Method for scheduling a multi-energy system, computer device and computer-readable storage medium

Abstract

Embodiments of the present application disclose a scheduling method of a multi-energy system, a computer device and a computer readable storage medium. In the embodiments, according to energy use and energy production characteristics of various resources, a certain energy model of certain energy in the multi-energy system and an uncertain energy model of renewable resources are respectively established, a two-stage robust scheduling model is finally established, and a final control decision is finally solved by a preset algorithm. Thus, flexible resources in the multi-energy system are reasonably classified and characterized, a suitable multi-energy system collaborative control method for various fixed resources and flexible resources is finally formulated according to the preset algorithm, flexibility of movable resources in the multi-energy system is fully utilized, and collaborative control among multi-region multi-energy systems is realized.

Description

Technical Field

[0001] This application belongs to the field of integrated energy system optimization operation and scheduling technology, and in particular relates to a scheduling method for a multi-energy system, a computer device, and a computer-readable storage medium. Background Technology

[0002] Faced with the increasingly severe global energy and climate crisis, the traditional energy industry structure is gradually developing towards a new type of energy system that is lower in carbon and more efficient, thereby promoting the consumption of renewable energy and reducing environmental pollution. As a major form of improving energy utilization efficiency and operational flexibility, multi-energy systems can give full play to the advantages of complementary and mutually reinforcing multiple energy sources and have been widely promoted in engineering applications.

[0003] However, for multi-energy systems encompassing multiple regions, the spatiotemporal distribution of multi-energy loads in each region may differ significantly, and the capacity, flexibility, and economic efficiency of the configured distributed equipment also vary. Optimizing scheduling only for a single region cannot fully leverage the coordinated role of flexible loads and equipment across regions. Current multi-energy system scheduling focuses primarily on the centralized optimization of the system's operation, achieving economic benefits through the regulation of fixed flexible resources within a region. However, when mobile flexible resources exist across multiple regions, such as mobile energy stations or electric vehicles, cross-regional collaborative scheduling is a prerequisite for optimizing the allocation of mobile flexible resources. Therefore, developing suitable collaborative regulation methods for multi-energy systems accommodating multiple types of fixed and flexible resources is a technical problem urgently needing to be solved by those skilled in the art.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] Based on this, it is necessary to propose a scheduling method, computer equipment, and computer-readable storage medium for multi-energy systems to address the above problems, which can flexibly and effectively realize the coordinated control of multi-energy systems with multiple types of fixed and flexible resources across regions.

[0006] The technical problem solved by this application is achieved by the following technical solution:

[0007] This application provides a scheduling method for a multi-energy system, comprising the following steps: acquiring first energy information of the multi-energy system; establishing a deterministic energy model based on the first energy information, wherein the first energy information is the energy information of certain energy devices in the multi-energy system, and the deterministic energy model is used to describe the operating load status of fixed energy sources and their equipment in the multi-energy system; acquiring second energy information of the multi-energy system; establishing an uncertain energy model based on the second energy information, wherein the second energy information is the energy information of renewable resource devices in the multi-energy system, and the uncertain energy model is used to describe the operating load status of renewable energy sources and their equipment in the multi-energy system; establishing a two-stage robust scheduling model based on the deterministic energy model and the uncertain energy model; and solving the two-stage robust scheduling model using a preset algorithm to obtain the control decision.

[0008] In an optional embodiment of this application, the deterministic energy model includes a regional flexible resource model and a system component model. Establishing the deterministic energy model based on first energy information includes: acquiring energy load information from the first energy information; establishing a regional flexible resource model based on the energy load information, where the energy load information is the operating information of each energy component in the multi-energy system; and the regional flexible resource model is used to describe the flexible load of energy in the multi-energy system and its energy conversion and mobile allocation characteristics. It also includes acquiring energy component information from the first energy information; establishing a system component model based on the energy component information, where the energy component information is the operating constraints of each energy component in the multi-energy system; and the system component model is used to describe the operating status of each energy component in the multi-energy system.

[0009] In an optional embodiment of this application, the regional flexible resource model includes a fixed regional flexible resource model and a mobile regional flexible resource model. Establishing the regional flexible resource model based on energy load information includes: acquiring energy load information including flexible thermal load and flexible electrical load; establishing a fixed regional flexible resource model based on the flexible thermal load and flexible electrical load; the fixed regional flexible resource model is used to describe the fixed thermal consumption and electrical consumption in the multi-energy system; acquiring energy load information including energy output power, energy consumption information, and spatial coefficient; establishing a mobile regional flexible resource model based on the energy output power, energy consumption information, and spatial coefficient; the mobile regional flexible resource model is used to describe the energy conversion and mobile allocation characteristics of the multi-energy system.

[0010] In an optional embodiment of this application, the system component model includes a device model and a network model; establishing the system component model based on energy component information includes: acquiring information including electrical energy component information and thermal energy component information, and establishing a device model and a network model based on the electrical energy component information and thermal energy component information. The device model is used to describe the operating status of electrical energy equipment and thermal energy equipment in the multi-energy system, and the network model is used to describe the energy transmission status in the multi-energy system.

