A supply-demand side collaborative optimization method for a district cooling system based on a principal-agent game

Abstract

The application discloses a kind of based on master-slave game's regional cooling system supply and demand side collaborative optimization method, comprising the following steps: obtaining the meteorological data and date attribute data of selected city in climatic region, determine typical building type, collect building information;Building energy consumption proxy model of user side is constructed;Cooling load demand is calculated based on building energy consumption proxy model, and target function of user side cooling cost is constructed based on cooling load demand;Cooling station operation model is constructed;Supply and demand side collaborative optimization model between cooling station and user side building is constructed, and building energy consumption proxy model meets thermal comfort constraint;With the maximum cooling income of cooling station and the minimum cooling cost of user side building as optimization goal, master-slave game is carried out between cooling station and cooling user;Supply and demand side collaborative optimization result is obtained by solving.This application includes demand side model in optimization problem to form global optimization, can effectively improve the flexibility of system, improve the stability of regional cooling system under extreme scenario operation.

Description

Technical Field

[0001] This invention relates to the field of district cooling optimization technology, specifically to a collaborative optimization method for the supply and demand sides of a district cooling system based on master-slave game theory. Background Technology

[0002] District cooling systems centrally produce chilled water from dedicated cooling plants and distribute it to various buildings via a pipeline network. They provide cooling services to densely populated public buildings such as large commercial buildings, central business districts, and university towns, characterized by high building density, a large number of users, and concentrated cooling load demand. The ever-increasing demand for cooling has made it one of the most energy-intensive building energy consumption areas. Currently, cooling electricity load accounts for more than 40% of total urban building electricity consumption, and in some cities, air conditioning electricity load has reached 60% of peak summer load. Especially in recent years, the frequent occurrence of extreme heat waves has led to a surge in cooling load demand, sometimes exceeding the maximum cooling capacity of the district cooling system. This surge in cooling demand caused by extreme heat waves puts enormous pressure on energy supply. Therefore, fully exploring the system's dispatchable potential is of great significance for district cooling systems to cope with extreme cooling scenarios.

[0003] The potential for rationally allocating demand-side resources and achieving coordinated optimization of cooling-side and demand-side operating strategies can improve system flexibility and meet the needs of stable system operation. Establishing an appropriate demand response mechanism to guide user interaction is an important way for district cooling systems to cope with extreme peak cooling loads. Therefore, researching supply-demand side coordinated optimization technology for district cooling systems based on master-slave game theory has important practical significance and application value. Summary of the Invention

[0004] To overcome the shortcomings and deficiencies of existing technologies, this invention provides a supply-demand side collaborative optimization method for district cooling systems based on master-slave game theory. This invention fully considers the economic benefits of cooling stations, as well as the impact of cooling costs and comfort on users. It incorporates the demand-side model into the optimization problem to form a global optimization, resulting in scientific and reasonable operating strategies for cooling stations and energy consumption strategies for users. This effectively improves the flexibility of the system, effectively balances the interests of multiple parties, achieves a win-win situation for both cooling stations and users, and fully taps the dispatchable potential of the user side to enable the stable operation of the district cooling system under extreme cooling scenarios.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a method for supply-demand side collaborative optimization of a district cooling system based on master-slave game theory, comprising the following steps:

[0007] Obtain meteorological and date attribute data for the climate zone of the selected city, determine typical building types, and collect building information;

[0008] Construct a user-side building energy consumption proxy model based on building type and building information;

[0009] The cooling load demand is calculated based on the building energy consumption proxy model, and an objective function for the user-side cooling purchase cost is constructed based on the cooling load demand.

[0010] Construct a cooling station operation model;

[0011] A supply-demand collaborative optimization model between the cooling station and the user-side building is constructed based on the cooling station operation model and the building energy consumption proxy model. The building energy consumption proxy model satisfies thermal comfort constraints.

[0012] The supply and demand side collaborative optimization model between the cooling station and the user-side building aims to maximize the cooling revenue of the cooling station and minimize the cooling purchase cost of the user-side building. The cooling station and the cooling user engage in a master-slave game.

[0013] When the game reaches Nash equilibrium, the optimized time-sharing cold selling price scheme is output as the result of the game problem and the collaborative optimization of supply and demand.

[0014] As a preferred technical solution, the meteorological data includes dry-bulb temperature, solar radiation, wet-bulb temperature, and relative humidity;

[0015] The date attribute data includes month attribute data and week attribute data;

[0016] The building information includes the building envelope selection, room thermal disturbance, interior design parameters, and room ventilation status.

