Offshore wind plant energy storage power station capacity optimization design method

Abstract

The invention discloses a capacity optimization design method for an energy storage power station of an offshore wind power plant, which comprises the following steps of: firstly, establishing a first objective function with minimum construction cost and operation cost of the energy storage power station, and then establishing a second objective function with the benefit of peak load shifting of the energy storage power station as a target; secondly, establishing constraint conditions including an energy storage power station in the power system, such as a power flow balance constraint condition, a system standby power constraint condition and the like; and finally, the objective function is solved by adopting a group intelligent optimization algorithm whale algorithm, and the position of each whale in the group of the whale algorithm represents the construction address, the construction capacity, the rated charging power, the rated discharging power and the like of the energy storage power station. Therefore, the specific technical indexes for constructing the energy storage power station can be determined through the optimal solution of the whale algorithm.

Description

Technical Field

[0001] The present invention relates to the field of energy storage power station capacity design, and in particular to a method for optimizing the capacity design of an offshore wind farm energy storage power station. Background Art

[0002] Offshore wind farms are one of the important ways to achieve the construction of a "clean, low-carbon, safe and efficient" energy system. However, building energy represented by "wind power generation" as an important part of the power system will lead to a decrease in the frequency regulation and peak regulation capabilities of the power system, and a decrease in the stability, reliability and safety of the power grid. In addition, the output power of offshore wind farms is volatile, but the supply and demand balance of the power system requires real-time performance. Therefore, offshore wind farms need to be equipped with energy storage power stations that can store and release electrical energy.

[0003] However, energy storage power stations have the characteristics of high investment costs and long payback periods. Therefore, from the perspective of energy storage power station manufacturers, it is necessary to comprehensively consider the investment costs of energy storage power stations and their ability to reduce peak loads when designing the capacity of energy storage power stations.

[0004] Therefore, there is still room for improvement in the prior art. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing the design of the capacity of an offshore wind farm energy storage power station, aiming to solve the technical problems of high initial construction cost, high operating cost and low peak shaving and valley filling capacity of the energy storage power station.

[0006] To achieve the above-mentioned purpose, the technical solution of the present invention is: a method for optimizing the design of the capacity of an offshore wind farm energy storage power station, which establishes an optimization model for the location and capacity of the energy storage power station, takes the minimization of the construction cost and operation cost of the energy storage system as the first objective function, and takes the peak shaving and valley filling benefit as the second objective function; and uses the swarm intelligence optimization algorithm whale algorithm to solve the objective function to obtain the optimal installation capacity of the energy storage power station.

[0007] Due to the volatility of offshore wind farms, in order to improve the power grid's ability to absorb wind energy, it is necessary to build an energy storage power station. The capacity of the energy storage power station is closely related to the construction cost and maintenance cost. The first objective function is to minimize the construction cost and operation cost of the energy storage power station. When designing the capacity of the energy storage power station, its first objective function is expressed as:

[0008] min(C T +C dep )(1)

[0009] In the formula, C T represents the cost of power system operation, C dep Represents the equivalent cost of the energy storage power station over its entire life cycle.

[0010] Furthermore, the constraints for designing the capacity of energy storage power stations include: the cost of power system operation, the equivalent cost of the energy storage power station over its entire life cycle, power system energy balance, power flow, system backup power demand, output of traditional power generation equipment, operating status of energy storage devices, fluctuations in output power of wind turbines, etc. (1) The constraints for the cost of power system operation can be expressed as:

[0011] C T =C op +C st (2)

[0012] In the formula, C op represents the operating cost of traditional thermal power generating units, hydropower generating units and other units, which can be expressed as formula (3); C st The startup cost of traditional thermal power generating units, hydropower generating units and other units can be expressed as formula (4).

[0013]

[0014] In the formula, T represents the total number of moments in a day, t represents the tth moment, and G n represents the set of traditional generator sets, i represents the i-th traditional generator set; u gi,t represents the state of the traditional generator set. If the i-th traditional generator set is in the startup state at time t, then u gi,t =1, if the i-th traditional generator set is in shutdown state at time t, then u gi,t =0; a gi is the variable cost coefficient of the i-th traditional generator set when it is in the startup state, b gi is the fixed cost coefficient of the i-th traditional generator set when it is in the startup state; P gi,t is the power generation of the i-th traditional generator set at the t-th moment; S gi,t represents the state of the traditional generator set. If the i-th traditional generator set is in the grid-connected state, then S gi,t =1; if the i-th traditional generator set is off-grid, then S gi,t =0; It represents the startup cost of a single startup of the i-th traditional generator set.

