A virtual power plant optimal scheduling method for heat network flow direction adaptation
Patent Information
- Application Number
- CN202611309462.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-27
- Publication Date
- 2026-10-27
AI Technical Summary
[0030]本发明实施例的具有以下有益效果：本发明面向多能耦合虚拟电厂内部电—热协同运行场景，突破了传统供热网络固定流向假设，通过设置正向流动指示变量和反向流动指示变量，使供热网络支路能够根据热源出力、热负荷分布和系统运行状态自适应选择流动方向，从而提高多热源供热网络建模的准确性；同时，本发明将供热网络流向状态嵌入水力约束、热力传输约束、节点热平衡约束和管道入口温度约束中，使热网反向流动不仅体现为流量符号变化，还能够反映温度传播方向、节点混合关系和热源供热范围的变化，提高虚拟电厂电—热耦合关系刻画的完整性；进一步地，本发明采用分段线性化、McCormick 包络和分段 McCormick 方法，对流向自适应热网调度模型中的非线性项进行统一线性化处理，将原本难以直接求解的非线性混合整数模型转化为可求解的混合整数线性规划模型，提高日前调度模型的计算效率和工程适用性；此外，本发明能够通过供热网络反向能量传输扩大燃气锅炉等热源的供热范围，使热负荷在不同热源之间重新分配，降低热电联产机组热出力需求及其耦合电出力，为风电等新能源提供更大的消纳空间。综上，本发明能够在满足电力网络安全约束、供热网络水力热力约束、电负荷需求和热负荷需求的前提下，获得虚拟电厂内部多类能源设备的日前协同调度方案，降低系统运行成本，提高新能源消纳能力和虚拟电厂运行灵活性。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of regional heating network optimization scheduling technology, and in particular to a virtual power plant optimization scheduling method with adaptive heating network flow direction. Background Technology
[0002] With the continuous growth of new energy installed capacity, the randomness, volatility, and anti-peak-shaving characteristics of renewable energy output such as wind power and photovoltaics have placed higher demands on the operational flexibility of the power system. As a collaborative operating entity that aggregates distributed power sources, controllable loads, energy storage devices, and energy conversion equipment, virtual power plants can achieve complementary operation of multiple types of internal resources through unified modeling, optimized scheduling, and control command issuance, and present adjustable, tradable, and responsive aggregation capabilities to the external power grid.
[0003] In a multi-energy coupled virtual power plant containing heat load and heat source equipment, combined heat and power (CHP) units, gas-fired boilers, circulating water pumps, and district heating networks together constitute an electro-thermal coupled operating system. Due to the transmission time lag, thermal inertia, and heat storage capacity of the heating network, rationally utilizing its hydraulic-thermal dynamic characteristics can effectively alleviate the "heat-driven power generation" constraint of CHP units, improve the virtual power plant's capacity to absorb new energy sources such as wind and solar power, and reduce system operating costs.
[0004] Existing day-ahead scheduling methods for multi-energy coupled virtual power plants typically simplify the heating network into a fixed-direction heating network model, or assume that the direction of heat medium flow within the heating pipeline remains constant throughout the scheduling cycle. In heating scenarios with a single heat source or a simple topology, these assumptions can reduce modeling and solution complexity. However, in district heating networks with multiple heat sources and load nodes, the direction of heat medium flow in some branches may reverse at different times due to changes in the output of different heat sources, heat load distribution, and the operating status of circulating pumps. If a fixed-direction model is still used, the pipeline inlet temperature, node mixing temperature relationships, hydraulic pressure drop relationships, and heat transfer direction will be incorrectly constrained, thus affecting the accuracy of the virtual power plant's adjustability assessment and day-ahead scheduling results.
[0005] Furthermore, the reversal of the flow direction in the heating network introduces coupling terms between flow direction state variables, flow rate variables, and temperature variables into the originally linear node energy balance and pipeline temperature transmission constraints. This further introduces flow rate square terms, flow rate-temperature product terms, and product terms between binary and continuous variables. Directly establishing a nonlinear mixed-integer model would significantly increase the difficulty of solving the problem, making it difficult to meet the requirements of computational efficiency and engineering feasibility for day-ahead scheduling of virtual power plants. Summary of the Invention
[0006] The present invention aims to at least partially solve one of the technical problems in the related art.
[0007] The main objective of this invention is to provide a virtual power plant optimization scheduling method with adaptive flow direction in the heating network. Without pre-fixing the flow direction of heating branches, it can automatically determine whether the branches flow in the forward or reverse direction based on the output of heat sources, heat load demand, and the operating status of the heating network within the virtual power plant. Furthermore, it transforms the electric-heat coordinated scheduling model with adaptive flow direction into a mixed integer linear programming model that can be solved efficiently, thereby improving the operating economy of the virtual power plant, the level of new energy consumption, and the reliability of scheduling results.
