Source-grid-load-storage bi-level collaborative planning method for power distribution network

Abstract

Disclosed in the present invention is a source-grid-load-storage bi-level collaborative planning method for a power distribution network. The method comprises carrying out planning prediction on source-grid-load-storage data of a power distribution network to obtain planned year load data; on the basis of power grid configuration and energy planning constraint conditions, by using the maximization of the total revenue of a main operator as an objective function, constructing a power grid planning model, wherein the objective function comprises an energy supply operator objective function, a power distribution network operator objective function, an energy storage operator objective function and a load aggregator objective function; on the basis of power generation resource operation costs, constructing an electric energy market simulation clearing model; using the power grid planning model as an upper-level planning layer, using the electric energy market simulation clearing model as a lower-level planning layer, inputting the planned year load data into the electric energy market simulation clearing model, and iteratively solving a bi-level collaborative planning model to obtain an optimal power grid planning scheme. The embodiments implement bi-level collaborative planning of the market and a power grid, and improve the safety and stability of the power grid.

Description

A two-layer collaborative planning method for source, grid, load and storage in distribution network Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a source-grid-load-storage dual-layer collaborative planning method for a distribution network. Background Art

[0002] With the development of new energy, the intermittent nature of renewable energy sources like wind and photovoltaics, as well as the addition of energy storage, traditional grid planning models face numerous challenges, necessitating the introduction of appropriate scenarios and constraints. In the context of spot markets and new power systems, uncertainty on both the power supply and demand sides is increasing, leading to significant uncertainty in grid planning at the source-load level.

[0003] When renewable energy penetration is high, grid planning may result in energy waste or fail to meet operational needs during the planning period. Even when renewable energy output fluctuates dramatically, insufficient regulation may lead to system failures. Spot market operational information can reflect grid operation, and grid planning directly impacts market outcomes, thereby affecting the interests of various market participants. In the context of the new power system and dual carbon policies, traditional grid planning methods that separate various entities are less scientific and economical, and coordination between grid components is weak, making it impossible to balance grid security and stability.

[0004] Summary of the Invention

[0005] The present invention provides a source-grid-load-storage dual-layer collaborative planning method for a distribution network, which realizes source-grid-load-storage dual-layer collaborative planning of a distribution network considering the spot electricity energy market, enhances the coordination of various parts of the power grid, and improves the security and stability of the power grid.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a source-grid-load-storage dual-layer collaborative planning method for a distribution network, including:

[0007] Plan and forecast the source, grid, load and storage data of the distribution network to obtain the load data for the planned year;

[0008] Based on the grid configuration and energy planning constraints, a grid planning model is constructed with the maximization of the total revenue of the main operators as the objective function. The objective functions include the objective functions of the energy supply operator, the distribution network operator, the energy storage operator, and the load aggregator.

[0009] According to the operating cost of power generation resources, a simulation clearing model of the electricity energy market is constructed;

[0010] The load data of the planning year is input into the electric energy market simulation clearing model, and the two-layer collaborative planning model is iteratively solved to obtain the optimal planning scheme for the power grid; wherein, the two-layer collaborative planning model is composed of the power grid planning model as the upper planning layer of the two-layer collaborative planning model and the electric energy market simulation clearing model as the lower operating layer of the two-layer collaborative planning model.

[0011] In implementing the embodiment of the present invention, the source, network, load and storage data of the distribution network are planned and predicted to obtain the load data for the planning year; according to the grid configuration and energy planning constraints, a grid planning model is constructed with the maximization of the total revenue of the main operator as the objective function; wherein the objective function includes the energy supply operator objective function, the distribution network operator objective function, the energy storage operator objective function and the load aggregator objective function; according to the operating cost of power generation resources, an electric energy market simulation clearing model is constructed; the load data for the planning year is input into the electric energy market simulation clearing model, and the two-layer collaborative planning model is iteratively solved to obtain the optimal planning scheme for the grid; wherein the two-layer collaborative planning model is composed of the grid planning model as the upper planning layer of the two-layer collaborative planning model and the electric energy market simulation clearing model as the lower operating layer of the two-layer collaborative planning model. In addition to considering the revenue of the operator, the grid planning takes into account targets such as carbon emissions and new energy development. The spot electricity market clearing model is combined with the power grid planning model, and a power grid planning method that coordinates source, grid, load and storage is adopted. Unlike the traditional power grid planning method with separate entities, through the renewable energy penetration constraint, the configuration capacity of energy storage under the corresponding situation can also be solved. The two-layer coordinated planning method of source, grid, load and storage is more scientific and economical, and can enhance the coordination between various parts of the power grid, making the planned power grid safer and more stable, and improving the security and stability of the power grid.

[0012] As a preferred solution, the load data of the planning year is input into the electric energy market simulation clearing model, and the two-layer collaborative planning model is iteratively solved to obtain the optimal planning scheme for the power grid. Specifically,

[0013] Inputting the load data of the planning year into the electric energy market simulation clearing model, solving the electric energy market simulation clearing model, and obtaining the first market planning data;

[0014] According to the first market planning data, the power grid planning model and the electric energy market simulation clearing model are solved cyclically and iteratively until the optimal solution of the power grid planning model is obtained, and the optimal solution of the power grid planning model is used as the optimal power grid planning scheme.

