A power grid regulation method and device based on dynamic resource prices and a storage medium

Abstract

The application relates to a power grid regulation method and device based on dynamic resource prices and a storage medium, wherein the power grid regulation based on dynamic resource prices comprises the following steps: constructing a multi-energy flow model of an energy region, determining the supply characteristics of the marginal cost of each energy resource under different power regulation amounts in the multi-energy flow model, and taking the comprehensive operation cost in a preset scheduling period as a cost objective function; determining the power regulation demand of the power grid; constructing a double-layer dynamic pricing model with an integrated energy system operator optimization model as an upper layer and a load aggregator optimization model as a lower layer, adjusting the electricity price and the heat price according to the power regulation demand and the supply characteristics; and the multi-energy flow model generates and executes a resource regulation scheme of the energy region with the cost objective function as an optimization target in response to the electricity price and the heat price. Through the application, the power grid regulation capability is improved.

Description

Technical Field

[0001] This application relates to the field of power grid regulation, and in particular to a power grid regulation method, apparatus and storage medium based on dynamic resource prices. Background Technology

[0002] In the context of the new power system, the power generation, grid, load, and storage systems exhibit diverse resource forms, complex energy flow characteristics, and multiple management entities. Traditional grid regulation methods, relying on centralized dispatch and passive load response, are not only ill-equipped to address the volatility and uncertainty brought about by the integration of new energy sources into the power system, as well as the challenges of high coupling among the power generation, grid, load, and storage systems, but also lead to increased system balancing costs and the idle and wasted resources. Therefore, improving the grid's regulation capacity has become an urgent problem to be solved in the context of the new power system. Summary of the Invention

[0003] This application provides a power grid regulation method, apparatus, and storage medium based on dynamic resource prices, to at least address the problem of how to improve the regulation capacity of the power grid in the context of new power systems in related technologies.

[0004] In a first aspect, embodiments of this application provide a power grid regulation method based on dynamic resource prices, comprising:

[0005] A multi-energy flow model is constructed based on different energy subsystems, distributed power sources and loads within an energy region. In the multi-energy flow model, the supply characteristics of the marginal cost of each energy resource under different power regulation quantities are determined. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as the cost objective function.

[0006] The power regulation demand of the power grid is determined based on economic goals, low-carbon goals, and power grid safety operation constraints.

[0007] Based on the Stackelberg game theory model architecture, a two-layer dynamic pricing model is constructed with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The two-layer dynamic pricing model adjusts the electricity price and the heat price according to the power regulation demand and the supply characteristics.

[0008] The multi-energy flow model responds to the electricity price and the heat price, and generates and executes a resource regulation scheme for the energy region with the cost objective function as the optimization objective.

[0009] In one embodiment, the energy subsystem includes a power grid subsystem, a heating network subsystem, and a gas network subsystem. The construction of a multi-energy flow model based on different energy subsystems, distributed power sources, and loads within the energy region includes:

[0010] Based on the connection and energy conversion relationships of the different energy subsystems, determine the physical topology between the power grid subsystem, the heating network subsystem, and the gas network subsystem;

[0011] Based on the physical topology, the load, the distributed power source, the energy conversion device, and the power supply equipment, determine the physical model of the energy region;

[0012] Based on the physical model, the adjustable power of the energy supply equipment is used as the decision variable, the comprehensive operating cost within the preset scheduling period is used as the cost objective function, and the constraints are equipment operation constraints, network operation constraints of different energy subsystems, load constraints, and energy supply and demand balance constraints of the energy region. The multi-energy flow model is then constructed.

[0013] In one embodiment, determining the power regulation demand of the power grid based on economic objectives, low-carbon objectives, and power grid safety operation constraints includes:

[0014] For the power system, a multi-objective operation optimization model is constructed with the optimization objectives of minimizing the operating cost and minimizing the total carbon emissions of the power system within the dispatch cycle, and with the constraints of power grid safe operation as the condition.

[0015] The resource regulation scheme in the power system is determined by solving the multi-objective optimization model, and the power regulation demand is determined based on the resource regulation scheme.

[0016] In one embodiment, the Stackelberg game theory model architecture, which constructs a two-layer dynamic pricing model with an integrated energy system operator optimization model as the upper layer and a load aggregator optimization model as the lower layer, includes:

[0017] Based on the Stackelberg game theory model architecture, in the upper layer, the load response of the lower layer is taken as input, the electricity price and heat price at each moment and the output of each device in the integrated energy system are taken as decision variables, the first objective function is to maximize the operating profit of the integrated energy system, and the first constraints are the KKT condition constraints, price constraints, equipment operation constraints of the integrated energy system and network constraints of the integrated energy system with respect to the optimal load response of the lower layer. The integrated energy operator optimization model is constructed.

[0018] In the lower layer, the electricity and heat prices published by the upper layer are used as inputs, the load response is used as the decision variable, the minimum operating cost of the entire load is used as the second objective function, and the building thermal dynamics constraint, comfort constraint and electrical load constraint are used as the second constraint conditions to construct the load aggregation quotient optimization model.

[0019] In the upper layer, the electricity price and the heat price are optimized based on the load response of the lower layer, the first objective function, and the first constraint condition; in the lower layer, the load response is optimized based on the electricity price and heat price published by the upper layer, the second objective function, and the second constraint condition.

[0020] The two-layer dynamic pricing model is constructed through the game between the upper and lower layers until both layers reach an equilibrium state.

[0021] In one embodiment, the first objective function is specifically configured as follows:

[0022] The first objective function includes the operating cost of the integrated energy system, heating revenue, and electricity revenue.

[0023] In one embodiment, the price constraint includes an electricity price constraint and a heat price constraint, and the first constraint condition is specifically configured as follows:

[0024] The KKT condition constraints are determined based on the optimal load response of the load aggregation quotient optimization model.

[0025] The electricity price constraint is determined based on the upper limit of the electricity price, the lower limit of the electricity price, and the electricity price discount coefficient;

[0026] The heat price constraint is determined based on the upper limit of the heat price, the lower limit of the heat price, and the heat price discount coefficient;

[0027] Based on the physical characteristics and operating limits of each device in the integrated energy system, the operating constraints of the devices are determined;

[0028] Based on the energy conservation law and multi-energy flow transmission characteristics of the electro-thermal coupled network, the network constraints of the integrated energy system are determined.

