Hybrid energy storage system power distribution method and device considering active deviation assessment

Abstract

The invention provides a hybrid energy storage system power distribution method and device considering active power deviation assessment, and the method comprises the steps: carrying out the adaptive energy adjustment of a lithium battery energy storage subunit in a new energy station hybrid energy storage system according to an electricity price when a new energy station is in an active power output deviation non-assessment time period, the super-capacitor energy storage subunit performs adaptive energy adjustment by taking 50% of SOC as an adjustment direction; in an active power output deviation assessment period, a hybrid energy storage system power distribution optimization model is established and solved to obtain an optimization result of the output of the lithium battery energy storage subunit and the total output of the super capacitor energy storage subunit; and establishing a super-capacitor energy storage subunit power distribution optimization model and solving to obtain a super-capacitor energy storage subunit output optimization result. The problem that after hybrid energy storage is configured in a new energy station, different types of energy storage resources are fully utilized to reduce power deviation assessment, and extra operation cost is brought can be solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of new energy station operation and energy storage control, and particularly relates to a mixed energy storage system power distribution method and device considering active deviation assessment, and is particularly suitable for real-time control scenarios of a station side configured with lithium batteries and flywheels / super capacitors and other power type and energy type energy storage. BACKGROUND

[0002] In recent years, the new energy industry has developed rapidly, and the installed capacity has increased significantly. New energy power generation participating in the power market will be subject to active power deviation assessment. From the perspective of engineering practice, for the minute-level demand of ultra-short-term power prediction deviation assessment, flywheels, mixed super capacitors, lithium iron phosphate batteries (lithium batteries), and other energy storage devices can be selected. Considering that the wind power station power prediction assessment time sequence is 96 instantaneous values per day, if the prediction deviation is to be avoided, the number of daily energy storage actions needs to be at least about 30. The initial investment of 2-minute mixed super capacitors and 1-hour lithium batteries is basically the same (the initial investment of flywheels is high, which is not considered in this scenario), and both can meet the demand of this scenario, but the cycle life of mixed super capacitors is more than 100,000 times, and the battery does not need to be replaced in the whole life cycle. The cycle life of lithium iron phosphate is about 6000 times, and the annual attenuation rate is about 2%, and the battery needs to be replaced after 13 years. Therefore, mixed super capacitors are more economical in this scenario. Based on the development of lithium battery technology, the industry application occupies a dominant position at the present stage, and has significant economic benefits in the energy type energy storage route, so for the demand of hour-level length and low-frequency calling, the configuration of lithium batteries has significant advantages.

[0003] The patent "A mixed energy storage power smoothing optimization method" (CN202410351118.2) discloses a mixed energy storage power smoothing optimization method, which relates to the field of new energy power generation control technology. After obtaining the mixed energy storage power, the method first decomposes and processes the power using a variational mode decomposition algorithm to obtain multiple modal components, and then optimizes the parameters of the variational mode decomposition algorithm in advance using the Sparrow algorithm. Then, the multiple modal components are subjected to Hilbert transform processing to obtain super capacitor power and battery power. Finally, the super capacitor power and battery power pass through the constraint layer, selection layer, optimization layer and compensation layer in the multi-layer energy management structure in turn, so that the state of charge of the mixed energy storage is optimized while the mixed energy storage power is smoothed. This not only achieves the global optimal effect of VMD (variational mode decomposition) parameters, but also optimizes the power redistribution of super capacitors and batteries based on SOC, so that the mixed energy storage is optimally distributed and the system is safely, stably and efficiently operated. The disadvantage of this technology is that it does not show the running aging cost of lithium batteries, and the modal decomposition method based on experience is not enough to achieve the optimal solution of the total running cost in the actual running stage. At the same time, this method only considers the wind power smoothing in a period of time, and does not consider the examination and punishment caused by the deviation between the output and clearing value of the new energy station in the existing rules. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art, and to provide a mixed energy storage system power distribution method and device considering active deviation examination. The present application can solve the problem of reducing the additional running cost caused by power deviation examination after configuring mixed energy storage in a new energy station, so as to improve the operation efficiency of the new energy station.

[0005] The first aspect of the present application provides a mixed energy storage system power distribution method considering active deviation examination, comprising:

[0006] When the new energy station is in the non-examination period of active power output deviation, the lithium battery energy storage subunit in the mixed energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, and the super capacitor energy storage subunit in the mixed energy storage system adjusts the adaptive energy with SOC as 50% as the adjustment direction;

[0007] When the new energy station is in the active power output deviation examination period, the optimization results of each lithium battery energy storage subunit output and the total output of the super capacitor energy storage subunit are obtained by establishing a mixed energy storage system power distribution optimization model and solving it. Then, the optimization results of each super capacitor energy storage subunit output are obtained by establishing a super capacitor energy storage subunit power distribution optimization model and solving it.

