A method, apparatus and medium for grid-accessible distributed capacity configuration

Abstract

The application discloses a kind of grid accessible distributed capacitance configuration method, device and medium, the method includes: according to the data information of photovoltaic distributed power, with the minimum compensation fee of adjustable load to user as target to establish first function, with the minimum loss of grid distribution side as target to establish second function to access capacitance, and establish the operation constraint set of distributed power generation device;First function, second function and operation constraint set are solved simultaneously, and configuration result is obtained, and according to configuration result, the accessible distributed capacitance of grid distribution side is configured.The application proposes a kind of grid accessible distributed capacitance configuration method, device and medium, by establishing objective function and operation constraint set, and solving simultaneously, so that configuration result can maintain equipment operation stable while ensuring the maximization of access capacitance, can solve the problem that grid access photovoltaic distributed capacitance configuration cannot be optimized, cause energy waste.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method, apparatus and medium for configuring distributed capacity that can be connected to the power grid. Background Technology

[0002] Demand-side response (DR) uses two-way communication technology as a basic bridge to establish a real-time information exchange architecture between power generation and consumption. Load users can adjust their original electricity consumption patterns based on real-time changes in electricity prices, effectively shifting peak electricity demand to off-peak periods. Distributed generation (DG) is a more decentralized power generation method that plays an increasingly important role in alleviating the pressure on the power grid's transmission and distribution sides. Currently, there are two main types of distributed generation in China: photovoltaic (PV) power generation and wind power generation. The large-scale integration of PV distributed power sources has a significant impact on the safe and stable operation of traditional power systems. Connecting distributed PV power sources to the grid requires ensuring that the power source capacity meets grid capacity limitations and power supply demands, and also necessitates equipment analysis and power system analysis.

[0003] However, the current power grid planning is still in the exploratory construction stage. In the process of connecting photovoltaic distributed power sources to the grid distribution network, the configuration of the grid-connected photovoltaic distributed power capacity cannot be optimized due to limitations in equipment operation and market transactions, resulting in energy waste and thus limiting the full realization of the advantages of photovoltaic distributed power generation. Summary of the Invention

[0004] This invention provides a method, device, and medium for configuring distributed capacity that can be connected to the power grid, in order to solve the problem that the configuration of distributed photovoltaic capacity connected to the power grid cannot be optimized, resulting in energy waste.

[0005] To address the above problems, this invention provides a method for configuring distributed electrical capacity that can be accessed by a power grid, comprising: Obtain data information on photovoltaic distributed power sources connected to the power grid distribution network side; Based on the equipment operating power parameters and price parameters in the data information, a first function is established with the goal of minimizing the compensation cost to users for adjustable loads. Based on the loss parameters of photovoltaic distributed power generation after being connected to the power grid distribution network side in the data information, a second function is established with the goal of minimizing the loss of the connected power capacity on the power grid distribution network side. Based on the equipment operating parameters in the data information, establish a set of operating constraints for the distributed generation device; The particle swarm optimization algorithm is used to solve the first function, the second function, and the set of operational constraints simultaneously to obtain the configuration result, and the accessible distributed power capacity on the power grid distribution network side is configured according to the configuration result.

[0006] This invention constructs a first function, a second function, and a set of operational constraints. The first function minimizes the compensation costs for adjustable load users, thereby avoiding ineffective or inefficient scheduling. The second function minimizes the losses on the grid distribution network caused by the connected capacity, ensuring high stability and reliability of distributed generation devices connected to the grid distribution network. The established set of operational constraints ensures that the distributed generation devices are always in a stable operating state, and that the distributed capacitors connected to the grid distribution network maintain a balance between supply and demand, guaranteeing efficient energy utilization. Finally, by solving these equations simultaneously, the configuration result can be obtained for resource allocation. This method of obtaining the configuration result is simple and quick, and maximizes the configuration of distributed capacity connected to the grid distribution network.

[0007] Compared to existing technologies, this solution establishes two objective functions and a comprehensive set of operational constraints. This allows for minimizing the compensation costs for users from adjustable loads while minimizing losses on the grid distribution network side due to the connected capacity. It also maintains the operational balance of distributed generation devices and the supply-demand balance of distributed capacitors connected to the grid distribution network. By using a particle swarm optimization algorithm to solve the simultaneous equations, the solution method is simple and fast, and the process of obtaining configuration results is accelerated for configuration. Therefore, this solution can solve the problem of energy waste caused by the inability to optimize the configuration of distributed photovoltaic capacity connected to the grid.

[0008] As a preferred embodiment, an operating constraint set for the distributed generation device is established based on the equipment operating parameters in the data information, specifically as follows: Based on the operating parameters of thermal power units, new energy units, electric heating equipment, electric vehicles, and distributed energy storage devices in the data information, an operating constraint set for distributed generation devices is established. The set of operational constraints includes power system power balance constraints, output constraints of new energy units, output constraints of thermal power units, ramping constraints of thermal power units, minimum start-up and shutdown time constraints of thermal power units, indoor temperature constraints for electric heating users, power constraints for electric heating loads, power constraints for electric vehicle charging, charging capacity constraints for electric vehicles, power constraints for distributed energy storage devices, capacity constraints for distributed energy storage devices, power flow constraints, node voltage constraints, branch capacity constraints, and total access constraints for distributed generation devices.