[0011] In an optional embodiment of this application, the equipment model includes an electrical energy equipment model and a thermal energy equipment model. Establishing the equipment model based on electrical and thermal component information includes: acquiring electrical component information including power generation equipment information and energy storage equipment information, and establishing an electrical energy equipment model, which describes the power generation output constraints and energy storage constraints of the electrical energy equipment; acquiring thermal component information including heat generation equipment information and heat storage equipment information, and establishing a thermal energy equipment model, which describes the heat generation efficiency and heat storage constraints of the thermal energy equipment.

[0012] In an optional embodiment of this application, the network model includes a power distribution network model and a heating network model. The network model is established based on electrical energy component information and thermal energy component information, including: acquiring transmission line information and establishing a power distribution network model based on the transmission line information, the power distribution network model being used to describe the power flow distribution in the multi-energy system; acquiring heat transmission pipeline information and establishing a heating network model based on the heat transmission pipeline information, the heating network model being used to describe the heat transmission status in the multi-energy system.

[0013] In an optional embodiment of this application, a two-stage robust scheduling model is established based on a deterministic energy model and an uncertain energy model, including: setting a first decision variable based on the deterministic energy model, setting an uncertainty variable and a second decision variable based on the uncertain energy model, wherein the first decision variable is used to determine the scheduling decision for mobile and flexible resources, and the second decision variable is used to determine the scheduling decision for load flexibility and renewable energy uncertainty; obtaining condition coefficients, and establishing a two-stage robust scheduling model based on the first decision variable, the second decision variable, the uncertainty variable, and the condition coefficients.

[0014] In an optional embodiment of this application, a two-stage robust scheduling model is solved using a preset algorithm to obtain a control decision. This includes: transforming the two-stage robust scheduling model into a mixed-integer linear programming main problem and a two-level subproblem; using the Karouch-Kun-Tucker condition to transform the two-level subproblem into a single-level linear programming problem, setting iteration parameters, and incorporating the iteration parameters into the main problem and subproblems; sequentially performing iterative parameter tuning to solve the main problem and subproblems using a row and column generation algorithm until the set iteration parameters satisfy the iteration conditions to obtain the decision variables; and generating and outputting a control decision based on the decision variables.

[0015] This application also provides a computer device including a processor and a memory: the processor is used to execute a computer program stored in the memory to implement the method as described above.

[0016] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described above.

[0017] The embodiments of this application have the following beneficial effects:

[0018] This application establishes deterministic energy models for fixed energy sources and uncertain energy models for renewable resources within a multi-energy system, based on the energy consumption and production characteristics of various resources. This leads to the creation of a two-stage robust scheduling model, which is then solved using a pre-defined algorithm to determine the final control decisions. This allows for the reasonable classification and characterization of flexible resources within the multi-energy system. By solving the problem using the pre-defined algorithm, a suitable collaborative control method for multi-energy systems, encompassing various types of fixed and flexible resources, is formulated. This fully leverages the flexibility of mobile resources within the multi-energy system and enables collaborative control between multi-regional multi-energy systems.

[0019] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it according to the contents of the specification, and to make the above and other objects, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] in:

[0022] Figure 1 A flowchart illustrating a scheduling method for a multi-energy system as provided in one embodiment;

[0023] Figure 2 This is a schematic diagram illustrating the process of obtaining decision variables for solving a two-stage robust scheduling model, as provided in one embodiment.

[0024] Figure 3 This is a schematic diagram of a multi-zone electrothermal multi-energy system provided in one embodiment;

[0025] Figure 4 A schematic diagram illustrating a multi-energy system purchasing power from an external power grid, as provided in one embodiment;

[0026] Figure 5 A schematic diagram of wind and solar power curtailment in a multi-energy system is provided as an example.

[0027] Figure 6 This is a schematic block diagram of the structure of a computer device provided in one embodiment. Detailed Implementation

[0028] 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 of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0029] For multi-energy systems comprising multiple regions, it is necessary to conduct more in-depth research on the interactive coupling relationships between these regions, building upon the physical interconnection and coupling of multiple regions at the multi-energy system level. This will allow for full utilization of the flexibility of mobile resources within the multi-energy system and the realization of coordinated control among the multi-region multi-energy systems. Based on this, the scheduling method for multi-energy systems provided in this application is proposed, including steps S110 to S130. For a clear description of the multi-energy system scheduling method provided in this embodiment, please refer to... Figures 1-5 .

[0030] Step S110: Obtain the first energy information of the multi-energy system, and establish a deterministic energy model based on the first energy information. The first energy information is the energy information of the determined energy equipment in the multi-energy system. The deterministic energy model is used to describe the fixed energy and its equipment operating load status in the multi-energy system.

[0031] In one embodiment, the deterministic energy model includes a regional flexible resource model and a system component model; step S110: establishing a deterministic energy model based on the first energy information includes: obtaining energy load information from the first energy information; establishing a regional flexible resource model based on the energy load information, wherein the energy load information is the operating information of each energy component in the multi-energy system; the regional flexible resource model is used to describe the flexible load of energy in the multi-energy system and its energy conversion and mobile allocation characteristics; obtaining energy component information from the first energy information; establishing a system component model based on the energy component information, wherein the energy component information is the operating constraint of each energy component in the multi-energy system; the system component model is used to describe the operating state of each energy component in the multi-energy system.

