A shared hybrid energy storage regulation strategy considering multi-time scale coordination of source, load and storage

Abstract

The present application relates to a kind of considering source load storage multi-time scale coordination's shared hybrid energy storage regulation strategy, which according to source load day-ahead forecast data, with pumped storage power lease net benefit maximum and grid power purchase curve hour-level fluctuation minimum as optimization goal, obtains the lease scale and operation strategy of pumped storage power resource;According to the source load forecast data obtained in daily rolling, with battery lease net benefit maximum and wind power, light power minimum as optimization goal, obtain the lease scale and operation strategy of battery resource in each stage;According to the source load ultra-short-term forecast data obtained in real time, with super capacitor net benefit maximum and net load minute-level fluctuation rate minimum as optimization goal, obtain the lease scale and operation strategy of hybrid energy storage resource in each stage.The present application not only can promote the full source consumption of renewable energy, but also promote industrial park energy saving and carbon reduction through the form of shared hybrid energy storage.

Description

TECHNICAL FIELD

[0001] The application relates to a shared hybrid energy storage regulation strategy considering source-load-storage multi-time scale cooperation, and belongs to the technical field of power system shared energy storage optimization. BACKGROUND

[0002] New energy power generation will become an important part of the future power market. However, in this process, wind power and photovoltaic power have relatively poor direct grid-connection benefits due to their anti-peaking and uncertainty, which has a very adverse effect on the safety and reliability of the power grid and restricts the development of wind power and photovoltaic power in the power system.

[0003] Energy storage systems are widely used because they can effectively alleviate the adverse effects of wind power and photovoltaic power on the power grid, and a reasonable control strategy and method are the key to their full effectiveness.

[0004] The accuracy of wind power and photovoltaic power prediction is closely related to the time scale. Long-time scale power prediction cannot meet the requirements of accurate energy storage control, and short-time scale power prediction can meet the requirements of accurate energy storage control, but short-time scale energy storage control can only ensure optimal energy storage control in the current period and cannot ensure that the energy storage is in a reasonable normal working state in a long time. SUMMARY

[0005] In order to solve the problems in the prior art, the application provides a shared hybrid energy storage regulation strategy considering source-load-storage multi-time scale cooperation, which introduces different types of shared hybrid energy storage systems under multi-time scale, so as to promote renewable energy consumption and energy saving and carbon reduction in industrial parks.

[0006] In order to achieve the above purpose, the technical scheme provided by the application is as follows: a shared hybrid energy storage regulation strategy considering source-load-storage multi-time scale cooperation, comprising:

[0007] For a park equipped with wind power and photovoltaic power; in the day-ahead stage, the day-ahead prediction data of the park load and the wind and light output are obtained with 24 hours as the time span and 1 hour as the sampling time interval; according to the day-ahead prediction data, the target function of pumped storage energy storage participating in the day-ahead stage is constructed with the maximum net benefit of pumped storage energy storage leasing and the minimum hourly fluctuation of power grid power purchase curve as the optimization target, and the optimal pumped storage energy storage resource leasing scale and operation strategy are obtained;

[0008] In the day-ahead stage, the whole day is divided into four optimization stages, each with a time span of 6 hours and a sampling time interval of 15 minutes. The intra-day prediction data of the park load and wind and light output are obtained in a rolling manner. According to the intra-day prediction data, the objective function of the battery participating in the intra-day rolling optimization is constructed with the maximum net benefit of battery leasing and the minimum abandoned wind and light as the optimization objectives, and the optimal battery resource leasing scale and operation strategy in each optimization stage are obtained.

[0009] In the real-time stage, the ultra-short-term prediction data of the park load and wind and light output are obtained with a time span of 15 minutes and a sampling time interval of 1 minute. According to the ultra-short-term prediction data, the objective function of the super capacitor participating in real-time correction is constructed with the maximum net benefit of super capacitor and the minimum net load minute-level fluctuation rate as the optimization objectives, and the optimal mixed energy storage resource leasing scale and operation strategy in each real-time stage are obtained.

[0010] The optimal pumped storage resource leasing scale and operation strategy, the optimal battery resource leasing scale and operation strategy in each optimization stage, and the optimal mixed energy storage resource leasing scale and operation strategy in each stage are integrated in the time scale to obtain the shared mixed energy storage control strategy.

