Differentiated building prosumer P2P electricity / heat energy interactive operation method and device

Abstract

This invention provides a method and device for interactive operation of P2P electricity / heat energy between differentiated building prosumers. This method, which belongs to the field of energy technology, includes the following steps: S1. Constructing a differentiated P2P electricity / heat energy interaction model for buildings; S2. Solving the differentiated P2P electricity / heat energy interaction model using an asynchronous distributed interactive solution algorithm based on ADMM. This invention utilizes the aforementioned method and device for interactive operation of differentiated P2P electricity / heat energy between buildings. This interactive method can significantly reduce the energy costs of each building prosumer, improve solution efficiency, and ensure a globally optimal solution.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a differentiated building prosumer P2P electricity / heat energy interactive operation method and device. Background Art

[0002] As the main energy consumers in urban integrated energy systems, building prosumers urgently need to be managed through reasonable and efficient energy management methods. P2P electricity exchange can be a key means to achieve efficient utilization of energy resources by building prosumers. However, building prosumers under P2P electricity exchange can only convert the electricity obtained through exchange to meet their heating needs, which may limit the efficiency of energy resource utilization and economic benefits of building prosumers. With the rapid development of urban integrated energy systems, coordinated optimization and complementary advantages between different energy systems can provide a physical system foundation for electricity / heat energy exchange between building prosumers. Therefore, carrying out P2P electricity / heat energy exchange between building prosumers in urban integrated energy systems is of great significance for further exploring and utilizing the flexibility of building prosumers and improving the economic efficiency of individual building prosumers and the overall urban integrated energy system.

[0003] During P2P electricity / heat energy exchange, building prosumers can leverage the thermal storage properties of buildings and the flexibility offered by their energy resources, such as multi-energy conversion equipment, to increase their economic returns. However, differences between building prosumers will impact the outcomes of P2P electricity / heat energy exchange, necessitating scientific consideration of these interactions. However, existing research has only considered differences in energy resource allocation and load distribution among building prosumers, lacking in-depth exploration of differentiated characteristics such as building insulation performance due to age, quality, or aging, as well as the performance of communication and computing infrastructure. These two characteristics directly impact both the efficiency of energy resource utilization within a building prosumer and the efficiency of solutions for interactions between them. Furthermore, as independent decision-makers, building prosumers have a need to protect their information privacy. Existing research generally employs distributed interactive solution algorithms, but variations in communication and computing infrastructure performance make it difficult for existing synchronous distributed interactive solution algorithms to achieve efficient solutions. The reason is that, after determining their interaction information, building prosumers with better communication and computing infrastructure performance must wait to receive interaction information from other buildings with poorer infrastructure performance, resulting in each iteration's solution time equal to that of the building with the worst infrastructure performance. To address this issue, some studies have proposed asynchronous distributed interactive solution algorithms. These algorithms enable building prosumers with better infrastructure performance to update their own interaction information using the interaction information from previous iterations of prosumers with poorer infrastructure performance. However, when the performance gap between building prosumers is too large, the building prosumer with better infrastructure performance must wait for a long time for interaction information from other prosumers and repeatedly use previously acquired information to update its own interaction information. This situation can cause the updated interaction information to drift towards suboptimal, making it impossible to ensure the optimal final interaction solution. Summary of the Invention

[0004] The purpose of the present invention is to provide a differentiated building prosumer P2P electricity / heat energy interactive operation method and device, which can significantly reduce the energy consumption cost of each building prosumer, improve the solution efficiency, and ensure the global optimal solution.

[0005] To achieve the above objectives, the present invention provides a differentiated building prosumer P2P electricity / heat energy interactive operation method, comprising the following steps:

[0006] S1. Construct a P2P electricity / heat energy interaction model for differentiated building prosumers;

[0007] The objective function of the differentiated building prosumer P2P electricity / heat energy interaction model is as follows:

[0008]

[0009] in, represents the energy storage battery charging and discharging cost of building prosumer i during period t; represents the heat storage and release cost of the thermal storage equipment of building prosumer i during period t; represents the cost of electricity purchased and sold by building prosumer i from the operator of the urban integrated energy system during period t; represents the cost of heat energy purchased by building prosumer i from the urban integrated energy system operator during period t; i represents the building prosumer number; t represents the time interval; T represents the interaction period; I represents the set of building prosumers; represents the charging power of the energy storage battery of building prosumer i during period t; represents the energy storage battery discharge power of building prosumer i during period t; represents the unit charging and discharging cost of the energy storage battery of building prosumer i during period t; represents the thermal storage power of the thermal storage equipment of building prosumer i during period t; represents the heat release power of the thermal storage equipment of building prosumer i during period t; represents the unit heat storage and release cost of thermal storage equipment of building prosumer i during period t; and They represent the power of electricity and heat purchased by building prosumer i from the urban integrated energy system operator during period t, respectively. A positive value indicates electricity purchase, and a negative value indicates electricity sale; and They represent the prices of electricity and heat purchased by building prosumer i from the operator of the urban integrated energy system during period t;

[0010] The constraints of the differentiated building prosumer P2P electricity / heat energy interaction model include the following six types:

[0011] Constraint 1: P2P electricity / heat energy interaction constraint;

[0012] Constraint 2: Constraints on purchasing and selling energy from urban integrated energy system operators;

[0013] Constraint three: building thermal balance constraint;

[0014] Constraint 4: Operation constraints of energy storage batteries, thermal storage equipment, radiators, and heat pumps;

[0015] Constraint 5: Electricity / heat power balance constraint for building prosumers;

[0016] Constraint 6: Distribution network constraints and heating network constraints;

[0017] S2. An asynchronous distributed interactive solution algorithm based on ADMM is used to solve the P2P electricity / heat energy interaction model of differentiated building prosumers, including the following sub-steps:

[0018] S21. Set the index k of each building prosumer to 1, and set the initial values of the coupling variables, auxiliary variables, Lagrange multipliers, and step sizes of each building prosumer. Use the ADMM algorithm to decompose the coupling variables to obtain the differentiated building prosumer P2P electricity / heat energy interaction subproblem.

[0019] S22, each building prosumer uses the latest auxiliary variable values and Lagrange multipliers to solve its subproblem in an asynchronous manner;

[0020] S23, executing the prediction step, updating the auxiliary variable value and Lagrange multiplier of the kth iteration;

[0021] S24, performing anomaly detection steps to evaluate the prediction results of the kth iteration;

[0022] When the prediction result does not cause an exception, proceed to step S25;

[0023] When the prediction result causes an exception, only the auxiliary variable value is updated, and k=k+1 is set, and then the process returns to step S22;

[0024] S25. When all the original residuals and the dual residuals are less than the convergence threshold, as shown in formula (2) and formula (3), the iteration stops; otherwise, return to step S22;

[0025]

[0026]

[0027] Among them, r primal represents the original residual; Indicates the kth i The coupling variables of the iteration; Indicates the kth i Auxiliary variable for iterations; r dual represents the dual residual; ρ represents the step size; Indicates the kth i -1 is an auxiliary variable for iteration; δ1 and δ2 both represent the convergence threshold.

[0028] Preferably, the P2P electrical / thermal energy interaction constraints are as follows:

[0029]

[0030]

[0031]

[0032]

[0033] in, and They represent the P2P electric energy and thermal energy exchange power between building prosumer i and building prosumer j during period t, respectively. A positive value indicates that energy is purchased from building prosumer j, and a negative value indicates that energy is sold to building prosumer j. and They represent the P2P electric energy and thermal energy exchange power between building prosumer j and building prosumer i during period t, respectively. A positive value indicates that energy is purchased from building prosumer i, and a negative value indicates that energy is sold to building prosumer i. and They represent the maximum interactive electric energy sold and purchased by building prosumer i from building prosumer j; and They represent the maximum interactive heat energy sold and purchased by building prosumer i to building prosumer j, respectively.

[0034] Formulas (4) and (5) represent the upper and lower limit constraints of the P2P electricity / heat energy interaction power of building prosumers, and formulas (6) and (7) represent the P2P electricity / heat energy interaction power balance constraints between building prosumers.