[0015] (2) The equivalent cost of the energy storage power station over its entire life cycle refers to the difference between the total initial investment cost of the energy storage power station and the residual value of the energy storage power station when it is scrapped, spread over the cost of each day in the entire life cycle of the energy storage power station, expressed as:

[0016]

[0017] In the formula, C EThe difference between the initial construction cost of the energy storage power station and its residual value after scrapping can be expressed as formula (6); r represents the discount rate, which can usually be 6%; n represents the life of the energy storage power station, in years;

[0018]

[0019] In the formula, z i represents the state of the energy storage power station at different power generation device nodes. If the energy storage power station is configured at the i-th node, then z i =1, if there is no energy storage power station at the i-th node, then z i =0; E i represents the capacity of the energy storage power station configured at the i-th node, represents the maximum value of the charging and discharging power of the energy storage device configured at the i-th node; v s Represents the residual value coefficient of the energy storage power station when it is scrapped; C esp Represents the cost per unit power of the energy storage power station, C ese represents the unit capacity cost of the energy storage power station; B n It represents the combination of nodes in the power system, that is, the combination of various power generation devices, including traditional generators and offshore wind turbines.

[0020] (3) The constraint condition of power system energy balance is expressed as:

[0021]

[0022] Where P gi,t represents the power generation of the i-th traditional generator set at the t-th time, P wi,t represents the power generation of the i-th offshore wind turbine at the t-th time, P pvi,t represents the output power of the ith photovoltaic power generation system at time t, P ij,t represents the power flow between the ith line and the jth line at time t, P loadi,t represents the composite power at the ith node at the tth moment; P ei,t ch represents the charging power of the i-th energy storage station at the t-th time, P ei,t dis represents the discharge power of the i-th energy storage station at the t-th moment; PV n represents the set of photovoltaic power generation systems, W n Represents a collection of wind turbines.

[0023] (4) The power flow constraint is expressed as:

[0024] P ij min ≤P ij,t=(θ i,t -θ j,t )B ij ≤P ij max (8)

[0025] Where P ij min represents the minimum value of the power flow between the ith line and the jth line, P ij max represents the maximum value of the power flow between the i-th line and the j-th line; θ i,t represents the phase angle of the ith line at time t, θ j,t represents the phase angle of the ith line at time t, θ i,t ,θ j,t It should satisfy equations (9)-(10).

[0026] θ i min ≤θ i,t ≤θ i max (9)

[0027] θ j min ≤θ j,t ≤θ j max (10)

[0028] In the formula, θ j min represents the minimum phase value of the jth node, θ j max represents the maximum phase value of the jth node, θ i min represents the minimum phase value of the i-th node, θ i max Indicates the maximum value of the phase of the i-th node.

[0029] (5) The system backup power demand constraint is expressed as:

[0030]

[0031] Where P gi,t min It represents the maximum output power of the i-th traditional generator set at the t-th time. R represents the system standby power coefficient.

[0032] (6) The output constraints of traditional power generation equipment can be divided into output power threshold limit, output power slope limit, start-up and shutdown limit and start-up time limit; the start-up and shutdown limit include start-up limit and shutdown limit; the start-up time limit includes start-up time and shutdown time; the output power threshold limit of traditional generator sets is expressed as:

[0033] s gi,t P gi min ≤P gi,t ≤s gi,t P gi max (12)

[0034] Where P gi min represents the minimum output power of the i-th traditional generator set, P gi max Represents the maximum output power of the i-th traditional generator set.

[0035] The output power slope limit of traditional generator sets can be expressed as:

[0036] P gi,t -P gi,t-1 ≤RU gi (13)

[0037] P gi,t-1 -P gi,t ≤RD gi (14)

[0038] Where P gi,t-1 represents the output power of the i-th traditional generator set at time t-1, RU gi is the maximum value of the output power of the i-th traditional generator set in a unit time, RD gi is the maximum value of the output power drop of the i-th traditional generator set in a unit time.

[0039] The constraints of the startup and shutdown restrictions of traditional generator sets can be expressed as:

[0040] -s gi,t-1 +s gi,t -v gi,t ≤0 (15)

[0041] s gi,t-1 -s gi,t -v gi,t ≤0 (16)

[0042] In the formula, v gi,t represents the shutdown action of the i-th traditional generator set. When the i-th traditional generator set is shut down at time t, vgi,t =0;S gi,t-1 It represents the grid-connected status of the i-th traditional generator set at the t-1th moment.

[0043] The constraints of the startup time and shutdown time of traditional generator sets are expressed as:

[0044] s gi,h ≥s gi,t -s gi,t-1 And 1≤h-(t-1)≤MU gi (17)

[0045] 1-s gi,h ≥s gi,t-1 -s gi,t And 1≤h-(t-1)≤MD gi (18)

[0046] In the formula, MU gi represents the minimum startup time of the i-th traditional generator set; MD gi is the minimum shutdown time of the i-th traditional generator set; S gi,h represents the startup time of the i-th traditional generator set.

[0047] (7) The constraints on the output power of offshore wind turbines are expressed as:

[0048] 0≤P wi,t ≤P wi,t max (19)

[0049] Where P wi,t max represents the maximum output power of the i-th offshore wind turbine at time t, P wi,t represents the output power of the i-th offshore wind turbine at the t-th moment.

[0050] (8) The constraints on the output power of the photovoltaic generator set are expressed as:

[0051] 0≤P pvi,t ≤P pvi,t max (20)

[0052] Where P pvi,t max represents the maximum output power of the ith photovoltaic generator set at the tth moment; P pvi,t Represents the output power of the i-th photovoltaic generator set at the t-th moment.