[0011] The further design of the above technical solution is that the objective function f1 of the pumped storage participating in the day-ahead stage is:

[0012] maxf1=(R PS -H PS -C grid )-α1Flu1

[0013]

[0014] H PS =h PS,P (P PS,Pmax +P PS,Gmax )+h PS,V (V u,max +V d,max )

[0015]

[0016] Wherein, R PS represents the arbitrage income of pumped storage in the day-ahead planning stage; H PS represents the leasing cost of pumped storage in a day; C grid represents the electricity purchase cost of the industrial park to the grid after configuring large-capacity pumped storage; Flu1 represents the hour-level fluctuation rate of grid electricity purchase curve, and α1 represents the hour-level fluctuation rate weighting coefficient; t1 represents the index variable of the day-ahead stage, t1=1,2,…,24, Δt1 represents the optimization decision time interval; ppv (t1) represents the peak-valley time-of-use electricity price at t1; P PS,G (t1) and P PS,P (t1) represent the generation power and pumping power of the pumped storage at t1, respectively; h PS,P and h PS,V represent the unit power leasing cost coefficient and unit capacity leasing cost coefficient of the pumped storage, respectively; P PS,Gmax represents the maximum generation power of the pumped storage; P PS,Pmax represents the maximum pumping power of the pumped storage; V u,max represent the maximum capacity of the upper reservoir, respectively; V d,max represent the maximum capacity of the lower reservoir, respectively; P da,gird (t1) represents the external power purchase of the industrial park at t1 in day-ahead optimization; E T represents the transformer capacity of the industrial park.

[0017] The constraint condition of the objective function f1 of the pumped storage participating in the day-ahead stage is:

[0018] (1) Power balance constraint in the day-ahead stage;

[0019] P da,grid (t1) = P da,load (t1) - P da,WP (t1) - P da,PV (t1) - P PS,G (t1) + P PS,P (t1)

[0020] Wherein, P da,load (t1), P da,WP (t1), P da,PV (t1) represent the predicted data of the industrial park electricity load and wind power and photovoltaic power at t1 in the day-ahead stage, respectively;

[0021] (2) Physical constraint of pumped storage;

[0022] V u (t1) = V u (t1-1) - Q G (t1) + Q P (t1)

[0023] V d (t1) = V d (t1-1) - Q P (t1) + Q G (t1)

[0024] V u,min ≤ V u (t1) ≤ V u,max

[0025] V d,min ≤V d (t1)≤V d,max

[0026] V u (1)=V u (24)

[0027] V d (1)=V d (24)

[0028] Q G (t1)=P PS,G (t1) / η PS,G ·ρ W ·g·h

[0029] Q P (t1)=η PS,P ·P PS,P (t1) / ρ W ·g·h

[0030] B PS,G (t1)·P PS,Gmin ≤P PS,G (t1)≤B PS,G (t1)·P PS,Gmax

[0031] B PS,P (t1)·P PS,Pmin ≤P PS,P (t1)≤B PS,P (t1)·P PS,Pmax

[0032] 0≤B PS,G (t1)+B PS,P (t1)≤1

[0033] Wherein, V u (t1) and V d (t1) represent the reservoir capacity of upper and lower reservoirs of pumped storage at t1 time; Q G (t1) and Q P (t1) represent the power generation flow and pumping flow of pumped storage at t1 time; V u,min represents the minimum capacity of upper reservoir; V d,min represents the minimum capacity of lower reservoir; η PS,G and η PS,S represent the power generation efficiency and pumping efficiency of pumped storage; ρ W represents water density; g represents gravitational acceleration; h represents the average water head height of pumped storage; B PS,G and B PS,Sis a Boolean variable, state 1 respectively represents the pumped storage power station in the power generation state and the pumping state; P PS,Gmin represents the minimum power generation of the pumped storage power station; P PS,Pmin represents the minimum pumping power of the pumped storage power station.

[0034] The target function f2 of the pumped storage battery participating in the intra-day rolling optimization is:

[0035] maxf2=(R BS -H BS )-α2C ab

[0036]

[0037] H BS =h BS,P ·P BS +h BS,E ·E BS

[0038]

[0039] Wherein, R BS represents the electricity price arbitrage income of the pumped storage battery in the rolling optimization stage; H BS represents the leasing cost of the pumped storage battery; C ab represents the wind and light curtailment cost; α2 represents the wind and light curtailment cost weighting coefficient; t2 represents the time index variable of the rolling optimization stage, t2=1,2,…,24, Δt2 optimization decision time interval; p pv (t2) represents the peak-valley time-of-use electricity price at t2; P BS ,dis (t2) and P BS,ch (t2) respectively represent the discharging power and charging power of the pumped storage battery at t2; h BS,P and h BS,E respectively represent the unit power leasing cost coefficient and unit capacity leasing cost coefficient of the pumped storage battery; P BS and E BS respectively represent the rated power and rated capacity of the pumped storage battery; λ ab represents the penalty coefficient for wind and light curtailment of the park renewable energy; p WP represents the wind power grid connection price; p PV represents the photovoltaic grid connection price; P WP,ab (t2) represents the power abandonment amount of the wind power station at t2; P PV,ab (t2) represents the power abandonment amount of the photovoltaic power station at t2.

