CN117035226A - Method and device for clearing distributed energy storage system - Google Patents

Method and device for clearing distributed energy storage system Download PDF

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Publication number
CN117035226A
CN117035226A CN202310897280.XA CN202310897280A CN117035226A CN 117035226 A CN117035226 A CN 117035226A CN 202310897280 A CN202310897280 A CN 202310897280A CN 117035226 A CN117035226 A CN 117035226A
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energy storage
energy
model
state
power
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顾慧杰
彭超逸
胡亚平
赵化时
李金�
马光
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China Southern Power Grid Co Ltd
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China Southern Power Grid Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch

Abstract

The application relates to a method and a device for a distributed energy storage system. The method comprises the following steps: acquiring user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost; according to energy storage parameters which change along with the energy state, an energy storage physical model is established, and constraint conditions of the energy state in power dispatching are determined; according to the user load parameters, the real-time electricity price, the energy storage cost and the energy storage parameters changing along with the energy state, a profit model of an energy storage holder in the electric power market is established based on a physical model, and the constraint condition of energy storage in electric power dispatching is determined; according to energy storage parameters, energy storage charge and discharge costs and the gain model which change along with the energy state, establishing an objective function of the power market clearing model and constraint conditions of the objective function so that the clearing result of the power market converges to a target value in iteration of the gain model and the clearing model; accordingly, the market benefit of each distributed energy storage holder can be effectively improved.

Description

Method and device for clearing distributed energy storage system
Technical Field
The present application relates to the field of distributed energy storage technologies, and in particular, to a method and apparatus for clearing a distributed energy storage system, a computer device, and a computer readable storage medium.
Background
With the increasing popularization of the distributed power generation technology and the continuous progress of the information communication technology, a transaction form based on distributed energy storage is gradually popular, and the energy transaction form allows owners of all energy storage to sell electricity to energy users directly, so that the utilization rate of energy storage at the user side is improved, and the high primary cost of installing the energy storage for all users is reduced.
Since most of the user-side energy storage is small-scale electrochemical energy storage, the parameter change in the process of charging and discharging each energy storage is not negligible. Therefore, how to combine the individual benefits of distributed energy storage and the social benefits of the electric power market becomes a current urgent problem to be solved.
Disclosure of Invention
Based on this, it is necessary to provide a method, an apparatus, a computer device and a computer readable storage medium for clearing a distributed energy storage system, which can effectively improve the market benefit of each distributed energy storage holder and efficiently and accurately describe the change of different energy storage parameters along with the energy state.
In a first aspect, the present application provides a method for clearing a distributed energy storage system. The method comprises the following steps:
acquiring electric power data, wherein the electric power data comprises user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost;
according to the energy storage parameters changing along with the energy state, an energy storage physical model is established, and constraint conditions of the energy state in power dispatching are determined;
according to the user load parameters, the real-time electricity price, the energy storage cost and the energy storage parameters changing along with the energy state, a profit model of an energy storage holder in an electric power market is built based on the physical model, and constraint conditions of energy storage in electric power dispatching are determined;
and establishing an objective function of an electric power market clearing model and a constraint condition of the objective function according to the energy storage parameter, the energy storage charge and discharge cost and the benefit model which change along with the energy state, so that the electric power market clearing result converges to a target value in iteration of the benefit model and the clearing model.
In one embodiment, the energy storage parameter that varies with energy state includes: charging and discharging efficiency of energy storage;
the physical model of energy storage includes:
wherein,for the initial value of the energy state of the corresponding energy storage, t is the current operation time of the electric power market, eta is the charge and discharge efficiency of the energy storage, I is the load current of the energy storage, U t Is the terminal voltage of the stored energy.
In one embodiment, the constraints on the energy state in power scheduling include:
wherein N is the number of stored energy forming distributed energy storage; t represents electric power cityThe run time of the field;respectively representing the lower energy state limit and the upper energy state limit of the ith energy storage, +.>For the charging power of the ith energy storage in the s-th section, deltat is the time step of charging and discharging the energy storage,/->For storing the discharge power in the s-th section.
