Optimal scheduling method and system for microgrids considering mass flow regulation of heating network

Abstract

This invention provides a microgrid optimization scheduling method and system considering the regulation of mass flow rate in a heating network. It belongs to the field of microgrid optimization scheduling technology. The microgrid optimization scheduling method includes: determining heating network equipment constraints based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heating network device, and the thermal power of each heating network device; determining energy storage constraints and system balance constraints based on the energy storage status and power in the microgrid; establishing a microgrid optimization scheduling model based on the heating network equipment constraints, energy storage constraints, and system balance constraints, with the goal of minimizing the microgrid's operation and maintenance costs and carbon emissions; and using a standardized multi-parameter de-aggregation method to de-aggregate the heating network equipment constraints and solve the microgrid optimization scheduling model to determine the optimal output of each grid device and the optimal mass flow rate and optimal temperature of each pipe of each heating network device, thereby improving the accuracy and efficiency of microgrid optimization scheduling.

Description

Technical Field

[0001] This invention relates to the field of microgrid optimization scheduling, and in particular to a microgrid optimization scheduling method and system that considers the regulation of heat network mass flow rate. Background Technology

[0002] Microgrids enable the efficient integration, multi-source complementarity, and coordinated operation of various energy sources, including cooling, heating, and electricity, within a region, ultimately achieving the goals of environmental protection and sustainable development. Effective optimization scheduling methods determine the quality of energy management and the overall performance of the microgrid system.

[0003] Current research primarily focuses on the optimal scheduling problem of microgrids incorporating three forms of energy: cooling, heating, and electricity. This involves establishing mathematical models of grid-side equipment, heating network-side equipment, and coupled equipment, along with system operating constraints, to solve the problem with economic optimization as the objective, obtaining the power output of each device to meet different load demands. However, current microgrid optimal scheduling problems largely remain at the power scheduling stage, focusing on the power output of equipment in the cooling, heating, and electricity systems, without considering the operating mode of the heating subsystem. This simplifies the scheduling problem to a linear convex problem, which is impractical for engineering applications and difficult to implement.

[0004] In practical engineering, the thermal subsystem transfers energy within the heating network pipelines using liquids such as hot water as a medium, thereby achieving thermal power scheduling. The mass flow rate and temperature of the liquid within the pipelines need to be considered, and their product term exists in the heating network model. The thermal subsystem has three regulation modes: quality regulation, quantity regulation, and mass-flow regulation. Quality regulation fixes the mass flow rate of the pipeline and adjusts the temperature to change the thermal power; quantity regulation fixes the liquid temperature within the pipeline and adjusts the flow rate to change the thermal power; mass-flow regulation simultaneously regulates both the mass flow rate and the liquid temperature, offering greater flexibility and enabling the attainment of the optimal scheduling scheme. Of the three methods, quality regulation and quantity regulation, because they both fix one of the two variables (mass flow rate and temperature), simplify the optimization problem to a linear problem, lacking flexibility and making it difficult to obtain the optimal scheduling scheme. In the mass-flow regulation mode, the product term is called a bilinear term, causing the microgrid optimization scheduling problem to be highly nonconvex, transforming it into a mixed-integer nonlinear programming problem containing bilinear terms, which cannot be solved using general linearization methods and solvers.

[0005] A common approach to handling non-convex problems is to generate a convex relaxation of the problem as a lower bound for the objective function, generate a feasible solution as an upper bound, and continuously update the upper and lower bounds until they are reduced to within the tolerance. Common linearization methods include refactoring linearization, generalized Benders decomposition, and convex relaxation. Refactoring linearization techniques enhance relaxation by reorganizing the constraints of the model and adding other constraints, which may be redundant in the original space. Generalized Benders decomposition decomposes the non-convex problem into linear programming and integer programming, using a cutting plane method to decompose the main problem and subproblems, and solving for the optimal value through iteration. It is mainly for specific mixed-integer nonlinear programming problems, but the solution obtained may not be the global optimum or even a local optimum, and its convergence is difficult to guarantee. The performance of convex relaxation techniques mainly depends on the relaxation boundary, and in order to make the relaxed problem convex, the feasibility of the original problem's solution is sacrificed.

[0006] Based on the above problems, there is an urgent need for a new microgrid optimization scheduling method to improve the accuracy and efficiency of optimization scheduling. Summary of the Invention

[0007] The purpose of this invention is to provide a micro-energy network optimization scheduling method and system that considers the regulation of heat network mass flow rate, which can improve the accuracy and efficiency of energy network optimization scheduling.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] A microgrid optimization scheduling method considering heat network mass flow regulation, wherein the microgrid includes multiple heat network hot standby and multiple grid equipment, and the microgrid optimization scheduling method considering heat network mass flow regulation includes:

[0010] The constraints of the heating network equipment are determined based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heating network equipment and the thermal power of each heating network equipment.

[0011] Based on the state of charge, charging and discharging power, charging and discharging efficiency, minimum charging and discharging power, maximum charging and discharging power, state of charge and discharge, state of charge and heat release, power of charge and heat release, efficiency of charge and heat release, minimum power of charge and heat release, maximum power of charge and heat release, energy storage capacity, minimum state of charge and maximum state of charge of energy storage in the microgrid, determine the energy storage constraints.

[0012] The system balance constraints are determined based on the maximum power that can be exchanged between the microgrid and the main grid, the output of each grid device, the thermal power, electrical load and thermal load of each heating network device;

[0013] Based on the constraints of the heating network equipment, the energy storage constraints, and the system balance constraints, an optimal scheduling model for the microgrid is established with the goal of minimizing the operation and maintenance costs and carbon emissions of the microgrid.

[0014] The constraints of the heating network equipment in the microgrid optimization scheduling model are de-aggregated using a standardized multi-parameter de-aggregation method, and the microgrid optimization scheduling model is solved to determine the optimal output of each grid equipment and the optimal mass flow rate and optimal temperature of each pipeline of each heating network equipment.