[0015] As a preferred solution, based on the first market planning data, the power grid planning model and the electric energy market simulation clearing model are solved iteratively, specifically as follows:

[0016] Inputting the first market planning data into a power grid planning model, solving the power grid planning model to obtain current power grid planning data, and inputting the current power grid planning data into an electric energy market simulation clearing model, solving the electric energy market simulation clearing model to obtain current market planning data;

[0017] The current market planning data and the current power grid planning data obtained are cyclically input into the power grid planning model and the electric energy market simulation clearing model, and the power grid planning model and the electric energy market simulation clearing model are cyclically iterated to obtain the current market planning data and the current power grid planning data;

[0018] Among them, the power grid planning data includes the number of power supply configurations, distribution network grid planning schemes and the number of energy storage device configurations.

[0019] As a preferred solution, a simulation clearing model for the electricity market is constructed based on the operating costs of power generation resources. Specifically:

[0020] Based on the safety-constrained unit commitment model, the safety-constrained economic dispatch model and the node electricity price calculation model, an electricity market simulation clearing model is constructed;

[0021] The objective function of the safety-constrained unit commitment model is to minimize the operating and startup costs of power generation resources, specifically:

[0022] Among them, f1 is the operation and startup cost of power generation resources, T0 is the set of clearing periods; G is the set of generator sets; is the electricity price declared by generator g during period t; is the winning bid power of generator group g in period t; ES is the set of energy storage devices; β cha,es Unit cost of charging the energy storage es, β dis,es The unit cost of discharging the energy storage es; is the successful bid charging capacity of energy storage es at time t, is the successful discharge capacity of energy storage es at time t; is the startup cost of generator set g; is the start-stop state variable of generator set g, Indicates that the unit is started. Indicates that the unit is stopped;

[0023] The constraints of the safety constraint unit combination model include: energy balance constraint, generator output constraint, generator ramp constraint, minimum continuous start-stop time constraint, line constraint, energy storage charge and discharge power constraint, energy storage charge and discharge state variable constraint, and energy storage capacity constraint;

[0024] The objective function of the security-constrained economic dispatch model is to minimize the operating cost of power generation resources, specifically:

[0025] Among them, f2 is the operating cost of power generation resources;

[0026] The constraints of the safety-constrained economic dispatch model include: energy balance constraints, generator output constraints, generator ramp constraints, minimum continuous start-stop time constraints, line constraints, energy storage charge and discharge power constraints, energy storage charge and discharge state variable constraints, and energy storage capacity constraints.

[0027] The node electricity price calculation model is calculated based on the node energy balance constraint of the security-constrained economic dispatch model, specifically:

[0028] in, is the load at node o during period t; G o is the set of units at node o; is the electricity price at node o.

[0029] As a preferred solution, the energy supply operator's objective function is specifically: F1 = W e1 +W s1 -C c1 -C o1 -C f1 -C en1 -C in1

[0030] Among them, F1 is the day-ahead dispatching income of the energy supply operator; W e1 W is the electricity sales income of conventional unit operators; s1 The income from electricity sales to distributed power generation operators; C c1 is the operation and maintenance cost of distributed power generation; C o1 is the conventional power supply operation and maintenance cost; C f1 is the fuel cost of conventional power supply; C en1 is the investment cost of conventional power supply; C in1 is the investment cost of distributed power generation; T is the total number of planning periods; E is the set of conventional power sources; is the unit electricity price of conventional power source e in time period t; is the output of conventional power source e in time period t; I is the set of distributed power sources; is the unit electricity price of distributed power generation i in time period t; is the output of distributed generation i in period t; α i is the unit capacity operating cost of distributed power source i; α e is the unit capacity operating cost of conventional power source e; β is the standard coal electricity price; is the start-stop state variable of the conventional power supply e at time t, Indicates shutdown status. Represents the power-on state; a e 、b e and c e are the quadratic coefficient, linear coefficient and constant coefficient of the operating cost of conventional power source e; r is the discount rate; n e For the full life cycle of conventional power supply; n i is the entire life cycle of distributed power supply; γ e is the unit capacity investment cost of a single conventional power supply; γ i N is the unit capacity investment cost of a single distributed power supply; e The number of units configured for conventional power supply; N i Configure the number of distributed power sources; P e,o is the rated power of a single conventional power supply; P i,o It is the rated power of a single distributed power supply.

