Day-ahead-real-time cooperative support method for power-energy multi-element energy storage and related device

Abstract

The invention discloses a day-ahead-real-time cooperative support method and a related device for power-energy multi-element energy storage, and belongs to the technical field of energy regulation and control, and the method comprises the following steps: obtaining photovoltaic predicted power, wind power predicted power and load predicted power of a power system, and inputting the photovoltaic predicted power, the wind power predicted power and the load predicted power to a capacity configuration optimization model at a day-ahead stage; the optimal energy capacity of the energy type energy storage and the optimal energy capacity of the power type energy storage and the standby power capacity distribution corresponding to each time period are obtained; detecting a power imbalance value of the electric power system in real time, and obtaining current residual power reserve capacity and residual energy reserve capacity of the energy type energy storage and the power type energy storage; and according to the current residual power reserve capacity and the residual energy reserve capacity, obtaining the power type energy storage residual capacity based on the updating, and completing day-ahead-real-time cooperative support for power-energy multi-element energy storage. According to the invention, coordination of capacity configuration and power release of the BESS and the SESS can be realized in the day-ahead stage and the real-time stage.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy regulation, and particularly relates to a day-ahead-real-time collaborative support method for power-energy multi-energy storage and a related device. BACKGROUND

[0002] With the increasing penetration of renewable energy (PV, WT) in power systems, the problems of volatility and uncertainty bring serious challenges to the reliability and economy of power grid operation. In the prior art, most studies focus on the configuration of energy storage in the day-ahead planning stage or separately discuss real-time scheduling, but it is difficult to consider the collaborative optimization of the two stages. On the one hand, there are problems of high operation cost or unreasonable configuration, and on the other hand, it may lead to insufficient response to real-time power deviation, affecting the stability and economy of microgrid.

[0003] In a multi-energy storage system, the energy storage system (in this application, lithium battery energy storage is taken as an example, BESS) has a large energy capacity and is suitable for long-term energy balance tasks; the power storage system (in this application, super capacitor energy storage is taken as an example, SESS) has high power capacity and charging and discharging efficiency, and is suitable for rapid response to power fluctuations. However, how to realize the collaboration of capacity configuration and power release of the two in the day-ahead and real-time two levels and reduce the operation cost of the energy storage system is a problem to be solved in the prior art. SUMMARY

[0004] The purpose of the present application is to provide a day-ahead-real-time collaborative support method for power-energy multi-energy storage and a related device, which can realize the collaboration of capacity configuration and power release of BESS (Battery Energy Storage System) and SESS (Stationary Energy Storage System) in the day-ahead stage and the real-time stage.

[0005] To achieve the above purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, a day-ahead-real-time collaborative support method for power-energy multi-energy storage includes the following steps:

[0007] Obtaining the predicted power of photovoltaic, the predicted power of wind power and the predicted power of load of the power system and inputting them to a capacity configuration optimization model in the day-ahead stage to obtain the optimal energy capacity of the energy storage and the power storage, and the standby power capacity distribution corresponding to each period, the capacity configuration optimization model in the day-ahead stage includes a target function and a constraint condition;

[0008] Real-time detect the power imbalance value of the power system, and based on the optimal energy capacity of the energy storage and the power storage, and the standby power capacity distribution corresponding to each time period, obtain the current remaining power standby capacity and the remaining energy standby capacity of the energy storage and the power storage;

[0009] According to the current remaining power standby capacity and the remaining energy standby capacity, judge whether the power imbalance value meets the remaining power standby capacity condition of the power storage, if yes, update the remaining capacity of the power storage, otherwise, the power storage updates the remaining capacity of the power storage in the way of maximum available power or exhausted energy capacity;

[0010] According to the power imbalance value and the remaining capacity of the power storage, calculate the remaining power deviation, and update the remaining capacity of the energy storage, to complete the day-ahead-real-time collaborative support for the power-energy multi-element storage.