[0040] The constraint condition of the target function f2 of the pumped storage battery participating in the intra-day rolling optimization is:

[0041] (1) Intra-day rolling stage power balance constraint;

[0042] P roll,load (t2)-P roll,grid (t2)-(P PS,G (t2)-P PS,P (t2))=

[0043] (P roll,WP (t2)-P WP,ab (t2))+(P roll,PV (t2)-P PV,ab (t2))+(P BS,dis (t2)-P BS,ch (t2))

[0044] Wherein, P roll,load (t2), P roll,grid (t2), P roll,WP (t2), P roll,PV (t2) represent the predicted data of industrial park electricity load, external power purchase and wind power output, photovoltaic output at time t2 of intra-day rolling optimization stage respectively;

[0045] (2) Battery physical constraint;

[0046]

[0047] In the formula, SOC BS (t2) represents the state of charge of the battery at time t2; η BS,ch and η BS,dis represent the charging efficiency and discharging efficiency of the battery respectively; SOC BS,min and SOC BS,max represent the minimum and maximum values of the state of charge of the battery respectively; P BS ,ch,max and η BS,dis,max represent the maximum charging power and maximum discharging power allowed by the battery respectively; B BS,ch (t2) and B BS,dis (t2) are both Boolean variables, and state 1 represents the charging state and discharging state of the battery at time t2 respectively.

[0048] The target function f3 of the super capacitor participating in real-time correction is:

[0049] maxf3=(R SC -H SC )-α3Flu3

[0050]

[0051] H SC= h SC,P · P SC + h SC,E · E SC

[0052]

[0053] wherein R SC represents the arbitrage profit of the super capacitor in the real-time correction stage; H SC represents the leasing cost of the super capacitor; Flu3 represents the minute-level fluctuation rate of the grid electricity purchasing curve, and a3 represents the minute-level fluctuation rate weighting coefficient; t3 represents the index variable of the day-ahead stage, t3 = 1, 2, …, 15, and At3 represents the optimization decision time interval; p pv (t3) represents the peak-valley time-of-use electricity price at t3; P SC,dis (t3) and P SC,ch (t3) represent the discharging power and charging power of the super capacitor at t3, respectively; h SC,P and h SC ,E represent the unit power leasing cost coefficient and the unit capacity leasing cost coefficient of the super capacitor, respectively; P SC and E SC represent the rated power and the rated capacity of the super capacitor, respectively; P real,grid (t3) represents the external electricity purchasing amount at t3, i.e., the net load demand of the industrial park.

[0054] The constraint condition of the objective function f3 of the super capacitor participating in real-time correction is:

[0055] (1) Real-time correction power balance constraint;

[0056] P real,load (t3) - P real,grid (t3) - (P PS,G (t3) - P PS,P (t3)) - (P BS,dis (t3) - P BS,ch (t3)) =

[0057] (P real,WP (t3) - P WP,ab (t3)) + (P real,PV (t3) - P PV,ab (t3)) + (P SC,dis (t3) - P SC,ch (t3))

[0058] wherein P real,load (t3), P real,grid (t3), P real,WP (t3), P real,PV(t3) represent the predicted data of the industrial park electricity load, external power purchase and wind power output, photovoltaic power output at time t3 of the real-time correction optimization stage t3, respectively.

[0059] (2) super capacitor physical constraints;

[0060]

[0061] In the formula, SOC SC (t3) represents the state of charge of the super capacitor at time t3; η SC,ch and η SC,dis represent the charging efficiency and discharging efficiency of the super capacitor, respectively; SOC SC,min and SOC SC,max represent the minimum value and maximum value of the state of charge of the super capacitor, respectively; P SC,ch,max and η SC,dis,max represent the maximum charging power and maximum discharging power allowed by the super capacitor, respectively; B SC,ch (t3) and B SC,dis (t3) are both Boolean variables, and state 1 represents the charging state and discharging state of the super capacitor at time t3, respectively.

[0062] The beneficial effects of the present application are:

[0063] (1) The present application takes the industrial park equipped with a wind-solar power generation field as the research object, and studies the leasing scale and operation strategy of shared hybrid energy storage from the day-ahead planning, intra-day rolling adjustment and real-time correction three time scales. The introduction of shared hybrid energy storage not only reduces the storage cost of the industrial park, but also increases the electricity flexibility of the industrial park.

[0064] (2) The present application comprehensively considers the multi-time scale uncertainty of renewable energy and the demand charge saving of net load under the background of two-part electricity price under multi-time scale, and improves the wind-solar consumption ratio through three-stage fine adjustment. At the same time, the introduction of shared hybrid energy storage also reduces the overall external power purchase demand and the fluctuation rate of the net load curve of the industrial park, which not only promotes the full source consumption of renewable energy, but also promotes the energy saving and carbon reduction of the industrial park through the form of shared hybrid energy storage. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The flowchart of the invention strategy. DETAILED DESCRIPTION

[0066] The present application will be described in detail below in combination with the drawings and specific embodiments.