[0031] As a preferred solution, the distribution network operator's objective function is: F2 = W s2 -C b2 -C l2 -C a2 -C g2

[0032] Among them, F2 is the day-ahead dispatching revenue of the distribution network operator; W s2 The electricity sales revenue for distribution network operators; C b2 The electricity purchase cost for distribution network operators; C l2 is the network loss cost; C a2 C is the cost of curtailing wind and solar power; g2 is the line investment cost; is the unit electricity price of the distribution network during period t; is the electricity sales of the distribution network during period t; is the unit electricity purchase price of the distribution network during period t; The amount of electricity purchased by the distribution network during period t; is the network loss of the distribution network during period t; a Penalty fees for units curtailing wind and solar power; is the total amount of wind and solar power abandoned in the distribution network during period t; nr is the entire life cycle of the line; N1 is the total number of newly built lines; N2 is the total number of upgraded and renovated lines; c1 is the cost of new construction per unit length of line; c2 is the cost of upgrading and renovating per unit length of line; L l,1 is the length of the l1th new line; L l,2 Summarize the length of the line for the l2th upgrade.

[0033] As a preferred solution, the energy storage operator's objective function is specifically: F3 = W s3 -C o3 -C sn3

[0034] Among them, F3 is the day-ahead dispatch revenue of the energy storage operator; W s3 The arbitrage income of low storage and high generation of energy storage; C o3 C is the energy storage operation and maintenance cost; sn3 is the energy storage investment cost; ES is the energy storage device set; is the unit price of charging and discharging of energy storage es in time period t; is the discharge power of energy storage es in time period t; is the charging power of energy storage es in time period t; α es The operating cost of energy storage unit charging and discharging power; is the charging or discharging power of the energy storage es in time period t; n es is the entire life cycle of energy storage; γ es Single energy storage installation investment cost; N es Configure the number of units for energy storage.

[0035] As a preferred solution, the load aggregator objective function is specifically: F4 = W u4 +W r4 -C l4

[0036] Among them, F4 is the increased revenue of load aggregators after participating in power market demand response; W u4 is the subsidy income for interruptible load; W r4 Cost reduction for transferable loads; C l4 The loss of electricity comfort caused by the load aggregator's participation in demand response; u Subsidy for interruptible load of units; is the load interruption amount in period t; r The electricity selling price for load aggregators; is the removed load in period t; is the incoming load in period t; a u 、a ro and a ri are the economic loss coefficients caused by interrupted load, removed load and moved load participating in demand response.

[0037] As the preferred solution, the grid configuration and energy planning constraints include: the number of conventional power sources, the number of distributed power sources, the number of energy storage devices, and the renewable energy penetration rate.

[0038] The number of conventional power supply configurations is as follows: N e <N e，max

[0039] Among them, N e The number of units configured for conventional power supply; N e,max Configure the upper limit of the number of conventional power supplies

[0040] Distributed power supply configuration number constraints, specifically: N i <N i,max

[0041] Among them, N i Configure the number of distributed power sources; N i,max Configure the upper limit of the number of distributed power sources

[0042] The number of energy storage devices is constrained, specifically: N es <N es，max

[0043] Among them, N es Configure the number of energy storage devices; N es,max Configure an upper limit on the number of energy storage devices

[0044] Renewable energy penetration constraint, specifically: N i P i,o ≥δN e P e,o

[0045] Where δ is the renewable energy penetration rate, P e,o is the rated power of a single conventional power supply; P i,o It is the rated power of a single distributed power supply.

[0046] As a preferred solution, the source, grid, load and storage data of the distribution network are planned and predicted to obtain the load data for the planned year, specifically:

[0047] Obtain the source, grid, load and storage data of the distribution network, and extract the source, grid, load and storage data of each hour for a preset historical year to obtain historical load data;

[0048] Based on the historical load data, the ARIMA method is used to predict the load level in the planning year and obtain the load data in the planning year. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] FIG1 is a flow chart of an embodiment of a method for source-grid-load-storage dual-layer collaborative planning of a distribution network provided by the present invention;

[0050] FIG2 is a simplified flow chart of a source-grid-load-storage dual-layer collaborative planning method for a distribution network according to an embodiment of the present invention;

[0051] FIG3 is a framework diagram of a two-layer collaborative planning model according to an embodiment of a source-grid-load-storage two-layer collaborative planning method for a distribution network provided by the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Example 1

[0054] Please refer to Figure 1, which is a flow chart of a source-grid-load-storage dual-layer collaborative planning method for a distribution network provided by an embodiment of the present invention, wherein a simplified process of the source-grid-load-storage dual-layer collaborative planning method is shown in Figure 2. The source-grid-load-storage dual-layer collaborative planning method of this embodiment, by constructing and solving a dual-layer collaborative planning model, obtains the optimal planning scheme for the power grid, enhances the coordination of various parts of the power grid, and makes the planned power grid more secure and stable. The source-grid-load-storage dual-layer collaborative planning method includes steps 101 to 104, each of which is specifically as follows:

[0055] Step 101: Plan and forecast the source, grid, load and storage data of the distribution network to obtain load data for the planned year.

[0056] Step 101 is specifically as follows: obtaining the source network load and storage data of the distribution network, and extracting the source network load and storage data per hour for a preset historical year to obtain historical load data; based on the historical load data, using the ARIMA method to predict the load level in the planning year to obtain the load data in the planning year.