[0009] This preferred solution is considered from the perspective of overall equipment operation. Because the set of operating constraints provides comprehensive constraints and protection for thermal power units, electric vehicles, distributed energy storage devices, indoor temperature of electric heating users, and power of electric heating loads, the distributed generation devices are always in a stable operating state, and the distributed capacitors are always in a supply-demand balance when connected to the grid distribution network, thus ensuring the efficient use of energy.

[0010] As a preferred embodiment, based on the equipment operating power parameters and price parameters in the data information, a first function is established with the objective of minimizing the compensation cost to the user from the adjustable load, specifically: Based on the total power of the equipment operating load, the total power of distributed energy storage, and the price parameters in the data information, a first function is established with the goal of minimizing the compensation cost to users for adjustable load. The first function is: in, In the case of participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of participating in load aggregation scheduling The total power of distributed energy storage at all times. for The compensation price offered by the time-based load aggregator to users participating in load aggregation. In the case of not participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of not participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of not participating in load aggregation scheduling The total load power of distributed energy storage loads at any given time. for Electricity purchase price at all times The duration of each scheduling period.

[0011] This preferred scheme constructs a first function by considering the total load power of different devices during operation, the total power of distributed energy storage, the compensation price, and the electricity purchase price. The establishment of this function can minimize the compensation cost payment for adjustable load users, thereby avoiding the occurrence of ineffective or inefficient scheduling.

[0012] As a preferred embodiment, the second function is specifically: in, The change in grid loss cost after distributed generation devices are connected to the distribution network side of the power grid. This refers to the change in the grid's electricity purchase cost after distributed generation devices are connected to the grid's distribution network side. This improves the voltage quality benefits of the power grid system after distributed generation devices are connected to the distribution network side of the power grid. The annual carbon emission costs of the power grid system after distributed generation devices are connected to the distribution network side of the power grid.

[0013] This preferred solution is based on the perspective of the distribution network company. By considering the changes in various aspects of the power grid after the distributed generation device is connected to the distribution network, a second function is established. The establishment of this function can minimize the loss of the connected capacity to the distribution network, thereby ensuring good stability and high reliability of the distributed capacitor when the distributed generation device is connected to the distribution network.

[0014] As a preferred approach, the particle swarm optimization algorithm is used to solve the simultaneous equations of the first function, the second function, and the set of operational constraints to obtain the configuration result, specifically: The first function and the second function are combined into a single objective function. The single objective function and the set of operational constraints are solved simultaneously according to the particle swarm optimization algorithm to obtain the configuration result.

[0015] The single objective function of this preferred scheme integrates the first function and the second function, enabling the single objective function to simultaneously guarantee the two optimization objectives of minimizing the compensation cost for users by adjustable load and minimizing the loss of the grid distribution side by the access capacity. Furthermore, by using the particle swarm optimization algorithm to solve the single objective function and the set of operating constraints simultaneously, the configuration result can be obtained quickly, saving computation time.

[0016] The present invention also provides a grid-accessible distributed capacity configuration device, including a data acquisition module, a first function construction module, a second function construction module, a constraint set construction module, and a solution module; The data acquisition module is used to acquire data information of photovoltaic distributed power sources connected to the power grid distribution network side. The first function construction module is used to establish a first function based on the equipment operating power parameters and price parameters in the data information, with the goal of minimizing the compensation cost to the user from the adjustable load; The second function construction module is used to establish a second function based on the loss parameters of the photovoltaic distributed power source connected to the power grid distribution network side in the data information, with the goal of minimizing the loss of the connected power capacity to the power grid distribution network side; The constraint set construction module is used to establish an operating constraint set for the distributed generation device based on the equipment operating parameters in the data information. The solution module is used to solve the first function, the second function, and the set of operational constraints simultaneously using the particle swarm optimization algorithm to obtain the configuration result, and to configure the accessible distributed power capacity on the power grid distribution network side according to the configuration result.

[0017] As a preferred embodiment, the constraint set construction module specifically comprises: Based on the operating parameters of thermal power units, new energy units, electric heating equipment, electric vehicles, and distributed energy storage devices in the data information, an operating constraint set for distributed generation devices is established. The set of operational constraints includes power system power balance constraints, output constraints of new energy units, output constraints of thermal power units, ramping constraints of thermal power units, minimum start-up and shutdown time constraints of thermal power units, indoor temperature constraints for electric heating users, power constraints for electric heating loads, power constraints for electric vehicle charging, charging capacity constraints for electric vehicles, power constraints for distributed energy storage devices, capacity constraints for distributed energy storage devices, power flow constraints, node voltage constraints, branch capacity constraints, and total access constraints for distributed generation devices.

[0018] As a preferred embodiment, the first function construction module specifically comprises: Based on the total power of the equipment operating load, the total power of distributed energy storage, and the price parameters in the data information, a first function is established with the goal of minimizing the compensation cost to users for adjustable load. The first function is: in, In the case of participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of participating in load aggregation scheduling The total power of distributed energy storage at all times. for The compensation price offered by the time-based load aggregator to users participating in load aggregation. In the case of not participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of not participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of not participating in load aggregation scheduling The total load power of distributed energy storage loads at any given time. for Electricity purchase price at all times The duration of each scheduling period.

[0019] As a preferred embodiment, the second function is specifically: in, The change in grid loss cost after distributed generation devices are connected to the distribution network side of the power grid. This refers to the change in the grid's electricity purchase cost after distributed generation devices are connected to the grid's distribution network side. This improves the voltage quality benefits of the power grid system after distributed generation devices are connected to the distribution network side of the power grid. The annual carbon emission costs of the power grid system after distributed generation devices are connected to the distribution network side of the power grid.

