Light-storage power station participation frequency modulation assisted market capacity configuration planning method and medium

Abstract

The invention relates to a light-storage power station participation frequency modulation auxiliary market capacity configuration planning method and a medium, and the method comprises the following steps: obtaining related operation parameters of a light-storage power station, considering a photovoltaic standby part, and building a cost-income model of light-storage power station participation in auxiliary frequency modulation under auxiliary market compensation; constructing a capacity configuration model of the light-storage power station, and setting constraint conditions; and based on the capacity configuration model, taking the maximum life cycle net income of the cost-income model as an objective function, and adopting a particle swarm optimization algorithm for solving to obtain an optimal capacity configuration planning scheme. Compared with the prior art, the method has the advantages that the stability and reliability of the system are improved by participating in frequency modulation auxiliary service while the economical efficiency is improved, and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage capacity configuration in the power market, and in particular to a capacity configuration planning method and medium for a photovoltaic-storage power station to participate in a frequency regulation auxiliary market. Background Art

[0002] The capacity configuration method is one of the important topics in the research of photovoltaic-storage power station energy storage under the power market, and is a key means to improve the stability, safety and economy of the system. The ancillary service market has gradually become an indispensable part of the power system. In this context, energy storage technology, as a flexible and efficient energy storage method, is gradually becoming an important participant in the ancillary service market. The photovoltaic-storage power station has the characteristics of rapid response, flexible scheduling, and efficient energy conversion, which gives it unique advantages in providing auxiliary services such as frequency regulation, spare capacity, and voltage support. With the participation of energy storage, the power system can better cope with load fluctuations, renewable energy fluctuations, and emergencies, and improve the flexibility and resilience of the system.

[0003] How to configure the capacity of photovoltaic-storage power stations has always been a very challenging research. The document "Design of the Power Ancillary Service Market System under the National Unified Power Market Environment" analyzes the situation of China's regional and provincial ancillary service market mechanisms, and proposes an overall framework for the national ancillary service market that adapts to different market development stages. The document "Research on the Bidding Strategy of Energy Storage and the Clearing Method of Power Market" proposes a bidding strategy for energy storage to declare the difference between charging and discharging prices, and establishes an electric energy market clearing model, which can improve the benefits of energy storage users. The document "Research on the Strategy of Energy Storage Participating in Power System Auxiliary Frequency Regulation under the Power Spot Market" analyzes the evaluation and compensation rules of different frequency regulation ancillary service markets, builds a cost-benefit model for battery energy storage to participate in power system auxiliary frequency regulation under different market compensation policies, and uses particle swarm algorithm to obtain the optimized battery system capacity configuration plan. Existing literature has also established a capacity planning model for photovoltaic-storage power stations to participate in power grid frequency regulation. Experiments show that hybrid energy storage combined with photovoltaic backup capacity can achieve reasonable resource planning and improve the economy and reliability of frequency regulation.

[0004] Energy storage in PV-storage power stations is an effective way to solve the problem of distributed resources being difficult to absorb after being connected to the power grid and to participate in the frequency regulation market. It can improve the stability and reliability of power system operation to a certain extent. However, the current research on energy storage capacity configuration of PV-storage power stations lacks exploration and analysis in the context of auxiliary services in the power market, especially the lack of in-depth research on energy storage capacity configuration in the frequency regulation auxiliary service market. It has failed to effectively model the energy storage capacity configuration of PV-storage power stations and cannot quantify its economic efficiency and impact on the power market. Summary of the invention

[0005] The purpose of the present invention is to provide a method and medium for capacity configuration planning of a photovoltaic-storage power station participating in a frequency modulation auxiliary market, which improves stability and reliability while improving economy.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for planning capacity allocation of a photovoltaic-storage power station participating in a frequency regulation auxiliary market comprises the following steps:

[0008] Obtain relevant operating parameters of the PV-storage power station, consider the PV reserve part, and establish a cost-benefit model for the PV-storage power station to participate in auxiliary frequency regulation under auxiliary market compensation;

[0009] Build a capacity configuration model for the solar-storage power station and set constraints;

[0010] Based on the capacity configuration model, taking the maximum net benefit of the whole life cycle of the cost-benefit model as the objective function, a particle swarm optimization algorithm is used to solve and obtain the optimal capacity configuration planning scheme.