[0011] In some embodiments, the objective function of the capacity configuration optimization model in the day-ahead stage is as follows:

[0012] min∑_t[C_PV_curt(PV_pred,t-PV,t)+C_WT_curt(WT_pred,t-WT,t)+C_BESS(P_BESS_charget,t,P_BESS_discharge,t)+C_SESS(P_SESS_charge,t,P_SESS_discharge,t)]

[0013] Wherein, PV_pred,t is the photovoltaic predicted power in the t time period, WT_pred,t is the wind power predicted power in the t time period, PV,t is the actual photovoltaic output power, WT,t is the actual wind power output power; C_PV_curt is the photovoltaic and wind power reduction cost unit, C_WT_curt is the wind power reduction cost unit; C_BESS is the charge and discharge cost function of the energy storage, C_SESS is the charge and discharge cost function of the power storage, P_BESS_charge,t is the charging power of the energy storage, P_BESS_discharge,t is the discharging power of the energy storage, P_SESS_charge is the charging power of the power storage, P_SESS_discharge,t is the discharging power of the power storage.

[0014] In some embodiments, the constraint conditions of the capacity configuration optimization model in the day-ahead stage include: photovoltaic and wind power output constraints, load output constraints, storage system power and energy constraints, power capacity constraints, energy capacity and state quantity constraints, and power balance constraints;

[0015] The photovoltaic and wind power output constraints are as follows:

[0016] 0≤PV,t≤PV_pred,t,0≤WT,t≤WT_pred,t

[0017] wherein PV,t is the actual power output of the photovoltaic, WT,t is the actual power output of the wind turbine, PV_pred,t is the predicted power output of the photovoltaic in the tth time period, and WT_pred,t is the predicted power output of the wind turbine in the tth time period;

[0018] The load power constraint is as follows:

[0019] P_load,t = P_load_pred,t

[0020] wherein P_load,t is the actual load power, and P_load_pred,t is the predicted load power;

[0021] The power and energy constraints of the energy storage system are directly separating the charging and discharging power of the energy storage system;

[0022] The power capacity constraint is as follows:

[0023] 0≤P_BESS_charge,t,P_BESS_discharge,t≤r_BESSE_BESS_cap

[0024] 0≤P_SESS_charge,t,P_SESS_discharge,t≤r_SESSE_SESS_cap

[0025] wherein r_BESS is the power-capacity ratio of the energy storage, r_SESS is the power-capacity ratio of the power storage, E_BESS_cap is the energy capacity of the BESS, E_SESS_cap is the energy capacity of the SESS, P_BESS_charge,t is the charging power of the energy storage, P_BESS_discharge,t is the discharging power of the energy storage, P_SESS_charge,t is the charging power of the power storage, and P_SESS_discharge,t is the discharging power of the power storage;

[0026] The energy capacity and state quantity constraint is as follows:

[0027] 0≤E_BESS,t≤E_BESS_cap,0≤E_SESS,t≤E_SESS_cap

[0028] wherein E_BESS,t is the stored energy of the BESS in the tth time period, and E_SESS,t is the stored energy of the SESS in the tth time period;

[0029] The power balance constraint is given by the following formula:

[0030] PV,t+WT,t+P_SESS_discharge,t+P_BESS_discharge,t=P_load,t+P_SESS_charge,t+P_BESS_charge,t.

[0031] In some embodiments, the remaining power reserve capacity condition of the power-type energy storage is:

[0032] |ΔP_t|≤R_SESS_p,t and |ΔP_t|Δt≤R_SESS_e,t

[0033] Where ΔP_t is the power loss measure, R_SESS_p,t is the remaining power reserve capacity of SESS at time t, and R_SESS_e,t is the remaining energy reserve capacity of SESS at time t.

[0034] The remaining capacity of the updated power storage is specifically expressed by the following formula:

[0035] R_SESS_p,t+1=R_SESS_p,t-|ΔP_t|,R_SESS_e,t+1

[0036] =R_SESS_e,t-|ΔP_t|Δt

[0037] Where R_SESS_p,t+1 is the remaining power reserve capacity of SESS at time t+1, and R_SESS_e,t+1 is the remaining energy reserve capacity of SESS at time t+1.

[0038] In some embodiments, the step of updating the remaining capacity of the power-type energy storage by means of maximum available power or depleted energy capacity specifically includes:

[0039] The power-type energy storage calculates the remaining power reserve capacity based on the maximum available power, and at the same time calculates the remaining energy reserve capacity. The smaller value between the remaining power reserve capacity and the remaining energy reserve capacity is used to update the remaining capacity of the power-type energy storage.

