A multi-stage heat supply system and layered collaborative scheduling method of power system

Abstract

The application discloses a kind of multistage heat supply system and electric power system layered collaborative scheduling method, it is related to electric heat integrated energy system scheduling field. Including the following steps: first, the collaborative scheduling model CDEPS of power grid, primary heat network and secondary heat network is established: the operation mode of primary heat network and secondary heat network is considered comprehensively;Second, the operation cost of three layers of power grid, primary heat network, secondary heat network is summed to establish objective function, and using transmission heat approximation, secondary heat network model remodeling method, form optimization problem;Finally, the solving method of optimization problem is designed, and the optimization problem is divided into inner and outer layer iteration layered solution, outer layer iteration solves EPS and DHS problem, and inner layer iteration solves primary heat network PHS and secondary heat network SHS problem.The application improves the economy and heating quality of system solves multistage cooperation structure, can protect the privacy problem between multiple subjects, also comply with the master-slave relationship of power grid-primary heat network-secondary heat network in actual system, has practical significance.

Description

Technical Field

[0001] This invention relates to the field of integrated electric and thermal energy system scheduling, specifically a hierarchical collaborative scheduling method for multi-level heating systems and power systems. Background Technology

[0002] Electric-thermal coupling systems (EDGs) are a typical integrated energy system. The flexibility of district heating systems (DHS) can provide additional regulation capabilities for electric power systems (EPS), thereby promoting the absorption of wind power. In northern regions, county towns are actively developing combined heat and power (CHP) clean heating methods, and achieving synergy between electricity and heat has become a significant practical requirement under the "dual carbon" context. However, there is no coordination between the power grid, primary heating network, and secondary heating network. The fragmented scheduling between different entities leads to insufficient utilization of flexible resources in the secondary heating network and causes additional economic scheduling costs and severe wind curtailment in EHPs. Traditional fragmented scheduling also results in excessive heat output from CHP units, causing uneven heating network temperatures.

[0003] The power grid, primary heating network, and secondary heating network belong to different entities and operate independently. Current scheduling methods fail to coordinate these three systems, treating the secondary heating network as a load on the primary network and ignoring its topology and the significant flexible resources distributed within it. This leads to heavy loads on CHP units, low wind power utilization, and uneven temperature distribution. In other words, there is no unified scheduling and management system for the power grid, primary heating network, and secondary heating network. While traditional distributed algorithms address privacy and data protection issues related to boundary information exchange, they neglect the master-slave characteristics of the three-tiered system. Therefore, they are unsuitable for coordinated scheduling models involving the power grid, primary heating network, and secondary heating network. Furthermore, previously constructed optimized scheduling models contain nonlinear variables (logarithmic mean temperature difference) and bilinear terms (the product of mass flow rate and temperature in the thermal system), making the models unsolvable. Therefore, a refined secondary heating network model is needed to enable coordinated scheduling of the power grid, primary heating network, and secondary heating network, while considering the master-slave characteristics to achieve more refined hierarchical scheduling. Summary of the Invention

[0004] To address the shortcomings of existing scheduling methods that fail to coordinate the power grid, primary heating network, and secondary heating network, resulting in heavy loads on CHP units, low wind power utilization, uneven temperature distribution, and neglect of the master-slave characteristics of the secondary heating network, as well as the unsolvability of previously established optimized scheduling models, this invention provides a hierarchical collaborative scheduling method for multi-level heating systems and power systems.

[0005] This invention is achieved through the following technical solution: a hierarchical coordinated scheduling method for multi-level heating systems and power systems, comprising the following steps:

[0006] S1: First, a coordinated dispatch model of integrated electric and primary-secondary heating systems (CDEPS) was established, comprehensively considering the operation modes of the primary and secondary heating networks; that is, considering the topology of the primary and secondary heating networks, the primary heating network adopts qualitative regulation, and the secondary heating network adopts both qualitative and quantitative regulation methods, establishing constraints that conform to reality; then, the coordinated cooperation of the power grid, primary heating network, and secondary heating network is realized; specifically as follows:

[0007] The three-tiered dispatch model structure includes the power grid EPS, the primary heating network PHS, and the secondary heating network SHS. The first tier consists of various generator sets connected to the power grid, including CHP units, thermal power units, and wind power units. The CHP units act as coupling devices between the first two tiers (EPS and PHS), generating both heat and electricity. The second tier is the primary heating network PHS, which uses a radial pipe network as the main urban heat pipeline. The third tier involves PHS transferring heat to the third-tier heat pipelines via HES (Heat Exchange Station). Distributed boilers generate heat and transfer it to the heat pipelines, merging it with the heat from PHS. Finally, the heat is distributed to the heating load from the SHS. Specifically:

[0008] S1-1: Establishing a primary heating network model:

[0009] The objective function of PHS is to reduce the operating costs of cogeneration units and improve their economic efficiency.

[0010] (1)

[0011] This is the operating cost of a heating network. Let the cost function of the i-th generating unit at time t be denoted as , and its constraints include:

[0012] ① CHP Unit Constraints: The CHP unit is modeled as a convex polygon, and its power and heat output are within the following constraints:

[0013] (2)

[0014] in, These are the electrical and thermal outputs of the i-th CHP unit at time t, respectively. Let be the coefficient variable of the k-th extreme point of the convex polygon of the i-th unit at time t. Let i be the k-th extreme point in the feasible region of the convex polygon representing the electrical and thermal outputs of CHP unit i, respectively. Its cost is expressed as follows:

[0015] (3);

[0016] in q is the cost operating coefficient of CHP unit i, where q is 0, 1, 2, 3, 4, or 5. These are the upper and lower limits of the electrical output of the CHP unit, respectively. These are the upper and lower limits of the thermal output of the CHP unit, respectively, and c is the specific heat capacity of water. It is the mass flow rate of node kc connected to the CHP unit. These are the temperatures at time t connected to node kc of the CHP unit in PHS and SHS, respectively. These are the upper and lower limits of the node temperature at node kc of the CHP unit, respectively.

