A multi-objective optimization method for an offshore wind power hydrogen production energy storage system

Abstract

The present invention discloses a multi-objective optimization method for a hydrogen production and energy storage system of offshore wind power, belonging to the field of new energy. The method specifically includes the following steps: establishing the reliability, economy and wind curtailment of the hydrogen production and energy storage system of offshore wind power as optimization objectives, constructing a fitness function for multiple optimization objectives, writing program code for solving the fitness function based on the multi-objective particle swarm optimization algorithm in application software, running the program to obtain the Pareto front surface of the multi-objective optimization problem, and determining the optimal solution of capacity ratio according to the Pareto front surface diagram and the preference for each objective. The multi-objective capacity optimization method for the hydrogen production and energy storage system of offshore wind power of the present invention helps decision-makers to determine the best capacity ratio of the system according to the preferences of economy, reliability and wind curtailment.

Description

Technical Field

[0001] The present invention relates to the technical field related to the accommodation of offshore wind power, and particularly relates to a multi-objective optimization method for an offshore wind power to hydrogen energy storage system. Background Art

[0002] Offshore wind resources are rich and widely distributed, which is an important field for the development and utilization of renewable energy. However, offshore wind energy has strong intermittency, randomness and volatility, bringing many problems to the safe grid connection and reliable accommodation of offshore wind power. Wind power to hydrogen can be used as an effective means to accommodate curtailed wind and improve the utilization of wind resources, and it is a hot topic in the current development and research of offshore wind power.

[0003] The multi-objective optimization of an offshore wind power to hydrogen energy storage system can improve the reliability and economy of the system and reduce curtailed wind. However, when multiple objectives are optimized simultaneously, due to the different relationships between the objectives, such as some objectives conflicting with each other and some objectives complementing each other, the multi-objective optimization of the wind power to hydrogen energy storage system is more complex than single-objective optimization. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the present invention provides a multi-objective optimization method for an offshore wind power to hydrogen energy storage system, which improves the reliability and economy of the system and reduces the amount of curtailed wind.

[0005] To achieve the above object, the present invention can be realized by the following technical solutions:

[0006] A multi-objective optimization method for an offshore wind power to hydrogen energy storage system, which is used for an offshore wind power to hydrogen energy storage system. The offshore wind power to hydrogen energy storage system includes a power grid, the power grid is electrically connected to a bus, the bus is electrically connected to a wind turbine generator through a transformer, and is respectively electrically connected to a battery pack, an electrolyzer, a compressor and a hydrogen storage tank through a transformer and an inverter in sequence;

[0007] The method includes the following steps:

[0008] Establish the optimization objectives of the offshore wind power to hydrogen energy storage system, wherein the optimization objectives at least include reliability, economy and the amount of curtailed wind;

[0009] Construct a fitness function for the optimization objectives of the offshore wind power to hydrogen energy storage system;

[0010] Based on the multi-objective particle swarm optimization algorithm, solve the fitness function of the optimization objectives to obtain the Pareto front surface;

[0011] Determine the optimal solution of the capacity ratio according to the Pareto front surface and the preference for each optimization objective.

[0012] The multi-objective optimization method of the above-mentioned offshore wind power hydrogen production energy storage system further determines the optimization objectives of the offshore wind power hydrogen production energy storage system, specifically including:

[0013] Taking the load deficit probability LDP as the evaluation index of the reliability of the system,

[0014]

[0015] In the formula, LDP is the load deficit probability of the system, and P grid (t) is the real-time grid-connected power demand of the system, and P b (t - 1) is the compensation power that the battery pack can provide;

[0016] Taking the annual net income as the evaluation index of the economy of the system,

[0017] Z = I tot - C tot-h2 - C tot-b - C w

[0018] In the formula, Z is the annual net income of the system, and I tot is the annual income of the system, and C tot-h2 is the annualized cost of hydrogen production energy storage, and C tot-b is the annualized cost of the battery pack, and C tot-w is the annualized cost of the offshore wind farm;

[0019] Among them, the annual income I of the system tot is specifically:

[0020]

[0021] Among them, p g (t) is the real-time electricity price of wind power grid connection, and P g (t) is the real-time grid-connected power, and p h is the unit price of hydrogen sales, and V h (t) is the hydrogen production rate of electrolyzed water;

[0022] The annualized cost C of hydrogen production energy storage tot-h2 is specifically:

[0023]

[0024] The annualized cost C of the battery pack tot-b is specifically:

[0025]

[0026] In the formula, C inv-h2 is the initial investment cost of hydrogen production energy storage, and C ope-h2For the annual operation and maintenance cost of hydrogen production and energy storage, C res-h2 For the annualized recovery residual value of hydrogen production and energy storage, i is the discount rate, n a For the equipment recovery period, C inv-b For the initial investment cost of the battery pack, C ope-b For the annual operation and maintenance cost of the battery pack, C res-b The annualized recovery residual value of the battery pack.