[0040] In some implementations, when updating the remaining capacity of the energy storage, the remaining power deviation satisfies the following condition:

[0041] |ΔP_BESS|≤R_BESS_p,t and |ΔP_BESS|Δt≤R_BESS_e,t

[0042] The remaining capacity of the updated energy storage is expressed by the following formula:

[0043] R_BESS_p,t+1 = R_BESS_p,t - |ΔP_BESS|, R_BESS_e,t+1 = R_BESS_e,t - |ΔP_BESS|Δt

[0044] = R_BESS_e,t - |ΔP_BESS|Δt

[0045] wherein R_BESS_p,t is the remaining power reserve capacity of the BESS at the tth time, R_BESS_e,t is the remaining energy reserve capacity of the BESS at the tth time, R_BESS_p,t+1 is the remaining power reserve capacity of the BESS at the t+1th time, R_BESS_e,t+1 is the remaining energy reserve capacity of the BESS at the t+1th time, and ΔP_BESS is the remaining power deviation of the BESS.

[0046] In a second aspect, a day-ahead-real-time collaborative support system for power-energy multi-element energy storage includes:

[0047] A day-ahead joint capacity and reserve capacity optimization module is configured to obtain predicted photovoltaic power, predicted wind power, and predicted load power of a power system and input the predicted photovoltaic power, the predicted wind power, and the predicted load power to a capacity configuration optimization model in a day-ahead stage to obtain optimal energy capacity of energy-type energy storage and power-type energy storage and reserve power capacity distribution corresponding to each time period, wherein the capacity configuration optimization model in the day-ahead stage includes a target function and a constraint condition.

[0048] A real-time detection module is configured to detect a power imbalance value of the power system in real time and obtain current remaining power reserve capacity and remaining energy reserve capacity of the energy-type energy storage and the power-type energy storage based on the optimal energy capacity of the energy-type energy storage and the power-type energy storage and the reserve power capacity distribution corresponding to each time period.

[0049] A real-time remaining capacity updating module is configured to determine whether the power imbalance value satisfies a remaining power reserve capacity condition of the power-type energy storage according to the current remaining power reserve capacity and the remaining energy reserve capacity, update the remaining capacity of the power-type energy storage if the power imbalance value satisfies the remaining power reserve capacity condition, otherwise, update the remaining capacity of the power-type energy storage by using the maximum available power or the exhausted energy capacity, calculate a remaining power deviation based on the power imbalance value and the remaining capacity of the power-type energy storage, and update the remaining capacity of the energy-type energy storage to complete the day-ahead-real-time collaborative support for the power-energy multi-element energy storage.

[0050] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, and the processor implements the steps of the day-ahead-real-time collaborative support method for the power-energy multi-element energy storage when executing the computer program.

[0051] In a fourth aspect, a computer readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for day-ahead-real-time collaborative support of power-energy multi-energy storage.

[0052] In a fifth aspect, a computer program product comprises a computer program, which, when executed by a processor, implements the steps of the method for day-ahead-real-time collaborative support of power-energy multi-energy storage.

[0053] Compared with the prior art, the present application has the following beneficial effects:

[0054] The present application provides a method for day-ahead-real-time collaborative support of power-energy multi-energy storage. In the day-ahead stage, the storage capacity and standby allocation are determined through an optimization model to avoid emergency costs caused by insufficient capacity in the real-time stage. In the real-time stage, a regular allocation is adopted to avoid complex optimization calculations and achieve millisecond-level response. At the same time, energy storage (BESS) and power storage (SESS) are jointly dispatched to take into account the energy storage and power support requirements and improve the system's ability to accommodate fluctuating renewable energy. The present application can realize the collaborative capacity configuration and power release of BESS and SESS in the day-ahead stage and the real-time stage.

[0055] Further, when updating the remaining capacity, the present application takes the smaller value of the remaining power capacity and the remaining energy capacity to ensure that the SESS outputs the maximum power within a safe range and avoids capacity waste. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 A schematic diagram of power storage and energy storage cooperating with each other to support power deviation in the real-time stage according to the day-ahead dispatching plan;

[0057] Figure 2 A flowchart of the method for day-ahead-real-time collaborative support of power-energy multi-energy storage provided by the embodiment of the present application;

[0058] Figure 3 A structural diagram of the system for day-ahead-real-time collaborative support of power-energy multi-energy storage provided by the embodiment of the present application. DETAILED DESCRIPTION

[0059] In order for those skilled in the art to better understand the present application, the technical solutions of the present application will be further described in detail below with reference to the accompanying drawings, which are explanatory rather than limiting.

