A method for coordinated and optimized allocation of energy storage power stations in the medium and long term

Abstract

This invention discloses a method for coordinated and optimized configuration of energy storage power stations in the medium and long term, belonging to the field of energy storage power station planning technology. The method includes: comprehensively considering the economic benefits during the construction of the planned energy storage power station, as well as the grid status, environmental factors, rated power, and capacity configuration of the completed energy storage power station, to construct a two-layer multi-objective optimization model and corresponding constraints; under the constraints, using a preset multi-objective optimization model solving algorithm, solving the multi-objective optimization model to obtain a Pareto optimal solution set for the energy storage power station configuration scheme; screening the schemes in the Pareto optimal solution set to obtain the optimal scheme, and configuring the planned energy storage power station with the obtained optimal scheme to achieve the optimal planning and configuration of the energy storage power station capacity. Using the technical solution of this invention can provide rational suggestions for the investment and construction of energy storage power stations.

Description

Technical Field

[0001] This invention relates to the field of energy storage power station planning technology, and in particular to a method for coordinated and optimized allocation of energy storage power station planning in the medium and long term. Background Technology

[0002] Because the abundance of non-renewable energy sources, such as oil and natural gas, on Earth is fixed, the government is vigorously promoting renewable energy sources, such as wind and solar power, to reduce dependence on them and meet the electricity needs of industry and daily life. However, renewable energy is easily affected by seasons and climate, exhibiting significant randomness and volatility. If the electricity generated by renewable energy is directly connected to the grid, this randomness and volatility will also affect grid stability. To avoid this impact on grid stability, energy storage power stations are currently being built to store the electricity generated by renewable energy before supplying stable power to the grid. In addition to providing electricity through renewable energy generation, energy storage stations can also supply power from the grid during off-peak hours, storing electricity during low demand periods and supplying it during peak hours, thus ensuring a stable power supply.

[0003] Because energy storage power stations are expensive, the configuration of energy storage equipment must comprehensively consider factors such as investment costs, operational efficiency, and application scenarios, and select a suitable construction plan based on specific uses. Currently, however, there is a lack of effective planning schemes for energy storage power station construction. Summary of the Invention

[0004] This invention provides a method for the coordinated and optimized allocation of medium- and long-term planning for energy storage power stations, in order to solve the technical problems of the current lack of effective construction planning for energy storage power stations, the inability to achieve rational utilization of energy storage power station resources, and the inability to ensure the effective and stable operation of energy storage power stations.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] Taking into account the economic benefits of the construction process of the planned energy storage power station, as well as the grid status, environmental factors, rated power and capacity configuration of the completed energy storage power station, a two-layer multi-objective optimization model and corresponding constraints are constructed.

[0007] Under the constraints, a preset multi-objective optimization model solving algorithm is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set of the energy storage power station configuration scheme;

[0008] The schemes in the Pareto optimal solution set are screened to obtain the optimal scheme, and the obtained optimal scheme is used to configure the planned energy storage power station to achieve the optimal planning and configuration of the energy storage power station capacity.

[0009] Furthermore, the multi-objective optimization model includes an upper-level objective optimization model and a lower-level objective optimization model; wherein, the upper-level objective optimization model includes an energy storage power station investment cost model, an energy storage operation benefit model, and an environmental protection effect model;

[0010] The lower-level target optimization model includes a net present value model, a return on investment model, a payback period model, a delayed equipment investment model, and an operation and maintenance cost model.

[0011] Furthermore, the expression for the investment cost model of the energy storage power station is as follows:

[0012] f A1 =γ p P rp +γ E E rc

[0013] Among them, f A1 Indicates the investment cost of an energy storage power station; γ p P represents the cost per unit of hardware equipment; rp Indicates the rated power of energy storage; γ E This indicates the unit cost of the energy storage battery pack and the cost of the battery management system; E rc Indicates the rated capacity of energy storage;

[0014] The expression for the energy storage operation benefit model is as follows:

[0015]

[0016] Among them, f A2 λ represents the annual economic benefit gained from utilizing the time difference in electricity prices; n represents the number of time periods for electricity pricing; λ i P represents the i-th electricity price range; i Δt represents the charging and discharging power of the energy storage power station in the i-th time period; i Indicates the step size of the i-th time period; η represents the charging and discharging efficiency of the energy storage power station;

[0017] The expression for the environmental protection effect model is:

[0018]

[0019] Among them, f A3 Indicates the environmental protection effect value; m represents the number of pollutant types; P ∑ V represents the annual power generation from energy storage; μ represents the power generation per unit of coal-fired power; jQ represents the environmental value of saving one unit of pollutant j; j I represents the content of pollutant j in a unit of coal combustion; I represents the cost of desulfurization and denitrification.

