Building P2P electric heat energy interaction network cost making strategy method and device

Abstract

The invention discloses a building P2P electric heat energy interaction network cost making strategy method and device, and the method comprises the steps: constructing an upper-layer city integrated energy system operator operation optimization model, and setting a first constraint condition; constructing an energy consumption optimization model for the lower-layer building producers and diasters, and setting a second constraint condition; according to the upper-layer city integrated energy system operator operation optimization model and the lower-layer building producer energy consumption optimization model, a network-crossing cost making strategy collaborative optimization model oriented to differentiated building P2P electricity / heat energy interaction is constructed; decomposing the collaborative optimization model to obtain an urban integrated energy system operator sub-problem and a plurality of building producer and disagger sub-problems; and solving through an acceleration asynchronous distributed optimization algorithm based on the ADMM to obtain an optimal operation strategy and an optimal transaction scheme. According to the method, the collaborative optimization model can be efficiently and quickly solved while the information privacy of the building producers and the urban integrated energy system operators is protected.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a method and device for formulating a network fee strategy for building P2P electric and thermal energy interaction. Background Art

[0002] With the rapid development of urban integrated energy systems and the widespread application of multi-energy conversion equipment, building prosumers will play a more flexible and active role in the optimization of urban integrated energy system operation. Existing technologies encourage building prosumers with various differentiated characteristics to conduct P2P electricity / heat energy transactions in urban integrated energy systems, thereby achieving complementary advantages between different types of energy, improving the efficiency of energy resource utilization, and reducing the energy costs of building prosumers. Distribution system operators have designed a network fee collection plan for building prosumers under P2P energy transactions to achieve a reasonable allocation of the cost of building prosumers using the distribution network, and indirectly guide them to optimize the P2P transaction plan so that the transaction can meet the operation constraints of the distribution network. Urban integrated energy system operators can configure a variety of different multi-energy conversion equipment, convert the electricity, gas, and heat energy purchased from the upper-level energy system, and then sell the converted energy to building prosumers under P2P electricity / heat energy transactions to obtain economic benefits. Formulating electricity and heat sales prices for urban integrated energy system operators and optimizing strategies for purchasing and selling energy to superior energy systems can further promote the interaction between urban integrated energy system operators and building prosumers, stimulate demand response from building prosumers, and ultimately achieve collaborative optimization for both parties.

[0003] At present, there are deficiencies in the technology for formulating the operation strategy of the urban integrated energy system operator that comprehensively considers the electricity / heat transmission fee price, the electricity and heat selling price, and the optimization of its energy resource scheduling. In the process of building prosumers transmitting and trading electricity and heat energy, it is necessary to ensure not only the safe operation of the distribution network, but also the safe operation of the heating network. In the process of building prosumers participating in the supply and demand interaction of the urban integrated energy system, both building prosumers and urban integrated energy system operators are independent interest decision-making entities. In order to further protect the information privacy of each decision-making entity, it is necessary to adopt distributed optimization algorithms, such as the alternating direction multiplier method (ADMM) and the target cascade analysis method (ATC), to solve the collaborative optimization problem of both parties. However, considering that in this collaborative optimization problem, the urban integrated energy system operator and all building prosumers present a two-layer structure, while the prosumers under P2P electricity / heat energy trading present a parallel structure. In addition, this collaborative optimization problem belongs to a high-dimensional mixed integer non-convex, nonlinear optimization model containing a large number of continuous and discrete variables. How to efficiently and quickly solve the model while protecting the information privacy of building prosumers and urban integrated energy system operators has become a research difficulty.

[0004] Therefore, how to invent a network fee formulation strategy for differentiated building P2P electricity / heat energy interaction has become an urgent problem to be solved. Summary of the invention

[0005] To this end, the present invention provides a method and device for formulating a network fee strategy for building P2P electric and thermal energy interaction. Through a new distributed optimization algorithm, it can efficiently and quickly solve the collaborative optimization model while protecting the information privacy of building prosumers and urban integrated energy system operators.

[0006] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for formulating a network fee strategy for building P2P electric and thermal energy interaction, comprising:

[0007] According to the changes in the energy selling price of the upper energy system, the P2P electricity / heat energy transactions of the lower building prosumers and the energy production and consumption of the lower building prosumers themselves, an upper city integrated energy system operator operation optimization model is constructed, and a first constraint condition is set for the upper city integrated energy system operator operation optimization model;

[0008] According to the electricity / heat transmission fee price and the electricity and heat selling price released by the urban integrated energy system operator, an energy consumption optimization model for the lower-level building prosumers is constructed, and a second constraint condition is set for the energy consumption optimization model for the lower-level building prosumers;

[0009] Based on the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model, a network fee formulation strategy collaborative optimization model for differentiated building P2P electricity / heat energy interaction is constructed;

[0010] The collaborative optimization model of the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction is decomposed to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems; the urban integrated energy system operator sub-problem and the building prosumer sub-problem are solved by an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy of the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers.

[0011] As a preferred solution for the grid fee formulation strategy method for building P2P electric and thermal energy interaction, the objective function expression of the upper-level urban integrated energy system operator running optimization model is:

[0012]

[0013] In the formula, and are the power of electrical energy and thermal energy sold to building prosumer i, respectively;

[0014] is the power of electricity purchased from building prosumer i; and are the prices of electricity and heat sold to building prosumer i, respectively; is the price of electricity purchased from building prosumer i; The price of electricity transmission fee; is the power transmission distance between building prosumers i and j; The price of the hot network fee; is the heat transfer distance between building prosumers i and j; is the P2P electricity trading power between building prosumer i and building prosumer j; is the P2P heat energy trading power between building prosumer i and building prosumer j;

[0015] and The power of purchasing electrical energy, gas energy and thermal energy from the upper energy system respectively; To sell power to the upper energy system; and are the prices for purchasing electricity, gas and heat from the parent energy system respectively; The price at which the operator of the urban integrated energy system sells electricity to the superior energy system is set as the purchase price multiplied by the relevant coefficient.

[0016] As a preferred scheme for a method of formulating a grid fee strategy for P2P electric and thermal energy interaction in a building, in the process of setting the first constraint condition for the upper-level urban integrated energy system operator to operate the optimization model, the first constraint condition includes: electricity / heat grid fee constraints, electricity and heat sales price constraints, heat pump and CHP operation constraints, urban integrated energy system operator electricity / heat power balance constraints, distribution network operation constraints, and heating network operation constraints.

[0017] As a preferred solution for the grid fee formulation strategy method for building P2P electric and thermal energy interaction, the objective function expression of the energy optimization model for the lower-level building prosumers is:

[0018]

[0019] In the formula, C ba The unit charging and discharging cost of the energy storage battery; To charge the energy storage battery;

[0020] is the discharge power of the energy storage battery; C TES is the unit heat storage and release cost of the thermal storage equipment; The heat storage power of the heat storage device; Provides heat dissipation power for thermal storage equipment.

[0021] As a preferred scheme for a method of formulating a network fee strategy for P2P electricity and heat energy interaction in a building, in the process of setting the second constraint condition for the energy consumption optimization model of the lower-level building prosumer, the second constraint condition includes: P2P electricity / heat energy trading constraints, energy purchase and sale constraints from urban integrated energy system operators, building thermal balance model constraints, energy storage batteries, heat storage equipment, heat pumps, radiators operation constraints and building prosumer electricity / heat power balance constraints.

