Hierarchical cooperative scheduling method for multi-stage heat supply system and electric power system

Abstract

The invention discloses a hierarchical cooperative scheduling method for a multi-stage heat supply system and an electric power system, and relates to the field of electric heating comprehensive energy system scheduling. Comprising the following steps: firstly, establishing a cooperative scheduling model CDEPS of a power grid, a primary heat supply network and a secondary heat supply network; comprehensively considering operation modes of the primary heat supply network and the secondary heat supply network; secondly, summing the operation cost of the power grid, the primary heat supply network and the secondary heat supply network to establish a target function, and forming an optimization problem by using methods of transfer heat approximation, secondary heat supply network model remodeling and the like; and finally, designing a solution method of an optimization problem, and dividing the optimization problem into inner and outer layer iteration layered solution, outer layer iteration solution of EPS and DHS problems, and inner layer iteration solution of primary heat supply network PHS and secondary heat supply network SHS problems. According to the method, the economical efficiency and the heat supply quality of the system are improved, a multi-stage cooperation structure is achieved, the privacy problem among multiple subjects can be protected, the method conforms to the master-slave relation of power grid-primary heat supply network-secondary heat supply network in an actual system, and the method has practical significance.

Description

Technical Field

[0001] The present invention relates to the field of electric-thermal integrated energy system scheduling, and in particular to a hierarchical coordinated scheduling method for a multi-level heating system and an electric power system. Background Art

[0002] The electric-thermal coupling system is a typical integrated energy system. The flexibility of the district heating system (hereinafter referred to as DHS) can provide additional adjustment capabilities for the electric power system (hereinafter referred to as EPS), thereby promoting the consumption of wind power. Counties in northern regions are actively developing clean heating methods using combined heat and power, and achieving electric-heat synergy has become a major practical need under the background of "dual carbon". However, there is no coordination between the power grid, the primary heating network, and the secondary heating network. The fragmented scheduling method between different entities leads to insufficient utilization of flexible resources in the secondary heating network, and causes additional economic scheduling costs and serious wind abandonment in the electric-thermal coupling system. The traditional fragmented scheduling method will also cause the combined heat and power (hereinafter referred to as CHP) unit to output too much heat, resulting in uneven temperature in the heating network.

[0003] The power grid, primary heating network and secondary heating network belong to different owners and operate independently. The existing dispatching method does not coordinate the power grid, primary heating network and secondary heating network, but regards the secondary heating network as the load of the primary heating network, ignoring its topological structure and a large number of flexible resources distributed in the secondary network, which will cause heavy burdens on CHP units, low wind power utilization, and uneven temperature distribution. That is, there is no unified dispatching and operation management method for the power grid, primary heating network and secondary heating network. Although the traditional distributed algorithm solves the privacy and data protection issues of boundary information exchange, it ignores the master-slave characteristics of the three-level system. Therefore, it is not suitable for the coordinated dispatching model of the power grid, primary heating network and secondary heating network. At the same time, the previously constructed optimization dispatching model contains nonlinear variables (logarithmic mean temperature difference) and bilinear terms (the product of mass flow and temperature in the thermal system), which will make the model unsolvable. Therefore, it is necessary to establish a refined secondary heating network model to enable the coordinated dispatching of the power grid, primary heating network and secondary heating network, while considering the master-slave characteristics to make the hierarchical dispatching more refined. Summary of the invention

[0004] In order to solve the problems that the existing scheduling method does not coordinate the power grid, primary heating network and secondary heating network, resulting in heavy burden on CHP units, low wind power utilization, uneven temperature distribution, and ignoring the master-slave characteristics of the secondary heating network, and the previously established optimization scheduling model still has problems that cannot be solved, the present invention provides a hierarchical coordinated scheduling method for a multi-level heating system and a power system.

[0005] The present invention is implemented by the following technical solution: a hierarchical coordinated dispatching method of a multi-level heating system and a power system, comprising the following steps:

[0006] S1: First, a coordinated dispatch of integrated electric and primary-secondary heating systems (CDEPS) model was established: the operation modes of the primary and secondary heating networks were considered comprehensively; that is, the topology of the primary and secondary heating networks was considered, the primary heating network adopted quality regulation, and the secondary heating network adopted quality and quantity regulation, and constraints that conform to reality were established; then the coordinated cooperation of the power grid, primary heating network, and secondary heating network was achieved; the details are as follows:

[0007] The three-level dispatch model structure includes power grid EPS, primary heat network PHS, and secondary heat network SHS; the first-level structure is: a variety of invented units are connected to the power grid, and the generator sets include CHP units, thermal power units and wind power units. The CHP units are the coupling devices of the first two levels, generating heat and electricity at the same time; the second-level structure is: DHS includes primary heat network PHS and secondary heat network SHS. PHS uses radial structure pipe network as the main urban heat pipeline. The third-level structure is: PHS transports heat to the heat pipeline of the third layer through HES (Heat exchange station). Distributed boilers generate heat and transfer the heat to the heat pipeline and merge with the heat of PHS; finally, it is distributed from SHS to the heating load; specifically:

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

[0009] The objective function of PHS is to reduce the operating cost of the cogeneration unit and improve the economic efficiency;

[0010]

[0011] C P is the operating cost of the primary heating network, It is expressed as the cost function of the i-th unit at time t, and its constraints include:

[0012] ① CHP unit constraints. The CHP unit is modeled as a convex polygon. The power and heat output of the CHP unit are within the following limits:

[0013]

[0014] in, are the electrical output and thermal output of the i-th CHP unit at time t, is the coefficient variable of the kth extreme point of the convex polygon of the i-th unit in period t, P i k is the kth extreme point in the convex polygonal feasible domain of the electrical output and thermal output of CHP unit i, respectively, and its cost is expressed as follows:

[0015]

[0016] where a q,i ,b q,i is the cost operation coefficient of CHP unit i; P i CHP are the upper and lower limits of the power output of CHP unit i thermal power unit i, are the upper and lower limits of the thermal output of CHP unit i and thermal power unit i, c is the specific heat capacity of water, is the mass flow rate of the node kc connected to the CHP unit, are the temperatures of the node kc connected to the CHP unit at time t in PHS and SHS, are the upper and lower limits of the node temperature of the CHP unit node k, respectively;

[0017] ② Establish a thermal network model:

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

[0019]

[0020] in, are the mass flow rate and temperature of the primary network water supply pipeline j starting and ending at time t, are the mass flow rate and temperature of the primary network return pipe j starting and ending at time t; are the supply and return water temperatures of the primary network node m, are the mass flow rates of primary network supply and return pipe j respectively; They are the beginning and end of the pipeline of the primary network node m respectively;

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

[0022]

[0023] symbol Numbers should be rounded to the nearest integer; and and is the intermediate variable of the delay, T t AT1 is the ambient temperature; Heat transfer time of heating pipe j in the heating and return network; ρ, A j ,L j ,ε j They are the density of water, the area, length and constant of the pipe;

[0024] S1-2: Establishing secondary thermal network model:

[0025] Compared with PHS, SHS has water pumps. In addition to the cost of distributed boilers, water pumps also incur costs. The water pump costs are as follows:

[0026]

[0027] C S is the operating cost of the secondary heating network, and They are respectively expressed as the cost functions of the zth boiler unit and the xth water pump at time t, and their constraints include:

[0028] ① Distributed boiler constraints:

[0029] The heat generated by the distributed boiler is transported to the SHS through the heat exchange station. The distributed boiler plays an important auxiliary combustion support role in the SHS, and the constraints are as follows:

[0030]

[0031] in, is the heat output of distributed boiler z at time t, is the fuel gas consumption of distributed boiler z at time t; is the conversion efficiency of 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 are:

[0032]

[0033] in, is the electrical output of pump x at time t, is the maximum / minimum power of pump x, r i PUMP is the cost coefficient of water pump x, is the flow rate on the primary side of the heat exchange station, is the pressure head at the node kh in the supply and return water network connected to the heat exchange station; is the conversion efficiency of the distributed boiler x;

[0034] ② Heat exchange station constraints:

[0035] The amount of heat transferred from the primary to the secondary is determined by the difference in log mean temperature using the following formula:

[0036]

[0037] in, is the heat transfer heat of the heat exchange station, are the heat transfer coefficient and heat transfer area of ​​the heat exchange station; is the inlet and outlet temperature of HES, is the primary mass flow rate, are the inlet and outlet temperatures of the mass flow rate during the secondary side;

[0038] After the heat transfer, the temperature in the main pipe decreases as follows:

[0039]

[0040] In the secondary pipe of HES, the heat output from the distributed boiler is combined with the heat transferred from PHS, and the formula is as follows:

[0041]

[0042] is the flow rate on the primary side of the heat exchange station, is the maximum and minimum heat exchange power of HES c;

[0043] ③Establish secondary thermal network model:

[0044] Different from the regulation mode in PHS, hydraulic constraints need to be considered in SHS. The incompressible mass flow in each node is zero, and the secondary thermal network model is:

[0045]

[0046] in, are the mass flow rates of secondary network supply and return water, are the mass flow rates of the heat exchange station and the load, respectively; are the beginning and end of the pipeline of the secondary network node n, respectively, and kl is the node connected to the load; the hydraulic constraints are as follows:

[0047]

[0048] are the maximum and minimum mass flow limits of heating pipe j; the Darcy–Weisbach equation states that the pressure loss caused by friction in the pipe is related to the square of the mass flow rate:

[0049]

[0050] Among them, μ j2 is the pressure loss coefficient of the mass flow in pipe j2, are the first section pressure heads at the node k connected to the pipeline in the supply and return networks respectively; are the end-stage pressure heads at the nodes k connected to the pipeline in the supply and return networks, respectively; in order to maintain the mass flow of HES, the load pressure must be greater than the specified level:

[0051]

[0052] in, is the minimum heating pressure of HES c;

[0053] SHS has thermal constraints, including temperature mixing constraints and heat loss constraints, and the formula is as follows:

[0054]

[0055]

[0056] in, are the mass flow rate and temperature of the secondary network water supply pipe j2 starting and ending at time t, are the mass flow rate and temperature of the secondary network return pipe j2 starting and ending at time t; 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.

