WO2024103450A1 - 多能系统的调度方法、计算机设备和计算机可读存储介质 - Google Patents

多能系统的调度方法、计算机设备和计算机可读存储介质 Download PDF

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WO2024103450A1
WO2024103450A1 PCT/CN2022/136211 CN2022136211W WO2024103450A1 WO 2024103450 A1 WO2024103450 A1 WO 2024103450A1 CN 2022136211 W CN2022136211 W CN 2022136211W WO 2024103450 A1 WO2024103450 A1 WO 2024103450A1
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energy
model
information
equipment
energy system
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French (fr)
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陆海
张�浩
罗恩博
唐立军
赵现平
王达达
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云南电网有限责任公司电力科学研究院
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

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  • the present application belongs to the technical field of optimized operation and scheduling of integrated energy systems, and in particular, relates to a scheduling method, computer equipment and computer-readable storage medium for a multi-energy system.
  • the present application provides a scheduling method for a multi-energy system, comprising the following steps: obtaining 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 energy information of energy equipment determined in the multi-energy system, and the deterministic energy model is used to describe the fixed energy and the operating load status of its equipment in the multi-energy system; obtaining 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 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 the 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 through a preset algorithm to obtain a control decision.
  • a deterministic energy model includes a regional flexible resource model and a system element model; a deterministic energy model is established based on the first energy information, including: obtaining energy load information in the first energy information, and 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, 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 movable deployment characteristics; obtaining energy element information in the first energy information, and establishing a system element model based on the energy element information, wherein the energy element information is the operating constraints of each energy component in the multi-energy system, and the system element model is used to describe the operating status of each energy component in the multi-energy system.
  • the regional flexible resource model includes a fixed regional flexible resource model and a movable regional flexible resource model; the regional flexible resource model is established according to the energy load information, including: obtaining energy load information including flexible thermal load and flexible electrical load, and establishing a fixed regional flexible resource model according to 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; obtaining energy load information including energy output power, energy consumption information and spatial coefficient, and establishing a movable regional flexible resource model according to the energy output power, energy consumption information and spatial coefficient, the movable regional flexible resource model is used to describe the energy conversion and movable deployment characteristics of the multi-energy system.
  • the system element model includes a device model and a network model; the system element model is established according to the energy element information, including: obtaining electrical energy element information and thermal energy element information, and establishing a device model and a network model according to the electrical energy element information and the thermal energy element 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 transmission status of energy in the multi-energy system.
  • the equipment model includes an electric energy equipment model and a thermal energy equipment model; the equipment model is established based on the electric energy element information and the thermal energy element information, including: obtaining electric energy element information including power generation equipment information and power storage equipment information, establishing an electric energy equipment model, and the electric energy equipment model is used to describe the power generation output constraints and power storage constraints of the electric energy equipment; obtaining thermal energy element information including heating equipment information and heat storage equipment information, and establishing a thermal energy equipment model, and the thermal energy equipment model is used to describe the heat production efficiency and heat storage constraints of the thermal energy equipment.
  • the network model includes a power distribution network model and a heating network model.
  • the network model is established based on the information of electric energy elements and thermal energy elements, including: obtaining transmission line information, establishing a distribution network model based on the transmission line information, and the distribution network model is used to describe the power flow distribution in the multi-energy system; obtaining heat transmission pipeline information, establishing a heating network model based on the heat transmission pipeline information, and the heating network model is used to describe the heat energy transmission status in the multi-energy system.
  • 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, the first decision variable being used to determine the scheduling decision of movable flexible resources, and the second decision variable being used to determine the scheduling decision of load flexibility and renewable energy uncertainty; obtaining a conditional coefficient, and establishing a two-stage robust scheduling model based on the first decision variable, the second decision variable, the uncertainty variable and the conditional coefficient.
  • a two-stage robust scheduling model is solved by a preset algorithm to obtain a control decision, including: converting the two-stage robust scheduling model into a main problem of a mixed integer linear programming and a two-level sub-problem; using the Karush Kuhn-Tucker condition to convert the two-level sub-problem into a single-level linear programming problem, setting iteration parameters, and incorporating the iteration parameters into the main problem and the sub-problem; using a row and column generation algorithm to iteratively adjust parameters in sequence to solve the main problem and the sub-problem until the set iteration parameters meet the iteration conditions to obtain the decision variables; generating and outputting the control decision based on the decision variables.
  • the present 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 aforementioned method.
  • the present application also provides a computer-readable storage medium storing a computer program, which implements the aforementioned method when the computer program is executed by a processor.
  • This application can establish a deterministic energy model for determined energy in a multi-energy system and an uncertain energy model for renewable resources according to the energy consumption and production capacity characteristics of various resources, so as to finally establish a two-stage robust scheduling model, and finally determine the control decision through a preset algorithm solution.
  • the flexible resources in the multi-energy system are reasonably classified and characterized, and the preset algorithm is used to solve and finally formulate a suitable multi-energy system coordinated control method for multiple types of fixed resources and flexible resources, so as to give full play to the flexibility of movable resources in the multi-energy system and realize coordinated control between multi-regional multi-energy systems.
  • FIG1 is a schematic flow chart of a scheduling method for a multi-energy system provided by an embodiment
  • FIG2 is a schematic diagram of a process for solving a two-stage robust scheduling model to obtain decision variables according to an embodiment
  • FIG3 is a schematic diagram of the structure of a multi-region electric heating multi-energy system provided by an embodiment
  • FIG4 is a schematic diagram of a situation where a multi-energy system purchases electricity from an external power grid according to an embodiment
  • FIG5 is a schematic diagram of wind and solar power abandonment in a multi-energy system provided by an embodiment
  • FIG6 is a schematic block diagram of the structure of a computer device provided by an embodiment.
  • Step S110 Acquire 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 energy information of energy equipment determined 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.
  • 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, including: obtaining energy load information in the first energy information, establishing a 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 movable deployment characteristics; obtaining energy component information in the first energy information, establishing a system component model based on the energy component information, 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.
  • the multi-energy system used in the present application includes photovoltaic, micro-turbines, diesel generators, distributed heat pumps, electric boilers, heat storage tanks and other power and heating equipment, mobile energy stations, electric vehicles and other mobile energy equipment, power grids, heat networks, heat exchange stations and other auxiliary equipment, as well as electric and thermal flexible loads. That is to say, there are relatively fixed energy loads and production capacities, as well as uncertain energy loads and production capacities, so separate modeling is required for analysis.
  • a deterministic energy model that describes the operating load status of fixed energy and its equipment in the multi-energy system is modeled.
  • the deterministic energy model can be further subdivided, and in this embodiment, it can include a flexible resource model and a system component model.
  • the regional flexible resource model is used to describe the flexible load of energy in the multi-energy system and its energy conversion and movable deployment characteristics
  • the system component model is used to describe the operating status of each energy component in the multi-energy system.
  • the regional flexible resource model includes a fixed regional flexible resource model and a movable regional flexible resource model; the regional flexible resource model is established according to the energy load information, including: obtaining energy load information including flexible thermal load and flexible electrical load, and establishing a fixed regional flexible resource model according to the flexible thermal load and flexible electrical load, and the fixed regional flexible resource model is used to describe the fixed thermal consumption and electrical consumption in the multi-energy system; obtaining energy load information including energy output power, energy consumption information and space coefficient, and establishing a movable regional flexible resource model according to the energy output power, energy consumption information and space coefficient, and the movable regional flexible resource model is used to describe the energy conversion and movable deployment characteristics of the multi-energy system.
  • the regional flexible resource model is used to describe the fixed heat consumption and electricity consumption in the multi-energy system.
  • it includes a fixed load flexible resource model and a movable flexible resource model: the former is used to describe the fixed heat consumption and electricity consumption in the multi-energy system; the latter is used to describe the energy conversion and movable deployment characteristics of the multi-energy system.
  • the fixed load flexible resources are divided into flexible electric loads and flexible heat loads.
  • the flexible electric load is the power consumption of heat pumps and electric boilers, which can be adjusted within a certain range according to the comfort level of the user side.
  • the model is:
  • the thermal resistance and capacitance model is used to model the building thermal load.
  • the discrete equation of the indoor temperature of the building can be expressed as:
  • ⁇ in,t represents the indoor temperature at time t
  • R t is the equivalent thermal resistance of the building's exterior wall
  • c a is the specific heat capacity of air. is the heating power provided by the heat exchange station at time t.
  • the building temperature must meet certain comfort requirements, namely:
  • ⁇ in,min and ⁇ in,max represent the upper and lower limits of the user's comfort, respectively.
