Energy storage cooperative configuration method and system considering photovoltaic output multi-time uncertainty

Abstract

The invention discloses an energy storage cooperative configuration method and system considering photovoltaic output multi-time uncertainty, and the method comprises the steps: constructing a photovoltaic output multi-time scale output uncertainty model which comprises a photovoltaic medium and long term output power uncertainty model, a photovoltaic intraday short-term power fluctuation model, and a photovoltaic extremely short-term output uncertainty model; the method comprises the following steps: constructing an energy storage inertia supporting capability and frequency response model based on analysis of operation characteristics of energy storage equipment, and constructing an energy storage collaborative configuration model of the optical storage power station by combining annual total cost, investment limitation, direct current transmission requirements and frequency stability requirements of the optical storage power station; according to the photovoltaic output multi-time scale output uncertainty model and the energy storage collaborative configuration model of the optical storage power station, constructing a distributed robust optimization model of energy storage collaborative configuration; and iterative solution is carried out to obtain a configuration scheme of the energy storage power and capacity. According to the method, the influence of photovoltaic uncertainty on the operation cost of the optical storage power station is reduced, and the reliability of an energy storage configuration scheme is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage optimization configuration, and in particular to an energy storage collaborative configuration method and system taking into account the multi-time uncertainty of photovoltaic output. Background Art

[0002] At present, in the existing energy storage coordinated optimization configuration, usually only the configuration of grid-following energy storage is considered to smooth the fluctuations of new energy, or the configuration of grid-building energy storage to provide inertial support and active frequency and voltage support, and it has the function of grid-following energy storage; however, traditional grid-following energy storage cannot provide inertial support and active frequency and voltage support, and the cost of grid-building energy storage equipment is relatively high, which cannot effectively guarantee the technical reliability of the energy storage configuration plan; in addition, in the characterization of multi-time scale uncertainty of photovoltaics, most of the focus is on characterizing the uncertainty of photovoltaic medium- and long-term output, some research focuses on short-term time scale uncertainty, and very few focus on the uncertainty of extremely short-term photovoltaic output. There is a lack of systematic characterization of photovoltaic multi-time scale uncertainty.

[0003] In view of this, this application is hereby filed. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for energy storage collaborative configuration that takes into account the multi-time uncertainty of photovoltaic output. Based on the characterization of the uncertainty of photovoltaic output in multiple time scales, such as medium-long-term, short-term, and very short-term, the present invention constructs a distributed robust optimization model for energy storage collaborative configuration, and iteratively solves the configuration scheme of energy storage power and capacity; so as to achieve the smoothing of the output volatility of photovoltaic power stations and meet their actual needs in terms of DC transmission and frequency stability. The present invention reduces the impact of photovoltaic uncertainty on the operating cost of photovoltaic power stations and effectively improves the robustness of energy storage configuration schemes.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a method for coordinated energy storage configuration considering multi-time uncertainty of photovoltaic output, the method comprising:

[0007] Based on the analysis of photovoltaic output characteristics, a photovoltaic output multi-time scale output uncertainty model is constructed; the photovoltaic output multi-time scale output uncertainty model includes photovoltaic medium- and long-term output power uncertainty model, photovoltaic intraday short-term power fluctuation model and photovoltaic extremely short-term output uncertainty model;

[0008] Based on the analysis of the operating characteristics of energy storage equipment, the energy storage inertial support capacity and frequency response model are constructed; the operating characteristics of energy storage equipment include the inertial support characteristics and primary frequency regulation characteristics of grid-type energy storage and grid-following energy storage with added virtual synchronous control links;

[0009] Based on the energy storage inertial support capacity and frequency response model, combined with the annualized total cost of the photovoltaic power station, investment restrictions, DC transmission requirements and frequency stability requirements, a storage coordination configuration model for the photovoltaic power station is constructed;

[0010] According to the multi-time-scale output uncertainty model of photovoltaic output and the energy storage coordinated configuration model of photovoltaic and energy storage power stations, a distributed and robust optimization model of energy storage coordinated configuration is constructed; and the distributed and robust optimization model of energy storage coordinated configuration is iteratively solved to obtain the configuration scheme of energy storage power and capacity.

[0011] Furthermore, photovoltaic output characteristics include medium- and long-term output characteristics, short-term output characteristics, and very short-term output characteristics;

[0012] The medium and long term includes months, quarters and weeks, the short term includes hours and days, and the very short term includes seconds and minutes.

[0013] Furthermore, the steps for constructing the uncertainty model of photovoltaic medium- and long-term output power are as follows:

[0014] Beta distribution is used to describe the probability distribution of photovoltaic power output on a medium- and long-term time scale. Based on the probability distribution, Monte Carlo simulation method is used to generate sufficient scenario data.

[0015] According to the scenario data, a clustering method is used to generate S typical photovoltaic output scenarios and probabilities, and a comprehensive norm constraint is added to the typical photovoltaic output scenarios and probabilities.

[0016] Furthermore, the steps for constructing the photovoltaic intraday short-term power fluctuation model are as follows:

[0017] Based on the typical photovoltaic output scenario generated by the photovoltaic medium- and long-term output power uncertainty model, the short-term photovoltaic output fluctuation is modeled by adding the fluctuation amount: Where: P represents the predicted available power of photovoltaic power in period t; t pre represents the predicted output in period t in a typical photovoltaic output scenario; ΔP t PV It represents the power deviation of the photovoltaic predicted output in period t;

[0018] An adjustable box uncertainty set is used to characterize the short-term power fluctuation of photovoltaic power within a day by setting the deviation value and the fluctuation period, and the uncertainty set of short-term power fluctuation of photovoltaic power within a day is obtained.

