CN-122000971-A - Multi-power-market-oriented hybrid energy storage capacity configuration and operation combined decision method

CN122000971ACN 122000971 ACN122000971 ACN 122000971ACN-122000971-A

Abstract

The invention discloses a hybrid energy storage capacity configuration and operation joint decision method for a multi-power market, which is used for realizing the collaborative optimization of a hybrid energy storage system in a planning and operation layer. The method comprises the steps of constructing a double-layer random planning model, wherein an upper-layer planning model aims at maximizing annual net benefit of the hybrid energy storage system and optimizing rated power and capacity configuration of an energy type battery and a power type battery, and a lower-layer running model aims at maximizing daily operation expected net benefit and optimizes a daily market declaration strategy and a real-time response power strategy of the hybrid energy storage system under a daily-real-time two-stage random scene set. The upper planning model and the lower running model carry out iterative interaction through capacity parameters and running feasibility information, so that the combined optimization of capacity configuration and multi-market running strategies is realized, and the technical problem that the hybrid energy storage system in the prior art is difficult to realize planning running collaborative optimization and internal power reasonable distribution under multiple uncertainties is solved.

Inventors

YAN WEN
LIU YUBIN
YU TAO
PAN ZHENNING
WU YUFENG
GONG YUSHEN
CUI KE

Assignees

华南理工大学

Dates

Publication Date: 20260508
Application Date: 20260126

Claims (10)

