CN107316152A - Electric automobile participates in planing method, device and the computing device of Demand Side Response - Google Patents

Electric automobile participates in planing method, device and the computing device of Demand Side Response Download PDF

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CN107316152A
CN107316152A CN201710509475.7A CN201710509475A CN107316152A CN 107316152 A CN107316152 A CN 107316152A CN 201710509475 A CN201710509475 A CN 201710509475A CN 107316152 A CN107316152 A CN 107316152A
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China
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mrow
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CN201710509475.7A
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黄俊辉
南开辉
祁万春
归三荣
葛毅
张文嘉
武赓
周鹏程
刘洋
冯俊杰
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国网江苏省电力公司经济技术研究院
国家电网公司
国网江苏省电力公司南京供电公司
华北电力大学
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Priority to CN201710509475.7A priority Critical patent/CN107316152A/en
Publication of CN107316152A publication Critical patent/CN107316152A/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0631Resource planning, allocation or scheduling for a business operation
    • G06Q10/06315Needs-based resource requirements planning or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/067Business modelling
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply

Abstract

The invention discloses planing method, device and the computing device that a kind of electric automobile participates in Demand Side Response, this method includes:Electric automobile cost model is set up, electric automobile cost model includes the minimum cost function for realizing electric automobile spare capacity cost minimization;The operation cost model of autonomous system operator is set up, operation cost model includes the operation cost function for calculating the actual energy expense of regulation operating service;System consumption model is set up, system consumption model includes the consuming cost function for calculating conventional cost correspondence expense;Corresponding constraints is constructed respectively to electric automobile cost model, operation cost module and system consumption model;With reference to electric automobile cost model, operation cost model and system consumption model, to generate system cost plan model;The optimal solution of system cost plan model is asked for, to optimize the planning that electric automobile participates in Demand Side Response.

Description

Electric automobile participates in planing method, device and the computing device of Demand Side Response

Technical field

The present invention relates to electricity power field, more particularly to electric automobile participates in the planing method of Demand Side Response, device And computing device.

Background technology

Nowadays, the overall sales volume of electric automobile is always in steady-state growth, and worldwide modern electric automobile sales volume is near Phase has exceeded 1,000,000, and this growth trend is facilitated by many reasons, and one is exactly that electric automobile is more saved than conventional internal combustion locomotive Save.The upsurge sold with electric car, the market share of electric automobile would be possible to increase in future.However, current many states The power network of family can not tackle the hypersynchronous of electric automobile completely.Therefore, charging electric vehicle is uncontrolled may result in The problems such as system overload, power attenuation and voltage pulsation.

In order to tackle this extra duty of electric automobile and alleviate the influence of these potential problems, it is necessary to charge into The appropriate control of row, such as control partly reduce these problems in low-valley interval (midnight to early morning) charging and improve existing The utilization rate of facility.Further, electric automobile is the potential distribution for supporting " power network to automobile " and " automobile to power network " pattern Formula energy resources, participating in the planning of the Demand Side Response based on the time (such as tou power price) and based on excitation (such as regulation service) has Help the risk for promoting the stabilization of power network and reducing power network.But, the prior art realization grid-connected to electric automobile is most It is the angle from electric automobile service aggregating station to consider, also without the demand considered based on the time and based on excitation simultaneously Side is responded, and the cooperation of polymerization station and autonomous system operator can must also be considered in practice, and the two all makes itself as possible Benefit.In addition, with electric automobile and grid-connected, the uncertainty possibility of any part of intelligent grid of regenerative resource Can increase, before to it is probabilistic consider be scattered, be concerned only with one or two aspect, lack systems approach synthetically consider with Machine factor, and these enchancement factors may high degree influence the efficiency and effect of built formwork erection type.

The content of the invention

Therefore, the present invention provides the technical scheme that a kind of electric automobile participates in the planning of Demand Side Response, to try hard to solve Or at least alleviate the problem of existing above.

According to an aspect of the present invention there is provided a kind of electric automobile participate in Demand Side Response planing method, suitable for Performed in computing device, electric automobile together determines demand by polymerization stand control discharge and recharge, polymerization station with autonomous system operator The planning of side response, this method comprises the following steps:Electric automobile cost model is initially set up, electric automobile cost model includes Realize the minimum cost function of electric automobile spare capacity cost minimization;Set up the operation cost mould of autonomous system operator Type, operation cost model includes the operation cost function for calculating the actual energy expense of regulation operating service;Set up system consumption Model, system consumption model includes the consuming cost function for calculating conventional cost correspondence expense;To electric automobile cost model, fortune Battalion's cost module and system consumption model construct corresponding constraints respectively;With reference to electric automobile cost model, operation cost Model and system consumption model, to generate system cost plan model;The optimal solution of system cost plan model is asked for, to optimize Electric automobile participates in the planning of Demand Side Response.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, minimum cost function Determined with equation below:

Wherein, V2G represent electric automobile discharge, G2V to charging electric vehicle,It is that a-th of polymerization station provides V2G Standby cost,It is the V2G capacity that a-th of polymerization station needs to provide in period t,It is a-th of polymerization station offer Cost standby G2V,Be a-th of polymerization station needs the G2V capacity that provides in period t, a=1 ..., and A, A is polymerization station Total quantity, t=1 ..., T, T is time total amount.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, operation cost function Determined with equation below:

Wherein,It is that the G2V discounts of service, b are provided in period ta,tBe a-th of polymerization station actual provides in the t periods G2V spare capacities,It is the electric discharge cost of electric automobile in period t,Be a-th of polymerization station actual provides in the t periods V2G spare capacities.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, conventional cost includes Cost of electricity-generating, removal of load cost and regenerative resource cut down cost expected from generating set, and consuming cost function is with equation below It is determined that:

Wherein,It is generating set g cost of electricity-generating, pg,tIt is the generated energy of generating set g in the t periods,It is k Node loses the unit punishment cost of load, zk,tIt is the load of t period internal losses,It is that regenerative resource is cut at node k Subtract cost, ck,tIt is the regenerative resource reduction of k nodes in the t periods, k=1 ..., K, K is node total number.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, constraints includes Electric quantity balancing constraint, default technological constraint, risk allow constraint, discharge and recharge constraint and spare capacity constraint.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, operation cost model Electric quantity balancing constraint is met with consuming cost model, electric quantity balancing constraint is represented with equation below:

Wherein, Hl,kFor the incidence matrix coefficient at circuit l k nodes, value is -1,0 or 1, fl,tIt is circuit l in t Electric current in section,It is the generated energy of wind power system j in the t periods, ψn,tIt is the generated energy of photovoltaic n in the t periods;λk,tIt is the t periods Load at interior k nodes,For the charge volume of a-th of polymerization station in the t periods, l=1 ..., L, L is circuit sum.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, fl,tAnd pg,tMeet Default technological constraint, default technological constraint is represented with equation below:

Wherein,For circuit l maximum current,For generating set g minimum generated energy,For generator Group g maximum generating watt,For generating set g drop units limits,For generating set g emersion force constraint.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, consuming cost model Meet risk and allow constraint, risk allows that constraint is represented with equation below:

Wherein, pr () represents to seek probability, and γ represents specific risk allowable limit.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention,WithMeet Discharge and recharge is constrained, and discharge and recharge constraint is represented with equation below:

Wherein, M is infinitely great number, qa,tFor 0-1 variables,The electricity of a-th of polymerization station in the t periods is represented,The remaining battery capacity of a-th of polymerization station in the t periods is represented,Filled for electric automobile in a-th of polymerization station Electrical efficiency,For the discharging efficiency of electric automobile in a-th of polymerization station,It is pre- for electric automobile in a-th of polymerization station State of charge when phase is left,For the battery capacity for the electric automobile that a-th of polymerization station is left in the t periods,To enter Enter the state of charge of the electric automobile of a-th of polymerization station,For the battery for the electric automobile for entering a-th of polymerization station in the t periods Capacity.