[0019] Furthermore, the uncertainty set expression of the photovoltaic intraday short-term power fluctuation is:

[0020]

[0021] Where: ΩPV represents the uncertainty set of the short-term power fluctuation of photovoltaic power within a day; It is a 0-1 variable, indicating whether fluctuation occurs during this period; T represents the total number of operating hours throughout the day; represents the maximum power deviation of the photovoltaic predicted output within the day; U represents the upper limit of the number of fluctuation periods throughout the day; λ PV Indicates the maximum power deviation rate of the photovoltaic predicted output within the day.

[0022] Furthermore, the steps for constructing the photovoltaic extremely short-term output uncertainty model are as follows:

[0023] The Weibull distribution is used to characterize the probability distribution of photovoltaic power for 1 minute. The probability density function is as follows:

[0024]

[0025] Among them, x represents the 1-minute fluctuation value of photovoltaic; k is the shape parameter; λ is the scale parameter;

[0026] Monte Carlo simulation of 1-minute power fluctuation was performed based on Weibull probability distribution and random sampling was performed. A total of T groups were selected, with N in each group. ss The number of sampling groups is the fluctuation of the corresponding period;

[0027] 1-minute power fluctuation value constraint:

[0028]

[0029] in, Indicates the extremely short-term fluctuation power of photovoltaic power during the period t+nΔt; λ max Indicates the maximum fluctuation rate of photovoltaic power in a short period of time.

[0030] Furthermore, the energy storage coordination configuration model of the photovoltaic power station is based on the coordinated optimization configuration model of the grid-building energy storage and the grid-following energy storage. The energy storage coordination configuration model of the photovoltaic power station constructs the objective function by minimizing the total cost, with equipment investment constraints, photovoltaic output constraints, energy storage constraints, photovoltaic power station balance constraints, DC transmission constraints and transient stability constraints as constraints;

[0031] The objective function is;

[0032] minF=C Inv +C OM +C Ope

[0033]

[0034]

[0035]

[0036] C Ope =C cur +C grid

[0037]

[0038] Where s represents the scenario; F is the total planning cost; C Inv , C OM , C Ope They represent the annual energy storage investment cost, annual operation and maintenance cost, and annual operation cost of the photovoltaic energy storage power station respectively; Respectively represent the unit power investment cost of grid-connected energy storage and grid-building energy storage and the investment cost of unit energy storage capacity; N GFL 、N GFM 、N ESS Respectively represent the investment amount of grid-following type and grid-building type energy storage power and energy storage capacity; Respectively represent the rated power of a single device of grid-following energy storage and grid-building energy storage and the rated capacity of energy storage; Represents the daily operation and maintenance cost per unit power of energy storage; R x is the annualized investment coefficient of x; r is the discount rate; T x is the service life of the equipment; represents the operation and maintenance cost of energy storage per unit storage power; C cur represents the penalty cost of photovoltaic abandonment; C grid It indicates the cost of purchasing electricity for the PV power station to obtain frequency support from the power grid when the frequency support is insufficient; It represents the frequency support power of the power grid in the period t+nΔt; Indicates the amount of electricity purchased by the system; c cur represents the unit abandoned light cost; c grid-f represents the unit frequency support cost; c grid Represents the system electricity purchase cost; N ss Indicates the number of fluctuations in each optimization period; p s represents the occurrence probability of scene s.

[0039] The equipment investment constraint is:

[0040]

[0041] in, Indicates the maximum investment quantity of equipment (grid-building energy storage, grid-connected energy storage, energy storage capacity);

[0042] The photovoltaic output constraint is:

[0043]

[0044] Among them, λ CurIndicates the maximum abandoned light rate of photovoltaics;

[0045] The energy storage constraint is:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] in, It is a 0-1 variable, indicating the discharge state of the grid-following and grid-building energy storage in time period t, which is used to ensure that the energy storage can only be in one state in a certain period of time; It represents the total power of the grid-following and grid-forming energy storage to maintain the stability of the system during the period t+nΔt; Indicates the energy storage charge during period t; η ch , η dis They represent the charging and discharging efficiency of energy storage respectively; μ t+nΔt Indicates the rate of change of inertial support power and frequency during the period t+nΔt; represents the power shortage of the photovoltaic power station during the period t+nΔt; f0 represents the rated frequency of the system; w l 、w h They represent the discharge depth and charge depth of energy storage respectively;

[0052] The balance constraint of the photovoltaic power station is:

[0053] P t s =P t PV +P t grid +P t GFM,dis +P t GFL,dis -P t GFM,ch -P t GFL,ch

[0054] Among them, P t s It represents the electric power of the solar energy storage system in the period t reported to the system on the previous day.

[0055] The DC transmission constraints are:

[0056]

[0057] in, P s , Indicates the upper and lower limits of the power transmission on the line; is a 0-1 variable, indicating whether the output of the photovoltaic storage system changes during the period t; τ represents the shortest maintenance time of the DC power; Indicates the maximum climbing capability of the solar-storage system;

[0058] The transient stability constraint is:

[0059]

[0060]

[0061] Among them, μ t+nΔt,max , They represent the maximum frequency change rate and actual power change per unit time in the period t+nΔt respectively; μ max Indicates the maximum frequency change rate that the photovoltaic power station can withstand; Δf ss Indicates the steady-state frequency of the system; Δf ss,max Indicates the maximum steady-state frequency deviation that the photovoltaic power station can withstand.