[0027] The multi-objective optimization method of the offshore wind power hydrogen production and energy storage system as described above. Further, the fitness function of the optimization objective specifically includes:

[0028]

[0029] In the formula, F1, F2, and F3 respectively represent the objective functions of the three optimization objectives of reliability, economy, and curtailment volume. C1, C2, and C3 respectively represent the capacities of the electrolyzer, hydrogen storage tank, and battery pack.

[0030] The multi-objective optimization method of the offshore wind power hydrogen production and energy storage system as described above. Further, the multi-objective particle swarm optimization algorithm specifically includes the following steps:

[0031] Under the condition of meeting the constraint conditions, randomly initialize a particle swarm with a population size of N, randomly generate the position and velocity of each particle, and initialize the external reserve set;

[0032] Calculate the fitness value of each particle of the objective functions F1, F2, and F3 respectively;

[0033] Update the particle individual leader p i , specifically, take the optimal position searched by the current particle as the particle individual leader p i , let the position of the particle in the t-th generation be x i (t), and the individual leader be p i (t). If the position of the next-generation particle x i (t + 1) is not dominated by p i (t), then p i (t + 1) is taken as x i (t + 1), otherwise the individual leader remains unchanged and is still p i (t);

[0034] Update the particle global leader g i , specifically, the particle global leader is selected as the optimal position obtained during the search of all particles within the current particle domain. Use the external reserve set to save the non-dominated solutions obtained by the particles during the search process, and then determine the global leader g according to the distribution density of the elements in the external reserve set i ; among them, the distribution density is represented by the particle density distance. For the particle xi (t), and its density distance is:

[0035]

[0036] In the formula, x j (t) and x k (t) are the two particles closest to particle x i (t), and are the maximum values of the three objective functions respectively; after calculating the distribution density of the elements in the external reserve set, select the one with the lowest density value as the global guide g for each particle i ;

[0037] According to the individual guide p i and the global guide g i update the position and velocity of each particle,

[0038] V i (t + 1) = w·v i (t) + η1·rand()·(p i -x i (t)) + η2·rand()·(g i -x i (t))

[0039] x i (t + 1) = x i (t) + v i (t + 1)

[0040] Among them, w represents the inertia weight, η1 and η2 represent the learning factors, and rand() represents a random number on [0, 1];

[0041] Sort the distribution densities of the elements in the external reserve set by size, and select the top N solutions with larger distribution densities to update the external reserve set;

[0042] Iterate until the termination condition is met, that is, output the Pareto solution after reaching the preset number of iterations.

[0043] In the multi-objective optimization method of the offshore wind power hydrogen production energy storage system as described above, further, in the Pareto front surface, each point is regarded as a three-dimensional variable, and among all the Pareto front points, the final optimal solution depends on the decision maker's preference for each objective.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] 1. The method of the present invention for converting the weighted sum of multiple objectives into a single-objective optimization is more reasonable than the traditional method, and can effectively show the trade-off relationship among the reliability, economy, and curtailment of the system; moreover, the optimal capacity ratio of the system can be determined according to the decision-making method.

[0046] 2. The present invention establishes a mathematical model for multi-objective optimal configuration of the capacity of an offshore wind power hydrogen production energy storage system, with the best system reliability, optimal economy, and minimum curtailment as the optimization objectives, and gives the system capacity configuration strategy from the perspectives of electrolytic hydrogen production and charge-discharge management of the energy storage system, and gives a multi-objective stochastic optimization configuration model based on the Pareto optimal solution. This method can effectively improve the grid connection reliability and economy of the system, and at the same time reduce the curtailment of the offshore wind farm in the system. Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings according to these drawings without creative efforts.

[0048] Figure 1 It is a flowchart of multi-objective optimization of the offshore wind power system applied in the embodiment of the present invention.

[0049] Figure 2 It is an offshore wind power hydrogen production energy storage grid-connected power generation system applied in the embodiment of the present invention.

