Power grid configuration operation cooperation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a power grid configuration operation cooperation method and device, electronic equipment and a storage medium. The method comprises the steps that an upper layer planning model with the purpose of minimizing the comprehensive planning cost of a power grid and with the installed capacity of a new energy unit and the installed capacity of an energy storage unit as optimization variables is constructed; a lower-layer operation simulation model with the aim of minimizing the comprehensive operation cost of the power grid and with the operation state of the thermal power generating unit, the operation state of the energy storage unit, the new energy abandoned power and the load shedding power as optimization variables is constructed; constructing a double-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model, and enabling the output of the upper-layer planning model and the output of the lower-layer operation simulation model to be mutually influenced; and performing multi-round iterative optimization on the double-layer decision model based on a multi-time-sequence typical source load matching scene. The embodiment of the invention can improve the power supply reliability of the power grid on the premise of ensuring the safety of the power grid and the lowest comprehensive cost.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a method, device, electronic device and storage medium for coordinating power grid configuration and operation. Background Art

[0002] With the rapid development of new energy technologies, existing technologies deeply integrate the power supply, power grid, load, and energy storage of the power grid through the integrated source-grid-load-storage system, and improve the safety, efficiency, and low-carbon nature of the power system through "source-source complementarity", "source-grid coordination", and "source-load interaction". Due to the high cost of energy storage, after the penetration rate of new energy increases, it is difficult to ensure the economy by relying solely on large-scale energy storage, and the peak-shaving capacity of thermal power is limited, which makes it difficult to cope with the peak-shaving pressure brought by the high penetration rate of new energy. When dealing with uncertain changes in the power market, such as changes in load under extreme weather, the source-grid-load-storage integrated system in the existing technology is difficult to quickly adjust the power supply, and the power supply reliability of the power grid is poor. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, electronic device and storage medium for coordinating the configuration and operation of a power grid, which can improve the power supply reliability of the power grid while ensuring the safety of the power grid and the lowest overall cost.

[0004] In a first aspect, an embodiment of the present invention provides a method for coordinating power grid configuration and operation, including:

[0005] Construct an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid, with the installed capacity of new energy generators and the installed capacity of energy storage generators as optimization variables. The comprehensive planning cost includes: equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost;

[0006] Construct a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost of the power grid, with the operation status of thermal power units, the operation status of energy storage units, the power abandonment of new energy sources and the power shedding of loads as optimization variables. The comprehensive operation cost includes the net load fluctuation cost, the simulated operation cost and the power abandonment and load shedding costs of new energy sources;

[0007] Building a two-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model so that the outputs of the upper-layer planning model and the lower-layer operation simulation model influence each other; and

[0008] Based on multi-time series typical source-load matching scenarios, the two-layer decision-making model is iteratively optimized for multiple rounds to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal new energy curtailment power and the optimal load shedding power.

[0009] In a second aspect, an embodiment of the present invention provides a power grid configuration and operation coordination device, including:

[0010] The upper-level planning model construction module is used to construct an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid, with the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables. The comprehensive planning cost includes: equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost;

[0011] A lower-level operation simulation model construction module is used to construct a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost of the power grid, with the operation status of thermal power units, the operation status of energy storage units, the power abandonment of new energy sources and the power shedding of loads as optimization variables. The comprehensive operation cost includes the net load fluctuation cost, the simulation operation cost and the power abandonment and load shedding costs of new energy sources;

[0012] a two-layer decision model construction module, used to construct a two-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model, so that the outputs of the upper-layer planning model and the lower-layer operation simulation model affect each other; and

[0013] The optimization module is used to perform multiple rounds of iterative optimization on the two-layer decision-making model based on multi-time series typical source-load matching scenarios to obtain the optimal installed capacity of new energy units, the installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal new energy power abandonment power and the optimal load shedding power.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a grid configuration and operation coordination method as described in any one of the embodiments of the present invention is implemented.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a power grid configuration and operation coordination method as described in any one of the embodiments of the present invention.

[0016] The embodiments of the present invention provide a method, device, electronic device and storage medium for coordinated operation of power grid configuration. The method aims to minimize the comprehensive planning cost including equipment investment cost, equipment maintenance cost, planned electricity purchase cost and source-load power imbalance risk cost, takes the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables, and takes into account the cost caused by the risk of source-load power imbalance; and aims to minimize the comprehensive operation cost including net load fluctuation cost, simulated operation cost and new energy power abandonment and load shedding cost, takes the operation status of thermal power units, the operation status of energy storage units, new energy power abandonment power and load shedding power as optimization variables, and takes into account the cost of ensuring safe operation of the system under extreme weather scenarios through new energy power abandonment and load shedding. Then, based on the operation status of thermal power units, the operation status of energy storage units, the installed capacity of new energy units and the installed capacity of energy storage units, the installed capacity of energy storage ... A lower-layer operation simulation model with the state, new energy power abandonment power and load shedding power as optimization variables is constructed, and a double-layer decision-making model with the upper and lower outputs influencing each other is further constructed based on the upper-layer planning model and the lower-layer operation simulation model. Then, based on the typical source-load matching scenario, the double-layer decision-making model is used to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating state of thermal power units, the optimal operating state of energy storage units, the optimal new energy power abandonment power, and the optimal load shedding power, which is beneficial to the planning of power grid configuration based on the optimal installed capacity of new energy units and the optimal installed capacity of energy storage units, and the operation of the power grid based on the optimal operating state of thermal power units, the optimal operating state of energy storage units, the optimal new energy power abandonment power, and the optimal load shedding power, and can improve the power supply reliability of the power grid system under the premise of ensuring power grid safety and the lowest comprehensive cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flowchart of a method for coordinated operation of power grid configuration provided by an embodiment of the present invention;

[0019] Figure 2 is another flowchart of the grid configuration and operation coordination method provided by an embodiment of the present invention;

[0020] Figure 3 is another flowchart of the grid configuration and operation coordination method provided by an embodiment of the present invention;

[0021] Figure 4is a structural diagram of a power grid configuration and operation coordination device provided by an embodiment of the present invention;

[0022] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Figure 1 A flowchart of a grid configuration and operation coordination method provided in an embodiment of the present invention. This embodiment is applicable to the scenario of optimizing a source-grid-load-storage integrated system. The method can be executed by a grid configuration and operation coordination device provided in an embodiment of the present invention, and the device can be implemented in software and / or hardware. In a specific embodiment, the device can be integrated in an electronic device, such as a computer, a server, etc. The following embodiments will be described by taking the device integrated in an electronic device as an example. Reference Figure 1 , the method may specifically include the following steps:

[0026] Step 101, construct an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid, with the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables, and the comprehensive planning cost includes: equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost. This step can facilitate the construction of a two-level decision model that considers the source-load power imbalance risk by establishing an upper-level planning model that considers the source-load power imbalance risk, and then after optimizing the two-level decision model, it can avoid the risk of source-load power imbalance as much as possible and improve the power supply reliability of the power grid system.