[0035] Preferably, the constraints on energy purchase and sale from the urban integrated energy system operator are as follows:

[0036]

[0037]

[0038] in, and They represent the maximum power of electricity and heat purchased by building prosumer i from the urban integrated energy system operator.

[0039] Preferably, the building heat balance constraints are as follows:

[0040]

[0041]

[0042] Among them, 1 represents the reference node of the heating area, denoted as node 1; 2 and 3 represent the nodes of the two areas adjacent to the heating area, denoted as nodes 2 and 3; 4 and 5 represent the nodes of the two outdoor areas, denoted as nodes 4 and 5; w represents the node number; there is a window installed on the wall of node 5; the heat capacity of the four walls around the heating area of building prosumer i is The thermal resistance is The wall temperatures during period t are The wall temperatures during period t+1 are represents the temperature of node 1 of building prosumer i during period t, They represent the temperature of node 2, node 3, node 4, and node 5 of building prosumer i in period t respectively; r 1,2 、r 1,3 、r 1,4 、r 1,5 They represent the state of sunlight exposure to the wall. If it is exposed to sunlight, the value is 1, otherwise it is 0; α 1,2 , α 1,3 , α 1,4 , α 1,5 Respectively represent the heat absorption rate of the four walls; Represent the surface areas of the four walls respectively; They represent the solar radiation intensity received by the four walls of building prosumer i; Δt represents the time difference; represents the indoor air heat capacity of node 1 of building prosumer i; T i out represents the outdoor temperature of building prosumer i; represents the indoor temperature of building prosumer i during period t; represents the indoor temperature of building prosumer i during period t+1; represents the temperature of the wall between nodes i and w of the building prosumer during period t; represents the thermal resistance of the windows of building prosumer i; represents the heat increment of the temperature control load in node 1 of building prosumer i during period t; represents the heat gain generated by indoor occupants and equipment in node 1 of building prosumer i during period t; τ win represents the transmittance of the window; represents the area of the windows of building prosumer i; represents the solar radiation intensity on the windows of building prosumer i during period t;

[0043]

[0044]

[0045] in, represents the heat load of building prosumer i at node 1 during period t; and They represent the upper and lower limits of the heat load of node 1 in building prosumer i during period t; represents the indoor temperature of node 1 in building prosumer i during period t; and They represent the upper and lower limits of the temperature comfort range during period t.

[0046] Preferably, the energy storage battery operation constraints include charge and discharge power constraints and SOC limit constraints:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] in, and They represent the charging and discharging power of the energy storage battery of building prosumer i during period t; P i ba,chg,max and P i ba,dischg,max They represent the maximum charging and discharging power of the energy storage battery of building prosumer i during period t; represents the SOC, i.e., the state of charge, of the energy storage battery of building prosumer i during period t; and They represent the upper and lower limits of the SOC of the energy storage battery of building prosumer i in period t; represents the capacity of the energy storage battery of building prosumer i; and denote the charging and discharging efficiency of the energy storage battery of building prosumer i, respectively; represents the initial SOC of the energy storage battery of building prosumer i, indicating that the SOC of the energy storage battery remains unchanged at the initial period t0 of each day, and there are d periods every day; Indicates the SOC of the energy storage battery in time period t0+d; Indicates the SOC of the energy storage battery in time period t-1;

[0054] The relationship between the heat storage capacity, heat storage power, heat release power and heat dissipation of the thermal storage equipment is as follows:

[0055]

[0056] in, represents the heat storage of the thermal storage equipment of building prosumer i in period t+1; represents the heat storage of the thermal storage equipment of building prosumer i during period t; represents the thermal storage power of the thermal storage equipment of building prosumer i during period t; represents the heat release power of the thermal storage equipment of building prosumer i during period t; ηTES Indicates the heat dissipation rate of the thermal storage device;

[0057] The operating constraints of thermal storage equipment are as follows:

[0058]

[0059]

[0060]

[0061]

[0062] in, and They represent the maximum heat storage and heat release power of the heat storage equipment of building prosumer i; W i TES,min and W i TES,max They represent the minimum and maximum heat storage capacity of the thermal storage equipment of building prosumer i respectively;

[0063] The heat sink operation constraints are as follows:

[0064]

[0065] Among them, c p represents the specific heat capacity of water; represents the water flow rate of the radiator in building prosumer i during period t; T s and T r Respectively represent the radiator water supply and return temperature; represents the heat power transferred by the radiator to the prosumer i of the building during period t;

[0066] The power consumption of the heat pump is as follows:

[0067]

[0068] in, represents the power consumption of the heat pump of building prosumer i during period t; represents the heating power of the heat pump of building prosumer i during period t; COP HP Indicates the energy efficiency ratio of the heat pump;

[0069] The heat pump operation constraints are as follows:

[0070]

[0071] Among them, P i HP,max represents the maximum power consumption of the heat pump of building prosumer i; represents the power consumption of the heat pump of building prosumer i during period t.

[0072] Preferably, the electricity / heat power balance constraints of the building prosumer are as follows:

[0073]

[0074]

[0075]

[0076] in, represents the total heat load of building prosumer i during period t; represents the power of the rigid electric load of building prosumer i during period t; Indicates the actual photovoltaic power generation output; represents the actual photovoltaic power generation output of building prosumer i during period t; j represents the building prosumer number; represents the power of heat energy produced by the heat pump of building prosumer i during period t; represents the heat load of heating zone n in building prosumer i during period t; n represents the heating zone number; N represents the total number of heating zones;

[0077] Formula (28) represents the electric power balance constraint of building prosumer i in period t; Formula (29) represents the thermal power balance constraint of building prosumer i in period t; Formula (30) represents that the total heat load of building prosumer i is equal to the sum of the heat loads of all its heating zones;

[0078] The distribution network constraints adopt the linearized DistFlow model. The active power, reactive power and voltage of any branch are expressed as:

[0079]

[0080]

[0081]

[0082] 1-ε≤V m ≤1+ε (34);

[0083] Among them, P m+1 represents the active power of node m+1; P m represents the active power of node m; r f Indicates line resistance; Q m represents the reactive power of node m; represents the active power of the load at node m+1; Q m+1 represents the reactive power of node m+1; x f Indicates line reactance; represents the reactive power of the load at node m+1; Pm +iQ m represents the complex power injected by node m; V m represents the voltage of node m; V m+1 represents the voltage of node m+1; represents the complex power of the load at node m+1; r f +ix f Represents line impedance; V0 represents the branch root node voltage; ε represents the node voltage operating fluctuation range;

[0084] The heating network constraint adopts the heating network model, which is as follows:

[0085] Y HDN m pipe =m node (35);

[0086]

[0087]

[0088]

[0089]

[0090] Among them, Y HDN Represents the incidence matrix of the heating network; m pipe and m node Represent each pipeline flow and node flow vector respectively; and They represent the maximum and minimum water flow rates in pipeline l during period t; N pipe Indicates the number of pipelines; p n,t represents the water pressure at node n during period t; ξ l represents the pipeline pressure coefficient; κ l 、L l and d l They represent the friction coefficient, length and inner diameter of the pipe l respectively; ρ water Indicates water density; N node Indicates the number of nodes; and Represent the maximum and minimum values of the node pressure respectively; represents the hot water flow in pipe l during period t; p n+1,t represents the water pressure at node n+1 during period t;

[0091] Formula (35) indicates that the flow rate injected into the node in the heating network is equal to the flow rate out of the node. At the same time, due to the short length and small scale of the heating network, the present invention ignores the heat loss in the heating network; Formula (36) indicates the water flow rate in the pipeline, which needs to be constrained within a certain range to prevent pipeline vibration; Formula (37) indicates that the pipeline pressure drop caused by the friction of water flow along the pipeline is proportional to the square of the water flow rate; Formula (39) indicates that the node pressure needs to be controlled within a certain range to ensure the safety of the heating network;

[0092] Among them, formula (37) contains the square term of the variable Therefore, the piecewise linearization method is used to deal with it; by reducing the range of formula (36) to Divide into Q segments, q∈Q and represent them on the abscissa, and the corresponding ordinate and slope are as follows:

[0093]

[0094] Then the nonlinear equation in formula (37) is processed piecewise and replaced by the sum of a set of linear equations, as shown in formulas (41) to (43):

[0095]

[0096]

[0097]

[0098] in, is a 0-1 variable, indicating Whether the value is in segment q.