[0020] As a preferred embodiment, the solution module specifically comprises: The first function and the second function are combined into a single objective function. The single objective function and the set of operational constraints are solved simultaneously according to the particle swarm optimization algorithm to obtain the configuration result.

[0021] The present invention also provides a storage medium storing a computer program, which is called and executed by a computer to implement the grid-accessible distributed capacity configuration method described above. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a method for configuring distributed electrical capacity that can be accessed by a power grid, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a distributed capacity configuration device for power grid access provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" and "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "several" means two or more.

[0025] Example 1: Please see Figure 1 The present invention provides a method for configuring distributed capacity that can be accessed by the power grid, including S1 to S4, and the specific implementation steps are as follows: S1. Obtain data information of photovoltaic distributed power sources connected to the power grid distribution network side.

[0026] Step S1 in this embodiment of the invention specifically includes: Data information on photovoltaic distributed power sources connected to the power grid distribution network is obtained from the power grid system.

[0027] S2. Based on the equipment operating power parameters and price parameters in the data information, establish the first function with the goal of minimizing the compensation cost to users for adjustable load.

[0028] Step S2 in this embodiment of the invention is specifically as follows: Based on the total power of the equipment operating load, the total power of distributed energy storage, and the price parameters in the data information, a first function is established with the goal of minimizing the compensation cost to users for adjustable load. The first function is: in, In the case of participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of participating in load aggregation scheduling The total power of distributed energy storage at all times. for The compensation price offered by the time-based load aggregator to users participating in load aggregation. In the case of not participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of not participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of not participating in load aggregation scheduling The total load power of distributed energy storage loads at any given time. for Electricity purchase price at all times The duration of each scheduling period.

[0029] This embodiment constructs a first function by considering the total load power of different devices during operation, the total power of distributed energy storage, the compensation price, and the electricity purchase price. The establishment of this function can minimize the compensation cost payment for adjustable load users, thereby avoiding the occurrence of ineffective or inefficient scheduling.

[0030] S3. Based on the loss parameters of photovoltaic distributed power sources connected to the power grid distribution network in the data information, establish a second function with the goal of minimizing the loss of the connected power capacity on the power grid distribution network side.

[0031] Step S3 in this embodiment of the invention is specifically as follows: Based on the parameters of changes in grid loss cost, electricity purchase cost and carbon emission cost after photovoltaic distributed power sources are connected to the grid distribution network, as well as voltage quality benefit parameters, a second function is established with the goal of minimizing the loss of the connected power capacity on the grid distribution network. The second function is: in, This refers to the change in grid loss costs after distributed generation devices are connected to the grid distribution network. This relates to the changes in the grid's electricity purchase cost after distributed generation devices are connected to the grid distribution network. To improve the voltage quality benefits of the power grid system after distributed generation devices are connected to the distribution network. The annual carbon emission costs of the power grid system after distributed generation devices are connected to the distribution network side of the grid.

[0032] This embodiment is generally considered from the perspective of the distribution network company. By considering the changes in various aspects of the power grid after the distributed generation device is connected to the distribution network, a second function is established. The establishment of this function can minimize the loss of the connected capacity to the distribution network, thereby making the distributed capacitor flow into the distribution network with good stability and high reliability when the distributed generation device is connected to the distribution network.

[0033] S4. Based on the equipment operating parameters in the data information, establish the operating constraint set of the distributed generation device.

[0034] Step S4 of this embodiment of the invention is specifically as follows: Based on the operating parameters of thermal power units, new energy units, electric heating equipment, electric vehicles, and distributed energy storage devices in the data information, establish a set of operating constraints for distributed generation devices; The set of operational constraints includes power system power balance constraints, output constraints of new energy units, output constraints of thermal power units, ramping constraints of thermal power units, minimum start-up and shutdown time constraints of thermal power units, indoor temperature constraints of electric heating users, power constraints of electric heating loads, charging power constraints of electric vehicles, charging amount constraints of electric vehicles, power constraints of distributed energy storage devices, capacity constraints of distributed energy storage devices, energy storage capacity constraints of distributed energy storage devices, power flow constraints, node voltage constraints, branch capacity constraints, and total access constraints of distributed generation devices. The constraints in the runtime constraint set are as follows: (4-1) The power balance constraint of the power system is: in, The output of the j-th thermal power unit at time t is expressed in MW. The output of the j-th wind turbine at time t is expressed in MW. Let t be the other loads in the power grid at time t, in MW. Before and after the load aggregator aggregates the dispersed adjustable loads to participate in the power grid peak shaving, this load is set to remain unchanged. The establishment of power balance constraints in the power system can ensure that the power system maintains equal supply and demand at any given time, that is, maintains power balance.

[0035] (4-2) The output constraint of the new energy unit is: in, The output of the kth new energy unit at time t is expressed in MW. The predicted output of the k-th renewable energy unit at time t is given in MW.

[0036] The establishment of output constraints for new energy generating units can ensure that the output level of new energy generating units does not exceed their day-ahead forecast level.

[0037] (4-3) The output constraint of thermal power units is: in, Let the output of the j-th thermal power unit be at time t. Let be the minimum output of the j-th thermal power unit, in MW; This represents the maximum output of the j-th thermal power unit, expressed in MW.