[0022] As a preferred solution for a method for formulating a grid fee strategy for building P2P electric / thermal energy interaction, in the process of decomposing the collaborative optimization model of grid fee formulation strategy for differentiated building P2P electric / thermal energy interaction to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems, the augmented Lagrangian function expression of the urban integrated energy system operator sub-problem is:

[0023]

[0024] The augmented Lagrangian function expression of the building prosumer subproblem is:

[0025]

[0026] Where χ is the local variable of the urban integrated energy system operator and the building prosumer; x is the coupling variable; z is the auxiliary variable; λ is the Lagrange multiplier; and ρ is the step size.

[0027] The present invention also provides a network fee formulation strategy device for building P2P electric and thermal energy interaction, based on the above-mentioned network fee formulation strategy method for building P2P electric and thermal energy interaction, comprising:

[0028] The upper-level city integrated energy system operator operation optimization model construction module is used to construct the upper-level city integrated energy system operator operation optimization model according to the changes in the energy sales price of the upper-level energy system, the P2P electricity / heat energy transactions of the lower-level building prosumers, and the energy production and consumption of the lower-level building prosumers themselves, and set the first constraint condition for the upper-level city integrated energy system operator operation optimization model;

[0029] A lower-level building prosumer energy consumption optimization model construction module is used to construct a lower-level building prosumer energy consumption optimization model according to the electricity / heat transmission fee price and the electricity and heat selling price released by the urban integrated energy system operator, and set a second constraint condition for the lower-level building prosumer energy consumption optimization model;

[0030] A collaborative optimization model construction module for the grid fee formulation strategy for P2P electricity / heat energy interaction of differentiated buildings, which is used to construct a collaborative optimization model for the grid fee formulation strategy for P2P electricity / heat energy interaction of differentiated buildings based on the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model;

[0031] A module for solving a collaborative optimization model for a network fee formulation strategy for differentiated building P2P electricity / heat energy interaction is used to decompose the collaborative optimization model for a network fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain a sub-problem of an urban integrated energy system operator and several building prosumer sub-problems; the urban integrated energy system operator sub-problem and the building prosumer sub-problem are solved by an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy for the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers.

[0032] As a preferred solution for a network fee formulation strategy device for building P2P electric and thermal energy interaction, in the upper-level city comprehensive energy system operator operation optimization model construction module, the objective function expression of the upper-level city comprehensive energy system operator operation optimization model is:

[0033]

[0034] In the formula, and are the power of electrical energy and thermal energy sold to building prosumer i, respectively;

[0035] is the power of electricity purchased from building prosumer i; and are the prices of electricity and heat sold to building prosumer i, respectively; is the price of electricity purchased from building prosumer i; The price of electricity transmission fee; is the power transmission distance between building prosumers i and j; The price of the hot network fee; is the heat transfer distance between building prosumers i and j; is the P2P electricity trading power between building prosumer i and building prosumer j; is the P2P heat energy trading power between building prosumer i and building prosumer j;

[0036] and The power of purchasing electrical energy, gas energy and thermal energy from the upper energy system respectively; To sell power to the upper energy system; and are the prices for purchasing electricity, gas and heat from the parent energy system respectively; The price at which the operator of the urban integrated energy system sells electricity to the superior energy system is set as the purchase price multiplied by the relevant coefficient.

[0037] As a preferred solution for a network fee formulation strategy device for P2P electric and thermal energy interaction in a building, in the upper-level urban integrated energy system operator operation optimization model construction module, in the process of setting the first constraint condition for the upper-level urban integrated energy system operator operation optimization model, the first constraint condition includes: electricity / heat network fee constraints, electricity and heat selling price constraints, heat pump and CHP operation constraints, urban integrated energy system operator electricity / heat power balance constraints, distribution network operation constraints and heating network operation constraints.

[0038] As a preferred solution of a network fee formulation strategy device for building P2P electric and thermal energy interaction, in the lower-level building prosumer energy optimization model construction module, the objective function expression of the lower-level building prosumer energy optimization model is:

[0039]

[0040] In the formula, C ba The unit charging and discharging cost of the energy storage battery; To charge the energy storage battery;

[0041] is the discharge power of the energy storage battery; C TES is the unit heat storage and release cost of the thermal storage equipment; The heat storage power of the heat storage device; Provides heat dissipation power for thermal storage equipment.

[0042] As a preferred solution for a network fee formulation strategy device for P2P electric and thermal energy interaction in a building, in the lower-level building prosumer energy optimization model construction module, in the process of setting the second constraint condition for the lower-level building prosumer energy optimization model, the second constraint condition includes: P2P electric / thermal energy trading constraints, energy purchase and sale constraints from urban integrated energy system operators, building thermal balance model constraints, energy storage batteries, heat storage equipment, heat pumps, radiator operation constraints and building prosumer electric / thermal power balance constraints.

[0043] As a preferred solution of a network fee formulation strategy device for building P2P electric / thermal energy interaction, in the network fee formulation strategy collaborative optimization model solving module for differentiated building P2P electric / thermal energy interaction, in the process of decomposing the network fee formulation strategy collaborative optimization model for differentiated building P2P electric / thermal energy interaction to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems, the augmented Lagrangian function expression of the urban integrated energy system operator sub-problem is:

[0044]

[0045] The augmented Lagrangian function expression of the building prosumer subproblem is:

[0046]

[0047] Where χ is the local variable of the urban integrated energy system operator and the building prosumer; x is the coupling variable; z is the auxiliary variable; λ is the Lagrange multiplier; and ρ is the step size.

[0048] The present invention has the following advantages: according to the changes in the energy sales price of the upper energy system, the P2P electricity / heat energy transactions of the lower-level building prosumers and the energy production and consumption of the lower-level building prosumers themselves, an upper-level urban comprehensive energy system operator operation optimization model is constructed, and a first constraint condition is set for the upper-level urban comprehensive energy system operator operation optimization model; according to the electricity / heat transmission fee price and the electricity and heat sales price issued by the urban comprehensive energy system operator, an energy consumption optimization model for the lower-level building prosumers is constructed, and a second constraint condition is set for the energy consumption optimization model for the lower-level building prosumers; according to the upper-level urban comprehensive energy system operator operation optimization model The model and the energy consumption optimization model of the lower-level building prosumers are used to construct a collaborative optimization model for the network fee formulation strategy for differentiated building P2P electricity / heat energy interaction; the collaborative optimization model for the network fee formulation strategy for differentiated building P2P electricity / heat energy interaction is decomposed to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems; the urban integrated energy system operator sub-problem and the building prosumer sub-problem are solved by an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy of the urban integrated energy system operator and the optimal transaction plan for the building prosumer P2P electricity / heat energy. The present invention uses a new distributed optimization algorithm to efficiently and quickly solve the collaborative optimization model while protecting the information privacy of building prosumers and urban integrated energy system operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0050] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0051] Figure 1 A schematic flow chart of a method for formulating a network fee strategy for building P2P electric and thermal energy interaction provided in Example 1 of the present invention;

[0052] Figure 2 This is a schematic diagram of a building RC network model in a method for formulating a network fee strategy for building P2P electric and thermal energy interaction provided in Example 1 of the present invention; wherein (a) is a single wall RC network model, and (b) is a single heating area RC network model;

[0053] Figure 3 A schematic diagram of a collaborative optimization framework for the interaction between building prosumers and urban integrated energy systems under P2P electricity / heat energy trading in a method for formulating a network fee strategy for building P2P electricity / heat energy interaction provided in Example 1 of the present invention;

[0054] Figure 4 A schematic diagram of an asynchronous distributed solution process based on ADMM in a method for formulating a network fee strategy for building P2P electric and thermal energy interaction provided in Example 1 of the present invention;

[0055] Figure 5 This is a schematic diagram of the optimization results of power generation and heat generation by an operator of a city integrated energy system in a possible embodiment provided in Embodiment 1 of the present invention; wherein (a) is the optimization result of power generation and (b) is the optimization result of heat generation and;