[0027] Specifically, the above-mentioned power grid may be a local power grid, such as a power grid of a city or an industrial park, or a regional power grid, such as a part of several cities or a province.

[0028] Optionally, the installed capacity of the above-mentioned new energy units includes the installed capacity of photovoltaic units and wind turbine units.

[0029] Specifically, the installed capacity of the above-mentioned new energy generators may also include biomass generator sets or hydropower generator sets.

[0030] Specifically, the above comprehensive planning cost can be understood as the planning cost corresponding to a multi-time series typical source-load matching scenario.

[0031] Optionally, the process of constructing the upper-level planning model includes: establishing a source-load power imbalance risk cost function based on conditional risk value.

[0032] Specifically, before establishing the source-load power imbalance risk cost function based on the conditional risk value, the historical data of the renewable energy power supply and the total load of the power grid can be obtained, and the above-mentioned conditional risk value (Conditional Value at Risk, CVaR) can be determined based on the historical data of the renewable energy power supply and the total load of the power grid.

[0033] Optionally, the process of determining the above-mentioned conditional risk value based on the historical data of new energy power supply and total grid load may include: predicting the net load of the grid within a preset time period in the future based on the historical data of new energy power supply and total grid load; determining the source-load mismatch loss function based on the net load of the grid within a preset time period in the future and the power adjustment of stable power supply resources; and determining the above-mentioned conditional risk value based on the source-load mismatch loss function.

[0034] Specifically, the above-mentioned net load of the power grid can be understood as the total load demand of the power grid minus the power supply of new energy within a certain period of time.

[0035] Specifically, the above-mentioned stable power supply resources may include generator sets, hydropower units, and / or energy storage units.

[0036] Optionally, x is used to represent the power adjustment of the stable power supply resource within the future preset time, y is used to represent the net load of the power grid within the future preset time, and f(x,y) is used to represent the source-load mismatch loss function. The above conditional risk value can be calculated by the following formula:

[0037]

[0038] α β (x)=min{α∈R:ψ(x,α)≥β}

[0039]

[0040] in, represents the conditional risk value; β(x) represents the risk value (Value at Risk, VaR) p(y) represents the probability density function of y; ψ(x,α) is the cumulative loss distribution function of f(x,y), which represents the probability that f(x,y) caused by y does not exceed the specific loss level value α; β∈[0,1] represents the confidence level that f(x,y) caused by y does not exceed the specific loss level value α.

[0041] Optionally, the above source-load power imbalance risk cost function can be expressed as:

[0042]

[0043] Among them, C flex_lack represents the risk cost of source-load power imbalance; c LIB represents the cost for balancing net load fluctuations; The total number of corresponding typical source-load matching scenarios; g is the serial number of the thermal power unit, y is the serial number of the typical scenario year, m is the serial number of the typical scenario month, E g,y,m represents the monthly power generation of thermal power unit g in the mth month of typical scenario year y; represents the unit variable operating cost of thermal power unit g; represents the unbalanced electricity in year y in each scenario; κ represents the confidence level of the monthly electricity imbalance risk event.

[0044] Optionally, the process of constructing the upper-level planning model includes: establishing an equipment investment cost function based on the installed capacity of the energy storage unit, the installed capacity of the new energy unit, and the full life cycle cost function of the energy storage unit.

[0045] Optionally, the expression of the above investment cost function is:

[0046]

[0047] Among them, C inv represents the equipment investment cost; and p correspond to the investment discount rate and investment payback period; I, L, S correspond to the equipment of energy storage, photovoltaic unit, and wind turbine; i, l, s correspond to the equipment number of energy storage, photovoltaic unit, and wind turbine; Correspondingly, it represents the unit investment cost of the energy storage unit over its entire life cycle, the unit investment cost of the photovoltaic unit, and the unit investment cost of the wind turbine unit; Correspondingly represents the installed capacity of energy storage units, photovoltaic units and wind turbine units; C i，ini represents the initial investment cost of the energy storage unit; C i，sub Represents the cost of auxiliary equipment of energy storage unit; C i，om represents the operation and maintenance cost of the energy storage unit; C i，rp represents the cost of updating and replacing energy storage equipment; C i，sd represents the scrapping and disposal cost of the energy storage unit; C i,sal Indicates the residual value of energy storage unit components.

[0048] Optionally, the process of constructing the upper-level planning model includes: establishing an equipment maintenance cost function based on the installed capacity of the energy storage unit and the installed capacity of the new energy unit.

[0049] Optionally, the expression of the above equipment maintenance cost function is:

[0050]

[0051] in, Correspondingly represents the installed capacity of energy storage units, photovoltaic units and wind turbine units; M ESS 、M PV and M WT It corresponds to the unit maintenance cost of energy storage units, photovoltaic units and wind turbine units, which is a constant within the service life.

[0052] Optionally, the process of constructing the upper-level planning model includes: constructing a planned electricity purchase cost function based on the threshold power and the market electricity price.

[0053] Optionally, the expression of the above planned electricity purchase cost function is:

[0054]

[0055] Among them, C opr It represents the annual planned electricity purchase cost, that is, the planned cost of purchasing electricity from outside; represents the threshold power in time period t; represents the electricity spot market price; T is the operation simulation period; Δt is the operation simulation time granularity; M represents the number of months, i.e. 12 months; and m represents the month number.

[0056] Optionally, the process of constructing the upper-level planning model includes: constructing an upper-level objective function based on a source-load power imbalance risk cost function, an equipment investment cost function and a planned power purchase cost function, and constructing an upper-level planning model based on the upper-level objective function.

[0057] Specifically, the above upper objective function can be expressed as:

[0058] minC Plan =C inv +C om +wC opr +(1-w)C flex_lack

[0059] Wherein, w represents the weight factor of normal operation cost to total operation cost, which is a preset value.

[0060] Optionally, before constructing the upper-level planning model based on the upper-level objective function, a planning investment constraint function, a maximum installed capacity constraint function, a power supply margin function, a flexibility margin constraint function, a carbon emission constraint function, a source-load matching constraint function, and a thermal power unit working time constraint function are established.

[0061] Specifically, a planning investment constraint function may be established based on the upper limit of the total investment amount allowed.

[0062] Optionally, the expression of the above planning investment constraint function is:

[0063]

[0064] in, Represents the investment cost required for unit installed capacity of photovoltaic power; Indicates the investment cost required for unit installed capacity of the wind turbine; Represents the investment cost required for energy storage unit installed capacity, G PV , G WT , G ESS , are the planned installed capacity of photovoltaic power, wind power and ESS respectively; C max The maximum amount of total investment allowed.