1. The multi-power market-oriented hybrid energy storage capacity configuration and operation combined decision method is characterized by constructing a double-layer random planning model combining planning and operation, and realizing the collaborative optimization of capacity configuration and operation strategies of an energy type battery and a power type battery in a hybrid energy storage system under the condition of simultaneously considering the uncertainty of new energy output, the price fluctuation of a power market and the real-time frequency modulation requirement, wherein the implementation steps are as follows: The method comprises the steps of S1, obtaining historical data of new energy output, market price and real-time frequency modulation signals, adopting a cluster analysis method to generate typical daily scenes representing annual operation characteristics as uncertainty description of a day-ahead stage, constructing real-time scenes representing prediction deviation of new energy in the day and real-time frequency modulation requirements under each typical daily scene, and constructing a day-ahead-real-time two-stage random scene; s2, maximizing annual net income of the hybrid energy storage system as an upper layer optimization target, and determining an upper layer planning model of capacity configuration of the energy type battery and the power type battery by combining the day-ahead-real-time two-stage random scene; S3, maximizing daily operation net income of the hybrid energy storage system as a lower-layer optimization target, constructing a lower-layer operation model of the hybrid energy storage system participating in a daily-real-time market according to the capacity configuration and a daily-real-time two-stage random scene, and determining corresponding constraint conditions; And S4, forming a double-layer random programming model combining programming and operation by the upper-layer programming model and the lower-layer operation model, and obtaining an optimal rated power and capacity configuration scheme of the hybrid energy storage system, and a daily market declaration strategy and a real-time power response strategy corresponding to the optimal rated power and capacity configuration scheme by iteratively solving the double-layer random programming model.
2. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 1, wherein, at step S1, constructing a pre-day-real-time two-phase random scenario comprises: Acquiring historical data of new energy output, market price and real-time frequency modulation signals, wherein the frequency modulation signals are real-time power regulation instructions issued by a power system dispatching mechanism or a power market platform; According to the historical data of the new energy output and the market price, a cluster analysis method is adopted to extract a plurality of typical daily scenes representing different seasons and typical load characteristics from annual operation data, and the typical daily scenes are used for describing prediction uncertainty on a annual scale; And constructing a plurality of real-time scenes based on the real-time frequency modulation signals and the predicted deviation of the new energy output in each typical daily scene so as to simulate power deviation and frequency modulation requirements possibly occurring in daily operation, thereby forming a daily-real-time two-stage random scene capable of simultaneously describing daily prediction uncertainty and real-time operation uncertainty.
3. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 2, wherein in step S2, the upper layer planning model targets maximization of the annual net gain of the hybrid energy storage system, the annual net gain being the difference of the annual profit of the hybrid energy storage system minus the annual investment cost and the annual operation maintenance cost of the hybrid energy storage system; The annual profit of the hybrid energy storage system is calculated based on the operation result of the lower layer operation model in a day-ahead-real-time two-stage random scene, and the rated power and capacity configuration schemes of the power type battery and the energy type battery are determined according to the operation result.
4. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 3, wherein the objective function of the upper layer planning model is: ; in the formula, For the purpose of the upper layer optimization, For the annual profit of the mixed energy storage, the annual profit is calculated by a lower layer operation model, For the cost of investment in hybrid energy storage, The operation and maintenance cost is the hybrid energy storage; cost of investment in hybrid energy storage The expression of (2) is: ; Hybrid energy storage operation and maintenance cost The expression of (2) is: ; in the formula, And The rated capacity and rated power of the energy type battery respectively, And The rated capacity and rated power of the power type battery respectively, 、 The investment cost of unit capacity and the investment cost of unit power of the energy type battery are respectively, 、 The investment cost of the unit capacity and the investment cost of the unit power of the power type battery are respectively, And The energy type battery unit capacity operation cost and the energy type battery unit power operation cost are respectively, 、 The operation cost per unit capacity and the operation cost per unit power of the power type battery are respectively, In order to ensure the service life of the equipment, Is the discount rate.
5. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 4, wherein the constraints of the upper layer planning model include hybrid energy storage power and capacity configuration boundary constraints, hybrid energy storage investment budget constraints, energy-to-power ratio constraints, and feasibility constraints that satisfy the lower layer operation model; The boundary constraint of the hybrid energy storage power and the capacity configuration means that the rated power and the rated capacity of the energy type battery and the power type battery are limited in the technical allowable range, and the minimum configurable value and the maximum configurable value are included to ensure the physical realizability of the hybrid energy storage system; the hybrid energy storage investment budget constraint means that the sum of the capacity investment cost and the power investment cost of the energy type battery and the power type battery must not exceed a preset investment budget so as to ensure that the planned scheme can be economically implemented; The