[0053] (9) The constraints for the operation of the energy storage device are expressed as:

[0054]

[0055]

[0056] In the formula, z i max represents the maximum number of energy storage devices, Indicates the charging state of the i-th energy storage device at the t-th time. If the value is 1, it means that the device is being charged; if the value is 0, it means that the device is not being charged; It shows the discharge state of the i-th energy storage device at the t-th time. If the value is 1, it means that the device is discharging; if the value is 0, it means that the device is not discharging.

[0057] (10) The storage capacity constraint of the energy storage device is expressed as:

[0058]

[0059] E li,t =δ loss E i,t (26)

[0060] In the formula, E0 represents the initial electric energy of the energy storage device, E i,t represents the storage capacity of the i-th energy storage device at the t-th time, E i,t-1 represents the storage capacity of the i-th energy storage device at the t-1th moment; η ch represents the efficiency of the energy storage device when charging, η dis Indicates the efficiency of the energy storage device during discharge; E li,t represents the natural loss of electricity of the i-th energy storage device at the t-th moment, δ loss Indicates the self-discharge rate of the energy storage device.

[0061] (11) The power constraint of the energy storage device can be expressed as:

[0062]

[0063] In the formula, Indicates the minimum value of the remaining capacity factor of the energy storage device, Indicates the maximum value of the remaining capacity factor of the energy storage device; Indicates the minimum value of the power coefficient of charging and discharging of the energy storage device. Indicates the maximum value of the power coefficient of charging and discharging of the energy storage device.

[0064] Furthermore, the purpose of configuring the energy storage power station is to reduce the peak load and fill the valley load. Then the energy storage power station takes the benefit of reducing the peak load and filling the valley load as the goal and establishes a second objective function. The second objective function is expressed as:

[0065]

[0066] In the formula, p t is the electricity price at the tth moment, is the charging power of the i-th energy storage device at the t-th moment, is the discharge power of the i-th energy storage device at the t-th moment.

[0067] Furthermore, the swarm intelligence optimization algorithm Whale Algorithm is used to solve the first objective function and obtain the capacity of the optimal offshore wind farm energy storage power station. The Whale Algorithm simulates the process of whale hunting, which includes three processes: surrounding prey, attacking prey, and searching for prey.

[0068] The mathematical model of whales surrounding their prey is expressed as:

[0069]

[0070] Where k is the number of iterations, X(k+1) represents the position of the whale at the k+1th iteration, and X * (k) represents the optimal position of the whale before the k+1th iteration, A is expressed as formula (32), and C is expressed as formula (33).

[0071] A=2a×ra (32)

[0072] C=2×r (33)

[0073] Where a represents the convergence factor, which is a linear decreasing function from 2 to 0, and can be expressed as formula (34). r is a random number between 0 and 1, and can be expressed as formula (35).

[0074]

[0075] r=rand(0,1) (35)

[0076] In the formula, K represents the total number of iterations.

[0077] There are two types of whale attack behaviors: constriction and spiral swimming. The probability of these two attack behaviors is 50%. The constriction behavior is expressed as:

[0078] X(k+1)=X * (k)-A·D p<0.5 (36)

[0079] Where p is a random number between 0 and 1.

[0080] The spiral swimming behavior can be expressed as

[0081]

[0082] Where l is a random number between -1 and 1, b is the shape coefficient, i.e., the coefficient representing the spiral shape; e bl A shape that represents the overall spiral motion.

[0083] The behavior of a whale searching for prey can be expressed as:

[0084] X(k+1)=X rand -ACX rand -X(k)| (38)

[0085] Where, X rand is the position of a randomly selected whale in the group of whales.

[0086] Furthermore, the swarm intelligence optimization algorithm, the whale algorithm, is used to solve the first objective function, and the steps are as follows: the first step is to initialize parameters and population;

[0087] The second step is to calculate the fitness of individuals in the population and find the location of the optimal whale;

[0088] Step 3: According to the number of iterations k, the coefficients in the algorithm are updated using formulas (32)-(35);

[0089] The fourth step is to determine whether the probability is less than 0.5. If it is less than 0.5, use formula (37) to update the whale position; if it is greater than 0.5, continue to determine the size of the coefficient A. If A is not greater than 1, use formula (38) to update the whale position; if A is greater than 1, use formula (36) to update the position;

[0090] In the fifth step, until the maximum number of iterations is reached, the optimal whale position of the current step is retained, which is the optimal solution. Furthermore, when the whale optimization algorithm is used to solve the objective function, the whale position is the energy storage power station capacity, energy storage power station location, energy storage power station charging power, and energy storage power station discharging power.

[0091] Beneficial effects: The present invention takes into account the volatility and randomness of the output power of new energy power plants. The use of energy storage power stations can solve this problem to a certain extent. However, the capacity and site selection of energy storage power stations are closely related to the performance of energy storage power stations in smoothing power fluctuations caused by new energy power generation. The present invention first studies the output power output model of new energy power plants, the output power mathematical model of traditional power plants, the charging and discharging power mathematical model of energy storage power stations, and the mathematical model of the flow constraints of each node, and establishes a first objective function with the lowest construction cost and operating cost of energy storage power stations, and a second objective function with the goal of peak shaving and valley filling. The whale algorithm is used to solve the objective function, which can improve the economic benefits of the construction and operation of energy storage power stations and improve the utilization rate of energy storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a step flowchart of the present invention.