[0020] Furthermore, the expression for the net present value model is:

[0021]

[0022] Among them, f B1 Represents net present value; N represents the lifecycle of the energy storage power station derived from the optimized charging and discharging power based on a typical daily load curve; y1(n) represents the net cash flow of the energy storage power station in year n; i0 represents the expected rate of return; c P Indicates the cost of electronic equipment in an energy storage power station; ε P Indicates the remaining efficiency of electronic equipment in an energy storage power station; c E Indicates investment in energy storage batteries; ε E Indicates the remaining cost rate of energy storage batteries;

[0023] The formula for the rate of return on investment is:

[0024]

[0025] Among them, f B2 C0 represents the return on investment; C0 represents the investment cost of the energy storage power station.

[0026] If y1(k)≥0 and y1(k-1)<0, then the investment payback period model expression is:

[0027]

[0028] Among them, f B3 Indicates the investment payback period; C NPV (k-1) represents the net present value over the past k-1 years; y1(k) represents the net cash flow in year k; y1(k-1) represents the net cash flow in year k-1; k represents the number of years;

[0029] The investment model expression for the delay equipment is:

[0030] f B4 =λ d C d ηP rp

[0031] Among them, f B4 Indicates delayed equipment investment; λ d Indicates the depreciation rate of fixed assets; C d P represents the unit production capacity cost of the equipment; rp Indicates the rated power of energy storage;

[0032] The expression for the operation and maintenance cost model is as follows:

[0033] f B5 =C m ×P ∑

[0034] Among them, f B5 Indicates the total operating and maintenance costs; C m P represents the cost of operation and maintenance per kWh; ∑ This indicates the annual power generation from energy storage.

[0035] Furthermore, the constraints include equality constraints, inequality constraints, energy storage power charging and discharging constraints, and energy storage state constraints.

[0036] Furthermore, the expression for the equality constraint is:

[0037]

[0038] Where j∈i indicates that the node labeled j after the ∑ sign must be directly connected to node i, including the case where j=i; P i Active power injection for node i; Q i Reactive power injection for node i; U i θ represents the voltage magnitude at node i; ij U is the voltage phase angle difference between node i and node j; j N represents the voltage magnitude at node j; b G represents the number of nodes in the power system. i,j B is the real part of the nodal admittance matrix; i,j This represents the imaginary part of the nodal admittance matrix;

[0039] The expression for the inequality constraint is:

[0040]

[0041] Where, N G P represents the total number of units in the system. G,i For the active power output of unit i; and Q represents the minimum and maximum active power output limits of unit i, respectively; G,i For the reactive power output of unit i; and These are the minimum and maximum reactive power output limits for unit i, respectively; and Let N be the minimum and maximum allowable voltages for node j under normal conditions. L P represents the total number of lines in the system. L,l P represents the active power flowing through line l.Lmax,l f is the maximum power that line l is allowed to transmit under normal conditions; f is the system frequency; f min and f max These are the minimum and maximum allowed frequencies of the system;

[0042] The expression for the charging and discharging power constraint of the energy storage power station is:

[0043] -P rp ≤P e,j (t)≤P rp

[0044] Among them, P rp Indicates the rated power of energy storage; P e,j (t) represents the charging and discharging power of the energy storage power station;

[0045] The expression for the energy state constraint of the energy storage power station is:

[0046] S oc,min ≤S e,j (t)≤S oc,max

[0047] Among them, S oc,min This represents the minimum power capacity for the energy storage power station; S oc,max This represents the maximum power output of the energy storage power station; S e,j (t) represents the electricity generated by the energy storage power station.

