Independent energy storage site selection and capacity optimization method and system under power spot market mechanism

Abstract

The application provides an independent energy storage site selection and capacity optimization method and system under an electricity spot mechanism, establishes a spot mechanism of independent energy storage participating in an electricity market and an auxiliary service market, constructs a whole life cycle planning index of the independent energy storage and converts the index to a daily average loss index; establishes a coal consumption model of operation and start-up of a conventional unit and a coal consumption model of frequency modulation, and simultaneously constructs a participation model of the independent energy storage; establishes a node power balance constraint, establishes a frequency modulation capacity and a frequency modulation mileage demand constraint according to a frequency modulation demand, and establishes a linearized power flow constraint through a direct current power flow model; constructs a site selection and capacity optimization model with the minimum daily average planning index of the energy storage, the coal consumption of operation and start-up of the conventional unit and the maximum participation of the energy storage as targets; linearizes the model, and obtains the optimal node selection and capacity configuration of the energy storage. The application fully considers the site selection and capacity of the independent energy storage under the spot mechanism, and makes up for the related blank.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage planning technology, specifically to a method and system for optimizing the location and capacity of an independent energy storage system under a power spot market mechanism. Background Technology

[0002] Energy storage capacity and operation planning fall under the category of power system energy storage planning. With the increasing proportion of renewable energy generation resources such as photovoltaics, the intermittent and uncertain nature of their output poses challenges to the safe and stable operation of the power system. Energy storage, as a flexible and adjustable resource, possesses bidirectional rapid adjustment capabilities, which helps to absorb renewable energy and improve grid stability. As my country's power market reform deepens, future energy storage construction and operation will be carried out under a spot market framework. However, energy storage planning targets are relatively high, the scale is small, and the locations are dispersed. Therefore, there is an urgent need for an energy storage site selection and capacity optimization method under a spot market mechanism to promote energy storage construction and improve energy storage utilization.

[0003] In recent years, new energy storage technologies, represented by electrochemical energy storage, have been developed rapidly, making energy storage configuration increasingly easier. Simultaneously, with policies favoring energy storage as an independent entity participating in the electricity spot market, more and more independent energy storage power plants are under construction. The evaluation indicators for energy storage under the spot market mechanism not only derive from the "low storage, high generation" and "peak shaving and valley filling" effects, but also from the regulation effects of ancillary services such as peak shaving and frequency regulation. Furthermore, line topology and power flow influence energy storage site selection, making energy storage configuration under the spot market mechanism a research hotspot. Therefore, site selection and capacity optimization methods for independent energy storage participating in the spot market mechanism are particularly important.

[0004] Currently, no descriptions or reports of technologies similar to this invention have been found, and no similar information has been collected domestically or internationally. Summary of the Invention

[0005] To address the aforementioned shortcomings in the existing technology, this invention provides a method and system for optimizing the location and capacity of independent energy storage under an electricity spot market mechanism.

[0006] According to one aspect of the invention, a method for optimizing the location and capacity of independent energy storage under an electricity spot market mechanism is provided, comprising:

[0007] Establish a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market. Based on the initial configuration indicators, maintenance indicators and decommissioning recovery indicators of independent energy storage, construct the full life cycle loss indicators of independent energy storage and convert them into daily average life cycle loss indicators.

[0008] By combining multiple typical operating days, we establish coal consumption models for the operation and startup of conventional units as well as coal consumption models for frequency regulation. At the same time, we construct a participation model for independent energy storage under the spot mechanism of the electricity market and the ancillary services market.

[0009] Energy storage location constraints are constructed based on the power system grid structure, capacity configuration constraints are constructed based on the upper limit of single-node energy storage configuration, node power balance constraints are established based on power balance requirements, frequency regulation capacity and frequency regulation mileage requirements are established based on frequency regulation requirements, and linear power flow constraints are established through DC power flow model.

[0010] Combining energy storage location constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints, a location and capacity optimization model is constructed with the goal of minimizing the average daily lifespan loss of energy storage, coal consumption during operation and startup of conventional units, and maximizing the participation of energy storage.

[0011] The site selection and capacity optimization model is linearized and solved to obtain the optimal node selection and capacity configuration for energy storage, thus completing the energy storage site selection and capacity optimization.

[0012] Preferably, the establishment of a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market includes:

[0013] In the spot market mechanism of the electricity market and ancillary services market, independent energy storage determines its charging and discharging plan through a full-volume bidding method; wherein, the full-volume bidding method includes: the generation side submits a quantity and bid, while the user side submits a quantity but does not bid; wherein, conventional generating units and independent energy storage on the generation side submit time-segmented operating parameters and parameter degree coefficients according to the application requirements of the operating agency;

[0014] The process involves constructing a full lifecycle loss index for independent energy storage based on initial configuration, maintenance, and decommissioning recovery indicators, and then converting this index to a daily average lifecycle loss index. This includes:

[0015] Let the index and set of the power system nodes be n and N, respectively, and the independent energy storage capacity and power configured for node n be respectively... and Then the initial energy storage configuration index C ESinv The model is as follows:

[0016]

[0017] In the formula, α is the configuration index parameter for energy storage unit power; β is the configuration index parameter for energy storage unit capacity.

[0018] Maintenance metrics are used to reflect the annual depreciation of independent energy storage. Let the operating life of the energy storage be Y. life Then maintain index C ESmai The model is as follows:

[0019]

[0020] In the formula, γ is the annual maintenance cost per unit capacity of energy storage;

[0021] The decommissioning recovery index is used to reflect the ability of energy storage equipment to recover a portion of its components after reaching the end of its lifespan, offsetting a certain portion of the initial energy storage configuration requirements. Therefore, the decommissioning recovery index C... ESres The model is as follows:

[0022] C ESres =σC ESinv (3)

[0023] In the formula, σ is the residual value rate of the energy storage equipment;

[0024] According to the independent energy storage initial planning index C ESinv The maintenance index C ESmai and the aforementioned decommissioning and recycling index C ESres The life-cycle loss index for constructing independent energy storage is as follows:

[0025] C ESlife =C ESinv +C ESmai -C ESres (4);

[0026] The life-cycle loss index of the independent energy storage is converted to a daily average using the CRF coefficient, and denoted as the daily average loss index C. ESimr The daily average loss index C ESimr The model is as follows:

[0027]

[0028]

[0029] In the formula, r is the discount rate.

[0030] Preferably, the process of establishing a coal consumption model for the operation and startup of conventional generating units and a frequency regulation loss model by combining multiple typical operating days, and simultaneously constructing a participation model for independent energy storage under the spot mechanism of the electricity market and ancillary services market, includes:

[0031] Let a be the index of a typical operating day in the target region, and g and G be the indexes and sets of the regular units, respectively;

[0032] Constructing a coal consumption model C for conventional units under a typical operating day a Gope,a for:

[0033]

[0034]

[0035] In the formula, t and Γ are the index and set of the time period, respectively; Δt is the optimization time interval; m and M are the index and set of the number of quotation segments for conventional units, respectively; The declared output of conventional unit g during time period t; For conventional unit g, the output is declared during the m-th segment of time period t; For a conventional unit g, the coal consumption coefficient at output level m;

[0036] Constructing a start-up coal consumption model C for conventional units under a typical operating day a Gsta,a for:

[0037]

[0038] In the formula, u g,t A 0-1 variable, representing the operating status of the unit, u g,t =1 indicates that the unit is in operation, u g,t =0 indicates that the unit is in machine mode; This is the start-up coal consumption coefficient for conventional unit g;

[0039] Constructing a frequency regulation coal consumption model C for conventional units under typical operating day a Gaux,a for:

[0040]

[0041] In the formula, These are the frequency regulation capacity participation coefficient, frequency regulation capacity, frequency regulation mileage participation coefficient, and frequency regulation mileage declared by conventional unit g in time period t.

[0042] The participation models for independent energy storage in the spot market and ancillary services market under a typical operating day 'a' are constructed as follows:

[0043]

[0044]

[0045] In the formula, C ESene,a C represents the energy participation rate of independent energy storage in the spot market mechanism of the electricity market. ESaux,a Frequency regulation participation of independent energy storage in the spot mechanism of the ancillary services market; These represent the charging power and charging participation coefficient reported by node n for energy storage during time period t, respectively. These represent the discharge power and discharge participation coefficient reported by node n for energy storage during time period t, respectively. These represent the frequency regulation capacity participation coefficient, frequency regulation capacity, frequency regulation mileage participation coefficient, and frequency regulation mileage declared by node n for energy storage during time period t.

