Power system evolution path planning method based on multi-element energy storage cooperation

Abstract

The invention relates to the technical field of electric power system analysis, and relates to an electric power system evolution path planning method based on multi-element energy storage collaboration, and the method comprises the steps: obtaining the related data of a multi-element energy storage system, and extracting the dynamic operation voltage, the water electrolysis operation power and the hydrogen flow; according to the dynamic operation voltage, the water electrolysis operation power and the hydrogen flow, a multi-energy energy storage model is built by combining related constraint conditions; constructing a battery energy storage model according to the battery energy balance related parameters; constructing a pumped storage model according to the pumped storage energy balance related parameters; constructing a cost function by taking minimization of power generation cost, new line construction cost and equipment cost as targets; and based on the multi-energy energy storage model, the battery energy storage model, the water pumping energy storage model and the cost function, solving an optimal power system evolution path. The objective of the invention is to realize co-evolution path planning for multi-energy storage, battery energy storage and water pumping energy storage.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a method for planning an evolutionary path of a power system based on multi-energy storage collaboration. Background Art

[0002] Energy storage using renewable energy has great potential in reducing carbon emissions in various fields. However, traditional water electrolysis technology may not be suitable for large-scale energy storage and utilization due to its high energy consumption and the complexity of storage and transportation. Therefore, coordinating and dispatching multiple energy storage modes through diversified energy types, such as electrochemical long-term energy storage, pumped storage and multi-energy energy storage, has become an effective way to increase the flexibility of the energy system while promoting carbon sequestration.

[0003] With the development of renewable energy (such as offshore wind power and distributed photovoltaics) and the continuous increase in power load, the problem of new power system evolution path planning has become the focus of research in the power field. The core of this problem is how to determine the optimal transmission line construction strategy to ensure that the growing energy demand is met and that energy can be efficiently delivered to end consumers. At present, relevant research has achieved certain results. For example, when facing the problem of new power system evolution path planning, the existing technology effectively reduces the combinatorial search space related to the optimal transmission line construction problem by introducing heuristic methods, and also considers factors such as energy storage and variable renewable energy; however, this method has certain limitations; because the existing technology cannot simultaneously take into account the refined energy storage and electric heat equipment models to solve the optimal transmission line planning and construction problem, the optimization ability of the power system in dealing with complex energy environments is severely restricted; especially in the face of the coordinated planning of multi-energy storage, battery storage, and pumped storage, the existing technology cannot provide an effective power system evolution path planning solution for this multi-coordinated planning architecture, thereby hindering the development of the power system towards a more efficient, flexible, and low-carbon direction. Summary of the invention

[0004] In order to realize the coordinated evolution path planning of multi-energy storage, battery storage, and pumped storage, the present invention provides a power system evolution path planning method based on multi-energy storage coordination, and the technical scheme adopted is as follows:

[0005] The technical solution of the present invention provides a method for planning the evolution path of a power system based on multi-energy storage collaboration, the method comprising:

[0006] Obtain data related to the multi-element energy storage system and extract dynamic operating voltage, water electrolysis operating power and hydrogen flow rate;

[0007] According to the dynamic operating voltage, water electrolysis operating power and hydrogen flow rate, a multi-energy storage model is constructed in combination with relevant constraints;

[0008] Construct a battery energy storage model based on battery energy balance related parameters;

[0009] Construct a pumped storage model based on pumped storage energy balance related parameters;

[0010] Construct a cost function with the goal of minimizing the cost of electricity generation, new line construction costs, and equipment costs;

[0011] Based on the multi-energy storage model, battery storage model, pumped storage model and cost function, the optimal power system evolution path is solved, and the relationship between the water electrolysis operating voltage and operating current density is extracted.

[0012] Furthermore, the relevant data of the multi-element energy storage system is obtained, and the dynamic operating voltage is extracted, including:

[0013] Obtaining the standard electrode potential of a multi-energy storage system;

[0014] Extracting thermodynamic parameters based on the free energy change of water decomposition during water electrolysis;

[0015] Based on the charge transfer coefficients and exchange current density of the positive and negative electrodes, the kinetic effect parameters in the charge transfer process are extracted;

[0016] The dynamic operating voltage is calculated based on the standard electrode potential, thermodynamic parameters and kinetic effect parameters.

[0017] Furthermore, the expression of the dynamic operating voltage is:

[0018]

[0019] Where V c,t represents the operating voltage of the water electrolysis unit at time t; V0 represents the standard electrode potential; i P represents the exchange current density of the positive electrode; i N represents the exchange current density of the negative electrode, represents the partial pressure of hydrogen; represents the partial pressure of oxygen, a w Indicates the activity of water; α P Represents the charge transfer coefficient of the positive electrode; α N represents the charge transfer coefficient of the negative electrode, T represents temperature; R represents the ideal gas constant; F represents the Faraday constant; R c represents ohmic resistance; i c,t Represents the operating current density of the water electrolysis unit at time t.

[0020] Furthermore, the expression for extracting the water electrolysis operating power is:

[0021]

[0022] In the formula, represents the operating power of water electrolysis at time t; N c Indicates the number of batteries in series; N s Indicates the number of batteries connected in parallel; A is a 0-1 variable, indicating whether electrolyzer i is running at time t; c Represents the effective area of ​​the electrolyzer; η f represents the Faraday efficiency; k1 represents the first conversion coefficient; V c,t represents the operating voltage of the water electrolysis unit at time t; i c,t Represents the operating current density of the water electrolysis unit at time t.