[0056] Figure 6 Schematic diagram of the relationship between the optimized electricity / heat transmission fee price and the P2P electricity / heat energy transaction volume of building prosumers in a possible embodiment provided in Embodiment 1 of the present invention; wherein (a) is the relationship between the electricity transmission fee price and the P2P electricity energy transaction, and (b) is the relationship between the heat transmission fee price and the P2P heat energy transaction;

[0057] Figure 7 A schematic diagram of P2P electricity / heat energy transaction volume between building prosumers in a possible embodiment provided in Embodiment 1 of the present invention;

[0058] Figure 8 This is a schematic diagram of the architecture of a network fee formulation strategy device for building P2P electric and thermal energy interaction provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0059] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Example 1

[0061] See also Figure 1 Embodiment 1 of the present invention provides a method for formulating a network fee strategy for building P2P electric and thermal energy interaction, comprising the following steps:

[0062] S1. According to the changes in the energy price of the upper energy system, the P2P electricity / heat energy transactions of the lower building prosumers, and the energy production and consumption of the lower building prosumers themselves, an upper-level city integrated energy system operator operation optimization model is constructed, and a first constraint condition is set for the upper-level city integrated energy system operator operation optimization model;

[0063] S2. According to the electricity / heat transmission fee price and the electricity and heat selling price released by the urban integrated energy system operator, an energy consumption optimization model for the lower-level building prosumers is constructed, and a second constraint condition is set for the energy consumption optimization model for the lower-level building prosumers;

[0064] S3. Based on the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model, a network fee formulation strategy collaborative optimization model for differentiated building P2P electricity / heat energy interaction is constructed;

[0065] S4. Decompose the collaborative optimization model of the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems; solve the urban integrated energy system operator sub-problem and the building prosumer sub-problem through the accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy of the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers.

[0066] In this embodiment, in step S1, according to the changes in the energy selling price of the upper energy system, the P2P electricity / heat energy transactions of the lower building prosumers, and the energy production and consumption of the lower building prosumers themselves, an upper-level city integrated energy system operator operation optimization model is constructed, and a first constraint condition is set for the upper-level city integrated energy system operator operation optimization model;

[0067] Specifically, in order to achieve the maximum economic benefits throughout the day, the urban integrated energy system operator needs to build an upper-level urban integrated energy system operator operation optimization model based on the changes in the energy sales price of the upper-level energy system, the P2P electricity / heat energy transactions of the lower-level building prosumers, and their own energy production and consumption, and adjust their operation strategies through the upper-level urban integrated energy system operator operation optimization model. The objective function expression of the upper-level urban integrated energy system operator operation optimization model is:

[0068]

[0069] In the formula, the first term represents the income from the purchase and sale of electricity and heat from building prosumers; the second term represents the income from charging electricity / heat transmission fees to building prosumers; the third term represents the cost of purchasing electricity, gas and heat from the upper energy system. The first and second terms contain nonlinear terms obtained by multiplying two variables, which makes the model a non-convex optimization problem. The present invention uses the McCormick envelope method to relax it. and are the power of electrical energy and thermal energy sold to building prosumer i, respectively; is the power of electricity purchased from building prosumer i; and are the prices of electricity and heat sold to building prosumer i, respectively; is the price of electricity purchased from building prosumer i; The price of electricity transmission fee; is the power transmission distance between building prosumers i and j; The price of the hot network fee; is the heat transfer distance between building prosumers i and j; is the P2P electricity trading power between building prosumer i and building prosumer j; is the P2P heat energy trading power between building prosumer i and building prosumer j;

[0070] and The power of purchasing electrical energy, gas energy and thermal energy from the upper energy system respectively; To sell power to the upper energy system; and are the prices for purchasing electricity, gas and heat from the parent energy system respectively; The price at which the operator of the urban integrated energy system sells electricity to the superior energy system is set as the purchase price multiplied by the relevant coefficient.

[0071] In the process of setting the first constraint condition for the upper-level urban integrated energy system operator to operate the optimization model, the first constraint condition includes: electricity / heat transmission fee constraints, electricity and heat sales price constraints, heat pump and CHP operation constraints, urban integrated energy system operator electricity / heat power balance constraints, distribution network operation constraints and heating network operation constraints.

[0072] Specifically,

[0073] 1) Electricity / heat transmission fee constraints:

[0074] The electrical distance between building prosumers under P2P power trading is a key factor in determining the network access fee income. The electrical distance between building prosumers i and j can be calculated from the impedance of the connecting line between the two:

[0075]

[0076] Where L is the set of lines connecting building prosumers i and j, which can be obtained through the shortest path algorithm; Z l,i(j) is the impedance of the lth line between building prosumers i and j.

[0077] At the same time, in order to ensure the profitability of distribution system operators and the willingness of building prosumers to participate in P2P power trading, the price of transmission fees needs to be constrained within a certain range:

[0078]

[0079] In the formula, and They respectively represent the upper and lower limits of the electricity transmission fee during period t.

[0080] As mentioned above, the electricity transmission fee is determined by the price of the electricity transmission fee and the electrical distance between the building prosumers. In view of this, the present invention assumes that the heat transmission fee is related to the price of the heat transmission fee and the heat transfer distance between the building prosumers. The heat transfer distance can be obtained by calculating the physical distance between the heating network pipelines of the two building prosumers participating in the P2P heat energy transaction, as shown in formula (4):

[0081]

[0082] Where D lHeat,i(j) The lth Heat The physical distance of the heating network pipeline of the line; L Heat Represents the set of heating network lines through which heat energy is transmitted between building prosumers i and j.

[0083] Similar to the electricity transmission fee, the heat transmission fee price needs to be constrained within a certain range, as shown in formula (5):

[0084]

[0085] In the formula, and They respectively represent the upper and lower limits of the heat transmission fee in time period t.

[0086] 2) Constraints on electricity and heat sales prices:

[0087] When setting the electricity and heat selling prices, the urban integrated energy system operator needs to take into account its own profits and the purchasing willingness of building prosumers. Therefore, the electricity and heat selling prices of the urban integrated energy system operator need to be limited to a certain range, as shown in equations (6) and (7);

[0088]

[0089]

[0090] In the formula, and They are the upper and lower limits of the electricity sales price for urban integrated energy system operators; and They are respectively the upper and lower limits of the operator's hot selling price.

[0091] At the same time, the average price of electricity and heat sold by the operator of the urban integrated energy system should not be higher than the average price of electricity and heat purchased directly from the superior energy system by the building prosumer, as shown in equations (8) and (9):

[0092]

[0093]

[0094] In the formula, and The prices for electricity and heat respectively purchased by building prosumers directly from the superior energy system.

[0095] 3) Heat pump and CHP operation constraints:

[0096] The air source heat pump driven by electric energy uses a fan to exchange heat between outdoor air and the refrigerator in the evaporator to provide indoor heating. The present invention uses a variable frequency air source heat pump (hereinafter referred to as a heat pump) to meet the heat demand of the building. Compared with the traditional 0-1 control method, the heat pump can extend the life of the compressor and reduce energy consumption by adjusting the compressor speed to avoid frequent start and stop. The power consumption of the heat pump is shown in formula (10):

[0097]

[0098] In the formula, is the power consumption of the heat pump during period t; is the heating power of the heat pump during period t; COP HP is the energy efficiency ratio of the heat pump.

[0099] During operation, the heat pump must satisfy the constraints shown in formula (11):

[0100]

[0101] Where P i HP,max is the maximum power consumption of the heat pump.