[0062] Furthermore, the distributed blue-rod optimization model for energy storage collaborative configuration is a two-stage distributed blue-rod optimization model for energy storage system collaborative optimization configuration: the first stage determines the energy storage system collaborative configuration plan, and the second stage is to conduct simulation operation under the known first stage investment plan to solve the worst photovoltaic fluctuation scenario;

[0063] The two-stage distributed robust optimization model for the coordinated optimization configuration of the energy storage system is:

[0064]

[0065] Among them, x, y s , s They represent investment decision variables, operation decision variables and photovoltaic output respectively; A, B, C, D, E, F, G, K, e, f, g are coefficient matrices and constraint matrices; st represents constraints.

[0066] Furthermore, the distributed robust optimization model of energy storage collaborative configuration is iteratively solved to obtain the configuration scheme of energy storage power and capacity, including:

[0067] The first stage is to solve the main problem: determine the coordinated configuration plan of the energy storage system, and the second stage is the sub-problem: under the known first stage investment plan, simulate and run to solve the worst photovoltaic fluctuation scenario;

[0068] Before solving, assign initial values ​​to variables and set upper and lower bounds of iterations, with the result of solving the main problem as the lower bound and the result of solving the subproblem as the upper bound;

[0069] Based on the initial value, the output curve reported by the photovoltaic power station on the day before is solved without considering the uncertainty, which serves as the benchmark data for subsequent operation;

[0070] Solve the main problem, obtain the coordinated configuration scheme of the energy storage system, and assign the comprehensive cost to the lower bound;

[0071] The configuration solution of the main problem is returned to the sub-problem. The sub-problem first solves the worst short-term photovoltaic fluctuation scenario based on the investment plan, then solves the probability distribution of the worst photovoltaic scenario, and calculates the new comprehensive cost to assign to the upper bound;

[0072] Determine whether the deviation between the upper and lower bounds is within the allowable range. If so, end the solution. Otherwise, return the uncertainty result of the sub-problem solution to the main problem. Repeat this process to obtain the final result, which is the configuration plan for energy storage power and capacity.

[0073] In a second aspect, the present invention further provides a storage coordination configuration system considering the multi-time uncertainty of photovoltaic output, the system comprising:

[0074] The photovoltaic output multi-time scale unit is used to construct a photovoltaic output multi-time scale output uncertainty model based on the analysis of photovoltaic output characteristics; the photovoltaic output multi-time scale output uncertainty model includes a photovoltaic medium- and long-term output power uncertainty model, a photovoltaic intraday short-term power fluctuation model, and a photovoltaic very short-term output uncertainty model;

[0075] The inertial support and frequency unit is used to construct the energy storage inertial support capability and frequency response model based on the analysis of the energy storage equipment operation characteristics; the energy storage equipment operation characteristics include the inertial support characteristics and primary frequency regulation characteristics of the grid-building energy storage and the grid-following energy storage with the virtual synchronization control link added;

[0076] The energy storage collaborative configuration model building unit is used to build the energy storage collaborative configuration model of the photovoltaic and energy storage power station based on the energy storage inertial support capacity and frequency response model, combined with the annualized total cost of the photovoltaic and energy storage power station, investment restrictions, DC transmission requirements and frequency stability requirements;

[0077] The distributed-bronze optimization model construction and solution unit is used to construct a distributed-bronze optimization model for energy storage coordination configuration based on the multi-time-scale output uncertainty model of photovoltaic output and the energy storage coordination configuration model of photovoltaic and energy storage power stations; and iteratively solve the distributed-bronze optimization model for energy storage coordination configuration to obtain the configuration plan of energy storage power and capacity.

[0078] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0079] 1. The present invention considers the uncertainty of photovoltaic output at multiple time scales, constructs a photovoltaic medium-long-term-short-term-very short-term time system uncertainty characterization model, and constructs a distributed blue bar optimization model for energy storage collaborative configuration, which reduces the impact of photovoltaic uncertainty on the operating cost of photovoltaic power stations and effectively improves the reliability of energy storage configuration solutions;

[0080] 2. The present invention takes into account the multi-time uncertainty of photovoltaic output, and the energy storage coordinated configuration method and system, fully considers the technical superiority of grid-building energy storage and the economic superiority of grid-following energy storage. By optimizing the coordinated configuration of grid-building energy storage and grid-following energy storage with virtual synchronous control links, it ensures that the planning scheme takes into account both economy and technology, and effectively improves the cost performance of energy storage configuration schemes;

[0081] 3. The present invention considers the multi-time uncertainty of photovoltaic output in the coordinated configuration method and system of energy storage, constructs the inertial support model and frequency response model of the grid-building type energy storage and the grid-following type energy storage with the virtual synchronization control link added, effectively quantifies the inertial support and frequency response capabilities of the energy storage configuration scheme, and effectively ensures the technical reliability of the energy storage configuration scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0083] Figure 1 This is a flow chart of the energy storage collaborative configuration method considering the multi-time uncertainty of photovoltaic output according to the present invention;

[0084] Figure 2 A detailed flow chart of the energy storage collaborative configuration method considering the multi-time uncertainty of photovoltaic output according to the present invention;

[0085] Figure 3 A flow chart of the distributed robust optimization model for solving energy storage collaborative configuration in the present invention;