[0065] Specifically, the above maximum installed capacity constraint function can be established based on the maximum allowable installed capacity that can be achieved by each photovoltaic unit, wind turbine unit and energy storage unit.

[0066] Optionally, the expression of the above maximum installed capacity constraint function is:

[0067]

[0068] in, It corresponds to the maximum allowable installed capacity that each photovoltaic unit, wind turbine unit and energy storage unit can achieve.

[0069] Specifically, the above power supply margin function can be established based on XX.

[0070] Optionally, the expression of the power supply margin function is:

[0071]

[0072] Among them, α represents the power supply margin, N t It represents the number of operating periods of the system throughout the year, 8760; t represents the period number; L represents the load identifier; Cut represents the load cut identifier; It is expressed as the load shedding amount in time period t; here The writing is wrong, it should be changed to P L,t , expressed as the load during period t.

[0073] Specifically, the flexibility margin constraint function may be determined based on the annual maximum load ramp demand factor and the annual maximum load.

[0074] Optionally, the expression of the flexibility margin constraint function is:

[0075]

[0076] Among them, Ω S represents all types of flexible power supply resources; f represents flexible power supply resources, k f and G f The corresponding expression is the ramp rate and capacity of each type of flexible resource; k r Represents the ramp-up demand factor of new energy; G r represents the installed capacity of new energy; k L and L max Corresponding to the ramp demand factor representing the annual maximum load and the annual maximum load.

[0077] Specifically, the carbon emission constraint function may be established based on the carbon emission limit.

[0078] Optionally, the above carbon emission constraint function can be expressed as:

[0079]

[0080] in, Indicates carbon emission intensity; represents the total power generation; ψ c represents the carbon emission limit; G represents the set of generator sets; g represents the generator set number.

[0081] Specifically, the source-load matching constraint function may be established based on a benchmark power load.

[0082] Optionally, the expression of the above source-load matching constraint function is:

[0083]

[0084] Wherein, d represents the period number of the source-load matching period; represents the output power of renewable energy at time t in period d; N represents the total number of areas covered by the corresponding power grid; and It represents the positive and negative deviations of load matching introduced at time t within period d; represents the matching load sequence at time t in period d; represents the reference load sequence at time t in period d; d represents the optimization variable of the d-period benchmark load adjustment coefficient, which represents the relationship between the matching load and the benchmark load, 0≤λ d ≤1; ε is the load matching deviation coefficient, which plays a constraining role in the entire optimization period to ensure that the deviation generated by the matching process does not exceed a specific ratio of the target load power; T represents the total duration.

[0085] Specifically, the operating time constraint of the thermal power unit may be determined based on predetermined upper and lower limits of the operating time of the thermal power unit.

[0086] Optionally, the above working time constraint function of the thermal power unit can be expressed as:

[0087]

[0088] Among them, tg represents the serial number of the thermal power unit; and Correspondingly, it indicates the upper and lower limits of the working hours of the thermal power unit, m indicates the month number, y indicates the year number, Indicates the installed capacity of thermal power unit tg; represents the planned monthly power generation of thermal power unit tg in the mth month of typical scenario year y; It means that any thermal power unit in the system satisfies this formula.

[0089] It is understandable that by establishing a constraint function for the working hours of thermal power units and optimizing the model based on the constraint function of the working hours of thermal power units, it is possible to limit the annual utilization hours of thermal power units. On the one hand, it can ensure the benefits of thermal power, and on the other hand, it can reduce the dependence on the "electricity supply" of thermal power, so as to gradually realize functional transformation and assist in the absorption of more new energy.

[0090] Optionally, the process of constructing the upper-level planning model based on the upper-level objective function includes: based on the upper-level objective function, planning investment constraint function, maximum installed capacity constraint function, power supply margin function, flexibility margin constraint function, flexibility margin constraint function, carbon emission constraint function, source-load matching constraint function, and thermal power unit working time constraint function.

[0091] Step 102, construct a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost of the power grid, with the operation status of the thermal power unit, the operation status of the energy storage unit, the power of the new energy abandonment and the load shedding power as the optimization variables, and the comprehensive operation cost includes the net load fluctuation cost, the simulated operation cost, and the cost of the new energy abandonment and load shedding. This step can be conducive to constructing a two-level decision-making model that considers ensuring the safe operation of the power grid in extreme weather scenarios and has a low comprehensive cost by establishing a lower-level simulation operation model that considers the cost of ensuring the safe operation of the system in extreme weather scenarios through the abandonment of new energy and load shedding, and then after optimizing the two-level decision-making model, it can ensure the safety of power supply in extreme weather, improve the power supply reliability of the power grid system, and control the comprehensive cost of the power grid system.

[0092] Optionally, the above process of constructing the lower-level operation simulation model includes: establishing a net load fluctuation cost function and a simulated operation cost function based on the operating status of the thermal power unit and the operating status of the energy storage unit.

[0093] Optionally, the expression of the net load fluctuation cost function is:

[0094]

[0095] in, is the net load of the system at time t; ρ L is the net load fluctuation cost coefficient.

[0096] Optionally, the operating status of the thermal power unit includes the power generation power of the corresponding thermal power unit; the operating status of the energy storage unit includes: the charging and discharging power of the energy storage unit.

[0097] Optionally, the expression of the above simulation running cost function is:

[0098]

[0099] C g_FT =C g_MF +C g_GD +C g_SP

[0100] C SB,ch-dis =C G / η ch / η dis +Cch +C dis

[0101] in, represents the simulation running cost, and represents the unit variable operating cost of thermal power unit g, covering the operating cost and deep regulation cost of thermal power unit; represents the unit startup cost of thermal power unit g; P g,k,t and Correspondingly, it represents the unit output and operating capacity of the thermal power unit in the tth period of the kth typical day; It is expressed as the unit load reduction cost of grid node n; It is expressed as the unit load reduction capacity of grid node n in the tth period of the kth typical day; C loss System network loss cost; n is the node number of the power grid; represents C SB,ch-dis Represents the charging cost of the energy storage unit; represents the regulation cost of thermal power units; C g represents the cost of power generation; C g_MF represents the flexibility transformation cost; C g_GD is the loss cost of deep peak regulation; C g_SP represents the start-stop peak load regulation cost; C G represents the external electricity price, η ch and η dis Corresponding to the charging and discharging efficiency of the energy storage unit, C ch and C dis It corresponds to the life loss cost of the energy storage unit during the charging and discharging process.

[0102] Optionally, the process of constructing the lower-level operation simulation model includes: establishing a new energy curtailment and load shedding cost function based on the new energy curtailment power and load shedding power.

