CN114997467B - Energy storage optimization configuration method considering state transition model - Google Patents

Energy storage optimization configuration method considering state transition model Download PDF

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CN114997467B
CN114997467B CN202210494134.8A CN202210494134A CN114997467B CN 114997467 B CN114997467 B CN 114997467B CN 202210494134 A CN202210494134 A CN 202210494134A CN 114997467 B CN114997467 B CN 114997467B
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energy storage
frequency modulation
power
period
state
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CN114997467A (en
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王满商
张子阳
赵琛胤
马嵩阳
李若冰
王吉
侯超
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State Grid Jiangsu Electric Power Co ltd Zhenjiang Power Supply Branch
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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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses an energy storage optimization configuration method considering a state transition model, which solves the problem that the randomness of an energy storage device participating in a primary frequency modulation auxiliary service signal reduces the accuracy of configuration energy storage. According to the invention, a mathematical model with maximum benefit and optimal frequency modulation effect is obtained under the condition that energy storage participates in frequency modulation auxiliary service is established so as to realize configuration of the energy storage, the mathematical model takes the punishment of the power deviation amount into consideration, and the constraint of primary frequency modulation effect is taken into consideration; and secondly, the energy storage optimization configuration comprises a large number of random, discrete and logic variable problems, the invention adopts a Markov decision process to describe the state transfer characteristics of the energy storage under the frequency modulation, can accurately simulate the energy storage frequency modulation signals, further adopts algorithms such as a dynamic programming method and the like to solve, obtains more reasonable energy storage configuration, and can effectively improve the energy storage economy and the frequency modulation performance.

Description

Energy storage optimization configuration method considering state transition model
Technical Field
The invention relates to an energy storage optimization configuration method considering a state transition model, and belongs to the technical field of new energy.
Background
Along with the continuous improvement of the permeability of new energy accessing to the power grid, the power system is gradually evolving into a power system mainly based on new energy. However, randomness and fluctuation of new energy output often lead to poor frequency modulation effect of the system, and the problem of broadband oscillation frequently occurs after the new energy is connected into the power grid, so that the risk of unsafe and stable operation of the power grid is increased. The energy storage system has the advantages of bidirectional power regulation, high response speed, flexible charge and discharge and the like, and can assist in active control of new energy and improve the frequency characteristic of the system. Therefore, the new energy station configures a certain proportion of energy storage according to relevant regulations, and the output capacity and the energy utilization rate of the new energy are improved.
Energy storage is one of key technologies for promoting efficient utilization of new energy and supporting construction of a novel power system. Along with the rapid development of new energy power generation technology, the energy storage configuration is also continuously increased so as to improve the output capacity and the energy utilization rate of new energy. With the continuous improvement of the permeability of the new energy station, in order to improve the stability of the power system, the power system network source coordination technical specification prescribes that the new energy station should have primary frequency modulation capability. The mode of realizing primary frequency modulation of the new energy station mainly comprises two modes of retaining active reserve and configuring energy storage, and the mode of retaining active reserve can lead to wind abandoning and light abandoning, so that the output capacity of the new energy is reduced, and the fluctuation of the output of the new energy also reduces the frequency modulation effect of the new energy. Therefore, the new energy station is configured with certain energy storage to share frequency modulation, so that the frequency modulation effect is improved, and the new energy station becomes a hotspot for industrial research. In the prior art, the randomness of the primary frequency modulation signal is considered, and the configuration of energy storage is realized by dividing a system time line into a series of primary frequency modulation action and non-action time intervals and modeling the interval duration by using a probability cumulative function. However, the influence of the current primary frequency modulation on the next primary frequency modulation is not considered, the reasonable energy storage capacity is difficult to effectively configure, and a large number of randomness, dispersion and logic variables exist in the mathematical model, so that the technical problems of low solving efficiency, unstable solving quality and the like easily occur.
Disclosure of Invention
The invention aims to provide an energy storage optimizing configuration method considering a state transition model, solves the problem that the randomness of an energy storage device participating in primary frequency modulation auxiliary service signals reduces the accuracy of configuration energy storage, and realizes reasonable configuration energy storage and maximum benefit of energy storage frequency modulation and frequency modulation effect optimization.