[0032] In one embodiment, the multi-energy system used in this application, in a preferred embodiment, includes power and heating equipment such as photovoltaics, micro-turbines, diesel generators, distributed heat pumps, electric boilers, and thermal storage tanks; mobile energy equipment such as mobile energy stations and electric vehicles; auxiliary equipment such as power grids, heating networks, and heat exchange stations; and flexible electrical and thermal loads. That is to say, it has both relatively fixed energy loads and production capacities, as well as uncertain energy loads and production capacities, thus requiring separate modeling and analysis. Step S110 involves modeling a deterministic energy model that describes the fixed energy sources and their operating load states within the multi-energy system. Specifically, the deterministic energy model can be further subdivided; in this embodiment, it may include a flexible resource model and a system component model. The regional flexible resource model describes the flexible load of energy in the multi-energy system and its energy conversion and mobile allocation characteristics; the system component model describes the operating states of each energy component in the multi-energy system.

[0033] In one embodiment, the regional flexible resource model includes a fixed regional flexible resource model and a mobile regional flexible resource model. Establishing the regional flexible resource model based on energy load information includes: acquiring energy load information including flexible thermal load and flexible electrical load; establishing a fixed regional flexible resource model based on the flexible thermal load and flexible electrical load; the fixed regional flexible resource model is used to describe the fixed thermal consumption and electrical consumption in the multi-energy system; acquiring energy load information including energy output power, energy consumption information, and spatial coefficient; establishing a mobile regional flexible resource model based on the energy output power, energy consumption information, and spatial coefficient; the mobile regional flexible resource model is used to describe the energy conversion and mobile allocation characteristics of the multi-energy system.

[0034] In one embodiment, a regional flexible resource model is used to describe the fixed heat and electricity consumption in a multi-energy system. Specifically, it includes a fixed load flexible resource model and a mobile flexible resource model: the former describes the fixed heat and electricity consumption in the multi-energy system; the latter describes the energy conversion and mobile allocation characteristics of the multi-energy system. Specifically, fixed load flexible resources are divided into flexible electrical loads and flexible heat loads. Flexible electrical loads are the power consumption of heat pumps and electric boilers, which can be adjusted within a certain range according to user comfort. The model is as follows:

[0035]

[0036] In the formula, and Let be the total electrical load and fixed electrical load at node i at time t. For flexible heat load, taking a building thermal model as an example, a thermal resistance-capacitance model is used to model the building heat load. The discrete equation for the building's indoor temperature can be expressed as:

[0037]

[0038] In the formula, τ in,t R represents the indoor temperature at time t. t c is the equivalent thermal resistance of the building's exterior wall. a The specific heat capacity of air, This refers to the heating capacity provided by the heat exchange station at time t. Simultaneously, the building temperature must meet certain comfort requirements, namely:

[0039] τ in,min ≤π in，t ≤π in,max (3)

[0040] In the above formula, τ in,min and τ in,max These represent the upper and lower limits of user comfort, respectively. Furthermore, mobile and flexible resources refer to the characteristics of flexible movement and allocation of various energy production and storage resources within a multi-regional energy system, enabling operators to achieve flexible control within the region with smaller resource consumption. This application utilizes the characteristics of numerous mobile and flexible resources—small installation scale and flexible configuration—to treat them as integrated mobile energy stations, and models their energy conversion characteristics and mobile allocation characteristics holistically based on the energy-hub method. In a preferred embodiment of this application, electric vehicles, small diesel generators, photovoltaics, and batteries are used as examples, utilizing their small installation scale and flexible configuration characteristics to treat them as integrated mobile energy stations (IMES), and their energy conversion characteristics and mobile allocation characteristics are modeled. First, an energy hub is used to describe the input-output relationship of the energy station, defining the input energy matrix as I. (3×1) The output energy matrix is ​​O (2×1) If the coupling coefficient matrix is ​​C(2×3), then:

[0041]

[0042]

[0043] In Equation 4, P PV, Let t be the active power generated by the photovoltaic power source in the mobile energy station. σ represents the mass of diesel fuel consumed by the diesel generators in the mobile energy station during time period t; E The charge / discharge conversion rate for the stored energy of a battery; W EES P represents the capacity of the storage battery. IMES, Let η be the active power output of the mobile energy station at time t. In Equation 5, η (2×3) D is the energy conversion coefficient matrix; (2×3) The input energy distribution coefficient matrix. Diesel mass can be represented as:

[0044]

[0045] In Equation 6, P represents the diesel consumption per kWh of electricity generated by a diesel generator. DG This represents the power output of the diesel generator in the mobile energy station, where Δt is the unit of time. Further, this can be further explained by introducing a 0-1 variable η. IMES,t Describe the spatiotemporal distribution of energy stations: η IMES,t =0 indicates that there is no energy station in the region during time period t; η IMES，t A value of 1 indicates that the region has an energy station during time period t. Based on this, the mobility characteristics of the energy station can be described by adjusting the upper and lower limits of the energy station.