[0015] To achieve the above objectives, the present invention also provides the following solution:

[0016] A microgrid optimization scheduling system considering heat network mass flow regulation, wherein the microgrid includes multiple heat network hot standby devices and multiple grid equipment, and the microgrid optimization scheduling system considering heat network mass flow regulation includes:

[0017] The heating network equipment constraint determination unit is connected to each heating network device and is used to determine the constraints of the heating network device based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heating network device and the thermal power of each heating network device.

[0018] The energy storage constraint determination unit is used to determine the energy storage constraints based on the state of charge, charging and discharging power, charging and discharging efficiency, minimum charging and discharging power, maximum charging and discharging power, state of charge and discharge, state of charge and heat release, power of charge and heat release, efficiency of charge and heat release, minimum power of charge and heat release, maximum power of charge and heat release, energy storage capacity, minimum state of charge and maximum state of charge of the energy stored in the micro energy grid.

[0019] The system balance constraint determination unit is connected to each heating network device and each power grid device. It is used to determine the system balance constraints based on the maximum power that can be exchanged between the micro energy network and the large power grid interconnection line, the output of each power grid device, the thermal power, electrical load and thermal load of each heating network device.

[0020] The optimized scheduling model establishment unit is connected to the heating network equipment constraint determination unit, the energy storage constraint determination unit, and the system balance constraint determination unit, respectively. It is used to establish an optimized scheduling model for the micro energy network based on the heating network equipment constraints, the energy storage constraints, and the system balance constraints, with the goal of minimizing the operation and maintenance cost and carbon emissions of the micro energy network.

[0021] The solution unit, connected to the optimization scheduling model establishment unit, is used to perform de-aggregation processing on the heating network equipment constraints in the microgrid optimization scheduling model using a standardized multi-parameter de-aggregation method, and to solve the microgrid optimization scheduling model to determine the optimal output of each grid equipment and the optimal mass flow rate and optimal temperature of each pipeline of each heating network equipment.

[0022] According to specific embodiments provided by the present invention, the following technical effects are disclosed: Based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heating network device, and the thermal power of each heating network device, heating network device constraints are determined, as well as energy storage constraints and system balance constraints. Based on the heating network device constraints, energy storage constraints, and system balance constraints, with the goal of minimizing the operation and maintenance cost and carbon emissions of the microgrid, an optimal scheduling model for the microgrid is established. This optimal scheduling model simultaneously adjusts the mass flow rate and liquid temperature of the heating network device pipes, providing greater flexibility and enabling the acquisition of the optimal scheduling scheme, thus improving scheduling accuracy. Furthermore, since the heating network device constraints include bilinear terms (the product of mass flow rate and temperature), a standardized multi-parameter de-aggregation method is used to de-aggregate the heating network device constraints, making the optimal scheduling problem easier to solve and improving the optimal scheduling efficiency of the microgrid. Finally, the model is solved to accurately obtain the optimal output of each grid device and the optimal mass flow rate and optimal temperature of each pipe of each heating network device. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 The flowchart of the microgrid optimization scheduling method considering heat network mass flow rate regulation according to the present invention is shown below.

[0025] Figure 2 This is a technical framework diagram of the microgrid optimization scheduling method considering the mass flow rate regulation of the heating network according to the present invention.

[0026] Figure 3 This is a schematic diagram of the module structure of the micro-energy network optimization scheduling system that considers the mass flow rate regulation of the heating network according to the present invention.

[0027] Symbol explanation:

[0028] Unit 1 for determining constraints of heating network equipment, unit 2 for determining constraints of energy storage, unit 3 for determining constraints of system balance, unit 4 for establishing an optimization scheduling model, and unit 5 for solving the problem. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0030] The purpose of this invention is to provide a microgrid optimization scheduling method and system that considers the mass flow rate regulation of the heating network. By linking the power of equipment within the heating network with the mass flow rate and temperature of the inlet and outlet pipes connecting these equipment, it offers greater flexibility, enabling the acquisition of optimal scheduling schemes that align with engineering realities. Furthermore, a standardized multi-parameter de-aggregation method is employed to de-aggregate the heating network equipment constraints containing bilinear terms, making the optimization scheduling problem easier to solve and improving the efficiency and accuracy of microgrid optimization scheduling.

[0031] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] like Figure 1 and Figure 2 As shown, the microgrid optimization scheduling method considering heat network mass flow rate regulation of the present invention includes:

[0033] S1: Determine the constraints of the heating network equipment based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heating network equipment and the thermal power of each heating network equipment.

[0034] S2: Determine the energy storage constraints based on the state of charge, charging and discharging power, charging and discharging efficiency, minimum charging and discharging power, maximum charging and discharging power, state of charge and discharge, state of charge and heat release, power of charge and heat release, efficiency of charge and heat release, minimum power of charge and heat release, maximum power of charge and heat release, energy storage capacity, minimum state of charge and maximum state of charge of the energy storage in the micro-energy grid.

[0035] S3: Determine the system balance constraints based on the maximum power allowed to be exchanged between the microgrid and the main grid, the output of each grid device, the thermal power, electrical load and thermal load of each heating network device.

[0036] S4: Based on the constraints of the heating network equipment, the energy storage constraints, and the system balance constraints, an optimal scheduling model for the micro-energy network is established with the goal of minimizing the operation and maintenance costs and carbon emissions of the micro-energy network.

[0037] S5: The constraints of the heating network equipment in the microgrid optimization scheduling model are de-aggregated using a standardized multi-parameter de-aggregation method, and the microgrid optimization scheduling model is solved to determine the optimal output of each grid equipment and the optimal mass flow rate and optimal temperature of each pipeline of each heating network equipment.