[0057] In this embodiment, the source, grid, load and storage data of the distribution network are obtained, and based on the hourly source, grid, load and storage data of the preset historical years (such as the hourly load data of the historical five years), the ARIMA method is adopted to predict the load level of the planning year to obtain the load curve of the planning year, that is, the load data of the planning year.

[0058] It should be noted that the ARIMA method is a time series analysis method, and its full name is the autoregressive, integrated moving average, and moving average model (ARIMA Model). The ARIMA model is widely used in the prediction and modeling of time series data. It can predict future trends and changes by analyzing and fitting historical time series data. The basic idea of ​​the ARIMA model is to establish a model that can describe the characteristics of the data through transformations such as autoregression, moving average, and differencing of time series data, and use this model to predict future data changes. The ARIMA model can effectively handle many characteristics of time series data, such as seasonality, trend, and periodicity, and can fit the data with fewer parameters.

[0059] The implementation of this invention combines a spot electricity market clearing model with a grid planning model and adopts a grid planning method that integrates sources, grids, loads, and storage. Unlike traditional grid planning methods that separate each entity, this planning method is more scientific and economical, can enhance coordination between various components of the grid, and make the planned grid more secure and stable. By using renewable energy penetration constraints, this invention can also solve for the energy storage configuration capacity under corresponding circumstances.

[0060] Step 102: Based on the grid configuration and energy planning constraints, a grid planning model is constructed with maximizing the total revenue of the main operator as the objective function; wherein the objective function includes the objective function of the energy supply operator, the objective function of the distribution network operator, the objective function of the energy storage operator, and the objective function of the load aggregator.

[0061] In this embodiment, the main operators include energy supply operators, distribution network operators, energy storage operators, and load aggregators. The goal is to maximize the total revenue of energy supply operators, distribution network operators, energy storage operators, and load aggregators. That is, the objective function for maximizing the total revenue of energy supply operators, distribution network operators, energy storage operators, and load aggregators is: max F = F1 + F2 + F3 + F4

[0062] Among them, F is the total revenue, F1 is the annual net revenue of the energy supplier, that is, the revenue of the energy supply operator's day-ahead dispatching operation; F2 is the annual net revenue of the distribution network operator, that is, the revenue of the distribution network operator's day-ahead dispatching operation; F3 is the annual net revenue of the energy storage operator, that is, the revenue of the energy storage operator's day-ahead dispatching; F4 is the annual revenue increased after the power users participate in demand response, that is, the revenue increased after the load aggregator participates in the power market demand response.

[0063] Optional, energy supply operator objective function, specifically: F1 = W e1 +Ws1 -C c1 -C o1 -C f1 -C en1 -C in1

[0064] Among them, F1 is the day-ahead dispatching income of the energy supply operator; W e1 W is the electricity sales income of conventional unit operators; s1 The income from electricity sales to distributed power generation operators; C c1 is the operation and maintenance cost of distributed power sources. Preferably, distributed power sources are wind power and photovoltaic power; C o1 is the conventional power supply operation and maintenance cost; C f1 is the fuel cost of conventional power supply; C en1 is the investment cost of conventional power supply; C in1 is the investment cost of distributed power generation; T is the total number of planning periods; E is the set of conventional power sources; is the unit electricity price of conventional power source e in time period t; is the output of conventional power source e in time period t; I is the set of distributed power sources; is the unit electricity price of distributed power generation i in time period t; is the output of distributed generation i in period t; α i is the unit capacity operating cost of distributed power source i; α e is the unit capacity operating cost of conventional power source e; β is the standard coal electricity price; is the start-stop state variable of the conventional power supply e at time t, Indicates shutdown status. Represents the power-on state; a e 、b e and c e are the quadratic coefficient, linear coefficient and constant coefficient of the operating cost of conventional power source e; r is the discount rate; n e For the full life cycle of conventional power supply; n i is the entire life cycle of distributed power supply; γ e is the unit capacity investment cost of a single conventional power supply (unit capacity investment cost of conventional power supply); γ i N is the unit capacity investment cost of a single distributed power supply; e The number of conventional power supply configurations (number of single conventional power supply configurations); N i The number of distributed power supply configurations (number of single distributed power supply configurations); Pe,o is the rated power of a single conventional power supply; P i,o It is the rated power of a single distributed power supply.

[0065] Optionally, the distribution network operator's objective function is: F2 = W s2 -C b2 -C l2 -C a2 -C g2

[0066] Among them, F2 is the day-ahead dispatching revenue of the distribution network operator; W s2 The electricity sales revenue for distribution network operators; C b2 The electricity purchase cost for distribution network operators; C l2 is the network loss cost; C a2 C is the cost of curtailing wind and solar power; g2 is the line investment cost; is the unit electricity price of the distribution network during period t; is the electricity sales of the distribution network during period t; is the unit electricity purchase price of the distribution network during period t; The amount of electricity purchased by the distribution network during period t; is the network loss of the distribution network during period t; a Penalty fees for units curtailing wind and solar power; is the total amount of wind and solar power abandoned in the distribution network during period t; n r is the entire life cycle of the line; N1 is the total number of newly built lines; N2 is the total number of upgraded and renovated lines; c1 is the cost of new construction per unit length of line; c2 is the cost of upgrading and renovating per unit length of line; L l,1 is the length of the l1th new line; L l,2 Summarize the length of the line for the l2th upgrade.