[0017] ② Establish a primary heat network model:

[0018] In the mass regulation and control strategy, hydraulic constraints are not considered; the mass flow rates from different pipelines converge at the same node before reaching the outlet, and the established primary heat network model is as follows:

[0019] (4);

[0020] in, These represent the mass flow rate and temperature of the primary water supply pipeline j at the start and end times of time t. These are the mass flow rate and temperature of the primary network return water pipe j at the beginning and end of time t, respectively. These are the supply and return water temperatures at primary network node m, respectively. These are the mass flow rates of the primary water supply and return water pipelines, respectively. These are the beginning and end of the pipe at node m in the primary network, respectively.

[0021] As heat is lost to the external environment, the temperature of the pipe will gradually decrease; in addition, there is a transmission delay during the pipe transmission process, and the parameters in formula (4) are as follows:

[0022] (5);

[0023] (6);

[0024] (7);

[0025] symbol Numbers should be rounded to the nearest integer; and and It is an intermediate variable of time delay. It is the ambient temperature; The heat transfer time of heating pipe j in the heating and return water network; These are the density of water, the area of ​​the pipe, its length, and a constant;

[0026] S1-2: Establishing a secondary heat network model:

[0027] Compared to PHS, SHS has a water pump. In addition to the cost of the distributed boiler, the water pump also incurs costs, which are as follows:

[0028] (8);

[0029] It is the operating cost of the secondary heating network. and Let z represent the cost functions of the z-th boiler unit and the x-th water pump at time t, respectively, and their constraints include:

[0030] ① Constraints of distributed boilers:

[0031] The heat generated by the distributed boiler is transferred to the SHS (Supply Heat Exchanger) via a heat exchange station. The distributed boiler plays an important auxiliary combustion support role in the SHS, subject to the following constraints:

[0032] (9);

[0033] in, It is the thermal output of the distributed boiler z at time t. Let z be the fuel gas consumption of the distributed boiler z at time t; The conversion efficiency of the distributed boiler z. Let z be the cost coefficient for the distributed boiler; in addition, the power consumption of the water pump has an upper and lower limit, specifically:

[0034] (10);

[0035] in, It is the electrical output of pump x at time t. These are the maximum and minimum power of water pump x. Let x be the cost coefficient of water pump. It is the flow rate on the primary side of the heat exchange station. It refers to the pressure head at node kh, which connects to the heat exchange station in the water supply and return network. It is the pressure head at node kh, which connects to the heat exchange station in the water supply and return network;

[0036] ②Constraints of heat exchange station:

[0037] The amount of heat transferred from the primary side to the secondary side is determined by the difference in logarithmic mean temperature, as shown in the following formula:

[0038] (11);

[0039] in, It refers to the heat exchange capacity of the heat exchange station. These are the heat transfer coefficient and heat transfer area of ​​the heat exchange station; HES refers to the inlet and outlet temperatures of the primary mass flow rate. These are the inlet and outlet temperatures of the mass flow rate during the secondary side phase;

[0040] After heat transfer, the temperature in the main pipe decreases, as shown in the following formula:

[0041] (12);

[0042] In the secondary piping of the HES, the heat output from the distributed boiler is combined with the heat transferred from the PHS, as shown in the following formula:

[0043] (13);

[0044] (14);

[0045] It is the flow rate on the primary side of the heat exchange station. These are the maximum and minimum heat exchange powers of HESg;

[0046] ③ Establish a secondary heat network model:

[0047] Unlike the regulation mode in PHS, SHS requires consideration of hydraulic constraints, with zero incompressible mass flow rate at each node. The secondary thermal network model is as follows:

[0048] (15);

[0049] in, These are the quality flow rates of the secondary water supply and return water, respectively. These are the load and the mass flow rate of the heat exchange station, respectively. These are the beginning and end points of the pipe at node n in the secondary network, respectively, and kl is the node connected to the load; the hydraulic constraints are as follows:

[0050] (16);

[0051] These are the maximum and minimum mass flow rate limits for the heating pipe j2; the Darcy–Weisbach equation indicates that the pressure loss caused by friction in the pipe is related to the square of the mass flow rate:

[0052] (17);

[0053] in, It is the pressure loss coefficient of the mass flow in pipe j2. These are the initial pressure heads at node k1, which connects to the pipeline in the supply and return water network; These are the end-stage pressure heads at node k2 connected to the pipeline in the supply and return water networks; to maintain the HES mass flow rate, the load pressure must be greater than the specified level:

[0054] (18);

[0055] in, It is the minimum heating pressure of HES g;

[0056] SHS has thermal constraints, including temperature mixing constraints and heat loss constraints, as shown in the following formula:

[0057] (19);

[0058] (20);

[0059] (twenty one);

[0060] in, These represent the mass flow rate and temperature of the secondary water supply pipeline j2 at the beginning and end of time t, respectively. These are the mass flow rate and temperature of the secondary network return water pipe j2 at the beginning and end of time t, respectively. It is the temperature of node n at time t in the SHS; in addition, since the pipes in the SHS are shorter than those in the PHS, the time delay of the mass flow rate is not considered.

[0061] In SHS (Supply, Heat, and Water) systems, buildings are considered heat loads. To maintain comfortable indoor temperatures, the temperature of the mass flow rate is limited, as shown in the following formula:

[0062] (twenty two);

[0063] in, It is the heat of the heat load. These are the upper and lower limits of the temperature at the load node kl;

[0064] S1-3: Establishing the power grid model:

[0065] EPS is constructed using a DC power flow model, as shown below:

[0066] (twenty three);

[0067] The power output of CHP units, thermal power units, and wind power units meets the load demand; among them... These represent the output electrical power of CHP unit i, thermal power unit v, and wind power unit e at time t, respectively. It is the electrical load at bus l at time t;

[0068] (twenty four);

[0069] The power flow through a transmission line must not exceed its maximum transmission power; among which, It is the power transmission distribution factor connecting bus l to line y. This is the maximum power flow transmission capacity limit of line y;

[0070] (25);

[0071] (26);

[0072] The power output of thermal power units should be within a certain range, while the power output of wind power units should be less than their predicted available power output. These are the maximum and minimum power output limits for thermal power units. This refers to the predicted output power of the wind turbine generator set.