[0099] Preferably, in step S22, each building prosumer uses the latest auxiliary variable value and Lagrange multiplier to solve its subproblem in an asynchronous manner, and the specific operations are as follows:

[0100] Determine the coupling variables in the building prosumer subproblem and then introduce auxiliary variables To ensure the equality of coupling variables, thus forming consistency constraints between subproblems:

[0101]

[0102]

[0103] The Lagrangian relaxation method is used to extend the above consistency constraint into the objective function (1):

[0104]

[0105] Among them, χ i Represents a local variable; xi represents the coupling variable, i.e., the P2P electricity / heat energy interaction power of building prosumer i; z i(j) Represents auxiliary variables; and They represent the P2P electric energy and thermal energy interaction power between building prosumer i and building prosumer j in the k+1th iteration period t; Cost i Same as the variables in the objective function (1), Both are used to represent the P2P electricity / heat energy exchange coupling between differentiated building prosumers; ρ represents the step size; It represents the Lagrange multiplier corresponding to the consistency constraint in time period t, that is, the P2P electricity / heat energy interaction price.

[0106] Preferably, in step S23, the prediction step is executed to update the auxiliary variable value and the Lagrange multiplier of the kth iteration. The specific operations are as follows:

[0107] Assume that building prosumer i is in the kth i In the kth iteration, if the coupling variable value transmitted by building prosumer p is not received, building prosumer i will predict the value of the variable and use the kth i -2nd and kth i -1 The value of the coupled variable in the iteration is predicted using momentum extrapolation:

[0108]

[0109]

[0110] in, Indicates the kth i -2nd and kth i -1 The trend of the coupling variable value in the iteration; μ and η are weight factors; Indicates the kth i -3rd and kth i -2 Trends in the values of coupled variables during the iterations; and Respectively represent the kth i -1 and k i -The coupling variables of building prosumer i and building prosumer p in the 2nd iteration; Indicates the kth i The predicted value of the coupling variable between building prosumer i and building prosumer p in the iteration;

[0111] It is worth noting that k in formula (47) and formula (48) i Need to be greater than 2; when k iWhen it is less than or equal to 2, the building prosumer will directly adopt the coupling variable value of the first iteration or the initial setting.

[0112] The predicted value of the coupling variable obtained based on formula (47) and formula (48) and the actual value of the coupled variable Calculate the kth i +1 iteration of auxiliary variable values and Lagrange multipliers:

[0113]

[0114]

[0115]

[0116]

[0117] Where, Indicates the kth i +1 iteration t period electric energy auxiliary variable of building prosumer i; Indicates the kth i The auxiliary heat energy variable of building prosumer i in the iteration t period; and Respectively represent the kth i The P2P electric and thermal energy interaction power between building prosumer i and building prosumer a during the iteration t period; and Respectively represent the kth i The P2P electric and thermal energy interaction power between building prosumer i and building prosumer p during the iteration t period; and Respectively represent the kth i The Lagrange multiplier of the electrical and thermal energy of building prosumer i during the iteration t period; and Respectively represent the kth i -1 Lagrange multipliers of electricity and heat energy of building prosumer i and building prosumer a during period t; and Respectively represent the kth i -1 Lagrange multipliers of electrical and thermal energy of building prosumer i and building prosumer p during time period t; and Respectively represent the kth i The auxiliary variables of electricity and heat energy of building prosumer i and building prosumer a during the iteration t period; and Respectively represent the kth i The auxiliary variables of electricity and heat energy of building prosumer i and building prosumer p in the iteration t period;

[0118] Construct the building prosumer i in the kth i The sub-problem of +1 iteration is as follows:

[0119]

[0120] Where N a represents the total number of building prosumer sellers; N p Represents the total number of construction prosumer buyers.

[0121] Preferably, in step S24, the abnormality detection step is as follows:

[0122] Based on the energy consumption cost of building prosumer i in the previous s iterations, the corresponding cost curve is obtained;

[0123] Wavelet threshold method and moving average method are used to reduce the noise of the cost curve until the noise-reduced curve and the original curve f i The difference between the original curve f i The ratio of is less than the threshold ε, as shown in formula (54) and formula (55):

[0124]

[0125]

[0126] in, Both represent the Lagrange multipliers corresponding to the consistency constraints;

[0127] Based on the above difference, anomaly detection is performed on the predicted value in the k+1th iteration.

[0128] Specifically, the K-Means method is used to divide the difference between the denoised curve and the original curve results of the s iterations into ψ groups, and then the group that will cause anomalies is determined. i When the difference of an iteration belongs to a group that will cause an exception, the Lagrange multiplier of that iteration will not be updated, which ensures that the asynchronous distributed solution algorithm described above generates the optimal interaction solution.

[0129] The present invention also provides an apparatus for implementing the above-mentioned differentiated building prosumer P2P electricity / heat energy interaction operation method, comprising a differentiated building prosumer P2P electricity / heat energy interaction model construction module and a differentiated building prosumer P2P electricity / heat energy interaction model solution module; the differentiated building prosumer P2P electricity / heat energy interaction model solution module comprises five submodules, namely, an initialization module, a subproblem solving module, a prediction module, an anomaly detection module, and a discrimination module;

[0130] Among them, the differentiated building prosumer P2P electricity / heat energy interaction model construction module is used to construct the differentiated building prosumer P2P electricity / heat energy interaction model;

[0131] The differentiated building prosumer P2P electricity / heat energy interaction model solving module is used to solve the differentiated building prosumer P2P electricity / heat energy interaction model through an asynchronous distributed interactive solving algorithm based on ADMM;

[0132] The initialization module is used to set the index of each building prosumer and set the initial values of each building prosumer's coupling variable, auxiliary variable, Lagrange multiplier, and step size. By using the ADMM algorithm to decompose the coupling variables, the differentiated building prosumer P2P electricity / heat energy interaction sub-problem can be obtained;

[0133] The subproblem solving module is used to solve the subproblems of each building prosumer in an asynchronous manner using the latest auxiliary variable values and Lagrange multipliers;

[0134] The prediction module is used to perform the prediction step and update the auxiliary variable values and Lagrange multipliers of the kth iteration;

[0135] The anomaly detection module is used to perform the anomaly detection step and evaluate the prediction results of the kth iteration;

[0136] The discrimination module is used to judge the original residual, dual residual and convergence threshold.

[0137] Therefore, the present invention adopts the above-mentioned differentiated building prosumer P2P electricity / heat energy interactive operation method and device. This interactive method can significantly reduce the energy consumption cost of each building prosumer, improve the solution efficiency, and ensure the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0138] Figure 1 is the building RC network model; where Figure 1 (a) is a single wall RC network model; Figure 1 (b) is the RC network model of a single heating area;

[0139] Figure 2 This is a schematic diagram of the distribution network branch;

[0140] Figure 3 Solve the P2P electricity / heat energy interaction process between building prosumers based on synchronous and asynchronous distributed algorithms;

[0141] Figure 4 A flowchart for P2P electricity / heat energy interactive solution for building prosumers based on asynchronous distributed interactive solution algorithm;

[0142] Figure 5Schematic diagram of the P2P electricity / heat energy interaction between differentiated building prosumers. DETAILED DESCRIPTION

[0143] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0144] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0145] Example 1

[0146] This paper proposes a differentiated P2P electricity / heat energy interaction model for building prosumers. This model optimizes the prosumer interaction process to minimize the building's own daily energy costs. The model's decision variables include the P2P electricity / heat energy interaction power, the operating power of controllable resources such as energy storage batteries, heat pumps, and thermal storage equipment, and the power purchased and sold from the city's integrated energy system operator.