[0011] Furthermore, the cost-benefit model includes a cost sub-model and a life cycle benefit sub-model. The cost sub-model includes the lithium battery cost and the attenuation cost of the photovoltaic backup capacity. The cost sub-model is:

[0012] C A,l =C B,l +C B,in

[0013] C B,s =C s P s E s

[0014] The full life cycle revenue sub-model is constructed by the frequency regulation mileage compensation revenue of the photovoltaic-storage power station in each trading cycle. The full life cycle revenue sub-model is:

[0015]

[0016] In the formula, C A,l is the total cost of lithium battery, C B,l is the operation and maintenance cost of lithium batteries, C B,in is the investment and construction cost of lithium batteries, C B,s is the attenuation cost of PV backup capacity, C s is the investment cost of the photovoltaic unit, P s is the spare capacity of the PV unit, E s is the attenuation coefficient of the photovoltaic module, R all is the total life cycle revenue of energy storage participating in the frequency regulation ancillary service market, d allK is the frequency regulation mileage involved in energy storage, all is the comprehensive adjustment performance index, Y all is the comprehensive frequency regulation mileage settlement price, C l It is the compensation price when conventional units adjust the frequency alone.

[0017] Furthermore, the expressions of the operation and maintenance cost of the lithium battery and the investment and construction cost of the lithium battery are respectively:

[0018] C B,l =C l Q l D l

[0019] C B,in =C z +C s +C f +C h

[0020] In the formula, C B,l is the operation and maintenance cost of lithium batteries, C B,in is the investment and construction cost of lithium batteries, C l is the unit investment cost of lithium battery energy storage, Q l is the configuration capacity, D l is the life loss coefficient, C z is the lithium battery component cost, C s is the construction cost, C f is the battery auxiliary equipment cost, C h The cost of electricity conversion.

[0021] Furthermore, the comprehensive frequency regulation performance index K of the photovoltaic-storage power station is all By the response time indicator K t , Adjustment accuracy index K a And the adjustment rate index K s After comprehensive weighting, the comprehensive frequency regulation performance index of the photovoltaic-storage power station is:

[0022]

[0023] Among them, the response time indicator Kt for:

[0024]

[0025] The adjustment accuracy index Ka for:

[0026]

[0027] The regulation rate indicator Ks for:

[0028]

[0029] In the formula, K all K is the comprehensive frequency modulation performance index, s is the adjustment rate index, K t is the response time indicator, K a is the regulation accuracy index, Δt is the delay time of the power generation unit responding to the AGC command, which is equal to the time of stepping out of the response dead zone minus the time of receiving the AGC adjustment command. is the deviation between the actual output of the power generation unit and the AGC command, Δp N is the allowable error of the power generation unit, v is the actual rate, v N is the rated speed.

[0030] Furthermore, the frequency modulation mileage compensation income for each transaction cycle is:

[0031] C b =D b Y b K b

[0032] In the formula, C b is the frequency modulation mileage compensation fee for the bth transaction cycle, D b Y is the frequency regulation mileage of energy storage in the bth trading cycle, b is the settlement price of frequency modulation mileage in the bth trading cycle, K b is the average value of the comprehensive frequency modulation performance index in the bth trading cycle.

[0033] Furthermore, the capacity configuration model is:

[0034]

[0035] In the formula, Q l is the configuration capacity, Q total is the total energy storage capacity, H1 and H2 are configuration coefficients, and P s is the spare capacity of the PV unit, Q PV is the total installed capacity of photovoltaic power.