[0102] The CHP configured by the operator of the urban integrated energy system is small-scale, distributed, and flexible in control, and can use natural gas to produce electricity and heat. Under the backpressure operation mode, the natural gas-to-electricity and heat efficiency of the CHP are fixed, as shown in equations (12) and (13):

[0103] P t CHP =η CHP,e P t Gas (12)

[0104] H t CHP =η CHP,h P t Gas (13)

[0105] Where P t CHP and H t CHP are the electrical and thermal power produced by CHP respectively; P t Gas is the power corresponding to the natural gas consumed by CHP; η CHP,e and Cη CHP,h HP gas-to-power and gas-to-heat efficiency.

[0106] In addition, the heat pumps configured by the urban integrated energy system operator must also meet the constraints of equations (10) and (11), and their capacity is larger than that of the building prosumer. At the same time, the energy purchased by the operator from the superior energy system must meet the constraints shown in equations (14)-(16):

[0107] 0≤P t Elec ≤P Elec,max (14)

[0108] 0≤P tHeat ≤P Heat,max (15)

[0109] 0≤P t Gas ≤P Gas,max (16)

[0110] Where P Elec,max , P Heat,max and P Gas,max They are the upper limits of the amount of electricity, heat and natural gas that the operator can purchase from the parent energy system.

[0111] 4) Electricity / heat power balance constraints for urban integrated energy system operators:

[0112]

[0113]

[0114] Where P t PV is the photovoltaic power generation of the urban integrated energy system operator during period t; P t wind is the wind turbine power generation of the operator in period t; Formula (17) represents the operator's electric power balance constraint; Formula (18) represents the operator's thermal power balance constraint.

[0115] 5) Distribution network operation constraints:

[0116] The present invention adopts the Distflow model to describe the operation law of the power distribution network. The node m of a branch is the starting point, the node m+1 is the end point, and the power flows from the node m to the node m+1. The active power, reactive power and voltage of the branch can be expressed as:

[0117]

[0118]

[0119]

[0120] 1-ε≤V m ≤1+ε(22)

[0121] Where P m +iQ m is the complex power injected into node m; V m is the voltage of node m; is the complex power of the load at node m+1; r f +ix f is the line impedance; ε is the node voltage operating fluctuation range.

[0122] 6) Heating network operation constraints:

[0123] The heating network model is as follows:

[0124] Y HDN m pipe =m node (twenty three)

[0125]

[0126]

[0127]

[0128]

[0129] Where, is Y HDN The connection matrix of the heating network; m pipe and m node are the flow vectors of each pipeline and node respectively; and are the maximum and minimum water flow rates in pipeline l during period t; N pipe is the number of pipelines; p n,t is the water pressure at node n during period t; l represents the pipeline pressure coefficient; κ l , L l and d l are the friction coefficient, length and inner diameter of the pipe l respectively; ρ water is the water density; N node is the number of nodes; and are the maximum and minimum values ​​of the node pressure, respectively. Formula (23) 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, this paper ignores the heat loss in the heating network; Formula (24) indicates that the water flow in the pipeline needs to be constrained within a certain range to prevent pipeline vibration; Formula (25) 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; Formula (27) indicates that the node pressure needs to be controlled within a certain range to ensure the safety of the heating network.

[0130] In this embodiment, in step S2, according to the electricity / heat transmission fee price and the electricity and heat selling price issued by the urban integrated energy system operator, an energy consumption optimization model for the lower-level building prosumer is constructed, and a second constraint condition is set for the energy consumption optimization model for the lower-level building prosumer;

[0131] Specifically, building prosumers will build an energy optimization model for lower-level building prosumers based on the electricity / heat transmission fee price and electricity and heat sales price released by the city's integrated energy system operator. The energy optimization model for lower-level building prosumers will optimize its P2P electricity / heat energy transactions with other building prosumers, energy purchase and sale from the city's integrated energy system operator, and its energy resource scheduling plan to minimize its energy costs.

[0132] The objective function expression of the energy optimization model for prosumers in lower buildings is:

[0133]

[0134] In the formula, the first item represents the cost of purchasing and selling energy from the city's integrated energy system operator; the second item represents the cost of the grid access fee paid to the city's integrated energy system operator; the third item represents the cost of charging and discharging the energy storage battery; the fourth item represents the cost of storing and releasing heat from the heat storage equipment; C ba The unit charging and discharging cost of the energy storage battery; To charge the energy storage battery; is the discharge power of the energy storage battery; C TES is the unit heat storage and release cost of the thermal storage equipment; The heat storage power of the heat storage device; Provides heat dissipation power for thermal storage equipment.

[0135] In the process of setting the second constraint condition for the energy consumption optimization model of the lower-level building prosumer, the second constraint condition includes: P2P electricity / heat energy trading constraints, energy purchase and sale constraints from urban integrated energy system operators, building thermal balance model constraints, energy storage batteries, heat storage equipment, heat pumps, radiator operation constraints and building prosumer electricity / heat power balance constraints.

[0136] Specifically,

[0137] 1) P2P electricity / heat energy trading constraints:

[0138]

[0139]

[0140] Equations (29) and (30) represent the upper and lower power constraints for building prosumer i to j to trade P2P electricity and heat;

[0141]

[0142]

[0143] Equations (31) and (32) represent the power balance constraints of P2P electricity / heat energy trading between building prosumers i and j.

[0144] 2) Constraints on the purchase and sale of energy from urban integrated energy system operators:

[0145]

[0146]

[0147]

[0148] Where P i Elec,max The maximum power of electricity purchased and sold by building prosumer i from the operator of the urban integrated energy system; The maximum power of heat energy purchased by building prosumer i from the operator of the urban integrated energy system.

[0149] 3) Constraints of building thermal balance model:

[0150] The present invention assumes that each floor of the building prosumer is composed of four heating zones. Taking heating zone 1 as an example, the RC network thermal dynamic model is established. Figure 2 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 2 As shown in (a). 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 ways: through walls or windows. out is the thermal resistance in the heat transfer process between outdoor air and the outer surface of the wall, R r is the thermal resistance in the heat transfer process between indoor air and the inner surface of the wall, R wall is the thermal resistance of heat transfer inside 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 from the sun, and the wall temperature T w Affected by wall thermal resistance, heat capacity and sunlight. rad,win The heat gained by the window from the sun. Indoor temperature T r The heat dissipation Q of indoor personnel and equipment is calculated by wall thermal resistance, heat capacity, window heat capacity, light intensity, and indoor personnel and equipment heat dissipation Q. int And the heat dissipation of temperature control load Q loadetc. factors jointly determine the

[0151] by Figure 2 As shown 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, 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 resistances are 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 the heating area 1 are as follows:

[0152]

[0153]

[0154] In the formula, 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 residents and equipment in heating zone 1, expressed as (38):

[0155] Q int =Q body +Q app (38)

[0156]

[0157] Q app =percent e ×(Light per +Appliance per )×S×ε(40)

[0158] In the formula, the heat generated by human activities is Q body and the hourly indoor rate of people b 、Average heat dissipation of the human body per and the indoor area S. The calculation process is shown in formula (39); the random heat load Q when lighting appliances and equipment are working app Instead of using percentage hourly e , indoor area S, heat dissipation ratio ε, and power density of electrical appliances and equipment (Light per +Appliance per ), and its calculation process is shown in formula (40).

[0159] Using the building RC network model, the wall thermal balance and indoor thermal balance constraints of a single heating area 1 in building prosumer i are constructed. Since the model belongs to a differential equation, it is difficult to solve, so it is converted and organized into a difference equation:

[0160]

[0161]

[0162] In formula (42), the heat load demand of heating area 1 is met by a variety of heating equipment;

[0163] Formula (43) indicates that the heat load needs to be controlled within a certain range;

[0164] Formula (44) represents the intelligent regulating valve configured for the radiator, which can set the indoor temperature comfort range of users in the building to ensure the user's comfort.