[0008] In one specific embodiment of the present application, the lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, including:

[0009] According to the day-ahead clearing time-of-use electricity price result, in the high electricity price period, the lithium battery energy storage subunit discharges according to the maximum feasible discharge power; in the low electricity price period, the lithium battery energy storage subunit charges according to the maximum feasible charging power; in the medium electricity price period, the lithium battery energy storage subunit adjusts the adaptive energy to the direction of SOC being 50%;

[0010] Wherein, the power calculation expression of the medium electricity price period is:

[0011]

[0012] In the formula, is the target power value of the lithium battery energy storage subunit at time ; is the power lower limit of the lithium battery energy storage subunit at time ; is the power upper limit of the lithium battery energy storage subunit at time ; is the state of charge of the lithium battery energy storage subunit at time ; is the capacity of the lithium battery energy storage subunit ; is the length of a single power control period.

[0013] In one specific embodiment of the present application, the supercapacitor energy storage subunit in the hybrid energy storage system adjusts the adaptive energy with SOC being 50% as the adjustment direction, and the expression is as follows:

[0014]

[0015] In the formula, is the target power value of the supercapacitor energy storage subunit at time ; is the power lower limit of the supercapacitor energy storage subunit at time ; is the power upper limit of the supercapacitor energy storage subunit at time ; is the state of charge of the supercapacitor energy storage subunit at time ; Capacity of supercapacitor energy storage subunit , Length of single power control period.

[0016] In one specific embodiment of the present application, further comprising:

[0017] The objective function of the hybrid energy storage system power distribution optimization model is as follows:

[0018]

[0019] Wherein, is the power deviation penalty price at time , is the number of lithium battery energy storage subunits, is the output power of the lithium battery energy storage subunit at time , is the output power of the supercapacitor energy storage subunit at time , is the sum of the powers of each supercapacitor energy storage subunit; the positive power of the hybrid energy storage system represents discharging, and the negative power represents charging; is the lithium battery aging cost at time , is the target power of the hybrid energy storage system at time , Length of single power control period.

[0020]

[0021] Wherein, , are the real-time clearing station power and new energy output at time , and are the upper and lower thresholds of the new energy station power deviation, respectively; is the day-ahead clearing time-of-use price at time , , are the high and low thresholds of the day-ahead clearing price, respectively;

[0022] The constraint conditions of the hybrid energy storage system power distribution optimization model include:

[0023]

[0024]

[0025]

[0026]

[0027] Wherein, lithium battery energy storage subunit at time lower bound of power feasible region, super capacitor energy storage subunit at time lower bound of power feasible region, lithium battery energy storage subunit at time upper bound of power feasible region, super capacitor energy storage subunit at time upper bound of power feasible region; number of super capacitor energy storage subunits lithium battery energy storage subunit at time power change amount compared to the previous time period;

[0028]

[0029]

[0030]

[0031] wherein, power limit value of energy storage subunit caused by PCS temperature at time , the energy storage subunit including lithium battery energy storage subunit and super capacitor energy storage subunit; rated power of energy storage subunit at time PCS temperature of energy storage subunit at time first temperature model parameter and second temperature model parameter of energy storage subunit at time , at time at time upper and lower limits of SOC operation of hybrid energy storage system; and upper and lower limits of SOC operation of hybrid energy storage system;

[0032] solving the hybrid energy storage system power distribution optimization model to obtain and optimization results.

[0033] In one specific embodiment of the present application, further comprising:

[0034] the objective function of the super capacitor energy storage subunit power distribution optimization model is:

[0035]

[0036] wherein,

[0037]

[0038]

[0039] wherein, is the super capacitor operation efficiency; is the SOC average value of the super capacitor energy storage subunit at the next time point;

[0040] The constraint condition of the super capacitor energy storage subunit power distribution optimization model is:

[0041]

[0042] Solving the super capacitor energy storage subunit power distribution optimization model obtains the optimization result of .

[0043] The second aspect of the application is a hybrid energy storage system power distribution device considering active deviation assessment, comprising:

[0044] The non-assessment period power distribution module is used for when the new energy station is in the active output deviation non-assessment period, the lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, and the super capacitor energy storage subunit in the hybrid energy storage system adjusts the adaptive energy with SOC as 50% as the adjustment direction;

[0045] The assessment period power distribution module is used for when the new energy station is in the active output deviation assessment period, by establishing a hybrid energy storage system power distribution optimization model and solving, the optimization result of each lithium battery energy storage subunit output and the total output of the super capacitor energy storage subunit is obtained; then by establishing a super capacitor energy storage subunit power distribution optimization model and solving, the optimization result of each super capacitor energy storage subunit output is obtained.

[0046] In one specific embodiment of the application, the lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, comprising:

[0047] According to the day-ahead clearing time-of-use electricity price result, in the high electricity price period, the lithium battery energy storage subunit discharges according to the maximum feasible discharge power; in the low electricity price period, the lithium battery energy storage subunit charges according to the maximum feasible charging power; in the medium electricity price period, the lithium battery energy storage subunit adjusts the adaptive energy to the direction of SOC being 50%;

[0048] Wherein, the medium electricity price period power calculation expression is:

[0049]

[0050] In the formula, Lithium-ion battery energy storage sub-unit At any moment Target power value, Lithium-ion battery energy storage sub-unit At any moment The lower limit of power; Lithium-ion battery energy storage sub-unit At any moment The upper limit of power; Lithium-ion battery energy storage sub-unit At any moment The state of charge, Lithium-ion battery energy storage sub-unit capacity, The length of a single power control period.