[0057] In SHS, buildings are considered as heat loads. In order to maintain a comfortable indoor temperature, the temperature of the mass flow is limited. The formula is as follows:

[0058]

[0059] in, is the heat of the heat load, are the upper and lower limits of the temperature at the load node kl;

[0060] S1-3: Establishing the power grid model:

[0061] EPS construction adopts DC power flow model as shown below:

[0062]

[0063] The power output of CHP units, thermal power units, and wind power units meets the load demand; among them, are the output power of CHP unit i, thermal power unit v, and wind power unit e at time t, respectively. l,t is the electric load at bus l at time t;

[0064]

[0065] The power flow through the transmission line cannot exceed its maximum transmission power; where LF y,r is the power transfer distribution factor for busbar r connected to line y, F y is the maximum power flow transmission capacity limit of line y;

[0066]

[0067] The power output of thermal power units should be within a certain range, and the power output of wind power units should be less than their predicted available power output. It is the maximum and minimum power output limit of thermal power units. is the predicted wind turbine output power of the wind turbine;

[0068]

[0069] The ramping and rotation reserve of thermal power units are also within a certain range. is the up and down ramp rate of the thermal power unit, is the uplink and downlink spinning reserve capacity of the thermal power unit at time t, SRU t ,SRD t is the total uplink and downlink spinning reserve capacity of the system at time t;

[0070] The operating cost of the power system is:

[0071]

[0072] in, is the operating cost of the thermal power unit, is the operating cost of the thermal power unit. When the actual wind power output deviates from the predicted available power, it will result in penalty costs; b m,i is the cost operation coefficient of thermal power units, υ i is the penalty coefficient of the wind turbine.

[0073] S2: On the basis of what is established in step S1, the operating costs of the power grid, primary heat network and secondary heat network are summed to establish the objective function, and the non-convex terms in the model are convexified by using the heat transfer approximation, secondary heat network model reshaping and bilinear term convex relaxation to form an optimization problem; the details are as follows:

[0074] The goal of the CDEPS model is to minimize the total operating cost, including the generator sets in EPS, PHS, and SHS. The minimum value of the total operating cost is as follows:

[0075]

[0076] The proposed CDEPS model is subject to the constraints of EPS, PHS and SHS, including (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 equations (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 convexed and solved:

[0077] S2-1: Approximate principle of heat transfer:

[0078] The logarithmic mean temperature difference is approximately the arithmetic mean temperature difference, and the approximate principle is as follows:

[0079]

[0080] Therefore, the heat transfer equation in formula (12) is approximately a linear function:

[0081]

[0082] S2-2: Secondary heat network model remodeling:

[0083] In order to solve the bilinear terms, the secondary heat network is reformulated as a linearized model related only to the proposed auxiliary variables, which are expressed as follows:

[0084]

[0085] Constraints (12) and (18) to (20) are equivalent to:

[0086]

[0087] Among them, ψ j2 , are auxiliary variables for pipeline j2, distributed boiler z, HES and heat loss j2 respectively;

[0088] The variables related to the upper and lower boundary conditions related to temperature and mass flow should be within the following ranges:

[0089]

[0090] The exponential function in constraint (38) is approximated by a first-order Taylor expansion:

[0091]

[0092] The SHS model is restated using constraints (2) to (7), (9) to (12), (14) to (18), (22) to (30), (36) to (38), and (40) to (41);

[0093] S2-3: Bilinear convex relaxation:

[0094] 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 the domain is divided into three non-overlapping regions. The role of the binary parameter is to determine whether the temperature variable belongs to the divided domain. Constraint (37) is relaxed as follows:

[0095]

[0096] After convex relaxation, the CDEPS reconstruction problem is transformed into a linearization problem; the constraints include the following: (2)~(7), (9)~(12), (14)~(18), (22)~(30), (36), (38), (40)~(42).

[0097] S3: Design a solution method for the optimization problem, nest the Benders algorithm into a three-level Benders hierarchical scheduling method, and divide the optimization problem into inner and outer iterative hierarchical solutions. The outer layer iteratively solves the EPS and DHS problems, and the inner layer iteratively solves the primary heating system (PHS) and secondary heating system (SHS) problems to achieve the optimal solution to the optimization problem. The details are as follows:

[0098] The proposed hierarchical solution strategy for the CDEPS problem includes EPS, PHS and SHS problems. The framework of the algorithm includes two inner iterations and an outer iteration:

[0099] 1) First initialize the outer iteration number N out =0, and set the upper and lower limits U b1 =+∞,L b1 = -∞;

[0100] 2) Determine the outer layer convergence condition U b1 -L b1 <δ, where δ is the outer layer convergence threshold, set to 0.001 or less;

[0101] 3) Use the interior point method to solve the model established based on the main problem EPS and obtain the solution x of the problem B1 ;

[0102] 4) Substitute the solution of step 3) into the subproblem. If the subproblem is feasible, the equality constraint is obtained. Lagrange multipliers Find the optimal feasible cut OC of the subproblem, α represents the boundary of the objective function of the subproblem;

[0103] 5) Determine the inner layer convergence condition U b2 -L b2 <γ, where γ is the inner layer convergence threshold, which can be set to 0.001 or less;

[0104] 6) Use the interior point method to solve the model established by the main problem PHS of the sub-problem and obtain the solution x of the problem B2 ;

[0105] 7) Substitute the solution of step 6) into the sub-problem of the sub-problem. If the sub-problem of the sub-problem is feasible, the equality constraint is obtained. Lagrange multipliers Find the optimal feasible cut of the subproblem, β represents the bound of the objective function of the master problem of the subproblem;

[0106] 8) If the sub-problem of the sub-problem is infeasible, introduce slack variables v1 and v2, remove infeasible solutions, and introduce feasible cuts of the sub-problem of the sub-problem that are feasible.

[0107] 9) Update the objective function of the main problem of the subproblem, U b2 and L b2 , the number of inner layer iterations N in =N in +1;

[0108] 10) Return to step 5), if the iteration convergence is satisfied, then jump out of the inner loop and execute step 11); if not, then continue the iteration loop;

[0109] 11) If the subproblem is infeasible, introduce slack variables m1 and m2, remove the infeasible solutions encountered in the main problem, and introduce the feasible cut FC of the subproblem.