In one embodiment, the user load parameters include: annual load data of users in the community-level microgrid; the energy storage cost comprises: cost coefficients of unit rated capacity and rated power of the stored energy; the energy storage parameters which change along with the energy state comprise: rated charge and discharge power of the stored energy;
the revenue model for the energy storage holder in the electricity market includes:
wherein p is b,t For the real-time electricity price of the node where the energy storage b is located at the time t, E des ,E tar Respectively representing an actual energy state and a target energy state at the final moment of energy storage scheduling, wherein lambda (E) is a quotation of the corresponding energy state;rated power for energy storage at node b, +.>For the rated capacity of the energy store at node b, +.>Representing the cost coefficients of the rated power and rated capacity of the corresponding energy storage, respectively.
In one embodiment, the constraint of energy storage in power scheduling includes:
wherein,a charging upper limit for storing energy on node b; />Representing the charging state of energy stored in the s-th section on the node b as a Boolean variable; />A discharge upper limit for the energy stored on node b; />Representing the discharge state of energy stored in the s-th section on the node b;E b ,/>representing the lower and upper limits, respectively, of the state of the stored energy at node b.
In one embodiment, the energy storage parameter that varies with energy state includes: rated charge and discharge power of the stored energy; the energy storage charge and discharge cost comprises: the cost coefficient of energy storage charging and discharging in the electric power market;
the objective functions of the power market clearing model include:
wherein N is g Indexing generators throughout the power market; c b,t The marginal cost of generator b at time t;generating power for the generator b at the time t; />Respectively representing the marginal cost of charging and discharging of the stored energy in the s-th energy state; />Respectively representing the charging and discharging power of the stored energy at the time t and the s-th section; lambda (lambda) b (E b,s,n ) For storing energy in the s-th section and in the energy state E b,s,n The charge and discharge loss coefficient at the time; v b,t Bidding price for the electric power market of the load; />A mid-scalar that is a load in the electricity market; pi is the total revenue of the distributed energy storage holder in the electricity market.
In one embodiment, the constraint of the objective function includes:
wherein,a load demand value for node b at time t; />Active power flow at time t for line l; />Generating power of the node b at the time t; />An upper power generation limit for the generator of node b; />An upper limit for the load demand of node b;an upper ramp limit for the rise in generator output at node b; />An upper ramp limit for a decrease in generator output at node b; />The upper limit of the tide of the line l; x is X l Is the reactance of line l; θ f(l),t ,θ t(l),t The voltage phase angles of the transmitting end and the receiving end of the line l are respectively; θ b,t A voltage phase angle of the node b at a time t; b=ref means that the node is a relaxed node and the corresponding phase angle is 0.
In a second aspect, the application further provides a clearing device of the distributed energy storage system. The device comprises:
the power data acquisition module is used for acquiring power data, wherein the power data comprises user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost;
the energy storage physical model module is used for establishing an energy storage physical model according to the energy storage parameters which change along with the energy state and determining constraint conditions of the energy state in power dispatching;
the profit model module is used for establishing a profit model of an energy storage holder in the electric power market based on the physical model according to the user load parameter, the real-time electricity price, the energy storage cost and the energy storage parameter changing along with the energy state, and determining constraint conditions of energy storage in electric power dispatching;
and the clearing model module is used for establishing an objective function of the electric power market clearing model and a constraint condition of the objective function according to the energy storage parameter, the energy storage charging and discharging cost and the income model which change along with the energy state so as to enable the clearing result of the electric power market to converge to a target value in the iteration of the income model and the clearing model.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring electric power data, wherein the electric power data comprises user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost;
according to the energy storage parameters changing along with the energy state, an energy storage physical model is established, and constraint conditions of the energy state in power dispatching are determined;
according to the user load parameters, the real-time electricity price, the energy storage cost and the energy storage parameters changing along with the energy state, a profit model of an energy storage holder in an electric power market is built based on the physical model, and constraint conditions of energy storage in electric power dispatching are determined;
and establishing an objective function of an electric power market clearing model and a constraint condition of the objective function according to the energy storage parameter, the energy storage charge and discharge cost and the benefit model which change along with the energy state, so that the electric power market clearing result converges to a target value in iteration of the benefit model and the clearing model.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring electric power data, wherein the electric power data comprises user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost;
according to the energy storage parameters changing along with the energy state, an energy storage physical model is established, and constraint conditions of the energy state in power dispatching are determined;
according to the user load parameters, the real-time electricity price, the energy storage cost and the energy storage parameters changing along with the energy state, a profit model of an energy storage holder in an electric power market is built based on the physical model, and constraint conditions of energy storage in electric power dispatching are determined;
and establishing an objective function of an electric power market clearing model and a constraint condition of the objective function according to the energy storage parameter, the energy storage charge and discharge cost and the benefit model which change along with the energy state, so that the electric power market clearing result converges to a target value in iteration of the benefit model and the clearing model.