[0048] Furthermore, under the aforementioned constraints, a pre-defined multi-objective optimization model solving algorithm is used to solve the multi-objective optimization model, obtaining a Pareto optimal solution set for the energy storage power station configuration scheme, including:

[0049] The multi-objective optimization model is mapped to the antigens invading the immune system, and the feasible solutions of the multi-objective optimization model are mapped to the antibodies produced by the immune system. An immune algorithm is used to solve the optimization problem corresponding to the multi-objective optimization model. When using the immune algorithm to solve the optimization problem corresponding to the multi-objective optimization model, each antibody represents a planning scheme, and the objective function value is calculated according to the scheme. A planning network is constructed according to the scheme, and power flow verification is performed. A penalty function method is used to handle schemes that exceed the limit.

[0050] Furthermore, when using the immune algorithm to solve the optimization problem corresponding to the multi-objective optimization model, the antibody encoding includes:

[0051] The power plant capacity is used as the length of the antibody. Each bit of the antibody represents a capacity level. The coding is in binary, with 1 indicating that the energy storage capacity is above this level and 0 indicating that the energy storage capacity is below this level.

[0052] Furthermore, when using immune algorithms to solve the optimization problem corresponding to a multi-objective optimization model, the evaluation methods include:

[0053] The quality of antibodies is evaluated using the expected reproduction probability.

[0054] Furthermore, the solutions in the Pareto optimal solution set are screened to obtain the optimal solution, including:

[0055] The fuzzy weight of each attribute is obtained using the fuzzy weighting method;

[0056] The objective weight of each attribute is obtained using the inverse entropy weight method;

[0057] The fuzzy weight of each attribute is combined with its objective weight, and then a multi-attribute decision algorithm based on the grey relational model is used to filter the solutions in the Pareto optimal solution set to obtain the optimal solution.

[0058] The beneficial effects of the technical solution provided by this invention include at least the following:

[0059] This invention proposes a method for coordinated and optimized allocation of energy storage power station capacity in the medium- and long-term planning process by analyzing key factors in such planning. Based on the medium- and long-term planning of the power system, this method comprehensively considers the economic benefits, grid conditions, and environmental factors during the construction of energy storage power stations. It selects the most influential factors as upper and lower level objective functions, respectively, and establishes a time-segmented two-level optimization model. An improved immune algorithm is used to solve the constructed time-segmented two-level optimization model to obtain the Pareto optimal solution set of the planning scheme. A multi-attribute decision-making method based on a gray-level correlation model using fuzzy weighting and anti-entropy weighting is then used to filter the Pareto optimal solution set of the scheme, obtaining the final satisfactory scheme. This achieves reasonable planning and allocation of energy storage power station capacity, thereby enabling the rational utilization of energy storage power station resources and ensuring the effective and stable operation of the energy storage power station. Attached Figure Description

[0060] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0061] Figure 1 A flowchart illustrating the method for coordinated and optimized configuration of energy storage power stations in the medium and long term, as provided in an embodiment of the present invention;

[0062] Figure 2 The flowchart illustrates the solution process for the immune algorithm provided in this embodiment of the invention. Detailed Implementation

[0063] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0064] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0065] This embodiment provides a method for coordinated and optimized configuration of medium- and long-term planning for energy storage power stations. This method can be implemented using electronic devices, such as terminals or servers. The execution flow of this method is as follows: Figure 1 As shown, it includes the following steps:

[0066] S1, taking into account the economic benefits of the construction process of the planned energy storage power station, as well as the grid status, environmental factors, rated power and capacity configuration of the completed energy storage power station, constructs a two-layer multi-objective optimization model and corresponding constraints.