[0110] 12) Update the objective function of the main problem, U b1 and L b1 , the number of outer iterations N out =N out +1;

[0111] 13) Return to step 2); if the iterative convergence is satisfied, then jump out of the outer loop and the algorithm ends; if not, then continue the iterative loop.

[0112] Compared with the prior art, the present invention has the following beneficial effects: the hierarchical collaborative scheduling method of a multi-level heating system and a power system provided by the present invention can improve the economy and heating quality of the system. At the same time, for the established mixed integer nonlinear problem, the piecewise McCormick technique is used for relaxation, which balances the solution speed and accuracy and ensures the convexity of the model. In addition, by extending the BD algorithm to a hierarchical solution strategy, the multi-level collaborative structure is solved, which can not only protect the privacy problem between multiple subjects, but also conform to the master-slave relationship of the power grid-primary heating network-secondary heating network in the actual system, which has practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Figure 1 This is a three-layer architecture diagram of the CDEPS of the present invention. DETAILED DESCRIPTION

[0114] The present invention is further described below in conjunction with specific embodiments.

[0115] A method for hierarchical coordinated scheduling of a multi-level heating system and a power system, comprising the following steps:

[0116] S1: First, the coordinated dispatch model CDEPS of the power grid, primary heating network and secondary heating network was established: the operation mode of the primary heating network and the secondary heating network was comprehensively considered; that is, the topology of the primary heating network and the secondary heating network was considered, the primary heating network adopted quality regulation, and the secondary heating network adopted quality and quantity regulation, and established constraints that conform to reality; then the coordinated cooperation of the power grid, the primary heating network and the secondary heating network was realized; the details are as follows:

[0117] The three-level dispatch model structure includes power grid EPS, primary heat network PHS, and secondary heat network SHS; the first-level structure is: a variety of invented units are connected to the power grid, and the generator sets include CHP units, thermal power units and wind power units. The CHP units are coupling devices of the first two levels, generating heat and electricity at the same time; the second-level structure is: DHS includes primary heat network PHS and secondary heat network SHS. PHS uses radial structure pipe network as the main urban heat pipeline. The third-level structure is: PHS transports heat to the heat pipeline of the third layer through HES, and the distributed boiler generates heat and transfers the heat to the heat pipeline and merges with the heat of PHS; finally, it is distributed from SHS to the heating load; specifically:

[0118] S1-1: Establish a primary heating network model:

[0119] The objective function of PHS is to reduce the operating cost of the cogeneration unit and improve the economic efficiency;

[0120]

[0121] C P is the operating cost of the primary heating network, It is expressed as the cost function of the i-th unit at time t, and its constraints include:

[0122] ① CHP unit constraints. The CHP unit is modeled as a convex polygon. The power and heat output of the CHP unit are within the following limits:

[0123]

[0124] in, are the electrical output and thermal output of the i-th CHP unit at time t, is the coefficient variable of the kth extreme point of the convex polygon of the i-th unit in period t, P i k is the kth extreme point in the convex polygonal feasible domain of the electrical output and thermal output of CHP unit i, respectively, and its cost is expressed as follows:

[0125]

[0126] where a q,i ,b q,i is the cost operation coefficient of CHP unit i; P i CHP are the upper and lower limits of the power output of CHP unit i thermal power unit i, are the upper and lower limits of the thermal output of CHP unit i and thermal power unit i, c is the specific heat capacity of water, is the mass flow rate of the node kc connected to the CHP unit, are the temperatures of the node kc connected to the CHP unit at time t in PHS and SHS, are the upper and lower limits of the node temperature of the CHP unit node k, respectively;

[0127] ② Establish a thermal network model:

[0128] In the quality regulation control strategy, hydraulic constraints are not considered; the mass flows from different pipelines converge at the same node before reaching the outlet, and the primary heat network model established is:

[0129]

[0130] in, are the mass flow rate and temperature of the primary network water supply pipeline j starting and ending at time t, are the mass flow rate and temperature of the primary network return pipe j starting and ending at time t; are the supply and return water temperatures of the primary network node m, are the mass flow rates of primary network supply and return pipe j respectively; They are the beginning and end of the pipeline of the primary network node m respectively;

[0131] As heat is lost to the external environment, the temperature of the pipeline will gradually decrease. In addition, there is a transmission delay during the pipeline transmission process. The parameters in formula (4) are as follows:

[0132]

[0133] symbol Numbers should be rounded to the nearest integer; and and is the intermediate variable of the delay, T t AT1 is the ambient temperature; Heat transfer time of heating pipe j in the heating and return network; ρ, A j ,L j ,ε j They are the density of water, the area, length and constant of the pipe;

[0134] S1-2: Establishing secondary thermal network model:

[0135] Compared with PHS, SHS has water pumps. In addition to the cost of distributed boilers, water pumps also incur costs. The water pump costs are as follows:

[0136]

[0137] C S is the operating cost of the secondary heating network, and They are respectively expressed as the cost functions of the zth boiler unit and the xth water pump at time t, and their constraints include:

[0138] ① Distributed boiler constraints:

[0139] The heat generated by the distributed boiler is transported to the SHS through the heat exchange station. The distributed boiler plays an important auxiliary combustion support role in the SHS, and the constraints are as follows:

[0140]

[0141] in, is the heat output of distributed boiler z at time t, is the fuel gas consumption of distributed boiler z at time t; is the conversion efficiency of 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 are:

[0142]

[0143] in, is the electrical output of pump x at time t, is the maximum / minimum power of pump x, r i PUMP is the cost coefficient of water pump x, is the flow rate on the primary side of the heat exchange station, is the pressure head at the node kh in the supply and return water network connected to the heat exchange station; is the conversion efficiency of the distributed boiler x;

[0144] ② Heat exchange station constraints:

[0145] The amount of heat transferred from the primary to the secondary is determined by the difference in log mean temperature using the following formula:

[0146]

[0147] in, is the heat transfer heat of the heat exchange station, are the heat transfer coefficient and heat transfer area of ​​the heat exchange station; is the inlet and outlet temperature of HES, is the primary mass flow rate, are the inlet and outlet temperatures of the mass flow rate during the secondary side;

[0148] After the heat transfer, the temperature in the main pipe decreases as follows:

[0149]

[0150] In the secondary pipe of HES, the heat output from the distributed boiler is combined with the heat transferred from PHS, and the formula is as follows:

[0151]

[0152] is the flow rate on the primary side of the heat exchange station, is the maximum and minimum heat exchange power of HES c;

[0153] ③Establish secondary thermal network model:

[0154] Different from the regulation mode in PHS, hydraulic constraints need to be considered in SHS. The incompressible mass flow in each node is zero, and the secondary thermal network model is:

[0155]

[0156] in, are the mass flow rates of secondary network supply and return water, are the mass flow rates of the heat exchange station and the load, respectively; are the beginning and end of the pipeline of the secondary network node n, respectively, and kl is the node connected to the load; the hydraulic constraints are as follows:

[0157]

[0158] are the maximum and minimum mass flow limits of heating pipe j; the Darcy–Weisbach equation states that the pressure loss caused by friction in the pipe is related to the square of the mass flow rate:

[0159]

[0160] Among them, μ j2 is the pressure loss coefficient of the mass flow in pipe j2, are the first section pressure heads at the node k connected to the pipeline in the supply and return networks respectively; are the end-stage pressure heads at the nodes k connected to the pipeline in the supply and return networks, respectively; in order to maintain the mass flow of HES, the load pressure must be greater than the specified level:

[0161]

[0162] in, is the minimum heating pressure of HES c;

[0163] SHS has thermal constraints, including temperature mixing constraints and heat loss constraints, and the formula is as follows:

[0164]

[0165]

[0166] in, are the mass flow rate and temperature of the secondary network water supply pipe j2 starting and ending at time t, are the mass flow rate and temperature of the secondary network return pipe j2 starting and ending at time t; 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.

[0167] In SHS, buildings are considered as heat loads. In order to maintain a comfortable indoor temperature, the temperature of the mass flow is limited. The formula is as follows:

[0168]

[0169] in, is the heat of the heat load, are the upper and lower limits of the temperature at the load node kl;

[0170] S1-3: Establishing the power grid model:

[0171] EPS construction adopts DC power flow model as shown below:

[0172]

[0173] The power output of CHP units, thermal power units, and wind power units meets the load demand; among them, are the output power of CHP unit i, thermal power unit v, and wind power unit e at time t, respectively. l,t is the electric load at bus l at time t;

[0174]

[0175] The power flow through the transmission line cannot exceed its maximum transmission power; where LF y,r is the power transfer distribution factor for busbar r connected to line y, F y is the maximum power flow transmission capacity limit of line y;

[0176]

[0177] The power output of thermal power units should be within a certain range, and the power output of wind power units should be less than their predicted available power output. It is the maximum and minimum power output limit of thermal power units. is the predicted wind turbine output power of the wind turbine;

[0178]

[0179] The ramping and rotation reserve of thermal power units are also within a certain range. is the up and down ramp rate of the thermal power unit, is the uplink and downlink spinning reserve capacity of the thermal power unit at time t, SRU t ,SRD t is the total uplink and downlink spinning reserve capacity of the system at time t;

[0180] The operating cost of the power system is:

[0181]

[0182] in, is the operating cost of the thermal power unit, is the operating cost of the thermal power unit. When the actual wind power output deviates from the predicted available power, it will result in penalty costs; b m,i is the cost operation coefficient of thermal power units, υ i is the penalty coefficient of the wind turbine.

[0183] S2: On the basis of what is established in step S1, the operating costs of the power grid, primary heat network and secondary heat network are summed to establish the objective function, and the non-convex terms in the model are convexified by using the heat transfer approximation, secondary heat network model reshaping and bilinear term convex relaxation to form an optimization problem; the details are as follows:

[0184] The goal of the CDEPS model is to minimize the total operating cost, including the generator sets in EPS, PHS, and SHS. The minimum value of the total operating cost is as follows:

[0185]

[0186] The proposed CDEPS model is subject to the constraints of EPS, PHS and SHS, including (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 equations (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 convexed and solved:

[0187] S2-1: Approximate principle of heat transfer:

[0188] The logarithmic mean temperature difference is approximately the arithmetic mean temperature difference, and the approximate principle is as follows:

[0189]

[0190] Therefore, the heat transfer equation in formula (12) is approximately a linear function:

[0191]

[0192] S2-2: Secondary heat network model remodeling:

[0193] In order to solve the bilinear terms, the secondary heat network is reformulated as a linearized model related only to the proposed auxiliary variables, which are expressed as follows:

[0194]

[0195] Constraints (12) and (18) to (20) are equivalent to:

[0196]

[0197] Among them, ψ j2 , are auxiliary variables for pipeline j2, distributed boiler z, HES and heat loss j2 respectively;

[0198] The variables related to the upper and lower boundary conditions related to temperature and mass flow should be within the following ranges:

[0199]

[0200] The exponential function in constraint (38) is approximated by a first-order Taylor expansion:

[0201]

[0202] The SHS model is restated using constraints (2) to (7), (9) to (12), (14) to (18), (22) to (30), (36) to (38), and (40) to (41);

[0203] S2-3: Bilinear convex relaxation:

[0204] 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 the domain is divided into three non-overlapping regions. The role of the binary parameter is to determine whether the temperature variable belongs to the divided domain. Constraint (37) is relaxed as follows:

[0205]

[0206] After convex relaxation, the CDEPS reconstruction problem is transformed into a linearization problem; the constraints include the following: (2)~(7), (9)~(12), (14)~(18), (22)~(30), (36), (38), (40)~(42).

[0207] S3: Design a solution method for the optimization problem, nest the Benders algorithm into a three-level Benders hierarchical scheduling method, and divide the optimization problem into inner and outer iterative hierarchical solutions. The outer layer iteratively solves the EPS and DHS problems, and the inner layer iteratively solves the primary heating network PHS and secondary heating network SHS problems to achieve the optimal solution to the optimization problem. The details are as follows:

[0208] The proposed hierarchical solution strategy for the CDEPS problem includes EPS, PHS and SHS problems. The framework of the algorithm includes two inner iterations and an outer iteration:

[0209] 1) First initialize the outer iteration number N out =0, and set the upper and lower limits U b1 =+∞,L b1 = -∞;

[0210] 2) Determine the outer layer convergence condition U b1 -L b1 <δ, where δ is the outer layer convergence threshold, set to 0.001 or less;

[0211] 3) Use the interior point method to solve the model established based on the main problem EPS and obtain the solution x of the problem B1 ;

[0212] 4) Substitute the solution of step 3) into the subproblem. If the subproblem is feasible, the equality constraint is obtained. Lagrange multipliers Find the optimal feasible cut OC of the subproblem, α represents the boundary of the objective function of the subproblem;

[0213] 5) Determine the inner layer convergence condition U b2 -L b2 <γ, where γ is the inner layer convergence threshold, which can be set to 0.001 or less;

[0214] 6) Use the interior point method to solve the model established by the main problem PHS of the sub-problem and obtain the solution x of the problem B2 ;

[0215] 7) Substitute the solution of step 6) into the sub-problem of the sub-problem. If the sub-problem of the sub-problem is feasible, the equality constraint is obtained. Lagrange multipliers Find the optimal feasible cut of the subproblem, β represents the bound of the objective function of the master problem of the subproblem;

[0216] 8) If the sub-problem of the sub-problem is infeasible, introduce slack variables v1 and v2, remove infeasible solutions, and introduce feasible cuts of the sub-problem of the sub-problem that are feasible.

[0217] 9) Update the objective function of the main problem of the subproblem, U b2 and L b2 , the number of inner layer iterations N in =N in +1;

[0218] 10) Return to step 5), if the iteration convergence is satisfied, then jump out of the inner loop and execute step 11); if not, then continue the iteration loop;

[0219] 11) If the subproblem is infeasible, introduce slack variables m1 and m2, remove the infeasible solutions encountered in the main problem, and introduce the feasible cut FC of the subproblem.

[0220] 12) Update the objective function of the main problem, U b1 and L b1 , the number of outer iterations N out =N out +1;

[0221] 13) Return to step 2); if the iterative convergence is satisfied, then jump out of the outer loop and the algorithm ends; if not, then continue the iterative loop.

[0222] The scope of protection claimed by the present invention is not limited to the above specific embodiments. For those skilled in the art, the present invention may have various deformations and changes. Any modifications, improvements and equivalent substitutions made within the concept and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A hierarchical coordinated dispatching method for a multi-level heating system and a power system, characterized in that: The following steps are involved: S1: First, the coordinated dispatch model CDEPS of the power grid, primary heating network and secondary heating network was established: the operation mode of the primary heating network and the secondary heating network was comprehensively considered; that is, the topology of the primary heating network and the secondary heating network was considered, the primary heating network adopted quality regulation, and the secondary heating network adopted quality and quantity regulation, and established constraints that conform to reality; then the coordinated cooperation of the power grid, the primary heating network and the secondary heating network was realized; S2: On the basis of what is established in step S1, the operating costs of the power grid, primary heat network and secondary heat network are summed to establish the objective function, and the non-convex terms in the model are convexified by using the heat transfer approximation, secondary heat network model reshaping and bilinear term convex relaxation to form an optimization problem; S3: Design a solution method for the optimization problem, nest the Benders algorithm into a three-level Benders hierarchical scheduling method, and divide the optimization problem into inner and outer layer iterative hierarchical solutions. The outer layer iteratively solves the EPS and DHS problems, and the inner layer iteratively solves the primary heating network PHS and secondary heating network SHS problems to achieve the optimal solution to the optimization problem.

