to receive data from several identified charging stations (106) and electric vehicles (109) from customers in locations distributed over a power network (104), where the data received includes selections from customers to allow load time planning, load location planning, and load duration planning for charging electric vehicles (109), and to receive a plurality of charging infrastructure parameters and power grid resource system (104); where the processor is configured to: analyze (720) the data related to the available energy for those locations and the usage history and customer profiles, where the analysis additionally includes determining, from the parameters, if there is enough energy available within the power network (104) to support the charging of electric vehicles (109) at charging station locations (106) within the power network (104); and send (730) commands to reallocate energy to the power grid resources, to deal with expected fluctuations or fluctuations in energy demand, based on the analysis, in which each power grid resource is one or more of: an electric vehicle (109); a charging station (106); a renewable energy source; a substation; and / or a transformer; the system characterized by the fact that said processor is configured to: simulate real conditions in the power grid (104), track individual smart meters inside charging stations (106) and inside identified electric vehicles (109) so that the planning can be coordinated or suggested to customers, dealing with intermittent power supplies; control via virtual automation, analysis and detection generators using dynamic voltage and capacitance adjustments, in which voltage profiles are leveled or limited by adding additional voltage regulators to the distribution system, and the voltage profile is moved up or down within the operational voltage limits, manage energy flow requirements to and from resources in the power grid (104), and perform loss minimization to reduce power losses inherent in the grid lines and loads. energy (104).
    [0008]
    8. System, according to claim 7, characterized by the fact that at least one processor is still configured to analyze one or a combination of an hour of the day and a day of the week; and / or where at least one processor is still configured to analyze preferences that are included in customer profiles; and / or at least one processor is further configured to analyze customer inputs in real time for planned trips or planned loads.
    [0009]
    9. System, according to claim 7, characterized by the fact that the resources include resources for transmission, distribution and supply of the power network (104), and in which the processor is also configured to: receive parameters related to the availability of transmission and distribution within the power network (104), for carrying out load at the charging stations (106), and relating to a level of use of power from a power feeder, which powers the charging stations; and analyze the parameters to determine if sufficient energy is available to support the charge of the electric vehicles (109) by the load feeders.
    [0010]
    10. System, according to claim 7, characterized by the fact that the at least one computer is additionally configured to receive the primary parameters related to the system and the load infrastructure within the transmission and energy distribution resources of a grid. energy (104); and secondary parameters related to electric vehicle customers (EVs) and customer preferences; and where the processor is configured to: analyze the primary parameters to determine the available energy for the charging stations (106) connected to the resources of the power network (104); analyze the primary and secondary parameters to determine whether the EV load, in relation to customer preferences, can be satisfied by the energy available at the charging stations (106); and in response to the determination that customer preferences cannot be satisfied by the determined available energy: executing a demand response on the power grid (104) to compensate for the lack of energy available for charging EVs; and enforce established economic rules for EV loading, to encourage EV customers to follow the demand response.
    [0011]
    11. System according to claim 10, characterized by the fact that to perform the demand response, the processor is configured to reallocate energy to and from various resources of the power network (104) to deal with expected fluctuations or fluctuations in demand energy, based on customer preferences that affect energy demand at charging stations (106); and / or in which the parameters related to the system and the load infrastructure are selected from the group consisting of: if there is an operational restriction with reference to a load station (106); if a charging station (106) is reserved by a customer; the presence of an abnormal event; and a level of infrastructure capacity relative to a charging station (106).
    [0012]
    12. System, according to claim 10, characterized by the fact that the processor is further configured to execute business rules in addition to economic rules, in which the parameters related to the business rules include whether an electric vehicle (109) is of a new or from an existing customer; and how the payment will be made by the customer; and / or where the parameters relating to the system and the load infrastructure include whether there is availability of generation on site, which contributes to the load of EVs.