[0103] Optionally, the expression of the above-mentioned new energy power abandonment and load shedding cost function is:

[0104]

[0105] in, represents the cost of new energy curtailment and load shedding, represents the load reduction cost at time t, which is usually negotiated with the user; Indicates load shedding power; represents the cost of photovoltaic power curtailment; Indicates the amount of photovoltaic power abandoned; represents the cost of wind power curtailment; Represents the cost of wind power curtailment.

[0106] Optionally, the above process of constructing the lower-level operation simulation model includes: establishing a lower-level objective function based on a net load fluctuation cost function, a simulation operation cost function, and a new energy power abandonment and load shedding cost function.

[0107] Optionally, the expression of the above lower layer objective function is:

[0108]

[0109] Where T represents the total duration of simulation operation; Correspondingly, it represents the net load fluctuation cost, the cost of new energy power abandonment and load shedding, and the simulated operation cost; w pnet 、w punish and w oper The corresponding weight factors represent the net load fluctuation cost, the cost of renewable energy curtailment and load shedding, and the simulated operation cost, w pnet +w punish +w oper =1.

[0110] Specifically, the weight factors of net load fluctuation cost, renewable energy power abandonment and load shedding cost, and simulated operation cost can be determined by analytic hierarchy process.

[0111] Optionally, the above process of constructing the lower-level operation simulation model includes: establishing a time-series power balance constraint function, a thermal power unit operation constraint function, an energy storage operation constraint function, a new energy power supply constraint function, a tie line interaction power constraint function, and a frequency regulation standby constraint function.

[0112] Optionally, the expression of the above-mentioned time-series power and quantity balance constraint function is:

[0113]

[0114] Among them, P t Gen represents the output power of the thermal power unit at time t; P t WT Represents the output power of the wind turbine at time t; P t PV Represents the output power of the photovoltaic unit at time t; P t ESS Represents the charging and discharging power of the energy storage unit at time t; P t Grid represents the tie line power at time t; P t Load represents the total load of the power grid at time t; Indicates the minimum power generation of thermal power units; represents the planned monthly power generation of thermal power unit tg in the mth month of typical scenario year y; Indicates the installed capacity of thermal power unit tg; Indicates the maximum utilization hours of the tg unit; It means that any thermal power unit in the system satisfies this formula; y represents the year number.

[0115] Optionally, the expression of the above thermal power unit operation constraint function is:

[0116] 0≤P g,t ≤G g

[0117]

[0118] Among them, P g,t G represents the output power of thermal power unit g during period t; g Indicates the installed capacity of thermal power units; Indicates the rate of descent; Indicates the rate of climb; Indicates the minimum output ratio of thermal power units; O g,t represents the capacity of online units at time t; It represents the startup capacity of thermal power unit at time t; represents the shutdown capacity of thermal power units at time t; Indicates the minimum start-up time of thermal power units; Indicates the minimum downtime of thermal power units.

[0119] Optionally, the expression of the above energy storage operation constraint function is:

[0120]

[0121] S i,0 =S i,T

[0122] Among them, P t cha and P t dis Correspondingly represents the charging and discharging power of the energy storage unit at time t; Representation and Corresponding to the 0-1 variable representing the charging and discharging status, charging is 1 and discharging is 0; indicating; P t chamax and P t dismax Correspondingly represents the maximum charging and discharging power of the energy storage unit; Represents the capacity of the energy storage unit at time t; and Corresponding to the upper and lower limits of the state of charge of the energy storage unit; η ESC and η ESDCorrespondingly represents the charging and discharging efficiency of the energy storage unit; S i,0 and S i,T Correspondingly represents the initial charge state and the final charge state of the energy storage unit.

[0123] Specifically, let S i,0 =S i,T , which can ensure that the ES capacity at the beginning and end of the scheduling cycle remains consistent, thereby ensuring the continuity of scheduling.

[0124] Optionally, the expression of the above renewable energy power supply constraint function is:

[0125]

[0126] Among them, P t WT and P t pv Corresponding to the output power of the wind turbine and photovoltaic unit at time t, it can specifically correspond to the normalized theoretical power output time series of the wind turbine and photovoltaic unit at time t; C WT and C PV Correspondingly represents the installed capacity of wind turbines and photovoltaic units.

[0127] Specifically, the renewable energy power supply constraint function can ensure that the actual output of renewable energy is within the maximum output limit, and with the help of the normalized theoretical power generation output time series, the capacity of the renewable energy station is linked to the power generation power.

[0128] Optionally, the expression of the above tie line interaction power constraint function is:

[0129] P grid,min <P grid (t) <P grid,max -P grid,R (t)

[0130] Among them, P grid (t) represents the interconnection power of the tie line at time t; P grid,min and P grid,max Corresponding to the minimum and maximum transmission power of the tie line, P grid,R (t) represents the spare capacity obtained from the external network.

[0131] Optionally, the above-mentioned frequency regulation standby constraint function is composed of a thermal power unit standby constraint function, an energy storage unit standby constraint function, and a power grid system standby constraint function.

[0132] Optionally, the expression of the reserve constraint function on the above thermal power unit is:

[0133]

[0134] in, and Correspondingly, it represents the upper reserve power and lower reserve power of the thermal power unit at time t; and Correspondingly represents the rising ramp rate and falling ramp rate of thermal power unit i; and Correspondingly, it represents the upper reserve power and lower reserve power of thermal power unit i at time t; u g,i,t Indicates the power-on status of thermal power unit i in period t (1 for power-on and 0 for power-off); P g,i,max represents the maximum technical output of thermal power unit i; P g,i,t represents the output of thermal power unit in period t; P g,i,1 Indicates the output of thermal power unit in period 1; u down,i,t Indicates the deep peak load regulation state of thermal power unit i in period t.

[0135] Optionally, the expression of the above energy storage unit standby constraint function is:

[0136]

[0137] in, and Correspondingly, it represents the power consumption of the energy storage power station at time t; P ess,max Indicates the maximum output power of the energy storage system; P ess,t represents the output power of the energy storage system at time t; Represents the discharge efficiency of the energy storage system; E ess,t Represents the amount of electricity in the energy storage system at time t; E ess Indicates the capacity of the energy storage system; Indicates the lower limit of the charge state of the energy storage system; Indicates the upper limit of the state of charge of the energy storage system; Δt indicates the time granularity, Indicates the charging efficiency of the energy storage system.

[0138] Optionally, the expression of the above power grid system reserve constraint function is:

[0139]

[0140] in, represents the reserve capacity of the pumped storage power station during period t; P r,t represents the output of the new energy unit in period t; P load,t represents the system load during period t, k e It represents the replacement ratio of thermal power to energy storage and frequency regulation capacity; ρ1 represents the new energy reserve rate, ρ2 represents the load reserve rate, and both ρ1 and ρ2 can be set to 10%.