[0067] Optional, energy storage operator objective function, specifically: F3 = W s3 -C o3 -C sn3

[0068] Among them, F3 is the day-ahead dispatch revenue of the energy storage operator; W s3 The arbitrage income of low storage and high generation of energy storage; C o3 C is the energy storage operation and maintenance cost; sn3 is the energy storage investment cost; ES is the energy storage device set; is the unit price of charging and discharging of energy storage es in time period t; is the discharge power of energy storage es in time period t; is the charging power of energy storage es in time period t; α es The operating cost of energy storage unit charging and discharging power; is the charging or discharging power of the energy storage es in time period t; n es is the entire life cycle of energy storage; γ es Single energy storage installation investment cost; N es Configure the number of units for energy storage.

[0069] Optional, load aggregator objective function, specifically: F4 = W u4 +W r4 -C l4

[0070] Among them, F4 is the increased revenue of load aggregators after participating in power market demand response; W u4 is the subsidy income for interruptible load; W r4 Cost reduction for transferable loads; C l4 The loss of electricity comfort caused by the load aggregator's participation in demand response; u Subsidy for interruptible load of units; is the load interruption amount in period t; r The electricity selling price for load aggregators; is the removed load in period t; is the incoming load in period t; a u 、a ro and a ri The economic loss coefficients generated by interrupted load, removed load and moved load participating in demand response can be obtained by fitting the long-term energy consumption data of the load.

[0071] Optional grid configuration and energy planning constraints include: number of conventional power sources, number of distributed power sources, number of energy storage devices, renewable energy penetration rate, and wind and solar power curtailment rate.

[0072] The number of conventional power supply configurations is as follows: N e <N e，max

[0073] Among them, N e The number of units configured for conventional power supply; N e,max Configure the upper limit of the number of conventional power supplies

[0074] Distributed power supply configuration number constraints, specifically: Ni <N i,max

[0075] Among them, N i Configure the number of distributed power sources; N i,max Configure the upper limit of the number of distributed power sources

[0076] The number of energy storage devices is constrained, specifically: N es <N es，max

[0077] Among them, N es Configure the number of energy storage devices; N es,max Configure an upper limit on the number of energy storage devices

[0078] Renewable energy penetration constraint, specifically: N i P i,o ≥δN e P e,o

[0079] Where δ is the renewable energy penetration rate, P e,o is the rated power of a single conventional power supply; P i,o It is the rated power of a single distributed power supply.

[0080] Step 103: Construct an electric energy market simulation clearing model based on the operating cost of power generation resources.

[0081] In this embodiment, the construction of the electric energy market simulation clearing model includes a safety-constrained unit commitment model, a safety-constrained economic dispatch model, and a node electricity price calculation model.

[0082] Step 103 is specifically as follows: constructing an electric energy market simulation clearing model based on the safety constraint unit commitment model, the safety constraint economic dispatch model and the node electricity price calculation model;

[0083] The objective function of the safety-constrained unit commitment model is to minimize the operating and startup costs of power generation resources, specifically:

[0084] Among them, f1 is the operation and startup cost of power generation resources, T0 is the set of clearing periods; G is the set of generator sets; is the electricity price declared by generator g during period t; is the winning bid power of generator group g in period t; ES is the set of energy storage devices; β cha,es Unit cost of charging the energy storage es, β dis,es The unit cost of discharging the energy storage es; is the successful bid charging capacity of energy storage es at time t, is the successful discharge capacity of energy storage es at time t; is the startup cost of generator set g; is the start / stop state variable of generator set g, a 0-1 variable, Indicates that the unit is started. Indicates that the unit is stopped;

[0085] The constraints of the safety constraint unit combination model include: energy balance constraint, generator unit output constraint, generator unit climbing constraint, minimum continuous start-stop time constraint, line constraint, energy storage charging and discharging power constraint, energy storage charging and discharging state variable constraint and energy storage power constraint.

[0086] The objective function of the security-constrained economic dispatch model is to minimize the operating cost of power generation resources, specifically:

[0087] Among them, f2 is the operating cost of power generation resources;

[0088] The constraints of the safety-constrained economic dispatch model include: energy balance constraints, generator output constraints, generator ramp constraints, minimum continuous start-stop time constraints, line constraints, energy storage charge and discharge power constraints, energy storage charge and discharge state variable constraints, and energy storage capacity constraints.

[0089] The node electricity price calculation model is calculated based on the node energy balance constraint of the security-constrained economic dispatch model, specifically:

[0090] in, is the load at node o during period t; G o is the set of units at node o; is the electricity price of node o, that is, the shadow price of the node energy balance constraint.