[0036] Furthermore, the constraints include energy storage system power constraints, energy storage system power charging and discharging efficiency constraints, energy storage system charge state constraints, and energy storage capacity constraints. The energy storage system power constraints are:

[0037] P f,max <P t <P c,max

[0038] The energy storage system power charging and discharging efficiency constraint is:

[0039] η charge ≥η min

[0040] η discharge ≥η min

[0041] The energy storage system charge state constraint is:

[0042] SOC min <SOC t <SOC min

[0043] The energy storage capacity constraint is:

[0044] E bat_min ≤E bat ≤E bat_max

[0045] Where P t Represents the power of the energy storage system, P c,max Indicates the maximum power limit of the energy storage system discharge, P f,max Represents the maximum power limit of the energy storage system charging, η charge Represents the charging efficiency of the energy storage system, η discharge Represents the discharge efficiency of the energy storage system, η min Indicates the minimum charge and discharge efficiency of the energy storage system, SOC t Indicates the state of charge of the energy storage system, SOC min Indicates the minimum state of charge of the energy storage system, SOC min Indicates the maximum state of charge of the energy storage system, E bat_min 、E bat_max They are the minimum and maximum configuration capacity of the energy storage battery, E bat Configure capacity for energy storage batteries.

[0046] Furthermore, the step of solving the problem using a particle swarm optimization algorithm includes:

[0047] Parameter setting: set the parameters of the particle swarm optimization algorithm, including the number of iterations and step size;

[0048] Initialization: Assign the initial particle position and velocity of each randomly selected particle to complete the particle population initialization. The particle position of each particle represents the capacity configuration scheme of different parts of the photovoltaic-storage power station, and the velocity of each particle represents the adjustment direction and amplitude of the capacity of different parts.

[0049] Fitness calculation: Based on the cost-benefit model, the objective function of maximizing the net benefit over the entire life cycle is obtained. Based on the entire life cycle and investment cost of the photovoltaic-storage power station and the entire life cycle benefit, the objective function of the payback period is obtained. The two objective functions are used as the fitness of the particles to determine the optimal fitness of each particle and particle population of the current generation and its particle position;

[0050] Update: Update the fitness of the next generation of particles, compare it with the best fitness of the previous generation of particles, and update the extreme values ​​of each particle and the population according to the fitness of the new population, and complete the particle position and speed update of the next generation of particles;

[0051] Judgment: Determine whether the termination condition is met. If so, output the final position of each particle. If not, perform iterative updates until the termination condition is met.

[0052] Furthermore, the full life cycle net benefit is the difference between the full life cycle benefit and cost of the solar-storage power station.

[0053] The present invention also provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the capacity configuration planner of the photovoltaic-storage power station participating in the frequency regulation assisted market as described above.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] (1) The present invention takes into account the frequency regulation support and capacity configuration issues of the photovoltaic backup part of the photovoltaic-storage power station, establishes a cost-benefit model for the photovoltaic-storage power station to participate in auxiliary frequency regulation under auxiliary market compensation, takes the maximum net profit of the photovoltaic-storage power station as the objective function, solves the capacity configuration of the photovoltaic-storage power station through the particle swarm optimization algorithm, improves stability and reliability by participating in frequency regulation auxiliary services, and also improves economy.

[0056] (2) The present invention divides a complete frequency regulation auxiliary service into three stages by analyzing various frequency regulation performance indicators of the frequency regulation auxiliary service, including the regulation rate indicator, the response time indicator, and the regulation accuracy indicator. Different charging and discharging strategies are selected in each time period according to whether the available battery capacity is sufficient. The frequency regulation compensation policy is analyzed, and a cost-benefit model of battery energy storage participating in auxiliary frequency regulation of the power system under the market compensation policy is established. The analysis is also conducted from the two perspectives of net income over the entire life cycle and the payback period, and the optimal capacity configuration plan is formulated, which not only prevents the battery from being overcharged or over-discharged, protects the battery service life, but also maximizes the economy.