[0141] Optionally, the process of constructing the lower-level operation simulation model includes: constructing the lower-level operation simulation model based on the lower-level objective function, the time-series power balance constraint function, the thermal power unit operation constraint function, the energy storage operation constraint function, the new energy power supply constraint function, the interconnection line interactive power constraint function, and the frequency regulation standby constraint function

[0142] Step 103, construct a two-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model, so that the outputs of the upper-layer planning model and the lower-layer operation simulation model influence each other. This step can be beneficial to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating state of thermal power units, the optimal operating state of energy storage units, the optimal new energy abandonment power, and the optimal load shedding power for the two-layer decision model based on typical source-load matching scenarios, and can improve the power supply reliability of the power grid system under the premise of ensuring power grid security and the lowest comprehensive cost.

[0143] Step 104, based on multi-time series typical source-load matching scenarios, multiple rounds of iterative optimization are performed on the two-layer decision model to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal new energy power abandonment, and the optimal load shedding power. Based on steps 101 to 103, this step takes into account the cost caused by the source-load power imbalance risk by constructing a comprehensive planning cost that minimizes equipment investment costs, equipment maintenance costs, planned electricity purchase costs, and source-load power imbalance risk costs, and takes the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables; and constructs a lower-level operation simulation model that takes into account the comprehensive operation costs that minimize net load fluctuation costs, simulated operation costs, and new energy power abandonment and load shedding costs, and takes into account the operation status of thermal power units, energy storage units, new energy power abandonment, and load shedding to ensure the optimal load. The cost of safe operation of the system under extreme weather scenarios is then calculated, and then a lower-level operation simulation model is constructed based on the operating status of thermal power units, the operating status of energy storage units, the power curtailment of new energy, and the load shedding power as optimization variables. A two-layer decision-making model with the outputs of the upper and lower layers influencing each other is further constructed based on the upper-layer planning model and the lower-layer operation simulation model. Then, based on the typical source-load matching scenario, the two-layer decision-making model is used to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal power curtailment of new energy, and the optimal load shedding power, which can improve the power supply reliability of the power grid system while ensuring the safety of the power grid and the lowest overall cost.

[0144] Optionally, the above-mentioned process of performing multiple rounds of iterative optimization of the two-layer decision-making model based on multi-time series typical source-load matching scenarios includes: when performing each round of iterative optimization, determining the wind turbine assembled unit capacity, photovoltaic motor assembled unit capacity and energy storage unit assembled unit capacity after this round of optimization through the upper-level planning model based on the multi-time series typical source-load matching scenario; based on the wind turbine assembled unit capacity, photovoltaic motor assembled unit capacity and energy storage unit assembled unit capacity after this round of optimization, determining the operating status of the thermal power unit, the operating status of the energy storage unit, the new energy abandoned power, the load shedding power and the comprehensive operating cost after this round of optimization through the lower-level operation simulation model; based on the comprehensive operating cost after this round of optimization, determining whether to terminate the optimization of the two-layer decision-making model. type of iterative optimization; when the result of the judgment is that the iterative optimization of the two-layer decision-making model is not terminated, the next round of iterative optimization is started; when the result of the judgment is that the iterative optimization of the two-layer decision-making model is terminated, the wind turbine assembled capacity, photovoltaic assembled capacity and energy storage assembled capacity after this round of optimization are respectively determined as the optimal wind turbine assembled capacity, the optimal photovoltaic assembled capacity, and the optimal energy storage assembled capacity, and the thermal power unit operating state, energy storage unit operating state, new energy power abandonment power and load shedding power after this round of optimization are respectively determined as the optimal thermal power unit operating state, the optimal energy storage unit operating state, the optimal new energy power abandonment power and the optimal load shedding power.

[0145] The following further describes the power grid configuration operation coordination method provided by the embodiment of the present invention. Figure 2 As shown, the following steps may be included:

[0146] Step 201, constructing an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid and with the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables.

[0147] Step 202, constructing a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost of the power grid, with the operation status of the thermal power units, the operation status of the energy storage units, the power abandonment of new energy sources and the load shedding power as optimization variables.

[0148] Step 203, constructing a two-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model, so that the outputs of the upper-layer planning model and the lower-layer operation simulation model influence each other.

[0149] Step 204, obtaining historical data of the renewable energy power supply and the total load of the power grid, and clustering the historical data of the renewable energy power supply and the total load of the power grid by a clustering algorithm to obtain multiple clusters of clustered data.

[0150] Specifically, the above-mentioned new energy power supply can be understood as the total power supply of photovoltaic units and wind turbine units.

[0151] Optionally, the above-mentioned process of clustering the historical data of renewable energy power supply and total grid load through a clustering algorithm to obtain multiple clustered data includes: clustering the historical data of renewable energy power supply and the historical data of total grid load respectively through a clustering algorithm, and obtaining multiple new energy data clusters and multiple load data clusters accordingly.

[0152] Specifically, the above clustering algorithm may be a Kmeans algorithm.

[0153] Step 205 : determining the historical source-load matching scenario corresponding to each cluster data based on the corresponding time information and meteorological data, and determining a multi-time series typical source-load matching scenario based on each historical source-load matching scenario.

[0154] Specifically, step 204 and step 205 may also be performed before step 201 , step 202 or step 203 .

[0155] Specifically, the above historical source-load matching scenarios may include: source-load matching scenarios in different seasons, different weather conditions, and daytime and nighttime.

[0156] Specifically, the above-mentioned multi-time series typical source-load matching scenarios may include: hourly-varying source-load matching scenarios, daily-varying source-load matching scenarios, and seasonal-varying source-load matching scenarios.

[0157] Optionally, the above process of determining the historical source-load matching scenarios corresponding to each cluster data based on corresponding time information and meteorological data includes: determining the association relationship between each new energy data cluster and the corresponding load data cluster and the corresponding meteorological data, and determining the historical source-load matching scenarios corresponding to each new energy data cluster and the corresponding load data cluster based on the corresponding association relationship.

[0158] Optionally, the process of determining multi-time series typical source-load matching scenarios based on various historical source-load matching scenarios includes: comprehensively considering the future development stages of the increase in new energy penetration and new load, such as an increase in new energy penetration by 20%, 30% and 40%, and an increase in load by 10%, 20% and 30%, and combining historical source-load matching scenarios to determine the multi-time series typical source-load matching scenarios.

[0159] Step 206 , performing multiple rounds of iterative optimization on the two-layer decision model based on multi-time series typical source-load matching scenarios.

[0160] Optionally, the above process of performing multiple rounds of iterative optimization on the double-layer decision model based on multiple time-series typical source-load matching scenarios includes: performing multiple rounds of iterative optimization on the double-layer decision model based on each time-series typical source-load matching scenario.