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, a-th of polymerization station exists The G2V spare capacities b of t periods actual offera,tSpare capacity constraint is met, spare capacity constraint is represented with equation below:

Alternatively, in the planing method of Demand Side Response is participated according to the electric automobile of the present invention, with reference to electric automobile Cost model, operation cost model and system consumption model, are included with generating the step of system cost plan model:Obtain minimum Cost function, operation cost function and consuming cost function sum, as object function;Constraints is linearized Processing, to obtain corresponding goal constraint;Object function is combined with goal constraint to generate system cost plan model.

There is provided the device for planning that a kind of electric automobile participates in Demand Side Response, the dress according to a further aspect of the invention Put suitable for residing in computing device, including first set up module, second set up module, the 3rd set up module, generation module, article Part constraints module and solution module.Wherein, first set up module and be adapted to set up electric automobile cost model, electric automobile expense mould Type includes the minimum cost function for realizing electric automobile spare capacity cost minimization;Second, which sets up module, is adapted to set up independent system Unite operator operation cost model, operation cost model include calculate regulation operating service actual energy expense operation into This function;3rd, which sets up module, is adapted to set up system consumption model, and system consumption model includes calculating conventional cost correspondence expense Consuming cost function;Constraint module is suitable to electric automobile cost model, operation cost module and system consumption model Corresponding constraints is constructed respectively;Generation module is suitable to combination electric automobile cost model, operation cost model and system and disappeared Model is consumed, to generate system cost plan model;The optimal solution that module is suitable to ask for system cost plan model is solved, to optimize Electric automobile participates in the planning of Demand Side Response.

According to a further aspect of the invention there is provided a kind of computing device, including according to the electric automobile participation of the present invention The device for planning of Demand Side Response.

According to a further aspect of the invention there is provided a kind of computing device, including one or more processors, memory with And one or more programs, wherein one or more program storages in memory and are configured as by one or more processors Perform, one or more programs participate in the planing method of Demand Side Response including the electric automobile for performing according to the present invention Instruction.

According to a further aspect of the invention, a kind of computer-readable storage medium for storing one or more programs is also provided Matter, one or more programs include instruction, and instruction is when executed by a computing apparatus so that computing device is according to the present invention's Electric automobile participates in the planing method of Demand Side Response.

The technical scheme of the planning of Demand Side Response is participated according to the electric automobile of the present invention, electronic vapour is set up respectively first Car cost model, operation cost model and system consumption model, construct the corresponding constraints of these three models, and by above-mentioned three Individual models coupling gets up to generate system cost plan model, and optimal solution is asked for the model, is optimized according to optimal solution electronic Automobile participates in the planning of Demand Side Response.In the above-mentioned technical solutions, by considering the conjunction of polymerization station and autonomous system operator Make, participate in the Demand Side Response project based on the time and based on excitation for electric automobile, it is proposed that system cost plan model, For time-based Demand Side Response, tou power price is focused on, and for the Demand Side Response based on excitation, focuses on regulation Service, contributes to the clear and definite risk level of policymaker, and balances costs and benefits by risk level, and the model is considering polymerization station While income, the spare capacity level required for autonomous system operator is optimized, autonomous system operator passes through Optimized Operation conventional energy resource generates electricity and cut down renewable energy power generation amount so as to the cost that runs minimized, and polymerization station is adjusted by optimizing The discharge and recharge of electric automobile is spent to obtain maximum-discount or income to minimize its electric cost expenditure, cannot be only used for optimizing electronic vapour Car charges, moreover it is possible to which support contributes to demand response and the assistant service of the stabilization of power grids.

Brief description of the drawings

In order to realize above-mentioned and related purpose, some illustrative sides are described herein in conjunction with following description and accompanying drawing Face, these aspects indicate the various modes of principles disclosed herein that can put into practice, and all aspects and its equivalent aspect It is intended to fall under in the range of theme claimed.The following detailed description by being read in conjunction with the figure, the disclosure it is above-mentioned And other purposes, feature and advantage will be apparent.Throughout the disclosure, identical reference generally refers to identical Part or element.

Fig. 1 shows the structured flowchart of computing device 100 according to an embodiment of the invention;

Fig. 2 shows that electric automobile according to an embodiment of the invention participates in the planing method 200 of Demand Side Response Flow chart;

Fig. 3 shows the schematic diagram of the node microgrids of IEEE 6 according to an embodiment of the invention;And

Fig. 4 shows that electric automobile according to an embodiment of the invention participates in the device for planning 300 of Demand Side Response Schematic diagram.

Embodiment

The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although showing the disclosure in accompanying drawing Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here Limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure Complete conveys to those skilled in the art.

Fig. 1 is the block diagram of Example Computing Device 100.In basic configuration 102, computing device 100, which is typically comprised, is System memory 106 and one or more processor 104.Memory bus 108 can be used in processor 104 and system storage Communication between device 106.

Depending on desired configuration, processor 104 can be any kind of processing, include but is not limited to:Microprocessor (μ P), microcontroller (μ C), digital information processor (DSP) or any combination of them.Processor 104 can be included such as The cache of one or more rank of on-chip cache 110 and second level cache 112 etc, processor core 114 and register 116.The processor core 114 of example can include arithmetic and logical unit (ALU), floating-point unit (FPU), Digital signal processing core (DSP core) or any combination of them.The Memory Controller 118 of example can be with processor 104 are used together, or in some implementations, Memory Controller 118 can be an interior section of processor 104.

Depending on desired configuration, system storage 106 can be any type of memory, include but is not limited to:Easily The property lost memory (RAM), nonvolatile memory (ROM, flash memory etc.) or any combination of them.System is stored Device 106 can include operating system 120, one or more apply 122 and routine data 124.In some embodiments, It may be arranged to be operated using routine data 124 on an operating system using 122.

Computing device 100 can also include contributing to from various interface equipments (for example, output equipment 142, Peripheral Interface 144 and communication equipment 146) to basic configuration 102 via the communication of bus/interface controller 130 interface bus 140.Example Output equipment 142 include graphics processing unit 148 and audio treatment unit 150.They can be configured as contributing to via One or more A/V port 152 is communicated with the various external equipments of such as display or loudspeaker etc.Outside example If interface 144 can include serial interface controller 154 and parallel interface controller 156, they can be configured as contributing to Via one or more I/O port 158 and such as input equipment (for example, keyboard, mouse, pen, voice-input device, touch Input equipment) or the external equipment of other peripheral hardwares (such as printer, scanner) etc communicated.The communication of example is set Standby 146 can include network controller 160, and it can be arranged to be easy to via one or more COM1 164 and one The communication that other individual or multiple computing devices 162 pass through network communication link.