In the method for clearing the distributed energy storage system, the electric power data is obtained, wherein the electric power data comprises a user load parameter, a real-time electricity price, an energy storage cost, an energy storage parameter changing along with an energy state and an energy storage charging and discharging cost, an energy storage physical model is built according to the energy storage parameter changing along with the energy state, constraint conditions of the energy state in the electric power dispatching are determined, a profit model of an energy storage holder in the electric power market is built according to the user load parameter, the real-time electricity price, the energy storage cost and the energy storage parameter changing along with the energy state, constraint conditions of an energy storage in the electric power dispatching are determined, and an objective function of the electric power market clearing model and constraint conditions of the objective function are built according to the energy storage parameter changing along with the energy state, the energy storage charging and discharging cost and the profit model, so that the clearing result of the electric power market converges to the objective value in iteration of the profit model and the clearing model.
Accordingly, the benefit model is used as an upper model, the clearing model is used as a lower model, the market benefit of each energy storage holder is maximized in the upper model, then the whole electric power market is cleared in the lower model so as to optimize the social benefit, and the clearing result of the whole electric power market is converged to an optimal value in the iteration of the upper model and the lower model. According to the embodiment of the application, the double-layer model is utilized, the upper-layer model is utilized to model the power generation and utilization behaviors of the distributed energy storage system in the power market, the lower-layer model is utilized to model the clearance of the distributed energy storage system after the distributed energy storage system participates in the power market, the whole power distribution network is ensured to run safely and stably, the market benefits of all distributed energy storage holders are improved, the social benefits are optimized, and the individual benefits of distributed energy storage and the social benefits of the power market are considered. In addition, the scheme of the embodiment of the application can accurately depict the charging and discharging behaviors of different energy storage based on the quotation of the energy state, and the double-layer model used gives consideration to the power generation and use requirements of all market participants and has higher use value.
Drawings
FIG. 1 is a schematic diagram of a distributed energy storage system participating in an electric market transaction in one embodiment;
FIG. 2 is a flow chart of a method of purging a distributed energy storage system according to one embodiment;
FIG. 3 is a schematic diagram of a flow of a distributed energy storage system participating in an electric power market clearing in one embodiment;
FIG. 4a is a diagram illustrating a bid amount of each energy producer when the total energy state is divided into 4 segments according to one embodiment;
FIG. 4b is a schematic diagram of charge-discharge revenue and cost curves for distributed energy storage based on energy status quotes in one embodiment;
fig. 5 is a schematic structural diagram of an outlet device of the distributed energy storage system in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for clearing the distributed energy storage system provided by the embodiment of the application can be applied to an application environment shown in fig. 1. In one embodiment, as shown in fig. 2, a method for clearing a distributed energy storage system is provided, and the scenario that the method is applied to the distributed energy storage system in fig. 1 to participate in electric power market transaction is taken as an example and illustrated, and the method includes the following steps 201 to 204:
step 201, obtaining electric power data, wherein the electric power data comprises user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost.
The distributed Energy storage system includes a plurality Of stored Energy, and the State Of Energy (SOE) refers to an Energy State Of the stored Energy. The stored state of charge, also known as the remaining charge, is SOC (State of Charge).
The power data may also include an upper grid transmission capacity limit, grid data, and energy storage charge-discharge losses. The power grid data comprises the connection relation between each micro power grid and the upper level power distribution grid, the resistance and reactance values of each branch and the upper limit of the transmission power of each branch. The energy storage charge-discharge loss comprises energy storage charge-discharge loss coefficients under different energy states. The user load parameters include annual load data of the users in each community-level microgrid, and the data acquisition interval is minimum 15 minutes. The real-time voltage adopts the national unified peak Gu Ping three-hour electricity price. The energy storage cost can be understood as the primary investment cost of energy storage, and mainly comprises the investment construction cost of each distributed energy storage, and the cost coefficient of unit rated capacity and rated power. The energy storage parameters which change along with the energy state mainly comprise rated charge and discharge power, charge and discharge efficiency and system capacity of energy storage.
Step 202, according to energy storage parameters changing along with the energy state, an energy storage physical model is established, and constraint conditions of the energy state in power dispatching are determined.