[0067] EMBODIMENT

[0068] The embodiment considers source load storage (combining the data of the power generation side and the load side (of the data, the configuration of energy storage) multi-time scale collaborative shared hybrid energy storage leasing strategy, facing an industrial park configured with a certain scale of wind power and photovoltaic, optimizing the leasing scale of shared hybrid energy storage at day-ahead, intraday, and real-time three time scales.

[0069] As a super-capacity energy storage device, pumped storage is particularly suitable for meeting the base load demand of an industrial park; compared with pumped storage, batteries have faster regulation speed and are suitable for intraday rolling optimization at the hour level; as a power-type energy storage, super-capacitors can be used to smooth the minute-level fluctuations of net load curve, so the shared hybrid energy storage resource of the embodiment corresponds to three different time scale energy storage resources, namely pumped storage, batteries, and super-capacitors.

[0070] The strategy of the embodiment is specifically as shown in Figure 1 , comprising the following steps:

[0071] In the day-ahead stage, the day-ahead prediction data of the park load and the wind and light output are obtained with 24h as the time span and 1h as the sampling time interval; according to the day-ahead prediction data, the objective function of pumped storage participating in the day-ahead stage is constructed with the maximum net benefit of pumped storage leasing and the minimum hour-level fluctuation of the grid power purchase curve as the optimization target, to obtain the optimal pumped storage resource leasing scale and operation strategy.

[0072] The objective function and constraint conditions of pumped storage participating in the day-ahead stage are as follows:

[0073] (1) Objective function f1: maximum net benefit of pumped storage leasing in the day-ahead stage and minimum hour-level fluctuation of the grid power purchase curve:

[0074] maxf1=(R PS -H PS -C grid )-α1Flu1

[0075]

[0076] H PS =h PS,P (P PS,Pmax +P PS,Gmax )+h PS,V (V u,max +V d,max )

[0077]

[0078] Wherein, R PS represents the arbitrage income of pumped storage in the day-ahead planning stage; H PS represents the leasing cost of pumped storage in a day; Cgrid represents the electricity purchase cost of the industrial park after the large-capacity pumped storage is configured; Flu1 represents the hourly fluctuation rate of the electricity purchase curve of the power grid; α1 represents the hourly fluctuation rate weighting coefficient; t1 represents the index variable of the day-ahead stage, t1 = 1, 2, …, 24, Δt1 represents the optimization decision time interval; P pv (t1) represents the peak-valley time-of-use electricity price at t1; P PS ,G (t1) and P PS,P (t1) represent the power generation and pumping power of the pumped storage at t1, respectively; h PS,P and h PS,V represent the unit power rental cost coefficient and the unit capacity rental cost coefficient of the pumped storage, respectively; P PS,Gmax represents the maximum power generation of the pumped storage; P PS,Pmax represents the maximum pumping power of the pumped storage; V u,max represent the maximum capacity of the upper reservoir, respectively; V d,max represent the maximum capacity of the lower reservoir, respectively; P da,gird (t1) represents the external electricity purchase power of the industrial park at t1 in the day-ahead optimization; E T represents the transformer capacity of the industrial park.

[0079] (II) Constraint conditions

[0080] (1) Power balance constraint in the day-ahead stage;

[0081] P da,grid (t1) = P da,load (t1) - P da,WP (t1) - P da,PV (t1) - P PS,G (t1) + P PS,P (t1)

[0082] wherein, P da,load (t1), P da,WP (t1), P da,PV (t1) represent the electricity load of the industrial park and the predicted data of wind power and photovoltaic power at t1 in the day-ahead stage, respectively.

[0083] (2) Physical constraint of pumped storage;

[0084] V u (t1) = V u (t1-1) - Q G (t1) + Q P (t1)

[0085] V d (t1) = V d (t1-1) - QP (t1)+Q G (t1)

[0086] V u,min ≤V u (t1)≤V u,max

[0087] V d,min ≤V d (t1)≤V d,max

[0088] V u (1)=V u (24)

[0089] V d (1)=V d (24)

[0090] Q G (t1)=P PS,G (t1) / η PS,G ·ρ W ·g·h

[0091] Q P (t1)=η PS,P ·P PS,P (t1) / ρ W ·g·h

[0092] B PS,G (t1)·P PS,Gmin ≤P PS,G (t1)≤B PS,G (t1)·P PS,Gmax

[0093] B PS,P (t1)·P PS,Pmin ≤P PS,P (t1)≤B PS,P (t1)·P PS,Pmax

[0094] 0≤B PS,G (t1)+B PS,P (t1)≤1

[0095] Wherein, V u (t1) and V d (t1) represent the reservoir capacity of upper and lower reservoirs of pumped storage at t1; Q G (t1) and Q P (t1) represent the power generation flow and pumping flow of pumped storage at t1; V u,min represents the minimum capacity of upper reservoir; V d,min represents the minimum capacity of lower reservoir; η PS,Gand η PS,S represent the power generation efficiency and the pumping efficiency of the pumped storage power station, respectively; p W represents the water density; g represents the acceleration of gravity; h represents the average water head height of the pumped storage power station; B PS,G and B PS,S are Boolean variables, and state 1 respectively represents that the pumped storage power station is in the power generation state and the pumping state; P PS,Gmin represents the minimum power generation power of the pumped storage power station; P PS,Pmin represents the minimum pumping power of the pumped storage power station.