[0051] In a specific embodiment of the present invention, the supercapacitor energy storage subunit in the hybrid energy storage system performs adaptive energy regulation with a SOC of 50% as the adjustment direction, as expressed below:

[0052]

[0053] In the formula, Supercapacitor energy storage subunit At any moment The target power value; Supercapacitor energy storage subunit At any moment The lower limit of power; Supercapacitor energy storage subunit At any moment The upper limit of power; Supercapacitor energy storage subunit At any moment The state of charge, Supercapacitor energy storage subunit capacity, The length of a single power control period.

[0054] In one specific embodiment of the present invention, it further includes:

[0055] The objective function of the power allocation optimization model for the hybrid energy storage system is shown below:

[0056]

[0057] in, For a moment Power deviation penalty price This refers to the number of lithium battery energy storage sub-cells. Lithium-ion battery energy storage sub-unit At any moment 'output power' For a moment The sum of the power of each supercapacitor energy storage sub-unit; in a hybrid energy storage system, positive power represents discharging and negative power represents charging; For a moment The aging cost of lithium batteries; For a moment Target power of hybrid energy storage system; The length of a single power control period;

[0058]

[0059] in, , They are time points Real-time power output of cleared power plants and output of new energy sources; and These are the upper and lower thresholds for power deviation at new energy power plants; For a moment Time-of-use electricity pricing was recently cleared out; , These are the high and low thresholds for day-ahead clearing electricity prices;

[0060] The constraints of the power allocation optimization model for the hybrid energy storage system include:

[0061]

[0062]

[0063]

[0064]

[0065] in, Lithium-ion battery energy storage sub-unit At any moment The lower bound of the feasible power domain, Supercapacitor energy storage subunit At any moment The lower bound of the feasible power domain, Lithium-ion battery energy storage sub-unit At any moment The upper bound of the power feasible domain, Supercapacitor energy storage subunit At any moment The upper bound of the power feasible domain; a number of supercapacitor energy storage sub-units; a lithium battery energy storage sub-unit at time a power change amount compared to a previous time period;

[0066]

[0067]

[0068]

[0069] wherein, an energy storage sub-unit caused by PCS temperature at time , the energy storage sub-unit including a lithium battery energy storage sub-unit and a supercapacitor energy storage sub-unit; a rated power of the energy storage sub-unit ; a PCS temperature of the energy storage sub-unit at time ; , a first temperature model parameter and a second temperature model parameter of the energy storage sub-unit at time ; and a mixed energy storage system SOC running upper and lower limit;

[0070] solving the mixed energy storage system power distribution optimization model to obtain and optimization results.

[0071] In one specific embodiment of the present application, further comprising:

[0072] The objective function of the supercapacitor energy storage sub-unit power distribution optimization model is:

[0073]

[0074] wherein,

[0075]

[0076]

[0077] wherein, a supercapacitor operating efficiency; a supercapacitor energy storage sub-unit SOC average value at a next time point;

[0078] The constraint condition of the supercapacitor energy storage sub-unit power distribution optimization model is:

[0079]

[0080] solving the supercapacitor energy storage subunit power distribution optimization model, obtaining optimization results.

[0081] The third aspect of the present application provides an electronic device, comprising:

[0082] at least one processor; and a memory connected to the at least one processor in communication;

[0083] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above-mentioned hybrid energy storage system power distribution method considering active deviation assessment.

[0084] The fourth aspect of the present application provides a computer readable storage medium, which stores computer instructions for executing the above-mentioned hybrid energy storage system power distribution method considering active deviation assessment.

[0085] The characteristics and benefits of the present application are:

[0086] The present application is aimed at the operation link of a new energy station equipped with a hybrid energy storage (lithium battery + supercapacitor / flywheel), considers the new energy active deviation assessment penalty and the lithium battery aging cost based on cycles, realizes the PCS-level hybrid energy storage system power distribution, and includes the power limiting caused by the temperature rise of the PCS in the model constraint condition, realizes more refined modeling. It can fully utilize the performance difference between the lithium battery energy storage and other types of power storage in the hybrid energy storage system, greatly reduce the active power output deviation assessment cost of the new energy station caused by insufficient prediction accuracy within the limited lithium battery energy storage aging cost, and at the same time, utilize the energy time shift characteristics of the storage, reduce the direct loss caused by light and wind curtailment, and improve the operation efficiency of the new energy station. BRIEF DESCRIPTION OF DRAWINGS

[0087] Figure 1 The present application provides a hybrid energy storage system power distribution method considering active deviation assessment. DETAILED DESCRIPTION

[0088] The present application provides a hybrid energy storage system power distribution method considering active deviation assessment and the following will be further described in detail in combination with the drawings and specific embodiments.

[0089] The first aspect of the present application provides a hybrid energy storage system power distribution method considering active deviation assessment, comprising:

[0090] When the new energy station is in the active power output deviation non-examination period, the lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, and the super capacitor energy storage subunit in the hybrid energy storage system adjusts the adaptive energy with SOC as 50% as the adjustment direction;

[0091] When the new energy station is in the active power output deviation examination period, the optimization results of the output of each lithium battery energy storage subunit and the total output of the super capacitor energy storage subunit are obtained by establishing a hybrid energy storage system power distribution optimization model and solving; then the optimization results of the output of each super capacitor energy storage subunit are obtained by establishing a super capacitor energy storage subunit power distribution optimization model and solving.