[0038] (4-4) The ramping constraint for thermal power units is: ; in, Let be the downhill ramp rate of the j-th thermal power unit, in MW / min; Let be the upward climbing rate of the j-th thermal power unit, in MW / min; Let be the output of the j-th thermal power unit at time t-1, in MW; the first equation in the thermal power unit ramping constraint is the load increase constraint, and the second equation is the load decrease constraint. The first equation and the second equation together constitute the thermal power unit ramping constraint.

[0039] The rate at which a thermal power unit can change its load is limited by its ramping capability. Therefore, establishing ramping constraints for thermal power units can constrain their operation from two perspectives, preventing overload operation from causing equipment overheating, frequent wear, and accelerated reduction of equipment life.

[0040] (4-5) The minimum start-up and shutdown time constraints for thermal power units are: ; in, The continuous operating time of the j-th thermal power unit is expressed in minutes. This represents the minimum operating time of the j-th thermal power unit, in minutes. The downtime of the j-th thermal power unit is expressed in minutes. The minimum downtime of the j-th thermal power unit is expressed in minutes.

[0041] During operation, thermal power units should meet their minimum start-stop time constraints. Therefore, the establishment of minimum start-stop time constraints for thermal power units can enable the equipment and components of thermal power units to operate fully, cool down, and remove residual charges, thus avoiding damage to the components of thermal power units due to frequent start-stop.

[0042] (4-6) The indoor temperature constraint for electric heating users is: in, This represents the maximum comfortable indoor temperature, expressed in °C. The indoor temperature at time t is expressed in °C. This represents the lowest comfortable indoor temperature, expressed in °C.

[0043] When regulating the electric heating load, the indoor temperature of electric heating users is constrained to keep the indoor temperature within a comfortable range, so as to ensure a good user experience.

[0044] (4-7) The power constraint for electric heating load is: in, Let be the operating power of the i-th electric heating device at time t, in kW; This represents the upper limit of the operating power of the i-th electric heating device, in kW.

[0045] (4-8) The charging power constraint for electric vehicles is: in, The minimum allowed charging power for the i-th electric vehicle, in kW; Let be the charging power of the i-th electric vehicle at time t, in kW; This represents the maximum allowed charging power for the i-th electric vehicle, expressed in kW.

[0046] (4-9) The charging capacity constraint for electric vehicles is: in, The expected battery charge capacity after the i-th electric vehicle owner's expected charging period ends is expressed in kWh. The amount of energy stored in the battery when the i-th electric vehicle finishes charging, in kWh; Let be the maximum energy storage capacity of the i-th electric vehicle, expressed in kWh.

[0047] The establishment of electric vehicle charging capacity constraints ensures that the battery storage capacity of electric vehicles can meet the minimum expectations of car owners after the dispatching process ends.

[0048] (4-10) The power constraint of distributed energy storage devices is: in, Let be the operating power of the j-th distributed energy storage device at time t, in MW; Let be the maximum discharge power of the j-th distributed energy storage device at time t, in MW; Let be the maximum charging power of the j-th distributed energy storage device at time t, in MW.

[0049] For distributed energy storage devices, the power cannot exceed its maximum power, whether charging or discharging. Therefore, the establishment of power constraints for distributed energy storage devices can ensure that the motors of distributed energy storage devices operate at the rated power limit, avoiding motor temperature rise and potential safety accidents such as fires.

[0050] (4-11) The capacity constraint of distributed energy storage devices is: in, Let MWh be the energy stored in the j-th distributed energy storage device at time t. The maximum energy storage capacity of the j-th distributed energy storage device is expressed in MWh.

[0051] (4-12) The energy storage capacity constraint of the distributed energy storage device is: At the end of each scheduling cycle, for any distributed energy storage device, it is necessary to ensure that its energy storage capacity is the same as the initial state. Here, the initial energy storage capacity is set to 0, that is, the energy storage capacity of the distributed energy storage device is 0 at the beginning and end of the load aggregation scheduling cycle.

[0052] (4-13) The power flow constraint is: Wherein, the subscript s indicates that the state is in the s-th scenario state; and , which represent the active power and reactive power injected into node i, respectively; and These are the voltage amplitudes at nodes i and j, respectively. The voltage phase angle difference between node i and node j; and Let be the conductance and susceptance between node i and node j, respectively.

[0053] (4-14) The node voltage constraint is: in, Let be the voltage amplitude of node i in the s-th scenario state. and These represent the upper and lower limits of the voltage amplitude of node i in the s-th scenario state, respectively.

[0054] (4-15) The branch capacity constraint is: in, Let be the capacity of branch ij in the s-th scene state. Let be the maximum capacity allowed for branch ij in the s-th scenario state.

[0055] It should be noted that i and j represent two nodes, and branch ij represents the line between node i and node j.

[0056] (4-16) The total limit for the connection of distributed generation devices is: in, This represents the total number of distributed generation (DG) devices connected to the distribution network. Let be the rated active power of the i-th distributed generation unit (DG).

[0057] The total capacity of distributed generation should not exceed 25% of the maximum load in the power supply area of ​​the upstream transformer. Therefore, by limiting the total amount of distributed generation (DG) devices connected, the total amount of distributed generation (DG) devices connected should not exceed 25% of the total maximum load to ensure the safe and stable operation of the power grid.