[0096] In the intra-day stage, the whole day is divided into 4 optimization stages, and the intra-day prediction data of the park load and the wind and light output are obtained in a rolling manner with a time span of 6 hours and a sampling time interval of 15 minutes; according to the intra-day prediction data, the objective function of the intra-day rolling optimization of the battery is constructed with the maximum net benefit of battery leasing and the minimum abandoned wind and light as the optimization objective, and the optimal battery resource leasing scale and operation strategy in each optimization stage are obtained.

[0097] (I) Objective function f2: maximum net benefit of battery leasing and minimum abandoned wind and light in intra-day rolling stage

[0098] maxf2=(R BS -H BS )-α2C ab

[0099]

[0100] H BS =h BS,P ·P BS +h BS,E ·E BS

[0101]

[0102] wherein, R BS represents the electricity price arbitrage income of the battery in the rolling optimization stage; H BS represents the leasing cost of the battery; C ab represents the cost of abandoned wind and light; α2 represents the weighting coefficient of the cost of abandoned wind and light; t2 represents the time index variable of the rolling optimization stage, t2=1,2,…,24, Δt2 optimization decision time interval; p pv (t2) represents the peak-valley time-of-use electricity price at t2; P BS ,dis (t2) and P BS,ch (t2) respectively represent the discharging power and the charging power of the battery at t2; h BS,P and h BS,Erespectively represent the unit power leasing cost coefficient and the unit capacity leasing cost coefficient of the battery; P BS and E BS respectively represent the rated power and the rated capacity of the battery; λ ab represents the penalty coefficient for the wind and light curtailment of the park; p WP represents the wind power grid connection price; p PV represents the photovoltaic power grid connection price; P WP,ab (t2) represents the power abandonment amount of the wind power station at t2; P PV,ab (t2) represents the power abandonment amount of the photovoltaic power station at t2.

[0103] (ii) Constraint conditions

[0104] (1) Power balance constraint in the intra-day rolling phase

[0105] Proll, load (t2) - Proll, grid (t2) - (PPS, G (t2) - PPS, P (t2)) = Pbat, load (t2) - Pbat, grid (t2) - (Pbat, G (t2) - Pbat, P (t2))

[0106] (P roll,WP (t2) - P WP,ab (t2)) + (P roll,PV (t2) - P PV,ab (t2)) + (P BS,dis (t2) - P BS,ch (t2))

[0107] wherein, P roll,load (t2), P roll,grid (t2), P roll,WP (t2), P roll,PV (t2) represent the predicted data of the industrial park electricity load, external power purchase amount, wind power output and photovoltaic power output at t2 in the intra-day rolling optimization phase, respectively.

[0108] (2) Physical constraint of the battery

[0109]

[0110] In the formula, SOC BS (t2) represents the state of charge of the battery at t2; η BS,ch and η BS,dis represent the charging efficiency and the discharging efficiency of the battery, respectively; SOC BS,min and SOC BS,max represent the minimum value and the maximum value of the state of charge of the battery, respectively; P BS ,ch,max and η BS,dis,max represent the maximum charging power and the maximum discharging power allowed by the battery, respectively; B BS,ch(t2) and B BS,dis (t2) are all Boolean variables, and state 1 represents the charging state and discharging state of the battery during time period t2.

[0111] In the real-time phase, ultra-short-term forecast data of park load and wind and solar power output are acquired with a time span of 15 minutes and a sampling interval of 1 minute. Based on the ultra-short-term forecast data, an objective function for supercapacitor participation in real-time correction is constructed with the optimization objectives of maximizing the net benefit of supercapacitors and minimizing the minute-level fluctuation of net load. The optimal leasing scale and operation strategy of hybrid energy storage resources in each real-time phase are obtained. The real-time phase divides a day into 15-minute spans, for a total of 96 time periods.

[0112] Real-time correction during the real-time phase, taking the time period from 8:00 to 8:15 as an example:

[0113] ① Initial optimization: At 8:00, the system obtains ultra-short-term forecast data of load and wind and solar power output for the next 15 minutes. Based on this forecast data, the system performs an optimization to obtain a preliminary operating plan for the supercapacitor from 8:00 to 8:15, including charging and discharging strategies.