[0038] The actual optimal solution obtained is the power of each device in the microgrid. However, since the heating network needs to adjust two variables—mass flow rate and temperature—in each pipe connecting the devices, the thermal power of the heating network's standby is controlled by adjusting these two variables. That is, the heating network equipment constraints in step S1 limit the relationship between mass flow rate, temperature, and power. Finally, instructions are issued based on the obtained optimal solution. The devices in the power grid directly output power according to the obtained power, while the heating network controls the mass flow rate and temperature of the inlet and outlet pipes of each device based on the solved mass flow rate and temperature, thereby controlling the thermal power of the devices.

[0039] This invention uses mass-flow regulation to model the thermal subsystem, which aligns with engineering practice. Then, the bilinear terms (mass flow rate and temperature product) that cause MINLP (Mixed Integer Nonlinear Programming) problems are handled using a standard multi-parameter decomposition method, making the problem easier to solve. First, a heating network pipeline model is established, using the mass flow rate and liquid temperature of each pipeline to represent the power of the node equipment. Then, constraints are established in conjunction with the grid equipment, and a mixed-integer nonlinear optimization scheduling model is established with economic optimization as the objective. Finally, the constraints of the bilinear terms that cause nonlinearity in the model are relaxed using a standard multi-parameter decomposition method, resulting in a MILP (Mixed Integer Linear Programming) problem that is easier to solve.

[0040] The heating network equipment in a microgrid includes solar collectors, bedrock energy storage, and electric heat pumps. The heat sources are solar collectors and bedrock energy storage, while the heat conversion equipment is the electric heat pump, which works together to meet the heat load demand. The heating network equipment is connected by pipes, and energy is transferred by the flow of liquid within the pipes. A circulating pump regulates the mass flow rate, which has both magnitude and direction. In this embodiment, it is assumed that the magnitude of the mass flow rate is variable but the direction is fixed; that is, the inlet and outlet pipes connecting each heating network device are fixed.

[0041] Suppose that heating network equipment a has i inlet pipes and j outlet pipes, then the power of heating network equipment a can be expressed as:

[0042]

[0043] in, Let be the thermal power of heating network equipment a at time t. Indicates endothermic flow, and vice versa; c represents the specific heat capacity of the liquid flowing in the pipe. a For the collection of liquid inflow pipes of heating network equipment a, Out a This is the collection of liquid outflow pipes for heating network equipment a. and Let represent the mass flow rate and temperature of the flushing liquid flowing into the heating network equipment a at time t. and Let represent the mass flow rate and temperature of pipe k that flows out of heating network equipment a at time t.

[0044] Based on the above formula for calculating the power of heating network equipment, the constraints on the heating network equipment in step S1 can be obtained as follows:

[0045]

[0046]

[0047]

[0048]

[0049] in, Let t be the output heat power of the electric heat pump, c be the specific heat capacity of the liquid flowing in the pipe, and In be the output heat power of the electric heat pump at time t. ehp For the liquid inflow pipe collection of the electric heat pump, out ehp This is the collection of liquid outflow pipes for the electric heat pump. Let be the mass flow rate of the liquid flowing into pipe i of the electric heat pump at time t. Let t be the temperature of the liquid flowing into pipe i of the heat pump. Let be the mass flow rate of pipe k exiting the heat pump at time t. Let K be the temperature of the pipe k flowing out of the heat pump at time t. Let In be the heat release power of bedrock energy storage at time t. bes For bedrock energy storage, liquid flows into the pipeline collection, out bes A collection of liquid outflow pipes for bedrock energy storage. Let be the mass flow rate of the liquid flowing into pipe i of the bedrock energy storage at time t during the bedrock energy storage heat release. Let t be the temperature of the liquid flowing into pipe i, which is used for bedrock energy storage, at time t during the heat release process. Let be the mass flow rate of the liquid flowing out of pipe k from the bedrock energy storage at time t during the bedrock energy release. Let t be the temperature of the liquid flowing out of the bedrock energy storage pipe k at time t during the bedrock energy storage heat release. Let be the thermal power of bedrock energy storage at time t. The mass flow rate of the liquid flowing into pipe i of the bedrock energy storage at time t during the bedrock energy storage heating process. The temperature of the liquid flowing into pipe i, which is used for heating bedrock energy storage at time t. The mass flow rate of the liquid flowing out of the bedrock energy storage in pipe k at time t during the bedrock energy storage heating process. The temperature of the liquid flowing out of the bedrock energy storage pipe k at time t during the bedrock energy storage heating process. Let In be the thermal power of the solar collector at time t. scs For the collection of liquid inflow pipes of solar collectors, out scs This is a collection of liquid outflow pipes for solar collectors. Let be the mass flow rate of the liquid flowing into pipe i of the solar collector at time t. Let t be the temperature of the liquid flowing into pipe i of the solar collector at time t. Let be the mass flow rate of pipe k exiting the solar collector at time t. Let t be the temperature of pipe k flowing out of the solar collector.

[0050] According to the above-mentioned constraint formulas for heating network equipment, all four constraints acting on the thermal power of the equipment involve the addition and subtraction of multiple bilinear terms, resulting in the microgrid optimization scheduling problem P being a mixed integer nonlinear problem.

[0051] Furthermore, in addition to the heating network equipment, the micro-energy network also includes grid equipment, which includes photovoltaics, wind turbines, electric energy storage, diesel generators, and the main power grid. The energy storage constraints in step S2 include energy storage charge and discharge state constraints, energy storage charge and heat release state constraints, upper and lower limit constraints of energy storage charge and discharge power, upper and lower limit constraints of energy storage charge and heat release power, electric energy storage capacity constraints, and bedrock energy storage capacity constraints.

[0052] Specifically, the energy storage charge / discharge state constraints are as follows:

[0053]

[0054] in, Let be a binary variable representing the charging state of the stored energy at time t. This indicates that the electrical energy storage is being charged at time t. This indicates that the stored energy has not been charged at time t. Let be a binary variable representing the discharge state of the stored energy at time t. This indicates the discharge of stored energy at time t. This indicates that the stored energy has not been discharged at time t.