[0058] This embodiment considers the overall equipment operation. Because the set of operating constraints provides comprehensive constraints and protection for thermal power units, electric vehicles, distributed energy storage devices, indoor temperature of electric heating users, and power of electric heating loads, the distributed generation devices are always in a stable operating state, and the distributed capacitors are always in a supply-demand balance when connected to the power grid distribution network, which can ensure the efficient use of energy.

[0059] S5. Using the particle swarm optimization algorithm, solve the first function, the second function, and the set of operating constraints simultaneously to obtain the configuration results, and configure the accessible distributed capacity on the power grid distribution network side according to the configuration results.

[0060] In step S5 of this embodiment of the invention, S5 includes S5.1 to S5.2, specifically as follows: S5.1. By using weighted summation or the analytic hierarchy process (AHP), the first and second functions are transformed into a single objective function. Based on the particle swarm optimization algorithm, the single objective function and the set of operational constraints are solved simultaneously to obtain the configuration result.

[0061] Alternatively, the particle swarm optimization algorithm can be used to solve the first function, the second function, and the set of operational constraints simultaneously, and the most suitable solution can be selected from the solution set as the configuration result.

[0062] S5.2 Configure the accessible distributed power capacity on the power grid distribution network side according to the configuration results.

[0063] This embodiment provides multiple methods for calculating configuration results. The single-objective function integrates the first and second functions, enabling it to simultaneously minimize both the compensation cost to users from adjustable loads and the loss to the power grid distribution side from connected capacity. This method is relatively simple to calculate. The method of directly solving the first function, the second function, and the set of operational constraints using the particle swarm optimization algorithm provides a more intuitive way to obtain the configuration results. Users can choose the appropriate calculation method based on their actual needs.

[0064] In general, the embodiments of the present invention have the following beneficial effects: This embodiment constructs a first function, a second function, and a set of operational constraints. The first function minimizes the compensation costs for adjustable load users, thus avoiding ineffective or inefficient scheduling. The second function minimizes the losses on the grid distribution network caused by the connected capacity, ensuring high stability and reliability of distributed generation devices connected to the grid distribution network. The established set of operational constraints ensures that the distributed generation devices are always in a stable operating state, and that the distributed capacitors connected to the grid distribution network maintain a balance between supply and demand, guaranteeing efficient energy utilization. Finally, these equations are solved using a particle swarm optimization algorithm to obtain the configuration result for resource allocation. This method of obtaining the configuration result is simple and fast, and by considering the balance of supply and demand and the stable operation of equipment, it maximizes the configuration of distributed capacity connected to the grid distribution network.

[0065] Example 2: Please see Figure 2 The embodiments of the present invention provide a grid-accessible distributed capacity configuration device, including a data acquisition module 10, a first function construction module 20, a second function construction module 30, a constraint set construction module 40, and a solution module 50; Among them, the data acquisition module 10 is used to acquire data information of photovoltaic distributed power sources connected to the power grid distribution network side; The first function construction module 20 is used to build a first function based on the equipment operating power parameters and price parameters in the data information, with the goal of minimizing the compensation cost to the user from the adjustable load. The second function construction module 30 is used to establish a second function based on the loss parameters of photovoltaic distributed power generation after the grid distribution network side is connected in the data information, with the goal of minimizing the loss of the connected power capacity on the grid distribution network side. The constraint set construction module 40 is used to establish the operating constraint set of the distributed generation device based on the equipment operating parameters in the data information. The solution module 50 is used to solve the first function, the second function and the set of operating constraints simultaneously using the particle swarm optimization algorithm to obtain the configuration result, and configure the accessible distributed capacity on the power grid distribution network side according to the configuration result.

[0066] In one embodiment, the data acquisition module 10 specifically comprises: Data information on photovoltaic distributed power sources connected to the power grid distribution network is obtained from the power grid system.

[0067] In one embodiment, the first function construction module 20 specifically comprises: Based on the total power of the equipment operating load, the total power of distributed energy storage, and the price parameters in the data information, a first function is established with the goal of minimizing the compensation cost to users for adjustable load. The first function is: in, In the case of participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of participating in load aggregation scheduling The total power of distributed energy storage at all times. for The compensation price offered by the time-based load aggregator to users participating in load aggregation. In the case of not participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of not participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of not participating in load aggregation scheduling The total load power of distributed energy storage loads at any given time. for Electricity purchase price at all times The duration of each scheduling period.

[0068] This embodiment constructs a first function by considering the total load power of different devices during operation, the total power of distributed energy storage, the compensation price, and the electricity purchase price. The establishment of this function can minimize the compensation cost payment for adjustable load users, thereby avoiding the occurrence of ineffective or inefficient scheduling.

[0069] In one embodiment, the second function construction module 30 specifically comprises: Based on the parameters of changes in grid loss cost, electricity purchase cost and carbon emission cost after photovoltaic distributed power sources are connected to the grid distribution network, as well as voltage quality benefit parameters, a second function is established with the goal of minimizing the loss of the connected power capacity on the grid distribution network. The second function is: in, This refers to the change in grid loss costs after distributed generation devices are connected to the grid distribution network. This relates to the changes in the grid's electricity purchase cost after distributed generation devices are connected to the grid distribution network. To improve the voltage quality benefits of the power grid system after distributed generation devices are connected to the distribution network. The annual carbon emission costs of the power grid system after distributed generation devices are connected to the distribution network side of the grid.