[0060] It is to be understood that the terms "including", "containing", "having" and variations thereof in the specification and claims of the application shall be interpreted as encompassing not only the presence of the stated features, but also the presence of other features not specifically stated. It is also to be understood that the terminology used in the description is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0061] As shown in the embodiment, the day-ahead-real-time collaborative support method for power-energy multi-element energy storage is provided, including the following steps: Figure 2

[0062] The photovoltaic predicted power, wind power predicted power and load predicted power of the power system are acquired and input to a capacity configuration optimization model in a day-ahead stage to obtain optimal energy capacity of energy storage and power storage, and reserve power capacity distribution corresponding to each period, the capacity configuration optimization model in the day-ahead stage including a target function and a constraint condition;

[0063] The power imbalance value of the power system is detected in real time, and based on the optimal energy capacity of energy storage and power storage, and the reserve power capacity distribution corresponding to each period, current remaining power reserve capacity and remaining energy reserve capacity of energy storage and power storage are acquired;

[0064] According to the current remaining power reserve capacity and the remaining energy reserve capacity, it is judged whether the power imbalance value meets the remaining power reserve capacity condition of power storage, if yes, the remaining capacity of power storage is updated, otherwise, the remaining capacity of power storage is updated by the maximum available power or the exhausted energy capacity; the remaining power deviation is calculated according to the power imbalance value and the remaining capacity of power storage, and the remaining capacity of energy storage is updated, to complete the day-ahead-real-time collaborative support for power-energy multi-element energy storage.

[0065] The specific content is as follows:

[0066] 1. Joint capacity and reserve capacity optimization in the day-ahead stage

[0067] (1) Modeling objective

[0068] In the day-ahead stage, the predicted power of photovoltaic (PV), wind power (WT) and load is taken as input, according to the demand of micro-grid power balance, while considering the operation cost of energy storage system, the following target function (economic optimization or mixed economic-stability target can be selected) is optimized:

[0069] ​min∑_t[C_PV_curt(PV_pred,t-PV,t)+C_WT_curt(WT_pred,t-WT,t)+C_BESS(P_BESS_charge,t,P_BESS_discharge,t)+C_SESS(P_SESS_charge,t,P_SESS_discharge,t)]

[0070] Wherein, PV_pred,t, WT_pred,t are the PV and WT predicted power in the t period, PV,t, WT,t are the actual power output; C_PV_curt, C_WT_curt are the PV and WT curtailment cost unit; C_BESS, C_SESS are the charge and discharge cost function of energy and power storage, P_BESS_charge,t is the charge power of energy storage, P_BESS_discharge,t is the discharge power of energy storage, P_SESS_charge is the charge power of power storage, P_SESS_discharge,t is the discharge power of power storage.

[0071] (2) Constraint conditions

[0072] PV and WT power output constraints:

[0073] 0≤PV,t≤PV_pred,t, 0≤WT,t≤WT_pred,t

[0074] Wherein, the upper limit is the predicted power multiplied by the unit of the respective installed capacity, to reflect the reduction behavior.

[0075] Load power output constraints:

[0076] P_load,t=P_load_pred,t

[0077] Wherein, P_load,t is the actual load power, P_load_pred,t is the predicted load power.

[0078] Energy storage system power and energy constraints:

[0079] For BESS and SESS, set the charge power P_BESS_charge,t, P_SESS_charge,t, and the discharge power P_BESS_discharge,t, P_SESS_discharge,t. Since the objective function in the present application has considered the charge and discharge cost, the charge and discharge mutual exclusion constraint can not be set, and the charge and discharge power can be separated directly.

[0080] Power capacity constraints:

[0081] 0 < P_BESS_charge,t, P_BESS_discharge,t < r_BESS * E_BESS_cap

[0082] 0 < P_SESS_charge,t, P_SESS_discharge,t < r_SESS * E_SESS_cap

[0083] Where, r_BESS, r_SESS are the power-capacity ratio of energy storage and power storage respectively; E_BESS_cap, E_SESS_cap are the energy capacity of BESS and SESS respectively.

[0084] Energy capacity and state constraints:

[0085] 0 < E_BESS,t < E_BESS_cap, 0 < E_SESS,t < E_SESS_cap

[0086] Where, the state of charge of energy storage system is updated as:

[0087] E_*,t = E_*,t-1 + η_*_chP_*_charge,tΔt - (1 / η_*_dis)P_*_discharge,tΔt

[0088] * can be replaced by BESS or SESS, η_*_ch, η_*_dis are the charging and discharging efficiency respectively.