[0046]

[0047] In the above formula, P DG,max The active power output of the diesel generator in the power station; Indicates the upper limit of the battery capacity; This indicates the upper limit of battery capacity.

[0048] In one embodiment, the system component model includes a device model and a network model; establishing the system component model based on energy component information includes: acquiring information including electrical energy component information and thermal energy component information, and establishing a device model and a network model based on the electrical energy component information and thermal energy component information. The device model is used to describe the operating status of electrical energy equipment and thermal energy equipment in the multi-energy system, and the network model is used to describe the energy transmission status in the multi-energy system.

[0049] In one embodiment, the equipment model includes an electrical equipment model and a thermal equipment model. Establishing the equipment model based on electrical component information and thermal component information includes: acquiring electrical component information including power generation equipment information and energy storage equipment information, and establishing an electrical equipment model, which describes the power generation output constraints and energy storage constraints of the electrical equipment; acquiring thermal component information including heat generation equipment information and heat storage equipment information, and establishing a thermal equipment model, which describes the heat generation efficiency and heat storage constraints of the thermal equipment.

[0050] In one embodiment, the network model includes a power distribution network model and a heating network model. The network model is established based on electrical energy component information and thermal energy component information, including: acquiring transmission line information and establishing a power distribution network model based on the transmission line information, the power distribution network model being used to describe the power flow distribution in the multi-energy system; acquiring heat transmission pipeline information and establishing a heating network model based on the heat transmission pipeline information, the heating network model being used to describe the heat transfer status in the multi-energy system.

[0051] In one embodiment, the system component model is used to describe the operating state of each energy component in a multi-energy system. Specifically, the system component model includes equipment models and network models: the equipment models include power supply equipment models and thermal energy equipment models. Power supply equipment in a multi-energy system generally includes photovoltaic, micro-turbine, and diesel generator equipment for self-sufficiency in electrical power, while also being equipped with batteries to mitigate power fluctuations. The micro-turbine is considered a distributed power source whose output and power factor can be continuously adjusted according to changes in the operating state of the distribution network. The equipment model can be constructed from the models or constraints described below. The prime mover output power constraint, power generation and heating power constraint, and active / reactive power constraint are as follows:

[0052] P MT,min ≤P MT ≤P MT,max (8)

[0053]

[0054]

[0055]

[0056]

[0057] In the formula, min and max represent the minimum and maximum values, respectively; P MT and Micro gas turbine Efficiency and energy loss rate. For a diesel generator model, this is considered as the power factor angle φ. DG For fixed distributed power sources, the output constraints are:

[0058] 0≤P DG ≤P DG,max (13)

[0059] Q DG =P DG ·tanφ DG (14)

[0060] In the formula, P DG and Q DG These represent the active and reactive power outputs of the diesel generator, respectively. Battery constraints include capacity constraints and charge / discharge constraints, which can be expressed as:

[0061]

[0062] In the formula, and These are the charging power and discharging power of the battery, respectively. and These represent the charge and discharge efficiencies of the battery, respectively; x EES,t This is a 0-1 variable used to indicate that a battery can only be in one of two states: charging or discharging. Furthermore, in heating equipment, the coefficient of performance (COP) is used to describe the electrical power consumed by a heat pump. and production heat power The relationship can be represented as:

[0063]

[0064]

[0065] In the formula, COP HP Coefficient of performance (COP) of heat pump This represents the maximum power consumption of the heat pump. Similar to a heat pump, an electric boiler model can be represented as:

[0066]

[0067]

[0068] In the formula, COP GB The coefficient of performance (COP) of an electric boiler. This represents the maximum power consumption of the electric boiler. Furthermore, the thermal storage tank model, similar to that of a battery, includes capacity constraints and storage thermal constraints, and can be expressed as:

[0069]

[0070] In the formula, and This indicates the upper limit of the thermal storage tank's capacity and the maximum charging / discharging power; W TES,t , and Let x represent the capacity, thermal storage power, and thermal power of the thermal storage tank at time t, respectively; 0-1 variable x TES，t This indicates the thermal state of the heat storage tank at time t, which prevents the heat storage tank from storing heat simultaneously. and These represent the storage thermal efficiency of the heat storage tank.

[0071] In one embodiment, the electrical energy equipment model and the thermal energy equipment model have been described in detail above. However, it is understood that a corresponding network model is needed for the transmission between the generation and consumption of electricity and heat; that is, a power distribution network model and a heating network model need to be established to describe the energy transmission state in the multi-energy system. In a preferred embodiment of this application, a DC power flow model can be used to describe the power flow distribution within the system, and the active and reactive power conservation equations at the nodes can be expressed as:

[0072]

[0073]

[0074] In the formula, NU(j) is the set of upstream nodes directly connected to node j within the power grid; R ij X represents the total resistance of branch ij; ij U represents the total line reactance of branch ij; i This represents the voltage magnitude at node i in the distribution network model; Let be the active power flowing into branch ij; P represents the reactive power flowing into branch ij. j Q represents the net outflow of active power at node j; j Let represent the net reactive power outflow at node j. The branch power equation can be expressed as:

[0075]