[0091] Step 104: Input the load data of the planning year into the electric energy market simulation clearing model, iteratively solve the two-layer collaborative planning model, and obtain the optimal planning scheme for the power grid; wherein the two-layer collaborative planning model is composed of the power grid planning model as the upper planning layer of the two-layer collaborative planning model and the electric energy market simulation clearing model as the lower operating layer of the two-layer collaborative planning model.

[0092] In this embodiment, the double-layer coordination of distribution network source, grid, load and storage in the spot electricity market is considered, and a double-layer coordinated planning model is established based on the grid planning model and the electricity market simulation clearing model. The framework diagram of the double-layer coordinated planning model is shown in Figure 3.

[0093] Optionally, step 104 specifically includes step 1041 to step 1042, and each step is specifically as follows:

[0094] Step 1041: inputting the load data for the planned year into the electric energy market simulation clearing model, solving the electric energy market simulation clearing model, and obtaining first market planning data;

[0095] Step 1042: Based on the first market planning data, the power grid planning model and the electric energy market simulation clearing model are solved iteratively until the optimal solution of the power grid planning model is obtained, and the optimal solution of the power grid planning model is used as the optimal power grid planning scheme.

[0096] In this embodiment, the electric energy market simulation clearing model and the power grid planning model form a two-layer collaborative planning model. Market planning data is obtained from the electric energy market simulation clearing model and input into the power grid planning model for solution to obtain power grid planning data. The power grid planning data is then input into the electric energy market simulation clearing model to obtain market planning data. The parameters obtained from the electric energy market simulation clearing model and the power grid planning model are mutually variable. In actual operation, the parameters are iterated in a loop until the optimal solution of the power grid planning model is obtained. Methods for solving the electric energy market simulation clearing model and the power grid planning model include, but are not limited to, particle swarm optimization.

[0097] Optionally, based on the first market planning data, the power grid planning model and the electric energy market simulation clearing model are solved iteratively, specifically as follows:

[0098] Inputting the first market planning data into a power grid planning model, solving the power grid planning model to obtain current power grid planning data, and inputting the current power grid planning data into an electric energy market simulation clearing model, solving the electric energy market simulation clearing model to obtain current market planning data;

[0099] The current market planning data and the current power grid planning data obtained are cyclically input into the power grid planning model and the electric energy market simulation clearing model, and the power grid planning model and the electric energy market simulation clearing model are cyclically iterated to obtain the current market planning data and the current power grid planning data;

[0100] Among them, the power grid planning data includes the number of power supply configurations, distribution network grid planning schemes and the number of energy storage device configurations.

[0101] In this embodiment, the grid planning data (the planned number of power sources, the distribution network grid planning scheme, and the planned number of energy storage configurations, i.e., data such as power source and energy storage capacity, start-up and shutdown times, power supply regulation rate, and predicted load) obtained by the grid planning model are input into the electric energy market simulation clearing model for solution, and the output is the market planning data (node ​​electricity prices, electricity purchase volume, and power generation data). The market planning data obtained by the electric energy market simulation clearing model is re-input into the grid planning model for solution, and the output is the grid planning data. This process is iterated until the optimal solution of the grid planning model is obtained. The optimal solution is the optimal grid planning data, i.e., the optimal grid planning scheme.

[0102] In implementing the embodiment of the present invention, the source, network, load and storage data of the distribution network are planned and predicted to obtain the load data for the planning year; according to the grid configuration and energy planning constraints, a grid planning model is constructed with the maximization of the total revenue of the main operator as the objective function; wherein the objective function includes the energy supply operator objective function, the distribution network operator objective function, the energy storage operator objective function and the load aggregator objective function; according to the operating cost of power generation resources, an electric energy market simulation clearing model is constructed; the load data for the planning year is input into the electric energy market simulation clearing model, and the two-layer collaborative planning model is iteratively solved to obtain the optimal planning scheme for the grid; wherein the two-layer collaborative planning model is composed of the grid planning model as the upper planning layer of the two-layer collaborative planning model and the electric energy market simulation clearing model as the lower operating layer of the two-layer collaborative planning model. Through the renewable energy penetration constraint, the configuration capacity of energy storage under the corresponding situation can be solved. In addition to considering the operator's revenue, grid planning takes carbon emissions, new energy development and other goals into consideration. The spot electricity market clearing model is combined with the power grid planning model, and a power grid planning method that coordinates source, grid, load and storage is adopted. Unlike the traditional power grid planning method with separate entities, through the renewable energy penetration constraint, the configuration capacity of energy storage under the corresponding situation can also be solved. The two-layer coordinated planning method of source, grid, load and storage is more scientific and economical, and can enhance the coordination between various parts of the power grid, making the planned power grid safer and more stable, and improving the security and stability of the power grid.

[0103] The above specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention for those skilled in the art.