[0055] The energy storage charge / discharge state constraint is as follows:

[0056]

[0057] in, For the thermal state of bedrock energy storage, a binary variable. This indicates that the bedrock is fully heated at time t. This indicates that the bedrock energy storage at time t is not yet fully heated. For the exothermic state of bedrock energy storage, (binary variable) This indicates that the bedrock energy storage releases heat at time t. express t The bedrock energy storage does not discharge heat at any given moment.

[0058] The upper and lower limits of the energy storage charging and discharging power are constrained as follows:

[0059]

[0060]

[0061] in, The minimum charging power for electrical energy storage. Let be a binary variable representing the charging state of the stored energy at time t. Let be the charging power of the electrical energy storage at time t. This is the maximum charging power for electrical energy storage. This is the minimum discharge power for electrical energy storage. Let be a binary variable representing the discharge state of the stored energy at time t. Let be the discharge power of the stored energy at time t. This represents the maximum discharge power of the electrical energy storage.

[0062] The upper and lower limits of the energy storage charging and discharging power are constrained as follows:

[0063]

[0064]

[0065] in, The minimum thermal power required for bedrock energy storage. For the thermal state of bedrock energy storage, a binary variable. Let be the thermal power of bedrock energy storage at time t. The maximum thermal power for bedrock energy storage This represents the minimum heat release capacity for bedrock energy storage. Let be the binary variable representing the exothermic state of the bedrock energy storage at time t. Let be the heat release power of the bedrock energy storage at time t. This represents the maximum heat release capacity of bedrock energy storage.

[0066] The energy storage capacity constraint is:

[0067]

[0068] in, This is the minimum state of charge for electrical energy storage. This represents the maximum state of charge of electrical energy storage. Let σ be the state of charge of the stored electrical energy at time t. eleThe self-discharge rate of electrical energy storage. Let be the charging power of the electrical energy storage at time t. For the charging efficiency of electrical energy storage, E ele For the capacity of electrical energy storage, Let be the discharge power of the stored energy at time t. Let t be the discharge efficiency of the electrical energy storage, where t > 0.

[0069] The bedrock energy storage capacity constraint is:

[0070]

[0071] in, This represents the minimum state of charge for bedrock energy storage. This represents the maximum state of charge for bedrock energy storage. Let σ be the state of charge of the bedrock energy storage at time t. bes The self-discharge rate of bedrock energy storage. Let be the thermal power of bedrock energy storage at time t. For the thermal efficiency of bedrock energy storage, E bes The capacity for bedrock energy storage, Let be the heat release power of the bedrock energy storage at time t. The heat release efficiency of bedrock energy storage.

[0072] Both electrical energy storage and bedrock energy storage follow energy storage constraints, with the only difference being the parameters.

[0073] In this embodiment, the coupling device is an electric heat pump, expressed by the following formula:

[0074]

[0075] in, and Let η represent the output thermal power and input electrical power of the heat pump at time t, respectively. ehp This indicates the conversion efficiency of the electric heat pump.

[0076] Furthermore, the system balance constraints in step S3 include power constraints, electrical balance constraints, and thermal balance constraints for the interconnection lines between the microgrid and the main grid.

[0077] Specifically, the power constraint of the interconnection line between the microgrid and the main grid is as follows:

[0078]

[0079] in, This represents the maximum power that can be exchanged between the microgrid and the main grid. Let t be the exchange power between the microgrid and the main power grid.

[0080] The electrical balance constraint is:

[0081]

[0082] in, Let be the discharge power of the stored energy at time t. Let be the charging power of the electrical energy storage at time t. Let t be the exchange power between the microgrid and the main grid. Let be the output power of the diesel generator at time t. Let t be the input electrical power of the heat pump. Let t be the electrical load of the microgrid. For the photovoltaic output at time t, The power output of the wind turbine at time t.

[0083] The thermal balance constraint is:

[0084]

[0085] in, Let t be the output heat power of the electric heat pump. Let t be the output thermal power of the solar collector. Let be the heat release power of the bedrock energy storage at time t. Let be the thermal power of bedrock energy storage at time t. Let t be the heat load of the microgrid at time t.

[0086] Furthermore, the objective function of the microgrid optimization scheduling model in step S4 is:

[0087]

[0088] Where C is the objective function value, T is the period of the optimization scheduling, and λe hp The operation and maintenance cost per unit output of the electric heat pump Let t be the input electrical power of the heat pump. Let t be the charging and discharging cost of the stored energy. Let be the cost of charging and releasing heat for bedrock energy storage at time t. Let be the power generation cost of the diesel generator at time t. Let β be the electricity purchase and sale cost between the microgrid and the main grid at time t, M be the number of carbon-emitting devices in the microgrid, and β be the cost of electricity purchase and sale between the microgrid and the main grid at time t. j Let j be the carbon emission coefficient of device j in the microgrid that emits carbon. Let j be the power of the carbon-emitting device j in the microgrid.

[0089] Because the product of mass flow rate and temperature in the heating network equipment constraints of step S1 is a bilinear term, the microgrid optimization scheduling model in step S4 becomes a mixed-integer nonlinear optimization model, which is difficult to solve. In this embodiment, the objective function and other constraints remain unchanged, and the Normalized Multiparameter Decomposition (NMDT) method is used to process the heating network equipment constraints containing bilinear terms.

[0090] For ease of representation, we will use x. i Represents bilinear terms mass flow rate x j Represents bilinear terms Temperature in Let w ij =x i x j , x i and x j The upper and lower limits. Introducing the auxiliary variable λ. j ∈[0,1],v ij =λ j x i 0-1 variable z jkl , Decimal digits k∈{0, 1, ..., 9}, l∈{p, p+1, ..., -1}, slack variable Δλ j Δv ij =x i ·Δλ j Then the bilinear term x i x j can be made by w ij x i x j The introduced variables represent:

[0091]

[0092] After processing using the standard multi-parameter decomposition method, all bilinear terms are replaced by newly introduced variables, transforming the original problem P into a non-convex MILP problem (PR), which can be solved directly. The optimal solution of PR provides a lower bound f0 for P. R The corresponding bilinear solution is (x R y R The parameter y in the bilinear term of the optimal solution obtained by fixing the PR problem. R Solve the original problem P to obtain the upper bound. Starting from the lowest accuracy level, PR and P are solved sequentially, and the upper and lower bounds are continuously updated until they are reduced to within the tolerance, at which point the algorithm terminates. The specific process is as follows:

[0093] 1. Transform the original problem P into problem PR using NMDT, initially setting p = -1, with an upper bound of f0. * =+∞.