Referring to fig. 3, the embodiment of the present application may segment the offers of each distributed energy storage based on the energy status, and each segment offer of each distributed energy storage based on the energy status needs to be submitted to the market operators, so the embodiment of the present application analyzes the charging and discharging process of each distributed energy storage, and establishes a physical model describing the energy storage. Because the energy stored in the stored energy is limited, the amount of remaining energy directly affects the scheduling scheme of the stored energy. Illustratively, embodiments of the present application use energy states to characterize the remaining power of a shared centralized energy storage based on voltammetry. In order to maximize the charge-discharge flexibility of utilizing the shared energy storage, embodiments of the present application divide the energy state (state of charge) of the entire energy storage into multiple segments, e.g., S segments, and determine the energy state constraints in the energy storage schedule.
And 203, establishing a profit model of an energy storage holder in the electric power market based on a physical model according to the user load parameters, the real-time electricity price, the energy storage cost and the energy storage parameters changing along with the energy state, and determining constraint conditions of energy storage in electric power dispatching.
Because the charge and discharge of the shared energy storage can be in different time periods, the electric energy prices of the corresponding time periods can be inconsistent, and the shared energy storage model can be charged and discharged at low level in different time periods, so that the income of the energy storage in the electric power market is maximized. An individual benefit maximization model for each energy storage holder may be built based on the cost of the energy storage being proportional to its rated capacity and rated power. The energy state of the residual power needs to be considered in the charging and discharging process of the energy storage device, so that the embodiment of the application establishes the constraint condition of the energy storage device in the whole dispatching process.
And 204, establishing an objective function of the power market clearing model and constraint conditions of the objective function according to the energy storage parameters, the energy storage charge and discharge cost and the gain model which change along with the energy state, so that the clearing result of the power market converges to a target value in iteration of the gain model and the clearing model.
Fig. 4a and fig. 4b are schematic diagrams of energy storage section bidding of a distributed energy storage system according to an embodiment of the present application, and refer to fig. 4a and fig. 4b, and analyze a model of the whole electric energy market, so as to maximize the social overall benefit of each distributed energy storage holder and other power generators in consideration of the participation of thermal power units in the market. After the distributed energy storage participates in the market, the whole market needs to maximize the social benefit, so that the embodiment of the application establishes a corresponding model objective function, and under the participation of the distributed energy storage, the embodiment of the application establishes the constraint condition of the whole electric power market.
In the embodiment of the application, the income model is used as an upper model, the clearing model is used as a lower model, each energy storage holder maximizes own market income in the upper model, then the sectional quotation based on the energy state is transmitted to the market operator, the market operator clears the whole electric power market in the lower model to optimize the social benefit, and the clearing result of the whole electric power market converges to an optimal value in the iteration of the upper model and the lower model. According to the embodiment of the application, the double-layer model is utilized, the upper-layer model models the power generation and utilization behaviors of the distributed energy storage system in the power market, and the lower-layer model models the clearance of the distributed energy storage system after the distributed energy storage system participates in the power market, so that the safe and stable operation of the whole power distribution network is ensured, and the market benefits of all distributed energy storage holders are improved. The quotation based on the energy state can accurately describe the charging and discharging behaviors of different energy storage, and the double-layer model used gives consideration to the power generation requirements of all market participants, so that the method has higher use value.
The method for clearing the distributed energy storage system is suitable for a plurality of distributed energy storage to participate in electric market transaction, maximizes market benefits of each user from the perspective of a distributed energy storage holder, considers energy storage parameters in different energy states based on segmented energy states, clears the whole market from the perspective of a market operator, ensures social benefits of all market participants, provides a new technical scheme for the distributed energy storage to participate in the electric market, and effectively improves the in-situ new energy consumption rate.
In one embodiment, the energy storage parameter as a function of energy state comprises: charging and discharging efficiency of energy storage; in step 202, the remaining energy of the stored energy is characterized by adopting an energy state based on a voltammetric integration method, and a physical model of the stored energy comprises:
wherein,for the initial value of the energy state of the corresponding energy storage, t is the current operation time of the electric power market, eta is the charge and discharge efficiency of the energy storage, I is the load current of the energy storage, U t Is the terminal voltage of the stored energy.
Optionally, the constraint condition of the energy state in the power scheduling includes:
wherein N is the number of stored energy forming distributed energy storage; t represents the run time of the whole power market;respectively representing the lower energy state limit and the upper energy state limit of the ith energy storage, +.>For the charging power of the ith energy storage in the s-th section, deltat is the time step of charging and discharging the energy storage,/->For storing the discharge power in the s-th section.