[0008] The second objective of this invention is to provide an electronic device.
[0009] A third objective of this invention is to provide a non-transitory computer-readable storage medium.
[0010] To achieve the above objectives, a first aspect of the present invention proposes a virtual power plant optimization scheduling method with adaptive heating network flow direction, comprising: S1. Establish a day-ahead scheduling model for a multi-energy coupled virtual power plant. The day-ahead scheduling model for a multi-energy coupled virtual power plant includes an objective function that aims to minimize the total operating cost of the virtual power plant within the day-ahead scheduling period, as well as constraints on power network operation, energy production and conversion equipment operation, heating network operation, and net power exchange between the virtual power plant and the grid connection point. S2. Establish a heating network flow direction adaptive model, which includes heating network flow direction state variables, hydraulic model and thermal model. By introducing forward flow indicator variables and reverse flow indicator variables, the heating network branches can automatically select the flow direction of the heat medium according to the operating status of the virtual power plant. S3. Linearize the nonlinear terms in the day-ahead scheduling model of the multi-energy coupled virtual power plant and the adaptive flow model of the heating network, and construct the resulting model as a mixed integer linear programming model. S4. Solve the mixed integer linear programming model to obtain the operating status of energy production and conversion equipment, the flow and direction status of each branch of the heating network, the operating status of heating network nodes, and the net external exchange power of the virtual power plant grid connection point within the day-ahead scheduling cycle, and generate the day-ahead operation control command of the virtual power plant based on the solution results.
[0011] Optionally, in step S1, the objective function is:
[0012] Where C represents the total day-ahead dispatch operating cost of the virtual power plant; The operating cost of the thermal power unit during time period t; The operating cost of the cogeneration unit during time period t; The operating cost of the gas-fired boiler during time period t; The power consumption cost of the circulating water pump during time period t; The cost of curtailment of renewable energy during period t; The power network operation constraints include power node power balance constraints, power line power flow constraints, power line transmission capacity constraints, unit output upper and lower limit constraints, unit ramping constraints, and wind power consumption constraints. The operational constraints of the energy production and conversion equipment include the thermoelectric coupling constraints of cogeneration units, the energy conversion constraints of gas-fired boilers, and the upper and lower limits of gas-fired boiler output.
[0013] Optionally, the net external power exchange of the virtual power plant grid connection point satisfies the following:
[0014] in, This represents the net external power exchanged by the virtual power plant at its grid connection point during time period t. Let g be the electrical power output of the thermal power unit during time period t. For the electrical power output of the combined heat and power unit v during time period t, The actual wind power absorbed during time period t. The virtual power plant's internal electrical load during time period t. The power consumption of the circulating water pump in the heating network during time period t; when When the time is positive, it indicates that the virtual power plant is sending electricity to the external power grid. A negative value indicates that the virtual power plant purchases electricity from an external power grid.
[0015] Optionally, in step S2, the process of establishing the state variables of the heating network branch flow includes: Set forward and reverse flow indicator variables. For heating pipe branches where reverse flow is permitted, set a forward flow indicator variable. and reverse flow indicator variables , used to characterize the direction of heat medium flow in branch m during time period t, satisfying:
[0016] when When, it indicates that the heat medium in branch m flows from node i to node j; when When, it indicates that the heat medium in branch m flows from node j to node i; Establish the association constraint between flow rate and flow direction indicator variables, where the association constraint between branch flow rate and flow direction indicator variables is expressed as follows:
[0017] in, Let M be the mass flow rate of the heat medium in branch m during time period t; M is a sufficiently large positive number. The above constraints are used to ensure that when the branch is in a forward flow state, It is a non-negative value; when the branch is in a reverse flow state, It is a non-positive value.