[0094] 2. Solve problem PR to obtain the lower bound f0. R The solution (x) corresponding to the bilinear term R y R ).

[0095] 3. Add the constraint y = y to problem P. R This yields the NLP (non-linear programming problems) of P, with x R Starting from this point, a local solver is used to solve the NLP problem, updating the upper bound f0 of the objective function. * and (x) * y * ), where y represents the 0-1 variables in the entire scheduling problem.

[0096] 4. If (f0) * -f0 R ) / f0 R ≤ε, then (x * y * If the solution is globally optimal, the algorithm terminates, and ε is the tolerance. Otherwise, set p = p-1 and return to process 2, repeating processes 2-4.

[0097] There are two types of variables in the entire scheduling problem: continuous variables, denoted by the set x, which includes all continuous variables in the entire microgrid scheduling problem, where x... i and x j yes and Other variables that do not require processing are also in set x. Another type is 0-1 variables, represented by set y. R It is a set, and is a lower bound of set x, y R It is also a set, and is a lower bound of set y. The global optimal solution (x) * y * () still refers to the set, x * For the optimal solution of the continuous variable set x, y * This is the optimal solution for the set of continuous variables y. A single solution yields all x and y variables, not just the nonlinear terms. Each solution provides all variables, including nonlinear terms and other variables that do not require processing, such as the output of various power grid devices.

[0098] In practical microgrid engineering applications, the heating network connects equipment via pipelines. In actual engineering, the controlled objects of the heating network in a microgrid are the mass flow rate and temperature of the pipelines, rather than directly controlling the thermal power of the heating network equipment. The output of the heating network equipment is adjusted by regulating the mass flow rate and temperature of the liquid medium within the connected pipelines. This transformation of the controlled variables for the heating network, considering practical problems, leads to model nonlinearity. Therefore, the existence of bilinear terms in the model makes it unsolvable by the solver. This invention can handle the nonlinear terms, linearizing the model and enabling the solver to solve it.

[0099] To better understand the solution of this invention, the derivation process of the standard multi-parameter depolymerization method used in this invention is explained in detail below.

[0100] Multi-parameter decomposition is a technique for generating mixed-integer linear relaxations for bilinear problems. It uses cardinality discretization to segment one of the two variables in the bilinear term to a specified precision level p, thus obtaining mixed-integer linear (MILP) relaxation. Canonical multi-parameter decomposition (NMDT) differs from multi-parameter decomposition by discretizing variables to all possible values. Instead, it introduces new variables to discretize the intervals within the upper and lower bounds of the variables to [0, 1]. Its advantage is that even if the ranges of each discrete variable are different, the number of segments remains consistent. Simultaneously, it allows the accuracy level parameter p to be directly related to the number of partitions N in the piecewise McCormick method (PMCR), thus flexibly utilizing both relaxation methods.

[0101] The original problem P is represented as:

[0102] min f0(x, y)

[0103]

[0104] Here, x and y are a vector of non-negative continuous variables and a 0-1 variable, respectively. x and y are the variables to be solved, and x is the set of all continuous variables in the entire scheduling problem, including x... i and x j ,Right now and In addition to other variables that do not need to be processed, such as the output of each device in the power grid, y is a 0-1 variable in the scheduling problem, such as the charging and discharging status of energy storage, m is the number of elements in the set x, and r is the number of 0-1 variables in the scheduling problem.

[0105] BL is a set indexed by (i, j) used to represent bilinear terms x. i x j When i = j, it represents a quadratic term; x L and x U They represent x respectively i and xj The lower and upper bounds of f; Q represents all functions f q The set of objective function f0 and constraints; a ijq and d q B is a scalar; q and C q Let f0 be a matrix. In this embodiment, the objective function f0 is the objective function of the microgrid optimization scheduling model, and the constraints are the constraints of the microgrid optimization scheduling model.

[0106] Use w ij =x i x j To represent a non-convex bilinear term, let the variable x j Discretize to a specified precision level p, and add slack variables to achieve the continuous domain. Introduce an auxiliary variable λ. j ∈[0,1], and used and The linear combination of x represents j Introduce 0-1 variables z jkl And a suitable decimal number k∈{0,1,…,9} represents λ j .

[0107]

[0108]

[0109]

[0110] in, It is the set of negative integers.

[0111] formula Multiply by x i The following formula is obtained, and v is used. ij Alternate λ j x i .

[0112]

[0113]

[0114] Introducing new continuous variables With exact linearization, then

[0115]

[0116]

[0117]

[0118] The original problem P is equivalent to P':

[0119] min f′0(x, y)

[0120]

[0121] f′0=min x，y f′0(x,y)=min x，y f0(x, y) = f0;

[0122] Since it is impossible to compute the infinite sum of all negative integers, we use l∈{p, p+1, ..., -1} instead. The value of the negative integer p is chosen according to the needs of the problem. A slack variable Δλ is introduced. j Reduce the interval between discrete points, so that λ j It can take all possible values. It is the set of real numbers.

[0123]

[0124]

[0125]

[0126] The newly emerging bilinear term x i ·Δλ j Relaxation was performed using the McCormick method, and Δv was used as the metric. ij Replace x i ·Δλ j :

[0127]

[0128]

[0129] Therefore, the optimization problem P' becomes the problem PR:

[0130]

[0131]

[0132] Problem PR may not satisfy constraint w ij =x i x j The solution to PR is the lower bound of problem P.