In one embodiment, the user load parameters include: annual load data of users in the community-level microgrid; the energy storage cost includes: cost coefficients of unit rated capacity and rated power of the stored energy; the energy storage parameters that vary with energy state include: rated charge and discharge power of the stored energy; the revenue model for the energy storage holder in the power market in step 203 includes:
the individual benefit maximization model for each energy storage holder includes:
wherein p is b,t For the real-time electricity price of the node where the energy storage b is located at the time t, E des ,E tar Respectively representing an actual energy state and a target energy state at the final moment of energy storage scheduling, wherein lambda (E) is a quotation of the corresponding energy state;rated power for energy storage at node b, +.>For the rated capacity of the energy store at node b, +.>Representing the cost coefficients of the rated power and rated capacity of the corresponding energy storage, respectively.
Optionally, the constraint condition of energy storage in power dispatching includes:
wherein,a charging upper limit for storing energy on node b; u (u) t,s Taking 0 or 1 as a Boolean variable; />Representing the charging state of energy stored in the s-th section on the node b as a Boolean variable; />A discharge upper limit for the energy stored on node b; />Representing the discharge state of energy stored in the s-th section on the node b; e (E) b ,/>Representing the lower and upper limits, respectively, of the state of the stored energy at node b.
In one embodiment, the energy storage parameter as a function of energy state comprises: rated charge and discharge power of the stored energy; the energy storage charge and discharge cost comprises: the cost coefficient of energy storage charging and discharging in the electric power market; the objective function (13) of the power market clearing model in step 204 includes:
wherein N is g Indexing generators throughout the power market; c b,t The marginal cost of generator b at time t;generating power for the generator b at the time t; />Respectively representing the marginal cost of charging and discharging of the stored energy in the s-th energy state; />Respectively representing the charging and discharging power of the stored energy at the time t and the s-th section; lambda (lambda) b (E b,s,n ) For storing energy in the s-th section and in the energy state E b,s,n The charge and discharge loss coefficient at the time; v b,t Bidding price for the electric power market of the load; />A mid-scalar that is a load in the electricity market; pi is the total revenue of the distributed energy storage holder in the electricity market.
Optionally, the constraint condition of the objective function includes:
wherein,a load demand value for node b at time t; />Active power flow at time t for line l; />Generating power of the node b at the time t; />An upper power generation limit for the generator of node b; />An upper limit for the load demand of node b;an upper ramp limit for the rise in generator output at node b; />An upper ramp limit for a decrease in generator output at node b; />The upper limit of the tide of the line l; x is X l Is the reactance of line l; θ f(l),t ,θ t(l),t The voltage phase angles of the transmitting end and the receiving end of the line l are respectively; θ b,t A voltage phase angle of the node b at a time t; b=ref means that the node is a relaxed node and the corresponding phase angle is 0.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a discharging device of the distributed energy storage system for realizing the discharging method of the distributed energy storage system. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the clearing device of one or more distributed energy storage systems provided below may refer to the limitation of the clearing method of the distributed energy storage system hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 5, there is provided a purge device of a distributed energy storage system, comprising: a power data acquisition module 10, an energy storage physical model module 20, a revenue model module 30, and a clearing model module 40. Wherein:
the power data acquisition module 10 is configured to acquire power data, where the power data includes a user load parameter, a real-time power price, an energy storage cost, an energy storage parameter that varies with an energy state, and an energy storage charging and discharging cost;
the energy storage physical model module 20 is used for establishing an energy storage physical model according to energy storage parameters which change along with the energy state, and determining constraint conditions of the energy state in power dispatching;
the profit model module 30 is configured to establish a profit model of an energy storage holder in the electric power market based on a physical model according to a user load parameter, a real-time electricity price, an energy storage cost and an energy storage parameter that varies with an energy state, and determine constraint conditions of energy storage in electric power dispatching;
the clearing model module 40 is configured to establish an objective function of the electric power market clearing model and a constraint condition of the objective function according to the energy storage parameter, the energy storage charge-discharge cost and the benefit model that vary with the energy state, so that the clearing result of the electric power market converges to a target value in iteration of the benefit model and the clearing model.