[0089] Power balance constraints (islanded microgrid mode)

[0090] PV,t + WT,t + P_SESS_discharge,t + P_BESS_discharge,t

[0091] = P_load,t + P_SESS_charge,t + P_BESS_charge,t

[0092] This formula ensures the dynamic balance between power generation, load and energy storage charging in the t period.

[0093] (3) Energy storage capacity and reserve capacity optimization in day-ahead stage

[0094] Through the above objective function and constraint conditions, the optimal E_BESS_cap, E_SESS_cap and the corresponding reserve power capacity (Reserve Capacity) distribution in each period can be obtained, so that BESS and SESS can ensure the economic operation of the entire microgrid, and also take into account the sufficient redundancy to cope with power deviation and energy regulation demand in the future real-time stage.

[0095] 2. Real-time stage regularized power allocation strategy

[0096] After the configuration of BESS and SESS capacity and reserve capacity is completed in the day-ahead stage, the real-time stage adopts the following regularized algorithm for dynamic allocation according to the real-time detected power imbalance (Power Imbalance), without calling the complex mathematical programming model again, thereby reducing the real-time calculation amount and accelerating the response speed. The specific content is as follows:

[0097] (1) Preliminary explanation

[0098] Since the power energy storage (SESS) has higher charging and discharging efficiency and lower operation cost, the application preferentially calls SESS for power imbalance compensation in the real-time stage. When the power reserve capacity or energy reserve capacity of SESS is exhausted, the energy storage (BESS) is called to supplement the remaining demand.

[0099] (2) Algorithm flow

[0100] Input: Real-time measured power imbalance ΔP_t (positive or negative value, positive value indicates that the load is greater than the power generation and needs to be discharged for compensation; negative value indicates that the power generation is greater than the load and needs to be stored)

[0101] SESS current remaining power reserve capacity R_SESS_p,t and remaining energy reserve capacity R_SESS_e,t;

[0102] BESS current remaining power reserve capacity R_BESS_p,t and remaining energy reserve capacity R_BESS_e,t.

[0103] Step 1: judge |ΔP_t|≤R_SESS_p,t and |ΔP_t|Δt≤R_SESS_e,t.

[0104] If both conditions are met, only SESS outputs or absorbs power ΔP_t, and the remaining capacity of SESS is updated by the following formula:

[0105] R_SESS_p,t+1=R_SESS_p,t-|ΔP_t|,R_SESS_e,t+1=R_SESS_e,t-|ΔP_t|Δt.

[0106] End of allocation, no need to call BESS in this period.

[0107] Step 2: Otherwise, first use SESS to exhaust the maximum available power ΔP_SESS=sgn(ΔP_t)×R_SESS_p,t or energy capacity ΔP_SESS=sgn(ΔP_t)×(R_SESS_e,t / Δt) (whichever is exhausted first), and update the remaining capacity of SESS.

[0108] Step 3: Calculate the remaining power deviation ΔP_res = ΔP_t - ΔP_SESS.

[0109] Step 4: Provide the remaining power ΔP_BESS = ΔP_res by the BESS, ensure that |ΔP_BESS| ≤ R_BESS_p,t and |ΔP_BESS|Δt ≤ R_BESS_e,t.

[0110] Step 5: Update the remaining capacity of the BESS:

[0111] R_BESS_p,t+1 = R_BESS_p,t - |ΔP_BESS|, R_BESS_e,t+1 = R_BESS_e,t - |ΔP_BESS|Δt.

[0112] Step 6: If the BESS backup capacity is insufficient, the system needs to trigger additional emergency measures (such as load reduction, dump or external networking support, etc.).

[0113] The pseudo code of the above algorithm is as follows:

[0114]

[0115] Based on the above day-ahead-real-time collaborative support method for power-energy multi-element energy storage, experimental simulation is carried out:

[0116] Set the PV and WT power prediction curve and load curve within 24 hours, the capacity is P_PV_max = 5MW, P_WT_max = 3MW, and the average load is 3MW.

[0117] According to the day-ahead model, the BESS capacity is 5MWh, the SESS capacity is 1MWh, and the power-capacity ratio is 0.2h-1 (BESS) and 1h-1 (SESS) respectively.

[0118] At a certain period (for example, 14 o'clock), the actual PV and WT combined output deviation is +1.2MW (power shortage), the algorithm makes the SESS 1MW output, and the remaining 0.2MW is compensated by the BESS, which maintains the grid balance at peak and valley alternately without manual intervention.