[0073] (27);

[0074] (28);

[0075] (29);

[0076] (30);

[0077] Thermal power units' ramp-up and rotational reserve are also within a certain range, among which It refers to the uphill and downhill gradient of thermal power units. It represents the uplink and downlink rotating reserve capacity of the thermal power unit at time t. It is the total uplink and downlink spinning reserve capacity of the system at time t;

[0078] The operating cost of the power system is:

[0079] (31);

[0080] (32);

[0081] (33);

[0082] in, For the operating costs of thermal power units, For the operating costs of thermal power units, when the actual wind power output deviates from the predicted available power, it will result in penalty costs. It is the cost operating coefficient of thermal power units. It is the penalty coefficient for wind turbine units.

[0083] S2: Based on the steps established in S1, the operating costs of the power grid, primary heating network, and secondary heating network are summed to establish the objective function. Then, using the heat transfer approximation, secondary heating network model reshaping, and bilinear term convex relaxation, the non-convex terms in the model are made convex, forming an optimization problem; specifically as follows:

[0084] The goal of the CDEPS model is to minimize total operating costs, including generator sets in EPS, PHS, and SHS. The minimum total operating cost is as follows:

[0085] (34);

[0086] The proposed CDEPS model is constrained by EPS, PHS, and SHS, including formulas (2)~(7) and (9)~(30); however, the CDEPS model is non-convex and nonlinear: due to the logarithmic mean temperature difference, there is a nonlinear term in formula (11), and the presence of multiple bilinear and exponential terms in formulas (13) and (19)~(21) makes the SHS model non-convex. In order to reduce the computational complexity of the CDEPS model, the CDEPS model is made convex and then solved:

[0087] S2-1: Approximate principle of heat transfer:

[0088] The logarithmic mean temperature difference is approximately equal to the arithmetic mean temperature difference, and the approximation principle is as follows:

[0089] (35);

[0090] Therefore, the heat transfer equation in formula (11) is approximately a linear function:

[0091] (36);

[0092] S2-2: Reshaping the Secondary Heating Network Model

[0093] To address the bilinear term, the secondary heating network is reformulated as a linearized model that depends only on the proposed auxiliary variables, which are represented as follows:

[0094] (37);

[0095] Constraints (12) and (18)~(20) are equivalent to:

[0096] (38);

[0097] (39);

[0098] in, These are auxiliary variables for pipeline j2, distributed boiler z, HES, and heat loss j2, respectively.

[0099] The variables related to the upper and lower boundary conditions for temperature and mass flow rate should be within the following ranges:

[0100] (40);

[0101] The exponential function in constraint (38) is approximated by a first-order Taylor expansion:

[0102] (41);

[0103] The SHS model is restated using constraint formulas (2)~(7), (9)~(12), (14)~(18), (22)~(30), (36)~(38), (40)~(41);

[0104] S2-3: Convex relaxation of bilinear terms:

[0105] The auxiliary variable is also a bilinear term that needs to be relaxed in the SHS model; according to the piecewise McCormick method, the domain of the temperature variable in the bilinear term is selected and divided into three non-overlapping regions; the role of the binary parameter is to determine whether the temperature variable belongs to the divided domain, and the constraint (37) is relaxed to:

[0106] (42)

[0107] The CDEPS reconstruction problem after convex relaxation is transformed into a linearization problem; the constraints include the following: formulas (2)~(7), (9)~(12), (14)~(18), (22)~(30), (36), (38), (40)~(42).

[0108] S3: Design a solution method for the optimization problem. Nest the Benders algorithm into a three-level Benders hierarchical scheduling method. Divide the optimization problem into inner and outer iterative hierarchical solutions. The outer layer iteratively solves the EPS and DHS problems, while the inner layer iteratively solves the primary heating system (PHS) and secondary heating system (SHS) problems, achieving the optimal solution to the optimization problem, as detailed below:

[0109] The proposed hierarchical solution strategy for the CDEPS problem includes EPS, PHS, and SHS problems. The algorithm framework consists of two stages: inner iteration and outer iteration.

[0110] 1) First, initialize the outer iteration count. And set upper and lower limits. , ;

[0111] 2) Determine the outer convergence condition ,in This is the outer convergence threshold, set to 0.001 or less;

[0112] 3) Using the interior-point method, the model established based on the main problem EPS is solved to obtain the solution to the problem. ;

[0113] 4) Substitute the solution from step 3) into the subproblem. If the subproblem is feasible, we obtain the equality constraints. Lagrange multipliers Find the optimal cut OC that is feasible for the subproblem. , The boundary of the objective function of the subproblem is represented;

[0114] 5) Determine the inner convergence condition ,in This is the inner convergence threshold, which can be set to 0.001 or less;

[0115] 6) Using the interior-point method, solve the model established by the main problem PHS with its subproblems to obtain the solution to the problem. ;

[0116] 7) Substitute the solution from step 6) into the subproblem of the subproblem. If the subproblem of the subproblem is feasible, we obtain the equality constraint. Lagrange multipliers Find the optimal cut that is feasible for the subproblem. , The boundary of the objective function of the main problem represents the subproblems;

[0117] 8) If a subproblem is infeasible, introduce slack variables. and And remove infeasible solutions, introduce feasible cuts for the subproblems of the subproblems. ;

[0118] 9) Update the objective function of the main problem in the subproblem. and Inner iteration count ;

[0119] 10) Return to step 5). If the iteration converges, exit the inner loop and execute step 11); otherwise, continue the iteration loop.