Claims

1. A source-grid-load-storage dual-layer collaborative planning method for a distribution network, characterized in that: include: Plan and forecast the source, network, load and storage data of the distribution network to obtain the load data for the planned year; According to the grid configuration and energy planning constraints, a grid planning model is constructed with the total revenue maximization of the main operator as the objective function; wherein the objective function includes the energy supply operator objective function, the distribution network operator objective function, the energy storage operator objective function and the load aggregator objective function; According to the operating cost of power generation resources, a simulation clearing model of the electric energy market is constructed; The load data of the planning year is input into the electric energy market simulation clearing model, and the two-layer collaborative planning model is iteratively solved to obtain the optimal planning scheme for the power grid; wherein the two-layer collaborative planning model is composed of the power grid planning model as the upper planning layer of the two-layer collaborative planning model and the electric energy market simulation clearing model as the lower operating layer of the two-layer collaborative planning model.

2. The source-grid-load-storage dual-layer collaborative planning method for a distribution network according to claim 1, characterized in that: The load data of the planned year is input into the electric energy market simulation clearing model, and the double-layer collaborative planning model is iteratively solved to obtain the optimal planning scheme of the power grid, specifically: Inputting the planned year load data into the electric energy market simulation clearing model, solving the electric energy market simulation clearing model, and obtaining the first market planning data; According to the first market planning data, the power grid planning model and the electric energy market simulation clearing model are solved in a cyclic and iterative manner until an optimal solution to the power grid planning model is obtained, and the optimal solution to the power grid planning model is used as the optimal planning scheme for the power grid.

3. The source-grid-load-storage dual-layer collaborative planning method for a distribution network according to claim 1, characterized in that: The step of iteratively solving the power grid planning model and the electric energy market simulation clearing model according to the first market planning data is specifically as follows: Inputting the first market planning data into the power grid planning model, solving the power grid planning model to obtain current power grid planning data, and inputting the current power grid planning data into the electric energy market simulation clearing model, solving the electric energy market simulation clearing model to obtain current market planning data; The obtained current market planning data and the current power grid planning data are inputted into the power grid planning model and the electric energy market simulation clearing model in a loop, the power grid planning model and the electric energy market simulation clearing model are solved in a loop iterative manner, and the current market planning data and the current power grid planning data are obtained in a loop; The power grid planning data includes the number of power supply configurations, the distribution network framework planning scheme and the number of energy storage device configurations.

4. The source-grid-load-storage dual-layer collaborative planning method for a distribution network according to claim 1, characterized in that: According to the operating cost of power generation resources, the electric energy market simulation clearing model is constructed, specifically: According to the safety-constrained unit commitment model, the safety-constrained economic dispatch model and the node electricity price calculation model, the electric energy market simulation clearing model is constructed; The objective function of the safety constraint unit commitment model is to minimize the operation and startup costs of power generation resources, specifically: Where f1 is the operating and startup cost of the power generation resource, T0 is the set of clearing periods; G is the set of generator sets; is the electricity price reported by generator g during period t; is the winning bid power of the generator set g in period t; ES is the energy storage device set; β cha,es Unit cost of charging the energy storage es, β dis,es a unit cost of discharging the energy storage es; is the bid charging capacity of the energy storage es at time t, is the winning discharge capacity of the energy storage es at time t; is the startup cost of the generator set g; is the start-stop state variable of the generator set g, Indicates that the unit is started. Indicates that the unit is stopped; The constraints of the safety constraint unit combination model include: energy balance constraint, generator unit output constraint, generator unit climbing constraint, minimum continuous start-stop time constraint, line constraint, energy storage charging and discharging power constraint, energy storage charging and discharging state variable constraint and energy storage quantity constraint; The objective function of the safety-constrained economic dispatch model is to minimize the operating cost of the power generation resources, specifically: Wherein, f2 is the operating cost of the power generation resource; The constraints of the safety-constrained economic dispatch model include: the energy balance constraint, the generator set output constraint, the generator set climbing constraint, the minimum continuous start-stop time constraint, the line constraint, the energy storage charging and discharging power constraint, the energy storage charging and discharging state variable constraint and the energy storage quantity constraint; The node electricity price calculation model is calculated based on the node energy balance constraint of the security-constrained economic dispatch model, specifically: in, is the load at node o during period t; G o is the set of units at the node o; is the electricity price of the node o.