Network communication link can be an example of communication media.Communication media can be generally presented as in such as carrier wave Or computer-readable instruction in the modulated data signal of other transmission mechanisms etc, data structure, program module, and can With including any information delivery media." modulated data signal " can such signal, one in its data set or many It is individual or it change can the mode of coding information in the signal carry out.As nonrestrictive example, communication media can be with Include the wire medium of such as cable network or private line network etc, and it is such as sound, radio frequency (RF), microwave, infrared (IR) the various wireless mediums or including other wireless mediums.Term computer-readable medium used herein can include depositing Both storage media and communication media.

Computing device 100 can be implemented as server, such as file server, database server, application program service Device and WEB server etc., can also be embodied as a part for portable (or mobile) electronic equipment of small size, these electronic equipments Can be such as cell phone, personal digital assistant (PDA), personal media player device, wireless network browsing apparatus, individual Helmet, application specific equipment or the mixing apparatus of any of the above function can be included.Computing device 100 can also be real It is now to include desktop computer and the personal computer of notebook computer configuration.In certain embodiments, the quilt of computing device 100 It is configured to perform the planing method 200 for participating in Demand Side Response according to the electric automobile of the present invention.Include using 122 according to this hair Bright electric automobile participates in the device for planning 300 of Demand Side Response.

It should be noted that electric automobile is by polymerization stand control discharge and recharge, polymerization station is together determined with autonomous system operator Determine the planning of Demand Side Response.Autonomous system operator plays central role in systems, and it is collected and issued in the market respectively The polymerization station of the information of class members, such as generating set, load center and electric automobile.Autonomous system operator is always in dimension Hold and seek system operation cost minimum in supply and demand balance.Power system is made up of distributed power generation unit, as conventional Generating set and renewable wind energy and solar energy system.Due to the limited battery capacity of each electric automobile, single motor automobile Contribution for power network can be ignored.The participation that small user is planned in whole electricity market is useless, but is passed through Aggregated service controls the discharge and recharge of a large amount of electric automobiles to be to realize that anticipated demand side responds a kind of effective means of target, therefore needs The bid and coordination of electric automobile charge and discharge electrical activity are participated in using the intermediate entities as polymerization station.

Fig. 2 shows that electric automobile according to an embodiment of the invention participates in the planing method 200 of Demand Side Response Flow chart.Electric automobile participates in the planing method 200 of Demand Side Response suitable for (such as the calculating shown in Fig. 1 is set in computing device Standby 100) middle execution.

As shown in Fig. 2 method 200 starts from step S210.In step S210, electric automobile cost model is set up, it is electronic Automobile expenses model includes the minimum cost function for realizing electric automobile spare capacity cost minimization.According to one of the present invention Embodiment, minimum cost function is determined with equation below:

Wherein, V2G represent electric automobile discharge, G2V to charging electric vehicle,It is that a-th of polymerization station provides V2G Standby cost,It is the V2G capacity that a-th of polymerization station needs to provide in period t,It is a-th of polymerization station offer Cost standby G2V,Be a-th of polymerization station needs the G2V capacity that provides in period t, a=1 ..., and A, A is polymerization station Total quantity, t=1 ..., T, T is time total amount.Formula (2) represents the scope of spare capacity.

Polymerization station supports G2V and V2G patterns, to enable polymerization station appropriately to arrange the discharge and recharge of electric automobile, electric automobile Car owner need to report its battery capacity and plan of travel, i.e., the estimated time arrived and departed from.To participate in peak-frequency regulation service, polymerization Autonomous system operator need to be reported by active volume and the state of charge of prediction by standing.The electric automobile for representing polymerization station with SOC holds Amount, RBC represents the remaining battery capacity of polymerization station, when electric automobile discharges in V2G patterns, and the total SOC of polymerization station is reduced, RBC increases, when electric automobile charges in G2V patterns, the total SOC increases of polymerization station, RBC is reduced.

Then, into step S220, the operation cost model of autonomous system operator is set up, operation cost model includes meter Calculate the operation cost function of the actual energy expense of regulation operating service.According to one embodiment of present invention, operation cost letter Number is determined with equation below:

Wherein,It is that the G2V discounts of service, b are provided in period ta,tBe a-th of polymerization station actual provides in the t periods G2V spare capacities,It is the electric discharge cost of electric automobile in period t,Be a-th of polymerization station actual provides in the t periods V2G spare capacities.

Next, performing step S230, system consumption model is set up, system consumption model includes calculating conventional cost correspondence The consuming cost function of expense.Wherein, conventional cost includes cost of electricity-generating expected from generating set, removal of load cost and renewable The energy cuts down cost.According to one embodiment of present invention, consuming cost function is determined with equation below:

Wherein,It is generating set g cost of electricity-generating, pg,tIt is the generated energy of generating set g in the t periods,It is k sections The unit punishment cost of point loss load, zk,tIt is the load of t period internal losses,It is the reduction of regenerative resource at node k Cost, ck,tIt is the regenerative resource reduction of k nodes in the t periods, k=1 ..., K, K is node total number.

By three models of above-mentioned foundation, in the Demand Side Response project, polymerization station can select timesharing electricity Valency provides peak regulation service as time-based project, if there is electric automobile participation in the project, and it tackles Ping Feng, half The peak price different with peak period payment, polymerization station may also participate in the project based on excitation to be needed to provide V2G and G2V The service such as regulation.These services allow autonomous system operator to produce two distinct types of cost:To polymerization station pay it is standby Ore-hosting rock series and energy cost.Ore-hosting rock series are relevant with the maximum capacity that each polymerization station can be provided within the contract provision time, It corresponds to electric automobile least cost model, and energy cost is relevant with cost of energy actual in V2G or G2V patterns, its Corresponding to operation cost model.In addition, in addition it is also necessary to consider cost of electricity-generating expected from a type of cost, i.e. conventional power unit, get rid of Load cost and regenerative resource cut down the summation of cost, and it corresponds to system consumption model.The electric automobile of certain level holds Amount is needed as standby to respond rapidly to and relax supply surplus or regulation service deficiency, and the stand by margin should be determined suitably, The decision relevant with various energy generation schedules with electric automobile discharge and recharge all should be careful, and should be looked to the future during decision has various The possibility scene of risk factors, the risk refers to load with supplying unbalanced possibility.

In step S240, electric automobile cost model, operation cost module and system consumption model are constructed respectively pair The constraints answered.Wherein, constraints allows constraint, discharge and recharge about including electric quantity balancing constraint, default technological constraint, risk Beam and spare capacity constraint.

According to one embodiment of present invention, operation cost model and consuming cost model meet electric quantity balancing constraint, institute Electric quantity balancing constraint is stated to represent with equation below:

Wherein, Hl,kFor the incidence matrix coefficient at circuit l k nodes, value is -1,0 or 1, fl,tIt is circuit l in t Electric current in section,It is the generated energy of wind power system j in the t periods, ψn,tIt is the generated energy of photovoltaic n in the t periods;λk,tIt is the t periods Load at interior k nodes,For the charge volume of a-th of polymerization station in the t periods, l=1 ..., L, L is circuit sum.Formula (7) table Show the electric quantity balancing of each node, flow into a certain node total electricity (including the electricity that sends of conventional power unit and regenerative resource with And polymerization station release electricity) should be equal to flow out the node total electricity (including meet base lod electricity, polymerization station charging Amount and regenerative resource cut down electricity).