[0018] Optionally, the hydraulic model of the heating network in step S2 includes constraints on branch hydraulic characteristics, circulating water pump power consumption, node flow conservation, branch flow upper and lower limits, and node pressure upper and lower limits; wherein: For a branch with a circulating water pump installed and where reverse flow is not permitted, the flow direction remains fixed, and the hydraulic characteristics of the branch are expressed as follows:
[0019] in, The pressure difference between the two ends of branch m; The head provided for the circulating water pump; Let m be the hydraulic impedance of branch line m; For a typical branch that allows reverse flow and does not have a circulating water pump installed, its hydraulic pressure drop relationship is determined by both the branch flow direction indicator variable and the branch flow rate, and can be expressed as follows: ; The power consumption of the circulating water pump is expressed as follows:
[0020] in, Density of the heat transfer medium; It is the acceleration due to gravity; For water pump efficiency; The flow conservation constraint at heating network nodes is established based on the node-branch relationship and combined with branch flow direction indicator variables to determine the inflow or outflow status of each branch at the node, expressed as:
[0021] in, The node-branch correlation coefficient is determined by both the branch connection relationship and the flow direction indicator variable. The injected traffic for node i during time period t; Within the upper and lower limits of hydraulic operation constraints, the branch flow and node pressure of the heating network satisfy the following:
[0022] Optionally, the heating network thermal model in step S2 includes dynamic temperature transfer constraints in pipes, pipe inlet temperature constraints, node heat power balance constraints, and node temperature upper and lower limit constraints; wherein: In the dynamic temperature transfer constraint of the pipeline, a heat transfer model of the heating pipeline is constructed based on the branch flow direction indicator variable, so that the pipeline temperature propagation direction can adaptively switch with the branch flow direction. For branch m, an explicit upwind scheme is used to describe the dynamic temperature transfer process of the pipeline:
[0023] in, For the kth spatial discrete point of branch m... The temperature of the heat transfer medium during the period; Let m be the cross-sectional area of branch m; The space is far from the walking distance; The heat transfer coefficient between the pipe and the soil; The temperature of the soil surrounding the pipeline; Specific heat capacity of the heat transfer medium. (Through...) and With the value of , the heat transfer equation of the pipeline can automatically switch between the forward flow discretization scheme and the reverse flow discretization scheme; In the pipe inlet temperature constraint, the branch inlet temperature is determined by the branch flow direction state, and the expression is:
[0024] in, Let be the inlet temperature of branch m during time period t; Let be the node temperatures at nodes i and j at the two ends of branch m, respectively. When the branch flows in the forward direction, the branch inlet temperature is equal to the temperature of node i; when the branch flows in the reverse direction, the branch inlet temperature is equal to the temperature of node j. In the heat power balance constraint of the heating network node, the heat power balance constraint of the heating network node is established based on the node-branch association relationship and the branch flow direction status. The inflow or outflow status of the branch at the node is determined by the flow direction indicator variable, which is expressed as:
[0025] in, These represent the inflow and outflow states of branch m at node i, determined by the branch flow direction indicator variable. Let m be the flow amplitude of branch m during time period t; These are the inlet and outlet temperatures of branch m, respectively. For the set of heat sources connected to node i; The heat power output of heat source s during time period t; Let i be the heat load of node i during time period t; In the upper and lower limits of thermal operation constraints, the temperatures of the heating network nodes and the supply water temperature of the heat source must satisfy:
[0026] Optionally, step S3 includes: Piecewise linearization is performed on the univariate nonlinear terms in the hydraulic model of the heating network. The range of variable values is divided into multiple linear intervals, and the corresponding linear approximation relationship is selected by interval selection of variables. An auxiliary variable is introduced into the bilinear product term consisting of two continuous variables, and McCormick envelope constraints are constructed based on the upper and lower limits of each continuous variable. For nonlinear terms that include the product of the forward or reverse flow indicator variable and a continuous variable, an auxiliary variable is introduced, and the logical linearization constraint of binary and continuous variables is used for processing. By utilizing the value characteristics of binary variables, the squared terms of binary variables are replaced with the corresponding binary variables, and the product terms of mutually exclusive forward flow indicator variables and reverse flow indicator variables are set to zero, thereby constructing the mixed integer linear programming model.
[0027] Optionally, in step S4, the day-ahead scheduling results include the power output of thermal power units, the power output and thermal output of cogeneration units, the thermal output of gas boilers, the power consumption of new energy sources, the power consumption of circulating water pumps, the net external exchange power of the virtual power plant grid connection point, the mass flow rate of heating network branches, the flow direction status of heating network branches, the pressure of heating network nodes, and the temperature of heating network nodes. Based on the day-ahead scheduling results for each scheduling period, corresponding equipment operation setpoints and heating network operation setpoints are generated, forming virtual power plant day-ahead operation control instructions.
[0028] To achieve the above objectives, a second aspect of this application provides an electronic device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory to implement the method described in the first aspect embodiment.
[0029] To achieve the above objectives, a third aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.