The aim of the invention is realized by the following technical scheme:
an energy storage optimization configuration method considering a state transition model comprises the following steps:
the mathematical model with maximum benefit and optimal frequency modulation effect is established under the condition that energy storage participates in frequency modulation auxiliary service is established as follows:
wherein N is the scene number; mu (mu) n Probability of being the nth scene; w (w) bs,n Peak valley arbitrage benefit for energy storage; w (w) fr,n The energy storage is participated in the benefits of primary frequency modulation auxiliary service; w (w) inv,n 、w om,n The investment cost and the operation maintenance cost of energy storage are respectively; w (w) u,n Bias punishment for energy storage participation in primary frequency modulation; m is the scene number when the absolute value of the frequency deviation is greater than 0.033, and the frequency deviation is the difference between the frequency obtained by the system after the frequency adjustment and the frequency of 50 Hz; Δf n A frequency deviation having an absolute value greater than 0.033;
(1) Peak Gu Taoli benefit
Wherein T is the transaction ending period;respectively sharing the discharge power and the charging power of the energy storage at the t-period peak Gu Taoli of the nth scene; />Respectively sharing the discharge electricity price and the charge electricity price of the energy storage in the t period of the nth scene;
(2) Frequency modulation auxiliary service benefit
In the method, in the process of the invention,the price of the frequency modulation capacity and the price of the frequency modulation mileage are respectively; Δt is the average duration of the frequency modulation mileage;the method comprises the steps of storing the power which participates in primary frequency modulation in the t period of an nth scene;
(3) Environmental benefit
The energy storage participates in primary frequency modulation service, so that the generated energy of a conventional unit can be effectively reduced, and the generated electricity emission of the conventional unit is reduced, and the reduced environmental treatment cost is the environmental benefit that the energy storage participates in primary frequency modulation:
in which Q n,l The first pollutant emission amount is generated for conventional coal burning power generation; r is R n,l Cost per unit of environmental load for the first pollutant; l is the total number of discharged pollutants;the discharge quantity stored in the nth scene is used as the energy;
(4) Energy storage investment cost:
wherein, c p 、c e The unit power cost and the capacity cost of energy storage are respectively; θ rate,n 、S rate,n Respectively the maximum charge and discharge power and capacity of the stored energy; r is the annual rate of funds; y is the life cycle of the stored energy;
(5) Energy storage operation maintenance cost:
w om,n ＝c om ·θ rate,n
wherein, c om Maintenance cost for unit operation of energy storage;
(6) Bias penalty for energy storage participation in primary frequency modulation
In the method, in the process of the invention,the power is respectively adjusted up and down for the requirements of the power grid when the power grid participates in frequency modulation in the t period of the nth scene; /> Penalty coefficients of energy storage participating in frequency modulation at the nth time period of the nth scene under the condition of up-regulation power and down-regulation power deficiency power are respectively obtained;
the constraint conditions of energy storage participation in frequency modulation are as follows:
(1) Power balance constraint
In the method, in the process of the invention,power is demanded for the load grid at the t-th period of the nth scenario;
(2) Energy storage power constraint
In θ max Maximum output active power for stored energy;
(3) Power constraint of up-down frequency modulation of energy storage
In the method, in the process of the invention,respectively regulating up-regulating power and down-regulating power when the nth time period of the energy storage nth scene participates in primary frequency modulation, and providing that a positive frequency modulation signal represents up-regulating, wherein the energy storage system needs to increase discharge power or reduce charging power, and a negative frequency modulation signal represents down-regulating, and the energy storage system needs to reduce discharge power or increase charging power;
(4) Capacity constraint of energy storage:
S min ≤S n,t,k+1 ≤S max
S min ≤S n,t ≤S max
wherein S is n,t,k+1 、S n,t,k The capacity of the (k+1) th and the (k) th signal period stored in the (t) th period of the (n) th scene；Respectively storing output power and charging power of a kth signal period of a kth period of an nth scene; s is S min 、S max Respectively storing energy with minimum capacity and maximum capacity; k represents the total signal period contained in the nth scene; η (eta) d 、η c Respectively discharging efficiency and charging efficiency of energy storage; Δk is the period from the kth signal period to the kth+1th signal period; Δt is a period from a kth signal period of an nth scene to a 1 st signal period of an n+1th scene;
the above-mentioned problems of a large number of random, discrete and logical variables contained in the energy storage optimization configuration are described as a markov decision process, the basic elements of which are as follows:
(1) State variables
Capacity S per moment of energy storage t Frequency modulation electric quantitySet as a state variable, expressed as:
wherein H is t Is a state variable;
(2) Decision variables