[0161] Optionally, the aforementioned process of predicting the net load of the power grid within a preset time period in the future based on the historical data of renewable energy power supply and the total load of the power grid includes: clustering the historical data of renewable energy power supply and the total load of the power grid through a clustering algorithm to obtain multiple clusters of clustered data, acquiring the historical data of the renewable energy power supply and the total load of the power grid, and clustering the historical data of the renewable energy power supply and the total load of the power grid through a clustering algorithm to obtain multiple clusters of clustered data, determining the historical source-load matching scenarios corresponding to each cluster of clustered data based on corresponding time information and meteorological data, and determining multiple time series typical source-load matching scenarios based on each historical source-load matching scenario, and determining the net load of the power grid under the typical source-load matching scenarios of each time series within the preset time period in the future.

[0162] The embodiments of the present invention can accurately depict the time series throughout the year, which is conducive to the quantitative analysis of the power matching capability between renewable energy output and load, and then evaluate the comprehensive benefits and potential risks of the planning scheme in multi-time series and massive scenarios. After the two-layer decision model is established, it can improve the power supply reliability of the power grid system while ensuring the safety of the power grid and the lowest overall cost.

[0163] The following further describes the power grid configuration operation coordination method provided by the embodiment of the present invention. Figure 3 As shown, Figure 1 Step 104 in may include the following steps:

[0164] Step 1041 , decomposing the net load in a multi-time series typical source-load matching scenario into a fast-varying component and a slow-varying component.

[0165] Optionally, the process of decomposing the net load in the multi-time series typical source-load matching scenario into a fast-varying component and a slow-varying component includes:

[0166] The recursive Hilbert transform and wavelet filtering method are used to decompose the net load in a multi-time series typical source-load matching scenario to obtain the above-mentioned fast-varying component and slow-varying component.

[0167] Step 1042, when performing each round of iterative optimization, the two-layer decision model is optimized by using the thermal power unit to adjust the slow-changing component and using the energy storage unit to adjust the fast-changing component.

[0168] The embodiment of the present invention can improve the regulation effect of the net load by utilizing a thermal power unit with a slower power supply reduction change rate to regulate the slow-changing component in the net load, and utilizing an energy storage unit with a faster power supply reduction change rate to regulate the fast-changing component in the net load.

[0169] Figure 4 is a structural diagram of a power grid configuration and operation coordination device provided in an embodiment of the present invention, and the device is suitable for executing the power grid configuration and operation coordination method provided in an embodiment of the present invention. Figure 4 As shown, the device may specifically include:

[0170] The upper-level planning model construction module 401 is used to construct an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid, with the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables. The comprehensive planning cost includes: equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost. This module can be used to construct a two-level decision model that considers the source-load power imbalance risk by establishing an upper-level planning model that considers the source-load power imbalance risk. After optimizing the two-level decision model, it can avoid the source-load power imbalance risk as much as possible and improve the power supply reliability of the power grid system.

[0171] Optionally, new energy units include: wind turbine units and photovoltaic units.

[0172] Optionally, the above-mentioned upper-level planning model construction module 401 can be specifically used to establish a source-load power imbalance risk cost function based on the conditional risk value; establish an equipment investment cost function based on the installed capacity of the energy storage unit, the installed capacity of the new energy unit, and the full life cycle cost function of the energy storage unit; establish an equipment maintenance cost function based on the installed capacity of the energy storage unit and the installed capacity of the new energy unit; construct a planned electricity purchase cost function based on the threshold power and the market electricity price; and construct an upper-level objective function based on the source-load power imbalance risk cost function, the equipment investment cost function and the planned electricity purchase cost function, and construct an upper-level planning model based on the upper-level objective function.

[0173] Optionally, the power grid configuration and operation coordination device provided by an embodiment of the present invention also includes: a conditional risk value determination module, which is used to obtain historical data on the power supply of new energy and the total load of the power grid before establishing a source-load power imbalance risk cost function based on the conditional risk value, and determine the above-mentioned conditional risk value based on the historical data on the power supply of new energy and the total load of the power grid.

[0174] Optionally, the above-mentioned source-load power imbalance risk cost function establishment module can be specifically used to predict the net load of the power grid within a preset time period in the future based on the historical data of the new energy power supply and the total load of the power grid; determine the source-load mismatch loss function based on the net load of the power grid within a preset time period in the future and the power adjustment of stable power supply resources; determine the above-mentioned conditional risk value based on the source-load mismatch loss function.

[0175] Optionally, the above-mentioned upper-level planning model construction module 401 can be specifically used to establish a planning investment constraint function, a maximum installed capacity constraint function, a power supply margin function, a flexibility margin constraint function, a carbon emission constraint function, a source-load matching constraint function, and a thermal power unit working time constraint function before constructing the upper-level planning model based on the upper-level objective function.

[0176] Optionally, the above-mentioned upper-level planning model construction module 401 can be specifically used based on the upper-level objective function, planning investment constraint function, maximum installed capacity constraint function, power supply margin function, flexibility margin constraint function, flexibility margin constraint function, carbon emission constraint function, source-load matching constraint function, and thermal power unit working time constraint function.

[0177] The lower-level operation simulation model construction module 402 is used to construct a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost of the power grid, with the operation status of the thermal power unit, the operation status of the energy storage unit, the power abandonment of new energy, and the power of load shedding as optimization variables. The comprehensive operation cost includes the net load fluctuation cost, the simulated operation cost, and the cost of abandonment of new energy and load shedding. This module can be used to establish a lower-level simulation operation model that considers the cost of ensuring the safe operation of the system in extreme weather scenarios through abandonment of new energy and load shedding, which can help to build a two-level decision-making model that considers ensuring the safe operation of the power grid in extreme weather scenarios and has a low comprehensive cost. After optimizing the two-level decision-making model, it can ensure the safety of power supply in extreme weather, improve the power supply reliability of the power grid system, and control the comprehensive cost of the power grid system.

[0178] Optionally, the above-mentioned lower-level operation simulation model construction module 402 can be specifically used to establish a net load fluctuation cost function and a simulated operation cost function based on the operating status of the thermal power unit and the operating status of the energy storage unit; establish a new energy power abandonment and load shedding cost function based on the new energy power abandonment power and the load shedding power; establish a lower-level objective function based on the net load fluctuation cost function, the simulated operation cost function and the new energy power abandonment and load shedding cost function; establish a time-series power balance constraint function, a thermal power unit operation constraint function, an energy storage operation constraint function, a new energy power supply constraint function, an interconnection line interactive power constraint function, and a frequency regulation standby constraint function; and construct a lower-level operation simulation model based on the lower-level objective function, the time-series power balance constraint function, the thermal power unit operation constraint function, the energy storage operation constraint function, the new energy power supply constraint function, the interconnection line interactive power constraint function, and the frequency regulation standby constraint function.