[0017] As a preferred technical solution, a user-side building energy consumption proxy model is constructed based on building type and building information, specifically including:

[0018] The cooling load of user-side buildings is calculated using building energy consumption simulation software. Experiments are conducted on the factors affecting the cooling load using experimental analysis software. The experimental results of the factors affecting the cooling load are sampled and fitted to obtain a building energy consumption proxy model. The factors affecting the cooling load include outdoor temperature, solar radiation, and indoor temperature at multiple times.

[0019] As a preferred technical solution, the objective function for constructing the user-side cooling cost based on cooling load demand is specifically expressed as follows:

[0020] C pc =L·CS

[0021] Where L is a matrix composed of cooling loads, and the hourly cooling load demand l of each element is calculated from outdoor and indoor characteristics through a building energy consumption model, expressed as:

[0022] l(O in O out Os A)

[0023] Among them, O in A matrix representing the indoor temperature of a building, O out O S These are the outdoor temperature matrix and the solar radiation matrix, respectively. A represents other inputs, and CS is the cooling price matrix.

[0024] As a preferred technical solution, the building energy consumption proxy model also satisfies user cost constraints, load transfer constraints, and maximum load constraints;

[0025] The thermal comfort constraint is expressed as follows:

[0026]

[0027] Where h is the discrete time step, h; l(t) is the building cooling demand, kW; o on (t), o side (t) represents the outdoor temperature at the top of the building and the outdoor temperature on the side of the building, in °C; in (t) represents the indoor temperature of the building, in °C; S east S south S west S north S ceiling S window S represents the area of ​​the east, south, west, and north walls of the building, the area of ​​the ceiling at the top of the building, the area occupied by the windows, and the area of ​​the thermally inertial building envelope, respectively, in meters. 2 ;R ceiling R east R south R west R north R window These represent the thermal resistance of the building's ceiling structure, the thermal resistance of the building's east, south, west, and north walls, and the thermal resistance of the building's window structure, respectively (m). 2 ·℃) / W;Q e Q h These represent the heat released by indoor equipment and the heat released by disturbance of people inside the building, respectively, in kW; S (t) represents solar radiation heat, in kW; C in Indicates the building's indoor heat capacity, kJ / (m³). 2 ·℃); O(h) 2 ) represents the truncation error;

[0028] The user fee constraint is expressed as follows:

[0029] L·CS≤L fs CS fs

[0030] Among them, L fs The base state cooling load matrix, CS fs The base state cold chain price matrix;

[0031] The load transfer constraint is expressed as follows:

[0032]

[0033] Among them, l i Indicates cooling load. This represents the cooling load in its ground state.

[0034] The maximum load constraint is expressed as follows:

[0035]

[0036] Where α represents the preset peak reduction rate.

[0037] As a preferred technical solution, the revenue objective function of the cooling station is expressed as:

[0038]

[0039] f1 = max(P) profit )

[0040] Among them, P profit L represents the revenue from cooling sales; CS represents the cooling load matrix; and L represents the cooling price matrix. This refers to the hourly partial power consumption of the cooling unit; PLR x,k,s,t This refers to the hourly partial load power of the refrigeration unit. Hourly partial power consumption of the refrigeration unit and its supporting equipment; ep t This represents the electricity price at time t.

[0041] As a preferred technical solution, the cooling station operation model satisfies the constraints of the district cooling system equipment model and the cooling load supply and demand balance constraints. The constraints of the district cooling system equipment model include partial load factor constraints, dual-condition refrigeration unit condition constraints, ice storage device status constraints, cold release rate constraints, and cold storage rate constraints.

[0042] The partial load factor constraint is expressed as follows:

[0043] 0≤PLR x,k (t)≤1

[0044] Among them, PLR x,k (t) represents the partial load rate of the refrigeration unit k under operating condition x;

[0045] The operating condition constraints of the dual-condition refrigeration unit are expressed as follows:

[0046] 0≤PLR re,k (t0)+PLR cha,k (t0)≤1

[0047] Among them, PLR re,k (t0), PLR cha,k (t0) represent the partial load rate of the refrigeration unit k under refrigeration and cold storage conditions, respectively;

[0048] The state constraints of the ice storage device are expressed as follows:

[0049] V(t)≥0

[0050] Where V(t) is the remaining cooling capacity of the cold storage device, in kW;

[0051] The cooling rate constraint is expressed as follows:

[0052]

[0053] Where M(t) represents the amount of cold energy released by the cold storage device, in kW; Cap storage The capacity of the cold storage device is expressed in kW. Indicates the cold release rate of the cold storage device;

[0054] The cold storage rate constraint is expressed as follows:

[0055]

[0056] in, This indicates the cold storage efficiency of the cold storage device; The cold storage rate of the cold storage device is represented by 'x', which is the power ratio between the cooling mode and the cold storage mode of the dual-mode refrigeration unit. d This indicates the capacity of the type D dual-mode chiller, in kW; PLR cha,d, s(t) represents the partial load rate of the s-th type d dual-condition refrigeration unit under cold storage condition; This indicates the efficiency of the d-type dual-condition refrigeration unit in cold storage mode;

[0057] The cooling load supply and demand balance constraint is expressed as follows:

[0058]

[0059] Where, η pipe To improve the transport efficiency of the pipeline system; The cooling capacity provided by the refrigeration unit; The cooling capacity provided to the cold storage device; n (t) represents the cooling load in kW; σ represents the supply-demand error.

[0060] As a preferred technical solution, the cooling station and the cooling user engage in a master-slave game, and the master-slave game model is specifically represented as follows:

[0061] H = {{P} scs UP user}{S scs US user}}

[0062] Among them, H is a master-slave game model, {P} scs ∪P user Let P be the set of participants. scs P represents a cooling station. user Indicates a cold user; {S scs ∪S user} represents the strategy set of the participants, S scs This indicates the strategy of the cooling station, s user The strategy of the cooling user is represented by the price at which the cooling station sells cooling to the user during each time period within the scheduling cycle; the strategy of the cooling user participating in the game is the energy consumed by the user.

[0063] As a preferred technical solution, the cooling station and the cooling user engage in a master-slave game, specifically including:

[0064] The master-slave game process involves the cooling station prioritizing the setting of cooling prices, while cooling users adjust their energy consumption behavior in response to price changes. In the game, the cooling station and cooling users achieve their maximum benefits through interactive game. When the game between the cooling station and cooling users reaches Nash equilibrium, the unique solution of the game model is obtained.

[0065] When the game reaches Nash equilibrium, both the cooling station and the cooling users obtain their maximum profit. The Nash equilibrium solution is obtained based on the conditions of the cooling station and the cooling users, which satisfy the following conditions:

[0066] Both the strategy sets of the cooling station and the cooling users are non-empty compact convex sets;

[0067] When the strategy of the cooling station is known, the cooling user has a unique optimal solution;

[0068] When the cooling user's strategy is known, the cooling station has a unique optimal solution for the cooling user's strategy.

[0069] As a preferred technical solution, when the game reaches Nash equilibrium, the optimized time-sharing cold storage price scheme is output as the result of the game problem and the collaborative optimization of supply and demand, specifically expressed as follows:

[0070] The cooling station operation model will determine the cooling price. The building energy consumption proxy model is passed to the user side, and the user-side building energy consumption proxy model is based on the known cooling price. The energy consumption scheme is obtained by solving the problem. and energy consumption plan Return to the cooling station operation model;

[0071] The cooling station operation model is based on the energy consumption plan. Optimize its own interests S1 and implement a new cold chain sales strategy Building energy consumption proxy model transmitted to the user side;

[0072] The user-side building energy consumption proxy model is based on the known cooling price. The solution yields a new energy consumption scheme. This is then returned to the cooling station operation model, which calculates its own benefit S2 based on the energy consumption plan.

[0073] Determine whether self-interest S2 is greater than self-interest S1. If so, update self-interest S1 to self-interest S2; otherwise, recalculate self-interest S2.

[0074] The game between the cooling station and the cooling user involves continuous iteration until an equilibrium is reached among the different stakeholders, and the strategy set {S} is then determined. scs ∪s user This serves as the equilibrium solution for the coordinated optimization of supply and demand in a district cooling system.

[0075] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0076] (1) This invention effectively solves the problem that the current optimization of district cooling systems only considers the optimization of the cold source side or directly increases the capacity of the refrigeration equipment, while ignoring the dispatchable potential of virtual energy storage on the user side. It provides methodological guidance for the coordinated optimization of the supply and demand sides of district cooling systems, and increases the overall scheduling flexibility of the system through the optimized operation strategy and optimized energy consumption strategy derived from master-slave game.