[0029] In one embodiment, the second objective function is specifically configured as follows:

[0030] The second objective function includes the total cost of comfort loss and the energy cost of all said loads.

[0031] In one embodiment, the second constraint condition is specifically configured as follows:

[0032] The thermal dynamic constraints of the building are determined based on the thermophysical properties of the building envelope.

[0033] The comfort constraints are determined based on the upper and lower limits of the acceptable indoor thermal environment for end users.

[0034] The electrical load constraint is determined based on a preset offset range of the electrical load.

[0035] Secondly, embodiments of this application provide a power grid regulation device based on dynamic resource prices, comprising:

[0036] The supply characteristics determination module is used to construct a multi-energy flow model based on different energy subsystems, distributed power sources and loads within an energy region. In the multi-energy flow model, the supply characteristics of the marginal cost of each energy resource under different power regulation quantities are determined. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as the cost objective function.

[0037] The regulation demand determination module is used to determine the power regulation demand of the power grid based on economic goals, low-carbon goals, and power grid safety operation constraints.

[0038] The price adjustment module is used to construct a two-layer dynamic pricing model based on the Stackelberg game model architecture, with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The two-layer dynamic pricing model adjusts the electricity price and the heat price according to the power regulation demand and the supply characteristics.

[0039] The resource adjustment module is used by the multi-energy flow model to generate and execute a resource adjustment scheme for the energy region in response to the electricity price and the heat price, with the cost objective function as the optimization objective.

[0040] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power grid regulation method based on dynamic resource prices as described in the first aspect above.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power grid regulation method based on dynamic resource prices as described in the first aspect above.

[0042] The power grid regulation method, apparatus, and readable storage medium based on dynamic resource pricing provided in this application have at least the following technical effects:

[0043] By constructing a multi-energy flow model for energy regions, the supply characteristics of marginal resource costs under different power regulation volumes are determined to initially identify the adjustable energy resources within the energy regions. Based on economic objectives, low-carbon goals, and grid safety operation constraints, the power regulation demand of the grid is determined. A two-layer dynamic pricing model is constructed, using an integrated energy system operator optimization model as the upper layer and a load aggregator optimization model as the lower layer. This two-layer dynamic pricing model adjusts electricity and heat prices according to power regulation demand and supply characteristics, enabling multi-energy regions to automatically respond to the adjusted electricity and heat prices, generate and execute resource regulation schemes, and adjust energy supply and power regulation within the energy regions. This achieves the integration of energy resources from multiple energy regions into grid regulation through price signals, not only rationally utilizing energy resources from multiple energy regions for grid regulation but also addressing the challenges of volatility, uncertainty, and high coupling between multiple links such as power generation, grid, load, and storage brought about by the integration of new energy into the power system. Through dynamic resource pricing, energy regions automatically respond to resource prices, adjusting the supply of different energy sources within the energy regions. This not only ensures the external supply of energy resources within the energy regions but also meets power regulation demand, thereby improving the grid's regulation capacity.

[0044] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent.

[0045] This application provides a power grid regulation method, apparatus, and storage medium based on dynamic resource prices, to at least address the problem of how to improve the regulation capacity of the power grid in the context of new power systems in related technologies.

[0046] In a first aspect, embodiments of this application provide a power grid regulation method based on dynamic resource prices, comprising:

[0047] A multi-energy flow model is constructed based on different energy subsystems, distributed power sources and loads within an energy region. In the multi-energy flow model, the supply characteristics of the marginal cost of each energy resource under different power regulation quantities are determined. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as the cost objective function.

[0048] The power regulation demand of the power grid is determined based on economic goals, low-carbon goals, and power grid safety operation constraints.

[0049] Based on the Stackelberg game theory model architecture, a two-layer dynamic pricing model is constructed with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The two-layer dynamic pricing model adjusts the electricity price and the heat price according to the power regulation demand and the supply characteristics.

[0050] The multi-energy flow model responds to the electricity price and the heat price, and generates and executes a resource regulation scheme for the energy region with the cost objective function as the optimization objective.

[0051] In one embodiment, the energy subsystem includes a power grid subsystem, a heating network subsystem, and a gas network subsystem. The construction of a multi-energy flow model based on different energy subsystems, distributed power sources, and loads within the energy region includes:

[0052] Based on the connection and energy conversion relationships of the different energy subsystems, determine the physical topology between the power grid subsystem, the heating network subsystem, and the gas network subsystem;

[0053] Based on the physical topology, the load, the distributed power source, the energy conversion device, and the power supply equipment, determine the physical model of the energy region;

[0054] Based on the physical model, the adjustable power of the energy supply equipment is used as the decision variable, the comprehensive operating cost within the preset scheduling period is used as the cost objective function, and the constraints are equipment operation constraints, network operation constraints of different energy subsystems, load constraints, and energy supply and demand balance constraints of the energy region. The multi-energy flow model is then constructed.

[0055] In one embodiment, determining the power regulation demand of the power grid based on economic objectives, low-carbon objectives, and power grid safety operation constraints includes:

[0056] For the power system, a multi-objective operation optimization model is constructed with the optimization objectives of minimizing the operating cost and minimizing the total carbon emissions of the power system within the dispatch cycle, and with the constraints of power grid safe operation as the condition.

[0057] The resource regulation scheme in the power system is determined by solving the multi-objective optimization model, and the power regulation demand is determined based on the resource regulation scheme.

[0058] In one embodiment, the Stackelberg game theory model architecture, which constructs a two-layer dynamic pricing model with an integrated energy system operator optimization model as the upper layer and a load aggregator optimization model as the lower layer, includes:

[0059] Based on the Stackelberg game theory model architecture, in the upper layer, the load response of the lower layer is taken as input, the electricity price and heat price at each moment and the output of each device in the integrated energy system are taken as decision variables, the first objective function is to maximize the operating profit of the integrated energy system, and the first constraints are the KKT condition constraints, price constraints, equipment operation constraints of the integrated energy system and network constraints of the integrated energy system with respect to the optimal load response of the lower layer. The integrated energy operator optimization model is constructed.

[0060] In the lower layer, the electricity and heat prices published by the upper layer are used as inputs, the load response is used as the decision variable, the minimum operating cost of the entire load is used as the second objective function, and the building thermal dynamics constraint, comfort constraint and electrical load constraint are used as the second constraint conditions to construct the load aggregation quotient optimization model.