[0119] The simulation results show that, compared with the scheme of using only BESS or only SESS, the overall operation cost of the energy storage system can be reduced by about 8% and the maximum grid frequency deviation can be reduced by 15% by using the scheme of the application.

[0120] Only relying on BESS: Because the charging and discharging efficiency of BESS (for example, 0.9) is lower than that of SESS (for example, 0.98), in the high-frequency power fluctuation scenario, frequent charging and discharging can cause large energy loss;

[0121] Only SESS: Although high efficiency, but the energy capacity is not enough to deal with the long period of time of power deviation, capacity design needs excessive redundancy, resulting in high construction cost;

[0122] As Figure 1 shown, the embodiment combines the advantages of both, the day-ahead stage has clear division of labor (SESS mainly undertakes short-term high-frequency power fluctuation, and BESS mainly undertakes cross-period or large-scale power deviation), and the real-time stage can quickly respond and minimize operating cost, which is significantly superior to single solution in terms of economy and reliability.

[0123] Therefore, the method provided by the embodiment has the following advantages: first, in the day-ahead stage, the optimal capacity and standby capacity distribution of BESS and SESS are determined jointly, the high-efficiency charging and discharging characteristics of SESS can be fully utilized, energy loss and overall operating cost in the real-time stage are reduced; the regularized distribution algorithm in the real-time stage further avoids time and equipment overhead caused by complex optimization calculation; second, the simple regularized real-time distribution logic can quickly respond to power deviation in milliseconds, greatly improving the system's ability to resist fluctuations; at the same time, the remaining capacity of the two kinds of energy storage is taken into account, ensuring that there is still redundant standby in the case of rare large-scale power fluctuation; third, the regularized real-time distribution algorithm does not need to be embedded in high-dimensional mathematical programming, and is easy to integrate and implement in existing microgrid controllers or energy storage coordination control systems; the planning in the day-ahead stage can be completed on a daily dispatching platform or an offline server, without affecting real-time control.

[0124] As Figure 3 shown, the embodiment provides a day-ahead-real-time collaborative support system for power-energy multi-element energy storage, including:

[0125] A day-ahead joint capacity and standby capacity optimization module is configured to obtain predicted photovoltaic power, predicted wind power and predicted load power of a power system and input the predicted photovoltaic power, the predicted wind power and the predicted load power to a capacity configuration optimization model in a day-ahead stage to obtain optimal energy capacity of energy storage and optimal energy capacity of power storage, and obtain standby power capacity distribution corresponding to each period, the capacity configuration optimization model in the day-ahead stage including a target function and a constraint condition;

[0126] A real-time detection module is configured to detect a power imbalance value of the power system in real time, and based on the optimal energy capacity of the energy storage and the optimal energy capacity of the power storage, and the standby power capacity distribution corresponding to each period, obtain current remaining power standby capacity and remaining energy standby capacity of the energy storage and the power storage;

[0127] A real-time residual capacity updating module is configured to determine whether the power imbalance value meets a residual power backup capacity condition of the power-type energy storage according to the current residual power backup capacity and the residual energy backup capacity, update the residual capacity of the power-type energy storage if the residual capacity meets the residual power backup capacity condition, or update the residual capacity of the power-type energy storage in a manner of maximum available power or exhausted energy capacity if the residual capacity does not meet the residual power backup capacity condition; calculate a residual power deviation according to the power imbalance value and the residual capacity of the power-type energy storage, and update the residual capacity of the energy-type energy storage, thereby completing day-ahead-real-time collaborative support for the power-energy multi-element energy storage.

[0128] The division of the modules in the embodiments of the present application is illustrative, and is merely a logical function division. In actual implementation, another division manner can be used. In addition, each function module in each embodiment of the present application can be integrated in one processor, or can be a separate physical existence, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0129] The embodiment further provides a computer device including a processor and a memory. The memory is used to store a computer program (the computer program in the embodiment includes a calculation component and an iteration component, and can perform model calculation and model updating). The computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor is a computing core and a control core of the terminal, and is suitable for implementing one or more instructions. Specifically, the processor is suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function. The processor in the embodiment of the present application can be used for the operation of the day-ahead-real-time collaborative support method for the power-energy multi-element energy storage and the related device.

[0130] The embodiment further provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer device and is used to store programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium in the computer device, and of course can include an extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, which stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the day-ahead-real-time collaborative support method for power-energy multi-element energy storage and the related device in the above embodiment.