[0133] This invention considers the actual operation mode and system structure of the heat subsystem of the micro-energy network. The heat subsystem uses a flow-mass regulation method to simultaneously regulate the mass flow rate and liquid temperature of the pipeline, linking the power of the equipment in the heat network with the mass flow rate and temperature of the inlet and outlet pipelines connecting the equipment. This provides greater flexibility, enables the acquisition of the optimal scheduling scheme, and conforms to engineering practice.

[0134] The canonical multi-parameter decomposition method avoids the shortcomings of traditional linearization methods in terms of solution quality and computational performance. Furthermore, compared to other multi-parameter decomposition methods, it maintains a consistent number of segments for discrete variables with different value ranges. This invention utilizes the canonical multi-parameter decomposition method to transform the MINLP problem into a MILP-compatible problem. The canonical multi-parameter decomposition method has no specific requirements for the MINLP problem it solves, and is particularly suitable for MINLP problems caused by bilinear terms. It also avoids complex branching and boundary tightening strategies. It ensures a consistent number of segments even when the value ranges of each discrete variable are different. Simultaneously, it allows the accuracy level parameter p to be directly related to the number of partitions N in the piecewise McCormick method (PMCR), thus flexibly utilizing both relaxation methods.

[0135] like Figure 3 As shown, the micro-energy network optimization scheduling system considering the mass flow regulation of the heating network of the present invention includes: a heating network equipment constraint determination unit 1, an energy storage constraint determination unit 2, a system balance constraint determination unit 3, an optimization scheduling model establishment unit 4, and a solution unit 5.

[0136] The heat network equipment constraint determination unit 1 is connected to each heat network device. The heat network equipment constraint determination unit 1 is used to determine the heat network equipment constraints based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heat network device and the thermal power of each heat network device.

[0137] The energy storage constraint determination unit 2 is used to determine energy storage constraints based on the state of charge, charge and discharge power, charge and discharge efficiency, minimum charge and discharge power, maximum charge and discharge power, charge and discharge state, charge and heat dissipation state, charge and heat dissipation power, charge and heat dissipation efficiency, minimum charge and heat dissipation power, maximum charge and heat dissipation power, energy storage capacity, minimum state of charge, and maximum state of charge of the energy stored in the micro energy grid.

[0138] The system balance constraint determination unit 3 is connected to each heating network device and each power grid device. The system balance constraint determination unit 3 is used to determine the system balance constraint based on the maximum power allowed to be exchanged between the micro energy network and the large power grid interconnection line, the output of each power grid device, the thermal power, electrical load and thermal load of each heating network device.

[0139] The optimized scheduling model establishment unit 4 is connected to the heating network equipment constraint determination unit 1, the energy storage constraint determination unit 2, and the system balance constraint determination unit 3, respectively. The optimized scheduling model establishment unit 4 is used to establish an optimized scheduling model for the micro energy network based on the heating network equipment constraints, the energy storage constraints, and the system balance constraints, with the goal of minimizing the operation and maintenance cost and carbon emissions of the micro energy network.

[0140] The solution unit 5 is connected to the optimization scheduling model establishment unit 4. The solution unit 5 is used to perform de-aggregation processing on the heating network equipment constraints in the micro-energy network optimization scheduling model using the standard multi-parameter de-aggregation method, and solve the micro-energy network optimization scheduling model to determine the optimal output of each power grid equipment and the optimal mass flow rate and optimal temperature of each pipeline of each heating network equipment.

[0141] Compared with the prior art, the microgrid optimization scheduling system of the present invention, which considers the regulation of the mass flow rate of the heating network, has the same beneficial effects as the microgrid optimization scheduling method that considers the regulation of the mass flow rate of the heating network described above, and will not be repeated here.