[0061] In the upper layer, the electricity price and the heat price are optimized based on the load response of the lower layer, the first objective function, and the first constraint condition; in the lower layer, the load response is optimized based on the electricity price and heat price published by the upper layer, the second objective function, and the second constraint condition.

[0062] The two-layer dynamic pricing model is constructed through the game between the upper and lower layers until both layers reach an equilibrium state.

[0063] In one embodiment, the first objective function is specifically configured as follows:

[0064] The first objective function includes the operating cost of the integrated energy system, heating revenue, and electricity revenue.

[0065] In one embodiment, the price constraint includes an electricity price constraint and a heat price constraint, and the first constraint condition is specifically configured as follows:

[0066] The KKT condition constraints are determined based on the optimal load response of the load aggregation quotient optimization model.

[0067] The electricity price constraint is determined based on the upper limit of the electricity price, the lower limit of the electricity price, and the electricity price discount coefficient;

[0068] The heat price constraint is determined based on the upper limit of the heat price, the lower limit of the heat price, and the heat price discount coefficient;

[0069] Based on the physical characteristics and operating limits of each device in the integrated energy system, the operating constraints of the devices are determined;

[0070] Based on the energy conservation law and multi-energy flow transmission characteristics of the electro-thermal coupled network, the network constraints of the integrated energy system are determined.

[0071] In one embodiment, the second objective function is specifically configured as follows:

[0072] The second objective function includes the total cost of comfort loss and the energy cost of all said loads.

[0073] In one embodiment, the second constraint condition is specifically configured as follows:

[0074] The thermal dynamic constraints of the building are determined based on the thermophysical properties of the building envelope.

[0075] The comfort constraints are determined based on the upper and lower limits of the acceptable indoor thermal environment for end users.

[0076] The electrical load constraint is determined based on a preset offset range of the electrical load.

[0077] Secondly, embodiments of this application provide a power grid regulation device based on dynamic resource prices, comprising:

[0078] The supply characteristics determination module is used to construct a multi-energy flow model based on different energy subsystems, distributed power sources and loads within an energy region. In the multi-energy flow model, the supply characteristics of the marginal cost of each energy resource under different power regulation quantities are determined. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as the cost objective function.

[0079] The regulation demand determination module is used to determine the power regulation demand of the power grid based on economic goals, low-carbon goals, and power grid safety operation constraints.

[0080] The price adjustment module is used to construct a two-layer dynamic pricing model based on the Stackelberg game model architecture, with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The two-layer dynamic pricing model adjusts the electricity price and the heat price according to the power regulation demand and the supply characteristics.

[0081] The resource adjustment module is used by the multi-energy flow model to generate and execute a resource adjustment scheme for the energy region in response to the electricity price and the heat price, with the cost objective function as the optimization objective.

[0082] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power grid regulation method based on dynamic resource prices as described in the first aspect above.

[0083] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power grid regulation method based on dynamic resource prices as described in the first aspect above.

[0084] The power grid regulation method, apparatus, and readable storage medium based on dynamic resource pricing provided in this application have at least the following technical effects:

[0085] By constructing a multi-energy flow model for energy regions, the supply characteristics of marginal resource costs under different power regulation volumes are determined to initially identify the adjustable energy resources within the energy regions. Based on economic objectives, low-carbon goals, and grid safety operation constraints, the power regulation demand of the grid is determined. A two-layer dynamic pricing model is constructed, using an integrated energy system operator optimization model as the upper layer and a load aggregator optimization model as the lower layer. This two-layer dynamic pricing model adjusts electricity and heat prices according to power regulation demand and supply characteristics, enabling multi-energy regions to automatically respond to the adjusted electricity and heat prices, generate and execute resource regulation schemes, and adjust energy supply and power regulation within the energy regions. This achieves the integration of energy resources from multiple energy regions into grid regulation through price signals, not only rationally utilizing energy resources from multiple energy regions for grid regulation but also addressing the challenges of volatility, uncertainty, and high coupling between multiple links such as power generation, grid, load, and storage brought about by the integration of new energy into the power system. Through dynamic resource pricing, energy regions automatically respond to resource prices, adjusting the supply of different energy sources within the energy regions. This not only ensures the external supply of energy resources within the energy regions but also meets power regulation demand, thereby improving the grid's regulation capacity.

[0086] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0087] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0088] Figure 1 This is a flowchart illustrating a power grid regulation method based on dynamic resource prices according to an exemplary embodiment;

[0089] Figure 2 This is a schematic diagram of a physical model of an energy region according to an exemplary embodiment;

[0090] Figure 3 This is a schematic diagram illustrating the structure of a two-tier dynamic pricing model according to an exemplary embodiment;

[0091] Figure 4 This is a schematic diagram of a power grid regulation device based on dynamic resource prices, according to an exemplary embodiment.

[0092] Figure 5 This is a block diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0093] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0094] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0095] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0096] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0097] In a first aspect, embodiments of this application provide a power grid regulation method based on dynamic resource prices. Figure 1 This is a flowchart illustrating a power grid regulation method based on dynamic resource prices according to an exemplary embodiment, such as... Figure 1 As shown, the power grid regulation methods based on dynamic resource prices include:

[0098] Step S101: Construct a multi-energy flow model based on different energy subsystems, distributed power sources and loads within the energy region. In the multi-energy flow model, determine the supply characteristics of the marginal cost of each energy resource under different power regulation quantities. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as its cost objective function.

[0099] The energy region contains various energy subsystems, including a power grid subsystem, a heating network subsystem, and a gas network subsystem. These subsystems supply electricity, heat, and gas to the energy region through their respective energy networks. Furthermore, these energy networks are coupled through energy conversion devices to achieve the conversion of different energy sources. Therefore, a multi-energy flow model is constructed based on the different energy subsystems, distributed power sources, and loads within the energy region to determine the energy conversion relationships within the region. This model also determines the supply characteristics of the marginal cost of each resource under different power regulation levels. The supply characteristics represent the marginal cost price of different resources per unit of power regulation. By understanding the supply characteristics, the cost of consuming other energy sources and the total amount of other energy sources that can be consumed within the energy region per unit of power regulation can be clearly identified. Based on these supply characteristics, the energy region can determine whether it can respond to power grid regulation according to a cost objective function.

[0100] The construction of the multi-energy flow model follows these steps:

[0101] Step S111: Determine the physical topology between the power grid subsystem, the heating network subsystem, and the gas network subsystem based on the connection and energy conversion relationships of different energy subsystems.