  • the movable flexible resources refer to the characteristics of flexible mobility and deployment of various energy production and storage in the multi-regional energy system, so that the operator can realize flexible regulation in the region by consuming smaller-scale resources.
  • This application takes advantage of the small installation scale and flexible configuration of many movable flexible resources, regards them as integrated integrated mobile energy stations, and models their energy conversion characteristics and movable deployment characteristics as a whole based on the energy-hub method.
  • the energy hub is used to describe the input-output relationship of the energy station, and the input energy matrix is defined as I (3 ⁇ 1) , the output energy matrix is defined as O (2 ⁇ 1) , and the coupling coefficient matrix is defined as C (2 ⁇ 3), then:
  • P PV,t is the active power generated by the photovoltaic power source in the mobile energy station at time t, is the mass of diesel consumed by the diesel generator in the mobile energy station during time period t; ⁇ E is the charge and discharge conversion rate of the battery storage capacity; W EES is the capacity of the battery; P IMES,t is the active power output of the mobile energy station at time t.
  • the mass of diesel can be expressed as:
  • P DG,max is the active power output of the diesel generator in the dynamic energy station; Indicates the upper limit of battery capacity; Indicates the upper limit of battery capacity.
  • the system element model includes a device model and a network model; the system element model is established according to the energy element information, including: obtaining electrical energy element information and thermal energy element information, and establishing a device model and a network model according to the electrical energy element information and the thermal energy element 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 transmission status of energy in the multi-energy system.
  • the device model includes an electric energy device model and a thermal energy device model; the device model is established based on the electric energy element information and the thermal energy element information, including: obtaining electric energy element information including power generation equipment information and power storage equipment information, establishing an electric energy device model, and the electric energy device model is used to describe the power generation output constraints and power storage constraints of the electric energy equipment; obtaining thermal energy element information including heating equipment information and heat storage equipment information, and establishing a thermal energy device model, and the thermal energy device model is used to describe the heat production efficiency and heat storage constraints of the thermal energy equipment.
  • the network model includes a power distribution network model and a heating network model.
  • the network model is established based on the information of electric energy elements and thermal energy elements, including: obtaining transmission line information, establishing a distribution network model based on the transmission line information, and the distribution network model is used to describe the power flow distribution in the multi-energy system; obtaining heat transmission pipeline information, establishing a heating network model based on the heat transmission pipeline information, and the heating network model is used to describe the heat energy transmission status in the multi-energy system.
  • the system component model is used to describe the operating status of each energy component in the multi-energy system.
  • the system component model includes a device model and a network model: the device model includes a power supply device model and a thermal energy device model.
  • the power supply equipment in the multi-energy system generally includes photovoltaics, micro-turbines, diesel generators and other equipment for self-sufficiency of electric power, and is equipped with batteries to smooth power fluctuations.
  • the micro-turbines are regarded as distributed power sources whose output and power factor can be continuously dispatched and adjusted as the operating status of the distribution network changes.
  • the device model can be composed of the models or constraints described below.
  • the prime mover output power constraints, power generation and heating power constraints and active/reactive power constraints are:
  • min and max represent the minimum and maximum values respectively;
  • P MT and are the prime mover output power and apparent power of the micro-turbine respectively; They are respectively the actual active/reactive power and thermal power output by the micro-turbine;
  • ⁇ HR are the power generation efficiency, waste heat recovery efficiency and energy loss rate of the micro-turbine respectively.
  • the diesel generator model it is regarded as a distributed power source with a fixed power factor angle ⁇ DG , and its output constraint is:
  • P DG and Q DG are the output active power and reactive power of the diesel generator respectively.
  • the battery constraint includes capacity constraint and charge and discharge constraint, which can be expressed as:
  • x EES,t is a 0-1 variable, used to indicate that the battery can only be in one state, either charging or discharging.
  • the heat pump uses the heating coefficient to describe its power consumption. and heat production The relationship can be expressed as:
  • COP is the heating coefficient of HP heat pump, is the maximum power consumption of the heat pump. Similar to the heat pump, the electric boiler model can be expressed as:
  • COP GB is the heating coefficient of the electric boiler, is the maximum power consumption of the electric boiler.
  • thermal storage tank model is similar to the battery, including capacity constraints and storage heat constraints, which can be expressed as:
  • W TES,t represent the capacity, thermal storage power and heat storage power of the thermal storage tank at time t respectively;
  • 0-1 variable x TES,t represents the heat storage state of the thermal storage tank at time t, so that the thermal storage tank cannot store heat at the same time; and They respectively represent the storage thermal efficiency of the heat storage tank.
  • the above text has described the electric energy equipment model and the thermal energy equipment model in detail.
  • the generation and consumption of electric heat also require corresponding network models for transmission, that is, it is necessary to establish a distribution network model and a heating network model accordingly to describe the transmission state of energy in the multi-energy system.
  • a DC power flow model can be used to describe the power flow distribution in the system, and the node active and reactive power conservation equations can be expressed as:
  • NU(j) is the set of upstream nodes directly connected to node j in the power grid;
  • R ij represents the total line resistance of branch ij;
  • Xij represents the total line reactance of branch ij;
  • Ui represents the voltage amplitude at node i in the distribution network model; is the active power flowing into branch ij; is the reactive power flowing into branch ij;
  • P j represents the net outflow active power at node j;
  • Q j represents the net outflow reactive power at node j.
  • the branch power equation can be expressed as:
  • Uj represents the voltage amplitude at node j of the distribution network model
  • ⁇ node and ⁇ line represent the node and pipeline set in the distribution network respectively.
  • the voltage amplitude and branch constraints in the distribution network model can be expressed as:
  • the heating network model may include a heating station, a water supply and return network, and a heat exchange station.
  • the heat exchange station composed of a micro-turbine (MT), a heat pump (HP), an electric boiler (EB), a thermal storage tank (TES), and the like, has an equivalent model as follows:
  • This model represents the conservation of heat energy at the heating station at node j.
  • the left side of the equal sign represents the sum of the heat power generated by various heating equipment collected by the heating station at node j, and the right side of the equal sign represents the heat power input by the heating station to the heat pipe network.
  • ⁇ HS represents the node set of the heating station
  • c is the specific heat capacity of water
  • the temperature of the water in the return pipe at node j is the temperature of the water in the return pipe at node j.
  • the node temperature of the heating station must meet the following requirements:
  • the heating network can be modeled by using the node method:
  • ⁇ p is the set of heating pipes
  • the set of pipes that flow into node k; is the set of pipes flowing out of node k
  • ⁇ in is the set of pipe intersection nodes
  • ⁇ ln is the set of heat load nodes
  • ⁇ sn is the set of heat source nodes
  • c and ⁇ are the density and specific heat capacity of water
  • ⁇ p , R p , ⁇ p , ⁇ p are all delay parameters of the pipe, and it can be understood that the delay parameters are coupled with the heat loss parameters to a certain extent, that is to say, the above parameters can also be expressed as heat loss parameters, and the coupling degree increases in turn, with ⁇ p having the highest coupling degree among the four; ⁇ j , l and p correspond to the heat transfer coefficient
  • the establishment of the deterministic energy model can be completed.
  • the above parameters including but not limited to the energy load information and energy component information listed above, are all included in the first energy information, or can be obtained through the first energy information to complete the above calculations and realize the establishment of the deterministic energy model.
  • 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 the renewable resource equipment in the multi-energy system.
  • the uncertain energy model is used to describe the operating load status of the renewable energy and its equipment in the multi-energy system.
  • renewable energy may include wind power generation and photovoltaic power generation, and the specific uncertainty energy modeling may be expressed as follows:
  • the actual power and predicted power of renewable energy is the maximum deviation ratio of renewable energy, and is the corresponding random variable, ⁇ res is the uncertainty budget, and the above parameters are all included in the second energy information.
  • Step S130 A two-stage robust scheduling model is established according to the deterministic energy model and the uncertain energy model, and the two-stage robust scheduling model is solved by a preset algorithm to obtain a control decision.
  • 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, the first decision variable being used to determine the scheduling decision of movable flexible resources, and the second decision variable being used to determine the scheduling decision of load flexibility and renewable energy uncertainty; obtaining a conditional coefficient, and establishing a two-stage robust scheduling model based on the first decision variable, the second decision variable, the uncertainty variable and the conditional coefficient.
  • the load flexibility resource is regarded as an uncertainty
  • the scheduling decision of the movable flexible resource is made in the first stage (that is, the first decision variable)
  • the robust scheduling problem considering the load flexibility and the uncertainty of renewable energy is carried out in the second stage (that is, determining the second decision variable).
  • x and y correspond to the first decision variable and the second decision variable respectively, and u represents the uncertainty variable.