[0050] Figure 3 It is a flowchart of the energy management rule strategy of the system applied in the embodiment of the present invention.

[0051] Figure 4 It is a flowchart of the multi-objective particle swarm optimization algorithm adopted in the embodiment of the present invention. Detailed Embodiments

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0053] Embodiment:

[0054] It should be noted that the terms "first", "second", etc. in the specification, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0055] See Figures 1 to 4 , Figure 1 is the offshore wind power hydrogen production energy storage grid-connected power generation system applied in the present invention, Figure 2 is the offshore wind power hydrogen production energy storage grid-connected power generation system applied in the present invention, Figure 3 This is an example of the energy management strategy given by the present invention, and the present invention is not limited to a single energy management strategy. Figure 3 The shown energy management strategy is that the offshore wind farm gives priority to grid connection. When the offshore wind power is less than the expected grid connection power, the battery releases electrical energy to supplement the system's deficit power, and the electrolyzer stops working; when the offshore wind power is greater than the expected grid connection power, the excess energy is preferentially used to produce hydrogen through the electrolyzer and stored in the hydrogen storage tank, and the remaining energy is supplied to the battery to smooth the system's grid connection power and reduce the wind curtailment amount. Figure 4 is the flowchart of the multi-objective particle swarm optimization algorithm adopted in the embodiment of the present invention.

[0056] The offshore wind power hydrogen production energy storage system includes a power grid, the power grid is electrically connected to a bus, the bus is electrically connected to a wind turbine generator through a transformer, and is respectively electrically connected to a battery pack, an electrolyzer, a compressor and a hydrogen storage tank through a transformer and an inverter in sequence

[0057] The present invention establishes a mathematical model for the multi-objective optimal configuration of the capacity of the offshore wind power hydrogen production energy storage system, with the best system reliability, the best economy and the minimum wind curtailment amount as the optimization objectives. From the perspectives of hydrogen production by the electrolyzer and charge and discharge management of the energy storage system, etc., a system capacity configuration strategy is given, and a multi-objective stochastic optimization configuration model based on Pareto optimal solutions is given. This method can effectively improve the grid connection reliability and economy of the system, and at the same time reduce the wind curtailment of the offshore wind farm in the system.

[0058] The multi-objective optimization method for offshore wind power hydrogen production energy storage includes the following steps:

[0059] S1: Establish the optimization goal of the offshore wind power hydrogen production energy storage system with the minimum of reliability, economy, and curtailment amount. The optimization goal includes but is not limited to reliability, economy, and curtailment amount;

[0060] Select the load deficit probability (LDP) as the system reliability evaluation index. The value range of LDP is 0 to 1, where the value 0 indicates that the system fully meets the load demand, and the value 1 indicates that the system does not provide any load.

[0061]

[0062] In the formula, LDP is the load deficit probability of the offshore wind power hydrogen production energy storage system, P grid (t) is the real-time grid-connected power demand of the system, and P b (t - 1) is the compensation power that can be provided by the battery energy storage.

[0063] To reasonably measure the economy of the system, use the annual net income of the system as the measurement index, as shown in the formula.

[0064] Z = I tot - C tot-h2 - C tot-b - C w (2)

[0065] In the formula, Z is the annual net income of the system, and I tot is the annual income of the system, and C tot-h2 is the annualized cost of hydrogen production energy storage, and C tot-b is the annualized cost of the battery pack, and C tot-w is the annualized cost of the offshore wind farm.

[0066] Among them, the system income consists of two parts, including the income generated by the fan transmitting power to the grid and selling hydrogen, as shown in the formula.

[0067]

[0068] Among them, p g (t) is the real-time electricity price of wind power grid connection, and P g (t) is the real-time grid-connected power, and p h is the unit price of hydrogen sold, and V h (t) is the hydrogen production rate of electrolyzed water.

[0069] The costs of hydrogen production energy storage and battery pack energy storage both consist of initial investment cost, operation and maintenance cost, and recovery salvage value. The full life cycle cost is calculated in the form of equal annual value. The annualized costs of hydrogen production energy storage and battery pack are shown in the formula respectively.

[0070]

[0071]

[0072] C inv-h2 is the initial investment cost of hydrogen production and energy storage, C ope-h2 is the annual operation and maintenance cost of hydrogen production and energy storage, C res-h2 is the annualized recovery residual value of hydrogen production and energy storage, i is the discount rate, n a is the equipment recovery period, C inv-b is the initial investment cost of the battery pack, C ope-b is the annual operation and maintenance cost of the battery pack, C res-b Annualized recovery residual value of the battery pack.