[0102] Different energy subsystems within an energy region are connected via busbars, and energy conversion occurs at these connections. Based on the connections and energy conversion relationships between these subsystems, the physical topology between the power grid subsystem, thermal monitoring subsystem, and gas grid subsystem is determined. This physical topology preliminarily reflects the complex energy conversion relationships within the energy region.

[0103] Step S112: Based on the physical topology, loads, distributed power sources, energy conversion devices, and energy supply equipment, determine the physical model of the energy area.

[0104] Based on the physical topology and the loads, distributed power sources, and functional devices connected to different energy subsystems, a preliminary energy architecture for the energy region is established. Building upon this preliminary architecture, and considering the energy conversion devices between different energy subsystems, the direction of energy flow is determined, thereby establishing the physical model of the energy region. This physical model enables the determination of the distribution of all resources within the energy region, the direction of energy flow, and the energy conversion process.

[0105] Step S113: Based on the physical model, a multi-energy flow model is constructed, with the adjustable power of the energy supply equipment as the decision variable, the comprehensive operating cost within the preset scheduling cycle as the cost objective function, and the constraints of equipment operation, network operation of different energy subsystems, load constraints, and energy supply and demand balance of energy regions as constraints.

[0106] Based on a physical model, this study uses the adjustable power of energy supply equipment in different energy subsystems as decision variables, and the comprehensive operating cost within a preset scheduling period as the cost objective function. The optimization objective is to minimize this objective function. The adjustable power of different energy supply equipment is adjusted to meet the optimization objective, and the optimization process satisfies constraints comprised of equipment operation constraints, network operation constraints of different energy subsystems, load constraints, and energy supply-demand balance constraints of the energy region. A multi-energy flow model is constructed. It should be noted that equipment operation constraints include the operation and power limitations of combined heat and power units (CHP units), gas boilers, and micro gas turbines, as well as the operation constraints, charging and discharging power limitations, and capacity limitations of energy storage devices. Network operation constraints of different energy subsystems include distribution network operation constraints, gas distribution network operation constraints, and heat network operation constraints. Energy supply-demand balance constraints of the energy region include electricity supply-demand balance constraints and heat supply-demand balance constraints.

[0107] The cost objective function in the multi-energy flow model satisfies the following formula:

[0108] ;

[0109] in, The day-ahead operating cost of the multi-energy flow model is expressed in yuan. This represents the gas purchase cost within time period t, expressed in yuan. The unit represents the operating cost of the thermal power unit within time period t, expressed in yuan. , The figures are the unit prices for operation and maintenance costs of photovoltaic and wind power, respectively, in yuan / kW·h; The operation and maintenance costs of photovoltaic systems during time period t. It represents the operation and maintenance cost of wind power during time period t; N represents the power exchange cost within time period t, expressed in yuan. t The scheduling period is 24 hours.

[0110] The gas purchase cost of the cost objective function satisfies:

[0111] ;

[0112] in, It refers to the cost of purchasing gas within time period t. This refers to the amount of gas purchased by gas source i during time period t, in Nm³. 3 ; It is the unit price of natural gas purchased from the external gas network of the gas source i; Δt represents the number of gas sources in the natural gas system, and Δt represents the time step.

[0113] The operating cost of a thermal power unit under the cost objective function satisfies:

[0114] ;

[0115] in, The operating cost of the thermal power unit within time period t. It is the cost coefficient of thermal power units. The output value of thermal power unit u during time period t is expressed in kW.

[0116] The operating costs of photovoltaic operation and wind power operation satisfy the following conditions for the cost objective function:

[0117] ;

[0118] in, The photovoltaic output at time t is At any given moment, the wind power output is... This is the unit price of operation and maintenance costs for photovoltaic systems, expressed in yuan / kW·h. This is the unit price of wind power operation and maintenance costs, expressed in yuan / kW·h; The operation and maintenance costs of photovoltaic systems during time period t. Δt represents the operation and maintenance cost of wind power within time period t, where Δt is the time step.

[0119] The grid interaction power cost of the cost objective function satisfies:

[0120] ;

[0121] in, The power exchange cost within time period t is the power cost of the power grid interaction. The unit price of electricity purchased from the main power grid at time t is expressed in yuan / kW·h. The unit price of electricity sold at time t is the price on the main power grid, expressed in yuan / kW·h. It is the electrical power purchased from the main grid at time t. Δt is the electrical power sold from the main grid at time t, where Δt is the time step.

[0122] In one embodiment, the energy zone comprises different types of energy networks, such as a power grid subsystem, a heating network subsystem, and a gas network subsystem, which are coupled together through energy conversion devices. Furthermore, distributed renewable energy sources and energy storage devices are integrated into the multi-energy flow system of the energy zone to achieve mutual conversion between electrical, thermal, and gas energy. Figure 2 This is a schematic diagram of a physical model of an energy region according to an exemplary embodiment, such as... Figure 2As shown, the power grid subsystem, heating network subsystem, and gas network subsystem are constructed through three centralized busbars: electrical, hot water, and natural gas. CHP units, renewable distributed power sources, and various types of loads are connected to the physical topology.

[0123] The CHP units use natural gas as fuel, and the combined heat and power (CHP) units convert chemical energy into electricity and heat, which are then supplied to users in the energy region. Some waste heat is recovered and reused through waste heat recovery devices. Distributed renewable energy sources mainly consist of wind and solar power, with wind turbines and photovoltaic panels connecting to the energy region to provide electricity.

[0124] In addition, the energy zone can exchange power with the external power grid. When the energy zone cannot meet its own load demand, it purchases electricity from the external grid; when the energy zone generates surplus electricity, it sells it to the external grid. The load of the energy zone includes: user electrical load and user heat load. The user electrical load is provided by renewable energy sources and CHP units; the heat load is provided by CHP units.

[0125] The physical model described above clarifies the direction of energy flow and energy supply within the energy region, providing a foundation for the subsequent construction of a multi-energy flow model.

[0126] Continuing with step S101, a multi-energy flow model is constructed based on the physical model within the energy region. This involves clarifying the energy flow and conversion within the energy region, and optimizing the cost objective function by adjusting decision variables. Furthermore, the multi-energy flow model, through the complementary interaction of different internal energy sources, can provide regulation capabilities to the external power grid. The multi-energy model determines the cost of consuming other energy sources and the total amount of other energy sources that can be consumed by each resource within the energy region under different power regulation levels. Based on supply characteristics, the energy region can determine whether it can respond to grid regulation according to the cost objective function.