  • the first decision variable x is the scheduling decision of the movable flexible resources
  • the second decision variable y is the interconnection line power, equipment output, battery charging and discharging power, thermal power and water temperature of the heating network, thermal demand and indoor temperature of the building, etc. of the multi-energy system under the uncertainty of uncertain load flexibility resources and renewable energy uncertainty.
  • E, h, G, and M are all related conditional coefficients, which can be set according to the pre-situation.
  • 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 upper power grid, fuel costs, distribution network power loss costs, wind and solar power abandonment costs, and heating network operation and maintenance costs.
  • the specific objective function can be expressed as:
  • F FOMC + FBEC + FBFC + FELC + RECC + HOC
  • F OMC is the equipment operation and maintenance cost
  • F BEC is the cost of purchasing electricity from the upper power grid
  • F BFC is the cost of purchasing fuel
  • F ELC is the cost of power loss in the distribution network
  • F RECC is the cost of abandoning wind and solar power from renewable resources
  • F HOC is the cost of maintaining the operation of the heating network.
  • the equipment operation and maintenance cost F OMC can be expressed as:
  • i is the node number in the system
  • t is the scheduling period number
  • n is the equipment type number, which refers to various types of equipment in the multi-region system; It represents the unit operating cost of equipment type n.
  • the cost of buying and selling electric energy with the main grid F BEC includes active and reactive exchange, which is expressed as:
  • the unit cost of the multi-energy system purchasing/selling electricity from the main grid with the unit costs of active and reactive power being the same; and They are respectively the active and reactive power purchased/sold by the regional system to the main grid.
  • the regional system purchases electricity, its value is positive, otherwise it is negative. Its interactive power needs to meet the following requirements:
  • the fuel purchase cost F BFC can be calculated by the following formula:
  • i is the micro-turbine number
  • n is the diesel engine number
  • C gas and C diesel are the unit prices of natural gas and diesel respectively
  • They are the unit gas consumption of the micro-turbine prime mover and the unit oil consumption of the diesel engine respectively
  • the power loss cost of the distribution network F ELC is:
  • the operation cost of the heating network mainly includes the power consumption of electric boilers, heat pumps and other equipment, as well as the electricity cost of water pumps used to maintain the circulation of hot water.
  • electric boilers, heat pumps and other equipment are regarded as electrical loads, and their operation costs have been reflected in the power supply cost of the power system; and according to relevant engineering standards, the electricity cost of water pumps can be estimated using the power-to-heat ratio (EHR):
  • step S130 solving the two-stage robust scheduling model through a preset algorithm to obtain a control decision, including: converting the two-stage robust scheduling model into a main problem of a mixed integer linear programming and a two-level sub-problem; using the Karush Kuhn-Tucker condition to convert the two-level sub-problem into a single-level linear programming problem, setting iteration parameters, and incorporating the iteration parameters into the main problem and the sub-problem; using the row and column generation algorithm to iteratively adjust parameters in sequence to solve the main problem and the sub-problem until the set iteration parameters meet the iteration conditions to obtain the decision variables; generating and outputting the control decision according to the decision variables.
  • the main problem and the sub-problem are solved by iteratively adjusting parameters in sequence through the row and column generation algorithm until the set iteration parameters meet the iteration conditions to obtain the decision variables.
  • FIG. 2 is a flow chart of solving the two-stage robust scheduling model to obtain the decision variables provided by one embodiment, including steps S210 to S260.
  • Step S210 setting iteration parameters including the number of iterations k, the lower bound LB, the upper bound UB, the iteration threshold ⁇ and the uncertainty variable u.
  • 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.
  • Step S230 Substitute the first decision variable x and the worst-case uncertainty variable u in the first stage into the sub-problem to solve the sub-problem, update the second decision variable y, the worst-case uncertainty variable u, and update the lower bound UB.
  • Step S240 Determine whether the sub-problem is feasible. If the sub-problem is not feasible, return to step S210 to perform the next iterative calculation.
  • step S250 determine whether the iteration condition is met according to the initial lower bound LB, the initial upper bound UB, and the iteration threshold ⁇ ; if not, return to step S210 for the next iterative calculation.
  • step S260 obtain the first decision variable x and the second decision variable y obtained by the final iterative settlement as decision variables.
  • the solution process can be to convert the original problem into a mixed integer linear programming master problem (MP) and a two-level subproblem (SP).
  • MP mixed integer linear programming master problem
  • SP two-level subproblem
  • the two-level SP problem is converted into a single-level linear programming problem using the KKT (Karush-Kuhn-Tucker) condition.
  • KKT Karush-Kuhn-Tucker
  • CC&G Cold-and-Constraint Generation Method
  • the iteration condition mentioned in step S250 can be specifically expressed as:
  • step S260 If not, return to step S210; if satisfied, execute step S260, so as to iteratively adjust the parameters and finally obtain the decision variables that meet the preset conditions.
  • the system includes a wind turbine, an electric energy storage device, a micro gas turbine, an electric drive compression refrigeration device, a thermal energy storage device, and an integrated mobile energy station integrating a diesel generator, a photovoltaic power source, an electric vehicle, and a battery. While adjusting the size of the electric load and adding a variety of distributed equipment and an integrated mobile energy station, the example also sets a thermal load node to reflect the multi-energy demand, and sets six regions to simulate the characteristics of multi-regional coordinated regulation.
  • the present invention takes 7 days as a scheduling cycle, and the time granularity is set to 1 hour, for a total of 168 time periods.
  • this paper divides 33 bus nodes into 6 regions as shown in FIG3, and each region is equipped with a mobile energy station for at least one day during the scheduling cycle; at the same time, each mobile energy station can only be fixed in one position within a day to reduce the cost of frequent movement. Since the scheduling instruction resolution is 1 hour, the model ignores the climbing power of the micro gas turbine and the diesel engine.
  • Example 1 the prior art does not consider the mobile flexible energy station
  • Example 2 the scheduling method of the multi-energy system provided by this application considers the mobile flexible energy station.
  • the scheduling results obtained by calculation are shown in Table 1.
  • this application can establish a deterministic energy model for determined energy in a multi-energy system and an uncertain energy model for renewable resources, respectively, according to the energy consumption and production capacity characteristics of various resources, so as to finally establish a two-stage robust scheduling model, and finally determine the control decision through a preset algorithm solution.
  • the flexible resources in the multi-energy system are reasonably classified and characterized, and the preset algorithm is used to solve the problem to finally formulate a suitable multi-energy system coordinated control method for multiple types of fixed resources and flexible resources, so as to give full play to the flexibility of movable resources in the multi-energy system and realize coordinated control between multi-regional multi-energy systems.
  • a computer device including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps: Step S110: Acquire first energy information of a multi-energy system, and establish a deterministic energy model based on the first energy information, wherein the first energy information is energy information of energy equipment determined in the multi-energy system, and the deterministic energy model is used to describe the fixed energy and the operating load status of its equipment in the multi-energy system; Step S120: Acquire second energy information of the multi-energy system, and establish an uncertain energy model based on the second energy information, wherein the second energy information is 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 the equipment in 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 through a preset algorithm to obtain
  • FIG6 shows an internal structure diagram of a computer device in an embodiment.
  • the computer device may be a terminal or a server.
  • the computer device includes a processor, a memory and a network interface connected via a system bus.
  • the memory includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium of the computer device stores an operating system and may also store a computer program.
  • the processor may implement a scheduling method for a multi-energy system.
  • the internal memory may also store a computer program.
  • the processor may execute an age recognition method.
  • FIG6 is only a block diagram of a partial structure related to the present application scheme, and does not constitute a limitation on the computer device to which the present application scheme is applied.
  • the specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
  • a computer-readable storage medium which stores a computer program.