[0023] Furthermore, the expression for extracting the hydrogen flow rate is:

[0024]

[0025] In the formula, represents the hydrogen flow rate of water electrolysis at time t; k2 represents the second conversion coefficient; F represents the Faraday constant.

[0026] Furthermore, the relevant constraints of the multi-energy storage model include:

[0027] The current density constraint is expressed as:

[0028]

[0029] In the formula, is a 0-1 variable, indicating whether the water electrolysis process is running at time t; i c,t represents the operating current density of the water electrolysis unit at time t; i c,min Indicates the minimum value of the operating current density; i c,max Indicates the maximum value of the operating current density;

[0030] The multi-energy storage constraint condition is expressed as:

[0031]

[0032] In the formula, represents the hydrogen flow rate generated by the electrolysis process at time t; represents the hydrogen input flow rate of the storage device at time t; represents the hydrogen output flow rate of the storage device at time t; Indicates the hydrogen storage capacity of the storage device at time t; Indicates the efficiency of the hydrogen input process; represents the efficiency of the hydrogen output process; Δt represents the time step; Indicates the minimum value of hydrogen input flow rate of the storage device; Indicates the maximum value of hydrogen input flow rate of the storage device; Indicates the minimum value of hydrogen output flow rate of the storage device; Indicates the maximum value of the hydrogen output flow rate of the storage device; Indicates the minimum hydrogen storage capacity of the storage device; Indicates the maximum hydrogen storage capacity of the storage device; Indicates the hydrogen input flow rate at the start time T0; Indicates the end time T e The amount of hydrogen input at .

[0033] Furthermore, constructing a battery energy storage model based on battery energy balance related parameters includes:

[0034] The battery energy balance constraint is expressed as:

[0035]

[0036] In the formula, E t Indicates the energy storage of the battery at time t; E t-1 represents the energy storage of the battery at time t-1; η c Indicates charging efficiency; P c,t represents the charging power at time t; Δt represents the time step; P d,t represents the discharge power at time t; η d Indicates discharge efficiency;

[0037] The power limit constraint is expressed as:

[0038] 0≤P c，t ≤P c，max

[0039] 0≤P d，t ≤P d，max

[0040] Where P c，max Indicates the maximum charging power; P d，max Indicates the maximum discharge power;

[0041] The capacity limit constraint is expressed as:

[0042] E min ≤E t ≤E max

[0043] In the formula, E minIndicates the minimum value of the battery's energy storage; E max Indicates the maximum value of the battery's stored energy.

[0044] Furthermore, the pumped storage model is constructed based on the pumped storage energy balance related parameters including:

[0045] Pumped storage energy balance constraints:

[0046]

[0047] In the formula, H t Indicates the water level at time t; H t-1 represents the water level at time t-1; η p Indicates pump efficiency; P pump，t represents the pump power at time t; Δt represents the time step; P gen，t represents the power generated at time t; η g Indicates power generation efficiency;

[0048] The power limit constraint is expressed as:

[0049] 0≤P gen，t ≤P gen，max

[0050] 0≤P pump，t ≤P pump，max

[0051] Where P gen，max Indicates the maximum power generation; P pump,max Indicates the maximum pump power;

[0052] The water level height limit constraint is expressed as:

[0053] H min ≤H t ≤H max

[0054] In the formula, H min Indicates the minimum water level height; H max Indicates the maximum water level height;

[0055] Energy storage capacity limitation constraint, its expression is:

[0056] E t =ρ·g·V t ·H t

[0057] In the formula, E t represents the energy storage capacity of pumped storage at time t; ρ represents the density of water; g represents the acceleration of gravity; V t represents the volume of the reservoir at time t;

[0058] The flow rate limit constraint is expressed as:

[0059] 0≤Qin,t≤Qi n,max

[0060] 0≤Q out，t ≤Q out,max

[0061] In the formula, Q in,t represents the inflow velocity at time t; Q in,max Indicates the maximum inflow velocity; Q out，t represents the outflow velocity at time t; Q out,max Indicates the maximum outflow velocity.

[0062] Furthermore, the cost function is constructed with the goal of minimizing the power generation cost, the new line construction cost, and the equipment cost, including:

[0063] Considering the impact of the discount rate, a cost function is constructed to minimize the cost of electricity generation, the cost of new line construction, and the cost of equipment, including:

[0064] The cost of electricity generation is calculated as:

[0065]

[0066] In the formula, λ G,i represents the electricity generation cost of node i; P G,i,y,t represents the power generated by node i at time t in year y; T represents the time deployment per year; I represents the set of nodes in the power grid;

[0067] Calculate the cost of building a new line, the expression is:

[0068]

[0069] In the formula, K l represents the construction cost of the new line l; w y,l is a 0-1 variable, used to indicate whether line l is built in year y; Ω L Represents a new line set;

[0070] Calculate the equipment cost, the expression is:

[0071]

[0072] In the formula, C w1 Represents the unit cost of electrolysis equipment; C w2 represents the unit cost of multi-energy storage equipment; C w3 represents the unit cost of the methanogenation equipment; represents the capacity of the electrolysis equipment in year y; represents the capacity of the multi-energy storage device in year y; Indicates the capacity of the methanogenation equipment in year y; Ω W Represents a collection of devices.