[0127] Step S102: Determine the power regulation demand of the power grid based on economic goals, low-carbon goals, and power grid safety operation constraints.

[0128] For the power system, a multi-objective operation optimization model is constructed with the optimization objectives of minimizing the operating cost and total carbon emissions of the power system within the dispatch cycle, and with grid security operation constraints as the condition. By solving the multi-objective optimization model, resource regulation schemes in the power system are determined, and power regulation demand is determined based on the resource regulation schemes.

[0129] It should be noted that in the optimization objective of minimizing low-carbon targets to meet total carbon emissions, total carbon emissions include carbon emissions generated by unit output, as well as carbon emissions generated due to increased power generation to meet electricity demand, grid transmission, losses, and load consumption. The economic objective is to determine the lowest-cost solution given a fixed total carbon emissions. Grid safety operation constraints ensure the safe and stable operation of the grid, specifically including grid safety operation constraints and resource and technical constraints. Resource and technical constraints include energy balance constraints, clean energy output constraints, equipment output and ramp-up constraints, and energy storage equipment constraints.

[0130] By solving a multi-objective operation optimization model and based on the determined resource regulation scheme in the power system, the resource regulation demand of the power grid in different regions is obtained, thereby determining the power regulation demand. This power regulation demand simultaneously satisfies low-carbon goals, economic goals, and power grid safety constraints, while also clearly defining the power regulation needs for different regions, enabling different energy regions to regulate according to power demand.

[0131] Step S103: Based on the Stackelberg game theory model architecture, a two-layer dynamic pricing model is constructed with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The electricity price and heat price are adjusted according to the electricity regulation demand and supply characteristics through the two-layer dynamic pricing model.

[0132] Based on the Stackelberg game theory model architecture, a two-layer dynamic pricing model is constructed, with the integrated energy system operator optimization model at the upper layer and the load aggregator optimization model at the lower layer. In this two-layer dynamic model, based on the characteristics of electricity regulation demand and supply, the integrated energy system operator optimization model adjusts electricity and heat prices in a master-slave game with the response strategy of the load aggregator optimization model. The final electricity and heat prices are obtained based on the final game result. These final prices satisfy both the integrated energy system operator optimization model's need to maximize economic benefits and the load aggregator's need to minimize costs. It should be noted that the load in the load aggregator model includes all loads within the energy region.

[0133] The two-tier dynamic pricing model is constructed by satisfying the following steps:

[0134] Step S131: Based on the Stackelberg game model architecture, in the upper layer, the load response of the lower layer is taken as input, the electricity price and heat price at each moment and the output of each device in the integrated energy system are taken as decision variables, the first objective function is to maximize the operating profit of the integrated energy system, and the first constraint conditions are the KKT condition constraints, price constraints, equipment operation constraints of the integrated energy system and network constraints of the integrated energy system regarding the optimal response of the lower layer load.

[0135] Based on the Stackelberg game theory model architecture, the upper layer is the energy operator optimization model. The input of the energy operator optimization model is the load response of the lower load aggregator optimization model. The electricity price and heat price at each moment, as well as the output of each device in the integrated energy system, are used as decision variables. By adjusting the heat price, electricity price, and the output of each device in the integrated energy system under the first constraint, the optimization objective of maximizing the operating profit of the integrated energy system is achieved, which is the first objective function.

[0136] The first objective function includes the operating cost, heating revenue, and electricity revenue of the integrated energy system, and specifically satisfies the following formula:

[0137] ;

[0138] in, It is the total cost of the integrated energy system operator. To account for the operating costs of the comprehensive energy system, It is heating revenue. It is revenue from electricity supply. It is the fuel cost of the comprehensive energy system. It is the start-up and shutdown cost of the micro gas turbine. It is the electricity trading cost of the integrated energy system. It is the operation and maintenance cost of the integrated energy system. The penalty costs of curtailing renewable energy.

[0139] The following formula applies to each cost item, heating revenue, and electricity revenue in the formula:

[0140] ;

[0141] Where T is the set of operation optimization periods. It is a set of electrical network nodes. The electrical power of the micro gas turbine, For the thermal power of the micro gas turbine, For natural gas prices, The scheduling interval length, It is the unit startup cost of the micro gas turbine at node i. It is the unit shutdown cost of the micro gas turbine at node i. It is a 0-1 variable representing the startup state of the micro gas turbine at node i. The variable representing the stopped state of the micro gas turbine at node i is a 0-1 variable. It is the electricity purchase price. It's the price of electricity. It purchases power from the main network. The minimum total cost is the amount of electricity sold to the main grid. It is the dispatch power of renewable energy. It refers to the power of the electric boiler. It refers to the battery's charging power. It is the battery's discharge power. It refers to the thermal power of a gas-fired boiler. , , , , These are the operation and maintenance cost coefficients for micro gas turbines, renewable energy power generation units, electric boilers, batteries, and gas boilers. It is the penalty price for curtailing renewable energy. This is the predicted value of the renewable energy functional rate. For a set of hot network nodes, It is the heat price at time t at node k in the hot network. It is the electricity price at time t at node i in the hot network; Let k be the building heat load at time t at node k in the heat network; Let be the electrical load at node i in the electrical network at time t.

[0142] The first set of constraints includes the KKT condition constraint for optimal response of the lower-level load, price constraint, equipment operation constraint of the integrated energy system, and network constraint of the integrated energy system. Each constraint satisfies the following:

[0143] Based on the optimal load response of the load aggregation quotient optimization model, determine the KKT condition constraints.

[0144] KKT constraints are optimal conditions that ensure the lower-level decision variables meet the optimization objective of the load aggregation quotient optimization model. This ensures that the lower-level decision-making behavior is the optimal response to the currently published price information from the upper level, thereby enabling the upper level to make more accurate price updates based on the lower-level response.

[0145] Electricity price constraints are determined based on the upper limit, lower limit, and discount factor for electricity prices. These constraints satisfy the following formula:

[0146] ;

[0147] in, It is the upper limit of electricity prices. This represents the lower limit of the electricity price, and N is the number of dispatch cycles. It is the electricity price discount factor. It is the average electricity price. This represents the electricity price at node i at time t.

[0148] The heat price constraint is determined based on the upper limit, lower limit, and discount coefficient of the heat price. The heat price constraint satisfies the following formula:

[0149] ;

[0150] in, It is the upper limit of electricity prices. This represents the lower limit of the electricity price, and N is the number of dispatch cycles. It is the heat price discount coefficient. It is the average heat price. It is the heat price at node k at time t.