[0067] Specifically, in this embodiment, the multi-objective optimization model includes an upper-level objective optimization model and a lower-level objective optimization model; wherein, the upper-level objective optimization model includes an energy storage power station investment cost model, an energy storage operation benefit model, and an environmental protection effect model; the lower-level objective optimization model includes a net present value model, a return on investment model, an investment payback period model, a delayed equipment investment model, and an operation and maintenance cost model. The objective functions are expressed as follows:

[0068] I. Upper-level objectives

[0069] (1) Investment cost of energy storage power station

[0070] The investment cost of energy storage is mainly related to the rated power, rated capacity and techno-economic characteristics of the energy storage power station, including the expenditure on energy storage battery packs, battery management systems, energy storage converters, monitoring systems, etc. Specifically, the expression for the investment cost of an energy storage power station is shown in equation (1):

[0071] f A1 =γ p P rp +γ E E rc (1)

[0072] Among them, f A1 For the investment cost of energy storage power stations; γ p The cost of the unit energy storage converter, monitoring equipment, and other expenses; P rp Rated power of energy storage; γ E Costs include the unit cost of the energy storage battery pack, the battery management system, etc.; E rc This refers to the rated capacity of the energy storage.

[0073] (2) Energy storage operation benefits

[0074] When the load peaks and electricity prices are high, the energy storage station is charged. When the power system load is low and the electricity price is low, the energy storage station discharges. The annual economic benefit obtained by utilizing the time difference in electricity prices is the operating efficiency of the energy storage station, and the expression for the energy storage operating efficiency is shown in equation (2):

[0075]

[0076] Among them, f A2 λ represents the annual economic benefit gained from utilizing the time difference in electricity prices; n represents the number of time periods for electricity pricing; λ i P represents the i-th electricity price range; i Δt represents the charging and discharging power of the energy storage power station in the i-th time period; i Indicates the step size of the i-th time period; η represents the charging and discharging efficiency of the energy storage power station;

[0077] (3) Environmental benefits

[0078] Energy storage power stations can save a significant amount of coal and reduce pollutant and greenhouse gas emissions by replacing thermal power units for power generation. The environmental benefits are shown in equation (3):

[0079]

[0080] Among them, f A3 Indicates the environmental protection effect value; m represents the number of pollutant types; P ∑ V represents the annual power generation from energy storage; μ represents the power generation per unit of coal-fired power; j Q represents the environmental value of saving one unit of pollutant j; j I represents the content of pollutant j in a unit of coal combustion; I represents the cost of desulfurization and denitrification.

[0081] II. Lower-level targets

[0082] (1) Net Present Value

[0083] Net present value (NPV) is the difference between the present value of future cash inflows and the present value of future cash outflows, as shown in equation (4):

[0084]

[0085] Among them, f B1 Represents net present value; N represents the lifecycle of the energy storage power station derived from the optimized charging and discharging power based on a typical daily load curve; y1(n) represents the net cash flow of the energy storage power station in year n; i0 represents the expected rate of return; c P Indicates the cost of electronic equipment in an energy storage power station; ε P Indicates the remaining efficiency of electronic equipment in an energy storage power station; cE Indicates investment in energy storage batteries; ε E Indicates the remaining cost rate of energy storage batteries;

[0086] (2) Return on investment

[0087] The ratio of the annual rate of return to the investment cost of the energy storage power station is shown in equation (5):

[0088]

[0089] Among them, f B2 C0 represents the return on investment; C0 represents the investment cost of the energy storage power station.

[0090] (3) Investment recovery period

[0091] The payback period for an energy storage project is the time required for the net cash inflow generated by the project to recover the initial total investment. If y1(k)≥0 and y1(k-1)<0, the payback period for the energy storage system can be expressed as:

[0092]

[0093] Among them, f B3 Indicates the investment payback period; C NPV (k-1) represents the net present value over the past k-1 years; y1(k) represents the net cash flow in year k; y1(k-1) represents the net cash flow in year k-1; k represents the number of years;

[0094] (4) Delayed equipment investment

[0095] The peak shaving and valley filling effect of energy storage power stations can delay the expansion of power equipment, and the resulting benefits can be expressed by equation (7):

[0096] f B4 =λ d C d ηP rp (7)

[0097] Among them, f B4 Indicates delayed equipment investment; λ d Indicates the depreciation rate of fixed assets; C d P represents the unit production capacity cost of the equipment; rp Indicates the rated power of energy storage;

[0098] (5) Operating and maintenance costs

[0099] The operation and maintenance costs of an energy storage power station are related to its operating status and can be expressed as follows:

[0100] f B5 =C m ×P∑ (8)

[0101] Among them, f B5 Total operating and maintenance costs; C m Operating and maintenance costs per kWh; P ∑ This is the annual power generation from energy storage.