    [0013]
    13. System, according to claim 10, characterized by the fact that the parameters related to economic rules include: a rate in force at the time of loading at a loading station (106); demand response rates in effect at peak pricing; and localized rates; and / or the parameters related to economic rules include penalties for the option of excluding a demand response, which would decrease or interrupt the energy available for loading at a desired time; and applicable fees or charges for a transaction during the demand response.
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    同族专利:
    公开号 | 公开日
    WO2013121291A2|2013-08-22|
    JP2018206438A|2018-12-27|
    CN108082002A|2018-05-29|
    US20180018007A1|2018-01-18|
    SG11201404797SA|2014-09-26|
    JP6408382B2|2018-10-17|
    CN108082002B|2021-06-22|
    AU2013220074B2|2015-04-16|
    NZ628578A|2016-05-27|
    RU2014137174A|2016-04-10|
    MY168153A|2018-10-11|
    AU2013220074A1|2014-08-28|
    US20130211988A1|2013-08-15|
    CN104540706A|2015-04-22|
    EP2814687B1|2019-04-10|
    US10126796B2|2018-11-13|
    ZA201405878B|2017-08-30|
    CA2864330A1|2013-08-22|
    JP2015510199A|2015-04-02|
    CA2864330C|2020-10-27|
    WO2013121291A3|2014-03-27|
    HK1205724A1|2015-12-24|
    EP2814687A2|2014-12-24|
    RU2633407C2|2017-10-12|
    JP6803884B2|2020-12-23|
    CN104540706B|2018-02-06|
    US9766671B2|2017-09-19|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US6673479B2|2001-03-15|2004-01-06|Hydrogenics Corporation|System and method for enabling the real time buying and selling of electricity generated by fuel cell powered vehicles|
    JP2003087969A|2001-09-17|2003-03-20|Mitsubishi Electric Corp|Power demand adjusting system|
    JP2003259696A|2002-02-28|2003-09-12|Jfe Engineering Kk|Generation control method and program thereof|
    US7747739B2|2006-08-10|2010-06-29|Gridpoint, Inc.|Connection locator in a power aggregation system for distributed electric resources|
    CN101678774A|2006-12-11|2010-03-24|威图格林股份有限公司|Power aggregation system for distributed electric resources|
    JP4144646B1|2007-02-20|2008-09-03|トヨタ自動車株式会社|Electric vehicle, vehicle charging device, and vehicle charging system|
    CA2730372A1|2008-09-19|2010-03-25|Better Place GmbH|System and method for operating an electric vehicle|
    JP2010540907A|2007-09-20|2010-12-24|ベタープレイスゲーエムベーハー|Electric vehicle network|
    WO2009075313A1|2007-12-13|2009-06-18|Toyota Jidosha Kabushiki Kaisha|Electric power calculation device, price calculation device for calculating price of consumed electric power and price calculation method, vehicle information output device and information output method|
    SG10201606766QA|2008-05-09|2016-10-28|Accenture Global Services Ltd|Method and system for managing a power grid|
    US20110004446A1|2008-12-15|2011-01-06|Accenture Global Services Gmbh|Intelligent network|
    US8892375B2|2008-05-09|2014-11-18|Accenture Global Services Limited|Power grid outage and fault condition management|
    US8266075B2|2008-06-16|2012-09-11|International Business Machines Corporation|Electric vehicle charging transaction interface for managing electric vehicle charging transactions|
    US20100138066A1|2008-11-14|2010-06-03|Thinkeco Power Inc.|System and method of democratizing power to create a meta-exchange|
    JP2010125868A|2008-11-25|2010-06-10|Denso Corp|Charge and discharge planning device|
    US8706650B2|2009-01-14|2014-04-22|Integral Analytics, Inc.|Optimization of microgrid energy use and distribution|
    US20100332373A1|2009-02-26|2010-12-30|Jason Crabtree|System and method for participation in energy-related markets|