In fact, formula (4), (5), (8) and (9) can be considered boundary condition, wherein, formula (4) represents the capacity limit of G2V services System, similarly, the discharge capacity of polymerization station is limited by the maximum V2G spare capacities that contract is determined, such as formula (5), and unsatisfied need The border of summation energy reduction is respectively by actual demand and available renewable energy source-representation, such as formula (4) and formula (5).

Further, fl,tAnd pg,tDefault technological constraint is met, default technological constraint is represented with equation below:

Wherein,For circuit l maximum current,For generating set g minimum generated energy,For generator Group g maximum generating watt,For generating set g drop units limits,For generating set g emersion force constraint.Formula (12) It ensure that generating set is run in the range of climbing limitation.

Consuming cost model meets risk and allows constraint, and risk allows that constraint is represented with equation below:

Wherein, pr () represents to seek probability, and γ represents specific risk allowable limit.It is general that formula (13) is based on venture worth Read, reflect the Risks of policymaker.According to this constraint, any energy resource supply and load are mismatched (by not meeting load Represented with energy reduction) probability all should be less than or equal to specific risk allowable limit γ.

Further,WithDischarge and recharge constraint is met, the discharge and recharge constraint is represented with equation below:

Wherein, M is infinitely great number, qa,tFor 0-1 variables,The electricity of a-th of polymerization station in the t periods is represented,The remaining battery capacity of a-th of polymerization station in the t periods is represented,Filled for electric automobile in a-th of polymerization station Electrical efficiency,For the discharging efficiency of electric automobile in a-th of polymerization station,It is pre- for electric automobile in a-th of polymerization station State of charge when phase is left,For the battery capacity for the electric automobile that a-th of polymerization station is left in the t periods,To enter Enter the state of charge of the electric automobile of a-th of polymerization station,For the battery for the electric automobile for entering a-th of polymerization station in the t periods Capacity.Formula (14) and (16) ensure that polymerization station is transported under a kind of pattern that each moment can only be in V2G or G2V both of which OK, formula (15) ensure charge volume be no more than the available residual capacity of polymerization station, formula (17) consider discharging efficiency, show discharge capacity by The limitation of utilisable energy.

On the other hand,WithIt can be represented with equation below:

Formula (18) illustrates the state of charge of each polymerization station, its change and battery efficiency with discharge and recharge situation and change Become.Polymerization station should be first to the charging electric vehicle that will be left, and leaving or adding for electric automobile can also influence polymerization station total Charging and discharging state.Formula (19) indicates the change of polymerization station remaining battery capacity, electric automobile reach stop time and Charge and discharge mode will all influence remaining battery capacity, and the state of charge for the electric automobile for adding or leaving can also influence remaining battery Capacity.

In this embodiment, G2V spare capacity b of a-th of polymerization station in the actual offer of t periodsa,tMeet spare capacity Constraint, spare capacity constraint is represented with equation below:

After the construction for completing constraints, into step S250, with reference to electric automobile cost model, operation cost model and System consumption model, to generate system cost plan model.According to one embodiment of present invention, it can generate in the following manner System cost plan model.First, the minimum cost function, operation cost function and consuming cost function sum are obtained, will It is determined as object function, the object function with equation below:

Then, linearization process is carried out to constraints, to obtain corresponding goal constraint, by object function and target about Shu Zuhe is to generate system cost plan model.In this embodiment, linearisation risk allows constraint and spare capacity respectively Constraint, i.e., with formula (23) and (24) instead of formula (13), introduction-type (25)~(28) linearize formula (20), to ensure all situations Considered, formula (23)~(28) are as follows:

Wherein, wtFor binary variable, w is set if energy resource supply and demand mismatchtFor 1, otherwise the variable is 0. Equally, w 'tWith w "tAlso it is 0-1 binary variable.

Finally, in step s 250, the optimal solution of system cost plan model is asked for, demand is participated in optimize electric automobile The planning of side response.According to one embodiment of present invention, it is that formula (22) is solved to object function based on goal constraint, obtains The optimal solution of system cost plan model is obtained, optimizes the planning that electric automobile participates in Demand Side Response according to the optimal solution.

To prove the validity of said system cost planning model, use one day of the node microgrids of IEEE 6 after improvement Plan to verify.Fig. 3 shows the schematic diagram of the node microgrids of IEEE 6 according to an embodiment of the invention.As shown in figure 3, should The node microgrids of IEEE 6 comprising three have it is identical climbing limitation conventional power units, i.e. 200kW per hour, the maximum of each unit Generated output is 700kW, and being not provided with minimum generated output for conventional power unit limits.The transmission capacity of every circuit is 500kW, Susceptance is 10 perunit values.It is assumed that three load centers have identical base lod, it is specific as shown in table 1.

Table 1

Charging electric vehicle can increase the extra duty being not included in base lod.Each the maximum capacity in parking lot is 200 automobiles, by a polymerization station administration.Assuming that the battery capacity of each electric automobile is 24kWh, and efficiency for charge-discharge is 99%, it is contemplated that 35% automobile is electric automobile in parking lot (level of interpenetration of electric automobile is 35%).Into parking lot The electricity of electric automobile residue 30%, battery electric quantity is 90% when leaving.The hair of wind-force (i.e. wind energy) and photovoltaic (i.e. solar energy) Electricity is reduced in proportion with Jia Lifo Leahs ISO wind-force and photovoltaic power generation quantity data.

The related cost of load is cut down and lost to regenerative resource is respectively set as 1.5 dollars/kWh and 5 dollar/kWh, The cost of conventional power unit Emergency electric generation is set to 0.2 dollar/kWh, and the income that polymerization station is obtained in V2G and G2V patterns is 0.02 Dollar/kWh, when need to abridge supply electric energy when, the discount of polymerization station charging available 100%.Autonomous system operator can be right The electricity that polymerization station discharges for peak regulation or other purposes is paid, and the expense of this service is different, depends primarily on market electricity Valency, in basic condition, electricity price is 0.01 dollar/kWh.It is determined that in the case of, relax Risk Constraint, setting random parameter Desired value after model be addressed, electric automobile is introduced in smooth load can better profit from regenerative resource, and will fill Electric load is transferred to the flat peak period.Electric automobile renewable energy power generation amount is big and provides G2V services during not enough base lod, And provide V2G services in peak period electric discharge.Substrate is insufficient in renewable energy power generation amount and electric automobile discharge capacity Generated electricity during load using conventional power unit.

Under conditions above setting, charging strategy, risk level, parking lot availability and load pattern are based respectively on, it is right System consumption model is analyzed.For charging strategy, point three kinds of strategies use in basic scenario cover half type really. In the first strategy, it is assumed that electric automobile is not involved in Demand Side Response project, and their entrance parking lots begin to fill Electricity;In second of strategy, it is assumed that electric automobile participates in time-based Demand Side Response project, by its charging of polymerization station plan Time to reduce the electricity charge and the smooth total load of power network, when polymerization station participates in time-based Demand Side Response project, independent system System operator only responds its charge mode by minimizing its operating cost;In the third strategy, it is assumed that electric automobile is joined With the Demand Side Response project based on excitation, it is activated for participating in V2G and G2V patterns.