[0142] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0143] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing the scheduling of a microgrid considering the mass flow rate regulation of a heating network, wherein the microgrid includes multiple heating network hot standby devices and multiple grid equipment, characterized in that, The microgrid optimization scheduling method considering the mass flow rate regulation of the heating network includes: Based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heating network device, and the thermal power of each heating network device, the constraints of the heating network device are determined; multiple heating network devices include solar collectors, bedrock energy storage, and electric heat pumps; multiple power grid devices include photovoltaics, wind turbines, electric energy storage, and diesel generators; The constraints on the heating network equipment are as follows: ； ； ； ； in, for t The output heat power of the electric heat pump at all times c This refers to the specific heat capacity of the liquid flowing inside the pipe. In ehp For the liquid inflow pipe collection of the electric heat pump, out ehp This is the collection of liquid outflow pipes for the electric heat pump. for t The pipes constantly flow into the heat pump i Mass flow rate of the liquid in the middle for t The pipes constantly flow into the heat pump i The temperature of the liquid in the middle, for t The pipes of the electric heat pump are constantly flowing out k mass flow rate for t The pipes of the electric heat pump are constantly flowing out k temperature, for t The heat release capacity of bedrock energy storage at any given time. In bes The liquid that is stored in the bedrock flows into the pipeline and collects. out bes A collection of liquid outflow pipes for bedrock energy storage. When the bedrock stores heat t Pipelines constantly flowing into bedrock energy storage i Mass flow rate of the liquid in the middle When the bedrock stores heat t Pipelines constantly flowing into bedrock energy storage i The temperature of the liquid in the middle, When the bedrock stores heat t Pipelines that constantly release energy from bedrock storage k Mass flow rate of the liquid in the middle When the bedrock stores heat t Pipelines that constantly release energy from bedrock storage k The temperature of the liquid in the middle, for t The thermal charging capacity of bedrock energy storage at any given time. When charging bedrock energy storage t Pipelines constantly flowing into bedrock energy storage i Mass flow rate of the liquid in the middle When charging bedrock energy storage t Pipelines constantly flowing into bedrock energy storage i The temperature of the liquid in the middle, When charging bedrock energy storage t Pipelines that constantly release energy from bedrock storage k Mass flow rate of the liquid in the middle When charging bedrock energy storage t Pipelines that constantly release energy from bedrock storage k The temperature of the liquid in the middle, for t The thermal power of the solar collector at any given time. In scs The liquid inflow pipes of the solar collector are collected. out scs This is a collection of liquid outflow pipes for solar collectors. for t Pipes constantly flowing into the solar collector i Mass flow rate of the liquid in the middle for t Pipes constantly flowing into the solar collector i The temperature of the liquid in the middle, for t Pipes that constantly flow out of the solar collector k mass flow rate for t Pipes that constantly flow out of the solar collector k Temperature; Based on the state of charge, charging and discharging power, charging and discharging efficiency, minimum charging and discharging power, maximum charging and discharging power, state of charge and discharge, state of charge and heat release, power of charge and heat release, efficiency of charge and heat release, minimum power of charge and heat release, maximum power of charge and heat release, energy storage capacity, minimum state of charge and maximum state of charge of energy storage in the microgrid, determine the energy storage constraints. The system balance constraints are determined based on the maximum power that can be exchanged between the microgrid and the main grid, the output of each grid device, the thermal power, electrical load and thermal load of each heating network device; Based on the constraints of the heating network equipment, the energy storage constraints, and the system balance constraints, an optimal scheduling model for the microgrid is established with the goal of minimizing the operation and maintenance costs and carbon emissions of the microgrid. The objective function of the microgrid optimization scheduling model is: ； in, C The objective function value, T To optimize the scheduling cycle, The operation and maintenance cost per unit output of the electric heat pump for t The input electrical power of the electric heat pump at all times. for t The charging and discharging cost of instantaneous energy storage for t The cost of charging and releasing heat for bedrock energy storage at any given time. for t The power generation cost of a diesel generator at any given time. for t The electricity purchase and sale costs between microgrids and large power grids. M The number of carbon-emitting devices in a microgrid. For devices with carbon emissions in microgrids j carbon emission coefficient, For devices with carbon emissions in microgrids j The power; The constraints of the heating network equipment in the microgrid optimization scheduling model are de-aggregated using a standardized multi-parameter de-aggregation method, and the microgrid optimization scheduling model is solved to determine the optimal output of each grid equipment and the optimal mass flow rate and optimal temperature of each pipeline of each heating network equipment.

2. The microgrid optimization scheduling method considering heat network mass flow rate regulation according to claim 1, characterized in that, The energy storage constraints include energy storage charge and discharge state constraints, energy storage charge and heat release state constraints, energy storage charge and discharge power upper and lower limit constraints, energy storage charge and heat release power upper and lower limit constraints, electrical energy storage capacity constraints, and bedrock energy storage capacity constraints.

3. The microgrid optimization scheduling method considering heat network mass flow rate regulation according to claim 2, characterized in that, The energy storage charge / discharge state constraint is as follows: ； in, for t A binary variable representing the charging state of the stored energy at any given time. express t Energy storage and charging at any time express t The energy storage device is not being charged at any given time. for t A binary variable representing the discharge state of the stored electrical energy at any given time. express t Energy storage and discharge at all times. express t The stored energy was not discharged at any given time. The energy storage charge / discharge state constraint is as follows: ； in, For the thermal state of bedrock energy storage, a binary variable. express t Bedrock energy storage and heat replenishment at all times express t At any given time, the bedrock energy storage was not fully heated. For the exothermic state of bedrock energy storage, (binary variable) express t Bedrock energy storage and heat release at all times express t The bedrock energy storage does not discharge heat at any given moment.

4. The microgrid optimization scheduling method considering heat network mass flow rate regulation according to claim 2, characterized in that, The upper and lower limits of the energy storage charging and discharging power are constrained as follows: ； ； in, The minimum charging power for electrical energy storage. for t A binary variable representing the charging state of the stored energy at any given time. for t The charging power of the energy storage device at all times. This is the maximum charging power for electrical energy storage. This is the minimum discharge power for electrical energy storage. for t A binary variable representing the discharge state of the stored electrical energy at any given time. for t The discharge power of the electrical energy storage at any given time. This is the maximum discharge power of the electrical energy storage; The upper and lower limits of the energy storage charging and discharging power are constrained as follows: ； ； in, The minimum thermal power required for bedrock energy storage. For the thermal state of bedrock energy storage, a binary variable. for t The thermal charging capacity of bedrock energy storage at any given time. The maximum thermal power for bedrock energy storage This represents the minimum heat release capacity for bedrock energy storage. for t The binary variable representing the exothermic state of bedrock energy storage at any given time. for t The heat release capacity of bedrock energy storage at any given time. This represents the maximum heat release capacity of bedrock energy storage.

5. The microgrid optimization scheduling method considering heat network mass flow rate regulation according to claim 2, characterized in that, The energy storage capacity constraint is: ； in, This is the minimum state of charge for electrical energy storage. This represents the maximum state of charge of electrical energy storage. for t The state of charge of electrical energy storage at any given time. The self-discharge rate of electrical energy storage. for t The charging power of the energy storage device at all times. The charging efficiency of electrical energy storage, E ele For the capacity of electrical energy storage, for t The discharge power of the electrical energy storage at any given time. The discharge efficiency of electrical energy storage. t >0; The bedrock energy storage capacity constraint is: ； in, This represents the minimum state of charge for bedrock energy storage. This represents the maximum state of charge for bedrock energy storage. for t The state of charge of bedrock energy storage at any given time. The self-discharge rate of bedrock energy storage. for t The thermal charging capacity of bedrock energy storage at any given time. For bedrock energy storage, the heat charging efficiency E bes The capacity for bedrock energy storage, for t The heat release capacity of bedrock energy storage at any given time. The heat release efficiency of bedrock energy storage.