[0165]

[0166]

[0167] In the formula, and They 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 area 1 in building prosumer i during period t; T t r,max and T t r,min are the upper and lower limits of the temperature comfort range respectively.

[0168] 4) Operation constraints of energy storage batteries, thermal storage equipment, heat pumps and radiators:

[0169] During the off-peak hours of building prosumers or when renewable energy sources such as solar photovoltaics are producing more electricity, the energy storage battery can be charged to store electricity; during the peak hours, the energy storage battery can be discharged to achieve a shift in electricity supply and demand. Its operating constraints include charging and discharging power constraints and SOC limits:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] In the formula, and are the charging and discharging power of the energy storage battery respectively; P i ba,chg,max and P i ba ,dischg,max are the maximum charging and discharging power of the energy storage battery respectively; is the SOC of the energy storage battery; and They are the upper and lower limits of the energy storage battery SOC respectively; is the capacity of the energy storage battery; and They are respectively the charging and discharging efficiency of the energy storage battery; is the initial SOC of the energy storage battery, 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.

[0177] Thermal storage equipment can store heat during periods of low electricity prices and release heat during periods of high electricity prices, thereby improving the flexibility of heat production and use by building prosumers. The relationship between the heat storage capacity, heat storage power, heat release power and heat dissipation of thermal storage equipment is shown in formula (51):

[0178]

[0179] In the formula, is the heat storage amount of the heat storage equipment during period t; is the heat storage power of the heat storage device during period t; is the heat release power of the heat storage device during period t;TES is the heat dissipation rate of the thermal storage equipment.

[0180] The following constraints need to be met when the thermal storage device is running:

[0181]

[0182]

[0183]

[0184]

[0185] In the formula, and are the maximum heat storage and heat release power of the heat storage equipment respectively; W i TES,min and W i TES,max are the maximum and minimum values ​​of the heat storage capacity of the thermal storage equipment respectively.

[0186] In the subsequent research of this invention, the hot water storage tank will be mainly discussed. The hot water in this equipment is only used for building heating and does not provide domestic water services for users. In addition, assuming that the upper and lower layers of hot water in the thermal storage device are evenly mixed and the temperature at any point in each layer is equal, only the heat transfer process along the flow direction of hot water in the thermal storage device needs to be considered.

[0187] Radiators can be used in two modes: P2P electricity trading and P2P electricity / heat trading. When the flow of the radiator is adjustable, its thermal characteristics directly affect the temperature comfort of users in the building. At the same time, the flow regulation mode will also affect the formulation of P2P energy trading strategies for building prosumers.

[0188] Building prosumers optimize the management of the thermal power delivered by the radiator according to the temperature comfort range. Assuming that the radiator is equipped with an intelligent regulating valve, the user can use it to adjust the water flow through the radiator, thereby changing its heat dissipation. Therefore, the thermal power delivered by the radiator in time period t is:

[0189]

[0190] In the formula, c p is the specific heat capacity of water; is the water flow rate of the radiator in period t; T s and T r are the supply and return water temperatures of the radiator, respectively. When building prosumers use flow control, it is assumed that the supply and return water temperatures are constant and known.

[0191] The air source heat pump driven by electric energy uses a fan to exchange heat between outdoor air and the refrigerator in the evaporator to provide indoor heating. The present invention uses a variable frequency air source heat pump (hereinafter referred to as a heat pump) to meet the heat demand of the building. Compared with the traditional 0-1 control method, the heat pump can extend the life of the compressor and reduce energy consumption by adjusting the compressor speed to avoid frequent start and stop. The power consumption of the heat pump is shown in the following formula:

[0192]

[0193] In the formula, is the power consumption of the heat pump during period t; is the heating power of the heat pump during period t; COP HP is the energy efficiency ratio of the heat pump.

[0194] During operation, the heat pump must meet the following constraints:

[0195]

[0196] Where P i HP,max is the maximum power consumption of the heat pump.

[0197] 5) Electricity / heat power balance constraints for building prosumers:

[0198]

[0199]

[0200] Formula (59) represents the electric power balance constraint of building prosumer i in period t; Formula (60) represents the thermal power balance constraint of building prosumer i in period t.

[0201] In this embodiment, in step S3, a collaborative optimization model for network fee formulation strategy for differentiated building P2P electricity / heat energy interaction is constructed based on the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model;

[0202] Specifically, the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model together constitute a collaborative optimization model for the grid-connection fee formulation strategy for differentiated building P2P electricity / heat energy interaction. The urban integrated energy system operator can purchase and sell energy with the upper-level energy system to meet its own and the energy production and consumption needs of each building prosumer; at the same time, it is equipped with a variety of multi-energy conversion equipment, such as combined heat and power units (CHP), heat pumps, etc., which can integrate the upper-level electricity, gas, and heat systems to achieve efficient energy utilization. In addition, during the electricity / heat energy transaction and transmission process between building prosumers, the urban integrated energy system operator will charge a certain grid-connection fee and ensure that various network operation constraints are met during the transaction. Different building prosumers integrate different types of energy resources, such as energy storage batteries, heat pumps, radiators, and thermal storage equipment. The energy management system (EMS) of building prosumers can optimize their P2P electricity / heat energy transactions and their own scheduling plans based on the electricity / heat transmission fee prices and electricity and heat sales prices released by the city's integrated energy system operators to minimize energy costs.

[0203] In this embodiment, in step S4, the collaborative optimization model of the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction is decomposed to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems; the urban integrated energy system operator sub-problem and the building prosumer sub-problem are solved by an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy of the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers.

[0204] Specifically, considering that building prosumers and urban integrated energy system operators are different independent interest decision-making entities, the interaction between the two needs to be coordinated and optimized. Figure 3 As shown, a collaborative optimization framework for building prosumers to participate in the supply and demand interaction of the urban integrated energy system is provided, which is a typical two-layer optimization problem. The urban integrated energy system operators at the upper level can achieve maximum economic benefits by formulating and optimizing their operation strategies. The building prosumers at the lower level perform P2P electricity / heat energy transactions to minimize their energy costs based on the various prices published by the urban integrated energy system operators. Therefore, the present invention hopes to achieve a balance of interests between the two parties by formulating reasonable strategies for urban integrated energy system operators and P2P electricity / heat energy trading schemes for building prosumers. In addition, an asynchronous distributed optimization algorithm will be introduced to protect the information privacy of urban integrated energy system operators and building prosumers.

[0205] The present invention first decomposes the original collaborative optimization model into the urban integrated energy system operator sub-problem and multiple building prosumer sub-problems, and then adopts an accelerated asynchronous distributed optimization algorithm based on ADMM to solve the double-layer structure and parallel structure between the sub-problems of each decision-making subject, and introduces two acceleration strategies to further improve the solution efficiency.

[0206] 1) Decomposition of two-level optimization problem

[0207] The key to decomposing the two-layer optimization problem is to determine the coupling variables and make the coupling variables satisfy the consistency constraints, which can then lead to multiple sub-problems, namely the urban integrated energy system operator sub-problem and the sub-problem of each building prosumer. Among them, the sub-problems of the urban integrated energy system operator and all building prosumers belong to a two-layer structure, and the coupling variables between the two are the electricity selling price and power of the urban integrated energy system operator, the heat selling price and power, the P2P electricity / heat energy trading power between building prosumers, and the electricity / heat transmission fee price; the sub-problems of each building prosumer belong to a parallel structure, and the coupling variable is the P2P electricity / heat energy trading power. Then, auxiliary variables (such as etc.) to constrain the coupling variables of each sub-problem to be consistent, thereby ensuring global consistency. Therefore, the consistency constraints between the urban integrated energy system operator sub-problem and all building prosumer sub-problems are shown in Equations (61) to (69):

[0208]

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217] The consistency constraints between the sub-problems of each building producer and consumer are shown in equations (70) and (71):

[0218]

[0219]

[0220] To this end, the above consistency constraints are relaxed using the Lagrangian relaxation method and then added to the objective functions of the urban integrated energy system operator subproblem and each building prosumer subproblem, so that their subproblems can be reconstructed.