[0092] In the embodiment, the hybrid energy storage system is a "lithium battery+" energy storage system, that is, the lithium battery is used as energy type energy storage, and is used for low charging and high discharging and power compensation at the same time, and the super capacitor or flywheel energy storage is used as power type energy storage, and is only used for power compensation, so as to realize the reduction of the active power deviation examination in the operation process of the new energy station. In one specific embodiment of the application, the hybrid energy storage system comprises a lithium battery energy storage subunit and a super capacitor energy storage subunit, and the hybrid energy storage system power distribution method considering active deviation examination comprises the following steps. Figure 1 As shown in the whole process of

[0093] 1) Determine whether the new energy station is in the active power output deviation examination period at the current time.

[0094] The new energy station will be subjected to active power output deviation examination in the operation process. Usually, fifteen minutes is taken as a cycle, and in the fifteen-minute cycle, there are several minutes of time as the examination period. The average value obtained by measuring multiple active power is used as the power value for examination, and other time is the non-examination period. In the embodiment, it is necessary to make a judgment every 30 seconds. According to the current time, the station can determine whether the current time is subjected to examination, so as to decide to use the corresponding hybrid energy storage system power distribution strategy in the method.

[0095] If the current time is not in the active power output deviation examination period, step 2) is entered; if the current time is in the active power output deviation examination period, step 3) is entered.

[0096] 2) Perform hybrid energy storage system power distribution in the non-examination period, and the specific steps are as follows:

[0097] 2-1) Calculate the target power of each subunit in the hybrid energy storage system.

[0098] In this embodiment, based on the day-ahead clearing time-of-use pricing results, during high-price periods, the lithium battery energy storage sub-unit in the hybrid energy storage system discharges at the maximum feasible discharge power; during low-price periods, the lithium battery energy storage sub-unit charges at the maximum feasible charging power; and during medium-price periods, the lithium battery energy storage sub-unit adaptively adjusts its energy towards a SOC of 50%.

[0099] The calculation method for power during medium-priced electricity periods is as follows:

[0100]

[0101] In this system, the charging power of the hybrid energy storage system is defined as negative, and the discharging power as positive. The hybrid energy storage system is divided into sub-units according to PCS (Power Conversion System), meaning that each lithium battery energy storage sub-unit and supercapacitor energy storage sub-unit has its own corresponding PCS. In the formula, Lithium-ion battery energy storage sub-unit At any moment Target power value, Lithium-ion battery energy storage sub-unit At any moment The lower limit of the power, i.e. the maximum charging power; Lithium-ion battery energy storage sub-unit At any moment The upper limit of power, i.e., the maximum discharge power; Lithium-ion battery energy storage sub-unit At any moment The state of charge, Lithium-ion battery energy storage sub-unit capacity, The length of a single power control period.

[0102] For the supercapacitor energy storage sub-unit in the hybrid energy storage system, adaptive energy regulation is only performed in the direction of 50% SOC during non-test periods. The calculation method is as follows:

[0103]

[0104] In the formula, Supercapacitor energy storage subunit At any moment The target power value; Supercapacitor energy storage subunit At any moment The lower limit of the power, i.e. the maximum charging power; Supercapacitor energy storage subunit At any moment The upper limit of power, i.e., the maximum discharge power; Supercapacitor energy storage subunit At any moment The state of charge, Supercapacitor energy storage subunit capacity, The length of a single power control period.

[0105] 2-2) Send the results obtained in step 2-1) to the corresponding sub-units for execution, so as to realize the power allocation of the hybrid energy storage system at the current moment.

[0106] 3) Conduct power allocation for the hybrid energy storage system during the assessment period. The specific steps are as follows:

[0107] 3-1) Calculate the target power of the hybrid energy storage system.

[0108] In this embodiment, if the current time is during the assessment period, firstly, the power station needs to obtain the output of new energy sources, the real-time cleared power station power, and the day-ahead cleared time-of-use electricity price data. Then, it calculates the target power of the hybrid energy storage system according to the upper limit of the power not subject to assessment during the high electricity price period, the cleared power value during the medium electricity price period, and the lower limit of the power not subject to assessment during the low electricity price period.

[0109]

[0110] in, For a moment Target power of hybrid energy storage system; , They are time points Real-time power output of cleared power plants and output of new energy sources; and These are the upper and lower threshold values ​​for the power deviation of new energy power stations. In a specific embodiment of this invention, according to the current rules of Hubei Province, they are 0.02 and 0.06, respectively. For a moment Time-of-use electricity pricing was recently cleared out; , These are the high and low thresholds for day-ahead clearing electricity prices, used to distinguish time periods. The current cleared electricity price category has been determined. For other provinces / regions, different cleared power calculation methods can be designed based on specific rules and the above principles.

[0111] 3-2) Based on the results of step 3-1), establish and solve the power distribution optimization model of the hybrid energy storage system to obtain the optimization results of the output of each lithium battery energy storage sub-unit and the total output of the supercapacitor energy storage sub-unit.