  • the processor executes the steps of the scheduling method of a multi-energy system as described in any one of the embodiments.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

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Abstract

本申请实施例公开了一种多能系统的调度方法、计算机设备和计算机可读存储介质。其中，本申请能够根据各类资源的用能、产能特性，分别建立多能系统中确定能源的确定性能源模型，和可再生资源的不确定性能源模型，以最终建立两阶段鲁棒调度模型，以最终通过预设算法求解最终确定调控决策。从而针对多能系统内的灵活资源进行合理的分类及表征，按照预设算法求解以最终制定合适的面向多类固定资源与灵活资源的多能系统协同调控方法，充分发挥多能系统中可移动资源的灵活性，实现多区域多能系统间的协同调控。

Description

多能系统的调度方法、计算机设备和计算机可读存储介质 技术领域
本申请属于综合能源系统优化运行调度技术领域，特别是涉及一种多能系统的调度方法、计算机设备和计算机可读存储介质。
背景技术
面对日益严峻的全球能源气候危机，传统的能源产业结构正逐渐向更低碳、高效的新型能源系统发展，从而促进可再生能源消纳、减少环境污染。作为提高能源利用率和运行灵活性的一种主要形式，多能系统可充分发挥多类能源互补互济的优势，在工程应用中得到了广泛推广。
然而，对于包含多个区域的多能系统来说，各区域的多能负荷在时空分布上可能存在较大差异，所配置的分布式设备的容量、灵活性和经济性也不尽相同；而只对单一区域进行优化调度，也无法充分发挥各区域灵活负荷和设备的协调作用。当前的多能系统调度多关注于多能系统的集中优化运行，通过调控区域内的固定的灵活资源实现经济效益，然而，当存在多区域间的可移动的灵活资源，如移动能源站或电动汽车时，跨区域协同调度是进行可移动灵活资源位置调配优化的前提条件。因此，如何制定合适的面向多类固定资源与灵活资源的多能系统协同调控方法，是本领域技术人员亟待解决的技术问题。
前面的叙述在于提供一般的背景信息，并不一定构成现有技术。
申请内容
基于此，有必要针对上述问题，提出了一种多能系统的调度方法、计算机设备和计算机可读存储介质，能够灵活有效地实现跨区域多类固定资源与灵活资源的多能系统协同调控。
本申请解决其技术问题是采用以下的技术方案来实现的：
本申请提供了一种多能系统的调度方法，包括如下步骤：获取多能系统的第一能源信息，根据第一能源信息建立确定性能源模型，第一能源信息为多能系统中确定的能源设备的能源信息，确定性能源模型用于描述多能系统内固定能源及其设备运行负荷状态；获取多能系统的第二能源信息，根据第二能源信息建立不确定性能源模型，第二能源信息为多能系统中可再生资源设备的能源信息，不确定性能源模型用于描述多能系统内可再生能源及其设备运行负荷状态；根据确定性能源模型和不确定性能源模型建立两阶段鲁棒调度模型，通过预设算法对两阶段鲁棒调度模型求解，以获取得到调控决策。
在本申请一可选实施例中，确定性能源模型包括区域灵活资源模型和系统原件模型；根据第一能源信息建立确定性能源模型，包括：获取第一能源信息中的能源负荷信息，根据能源负荷信息建立区域灵活资源模型，能源负荷信息为多能系统中各能源部件的运行信息，区域灵活资源模型用于描述多能系统中能源的灵活性负荷及其能源转化、可移动调配特性；获取第一能源信息中的能源原件信息，根据能源原件信息建立系统原件模型，能源原件信息多能系统中各能源部件的运行约束，系统原件模型用于描述多能系统中各能源部件的运行状态。
在本申请一可选实施例中，区域灵活资源模型包括固定区域灵活资源模型和可移动区域灵活资源模型；根据能源负荷信息建立区域灵活资源模型，包括：获取包括灵活热负荷和灵活电负荷的能源负荷信息，根据灵活热负荷和灵活电负荷建立固定区域灵活资源模型，固定区域灵活资源模型用于描述多能系统中的固定热消耗及电消耗；获取包括能源输出功率、能源消耗信息和空间系数的能源负荷信息，根据能源输出功率、能源消耗信息和空间系数建立可移动区域灵活资源模型，可移动区域灵活资源模型用于描述多能系统的能源转化、可移动调配特性。
在本申请一可选实施例中，系统原件模型包括设备模型和网络模型；根据能源原件信息建立系统原件模型，包括：获取包括电能原件信息和热能原件信 息，根据电能原件信息和热能原件信息建立设备模型和网络模型，设备模型用于描述多能系统中电能设备及热能设备的运行状态，网络模型用于描述多能系统中能源的传输状态。
在本申请一可选实施例中，设备模型包括电能设备模型和热能设备模型；根据电能原件信息和热能原件信息建立设备模型，包括：获取包括发电设备信息、蓄电设备信息的电能原件信息，建立电能设备模型，电能设备模型用于描述电能设备的发电出力约束和蓄电约束；获取包括发热设备信息、蓄热设备信息的热能原件信息，建立热能设备模型，热能设备模型用于描述热能设备的产热效率及蓄热约束。
在本申请一可选实施例中，网络模型包括配电网络模型和供热网络模型，根据电能原件信息和热能原件信息建立网络模型，包括：获取输电线路信息，根据输电线路信息建立配电网络模型，配电网络模型用于描述多能系统中的电力潮流分布；获取输热管路信息，根据输热管路信息建立供热网络模型，供热网络模型用于描述多能系统中的热能传输状态。
在本申请一可选实施例中，根据确定性能源模型和不确定性能源模型建立两阶段鲁棒调度模型，包括：根据确定性能源模型设定第一决策变量，根据不确定性能源模型设定不确定性变量和第二决策变量，第一决策变量用于确定可移动灵活资源的调度决策，第二决策变量用于确定负荷灵活性和可再生能源不确定性的调度决策；获取条件系数，根据第一决策变量、第二决策变量、不确定性变量和条件系数建立两阶段鲁棒调度模型。
在本申请一可选实施例中，通过预设算法对两阶段鲁棒调度模型求解，以获取得到调控决策，包括：将两阶段鲁棒调度模型转化为一个混合整数线性规划的主问题和一个两层的子问题；利用卡鲁什·库恩·塔克条件将两层子问题转化为单层线性规划问题，设置迭代参数，并将迭代参数纳入主问题和子问题中；通过行列生成算法依次进行迭代调参求解主问题和子问题，直至设定的迭代参数满足迭代条件时获取得到决策变量；根据决策变量生成调控决策并输出。
本申请还提供了一种计算机设备，包括处理器和存储器：处理器用于执行存储器中存储的计算机程序以实现如前述的方法。
本申请还提供了一种计算机可读存储介质，存储有计算机程序，当计算机程序被处理器执行时实现如前述的方法。
采用本申请实施例，具有如下有益效果：
本申请能够根据各类资源的用能、产能特性，分别建立多能系统中确定能源的确定性能源模型，和可再生资源的不确定性能源模型，以最终建立两阶段鲁棒调度模型，以最终通过预设算法求解最终确定调控决策。从而针对多能系统内的灵活资源进行合理的分类及表征，按照预设算法求解以最终制定合适的面向多类固定资源与灵活资源的多能系统协同调控方法，充分发挥多能系统中可移动资源的灵活性，实现多区域多能系统间的协同调控。
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其他目的、特征和优点能够更明显易懂，以下特举较佳实施例，并配合附图，详细说明。应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本申请。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
其中：
图1为一实施例提供的一种多能系统的调度方法的流程示意图；
图2为一实施例提供的一种对两阶段鲁棒调度模型求解获取决策变量的流程示意图；
图3为一实施例提供的多区域电热多能系统结构示意图；
图4为一实施例提供的多能系统从外部电网购电的情况示意图表；
图5为一实施例提供的多能系统弃风弃光的情况示意图表；
图6为一实施例提供的一种计算机设备的结构示意框图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
对于包含多个区域的多能系统来说，需要在物理层面多区域互联、多能系统耦合的基础上，进行更深入的研究多区域系统间的互动耦合关系，从而充分发挥多能系统中可移动资源的灵活性，实现多区域多能系统间的协同调控。基于此，提出了本申请所提供的多能系统的调度方法，包括有步骤S110～S130。为了清楚描述本实施例提供的一种多能系统的调度方法，请参考图1～图5。
步骤S110：获取多能系统的第一能源信息，根据第一能源信息建立确定性能源模型，第一能源信息为多能系统中确定的能源设备的能源信息，确定性能源模型用于描述多能系统内固定能源及其设备运行负荷状态。
在一实施方式中，确定性能源模型包括区域灵活资源模型和系统原件模型；步骤S110：根据第一能源信息建立确定性能源模型，包括：获取第一能源信息中的能源负荷信息，根据能源负荷信息建立区域灵活资源模型，能源负荷信息为多能系统中各能源部件的运行信息，区域灵活资源模型用于描述多能系统中能源的灵活性负荷及其能源转化、可移动调配特性；获取第一能源信息中的能源原件信息，根据能源原件信息建立系统原件模型，能源原件信息多能系统中各能源部件的运行约束，系统原件模型用于描述多能系统中各能源部件的运行状态。