[0030] The embodiments of this invention have the following beneficial effects: This invention addresses the electro-thermal coordinated operation scenario within a multi-energy coupled virtual power plant, breaking through the traditional fixed flow direction assumption of heating networks. By setting forward and reverse flow indicator variables, it enables heating network branches to adaptively select flow directions based on heat source output, heat load distribution, and system operating status, thereby improving the accuracy of multi-heat source heating network modeling. Simultaneously, this invention embeds the heating network flow direction status into hydraulic constraints, heat transfer constraints, node thermal balance constraints, and pipe inlet temperature constraints, ensuring that reverse flow in the heating network not only reflects changes in flow sign but also reflects changes in temperature propagation direction, node mixing relationships, and heat source heating range, improving the completeness of the electro-thermal coupling relationship characterization in the virtual power plant. Furthermore, this invention employs piecewise linearization, McCormick envelope, and piecewise McCormick... This method unifies and linearizes the nonlinear terms in the adaptive heating network scheduling model, transforming the originally difficult-to-solve nonlinear mixed-integer model into a solvable mixed-integer linear programming model, thereby improving the computational efficiency and engineering applicability of the day-ahead scheduling model. Furthermore, this invention can expand the heating range of heat sources such as gas-fired boilers through reverse energy transmission in the heating network, redistributing heat load among different heat sources, reducing the heat output demand of cogeneration units and their coupled electrical output, and providing greater capacity for the absorption of new energy sources such as wind power. In summary, this invention can obtain a day-ahead coordinated scheduling scheme for multiple energy devices within a virtual power plant, while meeting the constraints of power network security, hydraulic and thermal constraints of the heating network, and electrical and heat load demands, thus reducing system operating costs, improving the absorption capacity of new energy sources, and enhancing the operational flexibility of the virtual power plant. Attached Figure Description
[0031] The above-described and additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 The flowchart illustrates a virtual power plant optimization scheduling method with adaptive heating network flow direction, as provided in an embodiment of the present invention. Detailed Implementation
[0032] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0034] The following describes, with reference to the accompanying drawings, an embodiment of the present invention, a virtual power plant optimization scheduling method for adaptive heating network flow.
[0035] Example 1 This embodiment provides a virtual power plant optimization scheduling method with adaptive flow direction in the heating network. For example... Figure 1 As shown, the method includes the following steps: S1. Establish a day-ahead scheduling model for a multi-energy coupled virtual power plant. The day-ahead scheduling model for a multi-energy coupled virtual power plant includes an objective function that aims to minimize the total operating cost of the virtual power plant within the day-ahead scheduling period, as well as constraints on power network operation, energy production and conversion equipment operation, heating network operation, and net power exchange between the virtual power plant and the grid connection point.
[0036] In this embodiment of the invention, step S1 is specifically implemented through steps 1-1, 1-2, and 1-3. The details are as follows: (1-1) Establish the day-ahead scheduling objective function of the multi-energy coupled virtual power plant.
[0037] Specifically, it includes the following steps: (1-1-1) Establish the objective function for the total operating cost of the virtual power plant.
[0038] The objective function is constructed as follows, with the goal of minimizing the total operating cost of a multi-energy coupled virtual power plant within the daytime scheduling cycle:
[0039] Where C represents the total day-ahead dispatch operating cost of the virtual power plant; The operating cost of the thermal power unit during time period t; The operating cost of the cogeneration unit during time period t; The operating cost of the gas-fired boiler during time period t; The power consumption cost of the circulating water pump during time period t; The penalty cost for abandoning renewable energy during period t.
[0040] (1-1-2) Establish the operating cost of thermal power units.
[0041] The operating cost of thermal power units is expressed as follows:
[0042] in, A collection of thermal power units; The electrical power output of thermal power unit g during time period t; This refers to the coal consumption characteristic coefficient of thermal power units. The scheduling time interval; This refers to the price of coal.
[0043] (1-1-3) Establish the operating cost of cogeneration units.
[0044] The operating cost of a combined heat and power (CHP) unit is expressed as follows:
[0045] in, A collection of combined heat and power units; The electrical power output of the combined heat and power unit v during time period t; The thermal power output of the combined heat and power unit v during time period t; This is the coal consumption characteristic coefficient for combined heat and power (CHP) units.
[0046] (1-1-4) Establish the operating cost of gas-fired boilers.
[0047] The operating cost of a gas-fired boiler is expressed as follows:
[0048] in, A collection of gas-fired boilers; The thermal power output of gas-fired boiler b during time period t; For natural gas prices; Thermal efficiency of gas-fired boilers; It represents the lower heating value of natural gas.
[0049] (1-1-5) Establish penalties for abandoning renewable energy.
[0050] The penalty cost for curtailing renewable energy is expressed as follows:
[0051] in, Price as a penalty for wind curtailment; Forecast wind power output for period t; The actual wind power absorbed during time period t.
[0052] (1-2) Establish operation constraints for the multi-energy coupled virtual power plant power network.
[0053] Specifically, it includes the following steps: (1-2-1) Establish power balance constraints at power nodes The internal power network of a multi-energy coupled virtual power plant needs to satisfy node power balance, as shown below:
[0054] in, The virtual power plant's internal electrical load during time period t; The power consumption of the circulating water pump in the heating network during time period t; The transmission power of power network line l; A collection of power lines.