[0114] ② Minute-by-minute sampling and real-time correction: Real-time data is collected every minute. For example, actual load and wind and solar data are collected at 8:01. The real-time data is compared with the predicted data, the load deviation is calculated, and the supercapacitor is adjusted in real time at 8:01 to ensure that the operation of the supercapacitor is closer to the actual needs.

[0115] The above process was repeated at subsequent time points such as 8:02 and 8:03 to achieve point-by-point calibration of the supercapacitor.

[0116] (I) Objective function f3: Maximize the net benefit of supercapacitors and minimize the minute-level volatility of net load.

[0117] maxf3=(R SC -H SC )-α3Flu3

[0118]

[0119] H SC =h SC,P ·P SC +h SC,E ·E SC

[0120]

[0121] Among them, R SC This represents the electricity price arbitrage profit of supercapacitors during the real-time correction phase; H SCrepresents the leasing cost of super capacitor; Flu3 represents the minute-level fluctuation rate of grid electricity purchasing curve, and a3 represents the minute-level fluctuation rate weighting coefficient; t3 represents the index variable of day-ahead stage, t3 = 1, 2, …, 15, and At3 represents the optimization decision time interval; p pv (t3) represents the peak-valley time-of-use electricity price at t3; P SC,dis (t3) and P SC,ch (t3) represent the discharge power and charge power of super capacitor at t3, respectively; h SC,P and h SC ,E respectively represent the unit power leasing cost coefficient and unit capacity leasing cost coefficient of super capacitor; P SC and E SC respectively represent the rated power and rated capacity of super capacitor; P real,grid (t3) represents the external electricity purchasing amount at t3, i.e. the net load demand of industrial park.

[0122] (II) Constraint conditions

[0123] (1) Real-time correction power balance constraint

[0124] P real,load (t3)-P real,grid (t3)-(P PS,G (t3)-P PS,P (t3))-(P BS,dis (t3)-P BS,ch (t3))=

[0125] (P real,WP (t3)-P WP,ab (t3))+(P real,PV (t3)-P PV,ab (t3))+(P SC,dis (t3)-P SC,ch (t3)

[0126] wherein, P real,load (t3), P real,grid (t3), P real,WP (t3), P real,PV (t3) represent the electricity load of industrial park, the external electricity purchasing amount, and the predicted data of wind power and photovoltaic power at t3 in real-time correction optimization stage, respectively.

[0127] (2) Super capacitor physical constraint

[0128]

[0129] wherein, SOC SC (t3) represents the state of charge of super capacitor at t3; ηSC,ch and η SC,dis respectively represent the charging efficiency and discharging efficiency of the super capacitor; SOC SC,min and SOC SC,max respectively represent the minimum and maximum of the state of charge of the super capacitor; P SC,ch,max and η SC,dis,max respectively represent the maximum charging power and maximum discharging power allowed by the super capacitor; B SC,ch (t3) and B SC,dis (t3) are both Boolean variables, and state 1 respectively represents the charging state and discharging state of the super capacitor at the t3 period.

[0130] Finally, the leasing scale and operation strategy of the optimal pumped storage resource, the leasing scale and operation strategy of the optimal battery resource at each optimization stage, and the leasing scale and operation strategy of the optimal hybrid energy storage resource at each stage are integrated on the time scale to obtain a shared hybrid energy storage control strategy.

[0131] The technical solutions of the present application are not limited to the above embodiments, and any technical solution obtained by equivalent replacement falls within the scope of the present application.