[0086] Figure 4 This is a structural block diagram of the energy storage collaborative configuration system of the present invention that takes into account the multi-time uncertainty of photovoltaic output. DETAILED DESCRIPTION

[0087] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0088] The present invention considers the coordinated optimization configuration of grid-type energy storage and grid-following energy storage with an added virtual synchronous control link, taking into account both technological advancement and equipment economy; and considering that in terms of the characterization of multi-time-scale uncertainty in photovoltaics, most of the focus is on characterizing the uncertainty of medium- and long-term photovoltaic output, some research focuses on short-term time scale uncertainty, and very few focus on the uncertainty of extremely short-term photovoltaic output, and there is a lack of systematic characterization of multi-time-scale uncertainty in photovoltaics; therefore, the present invention constructs a photovoltaic uncertainty characterization system of a three-dimensional time system of medium- and long-term (months, seasons, weeks)-short-term (hours, days)-extremely short-term (minutes, seconds), which in turn affects the results of the coordinated optimization configuration of energy storage.

[0089] Based on the characterization of the uncertainty of photovoltaic output at multiple time scales, i.e., medium-long-term, short-term, and very short-term, the present invention constructs a distributed robust optimization model for energy storage collaborative configuration, and iteratively solves the configuration scheme of energy storage power and capacity, so as to achieve the goal of smoothing the output volatility of photovoltaic power stations and meet their practical needs in terms of DC transmission and frequency stability. The present invention reduces the impact of photovoltaic uncertainty on the operating cost of photovoltaic power stations and effectively improves the reliability of energy storage configuration schemes.

[0090] Example 1

[0091] like Figure 1 and Figure 2 As shown, the present invention considers the multi-time uncertainty of photovoltaic output and the energy storage coordinated configuration method, the method comprises:

[0092] Step 1: Based on the analysis of photovoltaic output characteristics, a photovoltaic output multi-time scale output uncertainty model is constructed; the photovoltaic output multi-time scale output uncertainty model includes a photovoltaic medium- and long-term output power uncertainty model, a photovoltaic intraday short-term power fluctuation model, and a photovoltaic very short-term output uncertainty model;

[0093] In this embodiment, the photovoltaic output characteristics include medium- and long-term output characteristics, short-term output characteristics, and very short-term output characteristics;

[0094] The medium and long term includes months, quarters and weeks, the short term includes hours and days, and the very short term includes seconds and minutes.

[0095] In this embodiment, the steps for constructing the photovoltaic medium- and long-term output power uncertainty model are as follows:

[0096] Step A1, Beta distribution is used to describe the probability distribution of photovoltaic power output on a medium- and long-term time scale. The probability density function of the probability distribution is as follows:

[0097]

[0098] Among them, α and β are state parameters; E and E maxis the actual radiation intensity and the maximum radiation intensity; Γ is the Gamma function;

[0099] Step A2, based on the probability distribution, using the Monte Carlo simulation method to generate enough scene data;

[0100] Step A3: Based on the scenario data, a clustering method is used to generate S typical photovoltaic output scenarios and probabilities. To ensure that the probability distribution of photovoltaic scenarios conforms to the actual situation, a comprehensive norm constraint is added to the typical photovoltaic output scenarios and probabilities. The constraints are as follows:

[0101]

[0102] Among them, Ω p represents the probability norm constraint set of photovoltaic output scenarios; p s represents the probability of the s-th photovoltaic output scenario; θ1, θ ∞ They correspond to the scene probability deviation limits under 1-norm and ∞-norm constraints respectively.

[0103] In this embodiment, the steps for constructing the photovoltaic intraday short-term power fluctuation model are as follows:

[0104] Step B1, based on the typical photovoltaic output scenario generated by the photovoltaic medium- and long-term output power uncertainty model, the short-term photovoltaic output fluctuation is modeled by adding the fluctuation amount: Where: P represents the predicted available power of photovoltaic power in period t; t pre represents the predicted output in period t in a typical photovoltaic output scenario; ΔP t PV It represents the power deviation of the photovoltaic predicted output in period t;

[0105] Step B2, using an adjustable box uncertainty set, characterizes the photovoltaic intraday short-term power fluctuation by setting the deviation value and the fluctuation period, and obtains the photovoltaic intraday short-term power fluctuation uncertainty set. Specifically, the photovoltaic intraday short-term power fluctuation uncertainty set expression is:

[0106]

[0107] Where: Ω PV represents the uncertainty set of the short-term power fluctuation of photovoltaic power within a day; It is a 0-1 variable, indicating whether fluctuation occurs during this period; T represents the total number of operating hours throughout the day; represents the maximum power deviation of the photovoltaic predicted output within the day; U represents the upper limit of the number of fluctuation periods throughout the day; λ PV Indicates the maximum power deviation rate of the photovoltaic predicted output within the day.

[0108] In this embodiment, the steps for constructing the photovoltaic extremely short-term output uncertainty model are as follows:

[0109] Step C1, using Weibull distribution to characterize the probability distribution of photovoltaic power for 1 minute, the probability density function is as follows:

[0110]

[0111] Among them, x represents the 1-minute fluctuation value of photovoltaic; k is the shape parameter; λ is the scale parameter;

[0112] Step C2: Monte Carlo simulation of the 1-minute power fluctuation is performed based on the Weibull probability distribution and random sampling is performed. A total of T groups are selected, each with N ss The number of sampling groups is the fluctuation of the corresponding period;

[0113] 1-minute power fluctuation value constraint:

[0114]

[0115] in, Indicates the extremely short-term fluctuation power of photovoltaic power during the period t+nΔt; λ max Indicates the maximum fluctuation rate of photovoltaic power in a short period of time.