[0073] The abandoned wind volume measurement index is as shown in the formula.

[0074] C curt = P w (t) - P g (t) - P el (t) - P b (t) (6)

[0075] In the formula, C curt is the abandoned wind volume, P g (t) is the real-time grid-connected power, P el (t) is the real-time power consumed by hydrogen production and energy storage, P b (t) is the real-time storage power of the battery.

[0076] S2: Construct a fitness function with multiple optimization objectives.

[0077] The fitness function of the multi-objective problem is as follows:

[0078]

[0079] In the formula, F1, F2, and F3 respectively represent the three objective functions of reliability, economy, and abandoned wind volume, and C1, C2, and C3 respectively represent the capacities of the electrolyzer, hydrogen storage tank, and battery pack.

[0080] S3. Write the program code for solving the fitness function based on the multi-objective particle swarm optimization algorithm in the application software.

[0081] The specific implementation steps of the multi-objective particle swarm optimization algorithm are as follows:

[0082] S31: Initialize the particle swarm. Under the condition of meeting the constraint conditions, randomly initialize a particle swarm with a population size of N, and randomly generate the position and velocity of each particle; initialize the external reserve set to store the non-dominated solutions in the search process.

[0083] S32: Calculate the particle fitness value. For the three objective functions F1, F2, and F3, calculate the fitness value of each particle respectively.

[0084] S33: Update the particle individual guide p i . The best position found by the current particle is used as the particle individual guide. Assume that the position of the particle in the t-th generation is x i (t), and the individual guide is p i (t). If the position of the next-generation particle x i (t + 1) is not dominated by p i (t), then p i (t + 1) is taken as x i (t + 1), otherwise the individual guide remains unchanged and is still p i (t).

[0085] S34: Update the global guide g i . The particle global guide is selected as the optimal position obtained during the search of all particles within the current particle domain. Use the external reserve set to save the non-dominated solutions obtained by the particles during the search process, and then determine the global guide according to the distribution density of the elements in the external reserve set.

[0086] S35: The distribution density is represented by the particle density distance. For the particle x i (t), its density distance is:

[0087]

[0088] In the formula, x j (t) and x k (t) are the two particles closest to the particle x i (t), and are the maximum values of the three objective functions respectively.

[0089] S36: After calculating the distribution density of the elements in the external reserve set, select the one with the lowest density value to select the global guide g for each particle i .

[0090] S37: Update the particle position. Use p i and g i calculated by S33 and S36 to update the position and velocity of each particle. The update formulas are as follows:

[0091]

[0092] Among them, w represents the inertia weight, η1 and η2 represent the learning factors, and rand() represents a random number on [0, 1];

[0093] S38: Update the external reserve set. Sort the distribution densities of the elements in the external reserve set calculated in S35 by magnitude, and select the top N solutions with larger distribution densities to update the external reserve set.

[0094] S39: Iterate until the termination condition is met, that is, output the Pareto solution after reaching the preset number of iterations.

[0095] S4. Run the program to obtain the Pareto front surface of the multi-objective optimization problem, and determine the optimal capacity ratio solution according to the Pareto front surface graph and the preferences for each objective.

[0096] In the Pareto front graph, each point is regarded as a three-dimensional variable. Among all the Pareto front points, the selection of the final optimal solution depends on the specific situation and the preferences of the decision maker for each objective.