[0076] In the above formula, U j This represents the voltage magnitude at node j in the distribution network model, in Ω. node and Ω line Let represent the sets of nodes and pipes within the distribution network, respectively. Furthermore, the voltage magnitude and branch constraints in the distribution network model can be expressed as follows:

[0077]

[0078]

[0079] In the formula, U min and U max These are the lower and upper limits of the node voltage amplitude, respectively. ij I represents the current transmitted between node i and node j. max This indicates the upper limit of the branch current. Furthermore, the heating network model may include heating stations, supply and return water networks, and heat exchange stations. In a preferred embodiment of this application, the equivalent model of a heat exchange station composed of equipment such as a micro-turbine (MT), heat pump (HP), electric boiler (EB), and thermal storage tank (TES) is as follows:

[0080]

[0081] This model represents the conservation of heat energy at the heating station at node j. The left side of the equation represents the sum of heat power generated by various heating devices collected by the heating station at node j, and the right side represents the heat power input from the heating station into the heat pipe network. Wherein, Ω HS This represents the set of nodes for the heating station; c is the specific heat capacity of water. Let be the mass flow rate of water transferred from the return water pipe to the supply water pipe by the heating station in node j; Let J be the temperature of the water in the water supply pipe at node j. Let be the temperature of the water in the return water pipe at node j. To ensure heating quality, the node temperature of the heating station must meet the following requirements:

[0082]

[0083] In the formula, and Let J represent the maximum and minimum water temperatures of the water supply pipe at node j. Let be the water temperature in the water supply pipeline at node j of the heating station during time period t. The heat exchange station model can be represented as:

[0084]

[0085] In the formula, Let be the mass flow rate of water transferred from the supply pipe to the return pipe at the heat exchange station in node j. Ω represents the heat load at node j. HES This represents the set of nodes in a heat exchange station. The upper and lower limits of the supply and return water temperatures for each node in the heat exchange station can be expressed as:

[0086]

[0087] In the formula, and Let J represent the maximum and minimum water temperatures in the return water pipe at node j. Based on the above, in a preferred embodiment of this application, the heating network can be modeled using the node method:

[0088]

[0089]

[0090]

[0091]

[0092] In the formula, These are the inlet and outlet water temperatures p of the water supply pipeline, respectively. The inlet / outlet water temperatures p of the return water pipe are respectively; The water temperature at node j in the supply / return water pipeline; ∏ p A collection of heating pipes; The set of pipes flowing into node k; φ is the sum of the pipes flowing out from node k; in φ is the set of intersecting nodes of the pipelines. ln The set of hot-load nodes; φ sn The set of heat source nodes; c and ρ are the density and specific heat capacity of water; γ p R p δp ξ p These are all time-delay parameters for the pipeline, and it can be understood that these time-delay parameters are coupled with heat loss parameters to some extent. That is, all of the above parameters can also be expressed as heat loss parameters, and the degree of coupling increases sequentially, with ξ... p The coupling degree is the highest among the four; λ j , Minimum node temperature. Based on the above description, the deterministic energy model can be established. It is understandable that the computational process for establishing a deterministic energy model is complex and diverse, involving multiple models and their corresponding parameters. Therefore, the parameters mentioned above, including but not limited to the energy load information and energy component information listed earlier, are all included in the first energy information, or can be obtained through the first energy information to complete the above calculations and establish the deterministic energy model.

[0093] Step S120: Obtain the second energy information of the multi-energy system, and establish an uncertain energy model based on the second energy information. The second energy information is the energy information of renewable resource equipment in the multi-energy system, and the uncertain energy model is used to describe the operating load status of renewable energy and its equipment in the multi-energy system.

[0094] In one embodiment, in a preferred embodiment of this application, renewable energy may include wind power generation and photovoltaic power generation, and the specific uncertainty energy modeling can be represented as follows:

[0095]

[0096]

[0097] In the formula, and For the actual and predicted power of renewable energy sources; The maximum deviation ratio for renewable energy. and It is the corresponding random variable, Γ res This is an uncertain budget, and all the above parameters are included in the second energy information.

[0098] Step S130: Establish a two-stage robust scheduling model based on the deterministic energy model and the uncertain energy model, and solve the two-stage robust scheduling model using a preset algorithm to obtain the control decision.

[0099] In one embodiment, step S130: establishing a two-stage robust scheduling model based on a deterministic energy model and an uncertain energy model, including: setting a first decision variable based on the deterministic energy model, setting an uncertainty variable and a second decision variable based on the uncertain energy model, wherein the first decision variable is used to determine the scheduling decision for mobile and flexible resources, and the second decision variable is used to determine the scheduling decision for load flexibility and renewable energy uncertainty; obtaining condition coefficients, and establishing a two-stage robust scheduling model based on the first decision variable, the second decision variable, the uncertainty variable, and the condition coefficients.