[0057] (3) The photovoltaic-storage power station of the present invention has a short response time and a fast adjustment speed in response to the frequency modulation signal. The output difference after the photovoltaic-storage power station participates in the frequency modulation and responds to the frequency modulation signal is reduced, so that energy storage and photovoltaic standby can well improve the frequency modulation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0059] Figure 2 This is a structural diagram of the photovoltaic-storage power station of the present invention;

[0060] Figure 3 A flow chart of the energy storage cost-benefit model of the present invention;

[0061] Figure 4 A flow chart of solving capacity configuration using a particle swarm algorithm according to the present invention;

[0062] Figure 5 The frequency modulation power curve of the photovoltaic-storage power station of the present invention;

[0063] Figure 6 The energy storage and photovoltaic standby output curve of the present invention;

[0064] Figure 7 The output difference of the photovoltaic-storage power station of the present invention that does not participate in frequency regulation;

[0065] Figure 8 The output difference of the photovoltaic-storage power station participating in frequency regulation of the present invention;

[0066] Fig. 9 It is an iterative diagram of energy storage capacity configuration and benefits of the present invention;

[0067] Fig.10 It is an iterative diagram of energy storage capacity configuration and net benefit of the present invention;

[0068] Fig.11 This is the simulation result of the relationship between the investment payback period and the energy storage configuration of the present invention. DETAILED DESCRIPTION

[0069] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0070] This embodiment provides a capacity configuration planning method for photovoltaic-storage power stations participating in the frequency regulation auxiliary market. The method establishes a cost-benefit model for photovoltaic-storage power stations participating in auxiliary frequency regulation under auxiliary market compensation, takes the maximum benefit of photovoltaic-storage power stations as the objective function, solves the capacity configuration of photovoltaic-storage power stations through particle swarm optimization algorithm, and considers the investment payback period of photovoltaic-storage power stations. It configures appropriate energy storage capacity configuration to maximize the return on investment, which not only improves the economic efficiency, but also improves the stability and reliability of the system by participating in frequency regulation auxiliary services. The core of this method mainly includes the following contents:

[0071] By analyzing the frequency regulation performance indicators of frequency regulation auxiliary services, the regulation rate indicator, the response time indicator, and the regulation accuracy indicator, a complete frequency regulation auxiliary service is divided into three stages. Different charging and discharging strategies are selected in each time period according to whether the available battery capacity is sufficient. The frequency regulation compensation policy is analyzed, and a cost-benefit model of battery energy storage participating in auxiliary frequency regulation of the power system under the market compensation policy is established. Then, the project is analyzed from the two perspectives of net income over the entire life cycle and payback period, and capacity configuration plans are formulated respectively. The effectiveness of the method of the present invention is verified through simulation on the MATLAB platform.

[0072] Specifically, Figure 1 As shown, the method comprises the following steps:

[0073] (1) Construct a cost sub-model for a solar-storage power station.

[0074] The photovoltaic-storage power station used in this embodiment is as follows Figure 2 As shown in the structural diagram, the photovoltaic panels are connected to the grid through the combiner box, and the energy storage unit is connected to the grid through the converter.

[0075] The operation and maintenance cost of lithium batteries is:

[0076] C B,l =C l Q l D l

[0077] Where: C l is the unit investment cost of lithium battery energy storage, Q l is the configuration capacity, D l is the life loss factor.

[0078] The investment and construction cost of lithium batteries is:

[0079] C B,in =C z +C s +C f +C h

[0080] C zis the lithium battery component cost, C s is the construction cost, C f is the battery auxiliary equipment cost, C h is the electricity conversion cost.

[0081] The total cost of lithium batteries is the sum of the operation and maintenance costs and investment and construction costs of lithium batteries. The total cost of lithium batteries is:

[0082] C A,l =C B,l +C B,in

[0083] The photovoltaic backup capacity can improve the frequency regulation reliability and stability of the photovoltaic-storage power station. The attenuation cost of the photovoltaic backup capacity is:

[0084] C B,s =C s P s E s

[0085] C s is the investment cost of the PV unit, P s is the reserve capacity of the PV unit, E s is the attenuation coefficient of the PV module.