[0116] Step 2: Based on the analysis of the operating characteristics of the energy storage equipment, an energy storage inertial support capacity and frequency response model is constructed; the operating characteristics of the energy storage equipment include the inertial support characteristics and primary frequency regulation characteristics of the grid-building energy storage and the grid-following energy storage with the virtual synchronous control link added;

[0117] In this embodiment, the energy storage inertial support capability and frequency response model is constructed, including:

[0118] Photovoltaic power generation does not have rotating elements and cannot provide inertia support capabilities by itself. It often needs to participate in inertia support and frequency regulation through load shedding or investment in energy storage. In order to improve the economy of photovoltaic power stations and reduce the amount of "abandoned light", in engineering applications at this stage, inertia support and frequency support for photovoltaic power stations are provided by configuring energy storage, and a linearized inertia support model for photovoltaic power stations with energy storage is constructed; the inertia support model is:

[0119]

[0120] Among them, H t PV-GFM , H t PV-GFL They represent the inertia time constants of grid-building type and grid-following type energy storage in time period t respectively; P t PV represents the electric power of photovoltaic in period t; Respectively represent the maximum and minimum historical output of photovoltaic power; Respectively represent the maximum and minimum values ​​of the inertia time constant of the grid-building / grid-following energy storage; represents the moment of inertia of the solar energy storage system during time period t; P GFM / P GFL Indicates the investment power of grid-connected / grid-following energy storage.

[0121] Without considering the control link, a certain amount of grid-building and grid-following energy storage power is reserved as backup in each period to cope with possible frequency stability requirements. The frequency response model is:

[0122]

[0123] in: P represents the primary frequency modulation power of grid-connected and grid-following energy storage in the period t+nΔt; t GFM,ch / P t GFL,ch , P t GFM,dis / P t GFL,ch They represent the charging and discharging power of the grid-building / grid-following energy storage in period t respectively; Indicates the short-term maximum overload capacity of grid-type energy storage.

[0124] Step 3: Based on the energy storage inertial support capacity and frequency response model, combined with the annualized total cost of the photovoltaic power station, investment restrictions, DC transmission requirements and frequency stability requirements, a storage coordination configuration model for the photovoltaic power station is constructed;

[0125] In this embodiment, in order to improve the economy of the planning scheme, the energy storage collaborative configuration model of the photovoltaic power station is based on the collaborative optimization configuration model of the grid-building energy storage and the grid-following energy storage. The energy storage collaborative configuration model of the photovoltaic power station constructs an objective function to minimize the total cost, with equipment investment constraints, photovoltaic output constraints, energy storage constraints, photovoltaic power station balance constraints, DC transmission constraints and transient stability constraints as constraints;

[0126] The objective function is;

[0127] minF=C Inv +C OM +C Ope

[0128]

[0129]

[0130]

[0131] C Ope=C cur +C grid

[0132]

[0133] Where s represents the scenario; F is the total planning cost; C Inv , C OM , C Ope They represent the annual energy storage investment cost, annual operation and maintenance cost, and annual operation cost of the photovoltaic energy storage power station respectively; Respectively represent the unit power investment cost of grid-connected energy storage and grid-building energy storage and the investment cost of unit energy storage capacity; N GFL 、N GFM 、N ESS Respectively represent the investment amount of grid-following type and grid-building type energy storage power and energy storage capacity; Respectively represent the rated power of a single device of grid-following energy storage and grid-building energy storage and the rated capacity of energy storage; Represents the daily operation and maintenance cost per unit power of energy storage; R x is the annualized investment coefficient of x; r is the discount rate; T x is the service life of the equipment; represents the operation and maintenance cost of energy storage per unit storage power; C cur represents the penalty cost of photovoltaic abandonment; C grid It indicates the cost of purchasing electricity for the PV power station to obtain frequency support from the power grid when the frequency support is insufficient; It represents the frequency support power of the power grid in the period t+nΔt; Indicates the amount of electricity purchased by the system; c cur represents the unit abandoned light cost; c grid-f represents the unit frequency support cost; c grid Represents the system electricity purchase cost; N ss Indicates the number of fluctuations in each optimization period; p s represents the occurrence probability of scene s.

[0134] The equipment investment constraint is:

[0135]

[0136] in, Indicates the maximum investment quantity of equipment (grid-building energy storage, grid-connected energy storage, energy storage capacity);

[0137] The photovoltaic output constraint is:

[0138]

[0139] Among them, λ Cur Indicates the maximum abandoned light rate of photovoltaics;

[0140] The energy storage constraint is:

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] in, It is a 0-1 variable, indicating the discharge state of the grid-following and grid-building energy storage in time period t, which is used to ensure that the energy storage can only be in one state in a certain period of time; It represents the total power of the grid-following and grid-forming energy storage to maintain the stability of the system during the period t+nΔt; Indicates the energy storage charge during period t; η ch , η dis They represent the charging and discharging efficiency of energy storage respectively; μ t+nΔt Indicates the rate of change of inertial support power and frequency during the period t+nΔt; represents the power shortage of the photovoltaic power station during the period t+nΔt; f0 represents the rated frequency of the system;; w l 、w h They represent the discharge depth and charge depth of energy storage respectively;

[0147] The balance constraint of the photovoltaic power station is:

[0148] P t s =P t PV +P t grid +P t GFM,dis +P t GFL,dis -P t GFM,ch -P t GFL,ch

[0149] Among them, P t s It represents the electric power of the solar energy storage system in the period t reported to the system on the previous day.