2. A hierarchical coordinated dispatching method for a multi-level heating system and a power system according to claim 1, characterized in that: Step S1 is specifically as follows: The three-level dispatch model structure includes power grid EPS, primary heat network PHS, and secondary heat network SHS; the first-level structure is: a variety of invented units are connected to the power grid, and the generator sets include CHP units, thermal power units and wind power units. The CHP units are coupling devices of the first two levels, generating heat and electricity at the same time; the second-level structure is: DHS includes primary heat network PHS and secondary heat network SHS. PHS uses radial structure pipe network as the main urban heat pipeline. The third-level structure is: PHS transports heat to the heat pipeline of the third layer through HES, and the distributed boiler generates heat and transfers the heat to the heat pipeline and merges with the heat of PHS; finally, it is distributed from SHS to the heating load; specifically: S1-1: Establish a primary heating network model: The objective function of PHS is to reduce the operating cost of the cogeneration unit and improve the economic efficiency; C P is the operating cost of the primary heating network, It is expressed as the cost function of the i-th unit at time t, and its constraints include: ① CHP unit constraints. The CHP unit is modeled as a convex polygon. The power and heat output of the CHP unit are within the following limits: in, are the electrical output and thermal output of the i-th CHP unit at time t, is the coefficient variable of the kth extreme point of the convex polygon of the i-th unit in period t, is the kth extreme point in the convex polygonal feasible domain of the electrical output and thermal output of CHP unit i, respectively, and its cost is expressed as follows: where a q,i ,b q,i is the cost operation coefficient of CHP unit i; are the upper and lower limits of the power output of CHP unit i thermal power unit i, are the upper and lower limits of the thermal output of CHP unit i and thermal power unit i, c is the specific heat capacity of water, is the mass flow rate of the node kc connected to the CHP unit, are the temperatures of the node kc connected to the CHP unit at time t in PHS and SHS, are the upper and lower limits of the node temperature of the CHP unit node k, respectively; ② Establish a thermal network model: In the quality regulation control strategy, hydraulic constraints are not considered; the mass flows from different pipelines converge at the same node before reaching the outlet, and the primary heat network model established is: in, are the mass flow rate and temperature of the primary network water supply pipeline j starting and ending at time t, are the mass flow rate and temperature of the primary network return pipe j starting and ending at time t; are the supply and return water temperatures of the primary network node m, are the mass flow rates of primary network supply and return pipe j respectively; They are the beginning and end of the pipeline of the primary network node m respectively; As heat is lost to the external environment, the temperature of the pipeline will gradually decrease. In addition, there is a transmission delay during the pipeline transmission process. The parameters in formula (4) are as follows: symbol Numbers should be rounded to the nearest integer; and and is the intermediate variable of the delay, is the ambient temperature; Heat transfer time of heating pipe j in the heating and return network; ρ, A j ,L j ,ε j They are the density of water, the area, length and constant of the pipe; S1-2: Establishing secondary thermal network model: Compared with PHS, SHS has water pumps. In addition to the cost of distributed boilers, water pumps also incur costs. The water pump costs are as follows: C S is the operating cost of the secondary heating network, and They are respectively expressed as the cost functions of the zth boiler unit and the xth water pump at time t, and their constraints include: ① Distributed boiler constraints: The heat generated by the distributed boiler is transported to the SHS through the heat exchange station. The distributed boiler plays an important auxiliary combustion support role in the SHS, and the constraints are as follows: in, is the heat output of distributed boiler z at time t, is the fuel gas consumption of distributed boiler z at time t; is the conversion efficiency of 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 are: in, is the electrical output of pump x at time t, is the maximum / minimum power of pump x, is the cost coefficient of water pump x, is the flow rate on the primary side of the heat exchange station, is the pressure head at the node kh in the supply and return water network connected to the heat exchange station; is the conversion efficiency of the distributed boiler x; ② Heat exchange station constraints: The amount of heat transferred from the primary to the secondary is determined by the difference in log mean temperature using the following formula: in, is the heat transfer heat of the heat exchange station, are the heat transfer coefficient and heat transfer area of ​​the heat exchange station; is the inlet and outlet temperature of HES, is the primary mass flow rate, are the inlet and outlet temperatures of the mass flow rate during the secondary side; After the heat transfer, the temperature in the main pipe decreases as follows: In the secondary pipe of HES, the heat output from the distributed boiler is combined with the heat transferred from PHS, and the formula is as follows: is the flow rate on the primary side of the heat exchange station, is the maximum and minimum heat exchange power of HESc; ③Establish secondary thermal network model: Considering hydraulic constraints in SHS, the incompressible mass flow in each node is zero, and the secondary thermal network model is: in, are the mass flow rates of secondary network supply and return water, are the mass flow rates of the heat exchange station and the load, respectively; are the beginning and end of the pipeline of the secondary network node n, respectively, and kl is the node connected to the load; the hydraulic constraints are as follows: are the maximum and minimum mass flow limits of heating pipe j; the Darcy–Weisbach equation states that the pressure loss caused by friction in the pipe is related to the square of the mass flow rate: Among them, μ j2 is the pressure loss coefficient of the mass flow in pipe j2, are the first section pressure heads at the node k connected to the pipeline in the supply and return networks respectively; are the end-stage pressure heads at the nodes k connected to the pipeline in the supply and return networks, respectively; in order to maintain the mass flow of HES, the load pressure must be greater than the specified level: in, is the minimum heating pressure of HESc; SHS has thermal constraints, including temperature mixing constraints and heat loss constraints, and the formula is as follows: in, are the mass flow rate and temperature of the secondary network water supply pipe j2 starting and ending at time t, are the mass flow rate and temperature of the secondary network return pipe j2 starting and ending at time t; is the temperature of node n at time t in the SHS; In SHS, buildings are considered as heat loads. In order to maintain a comfortable indoor temperature, the temperature of the mass flow is limited. The formula is as follows: in, is the heat of the heat load, are the upper and lower limits of the temperature at the load node kl; S1-3: Establishing the power grid model: EPS construction adopts DC power flow model as shown below: The power output of CHP units, thermal power units, and wind power units meets the load demand; among them, are the output power of CHP unit i, thermal power unit v, and wind power unit e at time t, respectively. l,t is the electric load at bus l at time t; The power flow through the transmission line cannot exceed its maximum transmission power; where LF y,r is the power transfer distribution factor for busbar r connected to line y, F y is the maximum power flow transmission capacity limit of line y; in, It is the maximum and minimum power output limit of thermal power units. is the predicted wind turbine output power of the wind turbine; The ramping and rotation reserve of thermal power units are also within a certain range. is the up and down ramp rate of the thermal power unit, is the uplink and downlink spinning reserve capacity of the thermal power unit at time t, SRU t ,SRD t is the total uplink and downlink spinning reserve capacity of the system at time t; The operating cost of the power system is: in, is the operating cost of the thermal power unit, is the operating cost of the thermal power unit. When the actual wind power output deviates from the predicted available power, it will result in penalty costs; b m,i is the cost operation coefficient of thermal power units, υ i is the penalty coefficient of the wind turbine.