[0093] Figure 2 It is a flow chart of the present invention using the swarm intelligence optimization algorithm whale algorithm to solve the objective function. DETAILED DESCRIPTION

[0094] In order to make the objectives, technical solutions and advantages of the present invention more clear and explicit, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0095] like Figure 1 As shown, the present invention discloses a method for optimizing the design of the capacity of an offshore wind farm energy storage power station, which establishes an optimization model for the location and capacity of the energy storage power station, takes the minimization of the construction cost and operation cost of the energy storage system as the first objective function, and takes the peak shaving and valley filling benefit as the second objective function; and adopts a swarm intelligence optimization algorithm, the whale algorithm, to solve the objective function to obtain the optimal installation address and installation capacity of the energy storage power station.

[0096] Due to the volatility of offshore wind farms, in order to improve the power grid's ability to absorb wind energy, it is necessary to build an energy storage power station. The capacity of the energy storage power station is closely related to the construction cost and maintenance cost. The construction cost and operation cost of the energy storage system are minimized. When designing the capacity of the energy storage power station, its first objective function is expressed as:

[0097] min(C T +C dep )(1)

[0098] In the formula, C T represents the cost of power system operation, C dep Represents the equivalent cost of the energy storage power station over its entire life cycle.

[0099] Furthermore, the constraints for designing the capacity of energy storage power stations include: the cost of power system operation, the equivalent cost of the energy storage power station over its entire life cycle, power system energy balance, power flow, system backup power demand, output of traditional power generation equipment, operating status of energy storage devices, fluctuations in output power of wind turbines, etc. (1) The constraints for the cost of power system operation can be expressed as:

[0100] C T =C op +C st (2)

[0101] In the formula, C op represents the operating cost of traditional thermal power generating units, hydropower generating units and other units, which can be expressed as formula (3); C st The startup cost of traditional thermal power generating units, hydropower generating units and other units can be expressed as formula (4).

[0102]

[0103] In the formula, T represents the total number of moments in a day, t represents the tth moment, and G n represents the set of traditional generator sets, i represents the i-th traditional generator set; u gi,t represents the state of the traditional generator set. If the i-th traditional generator set is in the startup state at time t, then u gi,t =1, if the i-th traditional generator set is in shutdown state at time t, then u gi,t =0; a gi is the variable cost coefficient of the i-th traditional generator set when it is in the startup state, b gi is the fixed cost coefficient of the i-th traditional generator set when it is in the startup state; P gi,t is the power generation of the i-th traditional generator set at the t-th moment; S gi,t represents the state of the traditional generator set. If the i-th traditional generator set is in the grid-connected state, then S gi,t =1; if the i-th traditional generator set is off-grid, then S gi,t =0. It represents the startup cost of a single startup of the i-th traditional generator set.

[0104] (2) The equivalent cost of the energy storage power station over its entire life cycle refers to the difference between the total initial investment cost of the energy storage power station and the residual value of the energy storage power station when it is scrapped, spread over the cost of each day in the entire life cycle of the energy storage power station, which can be expressed as:

[0105]

[0106] In the formula, C E The difference between the initial construction cost of the energy storage power station and its residual value after scrapping can be expressed as formula (6); r represents the discount rate, which can usually be 6%; n represents the life of the energy storage power station, in years.

[0107]

[0108] In the formula, z i represents the state of the energy storage power station at different power generation device nodes. If the energy storage power station is configured at the i-th node, then z i =1, if there is no energy storage power station at the i-th node, then z i =0; E i represents the capacity of the energy storage power station configured at the i-th node, Represents the maximum value of the charging and discharging power of the energy storage device configured at the i-th node. s Represents the residual value coefficient of the energy storage power station when it is scrapped; C esp Represents the cost per unit power of the energy storage power station, Cese represents the unit capacity cost of the energy storage power station; B n It represents the combination of nodes in the power system, that is, the combination of various power generation devices, including traditional generators and offshore wind turbines.

[0109] (3) The constraint condition of power system energy balance is expressed as:

[0110]

[0111] Where P gi,t represents the power generation of the i-th traditional generator set at the t-th time, P wi,t represents the power generation of the i-th offshore wind turbine at the t-th time, P pvi,t represents the output power of the ith photovoltaic power generation system at time t, P ij,t represents the power flow between the ith line and the jth line at time t, P loadi,t represents the composite power at the ith node at the tth moment; P ei,t ch represents the charging power of the i-th energy storage station at the t-th time, P ei,t dis represents the discharge power of the i-th energy storage station at the t-th moment; PV n represents the set of photovoltaic power generation systems, W n Represents a collection of wind turbines.