Claims

1. A shared hybrid energy storage regulation strategy considering source-load-storage multi-time scale cooperation, characterized in that: for a park equipped with wind power and photovoltaic power; in the day-ahead stage, the day-ahead prediction data of the park load and the wind and light output are obtained with 24 hours as the time span and 1 hour as the sampling time interval; according to the day-ahead prediction data, the objective function of pumped storage participating in the day-ahead stage is constructed with the maximum net benefit of pumped storage leasing and the minimum hourly fluctuation of grid power purchase curve as the optimization objectives, and the optimal pumped storage resource leasing scale and operation strategy are obtained; in the intra-day stage, the whole day is divided into four optimization stages, and the intra-day prediction data of the park load and the wind and light output are obtained with 6 hours as the time span and 15 minutes as the sampling time interval; according to the intra-day prediction data, the objective function of the battery participating in the intra-day rolling optimization is constructed with the maximum net benefit of battery leasing and the minimum abandoned wind and light as the optimization objectives, and the optimal battery resource leasing scale and operation strategy in each optimization stage are obtained; in the real-time stage, the ultra-short-term prediction data of the park load and the wind and light output are obtained with 15 minutes as the time span and 1 minute as the sampling time interval; according to the ultra-short-term prediction data, the objective function of the super capacitor participating in real-time correction is constructed with the maximum net benefit of super capacitor and the minimum net load minute-level fluctuation rate as the optimization objectives, and the optimal hybrid energy storage resource leasing scale and operation strategy in each real-time stage are obtained; the optimal pumped storage resource leasing scale and operation strategy, the optimal battery resource leasing scale and operation strategy in each optimization stage, and the optimal hybrid energy storage resource leasing scale and operation strategy in each stage are integrated in the time scale to obtain the shared hybrid energy storage regulation strategy. The objective function f1 of the pumped storage participating in the day-ahead stage is: The constraint condition of the objective function f1 of the pumped storage participating in the day-ahead stage is: (1) day-ahead stage power balance constraint; (2) pumped storage physical constraint; 2. The shared hybrid energy storage regulation strategy considering source-load storage multi-time scale collaboration according to claim 1, characterized in that: The objective function f2 of the battery participating in the intra-day rolling optimization is: maxf1 = (R PS -H PS -C grid )-α1Flu1 H PS = h PS,P (P PS,Pmax + P PS,Gmax ) + h PS,V (V u,max + V d,max ) wherein R PS represents arbitrage profit of pumped storage in day-ahead planning stage; H PS represents leasing cost of pumped storage in a day; C grid represents electricity purchasing cost of industrial park to power grid after large-capacity pumped storage is configured; Flu1 represents hourly fluctuation rate of power grid electricity purchasing curve, and α1 represents hourly fluctuation rate weighting coefficient; t1 represents index variable in day-ahead stage, t1 = 1, 2, …, 24, and Δt1 represents optimization decision time interval; p pv (t1) represents peak-valley time-of-use electricity price at t1; P PS ,G (t1) and P PS,P (t1) represent power generation and pumping power of pumped storage at t1 respectively; h PS,P and h PS,V respectively represent unit power leasing cost coefficient and unit capacity leasing cost coefficient of pumped storage; P PS,Gmax represents maximum power generation of pumped storage; P PS,Pmax represents maximum pumping power of pumped storage; V u,max respectively represent maximum capacity of upper reservoir; V d,max respectively represent maximum capacity of lower reservoir; P da,gird (t1) represents external electricity purchasing power of industrial park at t1 in day-ahead optimization; E T represents transformer capacity of industrial park.

3. The shared hybrid energy storage regulation strategy considering source-load storage multi-time scale collaboration according to claim 2, characterized in that: The constraint condition of the objective function f2 of the battery participating in the intra-day rolling optimization is: (1) intra-day rolling stage power balance constraint; P da,grid (t1) = P da,load (t1) - P da,WP (t1) - P da,PV (t1) - P PS,G (t1) + P PS,P (t1) wherein P da,load (t1), P da,WP (t1), P da,PV (t1) represent the predicted data of the industrial park electricity load and the wind power output and the photovoltaic output at the day-ahead stage t1, respectively. (2) battery physical constraint; V u (t1) = V u (t1-1) - Q G (t1) + Q P (t1) V d (t1) = V d (t1-1) - Q P (t1) + Q G (t1) V u,min ≤V u (t1)≤V u,max V d,min ≤V d (t1)≤V d,max V u (1) = V u (24) V d (1) = V d (24) Q G (t1) = P PS,G (t1) / η PS,G • ρ W • g • h Q P (t1) = η PS,P · P PS,P (t1) / p W · g · h B PS,G (t1)·P PS,Gmin ≤P PS,G (t1)≤B PS,G (t1)·P PS,Gmax B PS,P (t1)·P PS,Pmin ≤P PS,P (t1)≤B PS,P (t1)·P PS,Pmax 0 < B PS,G (t1) + B PS,P (t1) < 1 wherein V u (t1) and V d (t1) represent the reservoir capacity of the upper and lower reservoirs of the pumped storage at time t1, respectively; Q G (t1) and Q P (t1) represent the power generation flow and the pumping flow of the pumped storage at time t1, respectively; V u,min represents the minimum capacity of the upper reservoir; V d,min represents the minimum capacity of the lower reservoir; η PS,G and η PS,S represent the power generation efficiency and the pumping efficiency of the pumped storage, respectively; p W represents the water density; g represents the gravitational acceleration; h represents the average water head height of the pumped storage; B PS,G and B PS,S are Boolean variables, and state 1 represents that the pumped storage is in the power generation state and the pumping state, respectively; P PS,Gmin represents the minimum power generation power of the pumped storage; P PS,Pmin represents the minimum pumping power of the pumped storage.