[0131] The embodiment further provides a computer program product, which includes a computer program. When the computer program is executed by the processor, the corresponding steps of the day-ahead-real-time collaborative support method for power-energy multi-element energy storage and the related device in the above embodiment are implemented.

[0132] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0133] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The function of one flow or multiple flows and / or blocks Figure 1 The function of one block or multiple blocks.

[0134] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0136] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered in the protection scope of the claims of the present application.

Claims

1. A day-ahead-real-time coordinated support method for power-energy multi-element energy storage, characterized in that, Includes the following steps: The predicted power of photovoltaic power, wind power, and load of the power system are obtained and input into the day-ahead capacity configuration optimization model to obtain the optimal energy capacity of energy storage and power storage, as well as the corresponding reserve power capacity allocation for each time period. The day-ahead capacity configuration optimization model includes: objective function and constraints. Real-time detection of power imbalance in the power system, and based on the optimal energy capacity of energy storage and power storage, as well as the corresponding allocation of reserve power capacity for each time period, to obtain the current remaining power reserve capacity and remaining energy reserve capacity of energy storage and power storage. Based on the current remaining power reserve capacity and remaining energy reserve capacity, it is determined whether the power imbalance value meets the remaining power reserve capacity condition of power-type energy storage. If it does, the remaining capacity of power-type energy storage is updated; otherwise, the remaining capacity of power-type energy storage is updated by using the maximum available power or the exhausted energy capacity. The remaining power deviation is calculated based on the power imbalance value and the remaining capacity of power-type energy storage, and the remaining capacity of energy-type energy storage is updated to complete the day-ahead-real-time coordinated support for power-energy multi-electrode energy storage.

2. The day-ahead-real-time coordinated support method for power-energy multi-element energy storage according to claim 1, characterized in that, The objective function of the capacity allocation optimization model for the current phase is as follows: min∑_t[C_PV_curt(PV_pred,t-PV,t)+C_WT_curt(WT_pred,t-WT,t)+C_BESS(P_BESS_charge,t,P_BESS_discharge,t)+C_SESS(P_SESS_charge,t,P_SESS_discharge,t)] Wherein, PV_pred,t is the predicted photovoltaic power in time period t, WT_pred,t is the predicted wind power in time period t, PV,t is the actual photovoltaic power output, and WT,t is the actual wind power output; C_PV_curt is the cost reduction unit for photovoltaic and wind power, and C_WT_curt is the cost reduction unit for wind power; C_BESS is the charging and discharging cost function for energy storage, C_SESS is the charging and discharging cost function for power storage, P_BESS_charge,t is the charging power of energy storage, P_BESS_discharge,t is the discharging power of energy storage, P_SESS_charge is the charging power of power storage, and P_SESS_discharge,t is the discharging power of power storage.

3. The day-ahead-real-time coordinated support method for power-energy multi-element energy storage according to claim 1, characterized in that, The constraints of the capacity configuration optimization model in the current phase include: photovoltaic and wind power output constraints, load output constraints, energy storage system power and energy constraints, power capacity constraints, energy capacity and state quantity constraints, and power balance constraints. The constraints on photovoltaic and wind power output are as follows: 0≤PV,t≤PV_pred,t,0≤WT,t≤WT_pred,t Where PV,t is the actual power output of photovoltaics, WT,t is the actual power output of wind power, PV_pred,t is the predicted power output of photovoltaics in time period t, and WT_pred,t is the predicted power output of wind power in time period t. The load output constraint is given by the following formula: P_load,t = P_load_pred,t Where P_load,t is the actual load power and P_load_pred,t is the predicted load power; The power and energy constraints of the energy storage system are achieved by directly separating the charging and discharging power of the energy storage system. The power capacity constraint is given by the following formula: 0≤P_BESS_charge,t,P_BESS_discharge,t≤r_BESSE_BESS_cap 0≤P_SESS_charge,t,P_SESS_discharge,t≤r_SESSE_SESS_cap Where r_BESS is the power-capacity ratio of energy storage, r_SESS is the power-capacity ratio of power storage, E_BESS_cap is the energy capacity of BESS, E_SESS_cap is the energy capacity of SESS, P_BESS_charge,t is the charging power of energy storage, P_BESS_discharge,t is the discharging power of energy storage, P_SESS_charge,t is the charging power of power storage, and P_SESS_discharge,t is the discharging power of power storage. The energy capacity and state constraints are given by the following formula: 0≤E_BESS,t≤E_BESS_cap,0≤E_SESS,t≤E_SESS_cap Where E_BESS,t is the storage energy of BESS in time period t, and E_SESS,t is the storage energy of SESS in time period t. The power balance constraint is given by the following formula: PV,t+WT,t+P_SESS_discharge,t+P_BESS_discharge,t=P_load,t+P_SESS_charge,t+P_BESS_charge,t.