[0221] For ease of analysis, x will be used as a coupling variable and z as an auxiliary variable. The consistency constraint can be expressed as

[0222] Therefore, the augmented Lagrangian function expression of the urban integrated energy system operator subproblem is as follows:

[0223]

[0224] Similarly, the augmented Lagrangian function of each building prosumer subproblem can be expressed by equation (73):

[0225]

[0226] In the formula, χ represents the local variables of the urban integrated energy system operator and the building prosumer; is the Lagrange multiplier corresponding to the above consistency constraints (70)-(71), and can also be expressed as the price of P2P electricity / heat energy transaction between building prosumers.

[0227] 2) Accelerate the asynchronous solution process of two-level optimization problems

[0228] In view of the complex two-layer structure between the above sub-problems, an accelerated asynchronous distributed optimization algorithm based on ADMM will be adopted. Specifically, the sub-problem of the urban integrated energy system operator and the sub-problem of all building prosumers are solved asynchronously, so that all building prosumers can interact and iterate with the upper-level urban integrated energy system operator as a whole. In the k+1th iteration, the urban integrated energy system operator needs to be based on the coupling variables (i.e., the coupling variables shared by all lower-level building prosumers) in the previous iteration (i.e., the kth iteration). ) solves its sub-problems to obtain the coupling variables that need to be shared (i.e. ). Therefore, the auxiliary variables of the urban integrated energy system operator in the k+1th iteration can be calculated based on the coupling variables obtained in this iteration and the building prosumer coupling variables obtained in the previous iteration, as shown in equations (74) and (75):

[0229]

[0230]

[0231] Furthermore, all building prosumers need to solve their own subproblems based on the coupling variables shared by the above-mentioned urban integrated energy system operators. Considering that the subproblems of each building prosumer are parallel structures, each building prosumer will solve its subproblem in parallel to obtain the coupling variables that each building prosumer needs to share in this iteration. Therefore, the updated expressions of the auxiliary variables of each building prosumer in the k+1th iteration are equations (76) to (78). The updated auxiliary variables can be used to solve the subproblems of the urban integrated energy system operators in the next iteration (i.e., the k+2th iteration).

[0232]

[0233]

[0234]

[0235] The Lagrange multiplier can be updated by using the k+1th iteration coupling variables and auxiliary variables obtained above. The Lagrange multiplier expression of the k+1th iteration of the urban integrated energy system operator is expressed as equation (79)-equation (80), and the Lagrange multiplier expression of the k+1th iteration of the building prosumer is expressed as equation (81)-equation (83).

[0236]

[0237]

[0238]

[0239]

[0240]

[0241] After the above update, it is judged whether the collaborative optimization problem of the urban integrated energy system operator and each building prosumer has converged. The convergence criterion is that both the original residual and the dual residual must be less than the given convergence threshold:

[0242]

[0243] During the solution process, the present invention introduces a hot start strategy and a double update accelerated iteration strategy to further improve the convergence speed between each sub-problem. The hot start strategy can obtain the corresponding initial value of the coupling variable through optimization calculation. Compared with the initial value given by the cold start, the initial value obtained based on this strategy is closer to the optimal solution of the original problem, which can further improve the convergence performance of the algorithm and accelerate the convergence of the solution of each sub-problem, thereby reducing the number of solution iterations. The double update accelerated iteration strategy can be used to adjust the step size in the iteration process of each sub-problem at the end of each iteration, and at the same time, the Lagrange multiplier affected by the step size can be adjusted. Specifically, when the original residual of the iteration is greater than or equal to the given threshold, the step size of each sub-problem in the next iteration will be updated according to the relationship between its original residual and the dual residual, as shown in formula (86). Then, according to the obtained step size, the Lagrange multiplier for the next iteration is updated, as shown in formula (87):

[0244]

[0245] λ k+1 =λ k +ρ k+1 (x k+1 -z k+1 )(87)

[0246] In the formula, τ incr and τ decr They are the parameters used for increasing and decreasing the step size, and their values ​​are 1.5 and 2 respectively.

[0247] When the original residual of the iteration is less than the given threshold, the step size of each subproblem will be the same as the value of the previous iteration, and the Lagrange multiplier of each subproblem will be adjusted according to the original residual in the previous iterations to avoid affecting the efficiency of solving the subproblem, as shown in formula (88):

[0248]

[0249] In the formula, K int K is the parameter for accumulating the original residuals for multiple iterations; dual is the dual residual parameter.

[0250] like Figure 4 As shown in the figure, an asynchronous distributed solution process based on ADMM is given. The upper-level urban integrated energy system operator solves its sub-problems with the goal of optimizing its economic efficiency, and as a coordinator, sends limited shared coupling information to the lower-level building prosumers; the lower-level prosumers solve their respective sub-problems in parallel according to the information transmitted by the upper layer. Subsequently, the upper and lower layers solve in an asynchronous manner and iterate interactively until convergence.

[0251] The specific steps are as follows:

[0252] T1, set the iteration index k=0, and use the hot start strategy to obtain the initial values ​​of the coupling variables and their corresponding Lagrange multipliers;

[0253] T2, solve the relaxed urban integrated energy system operator subproblem by updating the auxiliary variable values ​​and their Lagrange multipliers using equations (74)-(76), and then publish these updated information to all building prosumers;

[0254] T3. According to the information released by the urban integrated energy system operator in step T2, solve the relaxed probabilities of each building prosumer in parallel, use equations (79) to (83) to update the auxiliary variable values ​​and Lagrange multipliers, and release them to the urban integrated energy system operator in the next iteration;

[0255] T4, using a double update iterative acceleration strategy to determine whether the original residual is greater than a threshold (the present invention takes 0.05). If so, use equations (86)-(87) to simultaneously update the step size and the Lagrange multiplier; if not, only use equation (8) to update the Lagrange multiplier;

[0256] T5. Use equation (84)-equation (85) to determine whether the solution process converges. If so, end the iteration; if not, set k=k+1 and go to step T2.

[0257] In a possible embodiment, a specific calculation example is provided as follows:

[0258] 1. Optimize scheduling results

[0259] Urban integrated energy system operators optimize their energy purchase plans from superior energy systems, their own energy production and consumption plans, electricity / heat transmission fees, and electricity and heat sales prices based on the energy prices released by superior energy systems and the energy production and consumption needs of lower-level building prosumers.

[0260] Figure 5 The optimization results of electricity and heat generation of the urban integrated energy system operator are shown. It can be seen that since the heat generation efficiency of CHP is lower than that of heat pump, the urban integrated energy system operator gives priority to using heat pump for heating during most of the day. However, due to the limitation of its heat pump capacity, in some periods, the urban integrated energy system operator needs to purchase heat directly from the upper energy system or choose to use CHP to meet its heat demand.

[0261] The optimized electricity / heat transmission fee price of the urban integrated energy system operator is as follows: Figure 6As shown in Figure 2. When the electricity grid price is at the peak period (i.e., 10:00 and 12:00), the total amount of P2P electricity trading is low. However, when the electricity grid price is at the low period (i.e., 11:00), the total amount of P2P electricity trading is high. Similarly, when the heat grid price increases, the total amount of P2P heat trading will decrease significantly. On the contrary, when the heat grid price decreases, the total amount of P2P heat trading will increase accordingly.