[0046] Preferably, the steps of constructing energy storage location constraints based on the power system grid structure, constructing capacity configuration constraints based on the upper limit of single-node energy storage configuration, establishing node power balance constraints based on power balance requirements, establishing frequency regulation capacity and frequency regulation mileage requirement constraints based on frequency regulation requirements, and establishing linearized power flow constraints through a DC power flow model include:

[0047] Let x n A 0-1 variable for whether to configure energy storage, where x n =1 indicates that energy storage is configured at node n, x n =0 indicates that no energy storage is configured at node n. Therefore, the location constraints and capacity configuration constraints for independent energy storage are expressed as follows:

[0048]

[0049]

[0050]

[0051] In the formula, E max The maximum configured capacity for energy storage in a single node; This is the maximum proportionality coefficient between energy storage power and capacity. The independent energy storage capacity configured for node n; Independent energy storage capacity configured for node n; N ES Configure the quantity of energy storage;

[0052] Let N(n) represent the set of nodes connected to node n, and G(n) represent the set of conventional units at node n. Let the load of node n in time period t be the load. Then the power balance constraint of node n is expressed as:

[0053]

[0054] In the formula, The declared output of conventional unit g during time period t; The discharge participation coefficient declared for energy storage at node n during time period t; The charging participation coefficient declared by node n for energy storage during time period t; For the power flow of line nw;

[0055] The power flow of line nw is constrained using a DC power flow model:

[0056]

[0057] In the formula, θ n,t and θ w,t B represents the voltage phase angles at nodes n and w, respectively; nw Let n be the admittance parameter of the line nw; Let nw be the capacity of the line;

[0058] The existing mechanisms of the ancillary services market are set to impose demand constraints on the frequency regulation capacity and frequency regulation history of the power system as follows:

[0059]

[0060]

[0061] In the formula, This refers to the frequency regulation capacity declared by the conventional generating unit g during time period t. The frequency regulation capacity declared for energy storage at node n during time period t; This refers to the frequency regulation mileage declared by the conventional generating unit g during time period t. The frequency regulation mileage declared for node n's energy storage during time period t; These represent the frequency regulation capacity requirement and frequency regulation mileage requirement of the power system during time period t, respectively.

[0062] Preferably, the site selection and capacity optimization model is constructed by combining energy storage site selection constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints, with the objectives of minimizing the average daily lifespan loss of energy storage, coal consumption during operation and startup of conventional units, and maximizing energy storage participation. This model includes:

[0063] The objective function of the site selection and capacity optimization model is determined to be minimizing the daily average lifetime loss of independent energy storage, the coal consumption during operation and startup of conventional units, and maximizing the participation of energy storage. Therefore, the objective function of the site selection and capacity optimization model is expressed as:

[0064]

[0065] C ES,a =C ESene,a +C ESaux,a (twenty one)

[0066] C G,a =C Gsta,a +C Gope,a +C Gaux,a (twenty two)

[0067] In the formula, C ESimr For the average daily lifespan loss of energy storage; ω a C represents the proportion of a typical operating day (a) in a year. G,a The starting and operating losses of conventional units under a typical operating day a; C ES,a C represents the participation rate of energy storage on a typical operating day a; ESene,a To determine the degree of participation of energy storage in the spot market mechanism of electricity on a typical operating day a; C ESaux,aFor energy storage, the participation rate in the spot market mechanism of ancillary services under typical operating day a; C Gsta,a C represents the start-up coal consumption of a conventional unit on a typical operating day (a). Gope,a C represents the operating coal consumption of a conventional unit on a typical operating day (a). Gaux,a This is a frequency regulation coal consumption model for conventional units under a typical operating day a;

[0068] Combining electrical energy constraints, frequency regulation demand constraints, and power flow constraints, the system-level constraints are constructed as follows:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] In the formula, The declared output of conventional unit g during time period t; The discharge participation coefficient declared for energy storage at node n during time period t; The charging participation coefficient declared by node n for energy storage during time period t; The load of node n during time period t; For the power flow of line nw; θ n,t and θ w,t B represents the voltage phase angles at nodes n and w, respectively; nw Let n be the admittance parameter of the line nw; Let nw be the capacity of the line; This refers to the frequency regulation capacity declared by the conventional generating unit g during time period t. The frequency regulation capacity declared for energy storage at node n during time period t; This refers to the frequency regulation mileage declared by the conventional generating unit g during time period t. The frequency regulation mileage declared for node n's energy storage during time period t; They are the frequency regulation capacity demand and frequency regulation mileage demand of the power system for time period t, respectively; k is the maximum proportion of the total frequency regulation capacity declared by energy storage to the frequency regulation demand of the power system; Equations (23) to (24) are the power balance constraints and power flow constraints of the power system, and Equations (25) to (27) are the demand constraints of the frequency regulation capacity and frequency regulation mileage of the power system.

[0075] Based on the physical characteristics of conventional generating units, the operational constraints for the participants in the power system spot market mechanism are as follows:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] In the formulas, equations (28) to (32) represent the operating constraints of conventional generating units. These are the minimum and maximum output of conventional unit i, respectively; These are the limits on the ramp power and landslide power of conventional unit i, respectively; These are the minimum operating time and minimum downtime for conventional generating units, respectively.

[0082] Based on the physical characteristics of energy storage, the operational constraints for the participants in the power system spot market mechanism are as follows:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] Equations (33) to (38) represent the operational constraints of energy storage. These are 0-1 variables describing the charge and discharge states of energy storage. This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a non-charging state. This indicates that the energy storage is in a discharging state. This indicates that the energy storage is in a non-discharge state; E n,t E represents the remaining energy stored during time period t. n,t-1 The remaining energy stored during period t-1; η ch For energy storage charging efficiency; η dis For energy storage discharge efficiency; These represent the remaining energy storage capacity at the beginning and end of the time periods, respectively. The independent energy storage capacity configured for node n; The initial energy storage capacity is the percentage of the total energy storage capacity.

[0090] Thus, a site selection and capacity optimization model for independent energy storage under the power system spot mechanism has been constructed.

[0091] Preferably, the linearization and solution of the site selection and sizing optimization model includes:

[0092] Based on the start-up coal consumption of conventional units, the location and capacity optimization model is linearized as follows:

[0093]

[0094]

[0095] In the formula, C Gsta This refers to the start-up coal consumption of a conventional unit; u g,t Indicates the operating status of the generator unit; y is the start-up coal consumption coefficient for conventional units g; g,t Auxiliary 0-1 variables are used to replace u. g,t u g,t-1 ;

[0096] An improved Benders decomposition algorithm is used to solve the linearized location and capacity optimization model:

[0097] min f T p+h T b (41)

[0098] K1p≥h1 (42)

[0099] J1b≥h2 (43)

[0100] K2p+J2b≥h3 (44)

[0101] p∈Ω p ,b∈{0,1} (45)

[0102] Where p is a continuous variable related to the energy storage capacity and power and the output of conventional generating units, including: the independent energy storage power configured at node n. Independent energy storage capacity configured for node n The charging participation coefficient of node n energy storage declared during time period t The discharge participation coefficient of node n energy storage declared during time period t The declared output of conventional unit g during time period t The frequency regulation capacity declared by node n for time period t The frequency regulation mileage declared by node n for energy storage during time period t Frequency regulation capacity declared by conventional unit g during time period t Frequency regulation mileage declared by conventional unit g during time period t And the remaining electricity E during energy storage period t n,t b represents variables related to site selection and operational status, including: a 0-1 decision variable x for deciding whether to configure energy storage at node n. n , Regular unit operating status during time period t u g,t Auxiliary variable y g,t Energy storage charging and discharging state variables z n,t f and h are the coefficient matrices in the objective function; J1, J2, K1, and K2 are the coefficient matrices in the constraints; Ω p The domain of a continuous variable;

[0103] Benders decomposition requires decomposing the location and sizing model into a main problem with 0-1 variables and subproblems with continuous variables. According to equations (41) to (45), the main problem is:

[0104]

[0105] In the formula, q is an auxiliary variable; h2 is the right-hand side value of the integer variable related constraints; cutting plane represents the cutting plane, including the optimal cutting plane and the combined integer cutting plane;

[0106] Find the optimal solution to the main problem. The following subproblem containing continuous variables and its dual problem are generated:

[0107]

[0108]

[0109] In the formulas, equations (47) and (48) represent the subproblem and dual problem, respectively; p is a continuous variable; h1 is the right-hand side value of the constraints related to the continuous variable; h3 is the right-hand side value of the constraints containing both the continuous variable and the 0-1 variable; v1 is the variable of the dual problem; v2 is the variable of the dual problem; and is the variable of the dual problem.