[0147] The objective function is as follows:

[0148]

[0149] in, represents the energy storage battery charging and discharging cost of building prosumer i during period t; represents the heat storage and release cost of the thermal storage equipment of building prosumer i during period t; represents the cost of electricity purchased and sold by building prosumer i from the operator of the urban integrated energy system during period t; represents the cost of heat energy purchased by building prosumer i from the urban integrated energy system operator during period t; i represents the building prosumer number; t represents the time interval; T represents the interaction period; I represents the set of building prosumers; represents the charging power of the energy storage battery of building prosumer i during period t; represents the energy storage battery discharge power of building prosumer i during period t; represents the unit charging and discharging cost of the energy storage battery of building prosumer i during period t; represents the thermal storage power of the thermal storage equipment of building prosumer i during period t; represents the heat release power of the thermal storage equipment of building prosumer i during period t; represents the unit heat storage and release cost of thermal storage equipment of building prosumer i during period t; and They represent the power of electricity and heat purchased by building prosumer i from the urban integrated energy system operator during period t, respectively. A positive value indicates electricity purchase, and a negative value indicates electricity sale; and They represent the prices of electricity and heat purchased by building prosumer i from the operator of the urban integrated energy system during period t.

[0150] There are a total of six constraints, namely Constraint 1: P2P electricity / heat energy interaction constraint; Constraint 2: Energy purchase and sale constraints from urban integrated energy system operators; Constraint 3: Building thermal balance constraints; Constraint 4: Energy storage battery, heat storage equipment, radiator and heat pump operation constraints; Constraint 5: Building prosumer electricity / heat power balance constraints; Constraint 6: Distribution network constraints and heating network constraints.

[0151] Constraint 1: P2P electricity / heat interaction constraints:

[0152]

[0153]

[0154]

[0155]

[0156] in, and They represent the P2P electric energy and thermal energy exchange power between building prosumer i and building prosumer j during period t, respectively. A positive value indicates that energy is purchased from building prosumer j, and a negative value indicates that energy is sold to building prosumer j. and They represent the P2P electric energy and thermal energy exchange power between building prosumer j and building prosumer i during period t, respectively. A positive value indicates that energy is purchased from building prosumer i, and a negative value indicates that energy is sold to building prosumer i. and They represent the maximum interactive electric energy sold and purchased by building prosumer i from building prosumer j; and They represent the maximum interactive heat energy sold and purchased by building prosumer i to building prosumer j, respectively.

[0157] Constraint 2: Constraints on the purchase and sale of energy from urban integrated energy system operators:

[0158]

[0159]

[0160] in, and They represent the maximum power of electricity and heat purchased by building prosumer i from the urban integrated energy system operator.

[0161] Constraint 3: Building thermal balance constraints:

[0162] The present invention assumes that each floor of the building of the building consumer consists of 4 heating zones. Among them, the RC network thermal dynamic model is established by taking heating zone 1 as an example. Figure 1 First, by selecting a single wall RC network model of heating area 1, the meaning of thermal resistance and heat capacity parameters in the wall thermal dynamic process is explained, as shown in Figure 1. Figure 1 As shown in (a) in the figure. The two sides of the wall are the outdoor and indoor environments, which can be divided into indoor air nodes, wall nodes, and outdoor air nodes. These nodes are connected to each other through thermal resistance and grounded through thermal capacitance. Their temperatures are T w 、T out and T r . C w 、C out and C r are the heat capacities of the three nodes. Heat can be transferred into the room through two channels: walls or windows. out is the thermal resistance in the heat transfer process between the outdoor air and the outer surface of the wall, R r is the thermal resistance during heat transfer between indoor air and the inner surface of the wall, R wall is the thermal resistance of heat transfer in the wall, and the sum of the three can be approximated as the total thermal resistance R of the wall. w . R win is the total thermal resistance of the window, Q rad is the heat gained by the wall due to sunlight, and the wall temperature T w Affected by the thermal resistance, heat capacity and sunlight of the wall. rad,win The amount of heat gained by the window due to sunlight. Indoor temperature T r The heat dissipation Q of indoor personnel and equipment is determined by wall thermal resistance, heat capacity, window heat capacity, light intensity, and int And the heat dissipation of temperature control load Q load and other factors jointly determined.

[0163] by Figure 1 In (b), the indoor air temperature of heating area 1 is the reference node. Assume that this node is node 1 and its temperature is T 1 , whose value is equal to T1 r The air nodes of other areas adjacent to heating area 1 (heating area 4, heating area 2 and two outdoor areas) are nodes 2, 3, 4 and 5 respectively, and their temperatures are T 2 、T 3 、T 4 、T 5 The two outdoor areas are nodes 4 and 5, and the sunlight intensity they receive is and The temperatures of the four walls separating the two areas are The heat capacities are The thermal resistance is The present invention assumes that the temperature distribution in the heating area is uniform and the total indoor air mass is constant. The wall thermal dynamic model and the indoor thermal dynamic model in heating area 1 are as follows:

[0164]

[0165]

[0166] Among them, r 1,2 、r 1,2 、r 1,2 、r 1,2 They are the states of sunlight exposure to the wall. If it is exposed to sunlight, the value is 1, otherwise it is 0; α 1,2 , α 1,3 , α 1,4 , α 1,5 are the heat absorption rates of the four walls, are the surface areas of the four walls respectively; is the indoor air heat capacity of heating zone 1; is the heat increment of the temperature control load in heating area 1; τ win is the window transmittance; A win is the window area. is the heat gain generated by indoor occupants and equipment in heating zone 1, and its expression is shown in formula (10):

[0167] Q int =Q body +Q app (10);

[0168]

[0169] Q app =percent e ×(Light per +Appliance per )×S×ε (12);

[0170] Among them, the heat generated by human activities Q body and the hourly indoor rate of personnel b 、Average heat dissipation of the human body per And it is related to the indoor area S. The calculation process is shown in formula (11); the random heat load Q when lighting appliances and equipment are working app Instead of hourly usage percentage e , indoor area S, heat dissipation ratio ε and power density of lighting appliances and equipment (Light per +Applianceper ), and its calculation process is shown in formula (12).

[0171] Using the building RC network model, the wall thermal balance and indoor thermal balance constraints of a single heating zone 1 in building prosumer i are constructed. Since this model is a differential equation and difficult to solve, it is converted and organized into a difference equation:

[0172]

[0173]

[0174] In formula (14), the heat load demand of heating area 1 is met by a variety of heating equipment; formula (15) indicates that the heat load needs to be controlled within a certain range; formula (16) indicates that the intelligent regulating valve configured on the radiator can set the indoor temperature comfort range of users in the building to ensure user comfort.

[0175]

[0176]

[0177] in, and are the upper and lower limits of heat load of heating zone 1 in building prosumer i during period t; is the indoor temperature of heating zone 1 in building prosumer i during period t; and are the upper and lower limits of the temperature comfort range respectively.

[0178] Constraint 4: Operation constraints for energy storage batteries, thermal storage equipment, radiators, and heat pumps:

[0179] During periods of low electricity load or high output from renewable energy sources such as solar photovoltaics, energy storage batteries can be charged to store electricity. During peak load periods, energy storage batteries can be discharged, thereby shifting electricity supply and demand. The operating constraints include charge and discharge power constraints and SOC limits.

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186] in, and They represent the charging and discharging power of the energy storage battery of building prosumer i during period t; and They represent the maximum charging and discharging power of the energy storage battery of building prosumer i during period t; represents the SOC, i.e., the state of charge, of the energy storage battery of building prosumer i during period t; and They represent the upper and lower limits of the SOC of the energy storage battery of building prosumer i in period t; represents the capacity of the energy storage battery of building prosumer i; and denote the charging and discharging efficiency of the energy storage battery of building prosumer i, respectively; represents the initial SOC of the energy storage battery of building prosumer i, indicating that the SOC of the energy storage battery remains unchanged at the initial time period t0 every day, and there are d time periods every day; Indicates the SOC of the energy storage battery in time period t0+d; Indicates the SOC of the energy storage battery in time period t-1.

[0187] Thermal storage equipment can store heat during periods of low electricity prices and release heat during periods of high electricity prices, thereby increasing the flexibility of building prosumers in heat production and use. The relationship between the heat storage capacity, heat storage power, heat release power, and heat dissipation of thermal storage equipment is shown in the following formula:

[0188]

[0189] in, represents the heat storage of the thermal storage equipment of building prosumer i in period t+1; represents the heat storage of the thermal storage equipment of building prosumer i during period t; represents the thermal storage power of the thermal storage equipment of building prosumer i during period t; represents the heat release power of the thermal storage equipment of building prosumer i during period t; η TES Indicates the heat dissipation rate of thermal storage equipment.