[0055] Furthermore, an expression for the net power exchange between the virtual power plant and the external power grid is established to characterize the power exchange between the virtual power plant and the external power grid:
[0056] in, The virtual power plant's net power exchange with the grid at time period t; when When the time is positive, it indicates that the virtual power plant is sending electricity to the external power grid. A negative value indicates that the virtual power plant purchases electricity from an external power grid.
[0057] (1-2-2) Establish power flow constraints for power lines.
[0058] The power transmission power of a power line is described using a DC power flow model, expressed as:
[0059] in, The susceptance of line l; These are the voltage phase angles at nodes i and j at both ends of line l during time period t.
[0060] (1-2-3) Establish line transmission capacity constraints.
[0061] The power transmission capacity of power lines meets the upper and lower limit constraints, as expressed as:
[0062] (1-2-4) Establish upper and lower limits of unit output constraints.
[0063] The output of thermal power units and combined heat and power units must meet the following constraints:
[0064]
[0065] (1-2-5) Establish unit ramp-up constraints.
[0066] The unit ramp-up constraint is expressed as:
[0067]
[0068] (1-2-6) Establish wind power consumption constraints.
[0069] Wind power absorption constraints are expressed as follows:
[0070] (1-3) Establish operational constraints for energy production and conversion equipment.
[0071] Specifically, it includes the following steps: (1-3-1) Establish thermoelectric coupling constraints for cogeneration units.
[0072] For back-pressure cogeneration units, their electrical power and thermal power satisfy the thermoelectric coupling relationship:
[0073] in, V represents the heat-to-power ratio of a combined heat and power (CHP) unit.
[0074] (1-3-2) Establish energy conversion constraints for gas-fired boilers.
[0075] Gas-fired boilers convert natural gas into heat energy, and their heat power output is expressed as:
[0076] in, Let t be the flow rate of natural gas consumed by gas-fired boiler b during time period t.
[0077] (1-3-3) Establish upper and lower limits of output constraints for gas-fired boilers.
[0078] The heat output of the gas-fired boiler meets the following requirements:
[0079] S2. Establish an adaptive model for the flow direction of the heating network. The adaptive model for the flow direction of the heating network includes state variables for the flow direction of the heating network, a hydraulic model, and a thermal model. By introducing forward flow indicator variables and reverse flow indicator variables, the heating network branches can automatically select the flow direction of the heat medium according to the operating status of the virtual power plant.
[0080] In this embodiment of the invention, step S1 is specifically implemented through steps 2-1, 2-2, and 2-3. The details are as follows: (2-1) Establish the state variables of the flow direction of the heating network branches.
[0081] Specifically, it includes the following steps: (2-1-1) Set forward and reverse flow indicator variables.
[0082] For heating pipe branches that allow reverse flow, set a forward flow indicator variable. and reverse flow indicator variables , used to characterize the direction of heat medium flow in branch m during time period t, satisfying:
[0083] when When, it indicates that the heat medium in branch m flows from node i to node j; when When, it indicates that the heat medium in branch m flows from node j to node i.
[0084] (2-1-2) Establish the association constraint between flow rate and flow direction indicator variables.
[0085] The association constraint between branch flow and flow direction indicator variables is expressed as follows:
[0086] in, Let M be the mass flow rate of the heat medium in branch m during time period t; M is a sufficiently large positive number. The above constraints are used to ensure that when the branch is in a forward flow state, It is a non-negative value; when the branch is in a reverse flow state, It is a non-positive value.
[0087] (2-2) Establish an adaptive hydraulic model for the flow direction of the heating network.
[0088] Specifically, it includes the following steps: (2-2-1) Establish hydraulic characteristic constraints for branch roads.
[0089] For a branch with a circulating water pump installed and where reverse flow is not permitted, the flow direction remains fixed, and the hydraulic characteristics of the branch are expressed as follows:
[0090] in, The pressure difference between the two ends of branch m; The head provided for the circulating water pump; Let m be the hydraulic impedance of branch.
[0091] For a typical branch that allows reverse flow and does not have a circulating water pump installed, its hydraulic pressure drop relationship is determined by both the branch flow direction indicator variable and the branch flow rate, and can be expressed as follows: The flow square term, absolute value term, and flow direction related term in this formula are processed in the subsequent linearization step through piecewise linearization, auxiliary variables, and big M constraints.
[0092] (2-2-2) Establish power consumption constraints for circulating water pumps.
[0093] The power consumption of the circulating water pump is expressed as follows:
[0094] in, Density of the heat transfer medium; It is the acceleration due to gravity; This refers to the efficiency of the water pump.
[0095] (2-2-3) Establish flow conservation constraints for heating network nodes.
[0096] The flow conservation constraint at heating network nodes is established based on the node-branch relationship and combined with branch flow direction indicator variables to determine the inflow or outflow status of each branch at the node, expressed as:
[0097] in, The node-branch correlation coefficient is determined by both the branch connection relationship and the flow direction indicator variable. This represents the injected traffic at node i during time period t.