[0102] Furthermore, the constraints include equality constraints, inequality constraints, charging and discharging power constraints of the energy storage power station, and state of charge constraints of the energy storage power station. These will be explained in detail below.

[0103] (1) Equality constraints

[0104] Contains N b For a power system with n nodes, the equality constraints are mainly power flow equation constraints, expressed as:

[0105]

[0106] Where j∈i indicates that the node labeled j after the ∑ sign must be directly connected to node i, including the case where j=i; P i Active power injection for node i; Q i Reactive power injection for node i; U i θ represents the voltage magnitude at node i; ij U is the voltage phase angle difference between node i and node j; j N represents the voltage magnitude at node j; b G represents the number of nodes in the power system. i,j B is the real part of the nodal admittance matrix; i,j This represents the imaginary part of the nodal admittance matrix;

[0107] (2) Inequality constraint, expressed as:

[0108]

[0109] Where, N G P represents the total number of units in the system. G,i For the active power output of unit i; and Q represents the minimum and maximum active power output limits of unit i, respectively; G,i For the reactive power output of unit i; and These are the minimum and maximum reactive power output limits for unit i, respectively; and Let N be the minimum and maximum allowable voltages for node j under normal conditions. L P represents the total number of lines in the system. L,l P represents the active power flowing through line l. Lmax,lf is the maximum power that line l is allowed to transmit under normal conditions; f is the system frequency; f min and f max These are the minimum and maximum allowed frequencies of the system;

[0110] (3) The charging and discharging power constraint of the energy storage power station is expressed as follows:

[0111] -P rp ≤P e,j (t)≤P rp

[0112] Among them, P rp Indicates the rated power of energy storage; P e,j (t) represents the charging and discharging power of the energy storage power station;

[0113] (4) The energy state constraint of the energy storage power station is expressed as follows:

[0114] S oc,min ≤S e,j (t)≤S oc,max

[0115] Among them, S oc,min This represents the minimum power capacity for the energy storage power station; S oc,max This represents the maximum power output of the energy storage power station; S e,j (t) represents the electricity generated by the energy storage power station.

[0116] S2, Under the constraints, a preset multi-objective optimization model solving algorithm is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set of the energy storage power station configuration scheme;

[0117] Specifically, in this embodiment, the solution to the above problem is an improved immune algorithm. The immune algorithm is a novel intelligent optimization algorithm developed based on immunology. It utilizes the diversity generation and maintenance mechanisms of the immune system to maintain population diversity, thus improving the preconception problem that is difficult to handle during optimization. The multi-objective function of the problem is mapped to the antigens invading the immune system, and the feasible solutions of the multi-objective function are mapped to the antibodies produced by the immune system. The affinity between the antibody and the antigen describes the approximation between the feasible and optimal solutions. The immune algorithm is used to solve this problem, with each antibody representing a planning scheme. The objective function value is calculated based on the scheme. A planning network is constructed based on the scheme, and power flow verification is performed. A penalty function method is used to handle schemes that exceed the limits. The antibody encoding and evaluation methods mainly include the following two aspects:

[0118] (1) Decision variable coding design. The capacity of the power plant is used as the antibody length. Each bit of the antibody code represents a capacity level. The coding adopts binary, where 1 indicates that the energy storage capacity is above this level and 0 indicates that the energy storage capacity is below this level.

[0119] (2) Determining the antibody evaluation method. The expected proliferation probability is used to evaluate the quality of antibodies, mainly based on the objective function. The calculation steps are as follows:

[0120] ① Affinity between antibody and antigen

[0121]

[0122] Where: m is the number of objective functions; r v,j Let be the normalized value of the objective function j of antibody v. The normalization method is shown in equations (9) and (10), where n is the antibody population size; M is the penalty for violating the constraints. If the constraints are met, M is 0; otherwise, M is a large positive number. Since this chapter is a multi-objective optimization problem, the sum of the affinity of each objective in the scheme is taken to represent the overall affinity.

[0123] Benefit indicators

[0124] Cost indicators

[0125] ②Affinity between antibodies

[0126]

[0127] Where: k v,s is the number of identical numbers in antibody v and antibody s; L is the antibody length.