[0112] (4) The power flow constraint can be expressed as:

[0113] P ij min ≤P ij,t =(θ i,t -θ j,t )B ij ≤P ij max (8)

[0114] Where P ij min represents the minimum value of the power flow between the ith line and the jth line, P ij max represents the maximum value of the power flow between the i-th line and the j-th line; θ i,t represents the phase angle of the ith line at time t, θ j,t represents the phase angle of the ith line at time t, θ i,t ,θ j,t It should satisfy equations (9)-(10).

[0115] θ i min ≤θi,t ≤θ i max (9)

[0116] θ j min ≤θ j,t ≤θ j max (10)

[0117] In the formula, θ j min represents the minimum phase value of the jth node, θ j max represents the maximum phase value of the jth node, θ i min represents the minimum phase value of the i-th node, θ i max Indicates the maximum value of the phase of the i-th node.

[0118] (5) The system backup power demand constraint is expressed as:

[0119]

[0120] Where P gi,t min It represents the maximum output power of the i-th traditional generator set at the t-th moment, and R represents the system standby power coefficient.

[0121] (6) The output constraints of traditional power generation equipment can be divided into output power threshold limit, output power slope limit, start-up and shutdown limit and start-up time limit; the start-up and shutdown limit include start-up limit and shutdown limit; the start-up time limit includes start-up time and shutdown time; the output power threshold limit of traditional generator sets is expressed as:

[0122] s gi,t P gi min ≤P gi,t ≤s gi,t P gi max (12)

[0123] Where P gi min represents the minimum output power of the i-th traditional generator set, P gi max Represents the maximum output power of the i-th traditional generator set.

[0124] The output power slope limit of traditional generator sets is expressed as:

[0125] P gi,t -Pgi,t-1 ≤RU gi (13)

[0126] P gi,t-1 -P gi,t ≤RD gi (14)

[0127] Where P gi,t-1 represents the output power of the i-th traditional generator set at time t-1, RU gi is the maximum value of the output power of the i-th traditional generator set in a unit time, RD gi is the maximum value of the output power drop of the i-th traditional generator set in a unit time.

[0128] The constraints of the startup and shutdown restrictions of traditional generator sets can be expressed as:

[0129] -s gi,t-1 +s gi,t -v gi,t ≤0 (15)

[0130] s gi,t-1 -s gi,t -v gi,t ≤0 (16)

[0131] In the formula, v gi,t represents the shutdown action of the i-th traditional generator set. When the i-th traditional generator set is shut down at time t, v gi,t =0;S gi,t-1 It represents the grid-connected status of the i-th traditional generator set at the t-1th moment.

[0132] The constraints of the startup time and shutdown time of traditional generator sets can be expressed as:

[0133] s gi,h ≥s gi,t -s gi,t-1 And 1≤h-(t-1)≤MU gi (17)

[0134] 1-s gi,h ≥s gi,t-1 -s gi,t And 1≤h-(t-1)≤MD gi (18)

[0135] In the formula, MU gi represents the minimum startup time of the i-th traditional generator set, MD gi is the minimum shutdown time of the i-th traditional generator set; S gi,h represents the startup time of the i-th traditional generator set.

[0136] (7) The constraints on the output power of offshore wind turbines can be expressed as:

[0137] 0≤P wi,t ≤P wi,t max (19)

[0138] Where P wi,t max represents the maximum output power of the i-th offshore wind turbine at time t, P wi,t represents the output power of the i-th offshore wind turbine at the t-th moment.

[0139] (8) The constraints on the output power of the photovoltaic generator set can be expressed as:

[0140] 0≤P pvi,t ≤P pvi,t max (20)

[0141] Where P pvi,t max represents the maximum output power of the ith photovoltaic generator set at time t, P pvi,t Represents the output power of the i-th photovoltaic generator set at the t-th moment.

[0142] (9) The constraints for the operation of the energy storage device can be expressed as:

[0143]

[0144] In the formula, z i max represents the maximum number of energy storage devices, Indicates the charging state of the i-th energy storage device at the t-th time. If the value is 1, it means that the device is being charged; if the value is 0, it means that the device is not being charged; It shows the discharge state of the i-th energy storage device at the t-th time. If the value is 1, it means that the device is discharging; if the value is 0, it means that the device is not discharging.

[0145] (10) The storage capacity constraint of the energy storage device can be expressed as:

[0146]

[0147] E li,t =δ loss E i,t (26)

[0148] In the formula, E0 represents the initial electric energy of the energy storage device, E i,t represents the storage capacity of the i-th energy storage device at the t-th time; ηch represents the efficiency of the energy storage device when charging, η dis Indicates the efficiency of the energy storage device during discharge; E li,t represents the natural loss of electricity of the i-th energy storage device at the t-th moment, δ loss Indicates the self-discharge rate of the energy storage device.

[0149] (11) The power constraint of the energy storage device can be expressed as:

[0150]

[0151] In the formula, Indicates the minimum value of the remaining capacity factor of the energy storage device, Indicates the maximum value of the remaining capacity factor of the energy storage device; Indicates the minimum value of the power coefficient of charging and discharging of the energy storage device. Indicates the maximum value of the power coefficient of charging and discharging of the energy storage device.