在一实施方式中，本申请所应用的多能系统，在较佳的实施方式中包含光 伏、微燃机、柴油发电机、分布式热泵、电热锅炉以及蓄热罐等供电和供热设备，移动能源站、电动车等移动能源设备，电网、热网、换热站等辅助设备，以及电、热灵活负荷。也即是说其中具有较为固定的能源负荷及产能，也有不确定的能源负荷及产能，因此需要进行分别的建模进行分析。而在步骤S110中即是对多能系统内固定能源及其设备运行负荷状态进行描述的确定性能源模型进行建模。具体的，确定性能源模型可以进行进一步的细分，在本实施例中可以包括有灵活资源模型和系统原件模型。其中，区域灵活资源模型用于描述多能系统中能源的灵活性负荷及其能源转化、可移动调配特性；系统原件模型用于描述多能系统中各能源部件的运行状态。
在一实施方式中，区域灵活资源模型包括固定区域灵活资源模型和可移动区域灵活资源模型；根据能源负荷信息建立区域灵活资源模型，包括：获取包括灵活热负荷和灵活电负荷的能源负荷信息，根据灵活热负荷和灵活电负荷建立固定区域灵活资源模型，固定区域灵活资源模型用于描述多能系统中的固定热消耗及电消耗；获取包括能源输出功率、能源消耗信息和空间系数的能源负荷信息，根据能源输出功率、能源消耗信息和空间系数建立可移动区域灵活资源模型，可移动区域灵活资源模型用于描述多能系统的能源转化、可移动调配特性。
在一实施方式中，区域灵活资源模型用于描述多能系统中的固定热消耗及电消耗。具体包括有固定负荷灵活资源模型和可移动灵活性资源建模：前者用于描述多能系统中的固定热消耗及电消耗；后者用于描述多能系统的能源转化、可移动调配特性。具体而言，固定负荷灵活资源分为灵活性电负荷和灵活性热负荷，灵活性电负荷为热泵、电锅炉的耗电功率，可根据用户侧舒适度在一定范围内调节，其模型为：
Figure PCTCN2022136211-appb-000001
式中，
Figure PCTCN2022136211-appb-000002
和
Figure PCTCN2022136211-appb-000003
为t时刻节点i的总电负荷和固定电负荷。灵活性热负 荷，以建筑物热力模型为例，采用热阻容模型对建筑物热负荷建模。建筑物室内温度的离散方程可表示为：
Figure PCTCN2022136211-appb-000004
式中，τ in,t表示t时刻的室内温度，R t为建筑物外墙的等效热阻，c a为空气比热容，
Figure PCTCN2022136211-appb-000005
为t时刻换热站提供的供热功率。同时，建筑物温度需满足一定的舒适度需求，即：
τ in,min≤τ in,t≤τ in,max
(3)
上式中，τ in,min和τ in,max分别表示用户的舒适度上下限。进一步地，可移动灵活性资源是指在多区域能源系统中，充分挖掘各类能源生产存储可灵活移动、调配的特点，使得运营商以消耗更小规模的资源实现区域内的灵活调控。本申请利用将众多可移动灵活资源安装规模较小、配置灵活的特征，将其视为集成的一体化可移动能源站，基于energy-hub方法对其能源转化特性和可移动调配特性进行整体建模。在本申请较佳实施例中，以电动车、小型柴油发电机、光伏和蓄电池为例，利用其安装规模较小、配置灵活的特征，将其视为集成的一体化可移动能源站(IntegratedMobile Energy Station,IMES)，对其能源转化特性和可移动调配特性进行建模。首先，采用能量枢纽描述能源站的输入-输出关系，定义输入能量矩阵为I (3×1)，输出能量矩阵为O (2×1)，耦合系数矩阵为C(2×3)，则有：
Figure PCTCN2022136211-appb-000006
Figure PCTCN2022136211-appb-000007
式4中，P PV,t为t时刻可移动能源站中光伏电源发出的有功功率，
Figure PCTCN2022136211-appb-000008
为时段t内可移动能源站中柴油发电机消耗的柴油的质量；σ E为蓄电池储存电量的充放电折算率；W EES为蓄电池的容量；P IMES,t为t时可移动能源站输出的有功功率大小。式5中，η (2×3)为能量转化系数矩阵；D (2×3)为输入能量分配系数矩阵。柴油质量可表示为：
Figure PCTCN2022136211-appb-000009
式6中，
Figure PCTCN2022136211-appb-000010
为柴油发电机每发出1kWh电的柴油消耗量，P DG是移动能源站里柴油发电机发出的功率，Δt为单位时间。进一步地，可以通过引入0-1变量η IMES,t描述能源站的时空分布：η IMES,t＝0表示区域在时段t内没有能源站；η IMES,t＝1则表示区域在时段t内有能源站。基于此，可通过修正能源站中的上下限值以描述能源站的可移动特性。
Figure PCTCN2022136211-appb-000011
上式中，P DG,max为动能源站里柴油发电机输出的有功功率；
Figure PCTCN2022136211-appb-000012
表示蓄电池的容量上限；
Figure PCTCN2022136211-appb-000013
表示电池容量上限。
在一实施方式中，系统原件模型包括设备模型和网络模型；根据能源原件信息建立系统原件模型，包括：获取包括电能原件信息和热能原件信息，根据电能原件信息和热能原件信息建立设备模型和网络模型，设备模型用于描述多能系统中电能设备及热能设备的运行状态，网络模型用于描述多能系统中能源的传输状态。
在一实施方式中，设备模型包括电能设备模型和热能设备模型；根据电能原件信息和热能原件信息建立设备模型，包括：获取包括发电设备信息、蓄电设备信息的电能原件信息，建立电能设备模型，电能设备模型用于描述电能设备的发电出力约束和蓄电约束；获取包括发热设备信息、蓄热设备信息的热能 原件信息，建立热能设备模型，热能设备模型用于描述热能设备的产热效率及蓄热约束。
在一实施方式中，网络模型包括配电网络模型和供热网络模型，根据电能原件信息和热能原件信息建立网络模型，包括：获取输电线路信息，根据输电线路信息建立配电网络模型，配电网络模型用于描述多能系统中的电力潮流分布；获取输热管路信息，根据输热管路信息建立供热网络模型，供热网络模型用于描述多能系统中的热能传输状态。
在一实施方式中，系统原件模型用于描述多能系统中各能源部件的运行状态。具体的，系统原件模型包括设备模型和网络模型：设备模型包括供电能设备模型和热能设备模型。供电设备在多能系统中一般包含光伏、微燃机、柴油发电机等设备用于电功率自给，同时配备蓄电池用于平抑功率波动。将微燃机视为出力和功率因数可以随着配电网运行状态的改变而进行连续调度调节的分布式电源。对于设备模型可以由如下描述的模型或约束构成。原动机输出功率约束、发电和供热功率约束和有功/无功功率约束分别为：
P MT,min≤P MT≤P MT,max
(8)
Figure PCTCN2022136211-appb-000014
Figure PCTCN2022136211-appb-000015
Figure PCTCN2022136211-appb-000016
式中，标志min和max分别代表最小值和最大值；P MT和
Figure PCTCN2022136211-appb-000017
分别为微燃机的原动机输出功率和视在功率功率；
Figure PCTCN2022136211-appb-000018
分别为微燃机输出的实 际有功/无功功率和热功率；
Figure PCTCN2022136211-appb-000019
η HR、
Figure PCTCN2022136211-appb-000020
分别为微燃机的发电效率、余热回收效率和能量损耗率。对于柴油发电机模型，将其视为功率因数角φ DG固定的分布式电源，其出力约束为：
0≤P DG≤P DG,max
(13)
Q DG＝P DG·tanφ DG
(14)
式中，P DG和Q DG分别为柴油发电机的输出有功功率和无功功率。蓄电池约束包含容量约束和充放电约束，可表示为：
Figure PCTCN2022136211-appb-000021
式中，
Figure PCTCN2022136211-appb-000022
和
Figure PCTCN2022136211-appb-000023
分别为蓄电池的充电功率和放电功率；
Figure PCTCN2022136211-appb-000024
和
Figure PCTCN2022136211-appb-000025
则分别为蓄电池的充放电效率；x EES,t为0-1变量，用于表明蓄电池仅能处于充电或放电中的一种状态。进一步，供热设备中，热泵采用制热系数描述其消耗电功率
Figure PCTCN2022136211-appb-000026
和生产热功率
Figure PCTCN2022136211-appb-000027
关系，可表示为：
Figure PCTCN2022136211-appb-000028
Figure PCTCN2022136211-appb-000029
式中，COP HP热泵制热系数，
Figure PCTCN2022136211-appb-000030
则为热泵的最大耗电功率。与热泵类似，电锅炉模型可表示为：
Figure PCTCN2022136211-appb-000031
Figure PCTCN2022136211-appb-000032
式中，COP GB为电锅炉制热系数，