[0179] The two-layer decision model construction module 403 is used to construct a two-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model, so that the outputs of the upper-layer planning model and the lower-layer operation simulation model affect each other. This module can be beneficial to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating state of thermal power units, the optimal operating state of energy storage units, the optimal new energy abandonment power, and the optimal load shedding power for the two-layer decision model based on typical source-load matching scenarios, and can improve the power supply reliability of the power grid system under the premise of ensuring power grid security and the lowest comprehensive cost.

[0180] Iterative optimization module 404 is used to perform multiple rounds of iterative optimization on the two-layer decision model based on multi-time series typical source-load matching scenarios to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal power abandonment of new energy, and the optimal load shedding power. This module combines modules 401 to 403, and takes the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables by constructing a comprehensive planning cost with the goal of minimizing the comprehensive planning cost including equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost, and takes the cost caused by the source-load power imbalance risk into consideration; and constructs a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost including net load fluctuation cost, simulated operation cost, and new energy abandonment and load shedding cost, and takes the operating status of thermal power units, energy storage units, new energy abandonment power, and load shedding power as optimization variables, and takes into consideration the extreme guarantee through new energy abandonment and load shedding. The cost of safe operation of the system under weather scenarios is then calculated, and then a lower-level operation simulation model is constructed based on the operating status of thermal power units, the operating status of energy storage units, the power curtailment of renewable energy, and the load shedding power as optimization variables. A two-layer decision-making model with the outputs of the upper and lower layers influencing each other is further constructed based on the upper-layer planning model and the lower-layer operation simulation model. Then, based on the typical source-load matching scenario, the two-layer decision-making model is used to obtain the optimal installed capacity of renewable energy units, the optimal installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal power curtailment of renewable energy, and the optimal load shedding power, which can improve the power supply reliability of the power grid system while ensuring the safety of the power grid and the lowest overall cost.

[0181] Optionally, the above-mentioned iterative optimization module 404 can be specifically used to, when performing each round of iterative optimization, determine the wind turbine assembly capacity, photovoltaic motor assembly capacity and energy storage assembly capacity after this round of optimization through the upper-level planning model based on the multi-time series typical source-load matching scenario; based on the wind turbine assembly capacity, photovoltaic motor assembly capacity and energy storage assembly capacity after this round of optimization, determine the operating status of the thermal power unit, the operating status of the energy storage unit, the new energy power abandonment, the load shedding power and the comprehensive operating cost after this round of optimization through the lower-level operation simulation model; determine whether to end the iterative optimization of the two-layer decision-making model based on the comprehensive operating cost after this round of optimization; in the judgment When the result is determined to be not to end the iterative optimization of the two-layer decision-making model, the next round of iterative optimization is started; when the result is determined to end the iterative optimization of the two-layer decision-making model, the wind turbine assembled unit capacity, the photovoltaic motor assembled unit capacity and the energy storage unit assembled unit capacity after this round of optimization are respectively determined as the optimal wind turbine assembled unit capacity, the optimal photovoltaic motor assembled unit capacity, and the optimal energy storage unit assembled unit capacity, and the thermal power unit operating state, energy storage unit operating state, new energy power abandonment power and load shedding power after this round of optimization are respectively determined as the optimal thermal power unit operating state, the optimal energy storage unit operating state, the optimal new energy power abandonment power and the optimal load shedding power.

[0182] Optionally, the above-mentioned iterative optimization module 404 can be specifically used to decompose the net load in a multi-time series typical source-load matching scenario into a fast-changing component and a slow-changing component; when performing each round of iterative optimization, the two-layer decision model is optimized by using thermal power units to adjust the slow-changing component and using energy storage units to adjust the fast-changing component.

[0183] Optionally, the power grid configuration and operation coordination device provided by an embodiment of the present invention also includes: a multi-time series typical source-load matching scenario determination module, which is used to obtain historical data on the power supply of new energy and the total load of the power grid, and cluster the historical data of the power supply of new energy and the total load of the power grid through a clustering algorithm to obtain multiple clusters of clustered data; determine the historical source-load matching scenario corresponding to each cluster of clustered data based on corresponding time information and meteorological data, and determine multiple time series typical source-load matching scenarios based on each historical source-load matching scenario.

[0184] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0185] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the grid configuration and operation coordination method provided in any of the above embodiments is implemented.

[0186] An embodiment of the present invention further provides a computer-readable medium having a computer program stored thereon, and when the program is executed by a processor, the method for coordinated operation of power grid configuration provided in any of the above embodiments is implemented.

[0187] The embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the power grid configuration and operation coordination method as described in any one of the embodiments of the present invention.

[0188] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 of an electronic device suitable for implementing an embodiment of the present invention. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0189] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0190] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0191] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0192] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0193] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0194] The modules and / or units involved in the embodiments of the present invention may be implemented in software or hardware. The modules and / or units described may also be arranged in a processor, for example, may be described as: a processor including an upper-level planning model construction module, a lower-level operation simulation model construction module, a two-level decision model construction module, and a two-level decision model construction module. The names of these modules do not, in some cases, constitute limitations on the modules themselves.

[0195] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by a device, the device includes: constructing an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid, with the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables, and the comprehensive planning cost includes: equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost; constructing an upper-level planning model with the goal of minimizing the comprehensive operating cost of the power grid, with the operating status of thermal power units, the operating status of energy storage units, the power abandonment of new energy, and the load shedding power as optimization variables. The lower-level operation simulation model of the variables is constructed, and the comprehensive operation cost includes the net load fluctuation cost, the simulated operation cost, and the cost of renewable energy power abandonment and load shedding; a two-level decision-making model is constructed based on the upper-level planning model and the lower-level operation simulation model, so that the outputs of the upper-level planning model and the lower-level operation simulation model influence each other; and the two-level decision-making model is iteratively optimized for multiple rounds based on multi-time series typical source-load matching scenarios to obtain the optimal installed capacity of renewable energy units, the optimal installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal renewable energy power abandonment power, and the optimal load shedding power.

[0196] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for coordinated operation of power grid configuration, characterized in that: include: Construct an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid, with the installed capacity of new energy generators and the installed capacity of energy storage generators as optimization variables. The comprehensive planning cost includes: equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost; Construct a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost of the power grid, with the operation status of thermal power units, the operation status of energy storage units, the power abandonment of new energy sources and the power shedding of loads as optimization variables. The comprehensive operation cost includes the net load fluctuation cost, the simulated operation cost and the power abandonment and load shedding costs of new energy sources; Building a two-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model so that the outputs of the upper-layer planning model and the lower-layer operation simulation model influence each other; and Based on multi-time typical source-load matching scenarios, the two-layer decision model is iteratively optimized for multiple rounds to obtain the optimal installed capacity of new energy units, the optimal installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal new energy curtailment power, and the optimal load shedding power.