[0098] (2-2-4) Establish upper and lower limits constraints for hydraulic operation.
[0099] The flow rate of the heating network branches and the pressure at the nodes meet the following requirements:
[0100] (2-3) Establish an adaptive thermodynamic model for the flow direction of the heating network.
[0101] Specifically, it includes the following steps: (2-3-1) Establish dynamic temperature transmission constraints in pipelines.
[0102] A heat transfer model for the heating pipeline is constructed based on branch flow direction indicators, enabling the pipeline temperature propagation direction to adaptively switch with the branch flow direction. For branch m, an explicit upwind scheme is used to describe the dynamic temperature transfer process in the pipeline.
[0103] in, For the kth spatial discrete point of branch m... The temperature of the heat transfer medium during the period; Let m be the cross-sectional area of branch m; The space is far from the walking distance; The heat transfer coefficient between the pipe and the soil; The temperature of the soil surrounding the pipeline; Specific heat capacity of the heat transfer medium. (Through...) and With the given value, the heat transfer equation for the pipeline can automatically switch between the forward flow discretization scheme and the reverse flow discretization scheme.
[0104] (2-3-2) Establish pipe inlet temperature constraints.
[0105] The branch inlet temperature is determined by the branch flow direction, as expressed by:
[0106] in, Let be the inlet temperature of branch m during time period t; Let be the node temperatures at nodes i and j at the two ends of branch m, respectively. When the branch flows in the forward direction, the branch inlet temperature is equal to the temperature of node i; when the branch flows in the reverse direction, the branch inlet temperature is equal to the temperature of node j.
[0107] (2-3-3) Establish heat power balance constraints for heating network nodes.
[0108] The heat power balance constraint of the heating network nodes is established based on the node-branch association and the branch flow direction status. The inflow or outflow status of the branch at the node is determined by the flow direction indicator variable, as shown in the following:
[0109] in, These represent the inflow and outflow states of branch m at node i, determined by the branch flow direction indicator variable. Let m be the flow amplitude of branch m during time period t; These are the inlet and outlet temperatures of branch m, respectively. For the set of heat sources connected to node i; The heat power output of heat source s during time period t; Let be the heat load of node i during time period t.
[0110] (2-3-4) Establish upper and lower limits constraints for thermal operation.
[0111] The heating network node temperatures and the heat source water supply temperatures meet the following requirements:
[0112] S3. Linearize the nonlinear terms in the day-ahead scheduling model of the multi-energy coupled virtual power plant and the adaptive model of the heating network flow direction, and construct the resulting model as a mixed integer linear programming model.
[0113] In this embodiment of the invention, the single-variable nonlinear term is first subjected to piecewise linearization.
[0114] Because the adaptive flow direction model for heating networks contains terms such as the square of the flow rate, the product of the flow rate and temperature, the product of the flow direction indicator variable and the continuous variable, and high-order nonlinear terms, it is difficult to solve directly. To improve the solvability of the model, this invention adopts a hierarchical linearization strategy to transform the original nonlinear model into a mixed-integer linear programming model.
[0115] Specifically, for univariate nonlinear terms The piecewise linearization method is used for processing. The range of values is divided into There are linear intervals, and a linear function is used to approximate each interval. And set an interval selection variable to ensure that only one segment interval is activated in each time period.
[0116] Next, the bilinear terms are processed using the McCormick envelope.
[0117] In one embodiment of the invention, for bilinear terms , and Convex relaxation is performed using the McCormick envelope. An auxiliary variable is introduced. It represents the product of two continuous variables, and constructs four sets of linear inequality constraints based on the upper and lower limits of the two variables, thereby forming a convex hull approximation of the corresponding bilinear terms.
[0118] In addition, higher-order terms containing flow direction indicator variables are processed using piecewise McCormick.
[0119] Specifically, for product terms containing flow direction indicator variables , And similar higher-order terms, this embodiment of the invention uses a piecewise McCormick method for processing. First, auxiliary variables are introduced to represent... or Then, the product of the auxiliary variable and the binary flow indicator variable is transformed into a linear constraint, thereby avoiding the direct handling of higher-order nonlinear terms.
[0120] Finally, the product terms of the binary variables are simplified.
[0121] In detail, for the product term of binary variables, the embodiments of the present invention simplify the process by utilizing the inherent properties of binary variables. Because... and Mutually exclusive, satisfying:
[0122] And there are:
[0123] Therefore, the square term of the binary variable can be directly replaced with itself, and the product term of the mutually exclusive binary variable can be replaced with 0.
[0124] Through the above processing, the embodiments of the present invention transform the day-ahead scheduling model of a multi-energy coupled virtual power plant that considers adaptive heating network flow into a mixed-integer linear programming model.