[0110] Initialize upper bound U B Lower bound L B and allowable error ε, when U B -L B For ≥ε, perform the following iterations:

[0111] Solve the main problem to obtain its optimal solution. Simultaneously update the lower bound L. B ;

[0112] The optimal solution Substitute the subproblem into the equation and solve its dual problem. If the subproblem has a solution, generate the optimal cutting plane and update the upper bound U. B If the subproblem has no solution, then the generated combinatorial integer cut plane is:

[0113]

[0114] In the formula, Φ0 and Φ1 are the sets of variable indices for y=0 and y=1, respectively, and |Φ1| represents the number of elements in the Φ1 set;

[0115] Add the generated cutting plane to the constraint "cutting plane" of the main problem;

[0116] If U B -L B If the value is less than ε, then stop the iteration and output the addressing and sizing results, including: independent energy storage power. Independent energy storage capacity And whether to configure energy storage at node n, a 0-1 variable x n Otherwise, return to the steps for solving the main problem.

[0117] Based on x above n , and To obtain the optimal node selection and capacity configuration for energy storage.

[0118] According to another aspect of the present invention, an independent energy storage location and capacity optimization system under an electricity spot market mechanism is provided, comprising:

[0119] The indicator construction module is used to establish a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market. Based on the initial configuration indicators, maintenance indicators and decommissioning recovery indicators of independent energy storage, it constructs the full life cycle loss indicators of independent energy storage and converts them into daily average life cycle loss indicators.

[0120] The model building module is used to combine multiple typical operating days to establish a coal consumption model for the operation and startup of conventional units, as well as a coal consumption model for frequency regulation. At the same time, it constructs a participation model for independent energy storage under the spot mechanism of the electricity market and ancillary services market.

[0121] The constraint construction module is used to construct energy storage location constraints based on the power system grid structure, capacity configuration constraints based on the single node energy storage configuration upper limit, node power balance constraints based on power balance requirements, frequency regulation capacity and frequency regulation mileage requirement constraints based on frequency regulation requirements, and linear power flow constraints through the DC power flow model.

[0122] The site selection and capacity optimization module combines energy storage site selection constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints to construct a site selection and capacity optimization model with the objectives of minimizing the average daily lifespan loss of energy storage, coal consumption during operation and startup of conventional units, and maximizing energy storage participation. The module then linearizes the site selection and capacity optimization model and solves it to obtain the optimal node selection and capacity configuration for energy storage, thus completing the energy storage site selection and capacity optimization.

[0123] According to a third aspect of the present invention, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can be used to perform the method described in any one of the above inventions, or to run the system described in the above inventions.

[0124] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, can be used to perform the method described in any one of the above-described inventions, or to run the system described in the above-described inventions.

[0125] By adopting the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0126] The method and system for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism provided by this invention can take into account the impact of the electricity spot market mechanism on the utilization rate of energy storage in the power system, which meets the needs of the development of new power systems.

[0127] The method and system for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism provided by this invention can improve the utilization rate of independent energy storage under the spot market mechanism and promote investment and construction of independent energy storage.

[0128] The method and system for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism provided by this invention can reduce the coal consumption of conventional units (such as traditional thermal power units), improve their operating conditions, and reduce carbon emission pollution. Attached Figure Description

[0129] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0130] Figure 1 This is a flowchart illustrating the process of the independent energy storage location and capacity optimization method under the electricity spot market mechanism in one embodiment of the present invention.

[0131] Figure 2 This is a flowchart illustrating the solution method for the location and volume optimization model in a preferred embodiment of the present invention.

[0132] Figure 3 This is a schematic diagram of the constituent modules of an independent energy storage location and capacity optimization system under an electricity spot market mechanism in one embodiment of the present invention. Detailed Implementation

[0133] The embodiments of the present invention are described in detail below: These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0134] The purpose of this invention is to consider the participation of independent energy storage in the spot market mechanism for electricity and ancillary services, and to propose a site selection and capacity optimization method for energy storage systems under the electricity spot market mechanism, thereby determining the capacity of the energy storage system and the optimal installation location.

[0135] like Figure 1 As shown in this embodiment, the method for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism can include the following operations:

[0136] S1. Establish a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market. Based on the initial configuration indicators, maintenance indicators and decommissioning recovery indicators of independent energy storage, construct the full life cycle loss indicators of independent energy storage and convert them into daily average life cycle loss indicators.

[0137] S2, combining multiple typical operating days, establish operation and start-up coal consumption models and frequency regulation coal consumption models for conventional units (such as traditional thermal power units), and at the same time construct a participation model for independent energy storage under the spot mechanism of the electric energy market and the ancillary service market;

[0138] S3, construct energy storage location constraints based on the power system grid structure, construct capacity configuration constraints based on the upper limit of single-node energy storage configuration, establish node power balance constraints based on power balance requirements, establish frequency regulation capacity and frequency regulation mileage requirement constraints based on frequency regulation requirements, and establish linear power flow constraints through DC power flow model;

[0139] S4 combines energy storage location constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints. With the goal of minimizing the average daily lifespan loss of energy storage, coal consumption during the operation and startup of conventional units, and maximizing the participation of energy storage, a location and capacity optimization model is constructed.

[0140] S5 linearizes the site selection and capacity optimization model and solves it to obtain the optimal node selection and capacity configuration for energy storage, thus completing the energy storage site selection and capacity optimization.

[0141] In some preferred embodiments, S1 above, which establishes a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market, may further include the following operations:

[0142] S11, in the spot mechanism of the electricity market and ancillary services market, independent energy storage determines its charging and discharging plan through full-volume bidding; the full-volume bidding method includes: the generation side submits a quantity and bid, while the user side submits a quantity but does not bid; the conventional generating units and independent energy storage on the generation side submit time-of-day operating parameters and parameter coefficients according to the application requirements of the operating agency.

[0143] S12, based on the initial configuration indicators, maintenance indicators, and decommissioning recovery indicators of independent energy storage, construct the full life cycle loss indicators of independent energy storage and convert them to daily average life cycle loss indicators, including:

[0144] Let the index and set of the power system nodes be n and N, respectively, and the independent energy storage capacity and power configured for node n be respectively... and Then the initial energy storage configuration index C ESinv The model is as follows:

[0145]

[0146] In the formula, α is the configuration index parameter for energy storage unit power; β is the configuration index parameter for energy storage unit capacity.

[0147] S13, the maintenance index is used to reflect the annual depreciation value of independent energy storage. Let the operating life of the energy storage be Y. life Then maintain index C ESmai The model is as follows:

[0148]

[0149] In the formula, γ is the annual maintenance cost per unit capacity of energy storage;

[0150] S14, the decommissioning recovery index is used to reflect the ability of energy storage equipment to recover a portion of the equipment after it reaches the end of its lifespan to offset a certain portion of the initial energy storage configuration target. Therefore, the decommissioning recovery index C... ESres The model is as follows:

[0151] C ESres =σC ESinv (3)

[0152] In the formula, σ is the residual value rate of the energy storage equipment;

[0153] S15, based on the initial planning index C for independent energy storage ESinv Maintenance indicator C ESmai and decommissioning recovery index C ESres The life-cycle loss index for constructing independent energy storage is as follows:

[0154] C ESlife =C ESinv +C ESmai -C ESres (4);

[0155] S16, using the CRF coefficient, converts the total lifecycle loss index of independent energy storage to a single day, denoted as the daily average loss index C. ESimr Then the daily average loss index C ESimr The model is as follows:

[0156]

[0157]

[0158] In the formula, r is the discount rate.