[0073] Furthermore, the cost function constructed with the goal of minimizing the power generation cost, the new line construction cost, and the equipment cost also includes:

[0074] Considering the total income of oxygen and methane, the expression is:

[0075]

[0076] In the formula, represents the unit selling price of oxygen; represents the mass of oxygen produced by the electrolysis equipment at time t in year y; represents the unit selling price of methane; It represents the methane mass of the methanogenation equipment at time t in year y.

[0077] The present invention has the following beneficial effects:

[0078] The method for planning the evolutionary path of an electric power system based on the synergy of multiple energy storage systems provided by the present invention obtains relevant data of the multiple energy storage system and extracts the dynamic operating voltage, water electrolysis operating power and hydrogen flow rate to construct a multi-energy energy storage model. At the same time, the battery energy storage model and the pumped storage model are combined to construct a cost function considering cost minimization, and then the optimal evolutionary path is solved. In this process, the influence of various factors on the multi-energy energy storage is fully considered, especially the influence of the dynamic operating voltage, water electrolysis operating power and hydrogen flow rate on the multi-energy energy storage. This not only enhances the optimization capability of the electric power system in complex energy scenarios, but also provides an effective solution for the synergistic planning of multiple energy storage, which effectively promotes the development of the electric power system in a more efficient, flexible and low-carbon direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 An embodiment of the present invention provides a method for planning the evolution path of a power system based on the collaboration of multiple energy storage systems. DETAILED DESCRIPTION

[0081] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a power system evolution path planning method based on multi-energy storage collaboration proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0082] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0083] The following is a specific solution of a method for planning an evolutionary path of a power system based on multi-energy storage collaboration provided by the present invention, with reference to the accompanying drawings.

[0084] See also Figure 1 , which shows a method flow chart of a method for planning an evolutionary path of a power system based on multi-energy storage collaboration provided by an embodiment of the present invention, the method comprising:

[0085] Step S100: Acquire data related to the multi-element energy storage system, and extract dynamic operating voltage, water electrolysis operating power, and hydrogen flow rate; specifically, the data related to the multi-element energy storage system at least include: electrochemical parameters: standard electrode potential, thermodynamic parameters, kinetic effect parameters; energy conversion related data: water electrolysis operating power related data, hydrogen flow related data; battery energy storage system related data: charging and discharging power limits, charging and discharging efficiency, upper and lower limits of storage capacity; pumped energy storage system related data: water level height related data, pump power and power generation power limits, pump and power generation efficiency, energy storage capacity limits, inlet and outlet water flow rate limits; for the above multi-element energy storage system related data, pre-processing including but not limited to data cleaning, data standardization, and data integration is also required to ensure the accuracy and integrity of the data;

[0086] Step S100 specifically includes:

[0087] Step S110: Acquire relevant data of the multi-element energy storage system and extract the dynamic operating voltage, including:

[0088] Step S111: obtaining the standard electrode potential of the multi-energy storage system; the standard electrode potential reflects the electrochemical characteristics of the electrode under the standard state, and different electrode materials and electrolyte systems will have different standard electrode potentials;

[0089] Step S112: extracting thermodynamic parameters based on the free energy change of water decomposition during water electrolysis; specifically, the thermodynamic parameters include: temperature, at different temperatures, the thermodynamic equilibrium of the water electrolysis reaction will change; partial pressures of hydrogen and oxygen: during the water electrolysis process, the pressures of the generated hydrogen and oxygen are important indicators reflecting the degree of reaction and the state of the system, which are closely related to the chemical equilibrium of the reaction, gas diffusion and other processes, and will directly affect the calculation of the dynamic operating voltage; water activity, which reflects the effective concentration of water in the electrolyte, which is affected by factors such as electrolyte composition and temperature;

[0090] Step S113: based on the charge transfer coefficients and exchange current density of the positive and negative electrodes, extract the kinetic effect parameters in the charge transfer process; the charge transfer coefficients of the positive and negative electrodes describe the ease of charge transfer on the electrode surface and are closely related to the kinetic process of the electrode reaction; the exchange current density of the positive and negative electrodes is a parameter for measuring the redox reaction rate of the electrode reaction in the equilibrium state, which reflects the electrochemical activity of the electrode surface and is a key data for understanding the kinetic characteristics of the electrode process and calculating the dynamic operating voltage;

[0091] Step S114: Calculate the dynamic operating voltage according to the standard electrode potential, thermodynamic parameters, and kinetic effect parameters; the expression of the dynamic operating voltage is:

[0092]

[0093] Where V c,t represents the operating voltage of the water electrolysis unit at time t; V0 represents the standard electrode potential; i P represents the exchange current density of the positive electrode; i N represents the exchange current density of the negative electrode, represents the partial pressure of hydrogen; represents the partial pressure of oxygen, a w Indicates the activity of water; α P Represents the charge transfer coefficient of the positive electrode; α N represents the charge transfer coefficient of the negative electrode, T represents temperature; R represents the ideal gas constant; F represents the Faraday constant; R c represents ohmic resistance; i c,t Represents the operating current density of the water electrolysis unit at time t.