[0120] 11) If the subproblem is infeasible, introduce slack variables. and And remove infeasible solutions encountered in the main problem, introducing feasible cuts (FCs) for subproblems. ;

[0121] 12) Update the objective function of the main problem. and Number of outer iterations ;

[0122] 13) Return to step 2); if the iteration convergence is satisfied, exit the outer loop and the algorithm ends; if not, continue the iteration loop.

[0123] Compared with existing technologies, this invention has the following advantages: The hierarchical collaborative scheduling method for multi-level heating systems and power systems provided by this invention can improve the system's economy and heating quality. Simultaneously, for the established mixed-integer nonlinear problem, piecewise McCormick relaxation is used, balancing solution speed and accuracy while ensuring the convexity of the model. Furthermore, by extending the BD algorithm to a hierarchical solution strategy, the multi-level collaborative structure is addressed, protecting the privacy of multiple stakeholders while conforming to the master-slave relationship of the power grid-primary heating network-secondary heating network in actual systems, thus possessing practical significance. Attached Figure Description

[0124] Figure 1 This is a three-layer architecture diagram of the CDEPS of this invention. Detailed Implementation

[0125] The present invention will be further described below with reference to specific embodiments.

[0126] A hierarchical coordinated scheduling method for multi-level heating systems and power systems includes the following steps:

[0127] S1: First, a coordinated dispatch model CDEPS (Digital-Oriented Power Grid, Primary Heating Network, and Secondary Heating Network) was established, comprehensively considering the operation modes of the primary and secondary heating networks; that is, considering the topology of the primary and secondary heating networks, the primary heating network adopts qualitative regulation, and the secondary heating network adopts both qualitative and quantitative regulation methods, establishing constraints that conform to reality; then, the coordinated cooperation of the power grid, primary heating network, and secondary heating network is realized; specifically as follows:

[0128] The three-tiered dispatch model structure includes the power grid EPS, the primary heating network PHS, and the secondary heating network SHS. The first tier consists of various generator sets connected to the power grid, including CHP units, thermal power units, and wind power units. The CHP units act as coupling devices between the first two tiers (EPS and PHS), generating both heat and electricity. The second tier is the primary heating network PHS, which uses a radial pipe network as the main urban heating pipeline. The third tier involves the PHS transmitting heat to the third-tier heating pipelines via HES. Distributed boilers generate heat and transfer it to the heating pipelines, merging it with the heat from the PHS. Finally, the heat is distributed to the heating load from the SHS. Specifically:

[0129] S1-1: Establishing a primary heating network model:

[0130] The objective function of PHS is to reduce the operating costs of cogeneration units and improve their economic efficiency.

[0131] (1);

[0132] This is the operating cost of a heating network. Let the cost function of the i-th generating unit at time t be denoted as , and its constraints include:

[0133] ① CHP Unit Constraints: The CHP unit is modeled as a convex polygon, and its power and heat output are within the following constraints:

[0134] (2);

[0135] in, These are the electrical and thermal outputs of the i-th CHP unit at time t, respectively. Let be the coefficient variable of the k-th extreme point of the convex polygon of the i-th unit at time t. Let i be the k-th extreme point in the feasible region of the convex polygon representing the electrical and thermal outputs of CHP unit i, respectively. Its cost is expressed as follows:

[0136] (3);

[0137] in q is the cost operating coefficient of CHP unit i, where q is 0, 1, 2, 3, 4, or 5. These are the upper and lower limits of the electrical output of the CHP unit, respectively. These are the upper and lower limits of the thermal output of the CHP unit, respectively, and c is the specific heat capacity of water. It is the mass flow rate of node kc connected to the CHP unit. These are the temperatures at time t connected to node kc of the CHP unit in PHS and SHS, respectively. These are the upper and lower limits of the node temperature at node kc of the CHP unit, respectively.

[0138] ② Establish a primary heat network model:

[0139] In the mass regulation and control strategy, hydraulic constraints are not considered; the mass flow rates from different pipelines converge at the same node before reaching the outlet, and the established primary heat network model is as follows:

[0140] (4);

[0141] in, These represent the mass flow rate and temperature of the primary water supply pipeline j at the start and end times of time t. These are the mass flow rate and temperature of the primary network return water pipe j at the beginning and end of time t, respectively. These are the supply and return water temperatures at primary network node m, respectively. These are the mass flow rates of the primary water supply and return water pipelines, respectively. These are the beginning and end of the pipe at node m in the primary network, respectively.

[0142] As heat is lost to the external environment, the temperature of the pipe will gradually decrease; in addition, there is a transmission delay during the pipe transmission process, and the parameters in formula (4) are as follows:

[0143] (5);

[0144] (6);

[0145] (7);

[0146] symbol Numbers should be rounded to the nearest integer; and and It is an intermediate variable of time delay. It is the ambient temperature; The heat transfer time of heating pipe j in the heating and return water network; These are the density of water, the area of ​​the pipe, its length, and a constant;

[0147] S1-2: Establishing a secondary heat network model:

[0148] Compared to PHS, SHS has a water pump. In addition to the cost of the distributed boiler, the water pump also incurs costs, which are as follows:

[0149] (8);

[0150] It is the operating cost of the secondary heating network. and Let z represent the cost functions of the z-th boiler unit and the x-th water pump at time t, respectively, and their constraints include:

[0151] ① Constraints of distributed boilers:

[0152] The heat generated by the distributed boiler is transferred to the SHS (Supply Heat Exchanger) via a heat exchange station. The distributed boiler plays an important auxiliary combustion support role in the SHS, subject to the following constraints:

[0153] (9);