3. A hierarchical coordinated dispatching method for a multi-stage heating system and a power system according to claim 2, characterized in that: Step S2 is specifically as follows: The goal of the CDEPS model is to minimize the total operating cost, including the generator sets in EPS, PHS, and SHS. The minimum value of the total operating cost is as follows: The proposed CDEPS model is subject to the constraints of EPS, PHS and SHS, including (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 equations (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 convexed and solved: S2-1: Approximate principle of heat transfer: The logarithmic mean temperature difference is approximately the arithmetic mean temperature difference, and the approximate principle is as follows: Therefore, the heat transfer equation in formula (12) is approximately a linear function: S2-2: Secondary heat network model remodeling: In order to solve the bilinear terms, the secondary heat network is reformulated as a linearized model related only to the proposed auxiliary variables, which are expressed as follows: Constraints (12) and (18) to (20) are equivalent to: Among them, ψ j2 , are auxiliary variables for pipeline j2, distributed boiler z, HES and heat loss j2 respectively; variables related to upper and lower boundary conditions related to temperature and mass flow should be within the following ranges: The exponential function in constraint (38) is approximated by a first-order Taylor expansion: The SHS model is restated using constraints (2) to (7), (9) to (12), (14) to (18), (22) to (30), (36) to (38), and (40) to (41); S2-3: Bilinear convex relaxation: 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 the domain is divided into three non-overlapping regions. The role of the binary parameter is to determine whether the temperature variable belongs to the divided domain. Constraint (37) is relaxed as follows: After convex relaxation, the CDEPS reconstruction problem is transformed into a linearization problem; the constraints include the following: (2)~(7), (9)~(12), (14)~(18), (22)~(30), (36), (38), (40)~(42).

4. The hierarchical coordinated scheduling method of a multi-stage heating system and a power system according to claim 1 is characterized in that: Step S3 is as follows: The proposed hierarchical solution strategy for the CDEPS problem includes EPS, PHS and SHS problems. The framework of the algorithm includes two inner iterations and an outer iteration: 1) First initialize the outer iteration number N out =0, and set the upper and lower limits U b1 =+∞,L b1 = -∞; 2) Determine the outer layer convergence condition U b1 -L b1 <δ, where δ is the outer layer convergence threshold, set to 0.001 or less; 3) Use the interior point method to solve the model established based on the main problem EPS and obtain the solution x of the problem B1 ; 4) Substitute the solution of step 3) into the subproblem. If the subproblem is feasible, the equality constraint is obtained. Lagrange multipliers Find the optimal feasible cut OC of the subproblem, α represents the boundary of the objective function of the subproblem; 5) Determine the inner layer convergence condition U b2 -L b2 <γ, where γ is the inner layer convergence threshold, which can be set to 0.001 or less; 6) Use the interior point method to solve the model established by the main problem PHS of the sub-problem and obtain the solution x of the problem B2 ; 7) Substitute the solution of step 6) into the sub-problem of the sub-problem. If the sub-problem of the sub-problem is feasible, the equality constraint is obtained. Lagrange multipliers Find the optimal feasible cut of the subproblem, β represents the bound of the objective function of the master problem of the subproblem; 8) If the sub-problem of the sub-problem is infeasible, introduce slack variables v1 and v2, remove infeasible solutions, and introduce feasible cuts of the sub-problem of the sub-problem that are feasible. 9) Update the objective function of the main problem of the subproblem, U b2 and L b2 , the number of inner layer iterations N in =N in +1; 10) Return to step 5), if the iteration convergence is satisfied, then jump out of the inner loop and execute step 11); if not, then continue the iteration loop; 11) If the subproblem is infeasible, introduce slack variables m1 and m2, remove the infeasible solutions encountered in the main problem, and introduce the feasible cut FC of the subproblem. 12) Update the objective function of the main problem, U b1 and L b1 , the number of outer iterations N out =N out +1; 13) Return to step 2); if the iterative convergence is satisfied, then jump out of the outer loop and the algorithm ends; if not, then continue the iterative loop.

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