[0152] Furthermore, the purpose of configuring the energy storage power station is to reduce the peak load and fill the valley load. When the energy storage power station takes the benefit of reducing the peak load and filling the valley load as the goal, its objective function is expressed as:

[0153]

[0154] In the formula, p t is the electricity price at the tth moment, is the charging power of the i-th energy storage device at the t-th moment, is the discharge power of the i-th energy storage device at the t-th moment.

[0155] Furthermore, the swarm intelligence optimization algorithm Whale Algorithm is used to solve the first objective function and obtain the capacity of the optimal offshore wind farm energy storage power station. The Whale Algorithm simulates the process of whale hunting, which includes three processes: surrounding prey, attacking prey, and searching for prey.

[0156] The mathematical model of whales surrounding their prey is expressed as:

[0157]

[0158] Where k is the number of iterations, X(k+1) represents the position of the whale at the k+1th iteration, and X * (k) represents the optimal position of the whale before the k+1th iteration, A is expressed as formula (32), and C is expressed as formula (33), where A and C are both random coefficients:

[0159] A=2a×ra (32)

[0160] C=2×r (33)

[0161] In the formula, a represents the convergence factor, which is a linear decreasing function from 2 to 0, which can be expressed as formula (34); r is a random number between 0 and 1, which can be expressed as formula (35);

[0162]

[0163] r=rand(0, 1) (35)

[0164] In the formula, K represents the total number of iterations.

[0165] There are two types of whale attack behaviors: constriction and spiral swimming. The probability of these two attack behaviors is 50%. The constriction behavior is expressed as:

[0166] X(k+1)=X * (k)-A·D p<0.5 (36)

[0167] Where p is a random number between 0 and 1.

[0168] The spiral swimming behavior is expressed as

[0169]

[0170] Where l is a random number between -1 and 1, b is the shape coefficient, i.e., the coefficient representing the spiral shape; e bl A shape that represents the overall spiral motion.

[0171] The behavior of a whale searching for prey is represented by:

[0172] X(k+1)=X rand -A|CX rand -X(k)| (38)

[0173] Where, X rand is the position of a randomly selected whale in the group of whales.

[0174] Furthermore, the flowchart of using the swarm intelligence optimization algorithm whale algorithm to solve the first objective function can be as follows Figure 2 As shown; the steps are as follows:

[0175] The first step is to initialize parameters and population;

[0176] The second step is to calculate the fitness of individuals in the population and find the location of the optimal whale;

[0177] Step 3: According to the number of iterations k, the coefficients in the algorithm are updated using formulas (32)-(35);

[0178] The fourth step is to determine whether the probability is less than 0.5. If it is less than 0.5, use formula (37) to update the whale position; if it is greater than 0.5, continue to determine the size of the coefficient A. If A is not greater than 1, use formula (38) to update the whale position; if A is greater than 1, use formula (36) to update the position;

[0179] In the fifth step, until the maximum number of iterations is reached, the optimal whale position of the current step is retained, which is the optimal solution. Furthermore, when the whale optimization algorithm is used to solve the objective function, the whale position is the energy storage power station capacity, energy storage power station location, energy storage power station charging power, and energy storage power station discharging power.

[0180] The present invention takes into account the volatility and randomness of the output power of renewable energy power plants. The use of energy storage power stations can solve this problem to a certain extent. However, the capacity and site selection of energy storage power stations are closely related to the performance of energy storage power stations in smoothing the power fluctuations caused by renewable energy power generation. The present invention proposes an objective function and constraint conditions for optimizing the capacity of energy storage power stations, and uses the whale algorithm to solve the objective function, which can improve the utilization rate and economic benefits of energy storage power stations.

[0181] The above are only preferred implementation modes of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. It should be pointed out that for technicians in this technical field, any modification or equivalent replacement of the technical solution of the present invention without creative work does not depart from the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing the capacity of an offshore wind farm energy storage power station, characterized in that: Offshore wind farms are volatile. In order to improve the power grid's ability to absorb wind energy, it is necessary to build energy storage power stations. The capacity of energy storage power stations is closely related to construction costs and maintenance costs. The first objective function is to minimize the construction cost and operation cost of the energy storage power station, and the second objective function is to reduce the peak and fill the valley. The swarm intelligence optimization algorithm, the whale algorithm, is used to solve the objective function to obtain the optimal installation address and installation capacity of the energy storage power station. When designing the capacity of the energy storage power station, the first objective function is expressed as: min(C T +C dep ) (1) In the formula, C T represents the cost of power system operation, C dep Represents the equivalent cost of the energy storage power station over its entire life cycle.