Decision variable x t Including frequency modulated electricityStored charge power->And discharge power->Rated power θ of energy storage rate,n And rated capacity S rate,n
(3) Equation of state transition
State transition equations are used to characterize the transition process from one state to the next, the state transition of stored energy is described as:
wherein S is t,1 、S t+1,1 Respectively representing the state quantity under the 1 st signal period under the t period and the state quantity under the 1 st signal period under the t+1 period; ΔS t,k Representing a state quantity at a kth signal period at a t-th period;
(4) External information process
External information Process W t Including the output theta of new energy t c Increment between adjacent primary frequency modulation signalsLoad demand->The specific description is as follows:
(5) Instant benefit function
The instant benefit function R is defined as the benefit of a certain decision made by a certain state for a certain period of time:
R(H t ,x t )＝w bs,n +w f,n -w inv,n -w om,n -w u,n
(6) Optimum value function
The optimal value function of the energy storage participation frequency modulation auxiliary service in the market is defined as the expected income from the beginning of the period to the end of all the periods, and the expression is as follows:
wherein Ω is a policy set composed of decisions at each stage; pi is the decision to transition the determined state to the next state;for a decision value given a state variable, E represents a revenue expectation;
the optimal value function is expressed as bellman optimality equation:
in the method, in the process of the invention,is shown in state H t Taking policy->After reaching the next state H t+1 Conditional probability of (2);
and finally, solving the problem of the Markov decision process to obtain the configuration of the stored energy.
The object of the invention can be further achieved by the following technical measures:
according to the energy storage optimization configuration method considering the state transition model, the dynamic programming method is not influenced by the non-convex, nonlinear, discrete and other properties of the model, and is convenient for processing logic variables, so that the problem of a Markov decision process is solved by adopting the dynamic programming method, and the configuration of energy storage is obtained.
According to the energy storage optimization configuration method considering the state transition model, a value function algorithm is adopted to solve the problem of the Markov decision process.
According to the energy storage optimization configuration method considering the state transition model, the problem of the Markov decision process is solved by adopting a stochastic simulation method.
According to the energy storage optimization configuration method considering the state transition model, the problem of the Markov decision process is solved by adopting a strategy search algorithm.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention establishes an optimal configuration model of the energy storage participated primary frequency modulation auxiliary service, wherein punishment of the power deviation amount is considered, and constraint of primary frequency modulation effect is considered, so that the frequency modulation effect can be effectively improved;
(2) The energy storage optimization configuration comprises a large number of random, discrete and logic variable problems, the state transfer characteristics of the energy storage under the frequency modulation are described by adopting a Markov decision process, the energy storage frequency modulation signals can be accurately simulated, and the energy storage configuration result can be accurately obtained.
(3) According to the invention, the energy storage is optimized to participate in primary frequency modulation of the power grid, so that the safe and stable operation capacity of the power grid can be effectively improved, and the energy utilization rate of the new energy station can be improved.
Detailed Description
The invention will be further illustrated with reference to specific examples.
The invention reduces the accuracy of energy storage configuration and the economical efficiency of energy storage aiming at the randomness of the energy storage participating in primary frequency modulation auxiliary service signals, and provides an energy storage optimizing configuration method considering a state transition model, which comprises the following steps:
establishing a mathematical model with maximum benefit and optimal frequency modulation effect under the condition that energy storage participates in frequency modulation auxiliary service, wherein the optimal frequency modulation effect means that: the primary frequency modulation time is generally 30 s-1 min, and the change range of the power grid frequency exceeds a dead zone, namely, Δf is more than or equal to 0.033Hz or Δf is less than or equal to 0.033Hz (Δf=f-50, wherein f is the power grid frequency), and the energy storage regulates the power grid frequency by generating or absorbing active power; if the grid frequency variation is within the dead zone, the energy storage does not regulate the grid frequency.
If the power grid frequency exceeds the dead zone, the energy storage needs to adjust the power grid frequency to restore the power grid frequency to the dead zone, if the energy storage adjusts the primary frequency of the power grid, if the power grid frequency changes are all in the dead zone, the frequency modulation effect is best; it is also possible that the frequency variation of part of the time period is outside the dead zone, the smaller the frequency variation range is, the better, i.e. the optimum frequency modulation performance is achieved.