5. The source-grid-load-storage dual-layer collaborative planning method for a distribution network according to claim 1, characterized in that: The energy supply operator objective function is specifically: F1 = W e1 +W s1 -C c1 -C o1 -C f1 -C en1 -C in1 Among them, F1 is the day-ahead dispatching revenue of the energy supply operator; W e1 W is the electricity sales income of conventional unit operators; s1 The income from electricity sales for distributed power generation operators; C c1 is the operation and maintenance cost of distributed power generation; C o1 is the conventional power supply operation and maintenance cost; C f1 is the fuel cost of conventional power supply; C en1 is the conventional power supply investment cost; C in1 is the investment cost of distributed power generation; T is the total number of planning periods; E is the set of conventional power sources; is the unit electricity price of conventional power source e in time period t; is the output of conventional power source e in time period t; I is the set of distributed power sources; is the unit electricity price of distributed power source i in time period t; is the output of the distributed power source i in time period t; α i is the unit capacity operating cost of the distributed power source i; α e is the unit capacity operating cost of the conventional power source e; β is the standard coal electricity price; is the start-stop state variable of the conventional power source e at time t, Indicates shutdown status. Represents the power-on state; a e 、b e and c e are the quadratic coefficient, linear coefficient and constant coefficient of the operating cost of the conventional power source e respectively; r is the discount rate; n e For the full life cycle of conventional power supply; n i For the full life of distributed power Period; γ e is the unit capacity investment cost of a single conventional power supply; γ i N is the unit capacity investment cost of a single distributed power source; e The number of units configured for conventional power supply; N i Configure the number of distributed power sources; P e,o is the rated power of a single conventional power supply; P i,o It is the rated power of a single distributed power supply.

6. The source-grid-load-storage dual-layer collaborative planning method for a distribution network according to claim 1, characterized in that: The distribution network operator objective function is specifically: F2 = W s2 -C b2 -C l2 -C a2 -C g2 Among them, F2 is the day-ahead dispatching revenue of the distribution network operator; W s2 The electricity sales revenue for distribution network operators; C b2 The cost of purchasing electricity for distribution network operators; C l2 is the network loss cost; C a2 C is the cost of curtailing wind and solar power; g2 is the line investment cost; is the unit electricity price of the distribution network during period t; The power sales of the distribution network during period t; is the unit electricity purchase price of the distribution network during period t; The amount of electricity purchased by the distribution network during period t; is the network loss of the distribution network during period t; a Penalty fees for units that abandon wind and solar power; is the total amount of wind and solar power abandoned in the distribution network during period t; n r is the entire life cycle of the line; N1 is the total number of newly built lines; N2 is the total number of upgraded and renovated lines; c1 is the cost of new construction per unit length of line; c2 is the cost of upgrading and renovating per unit length of line; L l,1 is the length of the l1th new line; L l,2 Summarize the length of the line for the l2th upgrade.

7. The source-grid-load-storage dual-layer collaborative planning method for a distribution network according to claim 1, characterized in that: The energy storage operator objective function is specifically: F3 = W s3 -C o3 -C sn3 Among them, F3 is the day-ahead dispatch revenue of the energy storage operator; W s3 It is the arbitrage income of low storage and high generation of energy storage; C o3 C is the energy storage operation and maintenance cost; sn3 is the energy storage investment cost; ES is the energy storage device set; is the charging and discharging unit price of energy storage es in time period t; is the discharge power of the energy storage es in time period t; is the charging power of the energy storage es in time period t; es It is the operating cost of energy storage unit charging and discharging power; is the charging or discharging power of the energy storage es in time period t; n es The whole life cycle of energy storage; es Single energy storage installation investment cost; N es Configure the number of units for energy storage.

8. The source-grid-load-storage dual-layer collaborative planning method for a distribution network as claimed in claim 1, characterized in that: The load aggregator objective function is specifically: F4 = W u4 +W r4 -C l4 Among them, F4 is the increased revenue of load aggregators after participating in the power market demand response; W u4 is the subsidy income for interruptible load; W r4 Cost reduction for transferable loads; C l4 The loss of electricity comfort caused by the load aggregator's participation in demand response; u Subsidy for interruptible load of units; is the load interruption amount in time period t; r The electricity selling price for load aggregators; is the removed load in period t; is the load transferred during period t; a u 、a ro and a ri They are the economic loss coefficients caused by interrupted load, removed load and moved load participating in demand response.

9. The source-grid-load-storage dual-layer collaborative planning method for a distribution network as claimed in claim 5, characterized in that: The grid configuration and energy planning constraints include: the number of conventional power sources, the number of distributed power sources, the number of energy storage devices and the penetration rate of renewable energy; The conventional power supply configuration number constraints are specifically: N e <N e，max Among them, N e The number of conventional power supplies configured; N e,max Configure the upper limit of the number of conventional power supplies The distributed power supply configuration number constraint is specifically: N i <N i,max Among them, N i The number of distributed power sources configured; N i,max Configure the upper limit of the number of distributed power sources The energy storage device configuration number constraint is specifically: N es <N es，max Among them, N es Configure the number of energy storage devices; N es,max Configure an upper limit on the number of energy storage devices The renewable energy penetration rate constraint is specifically: N i P i,o ≥δN e P e,o Where δ is the penetration rate of renewable energy, P e,o is the rated power of the single conventional power supply; P i,o is the rated power of the single distributed power supply.

10. The source-grid-load-storage dual-layer collaborative planning method for a distribution network according to claim 1, characterized in that: The source, network, load and storage data of the distribution network are planned and predicted to obtain the load data for the planned year, specifically: Obtain the source, network, load and storage data of the distribution network, and extract the source, network, load and storage data per hour for a preset historical year to obtain historical load data; According to the historical load data, the load level of the planning year is predicted using the ARIMA method to obtain the load data of the planning year.

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