Understood after the situation under to these three strategies is analyzed respectively, participating in Demand Side Response project can be reduced simultaneously Polymerization station and the cost of autonomous system operator.And with time-based Demand Side Response comparison of item, the demand based on excitation Response project in side can save more costs for autonomous system operator, be primarily due to electric automobile in G2V and V2G patterns Use reduce the cost related with energy reduction to load loss, the Demand Side Response project based on excitation of participation can also be Polymerization station brings income, and unplanned charging forces typical power system to generate more electricity in peak period.Need based on excitation Ask side to respond project by suitably using renewable resource, can almost be completely eliminated the need for being generated electricity for conventional energy resource.Demand Response item purpose main target is to reduce peak load, and participating in the Demand Side Response project based on the time and based on excitation can distinguish Reduce peak load 48% and 51%.Polymerization station, which participates in Demand Side Response project, can promote it to avoid charging in peak period, fill Electrical activity is all carried out in the flat peak period mostly.

For risk level, mismatch possibility risk level being predefined between 0.01, i.e. load and supply should Less than 1%.Assuming that load, renewable energy power generation, the behavior of electric automobile car owner, the charged state for adding or leaving polymerization station It is uncertain, and sets up the Analysis by Scenario Trees for there are 10 scenes, system consumption model is solved under various risk threshold values, Including 0,0.0001,0.1 and 1, it is known that unmatched possibility is higher between allowing supply load for higher risk threshold value, because This standby requirement is lower.

For the availability of parking lot, it is also to determine another influence factor of spare capacity, based on excitation In Demand Side Response project test three types car-parking model, using with test system same in Fig. 3, and assume in section The parking lot of point 4,5,6 is all located at residential block, shopping centre and industrial area respectively, and the total capacity in each parking lot is 160 automobiles. Residential block and industrial area polymerization station because it is available in late night to morning capacity, it is special on daytime so be more willing to provide G2V services It is not in peak period, it is not necessary that cut down renewable energy power generation.Shopping centre parking lot has more active volumes on daytime, Corresponding polymerization station may participate in G2V services when being necessary, but the region is not suitable for providing V2G services, because electronic vapour Car continually leaves or into influence state of charge and remaining battery capacity.For the charge mode in above three region, The electric automobile that residential block and industrial area are parked is because take part in G2V regulation services, so fully being filled from 3:00 AM to 6 points of holdings Electricity condition, and peak period reduce charge volume with provide V2G service, electric automobile suitably charging planning also prevent its Peak period fully charges.

For load pattern, it is contemplated that the load pattern of residential block, shopping centre and industrial area have very big difference, it is necessary to Treat with a certain discrimination.The electric automobile of residential block is generally parked the flat peak period in the morning in, thus can this period for its charge and Peak period provides V2G services.The parking lot availability of shopping centre is extremely unstable in peak period, because rather than V2G service Reliable sources, but its can in the morning with G2V services are provided after 21 points of night.The electric automobile of industrial area would generally be parked to work After time terminates, therefore, it is a good selection that electric automobile, which provides G2V services, while it is standby to provide limited V2G Capacity.In addition, being based on for time and Demand Side Response project based on excitation, it is to residential block, shopping centre and industrial area The influence of system operation cost, with no Demand Side Response participate in do not plan charged condition compared with, be still very different. Time-based Demand Side Response project is more effective for reducing peak load, and the Demand Side Response project based on excitation is flat The ability of load and supply respect is stronger in weighing apparatus power network.Compared with time-based Demand Side Response project, the need based on excitation More costs can be saved in three regions by asking side to respond project, and electric automobile participates in these Demand Side Response projects and helped In polymerization station power cost saving expenditure, and additional income is obtained in some cases.

Fig. 4 shows that the electric automobile of one embodiment of the invention participates in the signal of the device for planning 300 of Demand Side Response Figure.As shown in figure 4, the device for planning 300 that electric automobile participates in Demand Side Response sets up the foundation of module 310, second including first Module the 320, the 3rd sets up module 330, constraint module 340, generation module 350 and solves module 360.

First, which sets up module 310, is adapted to set up electric automobile cost model, and electric automobile cost model is electronic including realizing The minimum cost function that automobile standby ore-hosting rock series are minimized.First, which sets up module 310, is further adapted for according to equation below structure Build minimum cost function:

Wherein, V2G represent electric automobile discharge, G2V to charging electric vehicle,It is that a-th of polymerization station provides V2G Standby cost,It is the V2G capacity that a-th of polymerization station needs to provide in period t,It is a-th of polymerization station offer Cost standby G2V,Be a-th of polymerization station needs the G2V capacity that provides in period t, a=1 ..., and A, A is polymerization station Total quantity, t=1 ..., T, T is time total amount.

Second, which sets up module 320, sets up module 310 with first and is connected, and is adapted to set up the operation cost of autonomous system operator Model, operation cost model includes the operation cost function for calculating the actual energy expense of regulation operating service.Second sets up mould Block 320 is further adapted for building operation cost function according to equation below:

Wherein,It is that the G2V discounts of service, b are provided in period ta,tBe a-th of polymerization station actual provides in the t periods G2V spare capacities,It is the electric discharge cost of electric automobile in period t,Be a-th of polymerization station actual provides in the t periods V2G spare capacities.

3rd, which sets up module 330, sets up module 320 with second and is connected, and is adapted to set up system consumption model, system consumption mould Type includes the consuming cost function for calculating conventional cost correspondence expense.Wherein, conventional cost includes the expected generating of generating set Cost, removal of load cost and regenerative resource cut down cost.3rd, which sets up module 330, is further adapted for building according to equation below Consuming cost function:

Wherein,It is generating set g cost of electricity-generating, pg,tIt is the generated energy of generating set g in the t periods,It is k sections The unit punishment cost of point loss load, zk,tIt is the load of t period internal losses,It is the reduction of regenerative resource at node k Cost, ck,tIt is the regenerative resource reduction of k nodes in the t periods, k=1 ..., K, K is node total number.

Constraint module 340 sets up module 310 with first, second sets up module 320 and the 3rd and set up module 330 respectively It is connected, suitable for constructing corresponding constraints respectively to electric automobile cost model, operation cost module and system consumption model. Wherein, constraints allows constraint, discharge and recharge constraint and spare capacity including electric quantity balancing constraint, default technological constraint, risk Constraint.Operation cost model and consuming cost model meet electric quantity balancing constraint, and constraint module 340 is suitable to according to following public affairs Formula builds electric quantity balancing constraint:

Wherein, Hl,kFor the incidence matrix coefficient at circuit l k nodes, value is -1,0 or 1, fl,tIt is circuit l in t Electric current in section,It is the generated energy of wind power system j in the t periods, ψn,tIt is the generated energy of photovoltaic n in the t periods;λk,tIt is the t periods Load at interior k nodes,For the charge volume of a-th of polymerization station in the t periods, l=1 ..., L, L is circuit sum.fl,tWith pg,tDefault technological constraint is met, constraint module 340 is further adapted for determining default technological constraint according to equation below:

Wherein,For circuit l maximum current,For generating set g minimum generated energy,For generator Group g maximum generating watt,For generating set g drop units limits,For generating set g emersion force constraint.Consume into This model meets risk and allows constraint, and constraint module 340 is further adapted for determining that risk allows constraint according to equation below:

Wherein, pr () represents to seek probability, and γ represents specific risk allowable limit.WithMeet discharge and recharge about Beam, constraint module 340 is further adapted for building discharge and recharge constraint according to equation below:

Wherein, M is infinitely great number, qa,tFor 0-1 variables,The electricity of a-th of polymerization station in the t periods is represented,The remaining battery capacity of a-th of polymerization station in the t periods is represented,Filled for electric automobile in a-th of polymerization station Electrical efficiency,For the discharging efficiency of electric automobile in a-th of polymerization station,It is pre- for electric automobile in a-th of polymerization station State of charge when phase is left,For the battery capacity for the electric automobile that a-th of polymerization station is left in the t periods,To enter Enter the state of charge of the electric automobile of a-th of polymerization station,For the battery for the electric automobile for entering a-th of polymerization station in the t periods Capacity.G2V spare capacity b of a-th of polymerization station in the actual offer of t periodsa,tMeet spare capacity constraint, constraint module 340 are further adapted for building spare capacity constraint according to equation below:

Generation module 350 set up respectively with first module 310, second set up module the 320, the 3rd set up module 330 and article Part constraints module 340 is connected, suitable for combining electric automobile cost model, operation cost model and system consumption model, to generate System cost plan model.Generation module 350 is further adapted for obtaining minimum cost function, operation cost function and consuming cost Function sum, as object function;Linearization process is carried out to constraints, to obtain corresponding goal constraint;By mesh Scalar functions combine to generate system cost plan model with goal constraint.

Solve module 360 with generation module 350 to be connected, the optimal solution suitable for asking for system cost plan model, to optimize Electric automobile participates in the planning of Demand Side Response.

The specific steps and embodiment of the planning of Demand Side Response are participated on electric automobile, in retouching based on Fig. 2-3 It has been disclosed in detail in stating, here is omitted.

The prior art realization grid-connected to electric automobile, the overwhelming majority is come from the angle at electric automobile service aggregating station Consider, also without the Demand Side Response considered based on the time and based on excitation simultaneously, polymerization can must also be considered in practice The cooperation stood with autonomous system operator, and the two all makes the benefit of itself as possible.In addition, with electric automobile and can The renewable sources of energy it is grid-connected, the uncertainty of any part of intelligent grid may increase, and be point to probabilistic consideration before Scattered, one or two of aspect is concerned only with, lacks systems approach and synthetically considers enchancement factor, and these enchancement factors may be very big Degree influences the efficiency and effect of built formwork erection type.Electric automobile according to embodiments of the present invention participates in the planning of Demand Side Response Technical scheme, set up electric automobile cost model, operation cost model and system consumption model respectively first, construct these three The corresponding constraints of model, and above three models coupling is got up to generate system cost plan model, the model is asked Optimal solution is taken, optimizes the planning that electric automobile participates in Demand Side Response according to optimal solution.In the above-mentioned technical solutions, by examining Consider polymerization station and the cooperation of autonomous system operator, the Demand Side Response based on the time and based on excitation is participated in for electric automobile Project, it is proposed that system cost plan model, for time-based Demand Side Response, focuses on tou power price, and for base In the Demand Side Response of excitation, regulation service is focused on, contributes to the clear and definite risk level of policymaker, and balance by risk level Costs and benefits, the model is while polymerization station income is considered, to the spare capacity level required for autonomous system operator It is optimized, autonomous system operator is generated electricity and cut down renewable energy power generation amount by Optimized Operation conventional energy resource so as to most Smallization operating cost, polymerization station minimizes it by the discharge and recharge of Optimized Operation electric automobile to obtain maximum-discount or income Electric cost expenditure, cannot be only used for optimizing charging electric vehicle, moreover it is possible to which support contributes to the demand response and auxiliary of the stabilization of power grids Service.

A7. the method as described in A6, fl,tAnd pg,tDefault technological constraint is met, the default technological constraint is with equation below Represent:

Wherein,For circuit l maximum current,For generating set g minimum generated energy,For generator Group g maximum generating watt,For generating set g drop units limits,For generating set g emersion force constraint.

A8. the method as described in A6 or 7, the consuming cost model meets risk and allows constraint, and the risk is allowed about Beam is represented with equation below:

Wherein, pr () represents to seek probability, and γ represents specific risk allowable limit.

A9. the method as any one of A6-8,WithDischarge and recharge constraint is met, the discharge and recharge constraint is with such as Lower formula is represented:

Wherein, M is infinitely great number, qa,tFor 0-1 variables,The electricity of a-th of polymerization station in the t periods is represented,The remaining battery capacity of a-th of polymerization station in the t periods is represented,Filled for electric automobile in a-th of polymerization station Electrical efficiency,For the discharging efficiency of electric automobile in a-th of polymerization station,It is pre- for electric automobile in a-th of polymerization station State of charge when phase is left,For the battery capacity for the electric automobile that a-th of polymerization station is left in the t periods,To enter Enter the state of charge of the electric automobile of a-th of polymerization station,For the battery for the electric automobile for entering a-th of polymerization station in the t periods Capacity.

A10. the method as any one of A6-9, G2V spare capacity of a-th of polymerization station in the actual offer of t periods ba,tSpare capacity constraint is met, the spare capacity constraint is represented with equation below:

A11. the method as any one of A1-10, the electric automobile cost model with reference to described in, operation cost mould Type and system consumption model, are included with generating the step of system cost plan model:

The minimum cost function, operation cost function and consuming cost function sum are obtained, as object function;

Linearization process is carried out to the constraints, to obtain corresponding goal constraint;

The object function is combined with the goal constraint to generate system cost plan model.

B13. the device as described in B12, described first set up module be further adapted for according to equation below build described in most Small cost function:

Wherein, V2G represent electric automobile discharge, G2V to charging electric vehicle,It is that a-th of polymerization station provides V2G Standby cost,It is the V2G capacity that a-th of polymerization station needs to provide in period t,It is a-th of polymerization station offer Cost standby G2V,Be a-th of polymerization station needs the G2V capacity that provides in period t, a=1 ..., and A, A is polymerization station Total quantity, t=1 ..., T, T is time total amount.

B14. the device as described in B12 or 13, described second, which sets up module, is further adapted for building institute according to equation below State operation cost function:

Wherein,It is that the G2V discounts of service, b are provided in period ta,tBe a-th of polymerization station actual provides in the t periods G2V spare capacities,It is the electric discharge cost of electric automobile in period t,Be a-th of polymerization station actual provides in the t periods V2G spare capacities.

B15. the device as any one of B12-14, the conventional cost includes generating electricity into expected from generating set Originally, removal of load cost and regenerative resource cut down cost, and the described 3rd, which sets up module, is further adapted for building according to equation below The consuming cost function:

Wherein,It is generating set g cost of electricity-generating, pg,tIt is the generated energy of generating set g in the t periods,It is k Node loses the unit punishment cost of load, zk,tIt is the load of t period internal losses,It is that regenerative resource is cut at node k Subtract cost, ck,tIt is the regenerative resource reduction of k nodes in the t periods, k=1 ..., K, K is node total number.