[0112] In this embodiment, the power allocation of the hybrid energy storage system is performed with the goal of minimizing the sum of the target power deviation penalty of the hybrid energy storage system and the aging cost of the lithium battery. The allocation result will be sent to the PCS of each energy storage sub-unit.

[0113] In this embodiment, the objective function for establishing the power allocation optimization model of the hybrid energy storage system is as follows:

[0114]

[0115] in, For a moment Power deviation penalty price This refers to the number of lithium battery energy storage sub-cells. Lithium-ion battery energy storage sub-unit At any moment 'output power' For a moment The sum of the power of each supercapacitor energy storage sub-unit. In a hybrid energy storage system, positive power represents discharge, and negative power represents charging.

[0116] For a moment The aging cost of lithium batteries is calculated using an online rainflow counting method based on cycle aging. Based on the SOC change curves of the lithium batteries during past operation, the aging state sequence of unstable pairings can be obtained using the rainflow counting method. , can be represented as:

[0117]

[0118] in, Represents lithium battery energy storage sub-unit At any moment The The remaining unpaired charged state transition points This represents the number of unstable pairings in the aging state. Note that energy storage... State of charge at time t This is the last element in the inflection point sequence. Based on the different energy storage charging and discharging types in past and current periods, the cycle-based unit power aging cost is calculated using the following formula:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] in, To indicate The charging and discharging state is a 0-1 variable, where 1 represents discharging and 0 represents charging. If the lithium battery power is 0 at the current moment, it will maintain the same charging and discharging state as at the previous moment. This represents the unit energy aging cost for a scenario where the device is charging in the previous period and will be charging in the next period. This represents the unit energy aging cost for a period where the previous period was for charging and the next period is for discharging. This represents the unit energy aging cost for a period where the previous period was for discharging and the next period is for charging. This represents the unit energy aging cost for a period where the previous period was for discharging and the next period is for discharging. , The aging function coefficients can be obtained from the equipment manuals provided by lithium battery energy storage manufacturers. Indicates the loop depth; The efficiency of the lithium battery energy storage sub-unit can be set to a fixed value of 0.95 in the power optimization allocation stage; This represents the unit capacity price of a lithium-ion battery energy storage sub-unit. Different manufacturers may offer lithium-ion battery energy storage systems with different aging cost functions; this method can be used as long as the aging cost function is convex.

[0127] The constraints for establishing the power allocation optimization model for a hybrid energy storage system include:

[0128]

[0129]

[0130]

[0131]

[0132] in, Lithium-ion battery energy storage sub-unit At any moment The lower bound of the feasible power domain, Supercapacitor energy storage subunit At any moment The lower bound of the feasible power domain, Lithium-ion battery energy storage sub-unit At any moment The upper bound of the power feasible domain, Supercapacitor energy storage subunit At any moment The upper bound of the power feasible domain; This refers to the number of supercapacitor energy storage sub-units. Lithium-ion battery energy storage sub-unit At any moment Compared to the power change in the previous period, the above constraints ensure that the power of each lithium battery energy storage sub-unit does not change in the opposite direction, thus preventing power fluctuations.

[0133] During operation, the upper and lower power limits of lithium battery energy storage sub-units and supercapacitor energy storage sub-units are related to the State of Charge (SOC) and PCS temperature. When the SOC is too high / low or the PCS temperature is too high, power limiting will occur.

[0134]

[0135]

[0136]

[0137] in, For energy storage sub-units caused by PCS temperature At any moment The power limit of the energy storage subunit includes a lithium battery energy storage subunit and a supercapacitor energy storage subunit. When the superscript is B, it represents the lithium battery energy storage subunit, and when the superscript is S, it represents the supercapacitor energy storage subunit. For energy storage sub-units Rated power; For energy storage sub-units At any moment PCS temperature; , They are energy storage sub-units At any moment The parameters of the first temperature model and the parameters of the second temperature model can be set to... (°C), or set to other values ​​according to the actual operation of the site. For example, if the PCS used in the energy storage system will not have overheating problems, the constraints related to the PCS temperature in the model can be ignored. and These are the upper and lower limits of the SOC operation of the hybrid energy storage system, which can usually be set to suitable values ​​of 0.9~0.95 and 0.05~0.1.

[0138] Thus, a power allocation optimization model for the hybrid energy storage system has been established. By solving this optimization model, the power output of each lithium battery energy storage sub-unit can be obtained. Total output of supercapacitor energy storage subunit The optimization results.

[0139] 3-3) Based on the results of step 3-2), establish and solve the power allocation optimization model of the supercapacitor energy storage sub-unit to obtain the power output optimization results of each supercapacitor energy storage sub-unit.

[0140] Since supercapacitors have a sufficient number of cycles, there is no need to consider aging costs; for the power distribution of supercapacitor energy storage sub-units, only SOC balance needs to be considered.

[0141] In this embodiment, the objective function expression for establishing the power allocation optimization model of the supercapacitor energy storage sub-unit is as follows:

[0142]

[0143] in,

[0144]

[0145]

[0146] in, To optimize the operating efficiency of the supercapacitor, a fixed value of 0.95 is set in the power optimization allocation stage. The SOC average value of the supercapacitor energy storage subunit at the next time point (in this embodiment, for example, if the optimization program is executed every 30 seconds to update the power command calculation result, then t+1 represents 30 seconds later).