[0159] In some preferred embodiments, S2 above, which combines multiple typical operating days to establish a coal consumption model for the operation and startup of conventional units and a frequency regulation loss model, and simultaneously constructs a participation model for independent energy storage under the spot mechanism of the electricity market and ancillary services market, may further include the following operations:

[0160] S21, let a be the index of a typical operating day in the target area, and g and G be the index and set of the conventional units, respectively;

[0161] S22 Constructing a coal consumption model for conventional units on a typical operating day a C Gope,a for:

[0162]

[0163]

[0164] In the formula, t and Γ are the index and set of the time period, respectively; Δt is the optimization time interval; m and M are the index and set of the number of quotation segments for conventional units, respectively; The declared output of conventional unit g during time period t; For conventional unit g, the output is declared during the m-th segment of time period t; For a conventional unit g, the coal consumption coefficient at output level m;

[0165] S23, Construct a start-up coal consumption model for conventional units under a typical operating day a. Gsta,a for:

[0166]

[0167] In the formula, u g,t A 0-1 variable, representing the operating status of the unit, u g,t =1 indicates that the unit is in operation, ug,t =0 indicates that the unit is in machine mode; This is the start-up coal consumption coefficient for conventional unit g;

[0168] S24, Construct a frequency regulation coal consumption model for conventional units under a typical operating day a. Gaux,a for:

[0169]

[0170] In the formula, These are the frequency regulation capacity participation coefficient, frequency regulation capacity, frequency regulation mileage participation coefficient, and frequency regulation mileage declared by conventional unit g in time period t.

[0171] S25, constructing participation models for the spot mechanisms of independent energy storage in the electricity market and ancillary services market under a typical operating day a, respectively:

[0172]

[0173]

[0174] In the formula, C ESene,a C represents the energy participation rate of independent energy storage in the spot market mechanism of the electricity market. ESaux,a Frequency regulation participation of independent energy storage in the spot mechanism of the ancillary services market; These represent the charging power and charging participation coefficient reported by node n for energy storage during time period t, respectively. These represent the discharge power and discharge participation coefficient reported by node n for energy storage during time period t, respectively. These represent the frequency regulation capacity participation coefficient, frequency regulation capacity, frequency regulation mileage participation coefficient, and frequency regulation mileage declared by node n for energy storage during time period t.

[0175] In some preferred embodiments, the above-mentioned S3, which involves constructing energy storage location constraints based on the power system grid structure, constructing capacity configuration constraints based on the upper limit of single-node energy storage configuration, establishing node power balance constraints based on power balance requirements, establishing frequency regulation capacity and frequency regulation mileage requirement constraints based on frequency regulation requirements, and establishing linear power flow constraints through a DC power flow model, may further include the following operations:

[0176] S31, let x n A 0-1 variable for whether to configure energy storage, where x n =1 indicates that energy storage is configured at node n, x n =0 indicates that no energy storage is configured at node n. Therefore, the location constraints and capacity configuration constraints for independent energy storage are expressed as follows:

[0177]

[0178]

[0179] ∑ n x n =N ES (15)

[0180] In the formula, E max The maximum configured capacity for energy storage in a single node; This is the maximum proportionality coefficient between energy storage power and capacity. The independent energy storage capacity configured for node n; Independent energy storage capacity configured for node n; N ES Quantity of energy storage to be configured;

[0181] S32, Let N(n) represent the set of nodes connected to node n, and G(n) represent the set of conventional units at node n. Let the load of node n during time period t be denoted as , then the power balance constraint of node n is expressed as:

[0182]

[0183] In the formula, The declared output of conventional unit g during time period t; The discharge participation coefficient declared for energy storage at node n during time period t; The charging participation coefficient declared by node n for energy storage during time period t; For the power flow of line nw;

[0184] S33, constrains the power flow of line nw using a DC power flow model:

[0185]

[0186] In the formula, θ n,t and θ w,t B represents the voltage phase angles at nodes n and w, respectively; nw Let n be the admittance parameter of the line nw; Let nw be the capacity of the line;

[0187] S34, the existing mechanisms of the ancillary services market are set to impose demand constraints on the frequency regulation capacity and frequency regulation history of the power system as follows:

[0188]

[0189]

[0190] In the formula, This refers to the frequency regulation capacity declared by the conventional generating unit g during time period t. The frequency regulation capacity declared for energy storage at node n during time period t; This refers to the frequency regulation mileage declared by the conventional generating unit g during time period t. The frequency regulation mileage declared for node n's energy storage during time period t; These represent the frequency regulation capacity requirement and frequency regulation mileage requirement of the power system during time period t, respectively.

[0191] In some preferred embodiments, the above-mentioned S4, combining energy storage location constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints, aims to minimize the daily average lifespan loss of energy storage, the coal consumption of conventional unit operation and startup, and maximize the participation of energy storage, and constructs a location and capacity optimization model. It may further include the following operations:

[0192] S41, the objective function of the site selection and capacity optimization model is determined to be minimizing the daily average lifetime loss of independent energy storage, the coal consumption during operation and startup of conventional units, and maximizing the participation of energy storage. Therefore, the objective function of the site selection and capacity optimization model is expressed as:

[0193]

[0194] C ES,a =C ESene,a +C ESaux,a (twenty one)

[0195] C G,a =C Gsta,a +C Gope,a +C Gaux,a (twenty two)

[0196] In the formula, C ESimr For the average daily lifespan loss of energy storage; ω a C represents the proportion of a typical operating day (a) in a year. G,a The starting and operating losses of conventional units under a typical operating day a; C ES,a C represents the participation rate of energy storage on a typical operating day a; ESene,a To determine the degree of participation of energy storage in the spot market mechanism of electricity on a typical operating day a; C ESaux,a For energy storage, the participation rate in the spot market mechanism of ancillary services under typical operating day a; C Gsta,a C represents the start-up coal consumption of a conventional unit on a typical operating day (a). Gope,a C represents the operating coal consumption of a conventional unit on a typical operating day (a). Gaux,a This is a frequency regulation coal consumption model for conventional units under a typical operating day a;

[0197] S42, combining electrical energy constraints, frequency regulation demand constraints, and power flow constraints, constructs the following system-level constraints:

[0198]

[0199]

[0200]

[0201]

[0202]

[0203] In the formula, The declared output of conventional unit g during time period t; The discharge participation coefficient declared for energy storage at node n during time period t; The charging participation coefficient declared by node n for energy storage during time period t; The load of node n during time period t; For the power flow of line nw; θ n,t and θ w,t B represents the voltage phase angles at nodes n and w, respectively; nw Let n be the admittance parameter of the line nw; Let nw be the capacity of the line; This refers to the frequency regulation capacity declared by the conventional generating unit g during time period t. The frequency regulation capacity declared for energy storage at node n during time period t; This refers to the frequency regulation mileage declared by the conventional generating unit g during time period t. The frequency regulation mileage declared for node n's energy storage during time period t; They are the frequency regulation capacity demand and frequency regulation mileage demand of the power system for time period t, respectively; k is the maximum proportion of the total frequency regulation capacity declared by energy storage to the frequency regulation demand of the power system; Equations (23) to (24) are the power balance constraints and power flow constraints of the power system, and Equations (25) to (27) are the demand constraints of the frequency regulation capacity and frequency regulation mileage of the power system.

[0204] S43, based on the physical characteristics of conventional generating units (such as traditional thermal power units), the operational constraints for the participants in the power system spot market mechanism are as follows:

[0205]

[0206]

[0207]

[0208]

[0209]

[0210] In the formulas, equations (28) to (32) represent the operating constraints of conventional generating units. These are the minimum and maximum output of conventional unit i, respectively; These are the limits on the ramp power and landslide power of conventional unit i, respectively; These are the minimum operating time and minimum downtime for conventional generating units, respectively.

[0211] S44, based on the physical characteristics of energy storage, the operational constraints for the participants in the power system spot market mechanism are as follows:

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218] In the formula, equations (33) to (38) represent the operational constraints of energy storage. These are 0-1 variables describing the charge and discharge states of energy storage. This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a non-charging state. This indicates that the energy storage is in a discharging state. This indicates that the energy storage is in a non-discharge state; E n,t E represents the remaining energy stored during time period t. n,t-1 The remaining energy stored during period t-1; η ch For energy storage charging efficiency; η dis For energy storage discharge efficiency; These represent the remaining energy storage capacity at the beginning and end of the time periods, respectively. The independent energy storage capacity configured for node n; The initial energy storage capacity is the percentage of the total energy storage capacity.

[0219] Thus, a site selection and capacity optimization model for independent energy storage under the power system spot mechanism has been constructed.