[0151] Based on the physical characteristics and operating limits of each device in the integrated energy system, determine the operating constraints of the devices.

[0152] Equipment operation constraints include those for micro gas turbines, renewable energy units, electric boilers, gas boilers, and batteries.

[0153] Based on the energy conservation law and multi-energy flow transmission characteristics of electro-thermal coupled networks, network constraints for integrated energy systems are determined.

[0154] The constraints of the integrated energy system network include: tie line power constraints, grid node energy balance constraints, grid node voltage and line power upper and lower limit constraints, heat network source node power balance constraints, heat network source and load node power equation constraints, heat network pipeline delay and heat loss constraints, heat network junction node energy conservation and temperature equation constraints, and heat network supply and return water temperature upper and lower limit constraints.

[0155] Step S132: In the lower layer, using the electricity and heat prices published by the upper layer as inputs, load response as the decision variable, minimizing the operating cost of the entire load as the second objective function, and building thermal dynamic constraints, comfort constraints, and electrical load constraints as the second constraints, construct a load aggregation quotient optimization model.

[0156] Based on the Stackelberg game theory model architecture, the lower layer is the load aggregator optimization model. The load aggregator optimization model takes the electricity and heat prices published by the upper-level energy operator optimization model as input, uses load response as the decision variable, and achieves the optimization objective of minimizing the operating cost of the entire load by controlling the load response under the second constraint, which is the second objective function.

[0157] The second objective function includes the total cost of comfort loss and the energy purchase cost of the total load. Specifically, the second objective function satisfies the following formula:

[0158] ;

[0159] in, The scheduling interval length, It is a vector composed of the heat prices of node k in the hot network at each time step. It is a vector composed of the heat load at node k in the thermal network at each time step. It is the cost factor for the loss of thermal comfort; It is a vector consisting of the indoor temperature at each time point at node k of the thermal network; It is the ideal indoor temperature value at node k of the thermal network. It is the domain. , , Building parameters, Let K be the indoor temperature of the building at node k at time t. The outdoor temperature at time t. Let be the heat load of the building at node k at time t. Let be the adjustable indoor temperature, be the vector of indoor temperatures at each moment at node k in the thermal network, and N be the number of scheduling cycles. It is the downward allowable offset coefficient of node i in the electrical network at time t. It is the allowable upward offset coefficient of node i in the electrical network at time t. It is the predicted electrical load value of node i in the electrical network at time t. Let be the electrical load at node i in the electrical network at time t.

[0160] The second constraint includes building thermal dynamics constraints, comfort constraints, and electrical load constraints, which specifically satisfy the following:

[0161] The building's thermal dynamic constraints are determined based on the thermophysical properties of the building envelope. Comfort constraints are determined based on the upper and lower temperature limits of the acceptable indoor thermal environment for end users. Electrical load constraints are determined based on the preset offset range of the electrical load.

[0162] Step S133: In the upper layer, optimize the electricity price and heat price based on the load response of the lower layer, the first objective function and the first constraint condition; in the lower layer, optimize the load response based on the electricity price and heat price published by the upper layer, the second objective function and the second constraint condition.

[0163] In the upper layer, electricity and heat prices are continuously optimized based on the first objective function. During the price adjustment process, the upper layer implicitly includes the prediction of the response strategy of the integrated energy system operator to the load aggregator in the lower layer, so that electricity and heat prices can be adjusted more accurately.

[0164] In the lower layer, the load aggregator optimization model optimizes the load response strategy based on the electricity and heat prices published by the integrated energy system operator optimization model in the upper layer. That is, under the current electricity and heat prices, the resource allocation strategy aims to minimize the operating cost of the load aggregator.

[0165] Step S134: Through the game between the upper and lower layers, until both the upper and lower layers reach an equilibrium state, construct a two-layer dynamic pricing model.

[0166] The electricity and heat prices published by the upper-level integrated energy system operator optimization model continuously engage in a master-slave game with the load response strategy of the lower-level load aggregator optimization model. When both the first and second optimization objectives are simultaneously satisfied, an equilibrium state is reached, thus constructing a two-tier dynamic pricing model. The two-tier dynamic pricing model satisfies the following formulas and constraints:

[0167] ;

[0168] The constraints are:

[0169] ;

[0170] ;

[0171] ;

[0172] in, The scheduling interval length, It is a vector composed of the heat prices of node k in the hot network at each time step. It is a vector composed of the heat load at node k in the thermal network at each time step, c T It is the cost vector of decision variables, where x is the decision variable. It is a vector composed of the electricity prices at each time point of node i in the power network. It is a vector of electrical loads at node i in the electrical network at each time step. It is a set of hot network nodes. It is a set of electrical network nodes. It is the predicted electrical load value of node i in the power network at time t, where T is the set of operation optimization periods. It is the downward allowable offset coefficient of node i in the electrical network at time t. It is the allowable upward offset coefficient of node i in the electrical network at time t.

[0173] In one embodiment, Figure 3 This is a schematic diagram illustrating the structure of a two-tier dynamic pricing model according to an exemplary embodiment, such as... Figure 3As shown, the busbars of the power grid subsystem, heating network subsystem, and gas network subsystem are connected, and the busbars of energy subsystems capable of energy conversion are connected through energy conversion devices to construct a physical model. Based on the physical model, the upper-level integrated energy system operator model in the two-layer dynamic pricing model can be determined to minimize the total cost of the integrated energy system operator. The lower layer uses all loads as load aggregators, and the price response model of the load aggregators is determined according to the independent load aggregator model to minimize the cost of the load aggregators. It should be noted that... Figure 3 GB stands for gas-fired boiler, EB for electric boiler, TST for thermal storage tank, TLA for heat load, BT for battery, PV for photovoltaic power generation, and WT for wind turbine power generation.

[0174] Continuing with step 103, based on the constructed two-layer dynamic pricing model, the electricity price and heat price can be determined according to the power regulation demand and supply characteristics of the power grid. The electricity price and heat price can minimize the cost within the energy region, thereby enabling the energy region to automatically respond to the power regulation demand of the power grid and adjust the output of different resources, thus improving the regulation capability of the power grid.

[0175] Step S104: The multi-energy flow model responds to electricity and heat prices, uses the cost objective function as the optimization objective, and generates and executes a resource regulation scheme for the energy region.