Figure PCTCN2022136211-appb-000033
为电锅炉的最大耗电功率。进一步地，蓄热罐模型与蓄电池类似，包含容量约束和存放热约束，可表示为：
Figure PCTCN2022136211-appb-000034
式中，
Figure PCTCN2022136211-appb-000035
和
Figure PCTCN2022136211-appb-000036
表示蓄热罐的容量上限和最大充放热功率；W TES,t、
Figure PCTCN2022136211-appb-000037
和
Figure PCTCN2022136211-appb-000038
分别表示t时刻的蓄热罐的容量、蓄热功率和存热功率；0-1变量x TES,t表示蓄热罐在t时刻的存放热状态，使得蓄热罐无法同时存放热；
Figure PCTCN2022136211-appb-000039
和
Figure PCTCN2022136211-appb-000040
分别表示蓄热罐的存放热效率。
在一实施方式中，前文就电能设备模型和热能设备模型进行了详尽的描述。然而可以理解的是，电热的产生和消耗之间，还需要对应的网络模型进行传输，也即是说对此需要相应的建立配电网络模型和供热网络模型，以用于描述多能系统中能源的传输状态。在本申请较佳实施方式中，可以采用直流潮流模型描述系统内的电力潮流分布，节点有功和无功功率守恒方程可表示为：
Figure PCTCN2022136211-appb-000041
Figure PCTCN2022136211-appb-000042
式中，NU(j)电网内与节点j直接相连的上游节点集合；R ij表示支路ij的线路总电阻；X ij表示支路ij的线路总电抗；U i表示配电网模型节点i处的电压幅值大小；
Figure PCTCN2022136211-appb-000043
为流入支路ij的有功功率；
Figure PCTCN2022136211-appb-000044
为流入支路ij的无功功率；P j表示节点j处的净流出有功功率；Q j表示节点j处的净流出无功功率。支路功率方程可表示为：
Figure PCTCN2022136211-appb-000045
上式中，U j表示配电网模型节点j处的电压幅值大小，Ω node和Ω line分别表示配电网内的节点和管道集合。进一步地，对于配电网模型中的电压幅值和支路约束可以分别表示为：
Figure PCTCN2022136211-appb-000046
Figure PCTCN2022136211-appb-000047
式中，U min和U max分别为节点电压幅值的下限和上限，I ij表示节点i和节点j之间传输的电流，I max表示支路电流上限。此外，对于供热网络模型中可以包括有供热站、供回水管网和换热站。在本申请较佳实施方式中，由微燃机(MT)、热泵(HP)、电热锅炉(EB)、蓄热罐(TES)等设备构成的换热站，其等效模型为：
Figure PCTCN2022136211-appb-000048
该模型表示节点j处供热站的热能守恒，等号左侧表示由节点j处的供热站收集的各种供热设备产生的热功率之和，等号右侧表示供热站向热管网中输 入的热功率。其中，Ω HS表示供热站节点集；c为水的比热容；
Figure PCTCN2022136211-appb-000049
为节点j中供热站将水从回水管道向供水管道传输的质量流量；
Figure PCTCN2022136211-appb-000050
为节点j处供水管道内水的温度，
Figure PCTCN2022136211-appb-000051
为节点j处回水管道内水的温度。为保证供热质量，供热站节点温度需满足：
Figure PCTCN2022136211-appb-000052
式中，
Figure PCTCN2022136211-appb-000053
和
Figure PCTCN2022136211-appb-000054
为节点j处供水管道的最大、最小水温；
Figure PCTCN2022136211-appb-000055
为供热站节点j处t时段内供水管道的水温。换热站模型可表示为：
Figure PCTCN2022136211-appb-000056
式中，
Figure PCTCN2022136211-appb-000057
为节点j中换热站将水从供水管道向回水管道传输的质量流量；
Figure PCTCN2022136211-appb-000058
为节点j的热负荷；Ω HES表示换热站节点集。换热站节点的供水和回水温度的上下限可表示为：
Figure PCTCN2022136211-appb-000059
式中，
Figure PCTCN2022136211-appb-000060
和
Figure PCTCN2022136211-appb-000061
为节点j处回水管道的最大、最小水温。基于以上，在本申请较佳实施例中，即可通过采用节点法对供热网络建模：
Figure PCTCN2022136211-appb-000062
Figure PCTCN2022136211-appb-000063
Figure PCTCN2022136211-appb-000064
Figure PCTCN2022136211-appb-000065
Figure PCTCN2022136211-appb-000066
Figure PCTCN2022136211-appb-000067
式中，
Figure PCTCN2022136211-appb-000068
分别为供水管道进水/出水温度p；
Figure PCTCN2022136211-appb-000069
分别为回水管道进水/出水温度p；
Figure PCTCN2022136211-appb-000070
为供/回水管道在j节点的水温；∏ p为供热管道的集合；
Figure PCTCN2022136211-appb-000071
流入节点k的管道集；
Figure PCTCN2022136211-appb-000072
为从节点k流出的管道的集和；φ in为管道相交节点的集合；φ ln热负载节点的集合；φ sn热源节点的集合；c和ρ为水的密度和比热容；γ p、R p、δ p、ξ p都为管道的延时参数，并且可以理解的延时参数在一定程度上与热损失参数存在耦合，也即是说以上参数也都可以表示为热损失参数，且耦合程度依次增强，以ξ p的耦合程度为四者中最高；λ j、
Figure PCTCN2022136211-appb-000073
l p分别对应管道信息中的换热系数、管道截面积、管道长度；
Figure PCTCN2022136211-appb-000074
为管道的质量流量；
Figure PCTCN2022136211-appb-000075
τ s为供水管道的最大/最小节点温度；
Figure PCTCN2022136211-appb-000076
τ r为回水管道最大/最小节点温度。基于综上描述，即可完成对确定性能源模型的建立。并且可以理解的是，由于建立确定性能源模型的计算过程复杂多样，其中包括有多个模型及其对应实现其建立的参数。因此以上各参数包括但不限于前文所例举的能源负荷信息、能源原件信息等，都是包含在第一能源信息中的，或说可以通过第一能源信息进行获取，以完成以上的计算，实现确定性能源模型的建立。
步骤S120：获取多能系统的第二能源信息，根据第二能源信息建立不确定性能源模型，第二能源信息为多能系统中可再生资源设备的能源信息，不确定性能源模型用于描述多能系统内可再生能源及其设备运行负荷状态。
在一实施方式中，在本申请较佳实施例当中，可再生能源可以包括有风力 发电和光伏发电，具体的不确定性能源模型建模可以通过如下表示：
Figure PCTCN2022136211-appb-000077
Figure PCTCN2022136211-appb-000078
式中，
Figure PCTCN2022136211-appb-000079
和
Figure PCTCN2022136211-appb-000080
为可在生能源的实际功率和预测功率；
Figure PCTCN2022136211-appb-000081
为可再生能源的最大偏差比，
Figure PCTCN2022136211-appb-000082
和
Figure PCTCN2022136211-appb-000083
是相应的随机变量，Γ res是不确定性预算，以上各参数都包括在第二能源信息中。
步骤S130：根据确定性能源模型和不确定性能源模型建立两阶段鲁棒调度模型，通过预设算法对两阶段鲁棒调度模型求解，以获取得到调控决策。
在一实施方式中，步骤S130：根据确定性能源模型和不确定性能源模型建立两阶段鲁棒调度模型，包括：根据确定性能源模型设定第一决策变量，根据不确定性能源模型设定不确定性变量和第二决策变量，第一决策变量用于确定可移动灵活资源的调度决策，第二决策变量用于确定负荷灵活性和可再生能源不确定性的调度决策；获取条件系数，根据第一决策变量、第二决策变量、不确定性变量和条件系数建立两阶段鲁棒调度模型。
在一实施方式中，两阶段鲁棒调度模型中，将负荷灵活性资源视为一种不确定性，然后第一阶段进行可移动灵活资源的调度决策(也即是第一决策变量)，第二阶段进行考虑负荷灵活性和可再生能源不确定性的鲁棒调度问题(也即是确定第二决策变量)。所述的连接段调度模型的总体形式如下：