[0220] In some preferred embodiments, the above-mentioned S5, which linearizes the site selection and sizing optimization model and solves it, may further include the following operations:

[0221] S51, based on the start-up coal consumption of conventional units, the location and capacity optimization model is linearized as follows:

[0222]

[0223]

[0224] In the formula, C Gsta This refers to the start-up coal consumption of a conventional unit; u g,t Indicates the operating status of the generator unit; y is the start-up coal consumption coefficient for conventional units g; g,t Auxiliary 0-1 variables are used to replace u. g,t u g,t-1 ;

[0225] S52, the improved Benders decomposition algorithm is used to solve the linearized location and sizing optimization model:

[0226] min f T p+h T b (41)

[0227] K1p≥h1 (42)

[0228] J1b≥h2 (43)

[0229] K2p+J2b≥h3 (44)

[0230] p∈Ω p ,b∈{0,1} (45)

[0231] Where p is a continuous variable related to the energy storage capacity and power and the output of conventional generating units, including: the independent energy storage power configured at node n. Independent energy storage capacity configured for node n The charging participation coefficient of node n energy storage declared during time period t The discharge participation coefficient of node n energy storage declared during time period t The declared output of conventional unit g during time period t The frequency regulation capacity declared by node n for time period t The frequency regulation mileage declared by node n for energy storage during time period t Frequency regulation capacity declared by conventional unit g during time period t Frequency regulation mileage declared by conventional unit g during time period t And the remaining electricity E during energy storage period t n,t b represents variables related to site selection and operational status, including: a 0-1 decision variable x for deciding whether to configure energy storage at node n. n , Regular unit operating status during time period t u g,t Auxiliary variable y g,t Energy storage charging and discharging state variables z n,t f and h are the coefficient matrices in the objective function; J1, J2, K1, and K2 are the coefficient matrices in the constraints; Ωp The domain of a continuous variable;

[0232] S53, Benders decomposition requires decomposing the location and sizing model into a main problem with 0-1 variables and subproblems with continuous variables. According to equations (41) to (45), the main problem is:

[0233]

[0234] In the formula, q is an auxiliary variable; h2 is the right-hand side value of the integer variable related constraints; cutting plane represents the cutting plane, including the optimal cutting plane and the combined integer cutting plane;

[0235] S54, Find the optimal solution to the main problem. The following subproblem containing continuous variables and its dual problem are generated:

[0236]

[0237]

[0238] In the formulas, equations (47) and (48) represent the subproblem and dual problem, respectively; p is a continuous variable; h1 is the right-hand side value of the constraints related to the continuous variable; h3 is the right-hand side value of the constraints containing both the continuous variable and the 0-1 variable; v1 is the variable of the dual problem; v2 is the variable of the dual problem; and is the variable of the dual problem.

[0239] S55, initialize the upper bound U B Lower bound L B And the allowable error ε, to determine whether the iteration converges, when U B -L B For ≥ε, perform the following iterations:

[0240] S551, Solve the main problem to obtain the optimal solution to the main problem. Simultaneously update the lower bound L B ;

[0241] S552, the optimal solution Substitute the subproblem into the equation and solve its dual problem. If the subproblem has a solution, generate the optimal cutting plane and update the upper bound U. B If the subproblem has no solution, then the generated combinatorial integer cut plane is:

[0242]

[0243] In the formula, Φ0 and Φ1 are the sets of variable indices for y=0 and y=1, respectively, and |Φ1| represents the number of elements in the Φ1 set;

[0244] S553, add the generated cutting plane to the constraint "cutting plane" of the main problem;

[0245] S554, if U B -L B If the value is less than ε, then stop the iteration and output the addressing and sizing results, including: independent energy storage power. Independent energy storage capacity And whether to configure energy storage at node n, a 0-1 variable x n Otherwise, return to the steps for solving the main problem.

[0246] S56, according to x above n , and To obtain the optimal node selection and capacity configuration for energy storage.

[0247] The technical solution provided by the above embodiments of the present invention will be further described in detail below with reference to a preferred embodiment.

[0248] The preferred embodiment provides a method for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism, such as... Figure 1 As shown, it includes the following steps:

[0249] Step S1: Establish a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market. Based on the initial configuration indicators, maintenance indicators and decommissioning recovery indicators of independent energy storage, construct the full life cycle loss indicators of independent energy storage and convert them into daily average life cycle loss indicators.

[0250] Step S2: Considering multiple typical operating days, establish a coal consumption model for the operation and startup of conventional thermal power units and a coal consumption model for frequency regulation. At the same time, construct a participation model for independent energy storage under the spot mechanism of the electricity market and the ancillary services market.

[0251] Step S3: Construct energy storage location constraints based on the power system grid structure, construct capacity configuration constraints based on the upper limit of single-node energy storage configuration, establish node power balance constraints based on power balance requirements, establish frequency regulation capacity and frequency regulation mileage requirement constraints based on frequency regulation requirements, and establish linearized power flow constraints through DC power flow model;

[0252] Step S4: Combining node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints, a site selection and capacity optimization model is constructed with the goal of minimizing the average daily lifespan loss of energy storage, coal consumption during operation and startup of conventional units, and maximizing the participation of energy storage.

[0253] Step S5: Linearize the site selection and capacity optimization model and solve it to obtain the optimal node selection and capacity configuration for energy storage, thus completing the energy storage site selection and capacity optimization.

[0254] In a preferred embodiment, step S1 specifically includes the following steps:

[0255] Step S11: In the spot mechanism of the electricity market and ancillary services market, independent energy storage can determine its charging and discharging plan through competitive bidding. This invention adopts a full-capacity bidding format, where the generation side (independent energy storage and conventional thermal power units) submits bids, while the user side submits bids but does not. Conventional units submit bids in time slots according to their capacity, minimum technical output, and other operating parameters, as well as power generation losses, based on the operating agency's requirements. Independent energy storage needs to submit operating parameters such as capacity and power, as well as a participation coefficient.

[0256] Step S12: Set the index and set of the system nodes to n and N, respectively. The independent energy storage capacity and power configured for node n are respectively... and The initial investment index for energy storage can then be modeled as follows:

[0257]

[0258] In the formula, α is the investment index parameter per unit power of energy storage; β is the investment index parameter per unit capacity of energy storage.

[0259] Step S13, the maintenance index reflects the annual depreciation value of independent energy storage, which is related to its configured capacity. If the energy storage operation life is Y... life Then the maintenance index can be modeled as:

[0260]

[0261] In the formula, γ is the annual maintenance cost per unit capacity of energy storage (yuan / MWh / year).

[0262] Step S14, the decommissioning recovery index refers to the ability to recover a portion of the energy storage equipment after it reaches the end of its lifespan, offsetting a certain portion of the initial investment. It can be expressed as:

[0263] C ESres =σC ESinv (3)

[0264] In the formula, σ represents the residual value rate of the energy storage equipment.

[0265] Step S15, based on the initial planning index C for independent energy storage ESinv Maintenance indicator C ESmai and decommissioning recovery index C ESres The life-cycle loss index for constructing independent energy storage is as follows:

[0266] C ESlife =C ESinv +C ESmai -C ESres (4)

[0267] Step S16: The life-cycle planning indicators for energy storage can be converted to daily values ​​using the CRF index, and recorded as the daily average planning indicator C. ESimr Daily average loss index C ESimr It can be modeled as:

[0268]

[0269]

[0270] In the formula, r is the discount rate. Thus, the life-cycle planning indicators for independent energy storage are converted to a daily rate.

[0271] In a preferred embodiment, step S2 specifically includes the following steps:

[0272] Step S21: Set the index of a typical operating day in the target region as 'a'. Thermal power units remain the main power generation resource of the power system. Assume g and G are the index and set of conventional units, respectively. In its spot market mechanism, conventional thermal power units are usually allowed to report monotonically increasing segmented loss curves. Therefore, the operating coal consumption model of conventional thermal power units on typical day a can be expressed as:

[0273]

[0274]

[0275] In the formula, t and Γ are the index and set of the time period, respectively; Δt is the optimization time interval; m and M are the index and set of the number of unit bidding segments, respectively; The declared output of conventional unit g during time period t; The unit g is to declare its output during the m-th segment of time period t. is the loss coefficient of unit g in the output section m.

[0276] Step S22, the start-up coal consumption model for a typical daily conventional thermal power unit can be expressed as:

[0277]

[0278] In the formula, u g,t A 0-1 variable, representing the operating status of the unit, u g,t =1 indicates that the unit is in operation, u g,t =0 indicates that the unit is in machine mode; Let g be the starting coal consumption coefficient of unit g.