[0100] In one implementation, the two-stage robust scheduling model treats load flexibility resources as uncertainties. The first stage involves scheduling decisions for mobile flexible resources (i.e., the first decision variable), and the second stage addresses the robust scheduling problem considering both load flexibility and renewable energy uncertainties (i.e., determining the second decision variable). The overall form of the connector scheduling model is as follows:

[0101]

[0102] Where x and y correspond to the first and second decision variables, respectively, and u represents the uncertainty variable. The first decision variable x represents the scheduling decision for mobile flexible resources, and the second decision variable y represents the tie-line power, equipment output, battery charging and discharging power, heat power and water temperature of the heating network, building heat demand, and indoor temperature of the multi-energy system under uncertainties including uncertain load flexibility resources and renewable energy uncertainties. E, h, G, and M are relevant conditional coefficients that can be set based on pre-defined conditions. Specifically, the objective function of the two-stage robust scheduling model includes equipment operation and maintenance costs, electricity purchase costs, reactive power exchange costs with the upstream grid, fuel costs, distribution network power loss costs, wind and solar curtailment costs, and heating network operation and maintenance costs. The specific objective function can be expressed as:

[0103] F = F OMC +F BEC +F BFC +F ELC +F RECC +F HOC (34)

[0104] Among them, F OMC For equipment operation and maintenance costs, F BEC To reduce the cost of purchasing electricity from the upper-level power grid, F BFC To purchase fuel costs, F ELC For the cost of power loss in the distribution network, F RECC For the cost of curtailing renewable resources such as wind and solar power, F HOC To maintain the operating costs of the heating network. Specifically, the operating and maintenance costs of the equipment, F.OMC It can be represented as:

[0105]

[0106] In the formula, i is the node number in the system, t is the time period number of the scheduling, and n is the equipment type number, which refers to various types of equipment in the multi-region system; This represents the unit operating cost of equipment type n. It is also related to the electricity trading cost F with the main grid. BEC Including active and reactive power exchange, represented as:

[0107]

[0108] In the formula, The unit cost of electricity purchased / sold by a multi-energy system from the main grid is the same for active and reactive power. and These represent the active and reactive power that the regional system purchases / sells to the main grid, respectively. The value is positive when the regional system purchases electricity, and negative otherwise. The exchange power must satisfy the following:

[0109]

[0110] Fuel purchase cost F BFC It can be calculated using the following formula:

[0111]

[0112] In the formula, i is the micro gas engine number; n is the diesel engine number; C gas and C diesel These are the unit prices for natural gas and diesel, respectively. and These are the unit gas consumption of the micro gas turbine prime mover and the unit fuel consumption of the diesel engine, respectively. Let be the active power output of micro-turbine i at time t. Let F be the active power output of diesel engine n at time t. Similarly, the power loss cost F of the distribution network. ELC for:

[0113]

[0114] The parameters have already been explained in the preceding text and will not be repeated here. Regarding the cost F of curtailing renewable resources (wind and solar power), RECC It can be represented as:

[0115]

[0116] In the formula, and This refers to the unit cost of curtailed electricity and the amount of curtailed photovoltaic power. The operating cost of the heating network mainly includes the electricity consumption of equipment such as electric boilers and heat pumps, as well as the electricity cost of water pumps used to maintain hot water circulation. Among these, electric boilers and heat pumps are considered electrical loads, and their operating costs are already reflected in the power system's supply cost; while according to relevant engineering standards, the electricity cost of water pumps can be estimated using the energy-to-heat ratio (EHR).

[0117]

[0118] In the formula, i is the heat pump number in the heating network. This represents the amount of heat power flowing through the water pump. In summary, the construction of the two-stage robust scheduling model can be completed.

[0119] In one embodiment, step S130: Solving the two-stage robust scheduling model using a preset algorithm to obtain the control decision includes: transforming the two-stage robust scheduling model into a mixed-integer linear programming main problem and a two-level subproblem; using the Karouch-Kun-Tucker condition to transform the two-level subproblem into a single-level linear programming problem, setting iteration parameters, and incorporating the iteration parameters into the main problem and subproblems; sequentially performing iterative parameter tuning to solve the main problem and subproblems using a row and column generation algorithm until the set iteration parameters satisfy the iteration conditions to obtain the decision variables; generating and outputting the control decision based on the decision variables.

[0120] In one implementation, the decision variables are obtained by iteratively tuning the parameters of the main problem and subproblems using a row and column generation algorithm until the set iteration parameters meet the iteration conditions. This can be referenced from [the relevant documentation / reference]. Figure 2 , Figure 2 The present invention provides a flowchart for solving a two-stage robust scheduling model to obtain decision variables, including steps S210 to S260.

[0121] Step S210: Set the iteration parameters, including the number of iterations k, the lower bound LB, the upper bound UB, the iteration threshold ε, and the uncertainty variable u.

[0122] Step S220: Substitute the iteration parameters into the main problem to solve the main problem, obtain the first decision variable x, the second decision variable y, the worst-case uncertainty variable u, and update the lower bound LB.

[0123] Step S230: Substitute the first decision variable x and the worst-case uncertainty variable u into the subproblem to solve the subproblem, update the second decision variable y, the worst-case uncertainty variable u, and update the lower bound UB.

[0124] Step S240: Determine if the subproblem is feasible. If the subproblem is not feasible, return to step S210 for the next iteration calculation.

[0125] If the subproblem is feasible, proceed to step S250: determine whether the iteration conditions are met based on the initial lower bound LB, the initial upper bound UB, and the iteration threshold ε. If not, return to step S210 for the next iteration calculation.