    US20100217550A1|2009-02-26|2010-08-26|Jason Crabtree|System and method for electric grid utilization and optimization|
    US8346401B2|2009-07-17|2013-01-01|Gridpoint, Inc.|Smart charging value and guarantee application|
    US8013570B2|2009-07-23|2011-09-06|Coulomb Technologies, Inc.|Electrical circuit sharing for electric vehicle charging stations|
    JP5618511B2|2009-09-08|2014-11-05|株式会社東芝|Integrated monitoring and control system for smart grid and micro grid|
    US20110153474A1|2009-12-17|2011-06-23|Tormey Milton T|Electric vehicle charging and accounting|
    JP5585097B2|2010-01-25|2014-09-10|ソニー株式会社|Power management apparatus and electronic device registration method|
    US9043038B2|2010-02-18|2015-05-26|University Of Delaware|Aggregation server for grid-integrated vehicles|
    JP5417231B2|2010-03-17|2014-02-12|株式会社東芝|Electric energy management server, electric vehicle and electric energy management method|
    KR101132948B1|2010-05-13|2012-04-05|엘에스산전 주식회사|System, Apparatus and Method for Charge and Discharge Control of Electric Vehicle|
    US8521337B1|2010-07-20|2013-08-27|Calm Energy Inc.|Systems and methods for operating electrical supply|
    US8493026B2|2010-07-21|2013-07-23|Mitsubishi Electric Research Laboratories, Inc.|System and method for ad-hoc energy exchange network|
    US20120019215A1|2010-07-23|2012-01-26|Electric Transportation Engineering Corp., D/b/a ECOtality North America|Method for charging multiple rechargeable energy storage systems and related systems and methods|
    CN103190052B|2010-08-05|2016-06-08|三菱自动车工业株式会社|Power supply and demand leveling system|
    JP5615090B2|2010-08-20|2014-10-29|三菱重工業株式会社|Management device, management method, computer program, in-vehicle device, and communication method|
    US8594859B2|2010-10-18|2013-11-26|Qualcomm Incorporated|Method and system for real-time aggregation of electric vehicle information for real-time auctioning of ancillary services, and real-time lowest cost matching electric vehicle energy demand to charging services|
    CN102055217B|2010-10-27|2012-09-19|国家电网公司|Electric vehicle orderly charging control method and system|
    DK2495844T3|2011-03-04|2013-12-09|Siemens Ag|Method and system for managing a network of electric car charging stations|
    EP2683052A4|2011-03-04|2015-05-06|Nec Corp|Charging control system, charging control method and recording medium|
    CN102185375A|2011-03-24|2011-09-14|清华大学|Electric vehicle energy efficiency evaluation and supervision system and method thereof for intelligent power grid|
    JP5541221B2|2011-04-25|2014-07-09|株式会社デンソー|Charging reservation device for power storage charging system|
    CA2835425C|2011-05-20|2017-10-24|Siemens Corporation|Bidirectional demand response control|
    JP5909906B2|2011-07-21|2016-04-27|ソニー株式会社|Information processing apparatus, information processing method, program, recording medium, and information processing system|
    US8417391B1|2011-12-15|2013-04-09|Restore Nv|Automated demand response energy management system|
    US9379559B2|2012-02-03|2016-06-28|International Business Machines Corporation|System and method of charging a vehicle using a dynamic power grid, and system and method of managing power consumption in the vehicle|US8013570B2|2009-07-23|2011-09-06|Coulomb Technologies, Inc.|Electrical circuit sharing for electric vehicle charging stations|
    US9760875B2|2012-04-05|2017-09-12|Menolinx System Ltd.|Device and method for automatic billing of power consumption through street poles|
    DE102012008978A1|2012-05-03|2013-11-07|Audi Ag|Method for carrying out at least one calculation process in a motor vehicle and motor vehicle|