[0128] ③ Antibody concentration

[0129]

[0130] Where: N is the total number of antibodies; T is the set threshold.

[0131] ④ Expected fertility rate

[0132]

[0133] Where: α is the proportionality coefficient, 0≤α≤1.

[0134] As can be seen from the above formula, the higher the antibody affinity, the greater the expected proliferation rate; the higher the concentration, the lower the expected proliferation rate, thus ensuring antibody diversity. The specific steps for optimization using the immune algorithm are as follows: Figure 2 As shown.

[0135] S3. The schemes in the Pareto optimal solution set are screened to obtain the optimal scheme, and the obtained optimal scheme is used to configure the planned energy storage power station to achieve the optimal planning and configuration of the energy storage power station capacity.

[0136] It should be noted that when using the immune algorithm to solve the multi-objective optimization model and obtaining the Pareto optimal solution set of the scheme, it is necessary to determine the final satisfactory solution according to the preferences of the decision maker and the actual objective situation. The specific implementation process is as follows:

[0137] S31, fuzzy weight method

[0138] Fuzzy numbers reflect the subjective importance degree of decision makers for each attribute. In this embodiment, triangular fuzzy numbers are used to represent the subjective preferences of decision makers. Triangular fuzzy numbers where a1, a2, and a3 are all real numbers, and satisfy 0 < a1 < a2 < a3. The triangular fuzzy numbers (a1, a2, a3) corresponding to the fuzzy preference degree of the decision maker are shown in Table 1.

[0139] Table 1 Triangular fuzzy numbers corresponding to fuzzy preference degrees

[0140]

[0141] There are k decision makers. The fuzzy weight assigned by the l-th decision maker to the j-th attribute is Then the fuzzy weight of all decision makers for the j-th attribute is:

[0142]

[0143] Convert the fuzzy weights of each attribute into the best non-fuzzy performance values. The conversion formula is

[0144]

[0145] Normalize to obtain the fuzzy weight of the j-th attribute as

[0146]

[0147] S32, anti-entropy weight method

[0148] The characteristics of anti-entropy values are different from those of entropy values. The difference of indicators is proportional to both anti-entropy values and weight coefficients. The definition of anti-entropy value is shown in Equation (22).

[0149]

[0150] In the formula, 0 ≤ p j ≤ 1 and

[0151] First, construct a decision matrix B = (b ij ) n×m , i ∈ [1, n], j ∈ [1, m]. After normalization, the normalized matrix R = (r ij ) n×m . Then calculate the anti-entropy value H of attribute jj :

[0152] Standardization methods

[0153]

[0154]

[0155] The anti-entropy weight w of the j-th attribute j Assign objective weights:

[0156]

[0157] S33, Multi-attribute decision-making based on grey relational model

[0158] Grey relational analysis is a multi-attribute decision-making method that integrates grey system theory and approximation ideas. It can effectively solve grey multi-attribute decision-making problems in practical systems and has high decision sensitivity. This chapter uses the grey relational model for multi-attribute decision-making; the specific decision-making process is as follows:

[0159] (1) Construct a decision matrix F = (f_n) with n options and m attributes. ij ) n×m ,i∈[1,n],j∈[1,m], after normalization, we get the normalized matrix R=(r ij ) n×m .

[0160] (2) Let

[0161]

[0162]

[0163] The positive ideal solution and the negative ideal solution are shown in equations (29) and (30).

[0164]

[0165]

[0166] (3) The grey relational coefficients of scheme i and the positive and negative ideal schemes with respect to index j are calculated as follows:

[0167]

[0168]

[0169] Where: ρ is the resolution coefficient, ρ∈[0,1], and is generally taken as ρ=0.5.

[0170] (4) The correlation between scheme i and the positive and negative ideal schemes are calculated as follows:

[0171]

[0172]

[0173] The larger the value, the closer the solution i is to the ideal solution, and the better the solution is; Conversely, The smaller the size, the better the solution.