[0190] The following constraints must be met during the operation of the thermal storage device:

[0191]

[0192]

[0193]

[0194]

[0195] in, and They represent the maximum heat storage and heat release power of the thermal storage equipment of building prosumer i respectively; and They represent the minimum and maximum heat storage capacity of the thermal storage equipment of building prosumer i, respectively.

[0196] In this embodiment, the hot water in the thermal storage tank is used only for building heating and does not provide domestic water services. Furthermore, assuming that the hot water in the upper and lower layers of the thermal storage device is evenly mixed and the temperature at any point in each layer is equal, only the heat transfer process along the flow direction of the hot water within the thermal storage device needs to be considered.

[0197] Radiators can be used in two modes: P2P electricity exchange and P2P electricity / heat exchange. When the radiator flow rate is adjustable, its thermal characteristics directly affect the thermal comfort of building users. The flow rate adjustment mode also affects the P2P energy exchange strategy formulated by building prosumers.

[0198] Building prosumers optimize the thermal power delivered by radiators based on their temperature comfort range. Assuming the radiators are equipped with intelligent control valves, users can use them to adjust the water flow through the radiators, thereby varying the amount of heat dissipated. Therefore, the thermal power delivered by the radiator during period t is:

[0199]

[0200] Among them, c p represents the specific heat capacity of water; represents the water flow rate of building prosumer i in the radiator during period t; T s and T r Respectively represent the radiator water supply and return temperature; It represents the heat power delivered by the radiator to building prosumer i during period t. When the building prosumer adopts flow control, it is assumed that the supply and return water temperatures are constant and known.

[0201] An electrically powered air source heat pump uses a fan to exchange heat between outdoor air and the chiller in the evaporator to provide indoor heating. This embodiment uses a variable frequency air source heat pump (hereinafter referred to as a heat pump) to meet the building's heating needs. Compared to the traditional 0-1 control method, the heat pump can extend the compressor life and reduce energy consumption by adjusting the compressor speed to avoid frequent starts and stops. The heat pump power consumption is shown in the following formula:

[0202]

[0203] in, represents the power consumption of the heat pump of building prosumer i during period t; represents the heating power of the heat pump of building prosumer i during period t; COP HP Indicates the energy efficiency ratio of the heat pump.

[0204] During operation, the heat pump must meet the following constraints:

[0205]

[0206] in, represents the maximum power consumption of the heat pump of building prosumer i; represents the power consumption of the heat pump of building prosumer i during period t.

[0207] Constraint 5: Electricity / heat power balance constraints for building prosumers:

[0208]

[0209]

[0210]

[0211] in, represents the total heat load of building prosumer i during period t; represents the power of the rigid electric load of building prosumer i during period t; Indicates the actual photovoltaic power generation output; represents the actual photovoltaic power generation output of building prosumer i during period t; j represents the building prosumer number; represents the power of heat energy produced by the heat pump of building prosumer i during period t; represents the heat load of heating zone n in building prosumer i during period t; n represents the heating zone number; N represents the total number of heating zones;

[0212] Constraint 6: Distribution network constraints and heating network constraints:

[0213] It is worth noting that urban integrated energy system operators need to interact with building prosumers in P2P electricity / heat interactions to ensure that their interaction solutions meet the operational constraints of the distribution and heating networks. For distribution network constraints, a linearized DistFlow model is used. Figure 2 The figure shows one of the branches, with node m as the starting point and node m+1 as the end point. Power flows from node m to node m+1. The active power, reactive power, and voltage of this branch can be expressed as:

[0214]

[0215]

[0216]

[0217] 1-ε≤V m ≤1+ε(37);

[0218] Among them, P m+1 represents the active power of node m+1; P m represents the active power of node m; r f Indicates line resistance; Q m represents the reactive power of node m; represents the active power of the load at node m+1; Q m+1 represents the reactive power of node m+1; x f Indicates line reactance; represents the reactive power of the load at node m+1; P m +iQ m represents the complex power injected by node m; V m Represents the voltage of node m; V m+1 represents the voltage of node m+1; represents the complex power of the load at node m+1; r f +ix f Represents line impedance; V0 represents the branch root node voltage; ε represents the node voltage operating fluctuation range;

[0219] The heating network model is as follows:

[0220] Y HDN m pipe =m node (38);

[0221]

[0222]

[0223]

[0224]

[0225] Among them, Y HDN Represents the incidence matrix of the heating network; m pipe and m node Represent each pipeline flow and node flow vector respectively; and They represent the maximum and minimum water flow rates in pipeline l during period t; N pipe Indicates the number of pipelines; p n,t represents the water pressure at node n during period t; ξ l represents the pipeline pressure coefficient; κ l 、L l and d l They represent the friction coefficient, length and inner diameter of the pipe l respectively; ρ water Indicates water density; N node Indicates the number of nodes; and Represent the maximum and minimum values of the node pressure respectively; represents the hot water flow in pipe l during period t; p n+1,t represents the water pressure at node n+1 during period t;

[0226] Formula (38) indicates that the flow rate injected into the node in the heating network is equal to the flow rate out of the node. At the same time, due to the short length and small scale of the heating network, the present invention ignores the heat loss in the heating network; Formula (39) indicates the water flow rate in the pipeline, which needs to be constrained within a certain range to prevent pipeline vibration; Formula (40) indicates the pipeline pressure drop caused by the friction of water flow along the pipeline, which is proportional to the square of the water flow rate; Formula (42) indicates that the node pressure needs to be controlled within a certain range to ensure the safety of the heating network.

[0227] For the heating network constraints, the present invention will adopt the above heating network model, in which the hydraulic constraint (40) contains the square term of the variable Therefore, the piecewise linearization method is used to deal with it. By setting the range of constraint (39) Divide into Q (q∈Q) segments (with a value of 100) and represent them on the horizontal axis. The corresponding vertical axis and slope are shown as follows:

[0228]

[0229] Then the nonlinear equation in formula (40) is processed piecewise and replaced approximately by the sum of a set of linear equations, as shown in formulas (44) to (46):

[0230]

[0231]

[0232]

[0233] In the formula, is a 0-1 variable to represent Whether the value is in segment q.

[0234] The above objective functions and constraints constitute the differentiated building prosumer P2P electricity / heat energy interaction model of the present invention.

[0235] S2. An asynchronous distributed interactive solution algorithm based on ADMM is used to solve the P2P electricity / heat energy interaction model of differentiated building prosumers, including the following sub-steps:

[0236] Using the ADMM algorithm, coupled variables are decomposed into multiple differentiated P2P interaction subproblems for building prosumers. Based on this, an asynchronous distributed solution algorithm integrating prediction and anomaly detection steps is employed to asynchronously solve these decomposed P2P electricity / heat interaction subproblems for building prosumers.

[0237] Based on the ADMM algorithm, the P2P electricity / heat energy interaction model can be decomposed into multiple building prosumer interaction sub-problems. Each building prosumer can solve its sub-problem independently and share limited exchange information with other building prosumers. The key to decoupling is to determine the coupling variables in the building prosumer sub-problems and then introduce auxiliary variables to solve the problem. To ensure that the coupling variables are equal, a consistency constraint can be formed between the subproblems:

[0238]

[0239]

[0240] To this end, the Lagrangian relaxation method is used to extend the above consistency constraints into the objective function (1):

[0241]

[0242] Among them, χ i Represents a local variable; x i represents the coupling variable, i.e., the P2P electricity / heat energy interaction power of building prosumer i; z i(j) Represents auxiliary variables; and They represent the P2P electric energy and thermal energy interaction power between building prosumer i and building prosumer j in the k+1th iteration period t; Cost i Same as the variables in the objective function (1), Both are used to represent the P2P electric / thermal energy exchange coupling between differentiated building prosumers; ρ represents the step size; It represents the Lagrange multiplier corresponding to the consistency constraint in time period t, that is, the P2P electricity / heat energy interaction price.