[0070] This embodiment is generally considered from the perspective of the distribution network company. By considering the changes in various aspects of the power grid after the distributed generation device is connected to the distribution network, a second function is established. The establishment of this function can minimize the loss of the connected capacity to the distribution network, thereby making the distributed capacitor flow into the distribution network with good stability and high reliability when the distributed generation device is connected to the distribution network.

[0071] In one embodiment, the constraint set construction module 40 specifically comprises: Based on the operating parameters of thermal power units, new energy units, electric heating equipment, electric vehicles, and distributed energy storage devices in the data information, establish a set of operating constraints for distributed generation devices; The set of operational constraints includes power system power balance constraints, output constraints of new energy units, output constraints of thermal power units, ramping constraints of thermal power units, minimum start-up and shutdown time constraints of thermal power units, indoor temperature constraints of electric heating users, power constraints of electric heating loads, charging power constraints of electric vehicles, charging amount constraints of electric vehicles, power constraints of distributed energy storage devices, capacity constraints of distributed energy storage devices, energy storage capacity constraints of distributed energy storage devices, power flow constraints, node voltage constraints, branch capacity constraints, and total access constraints of distributed generation devices. The constraints in the runtime constraint set are as follows: (40-1) The power balance constraint of the power system is: in, The output of the j-th thermal power unit at time t is expressed in MW. The output of the j-th wind turbine at time t is expressed in MW. Let t be the other loads in the power grid at time t, in MW. Before and after the load aggregator aggregates the dispersed adjustable loads to participate in the power grid peak shaving, this load is set to remain unchanged. The establishment of power balance constraints in the power system can ensure that the power system maintains equal supply and demand at any given time, that is, maintains power balance.

[0072] (40-2) The output constraint of the new energy unit is: in, The output of the kth new energy unit at time t is expressed in MW. The predicted output of the k-th renewable energy unit at time t is given in MW.

[0073] The establishment of output constraints for new energy generating units can ensure that the output level of new energy generating units does not exceed their day-ahead forecast level.

[0074] (40-3) The output constraint of thermal power units is: in, Let the output of the j-th thermal power unit be at time t. Let be the minimum output of the j-th thermal power unit, in MW; This represents the maximum output of the j-th thermal power unit, expressed in MW.

[0075] (40-4) The ramping constraint for thermal power units is: ; in, Let be the downhill ramp rate of the j-th thermal power unit, in MW / min; Let be the upward climbing rate of the j-th thermal power unit, in MW / min; Let be the output of the j-th thermal power unit at time t-1, in MW; the first equation in the thermal power unit ramping constraint is the load increase constraint, and the second equation is the load decrease constraint. The first equation and the second equation together constitute the thermal power unit ramping constraint.

[0076] The rate at which a thermal power unit can change its load is limited by its ramping capability. Therefore, establishing ramping constraints for thermal power units can constrain their operation from two perspectives, preventing overload operation from causing equipment overheating, frequent wear, and accelerated reduction of equipment life.

[0077] (40-5) The minimum start-up and shutdown time constraints for thermal power units are: ; in, The continuous operating time of the j-th thermal power unit is expressed in minutes. This represents the minimum operating time of the j-th thermal power unit, in minutes. The downtime of the j-th thermal power unit is expressed in minutes. The minimum downtime of the j-th thermal power unit is expressed in minutes.

[0078] During operation, thermal power units should meet their minimum start-stop time constraints. Therefore, the establishment of minimum start-stop time constraints for thermal power units can enable the equipment and components of thermal power units to operate fully, cool down, and remove residual charges, thus avoiding damage to the components of thermal power units due to frequent start-stop.

[0079] (40-6) The indoor temperature constraint for electric heating users is: in, This represents the maximum comfortable indoor temperature, expressed in °C. The indoor temperature at time t is expressed in °C. This represents the lowest comfortable indoor temperature, expressed in °C.

[0080] When regulating the electric heating load, the indoor temperature of electric heating users is constrained to keep the indoor temperature within a comfortable range, so as to ensure a good user experience.

[0081] (40-7) The power constraint for electric heating load is: in, Let be the operating power of the i-th electric heating device at time t, in kW; This represents the upper limit of the operating power of the i-th electric heating device, in kW.

[0082] (40-8) The charging power constraint for electric vehicles is: in, The minimum allowed charging power for the i-th electric vehicle, in kW; Let be the charging power of the i-th electric vehicle at time t, in kW; This represents the maximum allowed charging power for the i-th electric vehicle, expressed in kW.

[0083] (40-9) The charging capacity constraint for electric vehicles is: in, The expected battery charge capacity after the i-th electric vehicle owner's expected charging period ends is expressed in kWh. The amount of energy stored in the battery when the i-th electric vehicle finishes charging, in kWh; Let be the maximum energy storage capacity of the i-th electric vehicle, expressed in kWh.

[0084] The establishment of electric vehicle charging capacity constraints ensures that the battery storage capacity of electric vehicles can meet the minimum expectations of car owners after the dispatching process ends.

[0085] (40-10) The power constraint of the distributed energy storage device is: in, Let be the operating power of the j-th distributed energy storage device at time t, in MW; Let be the maximum discharge power of the j-th distributed energy storage device at time t, in MW; Let be the maximum charging power of the j-th distributed energy storage device at time t, in MW.

[0086] For distributed energy storage devices, the power cannot exceed its maximum power, whether charging or discharging. Therefore, the establishment of power constraints for distributed energy storage devices can ensure that the motors of distributed energy storage devices operate at the rated power limit, avoiding motor temperature rise and potential safety accidents such as fires.