[0279] Step S23, construct the frequency regulation coal consumption model of a conventional unit under a typical operating day a as follows:

[0280]

[0281] In the formula, These are, respectively, the frequency regulation capacity participation coefficient, frequency regulation capacity, frequency regulation mileage participation coefficient, and frequency regulation mileage declared by conventional generating unit g in time period t.

[0282] Step S24, in a typical day a, the participation of independent energy storage in the spot market mechanism and ancillary service mechanism can be modeled as follows:

[0283]

[0284]

[0285] In the formula, C ESene,a C represents the energy participation rate of independent energy storage in the spot market mechanism of the electricity market. ESaux,a Frequency regulation participation of independent energy storage in the spot mechanism of the ancillary services market; These represent the charging power and charging participation coefficient reported by node n for energy storage during time period t, respectively. These represent the discharge power and discharge participation coefficient reported by node n for energy storage during time period t, respectively. These represent the frequency regulation capacity participation coefficient, frequency regulation capacity, frequency regulation mileage participation coefficient, and frequency regulation mileage declared by node n for energy storage during time period t.

[0286] In a preferred embodiment, step S3 specifically includes the following steps:

[0287] Step S31, considering the location of independent energy storage, assume x n x is a 0-1 variable indicating whether energy storage is configured. n =1 indicates that energy storage is configured at node n, x n =0; then the site selection and capacity configuration constraints for independent energy storage can be expressed as:

[0288]

[0289]

[0290] ∑ n x n =N ES (15)

[0291] Among them, E max This represents the maximum configured capacity of a single node's energy storage. N is the maximum proportionality coefficient of energy storage power to capacity; ES The number of energy storage units to be configured.

[0292] Step S32: Let N(n) represent the set of nodes connected to node n, and G(n) represent the set of conventional units at node n. Let the load at node n during time period t be the load. Then the power balance constraint at node n can be expressed as:

[0293]

[0294] In the formula, This refers to the current flow of line nw.

[0295] For high-voltage transmission networks, the power flow equations can be reasonably simplified, and the power flow of the lines can be constrained by a DC power flow model:

[0296]

[0297] In the formula, θ n,t and θ w,t B represents the voltage phase angles at nodes n and w, respectively; nw Let n be the admittance parameter of the line nw; Let nw be the capacity of the line.

[0298] Step S33, the frequency modulation auxiliary service mechanism requires both the system's frequency modulation capacity and frequency modulation history, and is configured as follows:

[0299]

[0300]

[0301] In the formula, These represent the frequency regulation capacity requirement and frequency regulation mileage requirement of the system during time period t, respectively.

[0302] In a preferred embodiment, step S4 specifically includes the following steps:

[0303] Step S41, the objective function of the site selection and capacity optimization model is to minimize the daily average planning index of independent energy storage, the operating and start-up coal consumption of conventional thermal power units, and maximize the utilization rate of energy storage. The objective function of the site selection and capacity optimization model can be expressed as follows:

[0304]

[0305] C ES,a =C ESene,a +C ESaux,a (twenty one)

[0306] C G,a =C Gsta,a +C Gope,a +C Gaux,a (twenty two)

[0307] In the formula, C ESimr For the average daily lifespan loss of energy storage; ω a C represents the proportion of a typical day (a) in a year; G,aThe starting and operating losses of conventional units under a typical operating day a; C ES,a These represent the participation rate of energy storage on a typical operating day a. (C) ESene The degree of participation of energy storage in the electrical energy mechanism; C ESaux For the participation of energy storage in the ancillary service mechanism; C Gsta Coal consumption for starting up a conventional unit; C Gope This refers to the operating losses of conventional generating units.

[0308] Step S42, combining electrical energy constraints, frequency regulation demand constraints, and power flow constraints, first construct system-level constraints:

[0309]

[0310]

[0311]

[0312]

[0313]

[0314] In the formula, k is the maximum proportion of the total frequency regulation capacity declared by energy storage to the system frequency regulation demand. Equations (23) to (24) are the system power balance and power flow constraints, and equations (25) to (27) are the constraints of system frequency regulation capacity and frequency regulation mileage demand.

[0315] Step S43: Combining the operating characteristics of conventional thermal power units and independent energy storage, construct the operating constraints for the participants in the power system spot market mechanism:

[0316]

[0317]

[0318]

[0319]

[0320]

[0321]

[0322]

[0323]

[0324]

[0325]

[0326]

[0327] Equations (28) to (32) represent the operating constraints of conventional generating units. These are the minimum and maximum output of unit i, respectively; These are the ramp and landslide power limits for unit i, respectively. These represent the minimum operating time and minimum downtime of the unit, respectively; equations (33) to (38) represent the operating constraints of energy storage. These are 0-1 variables describing the charge and discharge states of energy storage. This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a discharging state; These represent the remaining energy storage capacity at the beginning and end of the energy storage period, respectively. This refers to the initial energy storage capacity as a percentage of the total capacity.

[0328] At this point, the site selection and capacity optimization model for independent energy storage under the electricity spot market mechanism has been completed, which is a mixed integer nonlinear programming model.

[0329] In a preferred embodiment, such as Figure 2 As shown, step S5 specifically includes the following steps:

[0330] Step S51: Since the mixed-integer nonlinear programming model is difficult to solve and the optimal value is hard to obtain, it needs to be linearized. First, based on the start-up coal consumption of conventional units, the location and capacity optimization model is linearized as follows:

[0331]

[0332]

[0333] In the formula, y g,t Auxiliary 0-1 variables are used to replace u. g,t u g,t-1 .

[0334] Step S52: The linearized model is a large-scale mixed-integer linear programming model, containing many 0-1 variables, which is difficult to solve directly. This invention uses an improved Benders decomposition algorithm to solve it. The abstract form of the model is as follows:

[0335] min f T p+h T b (41)

[0336] K1p≥h1 (42)

[0337] J1b≥h2 (43)

[0338] K2p+J2b≥h3 (44)

[0339] p∈Ω p ,b∈{0,1} (45)

[0340] Where p is a continuous variable related to energy storage capacity and power output of conventional generating units, including E n,t b represents variables related to investment and operational status, including x. n u g,t y g,t z n,t f and h are the coefficient matrices in the objective function; J1, J2, K1, and K2 are the coefficient matrices in the constraints. Ω p The domain of a continuous variable.

[0341] Step S53, according to equations (41) to (45), the main problem of the original problem can be obtained:

[0342]

[0343] In the formula, the cutting plane represents the cutting plane, which includes the optimal cutting plane and the combined integer cutting plane.

[0344] Step S54: Find the optimal solution to the main problem. The following subproblems and their dual problems can be generated:

[0345]

[0346]

[0347] Equations (47) and (48) are the subproblem and its dual problem, respectively.

[0348] Step S55, initialize the upper bound U B Lower bound L B With the allowable error ε, when U B -L B For ≥ε, perform the following iterations:

[0349] Step a: Solve the main problem to obtain the optimal solution. Simultaneously update the lower bound L B ;

[0350] Step b, will Substitute the subproblem into the equation and solve its dual problem. If the subproblem has a solution, generate the optimal cut civilian population and update the upper bound U. B If the subproblem has no solution, then a combined cutting plane is generated.

[0351] Step c: Add the generated cutting plane to the constraints of the main problem;

[0352] Step d, if U B -L B If the result is less than ε, stop the iteration and output the addressing and sizing result; otherwise, return to step a.

[0353] One embodiment of the present invention provides an independent energy storage location and capacity optimization system under the electricity spot market mechanism, such as... Figure 3 As shown, the system may include the following modules:

[0354] The indicator construction module is used to establish a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market. Based on the initial configuration indicators, maintenance indicators and decommissioning recovery indicators of independent energy storage, it constructs the full life cycle loss indicators of independent energy storage and converts them into daily average life cycle loss indicators.

[0355] The model building module is used to combine multiple typical operating days to establish a coal consumption model for the operation and startup of conventional units, as well as a coal consumption model for frequency regulation. At the same time, it constructs a participation model for independent energy storage under the spot mechanism of the electricity market and ancillary services market.

[0356] The constraint construction module is used to construct energy storage location constraints based on the power system grid structure, capacity configuration constraints based on the single node energy storage configuration upper limit, node power balance constraints based on power balance requirements, frequency regulation capacity and frequency regulation mileage requirement constraints based on frequency regulation requirements, and linear power flow constraints through the DC power flow model.