[0154] in, It is the thermal output of the distributed boiler z at time t. Let z be the fuel gas consumption of the distributed boiler z at time t; The conversion efficiency of the distributed boiler z. Let z be the cost coefficient for the distributed boiler; in addition, the power consumption of the water pump has an upper and lower limit, specifically:

[0155] (10);

[0156] in, It is the electrical output of pump x at time t. These are the maximum and minimum power of water pump x. Let x be the cost coefficient of water pump. It is the flow rate on the primary side of the heat exchange station. It refers to the pressure head at node kh, which connects to the heat exchange station in the water supply and return network. It is the pressure head at node kh, which connects to the heat exchange station in the water supply and return network;

[0157] ②Constraints of heat exchange station:

[0158] The amount of heat transferred from the primary side to the secondary side is determined by the difference in logarithmic mean temperature, as shown in the following formula:

[0159] (11);

[0160] in,, It refers to the heat exchange capacity of the heat exchange station. These are the heat transfer coefficient and heat transfer area of ​​the heat exchange station; HES refers to the inlet and outlet temperatures of the primary mass flow rate. These are the inlet and outlet temperatures of the mass flow rate during the secondary side phase;

[0161] After heat transfer, the temperature in the main pipe decreases, as shown in the following formula:

[0162] (12);

[0163] In the secondary piping of the HES, the heat output from the distributed boiler is combined with the heat transferred from the PHS, as shown in the following formula:

[0164] (13);

[0165] (14);

[0166] It is the flow rate on the primary side of the heat exchange station. These are the maximum and minimum heat exchange powers of HESg;

[0167] ③ Establish a secondary heat network model:

[0168] Unlike the regulation mode in PHS, SHS requires consideration of hydraulic constraints, with zero incompressible mass flow rate at each node. The secondary thermal network model is as follows:

[0169] (15);

[0170] in, These are the quality flow rates of the secondary water supply and return water, respectively. These are the load and the mass flow rate of the heat exchange station, respectively. These are the beginning and end points of the pipe at node n in the secondary network, respectively, and kl is the node connected to the load; the hydraulic constraints are as follows:

[0171] (16);

[0172] These are the maximum and minimum mass flow rate limits for the heating pipe j2; the Darcy–Weisbach equation indicates that the pressure loss caused by friction in the pipe is related to the square of the mass flow rate:

[0173] (17);

[0174] in, It is the pressure loss coefficient of the mass flow in pipe j2. These refer to the initial pressure head at node kl, which connects to the pipeline in the supply and return water network. These are the end-stage pressure heads at node k2 connected to the pipeline in the supply and return water networks; to maintain the HES mass flow rate, the load pressure must be greater than the specified level:

[0175] (18);

[0176] in, It is the minimum heating pressure of HES g;

[0177] SHS has thermal constraints, including temperature mixing constraints and heat loss constraints, as shown in the following formula:

[0178] (19);

[0179] (20);

[0180] (twenty one);

[0181] in, These represent the mass flow rate and temperature of the secondary water supply pipeline j2 at the beginning and end of time t, respectively. These are the mass flow rate and temperature of the secondary network return water pipe j2 at the beginning and end of time t, respectively. It is the temperature of node n at time t in the SHS; in addition, since the pipes in the SHS are shorter than those in the PHS, the time delay of the mass flow rate is not considered.

[0182] In SHS (Supply, Heat, and Water) systems, buildings are considered heat loads. To maintain comfortable indoor temperatures, the temperature of the mass flow rate is limited, as shown in the following formula:

[0183] (twenty two);

[0184] in, It is the heat of the heat load. These are the upper and lower limits of the temperature at the load node kl;

[0185] S1-3: Establishing the power grid model:

[0186] EPS is constructed using a DC power flow model, as shown below:

[0187] (twenty three);

[0188] The power output of CHP units, thermal power units, and wind power units meets the load demand; among them... These represent the output electrical power of CHP unit i, thermal power unit v, and wind power unit e at time t, respectively. It is the electrical load at bus l at time t;

[0189] (twenty four);

[0190] The power flow through a transmission line must not exceed its maximum transmission power; among which, It is the power transmission distribution factor connecting bus l to line y. This is the maximum power flow transmission capacity limit of line y;

[0191] (25);

[0192] (26);

[0193] The power output of thermal power units should be within a certain range, while the power output of wind power units should be less than their predicted available power output. These are the maximum and minimum power output limits for thermal power units. This refers to the predicted output power of the wind turbine generator set.

[0194] (27);

[0195] (28);

[0196] (29);

[0197] (30);

[0198] Thermal power units' ramp-up and rotational reserve are also within a certain range, among which It refers to the uphill and downhill gradient of thermal power units. It represents the uplink and downlink rotating reserve capacity of the thermal power unit at time t. It is the total uplink and downlink spinning reserve capacity of the system at time t;

[0199] The operating cost of the power system is:

[0200] (31);

[0201] (32);

[0202] (33);

[0203] in, For the operating costs of thermal power units, For the operating costs of thermal power units, when the actual wind power output deviates from the predicted available power, it will result in penalty costs. It is the cost operating coefficient of thermal power units. It is the penalty coefficient for wind turbine units.