[0243] Through the above decoupling, if the synchronous ADMM algorithm is used to solve the subproblems of differentiated building prosumers, the building prosumers with better computing and communication facilities performance will have to wait for other building prosumers with poorer performance to share their coupling variables after solving their own subproblems, so as to update the auxiliary variable values and Lagrange multipliers in each interaction iteration, as shown in the following example: Figure 3 As shown, this will affect the computational efficiency of solving interactive problems.

[0244] like Figure 3As shown, the present invention adopts an asynchronous distributed interactive solution algorithm based on ADMM to solve the above challenges, and the specific implementation is as follows:

[0245] (1) Prediction step.

[0246] Assume that building prosumer i is in the kth i In the kth iteration, if the coupling variable value transmitted by building prosumer p is not received, building prosumer i will predict the value of the variable. i -2nd and kth i -1 The value of the coupled variable in the iteration is predicted using momentum extrapolation:

[0247]

[0248]

[0249] in, Indicates the kth i -2nd and kth i -1 The trend of the coupling variable value in the iteration; μ and η are weight factors; Indicates the kth i -3rd and kth i -2 Trends in the values of coupled variables during the iterations; and Respectively represent the kth i -1 and k i -The coupling variables of building prosumer i and building prosumer p in the 2nd iteration; Indicates the kth i The predicted value of the coupling variable between building prosumer i and building prosumer p in the iteration;

[0250] It is worth noting that k in formula (50) and formula (51) i Need to be greater than 2; when k i When it is less than or equal to 2, the building prosumer will directly adopt the coupling variable value of the first iteration or the initial setting.

[0251] The predicted value of the coupling variable obtained based on formula (50) and formula (51) and the actual value of the coupled variable The kth i +1 iteration of auxiliary variable values and Lagrange multipliers:

[0252]

[0253]

[0254]

[0255]

[0256] Where, Indicates the kth i +1 iteration t period electric energy auxiliary variable of building prosumer i; Indicates the kth i The auxiliary heat energy variable of building prosumer i in the iteration t period; and Respectively represent the kth i The P2P electric and thermal energy interaction power between building prosumer i and building prosumer a during the iteration t period; and Respectively represent the kth i The P2P electric and thermal energy interaction power between building prosumer i and building prosumer p during the iteration t period; and Respectively represent the kth i The Lagrange multiplier of the electrical and thermal energy of building prosumer i during the iteration t period; and Respectively represent the kth i -1 Lagrange multipliers of electricity and heat energy of building prosumer i and building prosumer a during period t; and Respectively represent the kth i -1 Lagrange multipliers of electrical and thermal energy of building prosumer i and building prosumer p during time period t; and Respectively represent the kth i The auxiliary variables of electricity and heat energy of building prosumer i and building prosumer a during the iteration t period; and Respectively represent the kth i The auxiliary variables of electricity and heat energy of building prosumer i and building prosumer p in the iteration t period;

[0257] To this end, we can construct the building prosumer i in the kth i The sub-problem of +1 iteration is as follows:

[0258]

[0259] Among them, N a N represents the total number of building prosumer sellers; p Represents the total number of construction prosumer buyers.

[0260] (2) Anomaly detection step.

[0261] Based on the building prosumer energy costs obtained through multiple iterations, the predicted values of the coupled variables are monitored and tested, and the need for adjustment is assessed. Prediction errors can affect the Lagrange multipliers updated based on the predicted values, resulting in a suboptimal solution for the interaction solution.

[0262] First, based on the energy consumption cost of the building prosumer i in the previous s iterations, the corresponding cost curve is obtained. Then, the wavelet threshold method and moving average method are used to reduce the noise of the curve until the noise-reduced curve is and the original curve f i The difference between them is less than the threshold ε, as shown in formula (57) and formula (58):

[0263]

[0264]

[0265] in, Both represent the Lagrange multipliers corresponding to the consistency constraints;

[0266] Then, based on the above difference, anomaly detection is performed on the predicted value in the k+1th iteration. Specifically, the difference between the denoised curve and the original curve result of the sth iteration is divided into ψ groups using the K-Means method, and then the group that will cause anomalies is determined. i When the difference of an iteration belongs to a group that will cause an exception, the Lagrange multiplier of that iteration will not be updated, which ensures that the asynchronous distributed solution algorithm described above generates the optimal interaction solution.

[0267] Figure 4 The solution process for asynchronous distributed interaction between building prosumers is given. The specific steps are as follows:

[0268] Step 1: Construct a differentiated building prosumer P2P electricity / heat energy interaction model and set the index k of each building prosumer to 1.

[0269] Step 2: Set the initial values of each building prosumer coupling variable, auxiliary variables, Lagrange multiplier, and step size.

[0270] Step 3: Decouple the original model in step 1 into multiple sub-problems described in formula (56) containing Lagrangian functions and internal constraints of building prosumers.

[0271] Step 4: Each building prosumer uses the latest auxiliary variable values and Lagrange multipliers to solve its subproblem in an asynchronous manner.

[0272] Step 5: Execute the prediction step and update the auxiliary variable values and Lagrange multipliers for the kth iteration using formula (52)-formula (55).

[0273] Step 6: Perform the anomaly detection step and evaluate the prediction result of iteration k. If the prediction value will cause an anomaly, only update the auxiliary variable value and set k = k + 1, then return to step 4.

[0274] Step 7: If all the primal residuals and dual residuals are less than the convergence threshold, as shown in Formula (59)-Formula (60), the iteration stops; otherwise, return to Step 4.

[0275]

[0276]

[0277] Among them, r primal represents the original residual; Indicates the kth i The coupling variables of the iteration; Indicates the kth i Auxiliary variable for the iteration; r dual represents the dual residual; ρ represents the step size; Indicates the kth i -1 is an auxiliary variable for iteration; δ1 and δ2 both represent the convergence threshold.

[0278] Example 2

[0279] like Figure 5 As shown, the present invention also provides a differentiated building prosumer P2P electricity / heat energy interactive operation device, including a differentiated building prosumer P2P electricity / heat energy interactive model construction module and a differentiated building prosumer P2P electricity / heat energy interactive model solution module; the differentiated building prosumer P2P electricity / heat energy interactive model solution module includes five submodules, namely, an initialization module, a subproblem solving module, a prediction module, an anomaly detection module, and a discrimination module;

[0280] Among them, the differentiated building prosumer P2P electricity / heat energy interaction model construction module is used to construct the differentiated building prosumer P2P electricity / heat energy interaction model;

[0281] The differentiated building prosumer P2P electricity / heat energy interaction model solving module is used to solve the differentiated building prosumer P2P electricity / heat energy interaction model through an asynchronous distributed interactive solving algorithm based on ADMM;

[0282] The initialization module is used to set the index of each building prosumer and set the initial values of each building prosumer's coupling variable, auxiliary variable, Lagrange multiplier, and step size. By using the ADMM algorithm to decompose the coupling variables, the differentiated building prosumer P2P electricity / heat energy interaction sub-problem can be obtained;

[0283] The subproblem solving module is used to solve the subproblems of each building prosumer in an asynchronous manner using the latest auxiliary variable values and Lagrange multipliers;

[0284] The prediction module is used to perform the prediction step and update the auxiliary variable values and Lagrange multipliers of the kth iteration;

[0285] The anomaly detection module is used to perform the anomaly detection step and evaluate the prediction results of the kth iteration;

[0286] The discrimination module is used to judge the original residual, dual residual and convergence threshold.

[0287] Therefore, the present invention adopts the above-mentioned differentiated building prosumer P2P electricity / heat energy interactive operation method and device. This interactive method can significantly reduce the energy consumption cost of each building prosumer, improve the solution efficiency, and ensure the global optimal solution.