[0094] Step S120: extracting the water electrolysis operating power, the expression of which is:

[0095]

[0096] In the formula, represents the operating power of water electrolysis at time t; N cIndicates the number of batteries in series; N s Indicates the number of batteries connected in parallel; A is a 0-1 variable, indicating whether electrolyzer i is running at time t; c Represents the effective area of ​​the electrolyzer; η f represents the Faraday efficiency; k1 represents the first conversion coefficient; V c,t represents the operating voltage of the water electrolysis unit at time t; i c,t Represents the operating current density of the water electrolysis unit at time t; the number of series cells and the number of parallel cells are usually fixed during the system design and installation stage; real-time monitoring of the operating status of the electrolyzer. It is possible to determine whether the electrolyzer is operating normally by installing corresponding sensors on each electrolyzer, such as current sensors, voltage sensors, etc. When the current, voltage and other parameters of the electrolyzer are monitored to be within the normal operating range, the corresponding The value is 1, indicating that the electrolyzer is in operation at time t; if the relevant parameters are abnormal or zero, it means that the electrolyzer is not in operation. The value is 0; the effective area is an inherent geometric parameter of the electrolyzer and is determined during the design and manufacturing stage of the electrolyzer; the first conversion coefficient is related to the specific physical and chemical properties of the multi-energy energy storage system and the equipment structure and other factors. Its value can usually be determined by energy analysis and theoretical modeling of the entire system. It can also refer to the empirical data of similar systems or consult the equipment manufacturer to obtain the value of the first conversion coefficient; step S120 provides a key energy conversion indicator for the evolution path planning of the power system based on multi-energy storage synergy by accurately extracting the water electrolysis operating power, which helps to gain a deep understanding of the energy output of the water electrolysis link in the multi-energy energy storage system, so as to better evaluate the energy conversion efficiency of the entire energy storage system;

[0097] Step S130: extract hydrogen flow, the expression is:

[0098]

[0099] In the formula, represents the hydrogen flow rate of water electrolysis at time t; k2 represents the second conversion coefficient; F represents the Faraday constant; specifically, the method for obtaining the second conversion coefficient can refer to the first conversion coefficient; step S130 extracts the hydrogen flow rate, thereby providing key material flow information for the evolution path planning of the power system based on multi-energy storage synergy. Understanding the hydrogen flow rate is helpful to deeply understand the material conversion efficiency and hydrogen output of the water electrolysis link in the multi-energy storage system;

[0100] In summary, step S100 can accurately describe the operating status of the multi-energy energy storage system by acquiring and calculating the dynamic operating voltage, water electrolysis operating power and hydrogen flow rate. The extraction of these key parameters is helpful to construct a multi-energy energy storage model, so that the model can truly reflect the physical and chemical processes of the multi-energy energy storage system, thereby more effectively optimizing the planning of the power system; among them, the accurate calculation of the dynamic operating voltage takes into account a variety of electrochemical factors, and provides a key indicator for evaluating the performance of the energy storage system; the acquisition of water electrolysis operating power is helpful for analyzing energy conversion efficiency; the determination of hydrogen flow rate is of great significance for understanding the material changes in the energy storage process and the utilization planning of hydrogen. Therefore, the acquisition of this series of data and the extraction of parameters can enhance the accuracy and reliability of the evolution path planning of the entire power system, thereby better responding to the complex energy environment and the needs of multi-energy storage coordination.

[0101] Step S200: construct a multi-energy storage model based on the dynamic operating voltage, water electrolysis operating power and hydrogen flow rate in combination with relevant constraints; the relevant constraints of the multi-energy storage model include:

[0102] The current density constraint is expressed as:

[0103]

[0104] In the formula, is a 0-1 variable, indicating whether the water electrolysis process is running at time t; i c,t represents the operating current density of the water electrolysis unit at time t; i c,min Indicates the minimum value of the operating current density; i c,max Represents the maximum value of the operating current density; this constraint condition limits the range of the operating current density of the water electrolysis unit at different times, ensuring that it operates under reasonable physical and chemical conditions. When constructing a multi-energy storage model, it is necessary to ensure that the model can accurately reflect the actual current density in the water electrolysis process;

[0105] The multi-energy storage constraint condition is expressed as:

[0106]

[0107] In the formula, represents the hydrogen flow rate generated by the electrolysis process at time t; represents the hydrogen input flow rate of the storage device at time t; represents the hydrogen output flow rate of the storage device at time t; Indicates the hydrogen storage capacity of the storage device at time t; Indicates the efficiency of the hydrogen input process; represents the efficiency of the hydrogen output process; Δt represents the time step; Indicates the minimum value of hydrogen input flow rate of the storage device; Indicates the maximum value of hydrogen input flow rate of the storage device; Indicates the minimum value of hydrogen output flow rate of the storage device; Indicates the maximum value of the hydrogen output flow rate of the storage device; Indicates the minimum hydrogen storage capacity of the storage device; Indicates the maximum hydrogen storage capacity of the storage device; Indicates the hydrogen input flow rate at the start time T0; Indicates the end time T e The above constraints regulate the multi-energy storage system in terms of hydrogen generation, input and output, and storage, ensuring that the dynamic changes of hydrogen in the system and the operating status of the storage equipment can be accurately reflected during the model construction process.