[0086] (2) Select frequency modulation performance indicators.

[0087] Comprehensive frequency modulation performance index K all By the response time indicator K t , Adjustment accuracy index K a And the adjustment rate index K s The response time index K is obtained by comprehensive weighting. t for:

[0088]

[0089] Δt is the delay time of the power generation unit responding to the AGC command, which is equal to the time of stepping out of the response dead zone minus the time of receiving the AGC adjustment command.

[0090] The regulation accuracy index is the deviation between the actual regulation and the target regulation. The regulation accuracy index K a for:

[0091]

[0092] Δp i · is the deviation between the actual output of the power generation unit and the AGC command, Δp N It is the allowable error of the power generation unit, which is 1.5% of the rated output.

[0093] Adjustment rate index Ks Equal to the actual speed divided by the rated speed.

[0094]

[0095] Comprehensive regulation performance index K all for:

[0096] (3) Construct a full life cycle benefit sub-model.

[0097] Frequency modulation compensation income C b equal

[0098] C b =D b Y b K b

[0099] C b is the frequency modulation mileage compensation income of the bth trading cycle, Y b is the FM mileage settlement price, K b It is the average value of comprehensive frequency modulation performance indicators.

[0100] The profit model of the total cycle:

[0101]

[0102] R all is the revenue of energy storage participating in the frequency regulation ancillary service market, D all is the frequency regulation mileage involved in energy storage, K all is the comprehensive regulation performance index, Y all Comprehensive frequency modulation mileage settlement price, C l It is the compensation price when a conventional unit adjusts the frequency alone.

[0103] The above cost sub-model and the life cycle benefit sub-model are composed as follows: Figure 3 The cost-benefit model of the photovoltaic-storage power station shown is used to calculate the net benefit over the entire life cycle.

[0104] (4) Build a capacity configuration model.

[0105] The energy storage unit and backup capacity in the solar-storage power station need to meet the frequency regulation target issued by the AGC. At the same time, the construction cost and operation and maintenance cost of the solar-storage power station need to be considered, as follows

[0106]

[0107] Q l is the configuration capacity, Q total is the total energy storage capacity, Q PV is the total installed capacity of photovoltaic power generation, H1 and H2 are configuration coefficients, and Ps is the spare capacity of the PV unit.

[0108] In the process of participating in frequency regulation, in order to allocate the frequency regulation power of photovoltaic backup and energy storage, factors such as the investment cost, loss cost, and frequency regulation effect of both parties must be considered.

[0109] (5) Set constraints.

[0110] Power constraints of energy storage systems:

[0111] P f,max <P t <P c,max

[0112] P t Represents the power of the energy storage system, P c,max Indicates the maximum power limit of the energy storage system discharge, P f,max Indicates the maximum power limit for charging the energy storage system.

[0113] The charging and discharging efficiency of the energy storage system cannot be lower than the minimum requirements:

[0114] η charge ≥η min

[0115] η discharge ≥η min

[0116] Among them, η charge Represents the charging efficiency of the energy storage system, η discharge Represents the discharge efficiency of the energy storage system, η min Indicates the minimum charging and discharging efficiency requirement of the energy storage system.

[0117] The state of charge of the energy storage system must be between the minimum and maximum state of charge. The state of charge refers to the ratio of the energy currently stored in the energy storage system to its maximum energy storage capacity. By limiting the range of the state of charge, it can be ensured that the energy storage system will not be over-discharged or over-charged during the capacity configuration process.

[0118] SOC min <SOC t <SOC min

[0119] SOC t Indicates the state of charge (SOC) of the energy storage system min Indicates the minimum state of charge requirement of the energy storage system, SOC min Indicates the maximum state of charge requirement of the energy storage system.

[0120] Energy storage capacity constraints:

[0121] E bat_min ≤E bat ≤E bat_max

[0122] In the formula, E bat_min 、E bat_max They are the minimum and maximum configuration capacities of the energy storage battery.