[0087] (40-11) The capacity constraint for distributed energy storage devices is: in, Let MWh be the energy stored in the j-th distributed energy storage device at time t. The maximum energy storage capacity of the j-th distributed energy storage device is expressed in MWh.

[0088] (40-12) The energy storage capacity constraint of the distributed energy storage device is: At the end of each scheduling cycle, for any distributed energy storage device, it is necessary to ensure that its energy storage capacity is the same as the initial state. Here, the initial energy storage capacity is set to 0, that is, the energy storage capacity of the distributed energy storage device is 0 at the beginning and end of the load aggregation scheduling cycle.

[0089] (40-13) The power flow constraint is: Wherein, the subscript s indicates that the state is in the s-th scenario state; and , which represent the active power and reactive power injected into node i, respectively; and These are the voltage amplitudes at nodes i and j, respectively. The voltage phase angle difference between node i and node j; and Let be the conductance and susceptance between node i and node j, respectively.

[0090] (40-14) The node voltage constraint is: in, Let be the voltage amplitude of node i in the s-th scenario state. and These represent the upper and lower limits of the voltage amplitude of node i in the s-th scenario state, respectively.

[0091] (40-15) The branch capacity constraint is: in, Let be the capacity of branch ij in the s-th scene state. Let be the maximum capacity allowed for branch ij in the s-th scenario state.

[0092] It should be noted that i and j represent two nodes, and branch ij represents the line between node i and node j.

[0093] (40-16) The total limit for the connection of distributed generation devices is: in, This represents the total number of distributed generation (DG) devices connected to the distribution network. Let be the rated active power of the i-th distributed generation unit (DG).

[0094] The total capacity of distributed generation should not exceed 25% of the maximum load in the power supply area of ​​the upstream transformer. Therefore, by limiting the total amount of distributed generation (DG) devices connected, the total amount of distributed generation (DG) devices connected should not exceed 25% of the total maximum load to ensure the safe and stable operation of the power grid.

[0095] This embodiment considers the overall equipment operation. Because the set of operating constraints provides comprehensive constraints and protection for thermal power units, electric vehicles, distributed energy storage devices, indoor temperature of electric heating users, and power of electric heating loads, the distributed generation devices are always in a stable operating state, and the distributed capacitors are always in a supply-demand balance when connected to the power grid distribution network, which can ensure the efficient use of energy.

[0096] In one embodiment, the solving module 50 includes a solving unit and a configuration unit; The solution unit is used to transform the first and second functions into a single objective function through weighted summation or the analytic hierarchy process (AHP); and then solves the single objective function and the set of operational constraints simultaneously using the particle swarm optimization algorithm to obtain the configuration result. The solution unit is also used, or directly through the particle swarm optimization algorithm, to solve the first function, the second function, and the set of operational constraints simultaneously, and select the most suitable solution from the solution set as the configuration result.

[0097] The configuration unit is used to configure the accessible distributed capacity on the power grid distribution network side according to the configuration results.

[0098] This embodiment provides multiple methods for calculating configuration results. The single-objective function integrates the first and second functions, enabling it to simultaneously minimize both the compensation cost to users from adjustable loads and the loss to the power grid distribution side from connected capacity. This method is relatively simple to calculate. The method of directly solving the first function, the second function, and the set of operational constraints using the particle swarm optimization algorithm provides a more intuitive way to obtain the configuration results. Users can choose the appropriate calculation method based on their actual needs.

[0099] This device has the following beneficial effects: This device constructs a first function, a second function, and a set of operational constraints. The first function minimizes the compensation costs for adjustable load users, thus avoiding ineffective or inefficient scheduling. The second function minimizes the losses on the grid distribution network caused by the connected capacity, ensuring high stability and reliability of distributed generation devices connected to the grid distribution network. The set of operational constraints ensures that the distributed generation devices are always in a stable operating state, and that the distributed capacitors connected to the grid distribution network maintain a balance between supply and demand, guaranteeing efficient energy utilization. Finally, by solving these equations simultaneously, the configuration result can be obtained for resource allocation. This method of obtaining the configuration result is simple and quick, and maximizes the configuration of distributed capacity connected to the grid distribution network.

[0100] Example 3: This invention provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the aforementioned method for configuring grid-accessible distributed capacity. The method for configuring distributed electrical capacity accessible to the power grid, if implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0101] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for configuring distributed capacity that can be connected to a power grid, characterized in that, include: Obtain data information on photovoltaic distributed power sources connected to the power grid distribution network side; Based on the equipment operating power parameters and price parameters in the data information, a first function is established with the objective of minimizing the compensation cost to users from adjustable loads, specifically: Based on the total power of the equipment operating load, the total power of distributed energy storage, and the price parameters in the data information, a first function is established with the goal of minimizing the compensation cost to users for adjustable load. The first function is: in, In the case of participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of participating in load aggregation scheduling The total power of distributed energy storage at all times. for The compensation price offered by the time-based load aggregator to users participating in load aggregation. In the case of not participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of not participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of not participating in load aggregation scheduling The total load power of distributed energy storage loads at any given time. for Electricity purchase price at all times The duration of each scheduling period; Based on the loss parameters of the photovoltaic distributed power generation after its connection to the power grid distribution network in the data information, a second function is established with the goal of minimizing the loss of the connected capacity on the power grid distribution network side; the second function is specifically: in, The change in grid loss cost after distributed generation devices are connected to the distribution network side of the power grid. This refers to the change in the grid's electricity purchase cost after distributed generation devices are connected to the grid's distribution network side. This improves the voltage quality benefits of the power grid system after distributed generation devices are connected to the distribution network side of the power grid. The annual carbon emission cost of the power grid system after distributed generation devices are connected to the distribution network side of the power grid; Based on the equipment operating parameters in the data information, establish a set of operating constraints for the distributed generation device; The particle swarm optimization algorithm is used to solve the first function, the second function, and the set of operational constraints simultaneously to obtain the configuration result, and the accessible distributed power capacity on the power grid distribution network side is configured according to the configuration result.