[0126] If the subproblem is feasible, then proceed to step S260: obtain the first decision variable x and the second decision variable y obtained from the final iterative settlement as decision variables.

[0127] In one implementation, the solution process can be transformed into a mixed-integer linear programming main problem (MP) and a two-level subproblem (SP). The two-level SP problem is then transformed into a single-level linear programming problem using the KKT (Karush-Kuhn-Tucker) conditions. The entire problem is then solved iteratively using the CC&G (Column-and-Constraint Generation Method). Specifically, the iterative conditions mentioned in step S250 can be expressed as follows:

[0128] (UB-LB) / UB<ε (42)

[0129] If the conditions are not met, return to step S210; if the conditions are met, proceed to step S260, thereby iteratively adjusting parameters to finally obtain decision variables that meet the preset conditions.

[0130] In one embodiment, to facilitate understanding of the multi-energy system scheduling method provided in this application, an example is presented for illustration. For the structure of the multi-energy system provided in this embodiment, please refer to... Figure 3 Among them, such as Figure 3 As shown, the system includes a wind turbine, an energy storage device, a micro gas turbine, an electrically driven compression refrigeration unit, a thermal energy storage device, and an integrated mobile energy station incorporating a diesel generator, photovoltaic power source, electric vehicle, and battery. The example, while adjusting the electrical load and adding various distributed devices and the integrated mobile energy station, also sets up thermal load nodes to reflect multi-energy demand and establishes six regions to simulate the characteristics of multi-regional coordinated control. To reflect the medium- to long-term time span of the regional multi-energy system, this invention uses a 7-day scheduling cycle with a time granularity of 1 hour, resulting in a total of 168 time periods. Regarding the mobile energy station scheduling strategy, this paper divides the 33 bus nodes into the following... Figure 3The system comprises six regions, each with a mobile energy station configured at least one day during the scheduling cycle. Furthermore, each mobile energy station remains stationary in one location per day to reduce the costs associated with frequent relocation. Since the scheduling command resolution is one hour, the model ignores the ramp power of the micro gas turbine and diesel engine. Based on this system structure, two calculation examples were obtained using the method provided in this application and existing technologies: Example 1, where existing technologies do not consider mobile flexible energy stations; and Example 2, which considers mobile flexible energy stations based on the multi-energy system scheduling method provided in this application. The calculated scheduling results are shown in Table 1.

[0131]

[0132] Table 1. Scheduling results of multi-energy systems under different mobile and flexible resource conditions

[0133] As shown in Table 1, the overall cost decreased significantly after adding the mobile energy station. In particular, the distribution network line losses, reactive power exchange with the upstream grid, and node voltage deviation all increased substantially by 5% to 10%, while the electricity purchase cost and average branch current amplitude also increased by about 3%. Due to the addition of multiple devices, the operation and maintenance costs inevitably increased by about 9%. (See also...) Figure 4 , Figure 5 , Figure 4 Two scenarios are presented where the system purchases electricity from an external power grid. Figure 5 The figure illustrates the curtailment of wind and solar power in two scenarios. As can be seen from the figure, without considering flexible resources, the system experiences severe wind and solar power curtailment between 0:00 and 4:00, and exhibits a high demand for external energy between 17:00 and 20:00. However, by introducing mobile flexible resources, the system's energy can be effectively transferred from periods of energy surplus to peak consumption periods, not only reducing the system's operating costs but also improving the absorption rate of distributed renewable energy.

[0134] Therefore, this application can establish deterministic energy models for deterministic energy sources and uncertain energy models for renewable resources in a multi-energy system based on the energy consumption and production characteristics of various resources. This leads to the establishment of a two-stage robust scheduling model, which is then solved using a pre-defined algorithm to determine the final control decision. This allows for the reasonable classification and characterization of flexible resources within the multi-energy system. By solving the problem using the pre-defined algorithm, a suitable collaborative control method for multi-energy systems, encompassing various types of fixed and flexible resources, is formulated. This fully leverages the flexibility of mobile resources within the multi-energy system, enabling collaborative control between multi-regional multi-energy systems. It performs well in meeting various demands between heat and electricity loads, improving the absorption capacity of renewable energy and reducing wind and solar curtailment, while also enhancing overall economic efficiency. In summary, by considering mobile and flexible resources across multiple regions, the economic efficiency and flexibility of the multi-energy system are significantly improved.

[0135] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the following steps: Step S110: Obtain first energy information of a multi-energy system, and establish a deterministic energy model based on the first energy information. The first energy information is the energy information of determined energy devices in the multi-energy system, and the deterministic energy model is used to describe the fixed energy and its equipment operating load status within the multi-energy system; Step S120: Obtain second energy information of the multi-energy system, and establish an uncertain energy model based on the second energy information. The second energy information is the energy information of renewable resource devices in the multi-energy system, and the uncertain energy model is used to describe the renewable energy and its equipment operating load status within the multi-energy system; Step S130: Establish a two-stage robust scheduling model based on the deterministic energy model and the uncertain energy model, and solve the two-stage robust scheduling model using a preset algorithm to obtain the control decision.