[0174] (5) Define the degree of superiority u i To comprehensively evaluate option S i The degree to which the solution approaches the positive ideal solution and deviates from the negative ideal solution. Solution S i Based on the degree of superiority u i Approaching the positive ideal solution, while using 1–u i Approaching the negative ideal solution. Based on the least squares criterion, establish the evaluation function for solution i:

[0175]

[0176] In the formula: make Easy to obtain:

[0177]

[0178] Degree of superiority u i As a comprehensive indicator of the merits and demerits of different participating schemes, they are ranked in descending order. i The solution with the largest value is the optimal solution.

[0179] In summary, this embodiment provides a method for the coordinated and optimized configuration of energy storage power stations in the medium and long term. This method models and analyzes the optimized configuration problem of a single energy storage power station, comprehensively considering the rated power and capacity configuration of the energy storage power station based on scenario requirements, market forecasts, and development trends. Based on multiple energy storage technologies and economic characteristics, it calculates the application scope and operating costs of the energy storage power station, thereby providing rational suggestions for investment and construction of energy storage power stations.

[0180] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0181] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0182] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0183] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0184] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for long-term planning and coordination optimization configuration of energy storage power stations, characterized in that, The application relates to a method for optimal planning of a storage power station. The method comprises the following steps: comprehensively considering economic benefits in the construction process of the to-be-planned storage power station, and power grid state, environmental protection factors, rated power and capacity configuration of the built storage power station, constructing a double-layer multi-objective optimization model and corresponding constraint conditions; under the constraint conditions, a preset multi-objective optimization model solving algorithm is adopted to solve the multi-objective optimization model, and a Pareto optimal solution set of a storage power station configuration scheme is obtained; schemes in the Pareto optimal solution set are screened, an optimal scheme is obtained, and the obtained optimal scheme is used to configure the to-be-planned storage power station, so that optimal planning and configuration of storage power station capacity are realized. The multi-objective optimization model comprises an upper-layer target optimization model and a lower-layer target optimization model; wherein, The upper-layer target optimization model comprises a storage power station investment cost model, a storage operation benefit model and an environmental protection effect model. The lower-layer target optimization model comprises a net present value model, an investment return rate model, an investment recovery period model, a delayed equipment investment model and an operation and maintenance cost model. The expression of the storage power station investment cost model is as follows: The expression of the storage operation benefit model is as follows: The expression of the environmental protection effect model is as follows: wherein, represents the investment cost of the energy storage power station; The expression of the net present value model is as follows: p represents the cost of a unit hardware device; P rp represents the energy storage rated power; The expression of the investment return rate model is as follows: E represents the cost of a unit energy storage battery pack and battery management system; E rc represents the energy storage rated capacity; The expression of the delayed equipment investment model is as follows: wherein, represents the economic benefit annual value obtained by using the electricity price time difference; n represents the number of time periods of the electricity price; The expression of the operation and maintenance cost model is as follows: i represents the first i time period electricity price interval; P i represents the first i time period charging and discharging power of the energy storage power station; t i represents the first i time period step; The constraint conditions comprise an equality constraint, an inequality constraint, a storage power station charging and discharging power constraint and a power state constraint of the storage power station. represents the charging and discharging efficiency of the energy storage power station; The expression of the equality constraint is as follows: Wherein, represents the environmental effect value; m represents the number of pollutant species; P ∑ represents the annual energy storage power generation; The expression of the inequality constraint is as follows: represents the unit coal power generation; V j represents the environmental value of saving unit pollutant j ; Q j represents the content of pollutants in unit coal j ; I represents the desulfurization and denitrification cost.