[0288] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A differentiated building prosumer P2P electricity / heat energy interactive operation method, characterized by: The following steps are involved: S1. Construct a P2P electricity / heat energy interaction model for differentiated building prosumers; The objective function of the differentiated building prosumer P2P electricity / heat energy interaction model is as follows: （1）； in, express Time-slot building prosumers The cost of charging and discharging energy storage batteries; express Time-slot building prosumers The heat storage and release costs of thermal storage equipment; express Time-slot building prosumers Cost of purchasing and selling electricity from the city's integrated energy system operator; express Time-slot building prosumers The cost of purchasing heat energy from the city's integrated energy system operator; Indicates the building prosumer number; Indicates a time interval; represents the interaction cycle; represents the set of building prosumers; express Time-slot building prosumers Energy storage battery charging power; express Time-slot building prosumers The discharge power of the energy storage battery; express Time-slot building prosumers Unit charge and discharge cost of energy storage batteries; express Time-slot building prosumers Thermal storage power of thermal storage equipment; express Time-slot building prosumers The heat release power of the thermal storage equipment; express Time-slot building prosumers Unit heat storage and release cost of thermal storage equipment; and Respectively Time-slot building prosumers The power of purchasing and selling electricity and heat energy from the city's integrated energy system operator, A positive value indicates electricity purchase, and a negative value indicates electricity sale; and Respectively Time-slot building prosumers The prices for purchasing and selling electricity and heat from the city's integrated energy system operator; The constraints of the differentiated building prosumer P2P electricity / heat energy interaction model include the following six types: Constraint 1: P2P electricity / heat energy interaction constraint; Constraint 2: Constraints on purchasing and selling energy from urban integrated energy system operators; Constraint three: building thermal balance constraint; Constraint 4: Operation constraints of energy storage batteries, thermal storage equipment, radiators, and heat pumps; Constraint 5: Electricity / heat power balance constraint for building prosumers; Constraint 6: Distribution network constraints and heating network constraints; S2. An asynchronous distributed interactive solution algorithm based on ADMM is used to solve the P2P electricity / heat energy interaction model of differentiated building prosumers, including the following sub-steps: S21. Set up the index of prosumers of each building , and set the initial values of the coupling variables, auxiliary variables, Lagrange multipliers and step sizes of each building prosumer. By using the ADMM algorithm, the coupling variables are decomposed to obtain the differentiated building prosumer P2P electricity / heat energy interaction sub-problems; S22, each building prosumer uses the latest auxiliary variable values and Lagrange multipliers to solve its subproblem in an asynchronous manner; S23, execute the prediction step, update the The values of auxiliary variables and Lagrange multipliers for the iterations; S24, perform anomaly detection step, evaluate the The prediction results of the iteration; When the prediction result does not cause an exception, proceed to step S25; When the prediction result causes an exception, only the auxiliary variable value is updated and , then returns to step S22; S25. When all the original residuals and the dual residuals are less than the convergence threshold, as shown in formula (2) and formula (3), the iteration stops; otherwise, return to step S22; （2）； （3）； in, represents the original residual; Indicates the The coupling variables of the iteration; Indicates the Auxiliary variables for iterations; represents the dual residual; Indicates the step length; Indicates the Auxiliary variables for iterations; and Both represent convergence thresholds; In step S23, the prediction step is performed to update the The auxiliary variable values and Lagrange multipliers of the iteration are as follows: Assuming a building prosumer In the In the iteration, no building prosumers were received The coupled variable values transferred, the building prosumer The variable value will be predicted using Second and The values of the coupled variables in the iteration are predicted using momentum extrapolation: （49）； （50）； in, Indicates the Second and The changing trend of the coupling variable values in the iteration; and is the weight factor; Indicates the Second and The changing trend of the coupling variable values in the iteration; and Respectively represent Second and Second iteration of building prosumers and construction prosumers The coupling variables; Indicates the Second iteration of building prosumers and construction prosumers The predicted value of the coupled variable; The predicted values of the coupled variables obtained based on formulas (49) and (50) are 、 and the actual value of the coupled variable 、 , calculate the Values of auxiliary variables and Lagrange multipliers for iterations: （51）； （52）； （53）； （54）； Where, Indicates the Iterations Time-slot building prosumers The auxiliary variable of electric energy; Indicates the Iterations Time-slot building prosumers The thermal energy auxiliary variable; and Respectively represent Iterations Time-slot building prosumers and construction prosumers P2P electrical and thermal energy interaction power; and Respectively represent Iterations Time-slot building prosumers and construction prosumers P2P electrical and thermal energy interaction power; and Respectively represent Iterations Time-slot building prosumers Lagrange multipliers of electrical energy and thermal energy; and Respectively represent Iterations Time-slot building prosumers and construction prosumers The Lagrange multipliers of electrical and thermal energy; and Respectively represent Iterations Time-slot building prosumers and construction prosumers The Lagrange multipliers of electrical and thermal energy; and Respectively represent Iterations Time-slot building prosumers and construction prosumers Auxiliary variables of electrical energy and thermal energy; and Respectively represent Iterations Time-slot building prosumers and construction prosumers Auxiliary variables of electrical energy and thermal energy; Constructing architectural prosumers In the The sub-problems of the iteration are as follows: （55）； Where, represents the total number of building prosumer sellers; represents the total number of construction prosumer buyers; Represents a local variable; Represents the coupling variable, i.e., the building prosumer P2P electricity / heat energy interaction power; Represents auxiliary variables; In step S24, the abnormality detection steps are as follows: Building Prosumer forward The energy cost of the iteration is calculated to obtain the corresponding cost curve; Wavelet threshold method and moving average method are used to reduce the noise of the cost curve until the noise-reduced curve and the original curve The difference between the original curve The ratio is less than the threshold , as shown in formula (4) and formula (5): （4）； （5）； in, 、 Both represent the Lagrange multipliers corresponding to the consistency constraints; According to the above difference, Anomaly detection is performed on the predicted values in the iteration.

2. A differentiated building prosumer P2P electricity / heat energy interactive operation method according to claim 1, characterized in that: The P2P electricity / thermal energy interaction constraints are as follows: （6）； （7）； （8）； （9）； in, and Respectively Time-slot building prosumers and construction prosumers The P2P electricity and heat interaction power, positive value indicates that the building prosumer Negative value indicates that the purchase is from construction producers and consumers. Sales capacity; and Respectively Time-slot building prosumers and construction prosumers The P2P electricity and heat interaction power, positive value indicates that the building prosumer Negative value indicates that the purchase is from construction producers and consumers. Sales capacity; and Represents construction prosumers To construction prosumers The maximum value of interactive energy sold and purchased; and Represents construction prosumers To construction prosumers The maximum amount of interaction heat for selling and buying.

3. A differentiated building prosumer P2P electricity / heat energy interactive operation method according to claim 2, characterized in that: The constraints on energy purchases and sales from urban integrated energy system operators are as follows: （10）； （11）； in, and Represents construction prosumers The maximum power of electricity and heat purchased from the city's integrated energy system operator.

4. A differentiated building prosumer P2P electricity / heat energy interactive operation method according to claim 3, characterized in that: The building heat balance constraints are as follows: （12）； （13）； Among them, 1 represents the reference node of the heating area, recorded as node 1; 2 and 3 represent the nodes of the two areas adjacent to the heating area, recorded as nodes 2 and 3; 4 and 5 represent the nodes of the two outdoor areas, recorded as nodes 4 and 5; Indicates the node number; there is a window installed on the wall of node 5; building prosumer The heat capacities of the four walls surrounding the heating area are 、 、 、 , the thermal resistances are 、 、 、 , The wall temperatures during the time periods are 、 、 、 , The wall temperatures during the time periods are 、 、 、 ; express Time-slot building prosumers The temperature of node 1, 、 、 、 Respectively Time-slot building prosumers The temperature of node 2, the temperature of node 3, the temperature of node 4, and the temperature of node 5; 、 、 、 They represent the state of sunlight exposure to the wall. If it is exposed to sunlight, the value is 1, otherwise it is 0. 、 、 、 Respectively represent the heat absorption rate of the four walls; 、 、 、 Represent the surface areas of the four walls respectively; 、 、 、 Represents construction prosumers The solar radiation intensity on the four walls of the building; Indicates time difference; Represents building prosumers Indoor air heat capacity of node 1; Represents building prosumers outdoor temperature; express Time-slot building prosumers indoor temperature; express Time-slot building prosumers indoor temperature; express Time-slot building prosumers and the temperature of the wall between nodes; Represents building prosumers thermal resistance of windows; express Time-slot building prosumers The heat increment of the temperature control load in node 1; express Time-slot building prosumers The heat gain generated by indoor occupants and equipment in node 1; represents the transmittance of the window; Represents building prosumers The area of the windows; express Time-slot building prosumers The windows are exposed to sunlight intensity; （14）； （15）； in, express Time-slot building prosumers Heat load at node 1; and Respectively Time-slot building prosumers In the middle, the upper and lower limits of the heat load of node 1; express Time-slot building prosumers middle, the indoor temperature of node 1; and Respectively The upper and lower limits of the temperature comfort range for the time period.