[0204] S2: Based on the steps established in S1, the operating costs of the power grid, primary heating network, and secondary heating network are summed to establish the objective function. Then, using the heat transfer approximation, secondary heating network model reshaping, and bilinear term convex relaxation, the non-convex terms in the model are made convex, forming an optimization problem; specifically as follows:

[0205] The goal of the CDEPS model is to minimize total operating costs, including generator sets in EPS, PHS, and SHS. The minimum total operating cost is as follows:

[0206] (34);

[0207] The proposed CDEPS model is constrained by EPS, PHS, and SHS, including formulas (2)~(7) and (9)~(30); however, the CDEPS model is non-convex and nonlinear: due to the logarithmic mean temperature difference, there is a nonlinear term in formula (11), and the presence of multiple bilinear and exponential terms in formulas (13) and (19)~(21) makes the SHS model non-convex. In order to reduce the computational complexity of the CDEPS model, the CDEPS model is made convex and then solved:

[0208] S2-1: Approximate principle of heat transfer:

[0209] The logarithmic mean temperature difference is approximately equal to the arithmetic mean temperature difference, and the approximation principle is as follows:

[0210] (35);

[0211] Therefore, the heat transfer equation in formula (11) is approximately a linear function:

[0212] (36);

[0213] S2-2: Reshaping the Secondary Heating Network Model

[0214] To address the bilinear term, the secondary heating network is reformulated as a linearized model that depends only on the proposed auxiliary variables, which are represented as follows:

[0215] (37);

[0216] Constraints (12) and (18)~(20) are equivalent to:

[0217] (38);

[0218] (39);

[0219] in, These are auxiliary variables for pipeline j2, distributed boiler z, HES, and heat loss j2, respectively.

[0220] The variables related to the upper and lower boundary conditions for temperature and mass flow rate should be within the following ranges:

[0221] (40);

[0222] The exponential function in constraint (38) is approximated by a first-order Taylor expansion:

[0223] (41);

[0224] The SHS model is restated using constraint formulas (2)~(7), (9)~(12), (14)~(18), (22)~(30), (36)~(38), (40)~(41);

[0225] S2-3: Convex relaxation of bilinear terms:

[0226] The auxiliary variable is also a bilinear term that needs to be relaxed in the SHS model; according to the piecewise McCormick method, the domain of the temperature variable in the bilinear term is selected and divided into three non-overlapping regions; the role of the binary parameter is to determine whether the temperature variable belongs to the divided domain, and the constraint (37) is relaxed to:

[0227] (42);

[0228] The CDEPS reconstruction problem after convex relaxation is transformed into a linearization problem; the constraints include the following: formulas (2)~(7), (9)~(12), (14)~(18), (22)~(30), (36), (38), (40)~(42).

[0229] S3: Design a solution method for the optimization problem. Nest the Benders algorithm into a three-level Benders hierarchical scheduling method. Divide the optimization problem into inner and outer iterative layers for solution. The outer layer iteratively solves the EPS and DHS problems, while the inner layer iteratively solves the primary heating network PHS and secondary heating network SHS problems, achieving the optimal solution to the optimization problem. Details are as follows:

[0230] The proposed hierarchical solution strategy for the CDEPS problem includes EPS, PHS, and SHS problems. The algorithm framework consists of two stages: inner iteration and outer iteration.

[0231] 1) First, initialize the outer iteration count. And set upper and lower limits. , ;

[0232] 2) Determine the outer convergence condition ,in This is the outer convergence threshold, set to 0.001 or less;

[0233] 3) Using the interior-point method, the model established based on the main problem EPS is solved to obtain the solution to the problem. ;

[0234] 4) Substitute the solution from step 3) into the subproblem. If the subproblem is feasible, we obtain the equality constraints. Lagrange multipliers Find the optimal cut OC that is feasible for the subproblem. , The boundary of the objective function of the subproblem is represented;

[0235] 5) Determine the inner convergence condition ,in This is the inner convergence threshold, which can be set to 0.001 or less;

[0236] 6) Using the interior-point method, solve the model established by the main problem PHS with its subproblems to obtain the solution to the problem. ;

[0237] 7) Substitute the solution from step 6) into the subproblem of the subproblem. If the subproblem of the subproblem is feasible, we obtain the equality constraint. Lagrange multipliers Find the optimal cut that is feasible for the subproblem. , The boundary of the objective function of the main problem represents the subproblems;

[0238] 8) If a subproblem is infeasible, introduce slack variables. and And remove infeasible solutions, introduce feasible cuts for the subproblems of the subproblems. ;

[0239] 9) Update the objective function of the main problem in the subproblem. and Inner iteration count ;

[0240] 10) Return to step 5). If the iteration converges, exit the inner loop and execute step 11); otherwise, continue the iteration loop.

[0241] 11) If the subproblem is infeasible, introduce slack variables. and And remove infeasible solutions encountered in the main problem, introducing feasible cuts (FCs) for subproblems. ;

[0242] 12) Update the objective function of the main problem. and Number of outer iterations ;

[0243] 13) Return to step 2); if the iteration convergence is satisfied, exit the outer loop and the algorithm ends; if not, continue the iteration loop.

[0244] The scope of protection claimed by this invention is not limited to the specific embodiments described above. Moreover, for those skilled in the art, this invention can have various modifications and alterations. Any modifications, improvements, and equivalent substitutions made within the concept and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A multi-stage heat supply system and power system hierarchical collaborative scheduling method, characterized in that: The method comprises the following steps: S1: First, a coordinated scheduling model CDEPS of the power grid, the primary heat network and the secondary heat network is established; the operation modes of the primary heat network and the secondary heat network are comprehensively considered; that is, the topologies of the primary heat network and the secondary heat network are considered; the primary heat network adopts quality regulation, the secondary heat network adopts quality and quantity regulation, and actual constraints are established; then, the coordinated cooperation of the power grid, the primary heat network and the secondary heat network is realized; The secondary heat network model is as follows: （15） wherein, are the mass flow rates of the secondary network supply and return water, respectively, are the mass flow rates of the load and heat exchange station, respectively; are the pipe start and end of the secondary network node n, and kl is the node connected to the load. S2: On the basis of step S1, the operation costs of the three layers of the power grid, the primary heat network and the secondary heat network are summed to establish a target function, and the non-convex terms in the model are convexified by using the approximate heat transfer, the secondary heat network model remodeling and the bilinear term convex relaxation, so as to form an optimization problem; S3: A solving method of the optimization problem is designed; the Benders algorithm is nested into a three-level Benders hierarchical scheduling method; the optimization problem is divided into inner and outer layer iteration hierarchical solving; the outer layer iteration solves the power grid EPS and the DHS problem; the DHS includes the primary heat network PHS and the secondary heat network SHS; the inner layer iteration solves the primary heat network PHS and the secondary heat network SHS problem; and the optimal solution of the optimization problem is realized.