2. The grid configuration and operation coordination method according to claim 1, characterized in that: The new energy generator set includes: a wind generator set and a photovoltaic generator set; the iterative optimization of the two-layer decision model includes: When performing each round of iterative optimization, the wind turbine assembly capacity, photovoltaic motor assembly capacity and energy storage assembly capacity after this round of optimization are determined through the upper-level planning model based on the multi-time series typical source-load matching scenario; Based on the wind turbine installed capacity, photovoltaic installed capacity and energy storage installed capacity after this round of optimization, the operating state of the thermal power unit, the operating state of the energy storage unit, the new energy power abandonment, the load shedding power and the comprehensive operating cost after this round of optimization are determined through the lower-level operation simulation model; Determining whether to end the iterative optimization of the two-layer decision model based on the comprehensive operating cost after the current round of optimization; When the result of the determination is that the iterative optimization of the two-layer decision model is not completed, starting to execute the next round of iterative optimization; When the judgment result is to end the iterative optimization of the two-layer decision-making model, the wind turbine assembled unit capacity, photovoltaic motor assembled unit capacity and energy storage unit assembled unit capacity after the optimization in this round are respectively determined as the optimal wind turbine assembled unit capacity, the optimal photovoltaic motor assembled unit capacity, and the optimal energy storage unit assembled unit capacity; and the operating state of the thermal power unit, the operating state of the energy storage unit, the new energy power abandonment power and the load shedding power after the optimization in this round are respectively determined as the optimal thermal power unit operating state, the optimal energy storage unit operating state, the optimal new energy power abandonment power and the optimal load shedding power.

3. The grid configuration and operation coordination method according to claim 1, characterized in that: Before performing multiple rounds of iterative optimization on the two-layer decision model based on the multi-time series typical source-load matching scenario, the power grid configuration operation coordination method further includes: Acquiring historical data of the renewable energy power supply and the total load of the power grid, and clustering the historical data of the renewable energy power supply and the total load of the power grid by a clustering algorithm to obtain multiple clusters of clustered data; and The historical source-load matching scenario corresponding to each cluster data is determined based on the corresponding time information and meteorological data, and the multi-time series typical source-load matching scenario is determined based on each historical source-load matching scenario.

4. The grid configuration and operation coordination method according to claim 1, characterized in that: The upper-level planning model constructed with the goal of minimizing the comprehensive planning cost of the power grid and taking the installed capacity of new energy generators and the installed capacity of energy storage generators as optimization variables includes: Establish source-load power imbalance risk cost function based on conditional risk value; Establishing an equipment investment cost function based on the installed capacity of the energy storage unit, the installed capacity of the new energy unit, and the full life cycle cost function of the energy storage unit; Establishing an equipment maintenance cost function based on the installed capacity of the energy storage unit and the installed capacity of the new energy unit; Constructing a planned electricity purchase cost function based on the threshold power and market electricity price; and An upper-level objective function is constructed based on the source-load power imbalance risk cost function, the equipment investment cost function and the planned power purchase cost function, and the upper-level planning model is constructed based on the upper-level objective function.

5. The grid configuration and operation coordination method according to claim 4, characterized in that: Before constructing the upper-level planning model based on the upper-level objective function, the power grid configuration and operation coordination method further includes: Establish planning investment constraint function, maximum installed capacity constraint function, power supply margin function, flexibility margin constraint function, carbon emission constraint function, source-load matching constraint function, and thermal power unit working time constraint function; The constructing the upper-level planning model based on the upper-level objective function comprises: Based on the upper-level objective function, the planned investment constraint function, the maximum installed capacity constraint function, the power supply margin function, the flexibility margin constraint function, the flexibility margin constraint function, the carbon emission constraint function, the source-load matching constraint function, and the thermal power unit working time constraint function.

6. The grid configuration and operation coordination method according to claim 1, characterized in that: The construction aims to minimize the comprehensive operation cost of the power grid, and takes the operation status of thermal power units, the operation status of energy storage units, the power abandonment of new energy sources and the load shedding power as optimization variables. The lower-level operation simulation model includes: Establishing a net load fluctuation cost function and a simulated operation cost function based on the operating status of the thermal power unit and the operating status of the energy storage unit; Establishing a new energy power abandonment and load shedding cost function based on the new energy power abandonment and load shedding power; Establishing a lower objective function based on the net load fluctuation cost function, the simulated operation cost function and the new energy power abandonment and load shedding cost function; Establishing a time-series power balance constraint function, a thermal power unit operation constraint function, an energy storage operation constraint function, a new energy power supply constraint function, a tie line interactive power constraint function, and a frequency regulation standby constraint function; and The lower-level operation simulation model is constructed based on the lower-level objective function, the sequential power and electricity balance constraint function, the thermal power unit operation constraint function, the energy storage operation constraint function, the new energy power supply constraint function, the interconnection line interaction power constraint function, and the frequency regulation standby constraint function.

7. The grid configuration and operation coordination method according to claim 1, characterized in that: The performing multiple rounds of iterative optimization on the two-layer decision model based on multi-time series typical source-load matching scenarios includes: Decomposing the net load in the multi-time series typical source-load matching scenario into a fast-varying component and a slow-varying component; When performing each round of iterative optimization, the two-layer decision model is optimized by using the thermal power unit to adjust the slow-changing component and using the energy storage unit to adjust the fast-changing component.

8. A power grid configuration and operation coordination device, characterized in that: include: The upper-level planning model construction module is used to construct an upper-level planning model with the goal of minimizing the comprehensive planning cost of the power grid, with the installed capacity of new energy units and the installed capacity of energy storage units as optimization variables. The comprehensive planning cost includes: equipment investment cost, equipment maintenance cost, planned electricity purchase cost, and source-load power imbalance risk cost; A lower-level operation simulation model construction module is used to construct a lower-level operation simulation model with the goal of minimizing the comprehensive operation cost of the power grid, with the operation status of thermal power units, the operation status of energy storage units, the power abandonment of new energy sources and the power shedding of loads as optimization variables. The comprehensive operation cost includes the net load fluctuation cost, the simulation operation cost and the power abandonment and load shedding costs of new energy sources; a two-layer decision model construction module, used to construct a two-layer decision model based on the upper-layer planning model and the lower-layer operation simulation model, so that the outputs of the upper-layer planning model and the lower-layer operation simulation model affect each other; and The iterative optimization module is used to perform multiple rounds of iterative optimization on the two-layer decision-making model based on multi-time series typical source-load matching scenarios to obtain the optimal installed capacity of new energy units, the installed capacity of energy storage units, the optimal operating status of thermal power units, the optimal operating status of energy storage units, the optimal new energy curtailment power and the optimal load shedding power.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the power grid configuration and operation coordination method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the power grid configuration and operation coordination method as described in any one of claims 1 to 7 is implemented.

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