[0108] Step S200 constructs a multi-energy energy storage model by combining key parameters such as dynamic operating voltage, water electrolysis operating power, and hydrogen flow, and incorporating current density constraints and multi-energy storage constraints, providing an accurate and comprehensive multi-energy energy storage system description framework for the evolution path planning of the power system based on multi-energy storage synergy. The model can accurately reflect the operating status of the multi-energy energy storage system under different working conditions, including the change in current density during water electrolysis, the production and storage of hydrogen, etc., thereby providing a reliable basis for the subsequent optimization planning of the power system. By accurately describing the physical and chemical processes of the system, it is helpful to analyze the synergy between different energy storage methods, and then optimize the energy storage strategy of the power system, improve energy utilization efficiency, ensure the stability of energy supply, and reduce the operating cost of the energy system, which is of great significance for promoting the power system to develop in a more efficient, flexible and sustainable direction.

[0109] Step S300: constructing a battery energy storage model according to battery energy balance related parameters;

[0110] Step S300 specifically includes:

[0111] Step S310: Establishing a battery energy balance constraint, the expression is:

[0112]

[0113] In the formula, E t Indicates the energy storage of the battery at time t; E t-1 represents the energy storage of the battery at time t-1; η c Indicates charging efficiency; P c,t represents the charging power at time t; Δt represents the time step; P d,t represents the discharge power at time t; η dIndicates discharge efficiency; step S310 is based on the principle of conservation of energy and describes the dynamic change of battery energy during the charging and discharging process; during the charging process, the battery adds the charging efficiency multiplied by the product of the charging power and the time step to the energy of the previous moment to achieve energy increase; during the discharging process, the battery energy minus the product of the discharge power and the time step divided by the discharge efficiency reflects the reduction of energy.

[0114] Step S320: Establish a power limit constraint, the expression of which is:

[0115] 0≤P c,t ≤P c,max

[0116] 0≤P d,t ≤P d,max

[0117] Where P c,max Indicates the maximum charging power; P d,max Indicates the maximum discharge power; step S320 can ensure that the power of the battery does not exceed its safety limit during the charging and discharging process, avoid damage to the battery due to excessive power, and ensure the safe operation and service life of the battery.

[0118] Step S330: Establish a capacity limit constraint, the expression of which is:

[0119] E min ≤E t ≤E max

[0120] In the formula, E min Indicates the minimum value of the battery's energy storage; E max Indicates the maximum value of the battery's energy storage; step S330 specifies the range of the battery's energy storage to prevent the battery from being overcharged or over-discharged, ensuring that the battery operates within a safe energy storage range and maintaining the stability of the battery's performance.

[0121] Step S300 constructs a battery energy storage model based on battery energy balance related parameters, providing an accurate and comprehensive description of the battery energy storage system for the planning of the evolution path of the power system based on multi-energy storage collaboration. The model can accurately reflect the changes in battery storage energy at different times, charging and discharging power limitations, and capacity limitations, so that the characteristics of battery energy storage can be fully considered when planning the power system. This helps to reasonably arrange the battery charging and discharging plan, improve the battery life and energy utilization efficiency, ensure the stable operation of the battery energy storage system in the power system, and then optimize the energy storage strategy of the entire power system, enhance the power system's ability to cope with different operating conditions and changes in energy demand, and promote the power system to develop in a more efficient, stable, and sustainable direction.

[0122] Step S400: constructing a pumped storage model according to pumped storage energy balance related parameters;

[0123] Step S400 specifically includes:

[0124] Step S410: Establishing pumped storage energy balance constraints:

[0125]

[0126] In the formula, H t Indicates the water level at time t; H t-1 represents the water level at time t-1; η p Indicates pump efficiency; P pump,t represents the pump power at time t; Δt represents the time step; P gen，t represents the power generated at time t; η g Indicates power generation efficiency; Step S410 describes the dynamic change of water level height of the pumped storage system during pumping and power generation based on the principle of energy conservation. During the pumping process, the reservoir water level is increased by adding the pump efficiency multiplied by the pump power and the time step on the basis of the previous moment; during the power generation process, the reservoir water level is reduced by the value of the product of the power generation power and the time step divided by the power generation efficiency, reflecting the drop in water level.

[0127] Step S420: Establish a power limit constraint, the expression of which is:

[0128] 0≤P gen，t ≤P gen，max

[0129] 0≤P pump,t ≤P pump，max

[0130] Where P gen,max Indicates the maximum power generation; P pump,max Indicates the maximum pump power; step S420 can ensure that during the operation of the pumped storage system, the power of the pump and the generator will not exceed their respective safety limits, avoid damage to the equipment due to excessive power, and ensure the safe operation and service life of the equipment.

[0131] Step S430: Establish a water level height limit constraint, the expression of which is:

[0132] H min ≤h t ≤H max

[0133] In the formula, H min Indicates the minimum water level height; H maxIndicates the maximum water level height; Step S430 stipulates the safe operating range of the reservoir water level to prevent the reservoir water level from being too high or too low, ensure that the reservoir operates within a safe water level range, avoid safety hazards such as dam breach, turbine idling, etc., and also ensure the stable operation of the system.