2. The method according to claim 1, characterized in that: Step S1 is specifically as follows: The three-level scheduling model structure includes the power grid EPS, the primary heat network PHS and the secondary heat network SHS; wherein the first layer structure is that: a plurality of generator units are connected in the power grid; the generator units include CHP units, thermal power units and wind power units; the CHP units are coupled devices of the first two levels of the power grid EPS and the primary heat network PHS, and simultaneously generate heat and power; the second layer structure is the primary heat network PHS; the PHS adopts a radial structure pipe network as a city main heat pipe; the third layer structure is that: the PHS delivers heat to the third layer heat pipe through the heat exchange station HES; the distributed boiler generates heat and transfers the heat to the heat pipe, and combines with the heat of the PHS; and finally is distributed to the heating load from the SHS; specifically: S1-1: A primary heat network model is established: The target function of the PHS is to reduce the operation cost of the combined heat and power unit, so as to improve the economy; （1） the operation cost of the primary heating network, is the cost function of the ith unit at time t, and the constraints include: ①CHP unit constraint: the CHP unit is modeled as a convex polygon; the power and heat output of the CHP unit is within the following limit range: （2） wherein, Pit(t) and Qit(t) are the electrical and thermal power output of the ith unit at time t, respectively, Pit(t) and Qit(t) are the electrical and thermal power output of the ith unit at time t, respectively, Pit(t) and Qit(t) are the electrical and thermal power output of the ith unit at time t, respectively, （3） wherein is the cost operation coefficient of the CHP unit i, q is 0, 1, 2, 3, 4, 5; are respectively the upper and lower limits of the electric power output of the CHP unit i, are respectively the upper and lower limits of the thermal power output of the CHP unit i, c is the specific heat capacity of water, is the mass flow connected to the node kc of the CHP unit, are respectively the temperature of the node kc connected to the CHP unit at time t in the PHS and SHS, are respectively the upper and lower limits of the node temperature of the node kc of the CHP unit. ②Establish a primary heat network model: In the mass regulation control strategy, the hydraulic constraint is not considered; the mass flow from different pipes converges at the same node before reaching the outlet; the established primary heat network model is as follows: （4） wherein, respectively the mass flow rate temperature of the pipe at time t for the beginning and end of the pipe of the primary network, respectively the mass flow rate temperature of the pipe at time t for the beginning and end of the pipe of the primary network; respectively the supply and return water temperatures at node m of the primary network, respectively the mass flow rates of the supply and return pipes j of the primary network; respectively the beginning and end of the pipe at node m of the primary network. With the heat loss to the external environment, the temperature of the pipe will gradually decrease; in addition, there is a transmission delay in the pipe transmission process; the parameters in formula (4) are as follows: （5） （6） （7） Symbols is rounded to the nearest integer; and and is an intermediate variable for the time delay, is the ambient temperature; is the heat transfer time of the heating pipe j in the heating and return water network; are the density of water, the area, the length and the constant of the pipe, respectively; S1-2: A secondary heat network model is established: Compared with the PHS, the SHS has a water pump; in addition to the cost of the distributed boiler, the water pump also generates a cost; the water pump cost is as follows: （8） is the operation cost of the secondary heating network, and Cz(t) and Cx(t) represent the cost functions of the zth boiler unit and the xth water pump at time t, respectively, with constraints including: ①Distributed boiler constraint: The heat generated by the distributed boiler is delivered to the SHS through the heat exchange station; the distributed boiler plays an important auxiliary combustion supporting role in the SHS; the constraint is as follows: （9） wherein, is the heat output of the distributed boiler z at time t, is the fuel gas consumption of the distributed boiler z at time t; is the conversion efficiency of the distributed boiler z, is the cost coefficient of the distributed boiler z; in addition, the water pump power consumption has an upper limit and a lower limit, which is: （10） wherein, is the electrical output of the water pump x at time t, is the maximum, minimum power of the water pump x, is the cost coefficient of the water pump x, is the flow rate of the primary side of the heat exchange station, is the pressure head at the node kh of the water supply network connected to the heat exchange station, is the pressure head at the node kh of the return water network connected to the heat exchange station; ②Heat exchange station constraint: The heat transferred from the primary side to the secondary side is determined by the difference of the logarithmic mean temperature; the formula is as follows: （11） wherein, is the heat exchange heat of the heat exchange station, is the heat exchange coefficient and the heat exchange area of the heat exchange station; is the inlet and outlet temperature of the primary side mass flow of the HES, is the inlet and outlet temperature of the secondary side mass flow during the HES; After heat transfer, the temperature in the main pipe decreases, and the formula is as follows: （12） In the secondary pipe of HES, the heat output of the distributed boiler is combined with the heat transferred from PHS, and the formula is as follows: （13） （14） is the flow rate of the primary side of the heat exchange station, is the maximum, minimum heat exchange power of HESg; ③ Establishing a secondary heat network model: In SHS, the hydraulic constraint is considered, and the incompressible mass flow in each node is zero, and the hydraulic constraint is as follows: （16） are the maximum and minimum mass flow limits for the heating pipe j2; the Darcy-Weisbach equation states that the pressure loss due to friction in a pipe is related to the square of the mass flow: （17） where, is the pressure loss coefficient of the mass flow in pipe j2, are the head losses at the beginning of the pipe at node k1 connected to the pipe in the supply, respectively return water network; are the head losses at the end of the pipe at node k2 connected to the pipe in the supply, respectively return water network; the load pressure must be greater than a specified level in order to maintain the mass flow of the HES: （18） wherein, is the minimum heating pressure of HES g; SHS has thermal constraints, including temperature mixing constraints and heat loss constraints, and the formula is as follows: （19） （20） （21） wherein, are the mass flow temperatures of the beginning and end of the pipe of the secondary network supply pipe j2 at time t, respectively, are the mass flow temperatures of the beginning and end of the pipe of the secondary network return pipe j2 at time t, respectively; is the temperature of node n in the SHS at time t; In SHS, the building is regarded as a thermal load, and in order to maintain a comfortable indoor temperature, the temperature of the mass flow is limited, and the formula is as follows: （22） wherein, is the heat of the thermal load, is the upper and lower temperature limits at the load node kl; S1-3: Establishing an electrical grid model: The EPS construction adopts a direct current flow model, as shown below: （23） The power output of the CHP unit, the thermal power unit and the wind power unit meets the load demand; wherein, respectively are the output electric power of the CHP unit i, the thermal power unit v and the wind power unit e at the time t, is the electric load at the bus l at the time t; （24） The power flow through the transmission line cannot exceed its maximum power transfer; wherein, is a power transfer distribution factor of the bus l to the line y, is a maximum power flow transfer capacity limit of the line y; （25） （26） wherein, is the maximum, minimum power output limit of the thermal power unit, is the wind power unit predicted output power of the wind power unit; （27） （28） （29） （30） The climbing and rotating reserve of the thermal power unit is also in a certain range, wherein is the up and down climbing rate of the thermal power unit, is the up and down rotating reserve capacity of the thermal power unit at time t, is the total up and down rotating reserve capacity of the system at time t; The operation cost of the power system is: （31） （32） （33） wherein, is the operation cost of the thermal power unit, is the operation cost of the thermal power unit, and when the actual wind power output deviates from the predicted available power, it will cause a penalty cost; is the cost operation coefficient of the thermal power unit, is the penalty coefficient of the wind power unit.