[0147] The power allocation optimization model for the supercapacitor energy storage sub-unit needs to satisfy the following power limiting constraints:

[0148]

[0149] By solving the power allocation optimization model of the supercapacitor energy storage sub-unit, the power output of each supercapacitor energy storage sub-unit can be obtained. The optimization results.

[0150] Thus, by constructing a hybrid energy storage power allocation optimization model and a supercapacitor energy storage sub-unit power allocation optimization model for the assessment period, the output power of each lithium battery energy storage sub-unit can be obtained. and the output of each supercapacitor energy storage subunit The optimization results.

[0151] 3-4) The output optimization results of each lithium battery energy storage sub-unit obtained in step 3-2) and the output optimization results of each supercapacitor energy storage sub-unit obtained in step 3-3) are sent to the corresponding sub-units for execution, so as to realize the power distribution of the hybrid energy storage system at the current moment.

[0152] To achieve the above embodiments, a second aspect of the present invention provides a power distribution device for a hybrid energy storage system that considers active power deviation assessment, comprising:

[0153] The non-assessment period power allocation module is used to adjust the energy of the lithium battery energy storage sub-unit in the hybrid energy storage system of the new energy power station according to the electricity price when the new energy power station is in the non-assessment period of active power output deviation. The supercapacitor energy storage sub-unit in the hybrid energy storage system adjusts the energy of the new energy power station with the SOC of 50% as the adjustment direction.

[0154] The power allocation module for the assessment period is used to obtain the optimized output of each lithium battery energy storage sub-unit and the total output of the supercapacitor energy storage sub-unit by establishing and solving a power allocation optimization model of the hybrid energy storage system when the new energy power station is in the active power output deviation assessment period; then, by establishing and solving a power allocation optimization model of the supercapacitor energy storage sub-unit, the optimized output of each supercapacitor energy storage sub-unit is obtained.

[0155] In one specific embodiment of the present invention, the lithium battery energy storage sub-unit in the hybrid energy storage system of the new energy power station performs adaptive energy adjustment according to the electricity price, including:

[0156] According to the recent results of clearing time-of-use pricing, during periods of high electricity price, the lithium battery energy storage sub-units discharge at the maximum feasible discharge power; during periods of low electricity price, the lithium battery energy storage sub-units charge at the maximum feasible charging power; and during periods of medium electricity price, the lithium battery energy storage sub-units adaptively adjust their energy output towards a SOC of 50%.

[0157] The formula for calculating power during the medium-price electricity period is as follows:

[0158]

[0159] In the formula, Lithium-ion battery energy storage sub-unit At any moment Target power value, Lithium-ion battery energy storage sub-unit At any moment The lower limit of power; Lithium-ion battery energy storage sub-unit At any moment The upper limit of power; Lithium-ion battery energy storage sub-unit At any moment The state of charge, Lithium-ion battery energy storage sub-unit capacity, The length of a single power control period.

[0160] In a specific embodiment of the present invention, the supercapacitor energy storage subunit in the hybrid energy storage system performs adaptive energy regulation with a SOC of 50% as the adjustment direction, as expressed below:

[0161]

[0162] In the formula, Supercapacitor energy storage subunit At any moment The target power value; Supercapacitor energy storage subunit At any moment The lower limit of power; Supercapacitor energy storage subunit At any moment The upper limit of power; Supercapacitor energy storage subunit At any moment The state of charge, Supercapacitor energy storage subunit capacity, The length of a single power control period.

[0163] In one specific embodiment of the present invention, it further includes:

[0164] The objective function of the power allocation optimization model for the hybrid energy storage system is shown below:

[0165]

[0166] in, For a moment Power deviation penalty price This refers to the number of lithium battery energy storage sub-cells. Lithium-ion battery energy storage sub-unit At any moment 'output power' For a moment The sum of the power of each supercapacitor energy storage sub-unit; in a hybrid energy storage system, positive power represents discharging and negative power represents charging; For a moment The aging cost of lithium batteries; For a moment Target power of hybrid energy storage system; The length of a single power control period;

[0167]

[0168] in, , They are time points Real-time power output of cleared power plants and output of new energy sources; and These are the upper and lower thresholds for power deviation at new energy power plants; For a moment Time-of-use electricity pricing was recently cleared out; , These are the high and low thresholds for day-ahead clearing electricity prices;

[0169] The constraints of the power allocation optimization model for the hybrid energy storage system include:

[0170]

[0171]

[0172]

[0173]

[0174] in, Lithium-ion battery energy storage sub-unit At any moment The lower bound of the feasible power domain, Supercapacitor energy storage subunit At any moment The lower bound of the feasible power domain, Lithium-ion battery energy storage sub-unit At any moment The upper bound of the power feasible domain, Supercapacitor energy storage subunit At any moment The upper bound of the power feasible domain; This refers to the number of supercapacitor energy storage sub-units. Lithium-ion battery energy storage sub-unit At any moment The change in power compared to the previous period;

[0175]

[0176]

[0177]

[0178] in, For energy storage sub-units caused by PCS temperature At any moment The power limit of the energy storage subunit includes a lithium battery energy storage subunit and a supercapacitor energy storage subunit. For energy storage sub-units Rated power; For energy storage sub-units At any moment PCS temperature; , They are energy storage sub-units At any moment The first temperature model parameters and the second temperature model parameters; and These are the upper and lower limits of the State of Charge (SOC) for hybrid energy storage systems.