[0134] Step S440: Establish energy storage capacity restriction constraint, the expression of which is:

[0135] E t =μ·g·V t ·H t

[0136] In the formula, E t represents the energy storage capacity of pumped storage at time t; ρ represents the density of water; g represents the acceleration of gravity; V t represents the volume of the reservoir at time t; step S440 describes the relationship between the energy storage of the pumped storage system and factors such as the water level and volume of the reservoir based on the gravitational potential energy formula. Through this constraint, the energy storage of the pumped storage system at different times can be clarified to ensure that the system operates within a safe energy storage capacity range, avoiding system failure or reduced efficiency due to energy storage capacity exceeding the limit.

[0137] Step S450: Establish a flow rate limit constraint, the expression of which is:

[0138] 0≤Q in,t ≤Q in,max

[0139] 0≤Q out,t ≤Q out，max

[0140] In the formula, Q in，t represents the inflow velocity at time t; Q in,max Indicates the maximum inflow velocity; Q out，t represents the outflow velocity at time t; Q out,max Indicates the maximum outflow velocity; step S450 can ensure that the flow rate of water in and out of the pumped storage system is within a safe and reasonable range, avoiding adverse effects on the system due to excessively fast or slow flow rates, such as pipeline scouring, reduced turbine efficiency, etc., to ensure the normal operation of the system and the service life of the equipment.

[0141] Step S400 provides a comprehensive and accurate description of the pumped storage system for the planning of the evolutionary path of the power system based on the coordination of multiple energy storage by constructing a pumped storage model that includes pumped storage energy balance constraints, power limit constraints, water level limit constraints, energy storage capacity limit constraints, and flow rate limit constraints. This enables full consideration of the energy conversion, equipment operation restrictions, reservoir water level, and water flow of the pumped storage system during the pumping and power generation process when planning the power system. It helps to reasonably arrange the operation plan of the pumped storage system, ensure the safe and stable operation of the system, avoid equipment damage and system failures caused by exceeding various restrictions, optimize the energy storage utilization efficiency of the pumped storage system, and thus provide strong support for the stable operation of the entire power system and the coordinated optimization of energy storage, and promote the development of the power system in an efficient and reliable direction.

[0142] Step S500: constructing a cost function with the goal of minimizing power generation cost, new line construction cost, and equipment cost;

[0143] Step S500 specifically includes:

[0144] Step S510: Considering the impact of the discount rate, a cost function is constructed with the goal of minimizing the cost of electricity generation, the cost of new line construction, and the cost of equipment, including:

[0145] The cost of electricity generation is calculated as:

[0146]

[0147] In the formula, λ G,i represents the electricity generation cost of node i; P G,i,y,t represents the power generated by node i at time t in year y; T represents the time deployment each year; I represents the set of nodes in the power grid; this formula starts from the micro level (each node) and comprehensively considers the power generation cost and power generation of different nodes at different times to fully reflect the cost status of the power generation link of the power grid;

[0148] Calculate the cost of building a new line, the expression is:

[0149]

[0150] In the formula, K l represents the construction cost of the new line l; w y,l is a 0-1 variable, used to indicate whether line l is built in year y; Ω L Represents a set of new lines; by introducing line construction variables, it is possible to accurately distinguish which lines need to be built in different years, and then accurately calculate the corresponding construction costs;

[0151] Calculate the equipment cost, the expression is:

[0152]

[0153] In the formula, C w1 Represents the unit cost of electrolysis equipment; C w2 represents the unit cost of multi-energy storage equipment; C w3 represents the unit cost of the methanogenation equipment; represents the capacity of the electrolysis equipment in year y; represents the capacity of the multi-energy storage device in year y; Indicates the capacity of the methanogenation equipment in year y; Ω W Represents a collection of equipment; takes into account the unit cost and capacity of different types of equipment to accurately reflect the proportion of equipment cost in the entire power system;

[0154] Step S520: Considering the total income of oxygen and methane, the expression is:

[0155]

[0156] In the formula, represents the unit selling price of oxygen; represents the mass of oxygen produced by the electrolysis equipment at time t in year y; represents the unit selling price of methane; Represents the methane quality of the methanogenization equipment at time t in year y; by considering the selling price and output of the two saleable gases, it can accurately reflect the economic benefits brought by gas production during the energy storage process

[0157] Finally, the objective function can be expressed as:

[0158]

[0159] In the formula, (1+r) y-1 represents the discount rate; its purpose is to find the optimal solution that minimizes the comprehensive cost of the entire power system in the long-term operation, that is, to maximize the economic benefits while meeting energy demand by adjusting relevant decision variables such as power generation, new line construction, and equipment configuration.

[0160] Step S500 provides a comprehensive and economically reasonable decision-making basis for the planning of the evolution path of the power system based on the coordination of multi-energy storage by constructing an objective function that takes into account the cost of electricity generation, the cost of new line construction, the cost of equipment, and the sales revenue of oxygen and methane and combines the discount rate. It can not only accurately reflect the various cost expenditures of the power system during operation and development, but also take into account the income brought by the saleable products generated in the energy storage process, while considering the time value of money. By optimizing and solving the cost function, the optimal evolution path of the power system can be obtained, so that the power system can minimize costs while meeting energy needs.