[0357] The site selection and capacity optimization module combines energy storage site selection constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints to construct a site selection and capacity optimization model with the objectives of minimizing the daily average lifespan loss of energy storage, coal consumption during operation and startup of conventional units, and maximizing energy storage participation. The module then linearizes the site selection and capacity optimization model and solves it to obtain the optimal node selection and capacity configuration for energy storage, thus completing the energy storage site selection and capacity optimization.

[0358] It should be noted that the steps in the method provided by the present invention can be implemented using corresponding modules, devices, units, etc. in the system. Those skilled in the art can refer to the technical solution of the method to realize the composition of the system. That is, the embodiments in the method can be understood as preferred examples for building the system, and will not be elaborated here.

[0359] An embodiment of the present invention provides a computer terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can be used to perform any of the methods in the above embodiments of the present invention, or to run any of the systems in the above embodiments of the present invention.

[0360] Optionally, the memory is used to store programs; the memory may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; the memory may also include non-volatile memory, such as flash memory. The memory is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc., and the aforementioned computer programs, computer instructions, etc., can be partitioned and stored in one or more memories. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by the processor.

[0361] The aforementioned computer programs, computer instructions, etc., can be stored in partitions within one or more memory locations. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by a processor.

[0362] A processor is used to execute computer programs stored in memory to implement the various steps of the methods or various modules of the systems involved in the above embodiments. For details, please refer to the relevant descriptions in the preceding method and system embodiments.

[0363] The processor and memory can be separate structures or integrated structures. When the processor and memory are separate structures, they can be coupled together via a bus.

[0364] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to perform the method of any of the above embodiments of the present invention, or to run the system of any of the above embodiments of the present invention.

[0365] Those skilled in the art will understand that, in addition to implementing the system and its various devices provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices of this invention function as logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices provided by this invention can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0366] Any matters not covered in the above embodiments of the present invention are well-known in the art.

[0367] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for optimizing the location and capacity of independent energy storage under an electricity spot market mechanism, characterized in that, include: Establish a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market. Based on the initial configuration indicators, maintenance indicators and decommissioning recovery indicators of independent energy storage, construct the full life cycle loss indicators of independent energy storage and convert them into daily average life cycle loss indicators. By combining multiple typical operating days, we establish coal consumption models for the operation and startup of conventional units as well as coal consumption models for frequency regulation. At the same time, we construct a participation model for independent energy storage under the spot mechanism of the electricity market and the ancillary services market. Energy storage location constraints are constructed based on the power system grid structure, capacity configuration constraints are constructed based on the upper limit of single-node energy storage configuration, node power balance constraints are established based on power balance requirements, frequency regulation capacity and frequency regulation mileage requirements are established based on frequency regulation requirements, and linear power flow constraints are established through DC power flow model. Combining energy storage location constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints, a location and capacity optimization model is constructed with the goal of minimizing the average daily lifespan loss of energy storage, coal consumption during operation and startup of conventional units, and maximizing the participation of energy storage. The location and capacity optimization model is linearized and solved to obtain the optimal node selection and capacity configuration for energy storage, thus completing the energy storage location and capacity optimization. The establishment of a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market includes: In the spot market mechanism of the electricity market and ancillary services market, independent energy storage determines its charging and discharging plan through a full-volume bidding method; wherein, the full-volume bidding method includes: the generation side submits a quantity and bid, while the user side submits a quantity but does not bid; wherein, conventional generating units and independent energy storage on the generation side submit time-segmented operating parameters and parameter degree coefficients according to the application requirements of the operating agency; The process involves constructing a full lifecycle loss index for independent energy storage based on initial configuration, maintenance, and decommissioning recovery indicators, and then converting this index to a daily average lifecycle loss index. This includes: Define the index and set of the power system nodes as follows: and ,node The configured independent energy storage capacity and power are respectively and Then the initial configuration index of energy storage The model is as follows: （1） In the formula, Configuration parameters per unit power of energy storage; Configuration parameters for energy storage unit capacity; Maintenance metrics are used to reflect the annual depreciation of independent energy storage, assuming the energy storage's operating life is... Then maintain indicators The model is as follows: （2） In the formula, Annual maintenance cost per unit capacity of energy storage; The decommissioning recovery index is used to reflect the ability of energy storage equipment to recover a portion of its components after reaching the end of its lifespan, offsetting a certain portion of the initial energy storage configuration requirements. The model is as follows: （3） In the formula, The residual value rate of energy storage equipment; According to the aforementioned independent energy storage initial planning indicators The aforementioned maintenance indicators and the aforementioned decommissioning and recycling indicators The life-cycle loss index for constructing independent energy storage is as follows: （4）； The life-cycle loss index of the independent energy storage is converted to a daily average using the CRF coefficient, and recorded as the daily average loss index. The daily average loss index The model is as follows: （5） （6） In the formula, This is the discount rate.

2. The method for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism according to claim 1, characterized in that, The above-mentioned model combines multiple typical operating days to establish a coal consumption model for the operation and startup of conventional generating units, as well as a frequency regulation loss model. Simultaneously, it constructs a participation model for independent energy storage under the spot market mechanism of the electricity market and ancillary services market, including: Let the index of the typical operating day of the target region be... The indexes and sets of conventional units are respectively and ; Construct a typical operating day Coal consumption model for conventional unit operation for: （7） （8） In the formula, , These are the index and set for the time period, respectively; To optimize the time interval; and These are the indexes and sets of the price ranges for regular generating units, respectively; For conventional units During the period The application and contribution; For conventional units During the period The Duan reported his efforts; For conventional units In the power output phase The coal consumption coefficient; Construct a typical operating day Start-up coal consumption model for conventional units for: （9） In the formula, These are 0-1 variables, representing the operating status of the unit. This indicates that the unit is in operation. This indicates that the unit is in a shutdown state; For conventional units The starting coal consumption coefficient; Construct a typical operating day Frequency regulation loss model of conventional units for: （10） In the formula, , , , They are conventional units During the period The declared frequency modulation capacity participation coefficient, frequency modulation capacity, frequency modulation mileage participation coefficient, and frequency modulation mileage; Construct a typical operating day The participation models for independent energy storage in the spot market and ancillary services market are as follows: （11） （12） In the formula, The energy participation rate of independent energy storage in the spot market mechanism of the electricity market. Frequency regulation participation of independent energy storage in the spot mechanism of the ancillary services market; , They are nodes Energy storage during the period The declared charging power and charging participation coefficient; , They are nodes Energy storage during the period The declared discharge power and discharge participation coefficient; , , , They are nodes Energy storage during the period The declared frequency modulation capacity participation coefficient, frequency modulation capacity, frequency modulation mileage participation coefficient, and frequency modulation mileage.

3. The method for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism according to claim 1, characterized in that, The process involves constructing energy storage location constraints based on the power system grid structure, capacity configuration constraints based on the upper limit of single-node energy storage configuration, node power balance constraints based on power balance requirements, frequency regulation capacity and mileage requirement constraints based on frequency regulation requirements, and linear power flow constraints through a DC power flow model, including: set up A 0-1 variable for whether to configure energy storage, where, Indicates at node Configure energy storage, Indicates not in node When configuring energy storage, the location constraints and capacity configuration constraints for independent energy storage are expressed as follows: （13） （14） （15） In the formula, The maximum configured capacity for energy storage in a single node; This is the maximum proportionality coefficient between energy storage power and capacity. For nodes The configured independent energy storage capacity; For nodes The configured independent energy storage capacity; Configure the quantity of energy storage; set up Represents nodes The set of connected nodes Represents a node The collection of conventional units at the location, For nodes Time period The load, then the node The power balance constraint is expressed as: （16） In the formula, For conventional units During the period The application and contribution; For nodes Energy storage during the period The declared discharge participation coefficient; For nodes Energy storage during the period The declared charging participation coefficient; For the line The trend; The line is analyzed using a DC power flow model. To constrain the trend: （17） In the formula, and Representing nodes respectively and The voltage phase angle; For the line Admittance parameters; For the line The capacity; The existing mechanisms of the ancillary services market are set to impose demand constraints on the frequency regulation capacity and mileage of the power system as follows: （18） （19） In the formula, For conventional units During the period The declared frequency modulation capacity; For nodes Energy storage during the period The declared frequency modulation capacity; For conventional units During the period The declared FM mileage; For nodes Energy storage during the period The declared FM mileage; , Time periods Frequency regulation capacity requirements and frequency regulation mileage requirements of power systems.