energy-power ratio constraint means that the capacities and powers of the energy battery and the power battery meet the technical characteristic relation, namely the energy battery is required to have a high energy capacity-power ratio, and the power battery is required to have a high power density, so that the energy battery and the power battery can bear corresponding energy supporting and quick response tasks in a subsequent operation stage; The meeting the feasibility constraint of the lower-layer operation model means that rated capacity and rated power configuration of an energy battery and a power battery given in an upper-layer planning model must fall into a capacity-power feasible domain capable of ensuring that the lower-layer operation model has feasible solutions in the day-ahead-real-time two-stage random scene, and if a certain candidate configuration causes the lower-layer operation model to have no feasible solution, the lower-layer operation model is excluded from the upper-layer planning model.
6. The multi-electric-market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 5, wherein the hybrid energy storage power and capacity configuration boundary constraint is expressed as: ; ; in the formula, 、 The power and capacity upper limit values of the battery respectively, 、 The power and capacity upper limit values of the super capacitor are respectively; the hybrid energy storage investment budget constraint is expressed as: ; in the formula, Representing a hybrid energy storage investment cost function, Calculating an upper limit for the investment; the energy-to-power ratio constraint is expressed as: ; ; in the formula, The minimum energy-to-power ratio allowed for the battery, For the maximum energy-to-power ratio allowed by the battery, The minimum energy-to-power ratio allowed for the super capacitor, The maximum energy-power ratio allowed for the super capacitor; The feasibility constraint meeting the underlying operational model is expressed as: ; in the formula, In order to ensure that the lower layer operation model has a feasible solution capacity-power set under a given day-ahead-real-time two-stage random scene.
7. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 6, wherein in step S3 the underlying operational model targets a single day operational net benefit of the hybrid energy storage system, the single day operational net benefit including day-ahead electric energy trading benefit, real-time electric energy bias costs, frequency modulation market benefit and related operational costs, and is calculated based on a day-ahead-real-time two-stage stochastic scenario, wherein: the objective function of the lower layer operation model is as follows: ; in the formula, For the purpose of the optimization of the lower layer, Indicating that the expectations are made with respect to the probability for the typical day d, Indicating that the real-time scene omega is probabilistic expected given a typical day d, As a typical collection of days, For a real-time scene set at a typical day d, For the electric energy market benefit of the typical day d, For the frequency modulated market return on a typical day d, For a typical d of the return to the smooth wave, For electric energy market revenue in a typical day d real-time scenario ω, For the frequency modulation market benefit in a typical day d real-time scenario omega, For a smooth wave return in a typical day d real-time scenario omega, Loss cost for a hybrid energy storage system in a typical day d real-time scenario ω; electric energy market benefit for typical day d The expression of (2) is: ; in the formula, The day-ahead electric energy market price for a typical day d period t, 、 The energy type battery participates in the discharging power and the charging power of the electric energy market in a typical day d period t respectively, For the day-ahead scheduling period, Is the time step before day; Frequency modulation market benefit for typical day d The expression of (2) is: ; in the formula, 、 Frequency modulation capacity unit price and frequency modulation mileage unit price of a typical day d period t respectively, For the pre-day frequency modulation capacity of the hybrid energy storage system declared during a typical day d period t, The average frequency modulation mileage is obtained from historical frequency modulation signal data; Mean wave return for typical day d The expression of (2) is: ; in the formula, 、 Respectively, the discharging power and the charging power of the energy type battery which are stabilized and fluctuated in the typical day d period t, 、 Respectively, the discharging power and the charging power of the power type battery which are stabilized and fluctuated in the typical day d period t, Rental unit power prices for typical day d period t hybrid energy storage systems; electric energy market revenue in a typical day d real-time scenario ω The expression of (2) is: ; in the formula, Omega period for typical day d real-time scene Is used for the real-time electricity market price of electricity, Penalty coefficients for the electrical energy market bias, 、 Omega time periods of typical day d real-time scene of energy type battery in electric energy market The declared charge-discharge power positive unbalance amount and negative unbalance amount, For the real-time scheduling of the cycles, Is a real-time step; frequency modulation market revenue under typical day d real-time scenario ω The expression of (2) is: ; in the formula, 、 Omega time periods of typical day d real-time scene The frequency modulation capacity unit price and the frequency modulation mileage unit price of the hybrid energy storage system, The penalty factor is for frequency-modulated market deviation, 、 Omega time periods of typical day d real-time scene of hybrid energy storage system in frequency modulation market Positive and negative unbalance of declared frequency modulation capacity; Stabilized surge yield in a typical day d real-time scenario ω The expression of (2) is: ; in the formula, Omega period for typical day d real-time scene Is a price of rental unit power, In order to stabilize the fluctuation deviation penalty coefficient, 、 Stabilizing fluctuation in omega time period of real-time scene of typical day d for hybrid energy storage system Positive and negative unbalance amounts of charge and discharge power; Loss cost of hybrid energy storage system in typical day d real-time scenario ω The expression of (2) is: ; ; ; ; ; in the formula, For energy cells to be used to stabilize the volatility and the loss costs involved in the electrical energy market, For power