[0123] (6) Model solution.

[0124] Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence that can be used to solve capacity configuration problems. Capacity configuration problems usually involve determining the capacity configuration of each part under given resource constraints to maximize a certain performance indicator or meet certain constraints. In the capacity configuration problem, the particle position (particle value) vector can represent the capacity configuration scheme of different parts, and the velocity vector can represent the adjustment direction and amplitude of the capacity. By continuously iterating and updating the position and velocity of particles, the particle swarm optimization algorithm can search for the global optimal capacity configuration scheme to achieve the optimal objective function value or meet the constraints.

[0125] Figure 4 This is the solution flow chart of the particle swarm algorithm, as follows:

[0126] 1) Set the parameters of the simulation model and randomly initialize the particle swarm. Randomly initialize a group of particles, assign the initial particle value and velocity of each randomly selected particle, and then complete the initialization of the entire population.

[0127] 2) Input data, input the cost-benefit model, obtain the objective function with the maximum net benefit over the entire life cycle, calculate the corresponding objective function, that is, the maximum benefit over the entire life cycle, input the entire life cycle and investment cost of the photovoltaic-storage power station and the entire life cycle benefit, calculate the investment payback period of the objective function, use the two objective functions as the fitness of the particles respectively, and determine the optimal fitness value of each particle and the population based on the initial fitness and record its particle position.

[0128] 3) Update and calculate the fitness value of the next generation of particles, and compare it with the optimal value of the previous generation. The larger the fitness value of the objective function with the maximum life cycle benefit, the better the particle solution is. The smaller the fitness value of the objective function with the shortest investment payback period is, the better the particle solution is. According to the fitness value of the particles of the new population, calculate the new extreme value of the individual and the new extreme value of the population, complete the update of the particle value and speed value of the next generation of particles, and move closer to the global optimal particle after each iterative update.

[0129] 4) Determine whether the termination conditions are met. Determine whether the maximum number of iterations is reached or other algorithm termination conditions are met. If the conditions are met, the algorithm ends and the global optimal value is output; if not, the particle speed and position are updated again until the maximum number of iterations and other algorithm termination conditions are finally met.

[0130] In order to verify the effectiveness of flexible resources in microgrid optimization scheduling, this embodiment sets up two scenarios for comparison:

[0131] Scenario 1: PV-storage power station capacity configuration scenario with the goal of maximizing the full life cycle benefits

[0132] Scenario 2: PV-storage power station capacity configuration scenario with the shortest payback period

[0133] Scenario 3: Scenario before optimization scheduling

[0134] The effectiveness of the present invention is verified using the Matlab platform.

[0135] Figure 5 It is the frequency modulation power curve of the photovoltaic-storage power station, which reaches the peak value of frequency modulation power output at 3-5 and the maximum value of frequency modulation absorption power at 10-12.

[0136] Figure 6 This is the energy storage and photovoltaic standby output curve. It can be seen that the energy storage has a short response time to the frequency modulation signal and a fast adjustment speed.

[0137] Figure 7 , 8 It can be seen that the output difference of the photovoltaic-storage power station participating in frequency regulation and responding to the frequency regulation signal is smaller than the output difference of the power station not participating in frequency regulation, indicating that energy storage and photovoltaic backup can well improve the frequency regulation performance.

[0138] Fig. 9 Through iteration, it was found that with the increase in the number of iterations, the energy storage capacity configuration tended to be stable. The energy storage configuration of the power market frequency regulation trading criteria in Region 1 was 26.552MWh, with the largest net profit over the entire life cycle, and the maximum profit was 15.4963 million yuan, which is the maximum net profit that can be obtained within the energy storage configuration range of the photovoltaic-storage power station [7MW, 30MW].

[0139] Fig.10 It can be seen from the iterative results that with the increase in the number of iterations, the energy storage capacity configuration tends to be stable. When the energy storage configuration capacity in the region 2 photovoltaic-storage power station is 26.599, the maximum net profit over the entire life cycle is 12.6799 million yuan.