[0179] Solving the power allocation optimization model of the hybrid energy storage system yields the following results. and The optimization results.

[0180] In one specific embodiment of the present invention, it further includes:

[0181] The objective function of the power allocation optimization model for the supercapacitor energy storage subunit is:

[0182]

[0183] in,

[0184]

[0185]

[0186] in, To improve the operating efficiency of supercapacitors; This represents the average SOC of the supercapacitor energy storage subunit at the next time point;

[0187] The constraints of the supercapacitor energy storage sub-unit power allocation optimization model are as follows:

[0188]

[0189] Solving the power allocation optimization model of the supercapacitor energy storage subunit, we obtain... The optimization results.

[0190] This can solve the problem of reducing the additional operating costs caused by power deviation assessment after configuring hybrid energy storage in new energy power plants, by making full use of different types of energy storage resources, thereby improving the operating efficiency of new energy power plants.

[0191] To implement the above embodiments, a third aspect of the present invention provides an electronic device, comprising:

[0192] At least one processor; and a memory communicatively connected to said at least one processor;

[0193] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-described power allocation method for a hybrid energy storage system that considers active power deviation assessment.

[0194] To implement the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described power allocation method for a hybrid energy storage system that considers active power deviation assessment.

[0195] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0196] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a power allocation method for a hybrid energy storage system considering active power deviation assessment according to the above embodiments.

[0197] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0198] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0199] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0200] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0201] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0202] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0203] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0204] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0205] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for power distribution of a hybrid energy storage system considering active deviation evaluation, characterized in that, The method comprises the following steps: When the new energy station is in the non-examination period of active power deviation, the lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, and the super capacitor energy storage subunit in the hybrid energy storage system adjusts the adaptive energy with SOC as 50% as the adjustment direction; When the new energy station is in the examination period of active power deviation, the optimization results of the output of each lithium battery energy storage subunit and the total output of the super capacitor energy storage subunit are obtained by establishing and solving the power distribution optimization model of the hybrid energy storage system; Then, the optimization results of the output of each super capacitor energy storage subunit are obtained by establishing and solving the power distribution optimization model of the super capacitor energy storage subunit.

2. The method of claim 1, wherein, The lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, which comprises the following steps: According to the day-ahead clearing sub-hourly electricity price result, the lithium battery energy storage subunit discharges according to the maximum feasible discharge power in the high electricity price period, charges according to the maximum feasible charge power in the low electricity price period, and adjusts the adaptive energy to the direction of SOC being 50% in the medium electricity price period; The power calculation expression of the medium electricity price period is as follows: wherein a lithium battery energy storage subunit at time a target power value, a lithium battery energy storage subunit at time a power lower limit; a lithium battery energy storage subunit at time a power upper limit; a lithium battery energy storage subunit at time a state of charge, a lithium battery energy storage subunit a capacity, a single power control period length.

3. The method of claim 1, wherein, The super capacitor energy storage subunit in the hybrid energy storage system adjusts the adaptive energy with SOC as 50% as the adjustment direction, and the expression is as follows: wherein a supercapacitor energy storage subunit at time a target power value; a supercapacitor energy storage subunit at time a lower power limit; a supercapacitor energy storage subunit at time an upper power limit; for the supercapacitor energy storage subunit at time of state of charge, for the supercapacitor energy storage subunit of capacity, is the length of a single power control period.

4. The method of claim 1, wherein, Further comprising: The objective function of the power distribution optimization model of the hybrid energy storage system is as follows: wherein, is the time instant, is the power deviation penalty price, is the number of lithium battery energy storage sub-units, is the lithium battery energy storage sub-unit at the time instant is the output power of the lithium battery energy storage sub-unit, is the time instant, is the sum of the power of each supercapacitor energy storage sub-unit; the positive power of the hybrid energy storage system represents discharging, and the negative power represents charging; is the time instant, is the lithium battery aging cost; is the time instant, is the target power of the hybrid energy storage system; is the length of a single power control period; Wherein, , are the real-time clearing station power and new energy output at time , and are the upper and lower thresholds of the new energy station power deviation; is the day-ahead clearing time hourly electricity price; , are the high and low thresholds of the day-ahead clearing price; The constraint conditions of the power distribution optimization model of the hybrid energy storage system comprise: wherein, is a lithium battery energy storage subunit at time is a lower bound of the power feasible region at time is a supercapacitor energy storage subunit at time is a lower bound of the power feasible region at time is a lithium battery energy storage subunit at time is an upper bound of the power feasible region at time is a supercapacitor energy storage subunit at time is an upper bound of the power feasible region at time is a number of supercapacitor energy storage subunits; is a lithium battery energy storage subunit at time is a power change amount compared to the previous time period; wherein, is the PCS temperature caused energy storage subunit at time is the power limit value, the energy storage subunit comprises a lithium battery energy storage subunit and a super capacitor energy storage subunit; is the rated power of the energy storage subunit ; is the PCS temperature of the energy storage subunit at time ; , are respectively the first temperature model parameter and the second temperature model parameter of the energy storage subunit at time ; and are respectively the upper and lower limits of the SOC of the hybrid energy storage system Solving the mixed energy storage system power distribution optimization model, obtaining and optimization results.