2. The method for optimizing the capacity of an offshore wind farm energy storage power station according to claim 1, characterized in that: The constraints for designing the capacity of energy storage power stations include: the cost of power system operation, the equivalent cost of the energy storage power station over its entire life cycle, power system energy balance, power flow, system backup power requirements, output of traditional power generation equipment, the operating status of energy storage devices, and fluctuations in wind turbine output power; (1) The cost constraint of power system operation is expressed as: C T =C op +C st (2) In the formula, C op represents the operating cost of traditional thermal power generating units, hydropower generating units, etc., expressed as formula (3); C st The startup cost of traditional thermal power generating units, hydropower generating units, etc. is expressed as formula (4); In the formula, T represents the total number of moments in a day, t represents the tth moment, and G n represents the set of traditional generator sets, i represents the i-th traditional generator set; u gi,t represents the state of the traditional generator set. If the i-th traditional generator set is in the startup state at time t, then u gi,t =1, if the i-th traditional generator set is in shutdown state at time t, then u gi,t =0; a gi is the variable cost coefficient of the i-th traditional generator set when it is in the startup state, b gi is the fixed cost coefficient of the i-th traditional generator set when it is in the startup state; P gi,t is the power generation of the i-th traditional generator set at the t-th moment; S gi,t represents the state of the traditional generator set. If the i-th traditional generator set is in the grid-connected state, then S gi,t =1; if the i-th traditional generator set is off-grid, then S gi,t =0; It represents the startup cost of a single startup of the i-th traditional generator set; (2) The equivalent cost of the energy storage power station over its entire life cycle refers to the difference between the total initial investment cost of the energy storage power station and the residual value of the energy storage power station when it is scrapped, spread over the cost of each day in the entire life cycle of the energy storage power station, expressed as: In the formula, C E The difference between the initial construction cost of the energy storage power station and its residual value after scrapping is expressed as formula (6); r represents the discount rate, which can usually be 6%; n represents the life of the energy storage power station, in years; In the formula, z i represents the state of the energy storage power station at different power generation device nodes. If the energy storage power station is configured at the i-th node, then z i =1, if there is no energy storage power station at the i-th node, then z i =0; E i represents the capacity of the energy storage power station configured at the i-th node, represents the maximum value of the charging and discharging power of the energy storage device configured at the i-th node; v s Represents the residual value coefficient of the energy storage power station when it is scrapped, C esp Represents the cost per unit power of the energy storage power station, C ese represents the unit capacity cost of the energy storage power station; B n It represents the combination of nodes in the power system, that is, the combination of various power generation devices, including traditional generators and offshore wind turbines; (3) The constraint condition of power system energy balance is expressed as: Where P gi,t represents the power generation of the i-th traditional generator set at the t-th time, P wi,t represents the power generation of the i-th offshore wind turbine at the t-th time, P pvi,t represents the output power of the ith photovoltaic power generation system at time t, P ij,t represents the power flow between the ith line and the jth line at time t, P loadi,t represents the composite power at the ith node at the tth moment; P ei,t ch represents the charging power of the i-th energy storage station at the t-th time, P ei,t dis represents the discharge power of the i-th energy storage station at the t-th moment; PV n represents the set of photovoltaic power generation systems, W n represents a collection of wind turbines; (4) The constraints of the power flow are expressed as: Where P ij min represents the minimum value of the power flow between the ith line and the jth line, P ij max represents the maximum value of the power flow between the i-th line and the j-th line; θ i,t represents the phase angle of the ith line at time t, θ j,t represents the phase angle of the ith line at time t, θ i,t and θ j,t Should satisfy equations (9)-(10); i i min ≤θ i,t ≤θ i max (9) i j min ≤θ j,t ≤θ j max (10) In the formula, θ j min represents the minimum phase value of the jth node, θ j max represents the maximum phase value of the jth node, θ i min represents the minimum phase value of the i-th node, θ i max represents the maximum value of the phase of the i-th node; (5) The constraint condition of system backup power demand is expressed as: Where P gi,t min It represents the maximum output power of the i-th traditional generator set at the t-th moment. R represents the system standby power factor; (6) The output constraints of traditional power generation equipment are divided into output power threshold limit, output power slope limit, start-up and shutdown limit and start-up time limit; the start-up and shutdown limit include start-up limit and shutdown limit; the start-up time limit includes start-up time and shutdown time; the threshold limit of the output power of traditional generator sets is expressed as: s gi,t P gi min≤P gi,t ≤s gi,t P g imax (12) Where P gi min represents the minimum output power of the i-th traditional generator set, P gi max represents the maximum output power of the i-th traditional generator set; The output power slope limit of traditional generator sets is expressed as: P gi,t -P gi,t-1 ≤RU gi (13) P gi,t-1 -P gi,t ≤RD gi (14) Where P gi,t-1 represents the output power of the i-th traditional generator set at time t-1, RU gi is the maximum value of the output power of the i-th traditional generator set in a unit time, RD gi is the maximum value of the output power drop of the i-th traditional generator set in a unit time; The constraints of the startup and shutdown restrictions of traditional generator sets can be expressed as: -s gi,t-1 +s gi,t -v gi,t ≤0 (15) s gi,t-1 -s gi,t -v gi,t ≤0 (16) In the formula, v gi,t represents the shutdown action of the i-th traditional generator set. When the i-th traditional generator set is shut down at time t, v gi,t =0;S gi,t-1 represents the grid-connected status of the i-th traditional generator set at time t-1; The constraints of the startup time and shutdown time of traditional generator sets are expressed as: s gi,h ≥s gi,t -s gi,t-1 And 1≤h-(t-1)≤MU gi (17) 1-s gi,h ≥s gi,t-1 -s gi,t And 1≤h-(t-1)≤MD gi (18) In the formula, MU gi represents the minimum startup time of the i-th traditional generator set, MD gi is the minimum shutdown time of the i-th traditional generator set; S gi,h represents the startup time of the i-th traditional generator set; (7) The constraints on the output power of offshore wind turbines are expressed as: 0≤P wi,t ≤P wi,t max (19) Where P wi,t max represents the maximum output power of the i-th offshore wind turbine at time t; (8) The constraints on the output power of the photovoltaic generator set are expressed as: 0≤P pvi,t ≤P pvi,t max (20) Where P pvi,t max represents the maximum output power of the i-th photovoltaic generator set at the t moment; (9) The constraints for the operation of the energy storage device are expressed as: In the formula, z i max represents the maximum number of energy storage devices, Indicates the charging state of the i-th energy storage device at the t-th time. If the value is 1, it means that the device is being charged; if the value is 0, it means that the device is not being charged; represents the discharge state of the i-th energy storage device at the t-th time. If the value is 1, it means that the device is discharging; if the value is 0, it means that the device is not discharging; (10) The storage capacity constraint of the energy storage device is expressed as: AND li,t =δ loss AND i,t (26) In the formula, E0 represents the initial electric energy of the energy storage device, E i,t represents the storage capacity of the i-th energy storage device at the t-th time; η ch represents the efficiency of the energy storage device when charging, η dis Indicates the efficiency of the energy storage device during discharge. li,t represents the natural loss of electricity of the i-th energy storage device at the t-th moment, δ loss Indicates the self-discharge rate of the energy storage device; (11) The power constraint of the energy storage device is expressed as: In the formula, Indicates the minimum value of the remaining capacity factor of the energy storage device, Indicates the maximum value of the remaining capacity factor of the energy storage device; Indicates the minimum value of the power coefficient of charging and discharging of the energy storage device. Indicates the maximum value of the power coefficient of charging and discharging of the energy storage device.