[0150] The DC transmission constraints are:

[0151]

[0152] in, Ps , Indicates the upper and lower limits of the power transmission on the line; is a 0-1 variable, indicating whether the output of the photovoltaic storage system changes during the period t; τ represents the shortest maintenance time of the DC power; Indicates the maximum climbing capability of the solar-storage system;

[0153] The transient stability constraint is:

[0154]

[0155]

[0156] Among them, μ t+nΔt,max , They represent the maximum frequency change rate and actual power change per unit time in the period t+nΔt respectively; μ max Indicates the maximum frequency change rate that the photovoltaic power station can withstand; Δf ss Indicates the steady-state frequency of the system; Δf ss,max Indicates the maximum steady-state frequency deviation that the photovoltaic power station can withstand.

[0157] Step 4: Based on the multi-time-scale output uncertainty model of photovoltaic output and the energy storage coordinated configuration model of photovoltaic and energy storage power stations, a distributed and robust optimization model of energy storage coordinated configuration is constructed; and the distributed and robust optimization model of energy storage coordinated configuration is iteratively solved to obtain a configuration plan for energy storage power and capacity.

[0158] In this embodiment, based on the characterization of the uncertainty of photovoltaic output at multiple time scales of medium-long-term, short-term and very short-term, the distributed robust optimization model of energy storage collaborative configuration is a two-stage distributed robust optimization model of energy storage system collaborative optimization configuration: the first stage determines the energy storage system collaborative configuration plan, and the second stage is to simulate and run under the known first stage investment plan to solve the worst photovoltaic fluctuation scenario;

[0159] The two-stage distributed robust optimization model for the coordinated optimization configuration of the energy storage system is:

[0160]

[0161] Among them, x, y s , s They represent investment decision variables, operation decision variables and photovoltaic output respectively; A, B, C, D, E, F, G, K, e, f, g are coefficient matrices and constraint matrices; st represents constraints.

[0162] In this embodiment, the iterative solution process is:

[0163] The above-mentioned robust optimization model of energy storage coordinated configuration is decomposed into the main problem of solving the energy storage configuration scheme and the sub-problem of solving the most robust photovoltaic scenario and probabilistic energy storage optimization operation; the main problem form is:

[0164]

[0165] Among them, the introduction of auxiliary variables st represents constraint;

[0166] The sub-problems are:

[0167]

[0168] Among them, x * is the solution result of the main problem; st represents the constraint;

[0169] The model energy storage constraints contain nonlinear terms, which are transformed using the Big M method. Taking grid-type energy storage as an example, the transformed form is as follows:

[0170]

[0171] Here, M represents a sufficiently large number.

[0172] Figure 3 The flowchart of the distributed robust optimization model for solving the energy storage collaborative configuration of the present invention is as follows: Figure 3 As shown in the figure, the first stage of the distributed blue bar optimization model for energy storage collaborative configuration is to solve the main problem: determine the energy storage system collaborative configuration plan, and the second stage is the sub-problem: under the known first stage investment plan, simulate and run to solve the worst photovoltaic fluctuation scenario;

[0173] Before solving, assign initial values ​​to variables and set upper and lower bounds of iterations, with the result of solving the main problem as the lower bound and the result of solving the subproblem as the upper bound;

[0174] Based on the initial value, the output curve reported by the photovoltaic power station on the day before is solved without considering the uncertainty, which serves as the benchmark data for subsequent operation;

[0175] Solve the main problem, obtain the coordinated configuration scheme of the energy storage system, and assign the comprehensive cost to the lower bound;

[0176] The configuration solution of the main problem is returned to the sub-problem. The sub-problem first solves the worst short-term photovoltaic fluctuation scenario based on the investment plan, then solves the probability distribution of the worst photovoltaic scenario, and calculates the new comprehensive cost to assign to the upper bound;

[0177] Determine whether the deviation between the upper and lower bounds is within the allowable range. If so, end the solution. Otherwise, return the uncertainty result of the sub-problem solution to the main problem. Repeat this process to obtain the final result, which is the configuration plan for energy storage power and capacity.

[0178] Example 2

[0179] like Figure 4 As shown, the difference between this embodiment and embodiment 1 is that this embodiment provides a system for coordinating energy storage configuration considering multi-time uncertainty of photovoltaic output, and the system corresponds to the function of the method for coordinating energy storage configuration considering multi-time uncertainty of photovoltaic output in embodiment 1 one by one; the system includes:

[0180] The photovoltaic output multi-time scale unit is used to construct a photovoltaic output multi-time scale output uncertainty model based on the analysis of photovoltaic output characteristics; the photovoltaic output multi-time scale output uncertainty model includes a photovoltaic medium- and long-term output power uncertainty model, a photovoltaic intraday short-term power fluctuation model, and a photovoltaic very short-term output uncertainty model;

[0181] The inertial support and frequency unit is used to construct the energy storage inertial support capability and frequency response model based on the analysis of the energy storage equipment operation characteristics; the energy storage equipment operation characteristics include the inertial support characteristics and primary frequency regulation characteristics of the grid-building energy storage and the grid-following energy storage with the virtual synchronization control link added;

[0182] The energy storage collaborative configuration model building unit is used to build the energy storage collaborative configuration model of the photovoltaic and energy storage power station based on the energy storage inertial support capacity and frequency response model, combined with the annualized total cost of the photovoltaic and energy storage power station, investment restrictions, DC transmission requirements and frequency stability requirements;

[0183] The distributed-bronze optimization model construction and solution unit is used to construct a distributed-bronze optimization model for energy storage coordination configuration based on the multi-time-scale output uncertainty model of photovoltaic output and the energy storage coordination configuration model of photovoltaic and energy storage power stations; and iteratively solve the distributed-bronze optimization model for energy storage coordination configuration to obtain the configuration plan of energy storage power and capacity.