5. A differentiated building prosumer P2P electricity / heat energy interactive operation method according to claim 4, characterized in that: Energy storage battery operation constraints include charge and discharge power constraints and SOC limit constraints: （16）； （17）； （18）； （19）； （20）； （21）； in, and Respectively Time-slot building prosumers The charging and discharging power of the energy storage battery; and Respectively Time-slot building prosumers The maximum charging and discharging power of the energy storage battery; express Time-slot building prosumers The SOC of the energy storage battery, i.e. the state of charge; and Respectively Time-slot building prosumers The upper and lower limits of the energy storage battery SOC; Represents building prosumers The capacity of the energy storage battery; and Represents construction prosumers Energy storage battery charging and discharging efficiency; Represents building prosumers The initial SOC of the energy storage battery, indicating the initial period of each day The SOC of the energy storage battery remains unchanged and has time periods; Indicates time period SOC of the energy storage battery; Indicates time period SOC of the energy storage battery; The relationship between the heat storage capacity, heat storage power, heat release power and heat dissipation of the thermal storage equipment is as follows: （22）； in, express Time-slot building prosumers The heat storage capacity of the thermal storage equipment; express Time-slot building prosumers The heat storage capacity of the thermal storage equipment; express Time-slot building prosumers The thermal storage power of the thermal storage equipment; express Time-slot building prosumers The heat release power of the heat storage equipment; Indicates the heat dissipation rate of the thermal storage device; The operating constraints of thermal storage equipment are as follows: （23）； （24）； （25）； （26）； in, and Represents construction prosumers The maximum heat storage and heat release power of the thermal storage equipment; and Represents construction prosumers The minimum and maximum heat storage capacity of the thermal storage equipment; The heat sink operation constraints are as follows: （27）； in, represents the specific heat capacity of water; Indicates that the radiator is Time-slot building prosumers Water flow rate; and Respectively represent the radiator water supply and return temperature; Indicates that the radiator is Time-slot building prosumers Thermal power transferred; The power consumption of the heat pump is as follows: （28）； in, express Time-slot building prosumers The power consumption of the heat pump; express Time-slot building prosumers The heating power of the heat pump; Indicates the energy efficiency ratio of the heat pump; The heat pump operation constraints are as follows: （29）； in, Represents building prosumers The maximum power consumption of the heat pump; express Time-slot building prosumers The power consumption of the heat pump.

6. A differentiated building prosumer P2P electricity / heat energy interactive operation method according to claim 5, characterized in that: The electricity / heat power balance constraints of building prosumers are as follows: （30）； （31）； （32）； in, express Time-slot building prosumers Total heat load; express Time-slot building prosumers The power of the rigid electric load; Indicates the actual photovoltaic power generation output; express Time-slot building prosumers The actual photovoltaic power generation output; Indicates the building prosumer number; express Time-slot building prosumers The power of the heat pump to produce heat energy; express Time-slot building prosumers Middle, heating area heat load; Indicates the heating zone number; Indicates the total number of heating zones; The distribution network constraints adopt the linearized DistFlow model. The active power, reactive power and voltage of any branch are expressed as: （33）； （34）； （35）； （36）； in, Representation node Active power; Representation node Active power; Indicates line resistance; Representation node Reactive power; Representation node Active power of the load; Representation node Reactive power; Indicates line reactance; Representation node Reactive power of the load; Representation node Injected complex power; Representation node voltage; Representation node voltage; Representation node The complex power of the load; Indicates line impedance; Represents the branch root node voltage; Indicates the node voltage operating fluctuation range; The heating network constraint adopts the heating network model, which is as follows: （37）； （38）； （39）； （40）； （41）； in, Represents the incidence matrix of the heating network; and Represent each pipeline flow and node flow vector respectively; and Respectively Time-of-day pipeline Maximum and minimum water flow rates; Indicates the number of pipelines; Representation node exist Water pressure during the period; Indicates the pipeline pressure coefficient; 、 and Represents pipelines The friction coefficient, length and inner diameter of the pipe; Indicates water density; Indicates the number of nodes; and Represent the maximum and minimum values of the node pressure respectively; express Time-of-day pipeline Medium-heat water flow rate; Representation node exist Water pressure during the period; Among them, formula (39) contains the square term of the variable , so the piecewise linearization method is used to deal with it; by reducing the range of formula (38) Divided into part, And it is expressed on the horizontal axis, and the corresponding vertical axis and slope are as follows: （42）； Then the nonlinear equation in formula (39) is processed piecewise and replaced by the sum of a set of linear equations, as shown in formulas (43) to (45): （43）； （44）； （45）； in, is a 0-1 variable, indicating Is the value of section.

7. A differentiated building prosumer P2P electricity / heat energy interactive operation method according to claim 6, characterized in that: In step S22, each building prosumer uses the latest auxiliary variable value and Lagrange multiplier to solve its subproblem in an asynchronous manner. The specific operations are as follows: Determine the coupling variables in the building prosumer subproblem and then introduce auxiliary variables 、 To ensure the equality of coupling variables, thus forming consistency constraints between subproblems: （46）； （47）； The Lagrangian relaxation method is used to extend the above consistency constraint into the objective function (1): （48）； in, Represents a local variable; Represents the coupling variable, i.e., the building prosumer P2P electricity / heat energy interaction power; Represents auxiliary variables; and Respectively represent Iterations Time-slot building prosumers and construction prosumers P2P electrical and thermal energy interaction power; The same variables as in the objective function (1), 、 、 、 Both are used to represent the P2P electricity / heat energy exchange coupling between differentiated building prosumers; Indicates the step length; 、 express The Lagrange multiplier corresponding to the time period consistency constraint is the P2P electricity / heat energy interaction price.

8. A differentiated building prosumer P2P electricity / heat energy interactive operation method according to claim 7, characterized in that: Formula (55) satisfies formulas (4)-(36), (38)-(39), and (41)-(45).

9. A device for implementing the differentiated building prosumer P2P electricity / heat energy interactive operation method according to any one of claims 1 to 8, characterized in that: It includes a differentiated building prosumer P2P electricity / heat energy interaction model construction module and a differentiated building prosumer P2P electricity / heat energy interaction model solution module; The differentiated building prosumer P2P electricity / heat energy interaction model solution module includes five submodules, namely, initialization module, subproblem solving module, prediction module, anomaly detection module and discrimination module; Among them, the differentiated building prosumer P2P electricity / heat energy interaction model construction module is used to construct the differentiated building prosumer P2P electricity / heat energy interaction model; The differentiated building prosumer P2P electricity / heat energy interaction model solving module is used to solve the differentiated building prosumer P2P electricity / heat energy interaction model through an asynchronous distributed interactive solving algorithm based on ADMM; The initialization module is used to set the index of each building prosumer and set the initial values of each building prosumer's coupling variable, auxiliary variable, Lagrange multiplier, and step size. By using the ADMM algorithm to decompose the coupling variables, the differentiated building prosumer P2P electricity / heat energy interaction sub-problem can be obtained; The subproblem solving module is used to solve the subproblems of each building prosumer in an asynchronous manner using the latest auxiliary variable values and Lagrange multipliers; The prediction module is used to perform the prediction step and update the The values of auxiliary variables and Lagrange multipliers for the iterations; The anomaly detection module is used to perform anomaly detection steps and evaluate the The prediction results of the iteration; The discrimination module is used to judge the original residual, dual residual and convergence threshold.

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