In one embodiment, the energy storage parameter as a function of energy state comprises: charging and discharging efficiency of energy storage; the stored energy physical model established by the stored energy physical model module 20 includes:
wherein,for the initial value of the energy state of the corresponding energy storage, t is the current operation time of the electric power market, eta is the charge and discharge efficiency of the energy storage, I is the load current of the energy storage, U t Is the terminal voltage of the stored energy.
Constraints on the energy state in the power schedule determined by the energy storage physical model module 20 include:
wherein N is the number of stored energy forming distributed energy storage; t represents the run time of the power market;
respectively representing the lower energy state limit and the upper energy state limit of the ith energy storage, +.>For the charging power of the ith energy storage in the s-th section, deltat is the time step of charging and discharging the energy storage,/->For storing the discharge power in the s-th section.
In one embodiment, the user load parameters include: annual load data of users in the community-level microgrid; the energy storage cost includes: cost coefficients of unit rated capacity and rated power of the stored energy; the energy storage parameters that vary with energy state include: rated charge and discharge power of the stored energy; the revenue model established by the revenue model module 30 for the energy storage holder in the power market includes:
wherein p is b,t For the real-time electricity price of the node where the energy storage b is located at the time t, E des ,E tar Respectively representing an actual energy state and a target energy state at the final moment of energy storage scheduling, wherein lambda (E) is a quotation of the corresponding energy state;rated power for energy storage at node b, +.>For the rated capacity of the energy store at node b, +.>Representing the cost coefficients of the rated power and rated capacity of the corresponding energy storage, respectively.
The constraints for energy storage in the power schedule determined by the revenue model module 30 include:
wherein,a charging upper limit for storing energy on node b; />Representing the charging state of energy stored in the s-th section on the node b as a Boolean variable; />A discharge upper limit for the energy stored on node b; />Representing the discharge state of energy stored in the s-th section on the node b;E b ,/>representing the lower and upper limits, respectively, of the state of the stored energy at node b.
In one embodiment, the energy storage parameter as a function of energy state comprises: rated charge and discharge power of the stored energy; the energy storage charge and discharge cost comprises: the cost coefficient of energy storage charging and discharging in the electric power market;
the objective functions of the electric power market skim model established by the skim model module 40 include:
wherein N is g Indexing generators throughout the power market; c b,t The marginal cost of generator b at time t;generating power for the generator b at the time t; />Respectively representing the marginal cost of charging and discharging of the stored energy in the s-th energy state; />Respectively representing the charging and discharging power of the stored energy at the time t and the s-th section; lambda (lambda) b (E b,s,n ) For storing energy in the s-th section and in the energy state E b,s,n The charge and discharge loss coefficient at the time; v b,t Bidding price for the electric power market of the load; />A mid-scalar that is a load in the electricity market; pi is the total revenue of the distributed energy storage holder in the electricity market.
Constraints of the objective function determined by the ex-definition model module 40 include:
/>
wherein,a load demand value for node b at time t; />Active power flow at time t for line l; />Generating power of the node b at the time t; />An upper power generation limit for the generator of node b; />An upper limit for the load demand of node b;an upper ramp limit for the rise in generator output at node b; />An upper ramp limit for a decrease in generator output at node b; f is the upper limit of the tide of the line l; x is X l Is the reactance of line l; θ f(l),t ,θ t(l),t The voltage phase angles of the transmitting end and the receiving end of the line l are respectively; θ b,t A voltage phase angle of the node b at a time t; b=ref indicates that the node is a relaxed node and the corresponding phase angle is 0.
The modules in the clearing device of the distributed energy storage system can be all or partially implemented by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims (10)

1. A method of clearing a distributed energy storage system, the method comprising:
acquiring electric power data, wherein the electric power data comprises user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost;
according to the energy storage parameters changing along with the energy state, an energy storage physical model is established, and constraint conditions of the energy state in power dispatching are determined;
according to the user load parameters, the real-time electricity price, the energy storage cost and the energy storage parameters changing along with the energy state, a profit model of an energy storage holder in an electric power market is built based on the physical model, and constraint conditions of energy storage in electric power dispatching are determined;
and establishing an objective function of an electric power market clearing model and a constraint condition of the objective function according to the energy storage parameter, the energy storage charge and discharge cost and the benefit model which change along with the energy state, so that the electric power market clearing result converges to a target value in iteration of the benefit model and the clearing model.
2. The method of claim 1, wherein the energy storage parameter as a function of energy state comprises: charging and discharging efficiency of energy storage;
the physical model of energy storage includes:
wherein,for the initial value of the energy state of the corresponding energy storage, t is the current operation time of the electric power market, eta is the charge and discharge efficiency of the energy storage, I is the load current of the energy storage, U t Is the terminal voltage of the stored energy.