3. The method for optimizing the capacity of an offshore wind farm energy storage power station according to claim 1, characterized in that: The purpose of configuring the energy storage power station is to reduce the peak load and fill the valley load. The energy storage power station establishes a second objective function with the benefit of reducing the peak load and filling the valley load as the goal. The second objective function is expressed as: In the formula, p t is the electricity price at the tth moment, is the charging power of the i-th energy storage device at the t-th moment, is the discharge power of the i-th energy storage device at the t-th moment.

4. The method for optimizing the capacity of an offshore wind farm energy storage power station according to claim 1, characterized in that: The swarm intelligence optimization algorithm, the whale algorithm, is used to solve the first objective function and obtain the optimal capacity of the offshore wind farm energy storage power station. The whale algorithm simulates the whale's hunting process, which includes three processes: surrounding prey, attacking prey, and searching for prey. The mathematical model of whales surrounding their prey is expressed as: Where k is the number of iterations, X(k+1) represents the position of the whale at the k+1th iteration, and X * (k) represents the optimal position of the whale before the k+1th iteration, A is expressed as formula (32), and C is expressed as formula (33); A=2a×ra (32) C＝2×r (33) In the formula, a represents the convergence factor, which is a linear decreasing function from 2 to 0, expressed as formula (34); r is a random number between 0 and 1, expressed as formula (35); r=rand(0,1) (35) In the formula, K represents the total number of iterations; There are two types of whale attack behaviors: constriction and spiral swimming. The probability of these two attack behaviors is 50%. The constriction behavior is expressed as: X(k+1)=X * (k)-A·D p<0.5 (36) In the formula, p is a random number between 0 and 1; The spiral swimming behavior is expressed as Where l is a random number between -1 and 1, b is the shape coefficient, i.e., the coefficient representing the spiral shape; e bl The shape that represents the entire spiral motion; The behavior of a whale searching for prey is represented by: X(k+1)=X rand -A|CX rand -X(k)| (38) Where, X rand is the position of a randomly selected whale in the group of whales.

5. The method for optimizing the capacity of an offshore wind farm energy storage power station according to claim 4, characterized in that: The swarm intelligence optimization algorithm, the whale algorithm, is used to solve the first objective function. The steps are as follows: Step 1: Initialize parameters and population; The second step is to calculate the fitness of individuals in the population and find the location of the optimal whale; Step 3: According to the number of iterations k, the coefficients in the algorithm are updated using formulas (32)-(35); The fourth step is to determine whether the probability is less than 0.

5. If it is less than 0.5, use formula (37) to update the whale position; if it is greater than 0.5, continue to determine the size of the coefficient A. If A is not greater than 1, use formula (38) to update the whale position; if A is greater than 1, use formula (36) to update the position; In the fifth step, until the maximum number of iterations is reached, the optimal whale position of the current step is retained, which is the optimal solution.

6. The method for optimizing the capacity of an offshore wind farm energy storage power station according to claim 4, characterized in that: When the whale optimization algorithm is used to solve the objective function, the position of the whale is the capacity of the energy storage power station, the location of the energy storage power station, the charging power of the energy storage power station, and the discharging power of the energy storage power station.

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