Figure PCTCN2022136211-appb-000084
其中，x、y分别对应第一决策变量和第二决策变量，u表示不确定性变量。一决策变量x是可移动灵活资源的的调度决策，第二决策变量y是在包括不确定性负荷灵活性资源的不确定性和可再生能源不确定性下多能系统的联络线功率、设备输出、电池的充放电功率、热网的热功率和水温、建筑物的热需求和室内温度等。其中的E、h、G、M都是相关的条件系数，可以通过预先的情况进行设定。具体地，两阶段鲁棒调度模型的目标函数包括设备运维成本、购电成本、与上级电网的无功交换成本、燃料成本、配电网电能损耗的成本、弃风弃光成本、热网运维成本。具体的目标函数可表示为：
F＝F OMC+F BEC+F BFC+F ELC+F RECC+F HOC
(34)
其中，F OMC为设备运行和维护成本，F BEC为从上级电网购买电能成本，F BFC为购买燃料成本，F ELC为配电网电能损耗成本，F RECC为可再生资源弃风弃光成本，F HOC为维持供热网运行成本。具体的，设备的运行维护成本F OMC可以表示为：
Figure PCTCN2022136211-appb-000085
式中，i为系统中的节点编号、t为调度的时段编号、n为设备类型编号，指代多区域系统内的各类设备；
Figure PCTCN2022136211-appb-000086
表示设备类型n的单位运行成本。与主网的电能买卖成本F BEC包含有功和无功交换，表示为：
Figure PCTCN2022136211-appb-000087
式中，
Figure PCTCN2022136211-appb-000088
为多能系统向主网购/售电的单位成本，有功和无功单位成本一 致；
Figure PCTCN2022136211-appb-000089
和
Figure PCTCN2022136211-appb-000090
分别为区域系统向主网购买/售卖的有功和无功功率。当区域系统购电时，其值为正，否则为负。其交互功率需满足：
Figure PCTCN2022136211-appb-000091
购买燃料成本F BFC可以通过下式计算：
Figure PCTCN2022136211-appb-000092
式中，i为微燃机编号；n为柴油机编号；C gas和C diesel分别为天然气和柴油的单价；
Figure PCTCN2022136211-appb-000093
和
Figure PCTCN2022136211-appb-000094
分别为微燃机原动机的单位耗气量和柴油机的单位耗油量；
Figure PCTCN2022136211-appb-000095
为i号微燃机在t时刻输出的有功功率，
Figure PCTCN2022136211-appb-000096
为n号柴油机在t时刻输出的有功功率。同理，配电网电能损耗成本F ELC为：
Figure PCTCN2022136211-appb-000097
其中各参数已经在前文中都有对应的解释，在此便不做赘述。对于可再生资源弃风弃光成本F RECC可以表示为：
Figure PCTCN2022136211-appb-000098
式中，
Figure PCTCN2022136211-appb-000099
和
Figure PCTCN2022136211-appb-000100
为单位弃电成本和光伏弃电量。供热网运行成本主要包含电锅炉、热泵等设备耗电量以及用于维持热水流通的水泵用电成本。其中，电锅炉、热泵等设备视为电负荷，其运行成本已反映在电力系统供电成本中；而根据相关工程标准，水泵用电成本可以用耗电输热比(EHR)估算：
Figure PCTCN2022136211-appb-000101
式中，i为供热网中的热泵编号，
Figure PCTCN2022136211-appb-000102
表示流经水泵的热功率大小。综上所述，即可完成对两阶段鲁棒调度模型的构建。
在一实施方式中，步骤S130：通过预设算法对两阶段鲁棒调度模型求解， 以获取得到调控决策，包括：将两阶段鲁棒调度模型转化为一个混合整数线性规划的主问题和一个两层的子问题；利用卡鲁什·库恩·塔克条件将两层子问题转化为单层线性规划问题，设置迭代参数，并将迭代参数纳入主问题和子问题中；通过行列生成算法依次进行迭代调参求解主问题和子问题，直至设定的迭代参数满足迭代条件时获取得到决策变量；根据决策变量生成调控决策并输出。
在一实施方式中，对于通过行列生成算法依次进行迭代调参求解主问题和子问题，直至设定的迭代参数满足迭代条件时获取得到决策变量，可以参考图2，图2为一实施例提供的一种对两阶段鲁棒调度模型求解获取决策变量的流程示意图，其中包括有步骤S210～S260.
步骤S210：设置包括迭代次数k、下界LB、上界UB、迭代阈值ε和不确定性变量u的迭代参数。
步骤S220：将迭代参数代入主问题中对主问题进行求解，获取得到第一决策变量x、第二决策变量y、最坏场景不确定性变量u，并更新下界LB。
步骤S230：将第一阶段第一决策变量x、最坏场景不确定性变量u代入子问题中对子问题进行求解，更新第二决策变量y、最坏场景不确定性变量u及更新下界UB。
步骤S240：判断子问题是否可行。若子问题不行则回到步骤S210进行下一次的迭代计算。
若子问题可行，则执行步骤S250：根据初始下界LB、初始上界UB、迭代阈值ε判断是否满足满足迭代条件，若不行则回到到步骤S210进行下一次的迭代计算。
若子问题可行，则执行步骤S260：获取包括最终迭代结算得到第一决策变量x、第二决策变量y为决策变量。
在一实施方式中，对于求解过程即可以是将原问题转化为一个混合整数线性规划的主问题(MP)和一个两层的子问题(SP)。利用KKT (Karush-Kuhn-Tucker，卡鲁什·库恩·塔克)条件将两层SP问题转化为单层线性规划问题。然后通过CC&G算法(Column-and-Constraint Generation Method，行列生成算法)迭代求解整个问题。具体的，对于步骤S250中所提到的迭代条件可以具体表示为：
(UB-LB)/UB<ε
(42)
若是不满足则回到步骤S210，若满足则执行步骤S260，从而迭代调参最终获取满足预设条件的决策变量。
在一实施方式中，为了便于理解本申请提供的多能系统的调度方法，现提出一实例进行说明。对于本实施例所提供的多能系统的结构，可以参考图3。其中，如图3所示：该系统包含风力发电机、电能存储装置、微型燃气轮机、电驱动压缩制冷装置、热能存储装置和集成有柴油发电机、光伏电源、电动车和电池的一体化可移动能源站。算例在调整电负荷大小并加入多种分布式设备和一体化可移动能源站的同时，还设置了热能负荷节点以体现多能需求，设置了六个区域以模拟多区域协同调控的特点。为体现区域多能系统的中长期时间跨度，本发明将7天作为一个调度周期，时间颗粒度设置为1小时，一共168个时间段。在移动能源站调度策略上，本文将33个母线节点分为如图3中的6个区域，调度周期内每个区域至少有一天配置有移动能源站；同时，每个移动能源站的在一天内只能固定在一个位置，以降低频繁移动带来的成本。由于调度指令分辨率为1小时，模型忽略了微型燃气轮机和柴油发动机的爬坡功率。基于以上系统结构，分别按照本申请提供的方法和现有技术的计算方式计算得到两个算例：算例1，现有技术中不考虑可移动灵活能源站；算例2，根据本申请提供的多能系统的调度方法考虑可移动灵活能源站。计算求得的调度结果如表1所示。
Figure PCTCN2022136211-appb-000103
表1.不同可移动灵活资源情况下多能系统的调度结果
由表1可见：在加入移动能源站后，总体成本均有明显下降。尤其是配电网线路损耗、与上级电网进行的无功功率交换量和节点电压偏移都有5％～10％的较大幅度提升，而购电成本和平均支路电流幅值也有3％左右的提升；由于新增了多个设备，因此运行维护成本不可避免的增加9％左右。同时参考图4、图5，图4给出了两种情况下系统从外部电网购电的情况。图5为两种情况下系统的弃风弃光情况。从图中可以看出，系统在不考虑灵活性资源时，在0:00～4:00时刻存在严重的弃风弃光现象，且在17:00～20:00时段，系统对于外部能源存在高度需求，而在引入可移动灵活性资源后，系统能源可以有效地从能源富余时段转移至用能高峰时段，不仅系统的运行成本下降，分布式可再生能源消纳水平也得到了提高。
因此，本申请能够根据各类资源的用能、产能特性，分别建立多能系统中确定能源的确定性能源模型，和可再生资源的不确定性能源模型，以最终建立两阶段鲁棒调度模型，以最终通过预设算法求解最终确定调控决策。从而针对多能系统内的灵活资源进行合理的分类及表征，按照预设算法求解以最终制定合适的面向多类固定资源与灵活资源的多能系统协同调控方法，充分发挥多能系统中可移动资源的灵活性，实现多区域多能系统间的协同调控。在满足热负荷和电力负荷之间的各种需求方面表现良好，既可以提高可再生能源的消纳能力，减少弃风、弃光现象，又能够提高整体的经济性，总体而言，通过考虑多 区域的可移动灵活资源，多能系统的经济性和灵活性得到了显著提高。