cells to be used to smooth out fluctuations and the loss costs involved in the electrical energy market, The cost of energy type batteries to participate in the frequency modulation market is lost, The loss cost of the power type battery participating in the frequency modulation market; For energy type battery in period The energy loss cost per unit energy can be changed along with the depth of discharge, For power type battery in period The cost per unit energy is averaged out of losses, 、 The energy type battery and the power type battery respectively have the mileage loss cost per unit frequency modulation, Omega period for typical day d real-time scene Is used for the frequency modulation mileage coefficient of (1), 、 Omega time period of real-time scene of typical day d for energy type battery respectively The charge and discharge power involved in the electrical energy market, 、 Omega time period of real-time scene of typical day d for energy type battery respectively Stabilizing the fluctuating charge power and discharge power, 、 Respectively, the omega time period of the real-time scene of the typical day d of the power type battery The charge and discharge power involved in the electrical energy market, 、 Respectively, the omega time period of the real-time scene of the typical day d of the power type battery Stabilizing the fluctuating charge power and discharge power, For energy type batteries during a typical day d period The frequency modulation capacity declared before the day, For power type battery in typical day d period Frequency modulation capacity declared before the day.
8. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 7, wherein the constraint conditions of the lower-layer operation model comprise a day-ahead phase constraint and a real-time phase constraint, and the day-ahead phase constraint comprises a new energy power fluctuation stabilizing constraint, a frequency modulation capacity reporting constraint, a hybrid energy storage power constraint and a hybrid energy storage energy constraint; the new energy power fluctuation stabilizing constraint is used for coupling energy storage stabilizing power and new energy output so as to limit the variation amplitude of grid-connected power in adjacent time periods; the frequency modulation capacity declaration constraint is used for determining the capacity of the hybrid energy storage system which can declare to the frequency modulation market in the day-ahead stage, and the declaration capacity is required to be matched with the rated charge-discharge power capacity of the hybrid energy storage system; the hybrid energy storage power constraint is used for limiting the charge and discharge power of the energy type battery and the power type battery at any moment not to exceed rated power, and preventing the charge and discharge from occurring simultaneously through a state mutual exclusion condition; The hybrid energy storage energy constraint is used for respectively applying upper and lower limits of states of charge to the energy type battery and the power type battery, and ensuring energy continuity in a time sequence operation process through an energy balance equation so as to avoid energy out-of-limit.
9. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 8, wherein the real-time phase constraints are structurally consistent with the day-ahead phase constraints and are differentiated on a scene and time scale, and further comprising power deviation constraints and hybrid energy storage internal power collaborative strategy constraints; The power deviation constraint is used for quantifying deviation of real-time operation relative to a day-ahead plan, and comprises electric energy deviation, frequency modulation capacity deviation and stabilized power deviation, and the influence of the real-time deviation on plan execution is reduced through a punishment term; The hybrid energy storage internal power collaborative strategy constraint is used for realizing complementary response of the energy type battery and the power type battery, and comprises the steps of constructing a dynamic power bearing weight coefficient based on the charge state deviation of the energy type battery, adjusting the real-time frequency modulation power duty ratio of the energy type battery in a real-time operation stage according to the dynamic power bearing weight coefficient, and controlling the power type battery to bear the residual real-time frequency modulation power which is not borne by the energy type battery through a power balance relation, so that the charge state and the operation feasibility of the energy type battery are maintained while the real-time response capability of the hybrid energy storage system is ensured.
10. The multi-power market oriented hybrid energy storage capacity configuration and operation joint decision method of claim 9, wherein in step S4, iteratively solving the two-layer stochastic programming model comprises the following solving process: The upper planning model solves the rated power and capacity configuration of the hybrid energy storage system, the capacity is used as an optimization variable, the running feasibility information and the running income information returned by the lower running model are converted into a cutting plane, the cutting plane is added into the upper planning model, and the gradual correction of the capacity configuration scheme is realized by continuously updating the cutting plane; Under the capacity configuration condition of the given upper planning model output, solving a lower running model, dividing a typical daily scene and a real-time scene contained in the typical daily scene into a plurality of sub-problems which can be solved in parallel, generating a cutting plane for describing daily declaration benefits and real-time response benefits by utilizing the solving result of each sub-problem, and returning to the main problem of the lower running model; Performing iterative interaction on the upper planning model and the lower running model, updating capacity configuration by the upper planning model according to feedback of the lower running model, and re-solving the running strategy by the lower running model according to the updated capacity configuration; And repeating the solving process until the objective functions of the upper planning model and the lower running model are converged or the newly added cutting plane does not change the solving result any more, and obtaining the capacity configuration scheme with the optimal full life cycle benefit of the hybrid energy storage system and the corresponding running strategy.