    KR20140011255A|2012-07-18|2014-01-28|한국전자통신연구원|Method for managing energy and energy managing system using the method|
    US20140304529A1|2013-04-08|2014-10-09|Ford Meazell|System and Method for Battery Power Transfer Between Mobile Devices|
    FR3008246B1|2013-07-03|2017-07-07|Schneider Electric Ind Sas|SYSTEM FOR ELECTRICALLY CHARGING A PLURALITY OF ELECTRIC VEHICLES AND METHOD FOR DISTRIBUTING THE ELECTRICAL POWER DELIVERED BY AN ELECTRIC POWER SUPPLY OF SUCH A SYSTEM|
    US10093194B2|2013-09-30|2018-10-09|Elwha Llc|Communication and control system and method regarding electric vehicle for wireless electric vehicle electrical energy transfer|
    US9199549B2|2013-09-30|2015-12-01|Elwha Llc|Communication and control system and method regarding electric vehicle for wireless electric vehicle electrical energy transfer|
    US20150091507A1|2013-09-30|2015-04-02|Elwha Llc|Dwelling related information center associated with communication and control system and method for wireless electric vehicle electrical energy transfer|
    JP6081940B2|2014-02-28|2017-02-15|三菱重工業株式会社|Power demand forecasting device, power supply system, power demand forecasting method and program|
    EP3119638B1|2014-03-20|2018-01-10|EverCharge, Inc.|Smart energy distribution methods and systems for electric vehicle charging|
    US20150294329A1|2014-04-11|2015-10-15|Nissan North America, Inc.|System and method of predicting usage of a charging station|
    US9409492B2|2014-04-21|2016-08-09|Honda Motor Co., Ltd.|Method for precise demand response and control, and a system thereof|
    US10017068B2|2014-06-19|2018-07-10|Proterra Inc.|Charging of a fleet of electric vehicles|
    CN106660461B|2014-06-20|2019-08-02|通用电气公司|The system and method for power control for energy stores charging station|
    US9789779B2|2014-08-25|2017-10-17|Toyota Jidosha Kabushiki Kaisha|Regional charging control service|
    US9987940B2|2014-09-16|2018-06-05|Honda Motor Co., Ltd.|Priority based vehicle control strategy|
    US10845822B2|2014-10-31|2020-11-24|Clearpath Robotics Inc.|Systems and methods for utilizing fleets of robots|
    EP3213388A4|2014-10-31|2017-11-22|ABB Schweiz AG|Control system for electric vehicle charging station and method thereof|
    CN105871946B|2015-01-19|2020-08-25|比亚迪股份有限公司|Data transmission system and data transmission method of charging station|
    US9731615B2|2015-03-24|2017-08-15|Honda Motor Co., Ltd.|Grid overlay for a zip coded map system and method therefor|
    US20170015210A1|2015-07-13|2017-01-19|EV Connect, Inc.|Charge prioritization to manage peak loads|
    US10647209B2|2015-09-28|2020-05-12|Siemens Industry, Inc.|Managing and optimizing energy supply from grid and renewable source with electric vehicle supply equipment|
    US10320203B2|2015-10-16|2019-06-11|California Institute Of Technology|Adaptive charging algorithms for a network of electric vehicles|
    US10336194B2|2015-11-13|2019-07-02|Nio Usa, Inc.|Electric vehicle charging device alignment and method of use|
    US10220717B2|2015-11-13|2019-03-05|Nio Usa, Inc.|Electric vehicle emergency charging system and method of use|
    US9944192B2|2015-11-13|2018-04-17|Nio Usa, Inc.|Electric vehicle charging station system and method of use|
    US10632852B2|2015-11-13|2020-04-28|Nio Usa, Inc.|Electric vehicle optical charging system and method of use|
    US10080318B2|2015-11-13|2018-09-18|Nio Usa, Inc.|Safety shield for charging|
    US10166875B2|2015-11-13|2019-01-01|Nio Usa, Inc.|Deployable safety shield for charging|
    US10427530B2|2015-11-13|2019-10-01|Nio Usa, Inc.|Vehicle charge query and exchange system and method of use|