在一个实施例中，提出了一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述计算机程序被所述处理器执行时，使得所述处理器执行以下步骤：步骤S110：获取多能系统的第一能源信息，根据第一能源信息建立确定性能源模型，第一能源信息为多能系统中确定的能源设备的能源信息，确定性能源模型用于描述多能系统内固定能源及其设备运行负荷状态；步骤S120：获取多能系统的第二能源信息，根据第二能源信息建立不确定性能源模型，第二能源信息为多能系统中可再生资源设备的能源信息，不确定性能源模型用于描述多能系统内可再生能源及其设备运行负荷状态；步骤S130：根据确定性能源模型和不确定性能源模型建立两阶段鲁棒调度模型，通过预设算法对两阶段鲁棒调度模型求解，以获取得到调控决策。
图6示出了一个实施例中计算机设备的内部结构图。该计算机设备具体可以是终端，也可以是服务器。如图6所示，该计算机设备包括通过系统总线连接的处理器、存储器和网络接口。其中，存储器包括非易失性存储介质和内存储器。该计算机设备的非易失性存储介质存储有操作系统，还可存储有计算机程序，该计算机程序被处理器执行时，可使得处理器实现多能系统的调度方法。该内存储器中也可储存有计算机程序，该计算机程序被处理器执行时，可使得处理器执行年龄识别方法。本领域技术人员可以理解，图6中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提出了一种计算机可读存储介质，存储有计算机程序，所述计算机程序被处理器执行时，使得所述处理器执行如任意一实施例所描述的多能系统的调度方法的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一非易 失性计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对本申请专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (10)

  1. 一种多能系统的调度方法，其特征在于，包括如下步骤：
    获取多能系统的第一能源信息，根据所述第一能源信息建立确定性能源模型，所述第一能源信息为所述多能系统中确定的能源设备的能源信息，所述确定性能源模型用于描述所述多能系统内固定能源及其设备运行负荷状态；
    获取所述多能系统的第二能源信息，根据所述第二能源信息建立不确定性能源模型，所述第二能源信息为所述多能系统中可再生资源设备的能源信息，所述不确定性能源模型用于描述所述多能系统内可再生能源及其设备运行负荷状态；
    根据所述确定性能源模型和所述不确定性能源模型建立两阶段鲁棒调度模型，通过预设算法对所述两阶段鲁棒调度模型求解，以获取得到调控决策。
  2. 如权利要求1所述的多能系统的调度方法，其特征在于，所述确定性能源模型包括区域灵活资源模型和系统原件模型；
    所述根据所述第一能源信息建立确定性能源模型，包括：
    获取所述第一能源信息中的能源负荷信息，根据所述能源负荷信息建立所述区域灵活资源模型，所述能源负荷信息为所述多能系统中各能源部件的运行信息，所述区域灵活资源模型用于描述所述多能系统中能源的灵活性负荷及其能源转化、可移动调配特性；
    获取所述第一能源信息中的能源原件信息，根据所述能源原件信息建立所述系统原件模型，所述能源原件信息所述多能系统中各能源部件的运行约束，所述系统原件模型用于描述所述多能系统中各能源部件的运行状态。
  3. 如权利要求2所述的多能系统的调度方法，其特征在于，所述区域灵活资源模型包括固定区域灵活资源模型和可移动区域灵活资源模型；
    所述根据所述能源负荷信息建立所述区域灵活资源模型，包括：
    获取包括灵活热负荷和灵活电负荷的所述能源负荷信息，根据所述灵活热负荷和所述灵活电负荷建立所述固定区域灵活资源模型，所述固定区域灵活资 源模型用于描述所述多能系统中的固定热消耗及电消耗；
    获取包括能源输出功率、能源消耗信息和空间系数的所述能源负荷信息，根据所述能源输出功率、所述能源消耗信息和所述空间系数建立所述可移动区域灵活资源模型，所述可移动区域灵活资源模型用于描述所述多能系统的能源转化、可移动调配特性。
  4. 如权利要求2所述的多能系统的调度方法，其特征在于，所述系统原件模型包括设备模型和网络模型；
    所述根据所述能源原件信息建立所述系统原件模型，包括：
    获取包括电能原件信息和热能原件信息，根据所述电能原件信息和所述热能原件信息建立所述设备模型和所述网络模型，所述设备模型用于描述所述多能系统中电能设备及热能设备的运行状态，所述网络模型用于描述所述多能系统中能源的传输状态。
  5. 如权利要求4所述的多能系统的调度方法，其特征在于，所述设备模型包括电能设备模型和热能设备模型；
    所述根据所述电能原件信息和所述热能原件信息建立所述设备模型，包括：
    获取包括发电设备信息、蓄电设备信息的所述电能原件信息，建立所述电能设备模型，所述电能设备模型用于描述所述电能设备的发电出力约束和蓄电约束；
    获取包括发热设备信息、蓄热设备信息的所述热能原件信息，建立所述热能设备模型，所述热能设备模型用于描述所述热能设备的产热效率及蓄热约束。
  6. 如权利要求4所述的多能系统的调度方法，其特征在于，所述网络模型包括配电网络模型和供热网络模型，
    所述根据所述电能原件信息和所述热能原件信息建立所述网络模型，包括：
    获取输电线路信息，根据所述输电线路信息建立所述配电网络模型，所述配电网络模型用于描述所述多能系统中的电力潮流分布；
    获取输热管路信息，根据所述输热管路信息建立所述供热网络模型，所述 供热网络模型用于描述所述多能系统中的热能传输状态。
  7. 如权利要求1所述的多能系统的调度方法，其特征在于，所述根据所述确定性能源模型和所述不确定性能源模型建立两阶段鲁棒调度模型，包括：
    根据所述确定性能源模型设定第一决策变量，根据所述不确定性能源模型设定不确定性变量和第二决策变量，所述第一决策变量用于确定可移动灵活资源的调度决策，所述第二决策变量用于确定负荷灵活性和可再生能源不确定性的调度决策；
    获取条件系数，根据所述第一决策变量、所述第二决策变量、所述不确定性变量和所述条件系数建立所述两阶段鲁棒调度模型。
  8. 如权利要求1所述的多能系统的调度方法，其特征在于，所述通过预设算法对所述两阶段鲁棒调度模型求解，以获取得到调控决策，包括：
    将所述两阶段鲁棒调度模型转化为一个混合整数线性规划的主问题和一个两层的子问题；
    利用卡鲁什·库恩·塔克条件将两层所述子问题转化为单层线性规划问题，设置迭代参数，并将所述迭代参数纳入所述主问题和所述子问题中；
    通过行列生成算法依次进行迭代调参求解所述主问题和所述子问题，直至设定的所述迭代参数满足迭代条件时获取得到决策变量；
    根据所述决策变量生成所述调控决策并输出。
  9. 一种计算机设备，其特征在于，包括处理器和存储器；
    所述处理器用于执行所述存储器中存储的计算机程序以实现如权利要求1到8中任一项所述方法。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1到8中任一项所述方法。
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106097154A (zh) * 2016-07-12 2016-11-09 广东电网有限责任公司电力科学研究院 一种工业园区能源中心的调度方法及系统
CN110443467A (zh) * 2019-07-18 2019-11-12 天津大学 一种区域综合能源系统太阳能消纳能力评估方法
CN112072712A (zh) * 2020-08-31 2020-12-11 合肥工业大学 综合能源系统多时间尺度优化调度方法、系统及存储介质
CN112084629A (zh) * 2020-08-11 2020-12-15 清华大学 一种基于两阶段鲁棒优化的多能虚拟电厂聚合方法
CN113256045A (zh) * 2020-08-04 2021-08-13 四川大学 考虑风光不确定性的园区综合能源系统日前经济调度方法
CN115115277A (zh) * 2022-08-23 2022-09-27 华北电力大学 园区综合能源调度方法、装置、计算机设备及存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106097154A (zh) * 2016-07-12 2016-11-09 广东电网有限责任公司电力科学研究院 一种工业园区能源中心的调度方法及系统
CN110443467A (zh) * 2019-07-18 2019-11-12 天津大学 一种区域综合能源系统太阳能消纳能力评估方法
CN113256045A (zh) * 2020-08-04 2021-08-13 四川大学 考虑风光不确定性的园区综合能源系统日前经济调度方法
CN112084629A (zh) * 2020-08-11 2020-12-15 清华大学 一种基于两阶段鲁棒优化的多能虚拟电厂聚合方法
CN112072712A (zh) * 2020-08-31 2020-12-11 合肥工业大学 综合能源系统多时间尺度优化调度方法、系统及存储介质
CN115115277A (zh) * 2022-08-23 2022-09-27 华北电力大学 园区综合能源调度方法、装置、计算机设备及存储介质

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