[0184] The execution process of each unit can be performed according to the process steps of the energy storage collaborative configuration method considering the multi-time uncertainty of photovoltaic output in Example 1, and will not be repeated in this embodiment.

[0185] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0186] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0187] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0189] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for coordinating energy storage configuration considering the multi-temporal uncertainty of photovoltaic output, characterized in that: The method includes: Based on the analysis of photovoltaic output characteristics, a photovoltaic output multi-time scale output uncertainty model is constructed; the photovoltaic output multi-time scale output uncertainty model includes a photovoltaic medium- and long-term output power uncertainty model, a photovoltaic intraday short-term power fluctuation model, and a photovoltaic extremely short-term output uncertainty model; Based on the analysis of the operating characteristics of energy storage equipment, an energy storage inertial support capacity and frequency response model is constructed; the operating characteristics of the energy storage equipment include the inertial support characteristics and primary frequency regulation characteristics of grid-building energy storage and grid-following energy storage with an added virtual synchronous control link; Based on the energy storage inertial support capacity and frequency response model, combined with the annualized total cost of the photovoltaic power station, investment restrictions, DC transmission requirements and frequency stability requirements, a storage coordination configuration model for the photovoltaic power station is constructed; According to the photovoltaic output multi-time scale output uncertainty model and the energy storage coordinated configuration model of the photovoltaic power station, a distributed robust optimization model of energy storage coordinated configuration is constructed; and the distributed robust optimization model of energy storage coordinated configuration is iteratively solved to obtain a configuration scheme of energy storage power and capacity.

2. The energy storage collaborative configuration method considering the multi-time uncertainty of photovoltaic output according to claim 1 is characterized in that: The photovoltaic output characteristics include medium- and long-term output characteristics, short-term output characteristics, and very short-term output characteristics; The medium and long term includes months, seasons and weeks, the short term includes hours and days, and the very short term includes seconds and minutes.

3. The energy storage coordinated configuration method considering the multi-time uncertainty of photovoltaic output according to claim 1 is characterized in that: The steps for constructing the photovoltaic medium- and long-term output power uncertainty model are as follows: Beta distribution is used to describe the probability distribution of photovoltaic power output on a medium- and long-term time scale. Based on the probability distribution, the Monte Carlo simulation method is used to generate scenario data. According to the scenario data, a clustering method is used to generate S types of typical photovoltaic output scenarios and probabilities, and comprehensive norm constraints are added to the typical photovoltaic output scenarios and probabilities.

4. The energy storage collaborative configuration method considering the multi-time uncertainty of photovoltaic output according to claim 1 is characterized in that: The steps for constructing the photovoltaic intraday short-term power fluctuation model are as follows: Based on the typical photovoltaic output scenario generated by the photovoltaic medium- and long-term output power uncertainty model, the short-term photovoltaic output fluctuation is modeled by adding the fluctuation amount: Where: P represents the available power predicted by photovoltaic power generation in period t; t pre represents the predicted output in period t in a typical photovoltaic output scenario; It represents the power deviation of the photovoltaic predicted output in period t; An adjustable box uncertainty set is used to characterize the photovoltaic intraday short-term power fluctuation by setting the deviation value and the fluctuation period, and the photovoltaic intraday short-term power fluctuation uncertainty set is obtained.

5. The energy storage coordinated configuration method considering the multi-time uncertainty of photovoltaic output according to claim 4 is characterized in that: The uncertainty set expression of the photovoltaic intraday short-term power fluctuation is: Where: Ω PV represents the uncertainty set of the short-term power fluctuation of photovoltaic power within a day; It is a 0-1 variable, indicating whether fluctuation occurs during this period; T represents the total number of operating hours throughout the day; represents the maximum power deviation of the photovoltaic predicted output within the day; U represents the upper limit of the number of fluctuation periods throughout the day; λ PV Indicates the maximum power deviation rate of the photovoltaic predicted output within the day.

6. The energy storage coordinated configuration method considering the multi-time uncertainty of photovoltaic output according to claim 1 is characterized in that: The steps for constructing the photovoltaic extremely short-term output uncertainty model are as follows: The Weibull distribution is used to characterize the probability distribution of photovoltaic power for 1 minute. The probability density function is as follows: Among them, x represents the 1-minute fluctuation value of photovoltaic; k is the shape parameter; λ is the scale parameter; Monte Carlo simulation of 1-minute power fluctuation was performed based on Weibull probability distribution and random sampling was performed. A total of T groups were selected, with N in each group. ss The number of sampling groups is the fluctuation of the corresponding period; Constraints on 1-minute power fluctuation values: in, Indicates the extremely short-term fluctuation power of photovoltaic power during the period t+nΔt; λ max Indicates the maximum fluctuation rate of photovoltaic power in a short period of time.