    US10124690B2|2015-11-13|2018-11-13|Nio Usa, Inc.|Electric vehicle charging device positioning and method of use|
    US10059213B2|2015-11-13|2018-08-28|Nio Usa, Inc.|Charging devices within wheel portions|
    US10131238B2|2015-11-13|2018-11-20|Nio Usa, Inc.|Charging transmission line under roadway for moving electric vehicle|
    US10093195B2|2015-11-13|2018-10-09|Nio Usa, Inc.|Integrated vehicle charging panel system and method of use|
    US10160339B2|2015-11-13|2018-12-25|Nio Usa, Inc.|Smart grid management|
    US10532663B2|2015-11-13|2020-01-14|Nio Usa, Inc.|Electric vehicle overhead charging system and method of use|
    US9964415B2|2015-11-13|2018-05-08|Nio Usa, Inc.|Tracking power consumption and payment|
    US10183584B2|2015-11-13|2019-01-22|Nio Usa, Inc.|Multi-mode rechargeable electric vehicle|
    US10252631B2|2015-11-13|2019-04-09|Nio Usa, Inc.|Communications between vehicle and charging system|
    US9709988B2|2015-12-09|2017-07-18|Ford Global Technologies, Llc|Identification of acceptable vehicle charge stations|
    DE102016202816A1|2016-02-24|2017-08-24|Bayerische Motoren Werke Aktiengesellschaft|Method for controlling the electrical charging of a group of vehicles|
    DE102016202813B4|2016-02-24|2022-01-27|Bayerische Motoren Werke Aktiengesellschaft|Method for controlling the electric charging of a group of vehicles|
    DE102016202808A1|2016-02-24|2017-08-24|Bayerische Motoren Werke Aktiengesellschaft|Method for controlling the electrical charging of a group of vehicles|
    WO2017147612A1|2016-02-25|2017-08-31|California Institute Of Technology|Adaptive charging network using adaptive charging stations for electric vehicles|
    KR20170101714A|2016-02-29|2017-09-06|하이리움산업|Method for providing hydrogen fuel cell vehicle charging service|
    US10150380B2|2016-03-23|2018-12-11|Chargepoint, Inc.|Dynamic allocation of power modules for charging electric vehicles|
    DE102016105614A1|2016-03-24|2017-09-28|EnBW Energie Baden-Württemberg AG|Charging system for electrical consumers, charging infrastructure device with a charging system and method for operating a charging infrastructure device|
    WO2017178401A1|2016-04-13|2017-10-19|Robert Bosch Gmbh|Smart dc microgrid parking structures using power line communications|
    EP3463970A4|2016-05-25|2020-01-08|Chargepoint, Inc.|Dynamic allocation of power modules for charging electric vehicles|
    WO2018046105A1|2016-09-12|2018-03-15|Innogy Se|Roaming method|
    US10699305B2|2016-11-21|2020-06-30|Nio Usa, Inc.|Smart refill assistant for electric vehicles|
    US10585440B1|2017-01-23|2020-03-10|Clearpath Robotics Inc.|Systems and methods for using human-operated material-transport vehicles with fleet-management systems|
    US10521525B2|2017-01-23|2019-12-31|International Business Machines Corporation|Quantifying a combined effect of interdependent uncertain resources in an electrical power grid|
    US10197412B2|2017-03-28|2019-02-05|Ford Global Technologies, Llc|Electric vehicle charging|
    WO2019020632A1|2017-07-24|2019-01-31|A.T. Kearney Limited|Aggregating energy resources|
    US10218196B1|2017-08-15|2019-02-26|GM Global Technology Operations LLC|Selection of charging modes for autonomous device|
    CN110015101B|2017-11-17|2021-05-14|宝沃汽车（中国）有限公司|Charging system and charging method|
    US11127056B2|2017-11-30|2021-09-21|Honda Motor Co., Ltd.|System and method for determining at least one demand response overhead percentage|
    EP3718073A4|2017-12-01|2021-08-25|California Institute of Technology|Optimization framework and methods for adaptive ev charging|
    CN110930687B|2017-12-18|2020-12-25|北京物资学院|Method and system for dispatching vehicles in peak vehicle time period|