[0125] S4. Solve the mixed integer linear programming model to obtain the operating status of energy production and conversion equipment, the flow and direction status of each branch of the heating network, the operating status of heating network nodes, and the net external exchange power of the virtual power plant grid connection point within the day-ahead scheduling cycle, and generate the day-ahead operation control command of the virtual power plant based on the solution results.
[0126] In one embodiment of the present invention, the day-ahead scheduling model established and linearized in steps S1 to S3 is transformed into a mixed-integer linear programming model, and an optimization solver is used to solve it to obtain the day-ahead scheduling result of the virtual power plant.
[0127] The output results include the output of thermal power units, the electrothermal output of cogeneration units, the thermal output of gas boilers, the wind power consumption, the wind curtailment power, the circulating water pump power, the net external exchange power of the virtual power plant grid connection point, the flow rate of each branch of the heating network, the flow direction status of the branch, the node pressure, the node temperature, the pipeline inlet temperature and the pipeline outlet temperature, and generate the day-ahead operation control command of the virtual power plant based on the optimization results.
[0128] Example 2 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.
[0129] Example 3 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.
[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
[0131] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.
Claims
1. A virtual power plant optimization scheduling method with adaptive flow direction in a heating network, characterized in that, Includes the following steps: S1. Establish a day-ahead scheduling model for a multi-energy coupled virtual power plant. The day-ahead scheduling model for a multi-energy coupled virtual power plant includes an objective function that aims to minimize the total operating cost of the virtual power plant within the day-ahead scheduling period, as well as constraints on power network operation, energy production and conversion equipment operation, heating network operation, and net power exchange between the virtual power plant and the grid connection point. S2. Establish a heating network flow direction adaptive model, which includes heating network flow direction state variables, hydraulic model and thermal model. By introducing forward flow indicator variables and reverse flow indicator variables, the heating network branches can automatically select the flow direction of the heat medium according to the operating status of the virtual power plant. S3. Linearize the nonlinear terms in the day-ahead scheduling model of the multi-energy coupled virtual power plant and the adaptive flow model of the heating network, and construct the resulting model as a mixed integer linear programming model. S4. Solve the mixed integer linear programming model to obtain the operating status of energy production and conversion equipment, the flow and direction status of each branch of the heating network, the operating status of heating network nodes, and the net external exchange power of the virtual power plant grid connection point within the day-ahead scheduling cycle, and generate the day-ahead operation control command of the virtual power plant based on the solution results.
2. The method according to claim 1, characterized in that, In step S1, the objective function is: Where C represents the total day-ahead dispatch operating cost of the virtual power plant; The operating cost of the thermal power unit during time period t; The operating cost of the cogeneration unit during time period t; The operating cost of the gas-fired boiler during time period t; The power consumption cost of the circulating water pump during time period t; The cost of curtailment of renewable energy during period t; The power network operation constraints include power node power balance constraints, power line power flow constraints, power line transmission capacity constraints, unit output upper and lower limit constraints, unit ramping constraints, and wind power consumption constraints. The operational constraints of the energy production and conversion equipment include the thermoelectric coupling constraints of cogeneration units, the energy conversion constraints of gas-fired boilers, and the upper and lower limits of gas-fired boiler output.
3. The method according to claim 2, characterized in that, The net external power exchange capacity of the virtual power plant grid connection point meets the following requirements: in, This represents the net external power exchanged by the virtual power plant at its grid connection point during time period t. Let g be the electrical power output of the thermal power unit during time period t. For the electrical power output of the combined heat and power unit v during time period t, The actual wind power absorbed during time period t. The virtual power plant's internal electrical load during time period t. The power consumption of the circulating water pump in the heating network during time period t; when When the time is positive, it indicates that the virtual power plant is sending electricity to the external power grid. A negative value indicates that the virtual power plant purchases electricity from an external power grid.
4. The method according to claim 1, characterized in that, In step S2, the process of establishing the state variables of the heating network branch flow includes: Set forward and reverse flow indicator variables. For heating pipe branches where reverse flow is permitted, set a forward flow indicator variable. and reverse flow indicator variables , used to characterize the direction of heat medium flow in branch m during time period t, satisfying: when When, it indicates that the heat medium in branch m flows from node i to node j; when When, it indicates that the heat medium in branch m flows from node j to node i; Establish the association constraint between flow rate and flow direction indicator variables, where the association constraint between branch flow rate and flow direction indicator variables is expressed as follows: in, Let M be the mass flow rate of the heat medium in branch m during time period t; M is a sufficiently large positive number; the above constraints are used to ensure that when the branch is in a forward flow state... It is a non-negative value; when the branch is in a reverse flow state, It is a non-positive value.