B16. the device as any one of B12-15, the constraints includes electric quantity balancing constraint, default technology Constraint, discharge and recharge constraint and spare capacity constraint are allowed in constraint, risk.

B17. the device as described in B16, the operation cost model and consuming cost model meet electric quantity balancing constraint, institute Constraint module is stated to be suitable to build the electric quantity balancing constraint according to equation below:

Wherein, Hl,kFor the incidence matrix coefficient at circuit l k nodes, value is -1,0 or 1, fl,tIt is circuit l in t Electric current in section,It is the generated energy of wind power system j in the t periods, ψn,tIt is the generated energy of photovoltaic n in the t periods;λk,tIt is the t periods Load at interior k nodes,For the charge volume of a-th of polymerization station in the t periods, l=1 ..., L, L is circuit sum.

B18. the device as described in B17, fl,tAnd pg,tMeet default technological constraint, the constraint module be further adapted for by The default technological constraint is determined according to equation below:

Wherein,For circuit l maximum current,For generating set g minimum generated energy,For generator Group g maximum generating watt,For generating set g drop units limits,For generating set g emersion force constraint.

B19. the device as described in B17 or 18, the consuming cost model meets risk and allows constraint, the constraint Module is further adapted for determining that the risk allows constraint according to equation below:

Wherein, pr () represents to seek probability, and γ represents specific risk allowable limit.

B20. the device as any one of B17-19,WithMeet discharge and recharge constraint, the constraint mould Block is further adapted for building the discharge and recharge constraint according to equation below:

Wherein, M is infinitely great number, qa,tFor 0-1 variables,The electricity of a-th of polymerization station in the t periods is represented,The remaining battery capacity of a-th of polymerization station in the t periods is represented,For the charging of electric automobile in a-th of polymerization station Efficiency,For the discharging efficiency of electric automobile in a-th of polymerization station,It is expected for electric automobile in a-th of polymerization station State of charge when leaving,For the battery capacity for the electric automobile that a-th of polymerization station is left in the t periods,To enter The state of charge of the electric automobile of a-th of polymerization station,The battery of electric automobile to enter a-th of polymerization station in the t periods holds Amount.

B21. the device as any one of B17-20, a-th of polymerization station standby is held in the G2V of t periods actual offer Measure ba,tSpare capacity constraint is met, the constraint module is further adapted for building the spare capacity constraint according to equation below:

B22. the device as any one of B12-21, the generation module is further adapted for:

The minimum cost function, operation cost function and consuming cost function sum are obtained, as object function;

Linearization process is carried out to the constraints, to obtain corresponding goal constraint;

The object function is combined with the goal constraint to generate system cost plan model.

In the specification that this place is provided, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention Example can be put into practice in the case of these no details.In some instances, known method, knot is not been shown in detail Structure and technology, so as not to obscure the understanding of this description.

Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect, exist Above in the description of the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention:It is i.e. required to protect The application claims of shield are than the feature more features that is expressly recited in each claim.More precisely, as following As claims reflect, inventive aspect is all features less than single embodiment disclosed above.Therefore, abide by Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself It is used as the separate embodiments of the present invention.

Those skilled in the art should be understood the module or unit or group of the equipment in example disclosed herein Between can be arranged in equipment as depicted in this embodiment, or alternatively can be positioned at and the equipment in the example In different one or more equipment.Module in aforementioned exemplary can be combined as a module or be segmented into addition multiple Submodule.

Those skilled in the art, which are appreciated that, to be carried out adaptively to the module in the equipment in embodiment Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment Member or group between be combined into one between module or unit or group, and can be divided into addition multiple submodule or subelement or Between subgroup.In addition at least some in such feature and/or process or unit exclude each other, it can use any Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power Profit is required, summary and accompanying drawing) disclosed in each feature can or similar purpose identical, equivalent by offer alternative features come generation Replace.

Although in addition, it will be appreciated by those of skill in the art that some embodiments described herein include other embodiments In included some features rather than further feature, but the combination of the feature of be the same as Example does not mean in of the invention Within the scope of and form different embodiments.For example, in the following claims, times of embodiment claimed One of meaning mode can be used in any combination.

In addition, be described as herein can be by the processor of computer system or by performing for some in the embodiment Method or the combination of method element that other devices of the function are implemented.Therefore, with for implementing methods described or method The processor of the necessary instruction of element forms the device for implementing this method or method element.In addition, device embodiment Element described in this is the example of following device:The device is used to implement as in order to performed by implementing the element of the purpose of the invention Function.

Various technologies described herein can combine hardware or software, or combinations thereof is realized together.So as to the present invention Method and apparatus, or the process and apparatus of the present invention some aspects or part can take embedded tangible media, such as it is soft The form of program code (instructing) in disk, CD-ROM, hard disk drive or other any machine readable storage mediums, Wherein when program is loaded into the machine of such as computer etc, and when being performed by the machine, the machine becomes to put into practice this hair Bright equipment.

In the case where program code is performed on programmable computers, computing device generally comprises processor, processor Readable storage medium (including volatibility and nonvolatile memory and/or memory element), at least one input unit, and extremely A few output device.Wherein, memory is arranged to store program codes;Processor is arranged to according to the memory Instruction in the described program code of middle storage, the electric automobile for performing the present invention participates in the planing method of Demand Side Response.

By way of example and not limitation, computer-readable medium includes computer-readable storage medium and communication media.Calculate Machine computer-readable recording medium includes computer-readable storage medium and communication media.Computer-readable storage medium storage such as computer-readable instruction, The information such as data structure, program module or other data.Communication media is general modulated with carrier wave or other transmission mechanisms etc. Data-signal processed passes to embody computer-readable instruction, data structure, program module or other data including any information Pass medium.Any combination above is also included within the scope of computer-readable medium.

As used in this, unless specifically stated so, come using ordinal number " first ", " second ", " the 3rd " etc. Description plain objects are merely representative of the different instances for being related to similar object, and are not intended to imply that the object being so described must Must have the time it is upper, spatially, in terms of sequence or given order in any other manner.

Although describing the present invention according to the embodiment of limited quantity, above description, the art are benefited from It is interior it is clear for the skilled person that in the scope of the present invention thus described, it can be envisaged that other embodiments.Additionally, it should be noted that The language that is used in this specification primarily to readable and teaching purpose and select, rather than in order to explain or limit Determine subject of the present invention and select.Therefore, in the case of without departing from the scope and spirit of the appended claims, for this Many modifications and changes will be apparent from for the those of ordinary skill of technical field.For the scope of the present invention, to this The done disclosure of invention is illustrative and not restrictive, and it is intended that the scope of the present invention be defined by the claims appended hereto.