[0140] Fig.11It can be seen that in the frequency regulation market of Region 1, when the energy storage configuration is 5MWh-13MWh, the payback period is 0, that is, the cost cannot be recovered. When the energy storage configuration exceeds 14MWh, the payback period is 415 days, and then the payback period increases linearly with the energy storage configuration.

[0141] The present invention relates to a capacity configuration planning method for a photovoltaic-storage power station to participate in the frequency regulation auxiliary market. The photovoltaic-storage power station can participate in the frequency regulation auxiliary service to obtain frequency regulation benefits. At the same time, participating in frequency regulation can improve the stable and safe operation of the system. Considering the SOC constraints of energy storage in the photovoltaic-storage power station and the optimal capacity configuration of the photovoltaic-storage power station, the battery will not be overcharged or over-discharged, so as to maximize the economy. Considering the investment payback period of the capacity configuration, the capacity configuration is determined. For investors, an appropriate investment payback period can increase the attractiveness of investment, promote the large-scale promotion and application of photovoltaic-storage power stations, and make important contributions to energy transformation and sustainable development.

[0142] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0143] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for planning capacity allocation of a photovoltaic-storage power station participating in a frequency regulation auxiliary market, characterized in that: The following steps are involved: Obtain relevant operating parameters of the PV-storage power station, consider the PV reserve part, and establish a cost-benefit model for the PV-storage power station to participate in auxiliary frequency regulation under auxiliary market compensation; Build a capacity configuration model for the solar-storage power station and set constraints; Based on the capacity configuration model, taking the maximum net benefit of the whole life cycle of the cost-benefit model as the objective function, a particle swarm optimization algorithm is used to solve and obtain the optimal capacity configuration planning scheme.

2. A method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 1, characterized in that: The cost-benefit model includes a cost sub-model and a full life cycle benefit sub-model. The cost sub-model includes the lithium battery cost and the attenuation cost of the photovoltaic backup capacity. The cost sub-model is: C A,l =C B,l +C B,in C B,s =C s P s E s The full life cycle revenue sub-model is constructed by the frequency regulation mileage compensation revenue of the photovoltaic-storage power station in each trading cycle. The full life cycle revenue sub-model is: In the formula, C A,l is the total cost of lithium battery, C B,l is the operation and maintenance cost of lithium batteries, C B,in is the investment and construction cost of lithium batteries, C B,s is the attenuation cost of PV backup capacity, C s is the investment cost of the photovoltaic unit, P s is the spare capacity of the PV unit, E s is the attenuation coefficient of the photovoltaic module, R all D is the total life cycle revenue of energy storage participating in the frequency regulation ancillary service market. all K is the frequency regulation mileage involved in energy storage, all is the comprehensive adjustment performance index, Y all is the comprehensive frequency regulation mileage settlement price, C l It is the compensation price when conventional units adjust the frequency alone.

3. A method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 2, characterized in that: The expressions for the operation and maintenance cost of the lithium battery and the investment and construction cost of the lithium battery are respectively: C B,l =C l Q l D l C B,in =C z +C s +C f +C h In the formula, C B,l is the operation and maintenance cost of lithium batteries, C B,in is the investment and construction cost of lithium batteries, C l is the unit investment cost of lithium battery energy storage, Q l is the configuration capacity, D l is the life loss coefficient, C z is the lithium battery component cost, C s is the construction cost, C f is the battery auxiliary equipment cost, C h The cost of electricity conversion.