2. The method for configuring distributed capacity that can be accessed by a power grid as described in claim 1, characterized in that, Based on the equipment operating parameters in the data information, an operating constraint set for the distributed generation device is established, specifically as follows: Based on the operating parameters of thermal power units, new energy units, electric heating equipment, electric vehicles, and distributed energy storage devices in the data information, an operating constraint set for distributed generation devices is established. The set of operational constraints includes power system power balance constraints, output constraints of new energy units, output constraints of thermal power units, ramping constraints of thermal power units, minimum start-up and shutdown time constraints of thermal power units, indoor temperature constraints for electric heating users, power constraints for electric heating loads, power constraints for electric vehicle charging, charging capacity constraints for electric vehicles, power constraints for distributed energy storage devices, capacity constraints for distributed energy storage devices, power flow constraints, node voltage constraints, branch capacity constraints, and total access constraints for distributed generation devices.

3. The method for configuring distributed capacity that can be connected to a power grid as described in claim 1, characterized in that, Using the particle swarm optimization algorithm, the first function, the second function, and the set of operational constraints are solved simultaneously to obtain the configuration result, specifically: The first function and the second function are combined into a single objective function. The single objective function and the set of operational constraints are solved simultaneously according to the particle swarm optimization algorithm to obtain the configuration result.

4. A power grid-accessible distributed capacity configuration device, characterized in that, It includes a data acquisition module, a first function construction module, a second function construction module, a constraint set construction module, and a solution module; The data acquisition module is used to acquire data information of photovoltaic distributed power sources connected to the power grid distribution network side. The first function construction module is used to establish a first function based on the equipment operating power parameters and price parameters in the data information, with the goal of minimizing the compensation cost to the user from the adjustable load. Specifically: Based on the total power of the equipment operating load, the total power of distributed energy storage, and the price parameters in the data information, a first function is established with the goal of minimizing the compensation cost to users for adjustable load. The first function is: in, In the case of participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of participating in load aggregation scheduling The total power of distributed energy storage at all times. for The compensation price offered by the time-based load aggregator to users participating in load aggregation. In the case of not participating in load aggregation scheduling The total load power of the temperature-controlled load at all times. In the case of not participating in load aggregation scheduling The total load power of electric vehicle charging load at any given time. In the case of not participating in load aggregation scheduling The total load power of distributed energy storage loads at any given time. for Electricity purchase price at all times The duration of each scheduling period; The second function construction module is used to establish a second function based on the loss parameters of the photovoltaic distributed power generation after its connection to the power grid distribution network side in the data information, with the goal of minimizing the loss of the connected capacity on the power grid distribution network side; the second function is specifically: in, The change in grid loss cost after distributed generation devices are connected to the distribution network side of the power grid. This refers to the change in the grid's electricity purchase cost after distributed generation devices are connected to the grid's distribution network side. This improves the voltage quality benefits of the power grid system after distributed generation devices are connected to the distribution network side of the power grid. The annual carbon emission cost of the power grid system after distributed generation devices are connected to the distribution network side of the power grid; The constraint set construction module is used to establish an operating constraint set for the distributed generation device based on the equipment operating parameters in the data information. The solution module is used to solve the first function, the second function, and the set of operational constraints simultaneously using the particle swarm optimization algorithm to obtain the configuration result, and to configure the accessible distributed power capacity on the power grid distribution network side according to the configuration result.

5. A power grid-accessible distributed capacity configuration device as described in claim 4, characterized in that, The constraint set construction module is specifically as follows: Based on the operating parameters of thermal power units, new energy units, electric heating equipment, electric vehicles, and distributed energy storage devices in the data information, an operating constraint set for distributed generation devices is established. The set of operational constraints includes power system power balance constraints, output constraints of new energy units, output constraints of thermal power units, ramping constraints of thermal power units, minimum start-up and shutdown time constraints of thermal power units, indoor temperature constraints for electric heating users, power constraints for electric heating loads, power constraints for electric vehicle charging, charging capacity constraints for electric vehicles, power constraints for distributed energy storage devices, capacity constraints for distributed energy storage devices, power flow constraints, node voltage constraints, branch capacity constraints, and total access constraints for distributed generation devices.

6. A power grid-accessible distributed capacity configuration device as described in claim 4, characterized in that, The solution module is specifically: The first function and the second function are combined into a single objective function. The single objective function and the set of operational constraints are solved simultaneously according to the particle swarm optimization algorithm to obtain the configuration result.

7. A storage medium, characterized in that, The storage medium stores a computer program, which is called and executed by a computer to implement any of the distributed capacity configuration methods for grid access as described in claims 1 to 3.

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