Claims (10)

1. a kind of electric automobile participates in the planing method of Demand Side Response, suitable for being performed in computing device, the electric automobile By polymerization stand control discharge and recharge, the polymerization station together determines the planning of the Demand Side Response, institute with autonomous system operator The method of stating includes:
Electric automobile cost model is set up, the electric automobile cost model includes realizing that electric automobile spare capacity expense is minimum The minimum cost function of change;
The operation cost model of autonomous system operator is set up, the operation cost model includes calculating the reality of regulation operating service The operation cost function of border energy cost;
System consumption model is set up, the system consumption model includes the consuming cost function for calculating conventional cost correspondence expense;
Corresponding constraints is constructed respectively to the electric automobile cost model, operation cost module and system consumption model;
With reference to the electric automobile cost model, operation cost model and system consumption model, to generate system cost planning mould Type;
The optimal solution of the system cost plan model is asked for, to optimize the planning that electric automobile participates in Demand Side Response.
2. the method as described in claim 1, the minimum cost function is determined with equation below:
<mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>A</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>C</mi> <mi>a</mi> <mrow> <mo>+</mo> <mi>A</mi> <mi>g</mi> <mi>r</mi> </mrow> </msubsup> <msubsup> <mi>x</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mrow> <mi>V</mi> <mn>2</mn> <mi>G</mi> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>C</mi> <mi>a</mi> <mrow> <mo>-</mo> <mi>A</mi> <mi>g</mi> <mi>r</mi> </mrow> </msubsup> <msubsup> <mi>x</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mrow> <mi>G</mi> <mn>2</mn> <mi>V</mi> </mrow> </msubsup> <mo>)</mo> </mrow> </mrow>
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>x</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mrow> <mi>G</mi> <mn>2</mn> <mi>V</mi> </mrow> </msubsup> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> <mo>,</mo> <msubsup> <mi>x</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mrow> <mi>V</mi> <mn>2</mn> <mi>G</mi> </mrow> </msubsup> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> </mrow> </mtd> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced>
Wherein, V2G represent electric automobile discharge, G2V to charging electric vehicle,It is that a-th of polymerization station offer V2G is standby Cost,It is the V2G capacity that a-th of polymerization station needs to provide in period t,It is that a-th of polymerization station offer G2V is standby Cost,Be a-th of polymerization station needs the G2V capacity that provides in period t, a=1 ..., and A, A is the sum of polymerization station Amount, t=1 ..., T, T is time total amount.
3. method as claimed in claim 1 or 2, the operation cost function is determined with equation below:
<mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>A</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>C</mi> <mi>t</mi> <mrow> <mi>D</mi> <mi>i</mi> <mi>s</mi> </mrow> </msubsup> <msub> <mi>b</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msubsup> <mi>C</mi> <mi>t</mi> <mrow> <mi>D</mi> <mi>c</mi> <mi>h</mi> </mrow> </msubsup> <msubsup> <mi>d</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>-</mo> </msubsup> <mo>)</mo> </mrow> </mrow>
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>b</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>&amp;le;</mo> <msubsup> <mi>x</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mrow> <mi>G</mi> <mn>2</mn> <mi>V</mi> </mrow> </msubsup> </mrow> </mtd> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced>
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>d</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>-</mo> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>x</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mrow> <mi>V</mi> <mn>2</mn> <mi>G</mi> </mrow> </msubsup> </mrow> </mtd> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced>
Wherein,It is that the G2V discounts of service, b are provided in period ta,tIt is G2V of a-th of polymerization station in the actual offer of t periods Spare capacity,It is the electric discharge cost of electric automobile in period t,It is V2G of a-th of polymerization station in the actual offer of t periods Spare capacity.
4. the method as any one of claim 1-3, the conventional cost include cost of electricity-generating expected from generating set, Removal of load cost and regenerative resource cut down cost, and the consuming cost function is determined with equation below:
<mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>C</mi> <mi>g</mi> <mrow> <mi>G</mi> <mi>e</mi> <mi>n</mi> </mrow> </msubsup> <msub> <mi>p</mi> <mrow> <mi>g</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msubsup> <mi>C</mi> <mi>k</mi> <mrow> <mi>U</mi> <mi>L</mi> </mrow> </msubsup> <msub> <mi>z</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msubsup> <mi>C</mi> <mi>k</mi> <mrow> <mi>C</mi> <mi>u</mi> <mi>r</mi> </mrow> </msubsup> <msub> <mi>c</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
Wherein,It is generating set g cost of electricity-generating, pg,tIt is the generated energy of generating set g in the t periods,It is k nodes Lose the unit punishment cost of load, zk,tIt is the load of t period internal losses,Be regenerative resource at node k reduction into This, ck,tIt is the regenerative resource reduction of k nodes in the t periods, k=1 ..., K, K is node total number.
5. the method as any one of claim 1-4, the constraints includes electric quantity balancing constraint, default technology about Beam, risk allow constraint, discharge and recharge constraint and spare capacity constraint.
6. method as claimed in claim 5, the operation cost model and consuming cost model meet electric quantity balancing constraint, institute Electric quantity balancing constraint is stated to represent with equation below:
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>L</mi> </munderover> <msub> <mi>H</mi> <mrow> <mi>l</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> <msub> <mi>f</mi> <mrow> <mi>l</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>p</mi> <mrow> <mi>g</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>z</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msubsup> <mi>d</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>-</mo> </msubsup> <mo>+</mo> <msub> <mi>&amp;phi;</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;psi;</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msubsup> <mi>d</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>+</mo> </msubsup> <mo>+</mo> <msub> <mi>c</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mtd> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced>
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>z</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mtd> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced>
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>c</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>&amp;phi;</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;psi;</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mtd> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>k</mi> <mo>,</mo> <mi>t</mi> </mrow> </mtd> </mtr> </mtable> </mfenced>
Wherein, Hl,kFor the incidence matrix coefficient at circuit l k nodes, value is -1,0 or 1, fl,tIt is circuit l within the t periods Electric current,It is the generated energy of wind power system j in the t periods, ψn,tIt is the generated energy of photovoltaic n in the t periods;λk,tIt is k in the t periods Load at node,For the charge volume of a-th of polymerization station in the t periods, l=1 ..., L, L is circuit sum.
7. a kind of electric automobile participates in the device for planning of Demand Side Response, suitable for residing in computing device, the electric automobile By polymerization stand control discharge and recharge, the polymerization station together determines the planning of the Demand Side Response, institute with autonomous system operator Stating device includes:
First sets up module, is adapted to set up electric automobile cost model, and the electric automobile cost model includes realizing electronic vapour The minimum cost function of car spare capacity cost minimization;
Second sets up module, is adapted to set up the operation cost model of autonomous system operator, and the operation cost model includes meter Calculate the operation cost function of the actual energy expense of regulation operating service;
3rd sets up module, is adapted to set up system consumption model, and the system consumption model includes the conventional cost correspondence of calculating and taken Consuming cost function;
Constraint module, suitable for distinguishing structure to the electric automobile cost model, operation cost module and system consumption model Make corresponding constraints;
Generation module, suitable for reference to the electric automobile cost model, operation cost model and system consumption model, being to generate System cost planning model;
Module is solved, the optimal solution suitable for asking for the system cost plan model participates in Demand-side sound to optimize electric automobile The planning answered.
8. a kind of computing device, including electric automobile as claimed in claim 7 participate in the device for planning of Demand Side Response.
9. a kind of computing device, including:
One or more processors;
Memory;And
One or more programs, wherein one or more of program storages are in the memory and are configured as by described one Individual or multiple computing devices, one or more of programs include being used to perform in the method according to claim 1 to 6 Either method instruction.
10. a kind of computer-readable recording medium for storing one or more programs, one or more of programs include instruction, The instruction is when executed by a computing apparatus so that in method of the computing device according to claim 1 to 6 Either method.
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