4. The shared hybrid energy storage regulation strategy considering source-load storage multi-time scale collaboration of claim 1, characterized in that: The objective function f3 of the super capacitor participating in real-time correction is: maxf2 = (R BS -H BS )-α2C ab H BS = h BS,P · P BS + h BS,E · E BS wherein R BS represents the arbitrage profit of the battery in the rolling optimization phase; H BS represents the leasing cost of the battery; C ab represents the cost of curtailment of wind and light; a2 represents the weighting coefficient of the cost of curtailment of wind and light; t2 represents the time index variable of the rolling optimization phase, t2 = 1, 2, …, 24, and At2 represents the optimization decision time interval; p pv (t2) represents the peak-valley time-of-use electricity price at t2; P BS,dis (t2) and P BS,ch (t2) represent the discharging power and charging power of the battery at t2, respectively; h BS,P and h BS,E represent the unit power leasing cost coefficient and the unit capacity leasing cost coefficient of the battery, respectively; P BS and E BS represent the rated power and the rated capacity of the battery, respectively; a ab represents the penalty coefficient for curtailment of renewable energy in the park; p WP represents the wind power grid connection price; p PV represents the photovoltaic grid connection price; P WP,ab (t2) represents the amount of power curtailment of the wind power station at t2; P PV,ab (t2) represents the amount of power curtailment of the photovoltaic power station at t2.

5. The shared hybrid energy storage regulation strategy considering source-load storage multi-time scale collaboration of claim 4, wherein: The constraint condition of the objective function f3 of the super capacitor participating in real-time correction is: (1) real-time correction power balance constraint; P roll,load (t2)-P roll,grid (t2)-(P PS,G (t2)-P PS,P (t2))=(P roll,WP (t2)-P WP,ab (t2))+(P roll,PV (t2)-P PV,ab (t2))+(P BS,dis (t2)-P BS,ch (t2) P roll,load (t2), P roll,grid (t2), P roll,WP (t2), P roll,PV (t2) represent the predicted data of the industrial park power consumption, external power purchase, and wind power output and photovoltaic power output at the rolling optimization stage t2, respectively. (2) super capacitor physical constraint; where SOC BS (t2) represents the state of charge of the battery at time t2; η BS,ch and η BS,dis represent the charging and discharging efficiency of the battery, respectively; SOC BS,min and SOC BS,max represent the minimum and maximum value of the state of charge of the battery, respectively; P BS,ch,max and η BS,dis,max represent the maximum charging and discharging power allowed by the battery, respectively; B BS,ch (t2) and B BS,dis (t2) are Boolean variables, state 1 representing the charging and discharging state of the battery at time t2, respectively.

6. The shared hybrid energy storage regulation strategy considering source-load storage multi-time scale collaboration of claim 1, wherein: ​ max f3 = (R SC -H SC )-α3Flu3 H SC = h SC,P • P SC + h SC,E • E SC wherein R SC represents the arbitrage profit of the super capacitor in the real-time correction stage; H SC represents the leasing cost of the super capacitor; Flu3 represents the minute-level fluctuation rate of the grid electricity purchasing curve, and a3 represents the minute-level fluctuation rate weighting coefficient; t3 represents the index variable of the day-ahead stage, t3 = 1, 2, …, 15, and At3 represents the optimization decision time interval; p pv (t3) represents the peak-valley time-of-use electricity price at t3; P SC,dis (t3) and P SC,ch (t3) represent the discharge power and the charge power of the super capacitor at t3, respectively; h SC,P and h SC,E represent the unit power leasing cost coefficient and the unit capacity leasing cost coefficient of the super capacitor, respectively; P SC and E SC represent the rated power and the rated capacity of the super capacitor, respectively; P real,grid (t3) represents the external electricity purchasing amount at t3, i.e., the net load demand of the industrial park.

7. The shared hybrid energy storage regulation strategy considering source-load storage multi-time scale collaboration of claim 6, wherein: ​ ​ P real,load (t3)-P real,grid (t3)-(P PS,G (t3)-P PS,P (t3))-(P BS,dis (t3)-P BS,ch (t3))=(P real,WP (t3)-P WP,ab (t3))+(P real,PV (t3)-P PV,ab (t3))+(P SC,dis (t3)-P SC,ch (t3)) P real,load (t3), P real,grid (t3), P real,WP (t3), P real,PV (t3) represent the predicted data of the industrial park power consumption, external power purchase, and wind power output and photovoltaic power output at the real-time correction optimization phase t3, respectively. ​ wherein SOC SC (t3) represents the state of charge of the supercapacitor at time t3; η SC,ch and η SC,dis represent the charging and discharging efficiency of the supercapacitor, respectively; SOC SC,min and SOC SC,max represent the minimum and maximum value of the state of charge of the supercapacitor, respectively; P SC,ch,max and η SC,dis,max represent the maximum charging and discharging power allowed for the supercapacitor, respectively; B SC,ch (t3) and B SC,dis (t3) are Boolean variables, state 1 representing the charging and discharging state of the supercapacitor at time t3, respectively.

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