[0176] The multi-energy flow model of the energy region responds to electricity and heat prices, takes minimizing the cost objective function as the optimization objective, generates resource regulation schemes within the energy region, and controls the output of corresponding energy supply equipment of different energy subsystems within the energy region according to the generated resource regulation schemes, so as to meet the power regulation needs of the power grid and the internal energy supply needs of the energy region, thereby improving the regulation capacity of the power grid.

[0177] In summary, the grid regulation based on dynamic resource pricing provided in this application constructs a multi-energy flow model for energy regions to determine the supply characteristics of the marginal cost of each resource under different power regulation levels. A two-layer dynamic pricing model determines electricity and heat prices based on power regulation demand and supply characteristics, enabling energy regions to automatically respond to electricity and heat prices and adjust their resource supply plans accordingly. This not only meets the power requirements of the grid but also satisfies the economic requirement of minimizing operating costs within the energy region. When the grid meets economic and low-carbon goals, the energy regions can automatically adjust their resource supply, providing energy resources to the grid while simultaneously supplying energy resources within the energy region, thus fulfilling the grid's regulation needs. The two-layer dynamic pricing model integrates energy resources from multiple energy regions into grid regulation through price signals. This not only rationally utilizes energy resources from multiple energy regions for grid regulation but also addresses the challenges of volatility, uncertainty, and high coupling between power generation, grid, load, and storage brought about by the integration of new energy sources into the power system, thereby improving the grid's regulation capabilities.

[0178] Secondly, embodiments of this application provide a power grid regulation device based on dynamic resource prices. Figure 4 This is a schematic diagram of a power grid regulation device based on dynamic resource prices, according to an exemplary embodiment, such as... Figure 4 As shown, the power grid regulation device based on dynamic resource prices includes:

[0179] The supply characteristics determination module is used to construct a multi-energy flow model based on different energy subsystems, distributed power sources and loads within an energy region. In the multi-energy flow model, the supply characteristics of the marginal cost of each energy resource under different power regulation quantities are determined. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as the cost objective function.

[0180] The regulation demand determination module is used to determine the power regulation demand of the power grid based on economic goals, low-carbon goals, and power grid safety operation constraints.

[0181] The price adjustment module is used to construct a two-layer dynamic pricing model based on the Stackelberg game model architecture, with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The two-layer dynamic pricing model adjusts the electricity price and the heat price according to the power regulation demand and the supply characteristics.

[0182] The resource adjustment module is used by the multi-energy flow model to generate and execute a resource adjustment scheme for the energy region in response to the electricity price and the heat price, with the cost objective function as the optimization objective.

[0183] In summary, the grid regulation device based on dynamic resource pricing provided in this application determines the supply characteristics of marginal resource costs under different power regulation volumes by constructing a multi-energy flow model of energy regions. This allows for the preliminary identification of adjustable energy resources within the energy regions. Based on economic objectives, low-carbon goals, and grid safety operation constraints, the power regulation demand of the grid is determined. A two-layer dynamic pricing model is constructed, with an integrated energy system operator optimization model at the upper layer and a load aggregator optimization model at the lower layer. This model adjusts electricity and heat prices according to power regulation demand and supply characteristics, enabling multi-energy regions to automatically respond to the adjusted prices and generate and execute resource regulation schemes to adjust energy supply and power regulation within the energy regions. This achieves the integration of energy resources from multiple energy regions into grid regulation through price signals. It not only rationally utilizes energy resources from multiple energy regions for grid regulation but also addresses the challenges of volatility, uncertainty, and high coupling between multiple links such as power generation, grid, load, and storage brought about by the integration of new energy sources into the power system. By using dynamic resource pricing, energy regions can automatically respond to resource prices and adjust the supply of different energy sources within the region. This not only enables the energy resources within the region to be supplied to the outside world but also meets the needs of power regulation, thereby improving the regulation capacity of the power grid.

[0184] It should be noted that the power grid regulation device based on dynamic resource pricing provided in this embodiment is used to implement the above-described embodiments, and details already described will not be repeated. As used above, terms such as "module," "unit," and "subunit" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the above embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0185] Thirdly, embodiments of this application provide an electronic device, Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. (e.g.) Figure 5 As shown, the electronic device may include a processor 81 and a memory 82 storing computer program instructions.

[0186] Specifically, the processor 81 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0187] The memory 82 may include a mass storage device for data or instructions. For example, and not limitingly, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include removable or non-removable (or fixed) media. Where appropriate, the memory 82 may be internal or external to a data processing device. In a particular embodiment, the memory 82 is non-volatile memory. In a particular embodiment, the memory 82 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0188] The memory 82 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 81.

[0189] The processor 81 reads and executes computer program instructions stored in the memory 82 to implement any of the power grid regulation methods based on dynamic resource prices in the above embodiments.

[0190] In one embodiment, the power grid regulation device based on dynamic resource pricing may further include a communication interface 83 and a bus 80. Wherein, as... Figure 5 As shown, the processor 81, memory 82, and communication interface 83 are connected through bus 80 and complete communication with each other.

[0191] The communication interface 83 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of this application. The communication interface 83 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0192] Bus 80, including hardware, software, or both, couples together components of a power grid regulation device based on dynamic resource pricing. Bus 80 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, and Local Bus. For example, and not as a limitation, bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 80 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0193] Fourthly, embodiments of this application provide a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the power grid regulation method based on dynamic resource prices provided in the first aspect.

[0194] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0195] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, causes the terminal device to perform steps implementing the power grid regulation method based on dynamic resource pricing provided in the first aspect.