    CN108009693B|2018-01-03|2021-09-07|上海电力学院|Grid-connected micro-grid double-layer optimization method based on two-stage demand response|
    WO2019140536A1|2018-01-22|2019-07-25|Clearpath Robotics Inc.|Systems and methods for measuring fleets of self-driving industrial vehicles|
    US11235778B2|2018-01-24|2022-02-01|Clearpath Robotics Inc.|Systems and methods for maintaining vehicle state information|
    WO2019153082A1|2018-02-07|2019-08-15|Clearpath Robotics Inc.|Communication systems for self-driving vehicles, and methods of providing thereof|
    DE102018202755A1|2018-02-23|2019-08-29|Bayerische Motoren Werke Aktiengesellschaft|Method and control device for adapting an electric power supply to an electrical power requirement in an electrical network|
    US20190275893A1|2018-03-06|2019-09-12|Wellen Sham|Intelligent charging network|
    JP6984555B2|2018-07-10|2021-12-22|トヨタ自動車株式会社|Server, vehicle and charging information provision method|
    GB201814092D0|2018-08-30|2018-10-17|Crowd Charge Ltd|Crowd Charge|
    CN109353242B|2018-11-13|2021-11-09|国网电动汽车（山西）服务有限公司|Charging algorithm for realizing bidirectional ordered charging and discharging of intelligent charging pile system|
    US10937113B2|2018-12-06|2021-03-02|GM Global Technology Operations LLC|Energy resource pre-allocation and delivery based on demand|
    DE102018131770A1|2018-12-11|2020-06-18|Innogy Se|Charging system for electric vehicles|
    CN109814992A|2018-12-29|2019-05-28|中国科学院计算技术研究所|Distributed dynamic dispatching method and system for the acquisition of large scale network data|
    CN109861208B|2019-01-07|2020-09-01|南京工程学院|Electric vehicle grid-connected optimization scheduling method based on two-stage preprocessing strategy|
    CN109813982B|2019-01-24|2021-05-14|深圳智链物联科技有限公司|Charging pile power failure monitoring method and device, charging pile management platform and storage medium|
    US10953767B2|2019-02-08|2021-03-23|Ford Global Technologies, Llc|System and method for battery-electric vehicle fleet charging|
    US10726642B1|2019-03-29|2020-07-28|Toyota Motor North America, Inc.|Vehicle data sharing with interested parties|
    US10896555B2|2019-03-29|2021-01-19|Toyota Motor North America, Inc.|Vehicle data sharing with interested parties|
    US10535207B1|2019-03-29|2020-01-14|Toyota Motor North America, Inc.|Vehicle data sharing with interested parties|
    US11176818B2|2019-05-02|2021-11-16|International Business Machines Corporation|Cluster-based management of vehicle power consumption|
    CN110588430B|2019-08-21|2021-06-08|深圳易马达科技有限公司|Charging method and charging equipment|
    DE102019125742A1|2019-09-25|2021-03-25|HELLA GmbH & Co. KGaA|Method for charging an energy store in a motor vehicle|
    WO2021229482A1|2020-05-14|2021-11-18|Emmanuel Bernard Jean Laurent|A distributed system to charge an electric vehicle and a method to operate the same|
    CN112092670A|2020-09-22|2020-12-18|南京林洋电力科技有限公司|Charging pile queuing and charging management method|
    CN112581313B|2020-12-23|2022-02-22|北京理工大学|Photovoltaic charging station resource distribution and adjustment method and system|
    法律状态:
    2018-12-04| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2019-11-12| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-12-29| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-02-23| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 13/02/2013, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US201261598109P| true| 2012-02-13|2012-02-13|
    US61/598,109|2012-02-13|
    PCT/IB2013/000666|WO2013121291A2|2012-02-13|2013-02-13|Electric vehicle distributed intelligence|
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