4. A method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 2, characterized in that: The comprehensive frequency regulation performance index K of the photovoltaic-storage power station all By the response time indicator K t , Adjustment accuracy index K a And the adjustment rate index K s After comprehensive weighting, the comprehensive frequency regulation performance index of the photovoltaic-storage power station is: Among them, the response time indicator K t for: The regulation accuracy index Ka for: The regulation rate indicator Ks for: In the formula, K all K is the comprehensive frequency modulation performance index, s is the adjustment rate index, K t is the response time indicator, K a is the regulation accuracy index, Δt is the delay time of the power generation unit responding to the AGC command, which is equal to the time of stepping out of the response dead zone minus the time of receiving the AGC adjustment command. is the deviation between the actual output of the power generation unit and the AGC command, Δp N is the allowable error of the power generation unit, v is the actual rate, v N is the rated speed.

5. A method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 2, characterized in that: The frequency adjustment mileage compensation income for each transaction cycle is: C b =D b Y b K b In the formula, C b is the frequency modulation mileage compensation fee for the bth transaction cycle, D b Y is the frequency regulation mileage of energy storage in the bth trading cycle, b is the settlement price of frequency modulation mileage in the bth trading cycle, K b is the average value of the comprehensive frequency modulation performance index in the bth trading cycle.

6. A method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 1, characterized in that: The capacity configuration model is: In the formula, Q l is the configuration capacity, Q total is the total energy storage capacity, H1 and H2 are configuration coefficients, and P s is the spare capacity of the PV unit, Q PV is the total installed capacity of photovoltaic power.

7. A method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 1, characterized in that: The constraints include energy storage system power constraints, energy storage system power charging and discharging efficiency constraints, energy storage system charge state constraints, and energy storage capacity constraints. The energy storage system power constraints are: P f,max <P t <P c,max The energy storage system power charging and discharging efficiency constraint is: or charge ≥η min or discharge ≥η min The energy storage system charge state constraint is: SOC min <SOC t <SOC min The energy storage capacity constraint is: AND bat_min ≤E bat ≤E bat_max Where P t Represents the power of the energy storage system, P c,max Indicates the maximum power limit of the energy storage system discharge, P f,max Represents the maximum power limit of the energy storage system charging, η charge Represents the charging efficiency of the energy storage system, η discharge Represents the discharge efficiency of the energy storage system, η min Indicates the minimum charge and discharge efficiency of the energy storage system, SOC t Indicates the state of charge of the energy storage system, SOC min Indicates the minimum state of charge of the energy storage system, SOC min Indicates the maximum state of charge of the energy storage system, E bat_min 、E bat_max They are the minimum and maximum configuration capacity of the energy storage battery, E bat Configure capacity for energy storage batteries.

8. The method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 1, characterized in that: The step of using the particle swarm optimization algorithm to solve the problem includes: Parameter setting: set the parameters of the particle swarm optimization algorithm, including the number of iterations and step size; Initialization: Assign the initial particle position and velocity of each randomly selected particle to complete the particle population initialization. The particle position of each particle represents the capacity configuration scheme of different parts of the photovoltaic-storage power station, and the velocity of each particle represents the adjustment direction and amplitude of the capacity of different parts. Fitness calculation: Based on the cost-benefit model, the objective function of maximizing the net benefit over the entire life cycle is obtained. Based on the entire life cycle and investment cost of the photovoltaic-storage power station and the entire life cycle benefit, the objective function of the payback period is obtained. The two objective functions are used as the fitness of the particles to determine the optimal fitness of each particle and particle population of the current generation and its particle position; Update: Update the fitness of the next generation of particles, compare it with the best fitness of the previous generation of particles, and update the extreme values ​​of each particle and the population according to the fitness of the new population, and complete the particle position and speed update of the next generation of particles; Judgment: Determine whether the termination condition is met. If so, output the final position of each particle. If not, perform iterative updates until the termination condition is met.

9. A method for capacity allocation planning of a photovoltaic-storage power station participating in a frequency regulation auxiliary market according to claim 8, characterized in that: The full life cycle net benefit is the difference between the full life cycle benefit and cost of the solar-storage power station.

10. A computer-readable storage medium, characterized in that: It includes one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing the capacity allocation planner of the photovoltaic-storage power station participating in the frequency regulation assisted market as described in any one of claims 1-9.

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