5. The method of claim 4, wherein, Further comprising: The objective function of the power distribution optimization model of the super capacitor energy storage subunit is as follows: The constraint conditions of the power distribution optimization model of the super capacitor energy storage subunit are as follows: wherein, is the supercapacitor operating efficiency; is the average value of the SOC of the supercapacitor energy storage subunit at the next time point. Comprising: Solving the super capacitor energy storage sub-unit power distribution optimization model, obtaining the optimization result.

6. A hybrid energy storage system power distribution device considering active deviation assessment, characterized in that, The non-examination period power distribution module is configured to, when the new energy station is in the non-examination period of active power deviation, the lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, and the super capacitor energy storage subunit in the hybrid energy storage system adjusts the adaptive energy with SOC as 50% as the adjustment direction; The examination period power distribution module is configured to, when the new energy station is in the examination period of active power deviation, the optimization results of the output of each lithium battery energy storage subunit and the total output of the super capacitor energy storage subunit are obtained by establishing and solving the power distribution optimization model of the hybrid energy storage system; Then, the optimization results of the output of each super capacitor energy storage subunit are obtained by establishing and solving the power distribution optimization model of the super capacitor energy storage subunit. The lithium battery energy storage subunit in the hybrid energy storage system of the new energy station adjusts the adaptive energy according to the electricity price, which comprises the following steps:

7. The apparatus of claim 6, wherein, According to the day-ahead clearing sub-hourly electricity price result, the lithium battery energy storage subunit discharges according to the maximum feasible discharge power in the high electricity price period, charges according to the maximum feasible charge power in the low electricity price period, and adjusts the adaptive energy to the direction of SOC being 50% in the medium electricity price period; The power calculation expression of the medium electricity price period is as follows: ​ wherein is a lithium battery energy storage subunit at time a target power value, is a lithium battery energy storage subunit at time a lower power limit; is a lithium battery energy storage subunit at time an upper power limit; is a lithium battery energy storage subunit at time a state of charge, is a lithium battery energy storage subunit a capacity, is a single power control period length.

8. The apparatus of claim 6, wherein, The super capacitor energy storage subunit in the hybrid energy storage system adjusts the performance energy adjustment with SOC as 50% as the adjustment direction, and the expression is as follows: wherein is a supercapacitor energy storage subunit at time a target power value; is a supercapacitor energy storage subunit at time a lower power limit; is a supercapacitor energy storage subunit at time an upper power limit; is a supercapacitor energy storage subunit at time a state of charge, is a supercapacitor energy storage subunit a capacity, is a single power control period length.

9. The apparatus of claim 6, wherein, Further comprising: The objective function of the power distribution optimization model of the hybrid energy storage system is as follows: wherein, is the time instant, is the power deviation penalty price, is the number of lithium battery energy storage subunits, is the lithium battery energy storage subunit at the time instant is the output power, is the time instant, is the sum of the powers of each supercapacitor energy storage subunit; the positive power of the hybrid energy storage system represents discharging and the negative power represents charging; is the time instant, is the lithium battery aging cost; is the time instant, is the target power of the hybrid energy storage system; is the length of a single power control period; Wherein, , are the real-time clearing station power and new energy output at time , respectively; and are the upper and lower threshold values of the new energy station power deviation, respectively; is the day-ahead clearing time-of-use electricity price; , , are the high and low threshold values of the day-ahead clearing electricity price, respectively; The constraint conditions of the power distribution optimization model of the hybrid energy storage system include: wherein, is a lithium battery energy storage subunit at time is a lower bound of the power feasible region, is a supercapacitor energy storage subunit at time is a lower bound of the power feasible region, is a lithium battery energy storage subunit at time is an upper bound of the power feasible region, is a supercapacitor energy storage subunit at time is an upper bound of the power feasible region; is a number of supercapacitor energy storage subunits; is a lithium battery energy storage subunit at time is a power change amount compared to the previous time period; wherein, is the PCS temperature caused energy storage subunit at time is the power limit value, the energy storage subunit comprises a lithium battery energy storage subunit and a super capacitor energy storage subunit; is the rated power of the energy storage subunit ; is the PCS temperature of the energy storage subunit at time ; , are respectively the first temperature model parameter and the second temperature model parameter of the energy storage subunit at time ; and are respectively the upper and lower limits of the SOC of the hybrid energy storage system Solving the hybrid energy storage system power distribution optimization model, obtaining and optimization results.

10. The apparatus of claim 9, wherein, Further comprising: The objective function of the power distribution optimization model of the super capacitor energy storage subunit is: Wherein, wherein, is the supercapacitor operating efficiency; is the average value of the SOC of the supercapacitor energy storage subunit at the next time point. The constraint conditions of the power distribution optimization model of the super capacitor energy storage subunit are: Solving the super capacitor energy storage sub-unit power distribution optimization model, obtaining the optimization result.

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