[0262] The electricity / heat transmission fee price released by the city's integrated energy system operator will directly affect the P2P electricity / heat energy transaction volume between all building producers and consumers. Figure 7 As shown in Figure 2. When the price of electricity / heat transmission fee increases, the P2P electricity / heat energy transaction volume will decrease. This is because the excessively high transmission fee price will lead to an increase in P2P transaction costs. and ), excessively high transmission fees will make P2P electricity / heat energy transactions uneconomical, resulting in zero P2P electricity / heat energy transactions between building producers and consumers. and When both are equal to 0), construction producers and consumers can conduct P2P transactions without being affected by network fees, so the transaction volume will reach the maximum value.

[0263] 2. Accelerating the performance analysis of asynchronous distributed optimization algorithms

[0264] In order to verify the effectiveness and feasibility of the accelerated asynchronous distributed optimization algorithm based on ADMM proposed in the present invention, the improved ADMM based on the dynamic step size strategy, the standard ADMM and the algorithm proposed in the present invention are used to solve the optimization problem respectively, and the solution time based on the above three algorithms is compared and analyzed.

[0265] Table 1 shows the solution time based on the above three algorithms. It can be seen that compared with the improved ADMM and standard ADMM with dynamic step size strategy, the solution time of the proposed algorithm is reduced by 6.6s and 30.3s respectively.

[0266] Therefore, the algorithm proposed in the present invention can effectively improve the calculation efficiency.

[0267]

[0268] Table 1 Solution time of three solution algorithms

[0269] In summary, the present invention constructs an upper-level city comprehensive energy system operator operation optimization model according to the changes in the energy sales price of the upper energy system, the P2P electricity / heat energy transactions of the lower-level building prosumers, and the energy production and consumption of the lower-level building prosumers themselves, and sets a first constraint condition for the upper-level city comprehensive energy system operator operation optimization model; constructs a lower-level building prosumer energy consumption optimization model according to the electricity / heat transmission fee price and the electricity and heat sales price issued by the city comprehensive energy system operator, and sets a second constraint condition for the lower-level building prosumer energy consumption optimization model; according to the upper-level city comprehensive energy system operator operation optimization model and the energy consumption optimization model of the lower-level building prosumers, to construct a collaborative optimization model for the network fee formulation strategy for differentiated building P2P electricity / heat energy interaction; decompose the collaborative optimization model for the network fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems; solve the urban integrated energy system operator sub-problem and the building prosumer sub-problem through an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy of the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers. The present invention uses a new distributed optimization algorithm to efficiently and quickly solve the collaborative optimization model while protecting the information privacy of building prosumers and urban integrated energy system operators.

[0270] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0271] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0272] Example 2

[0273] See also Figure 8 Embodiment 2 of the present invention further provides a network fee formulation strategy device for building P2P electric and thermal energy interaction, including:

[0274] The upper-level city integrated energy system operator operation optimization model construction module 001 is used to construct the upper-level city integrated energy system operator operation optimization model according to the changes in the energy sales price of the upper-level energy system, the P2P electricity / heat energy transactions of the lower-level building prosumers, and the energy production and consumption of the lower-level building prosumers themselves, and set the first constraint condition for the upper-level city integrated energy system operator operation optimization model;

[0275] The lower-level building prosumer energy consumption optimization model construction module 002 is used to construct the lower-level building prosumer energy consumption optimization model according to the electricity / heat transmission fee price and the electricity and heat selling price released by the urban integrated energy system operator, and set a second constraint condition for the lower-level building prosumer energy consumption optimization model;

[0276] A collaborative optimization model construction module 003 for the grid fee formulation strategy for differentiated building P2P electricity / heat energy interaction, which is used to construct a collaborative optimization model for the grid fee formulation strategy for differentiated building P2P electricity / heat energy interaction based on the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model;

[0277] The collaborative optimization model solving module 004 for the network fee formulation strategy for differentiated building P2P electricity / heat energy interaction is used to decompose the collaborative optimization model for the network fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems; the urban integrated energy system operator sub-problem and the building prosumer sub-problem are solved by an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy of the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers.

[0278] In this embodiment, in the upper-level city integrated energy system operator operation optimization model construction module 001, the objective function expression of the upper-level city integrated energy system operator operation optimization model is:

[0279]

[0280] In the formula, and are the power of electrical energy and thermal energy sold to building prosumer i, respectively; is the power of electricity purchased from building prosumer i; and are the prices of electricity and heat sold to building prosumer i, respectively; is the price of electricity purchased from building prosumer i; The price of electricity transmission fee; is the power transmission distance between building prosumers i and j; The price of the hot network fee; is the heat transfer distance between building prosumers i and j; is the P2P electricity trading power between building prosumer i and building prosumer j; is the P2P heat energy trading power between building prosumer i and building prosumer j; and The power of purchasing electrical energy, gas energy and thermal energy from the upper energy system respectively; To sell power to the upper energy system; and are the prices for purchasing electricity, gas and heat from the parent energy system respectively; The price at which the operator of the urban integrated energy system sells electricity to the superior energy system is set as the purchase price multiplied by the relevant coefficient.

[0281] In this embodiment, in the upper-level urban integrated energy system operator operation optimization model construction module 001, in the process of setting the first constraint condition for the upper-level urban integrated energy system operator operation optimization model, the first constraint condition includes: electricity / heat transmission fee constraints, electricity and heat sales price constraints, heat pump and CHP operation constraints, urban integrated energy system operator electricity / heat power balance constraints, distribution network operation constraints and heating network operation constraints.

[0282] In this embodiment, in the lower-level building prosumer energy optimization model construction module 002, the objective function expression of the lower-level building prosumer energy optimization model is:

[0283]

[0284] In the formula, C ba The unit charging and discharging cost of the energy storage battery; To charge the energy storage battery;

[0285] is the discharge power of the energy storage battery; C TES is the unit heat storage and release cost of the thermal storage equipment; The heat storage power of the heat storage device; Provides heat dissipation power for thermal storage equipment.

[0286] In this embodiment, in the lower-level building prosumer energy consumption optimization model construction module 002, in the process of setting the second constraint condition for the lower-level building prosumer energy consumption optimization model, the second constraint condition includes: P2P electricity / heat energy trading constraints, energy purchase and sale constraints from urban integrated energy system operators, building thermal balance model constraints, energy storage batteries, heat storage equipment, heat pumps, radiators operation constraints and building prosumer electricity / heat power balance constraints.

[0287] In this embodiment, in the collaborative optimization model solving module 004 for the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction, in the process of decomposing the collaborative optimization model for the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain the urban integrated energy system operator sub-problem and several of the building prosumer sub-problems, the augmented Lagrangian function expression of the urban integrated energy system operator sub-problem is:

[0288]

[0289] The augmented Lagrangian function expression of the building prosumer subproblem is:

[0290]

[0291] Where χ is the local variable of the urban integrated energy system operator and the building prosumer; x is the coupling variable; z is the auxiliary variable; λ is the Lagrange multiplier; and ρ is the step size.

[0292] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0293] Example 3

[0294] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a method for formulating a network fee strategy for P2P electric and thermal energy interaction in a building is stored. The program code includes instructions for executing embodiment 1 or any possible implementation method of a method for formulating a network fee strategy for P2P electric and thermal energy interaction in a building.

[0295] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0296] Example 4

[0297] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0298] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method for formulating a network fee strategy for building P2P electric and thermal energy interaction in Example 1 or any possible implementation thereof.