3. The method of claim 2, wherein: Step S2 is as follows: The objective of the CDEPS model is to minimize the total operation cost, including the generator units in EPS, PHS, and SHS, and the minimum value of the total operation cost is as follows: （34） The proposed CDEPS model is subject to the constraints of EPS, PHS, and SHS, including formulas (2)-(7), (9)-(30); however, the CDEPS model is non-convex and nonlinear: due to the logarithmic mean temperature difference, there is a nonlinear term in formula (11), and the existence of multiple bilinear terms and exponential terms in formulas (13) and (19)-(21) makes the SHS model non-convex, in order to reduce the computational complexity of the CDEPS model, the CDEPS model is convexized and solved: S2-1: Heat transfer approximation principle: The logarithmic mean temperature difference is approximated as the arithmetic mean temperature difference, and the approximation principle is as follows: （35） Therefore, the heat transfer equation in formula (11) is approximated as a linear function: （36） S2-2: Reshaping the secondary heat network model: In order to solve the bilinear term, the secondary heat network is re-expressed as a linearized model only related to the proposed auxiliary variables, which are represented as follows: （37） Constraints (12) and (18)-(20) are equivalent to: （38） （39） wherein, are auxiliary variables for pipe j2, distributed boiler z, HES and heat loss j2, respectively; The variables related to the upper and lower boundary conditions of temperature and mass flow should be within the following range: （40） The exponential function in constraint (38) is approximated by first-order Taylor expansion: （41） The SHS model is re-expressed with constraint formulas (2)-(7), (9)-(12), (14)-(18), (22)-(30), (36)-(38), (40)-(41); S2-3: Convex relaxation of bilinear terms: The auxiliary variable is also a bilinear term that needs to be relaxed in the SHS model; according to the piecewise McCormick method, the domain of the temperature variable in the bilinear term is divided into three disjoint regions; the role of the binary parameter is to determine whether the temperature variable belongs to the divided domain, and constraint (37) is relaxed as follows: （42） The convex relaxed CDEPS reconstruction problem is converted into a linearized problem; the constraints include the following: formulas (2)-(7), (9)-(12), (14)-(18), (22)-(30), (36), (38), (40)-(42).

4. The method of claim 1, wherein: Step S3 is as follows: The hierarchical solution strategy of the proposed CDEPS problem includes EPS, PHS and SHS problems, and the framework of the algorithm includes two parts: inner iteration and outer iteration. 1) First initialize the number of outer iterations and set upper and lower bounds , ; 2) judging outer layer convergence condition wherein is an outer layer convergence threshold value, set to 0.001 or less; 3) using the interior point method to solve the model established by the main problem EPS to obtain the solution of the problem ; 4) Substitute the solution of step 3) into the subproblem, if the subproblem is feasible, get the equality constraint the Lagrange multiplier of the equality constraint , find the optimal cut OC of the subproblem, , denotes the boundary of the objective function of the subproblem; 5) judge inner layer convergence condition wherein is an inner layer convergence threshold, which can be set to 0.001 or less; 6) using the interior point method, solve the model established by the main problem PHS of the sub-problem to obtain the solution of the problem ; 7) Substitute the solution of step 6) into the subproblem of the subproblem, if the subproblem of the subproblem is feasible, get the equality constraint the Lagrange multiplier of the equality constraint , find the optimal cut of the subproblem feasible, , denotes the boundary of the objective function of the master problem of the subproblem; 8) Introduce a slack variable if the subproblem of the subproblem is not feasible and and remove infeasible solutions, introduce a feasible cut of the subproblem of the subproblem is feasible ; 9) update the objective function of the master problem of the subproblem, and , the number of inner iterations ; 10) Return to step 5) again. If the iteration converges, then jump out of the inner loop and execute step 11); if not, then continue the iteration loop; 11) Introduce slack variables if subproblem is not feasible and and remove infeasibility encountered in the master problem, introduce a feasible cut FC, which is feasible for the subproblem, ; 12) update the objective function of the master problem, and , the number of outer iterations ; 13) Return to step 2) again. If the iteration converges, then jump out of the outer loop and the algorithm ends; if not, then continue the iteration loop.

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