4. The method for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism according to claim 1, characterized in that, The aforementioned optimization model for energy storage location and capacity allocation, combining constraints on energy storage location, capacity configuration, node power balance, frequency regulation capacity demand, frequency regulation mileage demand, and power flow, aims to minimize the average daily lifespan loss of energy storage, the coal consumption during operation and startup of conventional units, and maximize the participation of energy storage. The model includes: The objective function of the site selection and capacity optimization model is determined to be minimizing the daily average lifetime loss of independent energy storage, the coal consumption during operation and startup of conventional units, and maximizing the participation of energy storage. Therefore, the objective function of the site selection and capacity optimization model is expressed as: （20） （21） （22） In the formula, This refers to the average daily lifespan loss of energy storage. Typical operating day The proportion it accounts for in a year; For conventional units on a typical operating day Startup and operating losses; For energy storage on a typical operating day Participation rate; For energy storage on a typical operating day The degree of participation in the spot market mechanism of electricity; For energy storage on a typical operating day The degree of participation in the ancillary services market spot mechanism; For conventional units on a typical operating day The starting coal consumption is as follows; For conventional units on a typical operating day Coal consumption during operation; For conventional units on a typical operating day Frequency-modulated coal consumption model; Combining electrical energy constraints, frequency regulation demand constraints, and power flow constraints, the system-level constraints are constructed as follows: （23） （24） （25） （26） （27） In the formula, For conventional units During the period The application and contribution; For nodes Energy storage during the period The declared discharge participation coefficient; For nodes Energy storage during the period The declared charging participation coefficient; For nodes Time period The load; For the line The trend; and Representing nodes respectively and The voltage phase angle; For the line Admittance parameters; For the line The capacity; For conventional units During the period The declared frequency modulation capacity; For nodes Energy storage during the period The declared frequency modulation capacity; For conventional units During the period The declared FM mileage; For nodes Energy storage during the period The declared FM mileage; , Time periods Frequency regulation capacity requirements and frequency regulation mileage requirements of power systems; The total frequency regulation capacity declared for energy storage accounts for the largest proportion of the frequency regulation demand of the power system; Equations (23) to (24) are the power balance constraints and power flow constraints of the power system, and Equations (25) to (27) are the demand constraints of the frequency regulation capacity and frequency regulation mileage of the power system. Based on the physical characteristics of conventional generating units, the operational constraints for the participants in the power system spot market mechanism are as follows: （28） （29） （30） （31） （32） In the formulas, equations (28) to (32) represent the operating constraints of conventional generating units. , They are conventional units Minimum and maximum output; , They are conventional units Limitations on climbing power and landslide power; , These are the minimum operating time and minimum downtime for conventional generating units, respectively. Based on the physical characteristics of energy storage, the operational constraints for the participants in the power system spot market mechanism are as follows: （33） （34） （35） （36） （37） （38） In the formula, equations (33) to (38) represent the operational constraints of energy storage. , These are 0-1 variables describing the charge and discharge states of energy storage. This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a non-charging state. This indicates that the energy storage is in a discharging state. This indicates that the energy storage is in a non-discharge state; The remaining energy stored during time period t; The remaining energy stored during period t-1; To improve energy storage charging efficiency; For energy storage discharge efficiency; , These represent the remaining energy storage capacity at the beginning and end of the time periods, respectively. For nodes The configured independent energy storage capacity; The initial energy storage capacity is the proportion of the total energy storage capacity. Thus, a site selection and capacity optimization model for independent energy storage under the power system spot mechanism has been constructed.

5. The method for optimizing the location and capacity of independent energy storage under the electricity spot market mechanism according to claim 1, characterized in that, The linearization and solution of the site selection and capacity optimization model includes: Based on the start-up coal consumption of conventional units, the location and capacity optimization model is linearized as follows: （39） （40） In the formula, This refers to the start-up coal consumption of a conventional unit. Indicates the operating status of the generator unit; For conventional units The starting coal consumption coefficient; Auxiliary 0-1 variables are used to replace ; An improved Benders decomposition algorithm is used to solve the linearized location and capacity optimization model: （41） （42） （43） （44） （45） in, Continuous variables related to energy storage capacity and power output of conventional generating units include: nodes Configured independent energy storage capacity ,node Independent energy storage capacity configured ,node Energy storage during the period The declared charging participation coefficient ,node Energy storage during the period The declared discharge participation coefficient Conventional units During the period Application contribution ,node Energy storage during the period FM capacity declared ,node Energy storage during the period declared FM mileage Conventional units During the period FM capacity declared Conventional units During the period declared FM mileage and energy storage period Remaining battery power ; Variables related to location and operational status, including: whether it is on the node 0-1 decision variables for configuring energy storage Regular unit time period Running status Auxiliary variables Energy storage charging and discharging state variables ; , These are the coefficient matrices in the objective function; , , , These are the coefficient matrices in the constraint conditions; The domain of a continuous variable; Benders decomposition requires decomposing the site selection and sizing model into a main problem with 0-1 variables and subproblems with continuous variables. According to equations (41) to (45), the main problem is: （46） In the formula, As an auxiliary variable; The right-hand side value of the constraints related to integer variables; the cutting plane represents the cutting plane, including the optimal cutting plane and the combined integer cutting plane; Find the optimal solution to the main problem. The following subproblem containing continuous variables and its dual problem are generated: （47） （48） In the formula, equations (47) and (48) represent the subproblem and dual problem, respectively; For continuous variables; This represents the right-hand side value of the constraint related to continuous variables; The right-hand side value of the constraint that includes both continuous variables and 0-1 variables; For variables in the dual problem; For the variables of the dual problem Initialize upper bound Lower Boundary and allowable error ,when Perform the following iterations: Solve the main problem to obtain its optimal solution. At the same time, update the lower bound. ; The optimal solution Substitute the subproblem into the equation and solve its dual problem. If the subproblem has a solution, generate the optimal cutting plane and update the upper bound. If the subproblem has no solution, then the generated combinatorial integer cut plane is: （49） In the formula, , respectively and The set of variable indices express Number of elements in the set; Add the generated cutting plane to the constraint "cutting plane" of the main problem; like If the iteration stops, the location and capacity determination results are output, including: independent energy storage power. Independent energy storage capacity and whether it is in the node Configure 0-1 variables for energy storage Otherwise, return to the steps for solving the main problem. According to the above , , This allows for the selection of the optimal nodes and capacity configuration for energy storage.

6. A site selection and capacity optimization system for independent energy storage under an electricity spot market mechanism, wherein the system is applied to the site selection and capacity optimization method for independent energy storage under an electricity spot market mechanism as described in any one of claims 1-5, characterized in that, include: The indicator construction module is used to establish a spot mechanism for independent energy storage to participate in the electricity market and ancillary services market. Based on the initial configuration indicators, maintenance indicators and decommissioning recovery indicators of independent energy storage, it constructs the full life cycle loss indicators of independent energy storage and converts them into daily average life cycle loss indicators. The model building module is used to combine multiple typical operating days to establish a coal consumption model for the operation and startup of conventional units, as well as a coal consumption model for frequency regulation. At the same time, it constructs a participation model for independent energy storage under the spot mechanism of the electricity market and ancillary services market. The constraint construction module is used to construct energy storage location constraints based on the power system grid structure, capacity configuration constraints based on the single node energy storage configuration upper limit, node power balance constraints based on power balance requirements, frequency regulation capacity and frequency regulation mileage requirement constraints based on frequency regulation requirements, and linear power flow constraints through the DC power flow model. The site selection and capacity optimization module combines energy storage site selection constraints, capacity configuration constraints, node power balance constraints, frequency regulation capacity demand constraints, frequency regulation mileage demand constraints, and power flow constraints to construct a site selection and capacity optimization model with the objectives of minimizing the average daily lifespan loss of energy storage, coal consumption during operation and startup of conventional units, and maximizing energy storage participation. The module then linearizes the site selection and capacity optimization model and solves it to obtain the optimal node selection and capacity configuration for energy storage, thus completing the energy storage site selection and capacity optimization.

7. A computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it can be used to perform the method of any one of claims 1-5, or to run the system of claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program can be used to perform the method of any one of claims 1-5, or to run the system of claim 6.

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