[0196] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0197] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0198] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A power grid regulation method based on dynamic resource prices, characterized in that, include: A multi-energy flow model is constructed based on different energy subsystems, distributed power sources and loads within an energy region. In the multi-energy flow model, the supply characteristics of the marginal cost of each energy resource under different power regulation quantities are determined. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as the cost objective function. The power regulation demand of the power grid is determined based on economic goals, low-carbon goals, and power grid safety operation constraints. Based on the Stackelberg game theory model architecture, a two-layer dynamic pricing model is constructed with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The two-layer dynamic pricing model adjusts the electricity price and the heat price according to the power regulation demand and the supply characteristics. The multi-energy flow model responds to the electricity price and the heat price, and generates and executes the resource regulation scheme for the energy region with the cost objective function as the optimization objective. The Stackelberg game theory model architecture, with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer, constructs a two-layer dynamic pricing model, including: Based on the Stackelberg game model architecture, in the upper layer, the load response of the lower layer is taken as input, the electricity price and heat price at each moment and the output of each device in the integrated energy system are taken as decision variables, the first objective function is to maximize the operating profit of the integrated energy system, and the first constraints are the KKT condition constraints, price constraints, equipment operation constraints and network constraints of the integrated energy system with respect to the optimal load response of the lower layer. The integrated energy system operator optimization model is constructed. In the lower layer, the electricity and heat prices published by the upper layer are used as inputs, the load response is used as the decision variable, the minimum operating cost of the entire load is used as the second objective function, and the building thermal dynamics constraint, comfort constraint and electrical load constraint are used as the second constraint conditions to construct the load aggregation quotient optimization model. In the upper layer, the electricity price and the heat price are optimized based on the load response of the lower layer, the first objective function, and the first constraint condition; in the lower layer, the load response is optimized based on the electricity price and heat price published by the upper layer, the second objective function, and the second constraint condition. The two-layer dynamic pricing model is constructed through the game between the upper and lower layers until both layers reach an equilibrium state.

2. The power grid regulation method based on dynamic resource prices according to claim 1, characterized in that, The energy subsystem includes a power grid subsystem, a heating network subsystem, and a gas network subsystem. The construction of a multi-energy flow model based on different energy subsystems, distributed power sources, and loads within the energy region includes: Based on the connection and energy conversion relationships of the different energy subsystems, determine the physical topology between the power grid subsystem, the heating network subsystem, and the gas network subsystem; Based on the physical topology, the load, the distributed power source, the energy conversion device, and the power supply equipment, determine the physical model of the energy region; Based on the physical model, the adjustable power of the energy supply equipment is used as the decision variable, the comprehensive operating cost within the preset scheduling period is used as the cost objective function, and the constraints are equipment operation constraints, network operation constraints of different energy subsystems, load constraints, and energy supply and demand balance constraints of the energy region. The multi-energy flow model is then constructed.

3. The power grid regulation method based on dynamic resource prices according to claim 1, characterized in that, The process of determining the power regulation demand of the power grid based on economic objectives, low-carbon objectives, and power grid safety operation constraints includes: For the power system, a multi-objective operation optimization model is constructed with the optimization objectives of minimizing the operating cost and minimizing the total carbon emissions of the power system within the dispatch cycle, and with the constraints of power grid safe operation as the condition. The resource regulation scheme in the power system is determined by solving the multi-objective operation optimization model, and the power regulation demand is determined based on the resource regulation scheme.

4. The power grid regulation method based on dynamic resource prices according to claim 1, characterized in that, The first objective function is specifically configured as follows: The first objective function includes the operating cost of the integrated energy system, heating revenue, and electricity revenue.

5. The power grid regulation method based on dynamic resource prices according to claim 4, characterized in that, The price constraints include electricity price constraints and heat price constraints, and the first constraint condition is specifically configured as follows: The KKT condition constraints are determined based on the optimal load response of the load aggregation quotient optimization model. The electricity price constraint is determined based on the upper limit of the electricity price, the lower limit of the electricity price, and the electricity price discount coefficient; The heat price constraint is determined based on the upper limit of the heat price, the lower limit of the heat price, and the heat price discount coefficient; Based on the physical characteristics and operating limits of each device in the integrated energy system, the operating constraints of the devices are determined; Based on the energy conservation law and multi-energy flow transmission characteristics of the electro-thermal coupled network, the network constraints of the integrated energy system are determined.

6. The power grid regulation method based on dynamic resource prices according to claim 1, characterized in that, The second objective function is specifically configured as follows: The second objective function includes the cost of comfort loss and the energy cost of all said loads.

7. The power grid regulation method based on dynamic resource prices according to claim 6, characterized in that, The second constraint is specifically configured as follows: The thermal dynamic constraints of the building are determined based on the thermophysical properties of the building envelope. The comfort constraints are determined based on the upper and lower limits of the acceptable indoor thermal environment for end users. The electrical load constraint is determined based on a preset offset range of the electrical load.

8. A power grid regulation device based on dynamic resource prices, characterized in that, include: The supply characteristics determination module is used to construct a multi-energy flow model based on different energy subsystems, distributed power sources and loads within an energy region. In the multi-energy flow model, the supply characteristics of the marginal cost of each energy resource under different power regulation quantities are determined. The multi-energy flow model takes minimizing the comprehensive operating cost within a preset scheduling cycle as the cost objective function. The regulation demand determination module is used to determine the power regulation demand of the power grid based on economic goals, low-carbon goals, and power grid safety operation constraints. The price adjustment module is used to construct a two-layer dynamic pricing model based on the Stackelberg game model architecture, with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer. The two-layer dynamic pricing model adjusts the electricity price and the heat price according to the power regulation demand and the supply characteristics. The resource adjustment module is used by the multi-energy flow model to generate and execute a resource adjustment scheme for the energy region in response to the electricity price and the heat price, with the cost objective function as the optimization objective. The Stackelberg game theory model architecture, with the integrated energy system operator optimization model as the upper layer and the load aggregator optimization model as the lower layer, constructs a two-layer dynamic pricing model, including: Based on the Stackelberg game model architecture, in the upper layer, the load response of the lower layer is taken as input, the electricity price and heat price at each moment and the output of each device in the integrated energy system are taken as decision variables, the first objective function is to maximize the operating profit of the integrated energy system, and the first constraints are the KKT condition constraints, price constraints, equipment operation constraints and network constraints of the integrated energy system with respect to the optimal load response of the lower layer. The integrated energy system operator optimization model is constructed. In the lower layer, the electricity and heat prices published by the upper layer are used as inputs, the load response is used as the decision variable, the minimum operating cost of the entire load is used as the second objective function, and the building thermal dynamics constraint, comfort constraint and electrical load constraint are used as the second constraint conditions to construct the load aggregation quotient optimization model. In the upper layer, the electricity price and the heat price are optimized based on the load response of the lower layer, the first objective function, and the first constraint condition; in the lower layer, the load response is optimized based on the electricity price and heat price published by the upper layer, the second objective function, and the second constraint condition. The two-layer dynamic pricing model is constructed through the game between the upper and lower layers until both layers reach an equilibrium state.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the power grid regulation method based on dynamic resource prices as described in any one of claims 1 to 7.

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