The mathematical model is as follows:
wherein N is the scene number; mu (mu) n Probability of being the nth scene; w (w) bs,n Peak valley arbitrage benefit for energy storage; w (w) fr,n The energy storage is participated in the benefits of primary frequency modulation auxiliary service; w (w) inv,n 、w om,n The investment cost and the operation maintenance cost of energy storage are respectively; w (w) u,n Bias punishment for energy storage participation in primary frequency modulation; if the frequency change is within the dead zone after the energy storage is regulated under the condition that the energy storage participates in primary frequency regulation of the power grid, no punishment and assessment are carried out; if the frequency change is still outside the dead zone after the energy storage is regulated, punishment and assessment are carried out on the energy storage part;
m is the scene number when the absolute value of the frequency deviation is greater than 0.033, and the frequency deviation is the difference between the frequency obtained by the system after the frequency adjustment and the frequency of 50 Hz; Δf n A frequency deviation having an absolute value greater than 0.033;
(1) Peak Gu Taoli benefit
Wherein T is the transaction ending period;respectively are provided withThe discharge power and the charge power of the peak Gu Taoli of the nth period in the nth scene are shared for energy storage; />Respectively sharing the discharge electricity price and the charge electricity price of the energy storage in the t period of the nth scene;
(2) Frequency modulation auxiliary service benefit
In the method, in the process of the invention,the price of the frequency modulation capacity and the price of the frequency modulation mileage are respectively; Δt is the average duration of the frequency modulation mileage;the method comprises the steps of storing the power which participates in primary frequency modulation in the t period of an nth scene;
(3) Environmental benefit
The energy storage participates in primary frequency modulation service, so that the generated energy of a conventional unit can be effectively reduced, and the generated electricity emission of the conventional unit is reduced, and the reduced environmental treatment cost is the environmental benefit that the energy storage participates in primary frequency modulation:
in which Q n,l The first pollutant emission amount is generated for conventional coal burning power generation; r is R n,l Cost per unit of environmental load for the first pollutant; l is the total number of discharged pollutants;the discharge quantity stored in the nth scene is used as the energy;
(4) Energy storage investment cost:
wherein, c p 、c e The unit power cost and the capacity cost of energy storage are respectively; θ rate ,n、S rate,n Respectively the maximum charge and discharge power and capacity of the stored energy; r is the annual rate of funds; y is the life cycle of the stored energy;
(5) Energy storage operation maintenance cost:
w om,n ＝c om ·θ rate,n
wherein, c om Maintenance cost for unit operation of energy storage;
(6) Bias penalty for energy storage participation in primary frequency modulation
In the method, in the process of the invention,the power is respectively adjusted up and down for the requirements of the power grid when the power grid participates in frequency modulation in the t period of the nth scene; /> Penalty coefficients of energy storage participating in frequency modulation at the nth time period of the nth scene under the condition of up-regulation power and down-regulation power deficiency power are respectively obtained;
the constraint conditions of energy storage participation in frequency modulation are as follows:
(1) Power balance constraint
In the method, in the process of the invention,power is demanded for the load grid at the t-th period of the nth scenario;
(2) Energy storage power constraint
In θ max Maximum output active power for stored energy;
(3) Power constraint of up-down frequency modulation of energy storage
In the method, in the process of the invention,respectively regulating up-regulating power and down-regulating power when the nth time period of the energy storage nth scene participates in primary frequency modulation, and providing that a positive frequency modulation signal represents up-regulating, wherein the energy storage system needs to increase discharge power or reduce charging power, and a negative frequency modulation signal represents down-regulating, and the energy storage system needs to reduce discharge power or increase charging power;
(4) Capacity constraint of energy storage:
S min ≤S n,t,k+1 ≤S max
S min ≤S n,t ≤S max
wherein S is n,t,k+1 、S n,t,k The capacity of the k+1 and k signal periods stored in the t period of the nth scene;respectively storing output power and charging power of a kth signal period of a kth period of an nth scene; s is S min 、S max Respectively storing energy with minimum capacity and maximum capacity; k represents the total signal period contained in the nth scene; η (eta) d 、η c Respectively discharging efficiency and charging efficiency of energy storage; Δk is the period from the kth signal period to the kth+1th signal period; Δt is a period from a kth signal period of an nth scene to a 1 st signal period of an n+1th scene;
the above-mentioned problems of a large number of random, discrete and logical variables contained in the energy storage optimization configuration are described as a markov decision process, the basic elements of which are as follows:
(1) State variables