[0136] Figure 6 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a multi-functional system scheduling method. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to execute an age recognition method. Those skilled in the art will understand that… Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0137] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps of a scheduling method for a multi-functional system as described in any embodiment.

[0138] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0139] 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.

[0140] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. 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 scheduling method for a multi-energy system, characterized in that, Includes the following steps: Obtain first energy information of the multi-energy system, and establish a deterministic energy model based on the first energy information. The first energy information is the energy information of the energy devices determined in the multi-energy system, and the deterministic energy model is used to describe the fixed energy and its equipment operating load status in the multi-energy system. Acquire the second energy information of the multi-energy system, and establish an uncertain energy model based on the second energy information. The second energy information is the energy information of renewable resource equipment in the multi-energy system, and the uncertain energy model is used to describe the operating load status of renewable energy and its equipment in the multi-energy system. A two-stage robust scheduling model is established based on the deterministic energy model and the uncertain energy model. The two-stage robust scheduling model is solved by a preset algorithm to obtain the control decision. The deterministic energy model includes a regional flexible resource model and a system component model; The step of establishing a deterministic energy model based on the first energy information includes: Obtain energy load information from the first energy information, and establish the regional flexible resource model based on the energy load information. The energy load information is the operating information of each energy component in the multi-energy system, and the regional flexible resource model is used to describe the flexible load of energy in the multi-energy system and its energy conversion and mobile allocation characteristics. Obtain the energy component information from the first energy information, establish the system component model based on the energy component information, the energy component information is the operating constraint of each energy component in the multi-energy system, and the system component model is used to describe the operating state of each energy component in the multi-energy system; The establishment of a two-stage robust scheduling model based on the deterministic energy model and the uncertain energy model includes: A first decision variable is set according to the deterministic energy model, and an uncertainty variable and a second decision variable are set according to the uncertain energy model. The first decision variable is used to determine the scheduling decision of mobile and flexible resources, and the second decision variable is used to determine the scheduling decision of load flexibility and renewable energy uncertainty. Obtain the condition coefficients, and establish the two-stage robust scheduling model based on the first decision variable, the second decision variable, the uncertainty variable, and the condition coefficients.

2. The scheduling method for a multi-energy system as described in claim 1, characterized in that, The regional flexible resource model includes a fixed regional flexible resource model and a mobile regional flexible resource model. The step of establishing the regional flexible resource model based on the energy load information includes: The energy load information, including flexible heat load and flexible electrical load, is obtained. Based on the flexible heat load and flexible electrical load, a fixed area flexible resource model is established. The fixed area flexible resource model is used to describe the fixed heat consumption and electricity consumption in the multi-energy system. The energy load information, including energy output power, energy consumption information, and spatial coefficient, is acquired. Based on the energy output power, energy consumption information, and spatial coefficient, a flexible resource model for the mobile area is established. The flexible resource model for the mobile area is used to describe the energy conversion and mobile allocation characteristics of the multi-energy system.

3. The scheduling method for a multi-energy system as described in claim 1, characterized in that, The system component model includes a device model and a network model; The step of establishing the system component model based on the energy component information includes: Information including electrical components and thermal components is obtained. Based on the electrical components and thermal components, a device model and a network model are established. The device model is used to describe the operating status of electrical and thermal equipment in the multi-energy system, and the network model is used to describe the energy transmission status in the multi-energy system.

4. The scheduling method for a multi-energy system as described in claim 3, characterized in that, The equipment model includes an electrical energy equipment model and a thermal energy equipment model; The step of establishing the device model based on the electrical component information and the thermal component information includes: Acquire the information of the power components, including information of power generation equipment and information of energy storage equipment, and establish the power equipment model. The power equipment model is used to describe the power generation output constraints and energy storage constraints of the power equipment. The thermal energy component information, including information on heating devices and thermal storage devices, is obtained, and a thermal energy device model is established. The thermal energy device model is used to describe the heat generation efficiency and thermal storage constraints of the thermal energy device.

5. The scheduling method for a multi-energy system as described in claim 3, characterized in that, The network model includes a power distribution network model and a heating network model. The step of establishing the network model based on the electrical component information and the thermal component information includes: Obtain transmission line information, and establish the distribution network model based on the transmission line information. The distribution network model is used to describe the power flow distribution in the multi-energy system. Obtain heat transmission pipeline information, and establish the heating network model based on the heat transmission pipeline information. The heating network model is used to describe the heat energy transmission status in the multi-energy system.

6. The scheduling method for a multi-energy system as described in claim 1, characterized in that, The step of solving the two-stage robust scheduling model using a preset algorithm to obtain the control decision includes: The two-stage robust scheduling model is transformed into a main problem of mixed-integer linear programming and a two-level sub-problem. The two-level subproblem is transformed into a single-level linear programming problem using the Karouch-Kun-Tucker condition. Iteration parameters are set and incorporated into the main problem and the subproblems. The main problem and the sub-problems are solved by iterative parameter tuning through a row and column generation algorithm until the set iterative parameters meet the iterative conditions and the decision variables are obtained. The control decision is generated and output based on the decision variables.

7. A computer device, characterized in that, Including processor and memory; The processor is used to execute a computer program stored in the memory to implement the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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