6. The microgrid optimization scheduling method considering heat network mass flow rate regulation according to claim 1, characterized in that, The system balance constraints include power constraints, electrical balance constraints, and thermal balance constraints of the interconnection lines between the microgrid and the main power grid. The power constraint of the interconnection line between the microgrid and the main power grid is: ； in, This represents the maximum power that can be exchanged between the microgrid and the main grid. for t The exchange power of the microgrid and the main power grid interconnection line at any given time; The electrical balance constraint is: ； in, for t The discharge power of the electrical energy storage at any given time. for t The charging power of the energy storage device at all times. for t The exchange capacity of the microgrid and the main grid interconnection line at any time. for t The output power of the diesel generator at any given time. for t The input electrical power of the electric heat pump at all times. for t The electrical load of the microgrid at any time for t Solar power output at all times for t The wind turbine output at all times; The thermal balance constraint is: ； in, for t The output heat power of the electric heat pump at all times Let t be the output thermal power of the solar collector. for t The heat release capacity of bedrock energy storage at any given time. for t The thermal charging capacity of bedrock energy storage at any given time. for t Heat load of the micro-energy grid at any time.

7. A microgrid optimization scheduling system considering heat network mass flow regulation, wherein the microgrid includes multiple heat network hot backups and multiple grid equipment, characterized in that, The microgrid optimization scheduling system that considers the mass flow rate regulation of the heating network includes: A heat network equipment constraint determination unit is connected to each heat network device and is used to determine the constraints of the heat network device based on the specific heat capacity, mass flow rate, temperature of the liquid in each pipe of each heat network device and the thermal power of each heat network device; multiple heat network devices include solar collectors, bedrock energy storage and electric heat pumps; multiple power grid devices include photovoltaics, wind turbines, electric energy storage and diesel generators; The constraints on the heating network equipment are as follows: ； ； ； ； in, for t The output heat power of the electric heat pump at all times c This refers to the specific heat capacity of the liquid flowing inside the pipe. In ehp For the liquid inflow pipe collection of the electric heat pump, out ehp This is the collection of liquid outflow pipes for the electric heat pump. for t The pipes constantly flow into the heat pump i Mass flow rate of the liquid in the middle for t The pipes constantly flow into the heat pump i The temperature of the liquid in the middle, for t The pipes of the electric heat pump are constantly flowing out k mass flow rate for t The pipes of the electric heat pump are constantly flowing out k temperature, for t The heat release capacity of bedrock energy storage at any given time. In bes The liquid that is stored in the bedrock flows into the pipeline and collects. out bes A collection of liquid outflow pipes for bedrock energy storage. When the bedrock stores heat t Pipelines constantly flowing into bedrock energy storage i Mass flow rate of the liquid in the middle When the bedrock stores heat t Pipelines constantly flowing into bedrock energy storage i The temperature of the liquid in the middle, When the bedrock stores heat t Pipelines that constantly release energy from bedrock storage k Mass flow rate of the liquid in the middle When the bedrock stores heat t Pipelines that constantly release energy from bedrock storage k The temperature of the liquid in the middle, for t The thermal charging capacity of bedrock energy storage at any given time. When charging bedrock energy storage t Pipelines constantly flowing into bedrock energy storage i Mass flow rate of the liquid in the middle When charging bedrock energy storage t Pipelines constantly flowing into bedrock energy storage i The temperature of the liquid in the middle, When charging bedrock energy storage t Pipelines that constantly release energy from bedrock storage k Mass flow rate of the liquid in the middle When charging bedrock energy storage t Pipelines that constantly release energy from bedrock storage k The temperature of the liquid in the middle, for t The thermal power of the solar collector at any given time. In scs The liquid inflow pipes of the solar collector are collected. out scs This is a collection of liquid outflow pipes for solar collectors. for t Pipes constantly flowing into the solar collector i Mass flow rate of the liquid in the middle for t Pipes constantly flowing into the solar collector i The temperature of the liquid in the middle, for t Pipes that constantly flow out of the solar collector k mass flow rate for t Pipes that constantly flow out of the solar collector k Temperature; The energy storage constraint determination unit is used to determine the energy storage constraints based on the state of charge, charging and discharging power, charging and discharging efficiency, minimum charging and discharging power, maximum charging and discharging power, state of charge and discharge, state of charge and heat discharge, power of charge and heat discharge, efficiency of charge and heat discharge, minimum power of charge and heat discharge, maximum power of charge and heat discharge, energy storage capacity, minimum state of charge and maximum state of charge of the energy stored in the micro energy grid. The system balance constraint determination unit is connected to each heating network device and each power grid device. It is used to determine the system balance constraints based on the maximum power that can be exchanged between the micro energy network and the large power grid interconnection line, the output of each power grid device, the thermal power, electrical load and thermal load of each heating network device. The optimized scheduling model establishment unit is connected to the heating network equipment constraint determination unit, the energy storage constraint determination unit, and the system balance constraint determination unit, respectively. It is used to establish an optimized scheduling model for the micro energy network based on the heating network equipment constraints, the energy storage constraints, and the system balance constraints, with the goal of minimizing the operation and maintenance cost and carbon emissions of the micro energy network. The objective function of the microgrid optimization scheduling model is: ； in, C The objective function value, T To optimize the scheduling cycle, The operation and maintenance cost per unit output of the electric heat pump for t The input electrical power of the electric heat pump at all times. for t The charging and discharging cost of instantaneous energy storage for t The cost of charging and releasing heat for bedrock energy storage at any given time. for t The power generation cost of a diesel generator at any given time. for t The electricity purchase and sale costs between microgrids and large power grids. M The number of carbon-emitting devices in a microgrid. For devices with carbon emissions in microgrids j carbon emission coefficient, For devices with carbon emissions in microgrids j The power; The solution unit, connected to the optimization scheduling model establishment unit, is used to perform de-aggregation processing on the heating network equipment constraints in the microgrid optimization scheduling model using a standardized multi-parameter de-aggregation method, and to solve the microgrid optimization scheduling model to determine the optimal output of each grid equipment and the optimal mass flow rate and optimal temperature of each pipeline of each heating network equipment.

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