2. The method for long-term planning and coordination optimization configuration of energy storage power station of claim 1, wherein, The expression of the storage power station charging and discharging power constraint is as follows: wherein, represents the net present value; N represents the life cycle of the energy storage power station derived from the charging and discharging power optimized according to the typical daily load curve; y 1( n ) represents the net cash flow of the energy storage power station in the n year; i 0 represents the expected rate of return; c P represents the cost of the energy storage power station electronics; The expression of the power state constraint of the storage power station is as follows: P represents the residual rate of the energy storage power station electronics; c E represents the energy storage battery investment; Under the constraint conditions, a preset multi-objective optimization model solving algorithm is adopted to solve the multi-objective optimization model, and a Pareto optimal solution set of a storage power station configuration scheme is obtained. E represents the residual cost rate of the energy storage battery; The multi-objective optimization model corresponds to an antigen of an invasion immune system, and the feasible solution of the multi-objective optimization model corresponds to an antibody generated by the immune system, and an immune algorithm is adopted to solve the optimization problem corresponding to the multi-objective optimization model; wherein, when the immune algorithm is adopted to solve the optimization problem corresponding to the multi-objective optimization model, each antibody represents a planning scheme, and the target function value is calculated according to the scheme; a planning network frame is constructed according to the scheme, and a power flow check is carried out, and a penalty function method is adopted to process the check out-of-limit scheme. wherein, represents the rate of return on investment; C 0 represents the investment cost of the energy storage power station; If y 1( k ) ≥ 0 and y 1( k - 1) < 0, then the investment payback period model expression is: wherein, represents the payback period; C NPV ( k -1) represents the net present value for the past k -1 years; y 1( k ) represents the net cash flow in year k ; represents the net cash flow in year k- 1; k represents the number of years; When the immune algorithm is adopted to solve the optimization problem corresponding to the multi-objective optimization model, the antibody coding comprises the following steps: wherein, represents a delay in equipment investment; The power station capacity is taken as the antibody length, each coding of the antibody represents a capacity level, and the coding adopts binary, 1 represents that the storage capacity is above the level, and 0 represents that the storage capacity is below the level. d represents a depreciation rate of equipment fixed assets; C d represents a cost per unit of production capacity of equipment; P rp represents a rated power of energy storage; When the immune algorithm is adopted to solve the optimization problem corresponding to the multi-objective optimization model, the evaluation mode comprises the following steps: wherein, represents the total operation and maintenance cost; C m represents the operation and maintenance cost per kWh; P ∑ represents the annual energy production of the storage.

3. The method for long-term planning and coordination optimization configuration of energy storage power station of claim 2, wherein, The expected reproduction probability is adopted to evaluate the advantages and disadvantages of the antibody.

4. The method for long-term planning and coordination optimization configuration of energy storage power station of claim 3, wherein, The schemes in the Pareto optimal solution set are screened, and the optimal scheme is obtained, and the method comprises the following steps: wherein, j ∈ i denotes the label after the ∑ sign j The node of must be directly connected to the node of i including j = i the case; P i is the active power injection at the node i ; Q i is the reactive power injection at the node i ; U i is the voltage modulus at the node i ; A fuzzy weight method is adopted to obtain the fuzzy weight of each attribute; ij is the voltage phase angle difference between the node i and the node j ; is the voltage modulus at the node j ; is the number of nodes of the power system; is the real part of the node admittance matrix; is the imaginary part of the node admittance matrix; An anti-entropy weight method is adopted to obtain the objective weight of each attribute; wherein, N G is the total number of generating units in the system; is the active power output of the generating unit i ; and are the minimum and maximum active power limits of the generating unit i ; is the reactive power output of the generating unit i ; and are the minimum and maximum reactive power limits of the generating unit i ; and are the minimum and maximum voltage limits allowed at the node j under normal conditions; N L is the total number of lines in the system; P L,l is the active power flowing over the line l ; P Lmax,l is the maximum power allowed to be transmitted over the line l under normal conditions; f is the system frequency; f min and f max are the minimum and maximum frequency limits allowed in the system; ​ wherein, P rp represents the energy storage rated power; is the energy storage power station charge-discharge power; ​ wherein, S oc,min is the minimum energy of the energy storage plant; S oc,max is the maximum energy of the energy storage plant; is the energy of the energy storage plant.

5. The method for long-term planning and coordination optimization configuration of energy storage power station of claim 1, wherein, ​ ​ 6. The method for long-term planning and coordination optimization configuration of energy storage power station of claim 5, wherein, ​ ​ 7. The method for long-term planning and coordination optimization configuration of energy storage power station of claim 6, wherein, ​ ​ 8. The method for long-term planning and coordination optimization configuration of energy storage power station of claim 1, wherein, ​ ​ ​ The fuzzy weight of each attribute is combined with its objective weight, and then a multi-attribute decision algorithm based on a grey correlation model is used to screen the schemes in the Pareto optimal solution set, so as to obtain an optimal scheme.

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