4. The day-ahead-real-time coordinated support method for power-energy multi-element energy storage according to claim 1, characterized in that, The remaining power reserve capacity condition for the power-type energy storage is as follows: |ΔP_t|≤P_SESS_p,t and |ΔP_t|Δt≤R_SESS_e,t Where ΔP_t is the power loss measure, P_SESS_p,t is the remaining power reserve capacity of SESS at time t, and R_SESS_e,t is the remaining energy reserve capacity of SESS at time t. The remaining capacity of the updated power storage is specifically expressed by the following formula: R_SESS_p,t+1=R_SESS_p,t-|ΔP_t|, R_SESS_e,t+1=R_SESS_e,t-|ΔP_t|Δt Where R_SESS_p,t+1 is the remaining power reserve capacity of SESS at time t+1, and R_SESS_e,t+1 is the remaining energy reserve capacity of SESS at time t+1.

5. The day-ahead-real-time coordinated support method for power-energy multi-element energy storage according to claim 1, characterized in that, The step of updating the remaining capacity of the power-type energy storage by means of maximum available power or depleted energy capacity specifically includes: The power-type energy storage calculates the remaining power reserve capacity based on the maximum available power, and at the same time calculates the remaining energy reserve capacity. The smaller value between the remaining power reserve capacity and the remaining energy reserve capacity is used to update the remaining capacity of the power-type energy storage.

6. The day-ahead-real-time coordinated support method for power-energy multi-element energy storage according to claim 1, characterized in that, When updating the remaining capacity of the energy storage, the remaining power deviation satisfies the following condition: |ΔP_BESS|≤R_BESS_p,t and |ΔP_BESS|Δt≤R_BESS_e,t The remaining capacity of the updated energy storage is expressed by the following formula: R_BESS_p,t+1=R_BESS_p,t-|ΔP_BESS|, R_BESS_e,t+1=R_BESS_e,t-|ΔP_BESS|Δt Where R_BESS_p,t is the remaining power reserve capacity of BESS at time t, R_BESS_e,t is the remaining energy reserve capacity of BESS at time t, R_BESS_p,t+1 is the remaining power reserve capacity of BESS at time t+1, R_BESS_e,t+1 is the remaining energy reserve capacity of BESS at time t+1, and ΔP_BESS is the remaining power deviation of BESS.

7. A day-ahead-real-time coordinated support system for power-energy multi-element energy storage, characterized in that, include: The day-ahead joint capacity and reserve capacity optimization module is used to obtain the photovoltaic power forecast, wind power forecast, and load forecast of the power system and input them into the day-ahead capacity configuration optimization model to obtain the optimal energy capacity of energy storage and power storage, as well as the corresponding reserve power capacity allocation for each time period. The day-ahead capacity configuration optimization model includes: objective function and constraints. The real-time detection module is used to detect the power imbalance value of the power system in real time, and based on the optimal energy storage and power storage capacity, as well as the corresponding reserve power capacity allocation for each time period, obtain the current remaining power reserve capacity and remaining energy reserve capacity of energy storage and power storage. The real-time remaining capacity update module is used to determine whether the power imbalance value meets the remaining power reserve capacity condition of power-type energy storage based on the current remaining power reserve capacity and remaining energy reserve capacity. If it does, the remaining capacity of power-type energy storage is updated; otherwise, the remaining capacity of power-type energy storage is updated by using the maximum available power or the exhausted energy capacity. The remaining power deviation is calculated based on the power imbalance value and the remaining capacity of power-type energy storage, and the remaining capacity of energy-type energy storage is updated to complete the day-ahead-real-time coordinated support for power-energy multi-electrode energy storage.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of the day-ahead-real-time coordinated support method for power-energy multi-element storage as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the day-ahead-real-time coordinated support method for power-energy multi-element energy storage as described in any one of claims 1 to 6.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the day-ahead-real-time collaborative support method for power-energy multi-element energy storage as described in any one of claims 1 to 6.

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