Capacity S per moment of energy storage t Frequency modulation electric quantitySet as a state variable, expressed as:
wherein H is t Is a state variable;
(2) Decision variables
Decision variable x t Including frequency modulated electricityStored charge power->And discharge power->Rated power θ of energy storage rate,n And rated capacity S rate,n
(3) Equation of state transition
State transition equations are used to characterize the transition process from one state to the next, the state transition of stored energy is described as:
wherein S is t,1 、S t+1,1 Respectively representing the state quantity under the 1 st signal period under the t period and the state quantity under the 1 st signal period under the t+1 period; ΔS t,k Representing a state quantity at a kth signal period at a t-th period;
(4) External information process
External information Process W t Including the output of new energyIncrement between adjacent primary frequency modulation signals>Load demand->The specific description is as follows:
(5) Instant benefit function
The instant benefit function R is defined as the benefit of a certain decision made by a certain state for a certain period of time:
R(H t ,x t )＝w bs,n +w f,n -w inv,n -w om,n -w u,n
(6) Optimum value function
The optimal value function of the energy storage participation frequency modulation auxiliary service in the market is defined as the expected income from the beginning of the period to the end of all the periods, and the expression is as follows:
wherein Ω is a policy set composed of decisions at each stage; pi is the decision to transition the determined state to the next state;for a decision value given a state variable, E represents a revenue expectation;
the optimal value function is expressed as bellman optimality equation:
in the method, in the process of the invention,is shown in state H t Taking policy->After reaching the next state H t+1 Conditional probability of (2);
and finally, solving the problem of the Markov decision process to obtain the configuration of the stored energy.
The dynamic programming method is not influenced by the non-convex, nonlinear, discrete and other properties of the model, and is convenient for processing logic variables, so that the problem of the Markov decision process is solved by adopting the dynamic programming method, and the energy storage optimization configuration is obtained.
In the implementation process, the problem of the Markov decision process can be solved by a value function algorithm, a random simulation method, a strategy search algorithm and the like.
In addition to the above embodiments, other embodiments of the present invention are possible, and all technical solutions formed by equivalent substitution or equivalent transformation are within the scope of the present invention.

Claims (5)

1. The energy storage optimizing configuration method considering the state transition model is characterized by comprising the following steps:
the mathematical model with maximum benefit and optimal frequency modulation effect is established under the condition that energy storage participates in frequency modulation auxiliary service is established as follows:
wherein N is the scene number; mu (mu) n Probability of being the nth scene; w (w) bs,n Peak valley arbitrage benefit for energy storage; w (w) fr,n The energy storage is participated in the benefits of primary frequency modulation auxiliary service; w (w) en,n The energy storage is an environmental benefit of participating in primary frequency modulation; w (w) inv,n 、w om,n The investment cost and the operation maintenance cost of energy storage are respectively; w (w) u,n Bias punishment for energy storage participation in primary frequency modulation; m is the scene number when the absolute value of the frequency deviation is greater than 0.033, and the frequency deviation is the difference between the frequency obtained by the system after the frequency adjustment and the frequency of 50 Hz; Δf n A frequency deviation having an absolute value greater than 0.033;
(1) Peak Gu Taoli benefit
Wherein T is the transaction ending period;respectively sharing the discharge power and the charging power of the energy storage at the t-period peak Gu Taoli of the nth scene; />Respectively sharing the discharge electricity price and the charge electricity price of the energy storage in the t period of the nth scene;
(2) Frequency modulation auxiliary service benefit
In the method, in the process of the invention,the price of the frequency modulation capacity and the price of the frequency modulation mileage are respectively; Δt is the average duration of the frequency modulation mileage; />The method comprises the steps of storing the power which participates in primary frequency modulation in the t period of an nth scene;
(3) Environmental benefit
The energy storage participates in primary frequency modulation service, so that the generated energy of a conventional unit can be effectively reduced, and the generated electricity emission of the conventional unit is reduced, and the reduced environmental treatment cost is the environmental benefit that the energy storage participates in primary frequency modulation:
in which Q n,l Is conventional coalGenerating a first pollutant discharge amount; r is R n,l Cost per unit of environmental load for the first pollutant; l is the total number of discharged pollutants;the discharge quantity stored in the nth scene is used as the energy;
(4) Energy storage investment cost:
wherein, c p 、c e The unit power cost and the capacity cost of energy storage are respectively; θ rate,n 、S rate,n Respectively the maximum charge and discharge power and capacity of the stored energy; r is the annual rate of funds; y is the life cycle of the stored energy;