7. The energy storage coordinated configuration method considering the multi-time uncertainty of photovoltaic output according to claim 1 is characterized in that: The energy storage collaborative configuration model of the photovoltaic power station is based on the collaborative optimization configuration model of grid-type energy storage and grid-following energy storage. The energy storage collaborative configuration model of the photovoltaic power station constructs an objective function to minimize the total cost, with equipment investment constraints, photovoltaic output constraints, energy storage constraints, photovoltaic power station balance constraints, DC transmission constraints and transient stability constraints as constraints. The objective function is: minF=C Inv +C OM +C Ope C Ope =C cur +C grid Where s represents the scenario; F is the total planning cost; C Inv , C OM , C Ope They represent the annual energy storage investment cost, annual operation and maintenance cost, and annual operation cost of the photovoltaic energy storage power station respectively; Respectively represent the unit power investment cost of grid-connected energy storage and grid-building energy storage and the investment cost of unit energy storage capacity; N GFL 、N GFM 、N ESS Respectively represent the investment amount of grid-following type and grid-building type energy storage power and energy storage capacity; Respectively represent the rated power of a single device of grid-following energy storage and grid-building energy storage and the rated capacity of energy storage; Represents the daily operation and maintenance cost per unit power of energy storage; R x is the annualized investment coefficient of x; r is the discount rate; T x is the service life of the equipment; represents the operation and maintenance cost of energy storage per unit storage power; C cur represents the penalty cost of photovoltaic abandonment; C grid It indicates the cost of purchasing electricity for the PV power station to obtain frequency support from the power grid when the frequency support is insufficient; It represents the frequency support power of the power grid in the period t+nΔt; Indicates the amount of electricity purchased by the system; c cur represents the unit abandoned light cost; c grid-f represents the unit frequency support cost; c grid Represents the system electricity purchase cost; N ss Indicates the number of fluctuations in each optimization period; p s represents the occurrence probability of scene s.

8. The energy storage coordinated configuration method considering the multi-time uncertainty of photovoltaic output according to claim 1 is characterized in that: The distributed blue-rod optimization model for energy storage collaborative configuration is a two-stage distributed blue-rod optimization model for energy storage system collaborative optimization configuration: the first stage determines the energy storage system collaborative configuration plan, and the second stage is to perform simulation operation under the known first stage investment plan to solve the worst photovoltaic fluctuation scenario; The two-stage distributed robust optimization model for the coordinated optimization configuration of the energy storage system is: Among them, x, y s , s They represent investment decision variables, operation decision variables and photovoltaic output respectively; A, B, C, D, E, F, G, K, e, f, g are coefficient matrices and constraint matrices.

9. The energy storage coordinated configuration method considering the multi-time uncertainty of photovoltaic output according to claim 8 is characterized in that: The distributed robust optimization model of the energy storage collaborative configuration is iteratively solved to obtain a configuration scheme of energy storage power and capacity, including: The first stage is to solve the main problem: determine the coordinated configuration plan of the energy storage system, and the second stage is the sub-problem: under the known first stage investment plan, simulate and run to solve the worst photovoltaic fluctuation scenario; Before solving, assign initial values ​​to variables and set upper and lower bounds of iterations, with the result of solving the main problem as the lower bound and the result of solving the subproblem as the upper bound; Based on the initial value, the output curve reported by the photovoltaic power station on the day before is solved without considering the uncertainty, which serves as the benchmark data for subsequent operation; Solve the main problem, obtain the coordinated configuration scheme of the energy storage system, and assign the comprehensive cost to the lower bound; The configuration solution of the main problem is returned to the sub-problem. The sub-problem first solves the worst short-term photovoltaic fluctuation scenario based on the investment plan, then solves the probability distribution of the worst photovoltaic scenario, and calculates the new comprehensive cost to assign to the upper bound; Determine whether the deviation between the upper and lower bounds is within the allowable range. If so, end the solution. Otherwise, return the uncertainty result of the sub-problem solution to the main problem. Repeat this process to obtain the final result, which is the configuration plan for energy storage power and capacity.

10. A storage coordination configuration system considering the multi-time uncertainty of photovoltaic output, characterized in that: The system includes: A photovoltaic output multi-time scale unit is used to construct a photovoltaic output multi-time scale output uncertainty model based on the analysis of photovoltaic output characteristics; the photovoltaic output multi-time scale output uncertainty model includes a photovoltaic medium- and long-term output power uncertainty model, a photovoltaic intraday short-term power fluctuation model, and a photovoltaic very short-term output uncertainty model; The inertial support and frequency unit is used to construct an energy storage inertial support capability and frequency response model based on the analysis of the operating characteristics of the energy storage device; the operating characteristics of the energy storage device include the inertial support characteristics and primary frequency modulation characteristics of the grid-building type energy storage and the grid-following type energy storage with an additional virtual synchronous control link; An energy storage collaborative configuration model building unit is used to build an energy storage collaborative configuration model for a photovoltaic power station based on the energy storage inertial support capability and frequency response model, combined with the annualized total cost of the photovoltaic power station, investment restrictions, DC transmission requirements and frequency stability requirements; The distributed-brazil-rod optimization model construction and solution unit is used to construct a distributed-brazil-rod optimization model of energy storage coordinated configuration according to the photovoltaic output multi-time scale output uncertainty model and the energy storage coordinated configuration model of the photovoltaic power station; and iteratively solve the distributed-brazil-rod optimization model of energy storage coordinated configuration to obtain a configuration plan of energy storage power and capacity.

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