5. The method according to claim 4, characterized in that, The hydraulic model of the heating network in step S2 includes constraints on branch hydraulic characteristics, circulating water pump power consumption, node flow conservation, branch flow upper and lower limits, and node pressure upper and lower limits; among which: For a branch with a circulating water pump installed and where reverse flow is not permitted, the flow direction remains fixed, and the hydraulic characteristics of the branch are expressed as follows: in, The pressure difference between the two ends of branch m; The head provided for the circulating water pump; Let m be the hydraulic impedance of branch line m; For a typical branch that allows reverse flow and does not have a circulating water pump installed, its hydraulic pressure drop relationship is determined by both the branch flow direction indicator variable and the branch flow rate, and can be expressed as follows: ; The power consumption of the circulating water pump is expressed as follows: in, Density of the heat transfer medium; It is the acceleration due to gravity; For water pump efficiency; The flow conservation constraint at heating network nodes is established based on the node-branch relationship and combined with branch flow direction indicator variables to determine the inflow or outflow status of each branch at the node, expressed as: in, The node-branch correlation coefficient is determined by both the branch connection relationship and the flow direction indicator variable. The injected traffic for node i during time period t; Within the upper and lower limits of hydraulic operation constraints, the branch flow and node pressure of the heating network satisfy the following: 。 6. The method according to claim 5, characterized in that, The heating network thermal model in step S2 includes constraints on dynamic temperature transfer in pipelines, pipeline inlet temperature, node heat power balance, and node temperature upper and lower limits; among which: In the dynamic temperature transmission constraint of the pipeline, a heat transmission model for the heating pipeline is constructed based on the branch flow direction indicator variable, enabling the pipeline temperature propagation direction to adaptively switch with the branch flow direction; for branch m, an explicit upwind scheme is used to describe the dynamic temperature transmission process of the pipeline. in, For the kth spatial discrete point of branch m... The temperature of the heat transfer medium during the period; Let m be the cross-sectional area of branch m; The space is far from the walking distance; The heat transfer coefficient between the pipe and the soil; The temperature of the soil surrounding the pipeline; Specific heat capacity of the heat transfer medium; through and With the value of , the heat transfer equation of the pipeline can automatically switch between the forward flow discretization scheme and the reverse flow discretization scheme; In the pipe inlet temperature constraint, the branch inlet temperature is determined by the branch flow direction state, and the expression is: in, Let be the inlet temperature of branch m during time period t; Let i and j be the node temperatures at the two ends of branch m, respectively; when the branch flows in the forward direction, the branch inlet temperature is equal to the temperature of node i; when the branch flows in the reverse direction, the branch inlet temperature is equal to the temperature of node j. In the heat power balance constraint of the heating network node, the heat power balance constraint of the heating network node is established based on the node-branch association relationship and the branch flow direction status. The inflow or outflow status of the branch at the node is determined by the flow direction indicator variable, which is expressed as: in, These represent the inflow and outflow states of branch m at node i, determined by the branch flow direction indicator variable. Let m be the flow amplitude of branch m during time period t; These are the inlet and outlet temperatures of branch m, respectively. For the set of heat sources connected to node i; The heat power output of heat source s during time period t; Let i be the heat load of node i during time period t; In the upper and lower limits of thermal operation constraints, the temperatures of the heating network nodes and the supply water temperature of the heat source must satisfy: 。 7. The method according to claim 1, characterized in that, Step S3 includes: Piecewise linearization is performed on the univariate nonlinear terms in the hydraulic model of the heating network. The range of variable values is divided into multiple linear intervals, and the corresponding linear approximation relationship is selected by interval selection of variables. An auxiliary variable is introduced into the bilinear product term consisting of two continuous variables, and McCormick envelope constraints are constructed based on the upper and lower limits of each continuous variable. For nonlinear terms that include the product of the forward or reverse flow indicator variable and a continuous variable, an auxiliary variable is introduced, and the logical linearization constraint of binary and continuous variables is used for processing. By utilizing the value characteristics of binary variables, the squared terms of binary variables are replaced with the corresponding binary variables, and the product terms of mutually exclusive forward flow indicator variables and reverse flow indicator variables are set to zero, thereby constructing the mixed integer linear programming model.
8. The method according to claim 1, characterized in that, In step S4, the day-ahead scheduling results include the power output of thermal power units, the power output and thermal output of cogeneration units, the thermal output of gas boilers, the power consumption of new energy sources, the power consumption of circulating water pumps, the net external exchange power of the virtual power plant grid connection point, the mass flow rate of heating network branches, the flow direction status of heating network branches, the pressure of heating network nodes, and the temperature of heating network nodes. Based on the day-ahead scheduling results for each scheduling period, corresponding equipment operation setpoints and heating network operation setpoints are generated, forming virtual power plant day-ahead operation control instructions.
9. An electronic device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-8.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.