[0299] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0300] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0301] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0302] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for formulating a network fee strategy for building P2P electric and thermal energy interaction, characterized in that: include: According to the changes in the energy selling price of the upper energy system, the P2P electricity / heat energy transactions of the lower building prosumers and the energy production and consumption of the lower building prosumers themselves, an upper city integrated energy system operator operation optimization model is constructed, and a first constraint condition is set for the upper city integrated energy system operator operation optimization model; According to the electricity / heat transmission fee price and the electricity and heat selling price released by the urban integrated energy system operator, an energy consumption optimization model for the lower-level building prosumers is constructed, and a second constraint condition is set for the energy consumption optimization model for the lower-level building prosumers; Based on the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model, a network fee formulation strategy collaborative optimization model for differentiated building P2P electricity / heat energy interaction is constructed; The collaborative optimization model of the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction is decomposed to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems; the urban integrated energy system operator sub-problem and the building prosumer sub-problem are solved by an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy of the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers.

2. A method for formulating a network fee strategy for building P2P electric and thermal energy interaction according to claim 1, characterized in that: The objective function expression of the optimization model for the upper-level urban integrated energy system operator is: In the formula, and are the power of electrical energy and thermal energy sold to building prosumer i, respectively; is the power of electricity purchased from building prosumer i; and are the prices of electricity and heat sold to building prosumer i, respectively; is the price of electricity purchased from building prosumer i; The price of electricity transmission fee; is the power transmission distance between building prosumers i and j; The price of the hot network fee; is the heat transfer distance between building prosumers i and j; is the P2P electricity trading power between building prosumer i and building prosumer j; is the P2P heat energy trading power between building prosumer i and building prosumer j; and The power of purchasing electrical energy, gas energy and thermal energy from the upper energy system respectively; To sell power to the upper energy system; and are the prices for purchasing electricity, gas and heat from the parent energy system respectively; The price at which the operator of the urban integrated energy system sells electricity to the superior energy system is set as the purchase price multiplied by the relevant coefficient.

3. A method for formulating a network fee strategy for building P2P electric and thermal energy interaction according to claim 2, characterized in that: In the process of setting the first constraint condition for the upper-level urban integrated energy system operator to operate the optimization model, the first constraint condition includes: electricity / heat transmission fee constraints, electricity and heat sales price constraints, heat pump and CHP operation constraints, urban integrated energy system operator electricity / heat power balance constraints, distribution network operation constraints and heating network operation constraints.

4. A method for formulating a network fee strategy for building P2P electric and thermal energy interaction according to claim 3, characterized in that: The objective function expression of the energy optimization model for prosumers in lower buildings is: In the formula, C ba The unit charging and discharging cost of the energy storage battery; To charge the energy storage battery; is the discharge power of the energy storage battery; C TES is the unit heat storage and release cost of the thermal storage equipment; The heat storage power of the heat storage device; Provides heat dissipation power for thermal storage equipment.

5. A method for formulating a network fee strategy for building P2P electric and thermal energy interaction according to claim 4, characterized in that: In the process of setting the second constraint condition for the energy consumption optimization model of the lower-level building prosumer, the second constraint condition includes: P2P electricity / heat energy trading constraints, energy purchase and sale constraints from urban integrated energy system operators, building thermal balance model constraints, energy storage batteries, heat storage equipment, heat pumps, radiator operation constraints and building prosumer electricity / heat power balance constraints.

6. A method for formulating a network fee strategy for building P2P electric and thermal energy interaction according to claim 5, characterized in that: In the process of decomposing the collaborative optimization model of the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain the urban integrated energy system operator sub-problem and several building prosumer sub-problems, the augmented Lagrangian function expression of the urban integrated energy system operator sub-problem is: The augmented Lagrangian function expression of the building prosumer subproblem is: Where χ is the local variable of the urban integrated energy system operator and the building prosumer; x is the coupling variable; z is the auxiliary variable; λ is the Lagrange multiplier; and ρ is the step size.

7. A network fee formulation strategy device for building P2P electric and thermal energy interaction, using a network fee formulation strategy method for building P2P electric and thermal energy interaction as described in any one of claims 1 to 6, characterized in that: include: The upper-level city integrated energy system operator operation optimization model construction module is used to construct the upper-level city integrated energy system operator operation optimization model according to the changes in the energy sales price of the upper-level energy system, the P2P electricity / heat energy transactions of the lower-level building prosumers, and the energy production and consumption of the lower-level building prosumers themselves, and set the first constraint condition for the upper-level city integrated energy system operator operation optimization model; A lower-level building prosumer energy consumption optimization model construction module is used to construct a lower-level building prosumer energy consumption optimization model according to the electricity / heat transmission fee price and the electricity and heat selling price released by the urban integrated energy system operator, and set a second constraint condition for the lower-level building prosumer energy consumption optimization model; A collaborative optimization model construction module for the grid fee formulation strategy for P2P electricity / heat energy interaction of differentiated buildings, which is used to construct a collaborative optimization model for the grid fee formulation strategy for P2P electricity / heat energy interaction of differentiated buildings based on the upper-level urban integrated energy system operator operation optimization model and the lower-level building prosumer energy optimization model; A module for solving a collaborative optimization model for a network fee formulation strategy for differentiated building P2P electricity / heat energy interaction is used to decompose the collaborative optimization model for a network fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain a sub-problem of an urban integrated energy system operator and several building prosumer sub-problems; the urban integrated energy system operator sub-problem and the building prosumer sub-problem are solved by an accelerated asynchronous distributed optimization algorithm based on ADMM to obtain the optimal operation strategy for the urban integrated energy system operator and the optimal P2P electricity / heat energy transaction plan for building prosumers.

8. A network fee formulation strategy device for building P2P electric and thermal energy interaction according to claim 7, characterized in that: In the upper-level city integrated energy system operator operation optimization model construction module, the objective function expression of the upper-level city integrated energy system operator operation optimization model is: In the formula, and are the power of electrical energy and thermal energy sold to building prosumer i, respectively; is the power of electricity purchased from building prosumer i; and are the prices of electricity and heat sold to building prosumer i, respectively; is the price of electricity purchased from building prosumer i; The price of electricity transmission fee; is the power transmission distance between building prosumers i and j; The price of the hot network fee; is the heat transfer distance between building prosumers i and j; is the P2P electricity trading power between building prosumer i and building prosumer j; is the P2P heat energy trading power between building prosumer i and building prosumer j; and The power of purchasing electrical energy, gas energy and thermal energy from the upper energy system respectively; To sell power to the upper energy system; and are the prices for purchasing electricity, gas and heat from the parent energy system respectively; The price at which the operator of the urban integrated energy system sells electricity to the superior energy system is set as the purchase price multiplied by the relevant coefficient.

9. A network fee formulation strategy device for building P2P electric and thermal energy interaction according to claim 8, characterized in that: In the lower-level building prosumer energy optimization model construction module, the objective function expression of the lower-level building prosumer energy optimization model is: In the formula, C ba The unit charging and discharging cost of the energy storage battery; To charge the energy storage battery; is the discharge power of the energy storage battery; C TES is the unit heat storage and release cost of the thermal storage equipment; The heat storage power of the heat storage device; Provides heat dissipation power for thermal storage equipment.

10. A network fee formulation strategy device for building P2P electric and thermal energy interaction according to claim 9, characterized in that: In the collaborative optimization model solving module for the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction, in the process of decomposing the collaborative optimization model for the transmission fee formulation strategy for differentiated building P2P electricity / heat energy interaction to obtain the urban integrated energy system operator sub-problem and several of the building prosumer sub-problems, the augmented Lagrangian function expression of the urban integrated energy system operator sub-problem is: The augmented Lagrangian function expression of the building prosumer subproblem is: Where χ is the local variable of the urban integrated energy system operator and the building prosumer; x is the coupling variable; z is the auxiliary variable; λ is the Lagrange multiplier; and ρ is the step size.

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