Description

Multi-power-market-oriented hybrid energy storage capacity configuration and operation combined decision method Technical Field The invention relates to the technical field of energy storage, in particular to a hybrid energy storage capacity configuration and operation combined decision method for a multi-power market. Background With the transformation of global energy structures, the permeability of new energy sources represented by wind energy and solar energy in an electric power system is increasingly improved. However, the intermittence and fluctuation of the new energy output bring great challenges to the safe and stable operation of the power grid. The energy storage system, in particular electrochemical energy storage, has the advantages of high response speed, flexible configuration and the like, and becomes a key technology for stabilizing new energy fluctuation and improving the flexibility and stability of a power grid. The energy storage system is taken as an independent market main body, participates in the electric energy market, the frequency modulation and other auxiliary service markets, and is an important way for realizing the commercial value and promoting the large-scale application of the energy storage system. In order to meet the diversified demands of energy and power, a hybrid energy storage system (Hybrid Energy Storage System, HESS) is formed by energy storage such as a battery and power storage such as a super capacitor. The core idea is to realize the improvement of the overall performance by utilizing the complementary advantages of different energy storage elements in technical characteristics, wherein the cooperative distribution of power is a key technical problem. In the prior art, the hybrid energy storage power distribution is mostly dependent on a fixed filtering strategy, and the power demand signal is distributed to the super capacitor and the battery according to the frequency characteristic, so that the battery can be protected to a certain extent, but the dynamic response to the battery SOC and the market price is lacking, so that the battery can be still used excessively in an unfavorable state, the performance decline is accelerated, and the system economy is reduced. Meanwhile, the capacity planning and operation strategy of the energy storage are commonly split, namely, the planning stage estimates the benefits based on a simplified model, the service life cost and multi-market coupling are often ignored, the capacity configuration deviates from the optimal, the operation stage is limited by the set capacity, and the economic benefit maximization of the whole life cycle is difficult to realize. In addition, uncertainty in the price of the electric market and the output of new energy is a key factor affecting the decision of the HESS. In the prior art, when the uncertainty is processed, the uncertainty of two different time scales of daily macroscopic operation mode change and daily real-time random deviation is difficult to be considered, so that the robustness and the accuracy of a constructed model are insufficient. Disclosure of Invention The invention aims to provide a hybrid energy storage capacity configuration and operation joint decision method for a multi-power market, which solves the technical problems that a hybrid energy storage system is difficult to realize planning operation collaborative optimization and internal power reasonable distribution under multiple uncertainties in the prior art. In order to achieve the above purpose, the technical scheme provided by the invention is that a multi-electric-market-oriented hybrid energy storage capacity configuration and operation joint decision method is used for constructing a double-layer random planning model combining planning and operation, and under the condition of simultaneously considering the uncertainty of new energy output, the fluctuation of electric market price and the real-time frequency modulation requirement, the capacity configuration and operation strategy of an energy type battery and a power type battery in a hybrid energy storage system are cooperatively optimized, and the specific implementation steps are as follows: The method comprises the steps of S1, obtaining historical data of new energy output, market price and real-time frequency modulation signals, adopting a cluster analysis method to generate typical daily scenes representing annual operation characteristics as uncertainty description of a day-ahead stage, constructing real-time scenes representing prediction deviation of new energy in the day and real-time frequency modulation requirements under each typical daily scene, and constructing a day-ahead-real-time two-stage random scene; s2, maximizing annual net income of the hybrid energy storage system as an upper layer optimization target, and determining an upper layer planning model of capacity configuration of the energy type battery and the power type battery by