[0097] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0098] The above embodiments are only for explaining the technical concept and characteristics of the present invention, and their purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly. It should not be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A multi-objective optimization method for an offshore wind power hydrogen production energy storage system, characterized in that, For an offshore wind power hydrogen production energy storage system, the offshore wind power hydrogen production energy storage system includes a power grid, the power grid is electrically connected to a bus, the bus is electrically connected to a wind turbine generator set through a transformer, and is respectively electrically connected to a battery pack, an electrolyzer, a compressor and a hydrogen storage tank through a transformer and an inverter in sequence; The method includes the following steps: Establish the optimization objectives of the offshore wind power hydrogen production energy storage system, wherein the optimization objectives at least include reliability, economy and wind curtailment; Construct a fitness function for the optimization objectives of the offshore wind power hydrogen production energy storage system; Solve the fitness function of the optimization objectives based on the multi-objective particle swarm optimization algorithm to obtain the Pareto front surface; Determine the optimal solution of the capacity ratio according to the Pareto front surface and the preference for each optimization objective; Among them, establishing the optimization objectives of the offshore wind power hydrogen production energy storage system specifically includes: Taking the load loss probability LDP as the evaluation index of the reliability of the system, where LDP is the load deficit probability of the system, P grid (t) is the real-time grid-connected power demand of the system, P b (t - 1) is the compensation power that the battery pack can provide; Taking the annual net income as the evaluation index of the economy of the system, Z = I tot -C tot-h2 -C tot-b -C tot-w where Z is the annual net income of the system, I tot is the annual income of the system, C tot-h2 is the annualized cost of hydrogen production and energy storage, C tot-b is the annualized cost of the battery pack, C tot-w is the annualized cost of the offshore wind farm; Among them, the annual income I of the system tot Specifically: Among them, p g (t) is the real-time electricity price for wind power grid connection, P g (t) is the real-time grid connection power, p h is the unit price of hydrogen sales, V h (t) is the hydrogen production rate by electrolyzing water; The annualized cost C of hydrogen production and energy storage tot-h2 Specifically: The annualized cost C of the battery pack tot-b Specifically: Where, C inv-h2 is the initial investment cost of hydrogen production and energy storage, C ope-h2 is the annual operation and maintenance cost of hydrogen production and energy storage, C res-h2 is the annualized recovery residual value of hydrogen production and energy storage, i is the discount rate, n a is the equipment recovery period, C inv-b is the initial investment cost of the battery pack, C ope-b is the annual operation and maintenance cost of the battery pack, C res-b Annualized recovery residual value of the battery pack.

2. The multi-objective optimization method for the offshore wind power hydrogen production and energy storage system according to claim 1, wherein The fitness function of the optimization objective specifically includes: Wherein, F1, F2 and F3 respectively represent the objective functions of three optimization objectives of reliability, economy and air rejection volume, C1, C2, C3 respectively represent the capacities of the electrolyzer, hydrogen storage tank and battery pack, and C curt is the air rejection volume.

3. The multi-objective optimization method for the offshore wind power hydrogen production and energy storage system according to claim 1, characterized in that, The multi-objective particle swarm optimization algorithm specifically includes the following steps: Under the condition of satisfying the constraint conditions, randomly initialize a particle swarm with a population size of N, randomly generate the position and velocity of each particle, and initialize the external reserve set; Calculate the fitness value of each particle of the objective functions F1, F2 and F3 respectively; Update the particle individual guide p i , specifically, take the optimal position found by the current particle as the particle individual guide p i , let the position of the particle in the t-th generation be x i (t), and the individual guide be p i (t). If the position of the next-generation particle x i (t + 1) is not dominated by p i (t), then p i (t + 1) is taken as x i (t + 1), otherwise the individual guide remains unchanged and is still p i (t); Update the global leader g of the particles i , specifically, the global leader of the particles is selected as the optimal position obtained during the search of all particles within the current particle domain. The external reserve set is used to save the non-dominated solutions obtained by the particles during the search process, and then the global leader g is determined according to the distribution density of the elements in the external reserve set i ; among them, the distribution density is represented by the particle density distance. For the particle x i (t), its density distance is: where x j (t) and x k (t) are the two particles closest to the particle x i (t); and are the maximum values of the three objective functions respectively; after calculating the distribution density of the elements in the external reserve set, the one with the lowest density value is selected as the global guide g i for each particle; According to the individual guide p i and the global guide g i Update the position and velocity of each particle V i (t + 1)= w·v i (t)+η1·rand()·(p i -x i (t))+η2·rand()·(g i -x i (t)) x i (t + 1)=x i (t)+v i (t + 1) Among them, w represents the inertia weight, η1 and η2 represent the learning factors, and rand() represents a random number on [0, 1]; Sort the distribution densities of the elements in the external reserve set by size, and select the top N solutions with larger distribution densities to update the external reserve set; Iterate until the termination condition is met, that is, output the Pareto solution after reaching the preset number of iterations.

4. The multi-objective optimization method of the offshore wind power hydrogen production energy storage system according to claim 1, characterized in that, In the Pareto front surface, each point is regarded as a three-dimensional variable. Among all the Pareto front points, the final optimal solution depends on the decision maker's preference for each objective.

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