[0161] Step S600: Based on the multi-energy storage model, the battery storage model, the pumped storage model and the cost function, the optimal power system evolution path is solved, and the relationship between the water electrolysis operating voltage and the operating current density is extracted; specifically, in addition to the constraints of the multi-energy storage model, the battery storage model and the pumped storage model, the constraints of the new power system evolution path planning model that considers the dynamic energy storage model in a refined manner include power generation capacity, renewable energy capacity and utilization rate, power flow constraints, power balance, energy storage capacity, and construction can be expressed as:

[0162] Power generation capacity constraints:

[0163] P G,i,min ≤P G,i,y,t ≤P G,i,max

[0164] Where P G,i,min , P G,i,max Indicates the minimum and maximum power generation corresponding to node i;

[0165] Renewable energy capacity and utilization rate:

[0166] P re,i,min ≤P re,i,y,t ≤P re,i,max

[0167] Where P re,i,y,t represents the renewable energy generation power of node i at time t in year y;

[0168] Power flow constraints:

[0169] p l,y,t =B i，j (θ i，y,t -θ j,y,t ),l∈Ω i，j

[0170]

[0171] In the formula, p l ,y , t represents the power flow of line l at time t, B i , j represents the susceptance of the line from point i to node j, θ i , y , t represents the phase angle of i; represents the minimum power flow of line l; represents the maximum power flow of line l;

[0172] Power flow constraints for the new line:

[0173] p l,y,t =w y,l B i，j (θ i，y,t -θ j，y,t ),l∈Ω L

[0174]

[0175] In the formula, w y , l is a 0-1 variable, used to indicate whether line l was built in year y;

[0176] Power Balance:

[0177]

[0178] Among them, P D,i , y , t The load demand power of node i at time t in year y;

[0179] Energy storage capacity:

[0180]

[0181] This formula is used for power control (charging and discharging power) of energy storage equipment. Through the management system of energy storage equipment, power is reasonably allocated according to its capacity limit to avoid damage to the equipment caused by excessive power or power exceeding the capacity range causing the equipment to fail to operate normally. For the storage volume of substances such as hydrogen, real-time monitoring and control of its input and output operations are carried out to ensure that the storage volume is within a safe and reasonable range, thereby ensuring the stable and reliable operation of the energy storage system and the normal realization of the energy storage and release functions of the entire power system.

[0182] Construction constraints:

[0183] W y-1,l ≤w y，l

[0184] This constraint ensures that already constructed lines will not be demolished, that is, if a line has been constructed in year y-1, its construction status cannot be changed to unconstructed in subsequent years.

[0185] Finally, the selected nonlinear programming algorithm, such as the interior point method or sequential quadratic programming method, is started with the set initial value. The algorithm will adjust the values ​​of each variable through continuous iterative calculations based on the cost function and various constraints. During each iteration, the algorithm will evaluate whether the current solution meets all constraints and calculate the value of the cost function. If the current solution does not meet the constraints, the algorithm will make corresponding adjustments; if the constraints are met but the value of the cost function is not the minimum, the algorithm will continue to iterate to find a better solution. This process will continue until the optimal solution that meets all constraints and minimizes the cost function is found. This optimal solution is the optimal power system evolution path.

[0186] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0187] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A power system evolution path planning method based on multi-energy storage collaboration, characterized in that: The method comprises: Obtain data related to the multi-element energy storage system and extract dynamic operating voltage, water electrolysis operating power and hydrogen flow rate; According to the dynamic operating voltage, water electrolysis operating power and hydrogen flow rate, a multi-energy storage model is constructed in combination with relevant constraints; Construct a battery energy storage model based on battery energy balance related parameters; Construct a pumped storage model based on pumped storage energy balance related parameters; Construct a cost function with the goal of minimizing the cost of electricity generation, new line construction costs, and equipment costs; Based on the multi-energy storage model, battery storage model, pumped storage model and cost function, the optimal power system evolution path is solved, and the relationship between the water electrolysis operating voltage and operating current density is extracted.

2. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 1, characterized in that: Obtain relevant data of the multi-energy storage system and extract dynamic operating voltage including: Obtaining the standard electrode potential of a multi-energy storage system; Extracting thermodynamic parameters based on the free energy change of water decomposition during water electrolysis; Based on the charge transfer coefficients and exchange current density of the positive and negative electrodes, the kinetic effect parameters in the charge transfer process are extracted; The dynamic operating voltage is calculated based on the standard electrode potential, thermodynamic parameters and kinetic effect parameters.

3. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 2, characterized in that: The expression of the dynamic operating voltage is: Where V c，t represents the operating voltage of the water electrolysis unit at time t; V0 represents the standard electrode potential; i P represents the exchange current density of the positive electrode; i N represents the exchange current density of the negative electrode, represents the partial pressure of hydrogen; represents the partial pressure of oxygen, a w Indicates the activity of water; α P Represents the charge transfer coefficient of the positive electrode; α N represents the charge transfer coefficient of the negative electrode, T represents temperature; R represents the ideal gas constant; F represents the Faraday constant; R c represents ohmic resistance; i c，t Represents the operating current density of the water electrolysis unit at time t.

4. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 1, characterized in that: The expression for extracting the operating power of water electrolysis is: In the formula, represents the operating power of water electrolysis at time t; N c Indicates the number of batteries in series; N s Indicates the number of batteries connected in parallel; A is a 0-1 variable, indicating whether electrolyzer i is running at time t; c Represents the effective area of ​​the electrolyzer; η f represents the Faraday efficiency; k1 represents the first conversion coefficient; V c，t represents the operating voltage of the water electrolysis unit at time t; i c，t Represents the operating current density of the water electrolysis unit at time t.

5. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 4, characterized in that: The expression for extracting hydrogen flow is: In the formula, represents the hydrogen flow rate of water electrolysis at time t; k2 represents the second conversion coefficient; F represents the Faraday constant.

6. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 1, characterized in that: The relevant constraints of the multi-energy storage model include: The current density constraint is expressed as: In the formula, is a 0-1 variable, indicating whether the water electrolysis process is running at time t; i c，t represents the operating current density of the water electrolysis unit at time t; i c，min Indicates the minimum value of the operating current density; i c，max Indicates the maximum value of the operating current density; The multi-energy storage constraint condition is expressed as: In the formula, represents the hydrogen flow rate generated by the electrolysis process at time t; represents the hydrogen input flow rate of the storage device at time t; represents the hydrogen output flow rate of the storage device at time t; Indicates the hydrogen storage capacity of the storage device at time t; Indicates the efficiency of the hydrogen input process; represents the efficiency of the hydrogen output process; Δt represents the time step; Indicates the minimum value of hydrogen input flow rate of the storage device; Indicates the maximum value of hydrogen input flow rate of the storage device; Indicates the minimum value of hydrogen output flow rate of the storage device; Indicates the maximum value of the hydrogen output flow rate of the storage device; Indicates the minimum hydrogen storage capacity of the storage device; Indicates the maximum hydrogen storage capacity of the storage device; Indicates the hydrogen input flow rate at the start time T0; Indicates the end time T e The amount of hydrogen input at .

7. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 1, characterized in that: The battery energy storage model is constructed based on the battery energy balance related parameters including: The battery energy balance constraint is expressed as: In the formula, E t Indicates the energy storage of the battery at time t; E t-1 represents the energy storage of the battery at time t-1; η c Indicates charging efficiency; P c，t represents the charging power at time t; Δt represents the time step; P d，t represents the discharge power at time t; η d Indicates discharge efficiency; The power limit constraint is expressed as: 0≤P c，t ≤P c，max 0≤P d，t ≤P d，max Where P c，max Indicates the maximum charging power; P d，max Indicates the maximum discharge power; The capacity limit constraint is expressed as: AND min ≤E t ≤E max In the formula, E min Indicates the minimum value of the battery's energy storage; E max Indicates the maximum value of the battery's stored energy.

8. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 1, characterized in that: The pumped storage model is constructed based on the relevant parameters of pumped storage energy balance, including: Pumped storage energy balance constraints: In the formula, H t Indicates the water level at time t; H t-1 represents the water level at time t-1; η p Indicates pump efficiency; P pump，t represents the pump power at time t; Δt represents the time step; P gen，t represents the power generated at time t; η g Indicates power generation efficiency; The power limit constraint is expressed as: 0≤P gen，t ≤P gen，max 0≤P pump，t ≤P pump，max Where P gen，max Indicates the maximum power generation; P pump，max Indicates the maximum pump power; The water level height limit constraint is expressed as: H min ≤H t ≤H max In the formula, H min Indicates the minimum water level height; H max Indicates the maximum water level height; Energy storage capacity limitation constraint, its expression is: E t =ρ·g·V t ·H t In the formula, E t represents the energy storage capacity of pumped storage at time t; ρ represents the density of water; g represents the acceleration of gravity; V t represents the volume of the reservoir at time t; The flow rate limit constraint is expressed as: 0≤Q in，t ≤Q in，max 0≤Q out，t ≤Q out，max In the formula, Q in，t represents the inflow velocity at time t; Q in，max Indicates the maximum inflow velocity; Q out，t represents the outflow velocity at time t; Q out，max Indicates the maximum outflow velocity.

9. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to any one of claims 1 to 8, characterized in that: The cost function constructed to minimize the cost of electricity generation, new line construction cost, and equipment cost includes: Considering the impact of the discount rate, a cost function is constructed to minimize the cost of electricity generation, the cost of new line construction, and the cost of equipment, including: The cost of electricity generation is calculated as: In the formula, λ G，i represents the electricity generation cost of node i; P G，i，y，t represents the power generated by node i at time t in year y; T represents the time deployment per year; I represents the set of nodes in the power grid; Calculate the cost of building a new line, the expression is: In the formula, K l represents the construction cost of the new line l; w y，l is a 0-1 variable, used to indicate whether line l is built in year y; Ω L Represents a new line set; Calculate the equipment cost, the expression is: In the formula, C w1 Represents the unit cost of electrolysis equipment; C w2 represents the unit cost of multi-energy storage equipment; C w3 represents the unit cost of the methanogenation equipment; represents the capacity of the electrolysis equipment in year y; represents the capacity of the multi-energy storage device in year y; represents the capacity of the methanogenation equipment in year y; Ω W Represents a collection of devices.

10. The method for planning the evolutionary path of a power system based on multi-energy storage collaboration according to claim 9, characterized in that: The cost function constructed to minimize the cost of electricity generation, new line construction cost, and equipment cost also includes: Considering the total income of oxygen and methane, the expression is: In the formula, represents the unit selling price of oxygen; represents the mass of oxygen produced by the electrolysis equipment at time t in year y; represents the unit selling price of methane; It represents the methane mass of the methanogenation equipment at time t in year y.

Images