3. The method of claim 2, wherein the constraint on the energy state in the power schedule comprises:
wherein N is the number of stored energy forming distributed energy storage; t represents operation of the electric marketTime;respectively representing the lower energy state limit and the upper energy state limit of the ith energy storage, +.>For the charging power of the ith energy storage in the s-th section, deltat is the time step of charging and discharging the energy storage,/->For storing the discharge power in the s-th section.
4. The method of claim 1, wherein the user load parameters comprise: annual load data of users in the community-level microgrid; the energy storage cost comprises: cost coefficients of unit rated capacity and rated power of the stored energy; the energy storage parameters which change along with the energy state comprise: rated charge and discharge power of the stored energy;
the revenue model for the energy storage holder in the electricity market includes:
wherein p is b,t For the real-time electricity price of the node where the energy storage b is located at the time t, E des ,E tar Respectively representing an actual energy state and a target energy state at the final moment of energy storage scheduling, wherein lambda (E) is a quotation of the corresponding energy state;rated power for energy storage at node b, +.>For the rated capacity of the energy store at node b, +.>Representing the cost coefficients of the rated power and rated capacity of the corresponding energy storage, respectively.
5. The method of claim 4, wherein the constraint of energy storage in power scheduling comprises:
wherein,a charging upper limit for storing energy on node b; />Representing the charging state of energy stored in the s-th section on the node b as a Boolean variable; />A discharge upper limit for the energy stored on node b; />Representing the discharge state of energy stored in the s-th section on the node b; e (E) b ,Representing the lower and upper limits, respectively, of the state of the stored energy at node b.
6. The method of claim 1, wherein the energy storage parameter as a function of energy state comprises: rated charge and discharge power of the stored energy; the energy storage charge and discharge cost comprises: the cost coefficient of energy storage charging and discharging in the electric power market;
the objective functions of the power market clearing model include:
wherein N is g Indexing generators throughout the power market; c b,t The marginal cost of generator b at time t;generating power for the generator b at the time t; />Respectively representing the marginal cost of charging and discharging of the stored energy in the s-th energy state; />Respectively representing the charging and discharging power of the stored energy at the time t and the s-th section; lambda (lambda) b (E b,s,n ) For storing energy in the s-th section and in the energy state E b,s,n The charge and discharge loss coefficient at the time; v b,t Bidding price for the electric power market of the load; />A mid-scalar that is a load in the electricity market; pi is the total revenue of the distributed energy storage holder in the electricity market.
7. The method of claim 6, wherein the constraints of the objective function include:
wherein,a load demand value for node b at time t; />Active power flow at time t for line l; />Generating power of the node b at the time t; />An upper power generation limit for the generator of node b; />An upper limit for the load demand of node b; />Climbing for generator output rise at node bAn upper slope limit; />An upper ramp limit for a decrease in generator output at node b; />The upper limit of the tide of the line l; x is X l Is the reactance of line l; θ f(l),t ,θ t(l),t The voltage phase angles of the transmitting end and the receiving end of the line l are respectively; θ b,t A voltage phase angle of the node b at a time t; b=ref means that the node is a relaxed node and the corresponding phase angle is 0.
8. A purge device for a distributed energy storage system, the device comprising:
the power data acquisition module is used for acquiring power data, wherein the power data comprises user load parameters, real-time electricity price, energy storage cost, energy storage parameters changing along with energy state and energy storage charging and discharging cost;
the energy storage physical model module is used for establishing an energy storage physical model according to the energy storage parameters which change along with the energy state and determining constraint conditions of the energy state in power dispatching;
the profit model module is used for establishing a profit model of an energy storage holder in the electric power market based on the physical model according to the user load parameter, the real-time electricity price, the energy storage cost and the energy storage parameter changing along with the energy state, and determining constraint conditions of energy storage in electric power dispatching;
and the clearing model module is used for establishing an objective function of the electric power market clearing model and a constraint condition of the objective function according to the energy storage parameter, the energy storage charging and discharging cost and the income model which change along with the energy state so as to enable the clearing result of the electric power market to converge to a target value in the iteration of the income model and the clearing model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
CN202310897280.XA 2023-07-20 2023-07-20 Method and device for clearing distributed energy storage system Pending CN117035226A (en)

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