(5) Energy storage operation maintenance cost:
w om,n ＝c om ·θ rate,n
wherein, c om Maintenance cost for unit operation of energy storage;
(6) Bias penalty for energy storage participation in primary frequency modulation
In the method, in the process of the invention,the power is respectively adjusted up and down for the requirements of the power grid when the power grid participates in frequency modulation in the t period of the nth scene; /> Respectively performing punishment on the energy storage participating in frequency modulation in the nth period of the nth scene under the condition of up-regulation power and down-regulation power deficiency powerPenalty coefficients;
the constraint conditions of energy storage participation in frequency modulation are as follows:
(1) Power balance constraint
In the method, in the process of the invention,power is demanded for the load grid at the t-th period of the nth scenario;
(2) Energy storage power constraint
In θ max Maximum output active power for stored energy;
(3) Power constraint of up-down frequency modulation of energy storage
In the method, in the process of the invention,respectively regulating up-regulating power and down-regulating power when the nth time period of the energy storage nth scene participates in primary frequency modulation, and providing that a positive frequency modulation signal represents up-regulating, wherein the energy storage system needs to increase discharge power or reduce charging power, and a negative frequency modulation signal represents down-regulating, and the energy storage system needs to reduce discharge power or increase charging power;
(4) Capacity constraint of energy storage:
S min ≤S n,t,k+1 ≤S max
S min ≤S n,t ≤S max
wherein S is n,t,k+1 、S n,t,k The capacity of the k+1 and k signal periods stored in the t period of the nth scene;respectively storing output power and charging power of a kth signal period of a kth period of an nth scene; s is S min 、S max Respectively storing energy with minimum capacity and maximum capacity; k represents the total signal period contained in the nth scene; η (eta) d 、η c Respectively discharging efficiency and charging efficiency of energy storage; Δk is the period from the kth signal period to the kth+1th signal period; Δt is a period from a kth signal period of an nth scene to a 1 st signal period of an n+1th scene;
the random, discrete and logic variable problems contained in the energy storage optimization configuration are described as a Markov decision process, and the basic elements of the Markov decision process are as follows:
(1) State variables
Capacity S per moment of energy storage t Frequency modulation electric quantitySet as a state variable, expressed as:
wherein H is t Is a state variable;
(2) Decision variables
Decision variable x t Including frequency modulated electricityStored charge power->And discharge power->Rated power θ of energy storage rate,n And rated capacity S rate,n
(3) Equation of state transition
State transition equations are used to characterize the transition process from one state to the next, the state transition of stored energy is described as:
wherein S is t,1 、S t+1,1 Respectively representing the state quantity under the 1 st signal period under the t period and the state quantity under the 1 st signal period under the t+1 period; ΔS t,k Representing a state quantity at a kth signal period at a t-th period;
(4) External information process
External information Process W t Including the output theta of new energy t c Increment between adjacent primary frequency modulation signalsLoad demand->The specific description is as follows:
(5) Instant benefit function
The instant benefit function R is defined as the benefit of a certain decision made by a certain state for a certain period of time:
R(H t ,x t )＝w bs,n +w fr,n -w inv,n -w om,n -w u,n
(6) Optimum value function
The optimal value function of the energy storage participation frequency modulation auxiliary service in the market is defined as the expected income from the beginning of the period to the end of all the periods, and the expression is as follows:
wherein Ω is a policy set composed of decisions at each stage; pi is the decision to transition the determined state to the next state;for a decision value given a state variable, E represents a revenue expectation;
the optimal value function is expressed as bellman optimality equation:
in the method, in the process of the invention,is shown in state H t Taking policy->After reaching the next state H t+1 Conditional probability of (2);
and finally, solving the problem of the Markov decision process to obtain the configuration of the stored energy.
2. The energy storage optimization configuration method considering a state transition model as claimed in claim 1, wherein a dynamic programming method is adopted to solve the problem of the markov decision process.
3. The energy storage optimization configuration method considering a state transition model according to claim 1, wherein a value function algorithm is adopted to solve the problem of the markov decision process.
4. The energy storage optimization configuration method considering a state transition model as claimed in claim 1, wherein a random simulation method is adopted to solve the problem of the markov decision process.
5. The energy storage optimization configuration method considering a state transition model according to claim 1, wherein a strategy search algorithm is adopted to solve the problem of the markov decision process.
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