[0077] (2) This invention fully considers the impact of extreme cooling scenarios on the district cooling system, and can effectively reduce the peak cooling load. It solves the problem that the existing technology only considers a single typical daily cooling load scenario and ignores the difficulty of maintaining stable operation and economic benefits of the district cooling system under extreme cooling scenarios.

[0078] (3) This invention constructs a supply and demand side collaborative optimization of the district cooling system based on master-slave game, constraint conditions and objective function, fully considers the user's requirements for thermal comfort, and uses optimization methods to solve the game model, which solves the problem that the existing technology only considers the performance optimization of the district cooling system and ignores the need to consider the user's thermal comfort in the comfort cooling load of the urban energy system.

[0079] Based on the optimization of the operation strategy of the district cooling system, the demand-side model is incorporated into the optimization problem, forming a collaborative optimization of the cold source side and the load side. This takes into account the interests of both the supply and demand sides and effectively ensures the stability of the district cooling system in extreme scenarios. It solves the problems of existing technologies that only consider the optimization of the cold source side and fail to tap the dispatchable potential of the user side, as well as the inability to guarantee system stability and user thermal comfort under extreme conditions. Attached Figure Description

[0080] Figure 1 This is a flowchart illustrating the supply and demand side collaborative optimization method for a regional cooling system based on master-slave game theory, as described in this invention.

[0081] Figure 2 A schematic diagram of an office building room, the minimum energy consumption unit constructed according to this invention;

[0082] Figure 3 This is a schematic diagram of the optimized time-sharing refrigeration pricing of the present invention.

[0083] Figure 4 This is a schematic diagram of the load distribution after user response under the time-of-use cooling price of this invention. Detailed Implementation

[0084] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0085] Example

[0086] like Figure 1 As shown, this embodiment provides a supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory, including the following steps:

[0087] S1: Select climate zones and cities, determine typical building types, and collect basic building information.

[0088] Obtain meteorological data and date attribute data for the corresponding city's climate zone. The meteorological data includes dry-bulb temperature, solar radiation, wet-bulb temperature, and relative humidity. The date attribute data includes monthly and weekly data. The building type and building information include building envelope selection, room thermal disturbance, interior design parameters, and room ventilation status.

[0089] like Figure 2 As shown in the figure, this embodiment takes a single room in an office building in a southern city as an example. First, meteorological data and building envelope information are collected. The collected building parameter information is shown in Table 1 below:

[0090] Table 1 Case Building Parameters

[0091]

[0092]

[0093] S2: In this embodiment, a user-side building energy consumption proxy model based on an RC thermal network model is constructed using building energy consumption simulation software according to the building type and building information. The cooling load of the user-side building is calculated using the building energy consumption simulation software DeST. The experimental analysis software Design-Expert is used to conduct multi-factor, multi-level response surface experimental design for cooling load. The multi-level includes outdoor temperature, solar radiation, current indoor temperature, previous indoor temperature, and indoor temperature two times previous. The experimental data of the factors affecting the cooling load are sampled, and finally, the building energy consumption proxy model is obtained by fitting, as shown in Table 2.

[0094] Table 2 Building Energy Consumption Proxy Model

[0095]

[0096] S3: In this embodiment, the cold source side comprehensively considers the pricing strategy for cold storage and constructs an operation model for the cooling station. The ice storage device with a capacity of 47344 kWh, obtained from the configuration combination scheme of the district cooling system configuration method, is used as the research object for the case cooling side.

[0097] S4: In this embodiment, the objective function for the user-side purchase cost of cooling is:

[0098] C pc =L·CS

[0099] In the formula, L is a matrix composed of cooling loads, where the hourly cooling load demand l of each element is calculated from the outdoor and indoor characteristics using a building energy consumption model: l(O in O out O S A), O in A matrix representing the indoor temperatures of a building; O out O S These are the outdoor temperature matrix and the solar radiation matrix, respectively; A represents other inputs; CS is the cooling price matrix.

[0100] The building energy consumption proxy model satisfies thermal comfort constraints, user cost constraints, load transfer constraints, and maximum load constraints, where the thermal comfort constraints are:

[0101]

[0102] In the formula, h is the discrete time step, h; l(t) is the building cooling demand, kW; o on (t), o side(t) represents the outdoor temperature at the top of the building and the outdoor temperature on the side of the building, in °C; in (t) represents the indoor temperature of the building, in °C; S east S south S west S north S ceiling S window S represents the area of ​​the east, south, west, and north walls of the building, the area of ​​the ceiling at the top of the building, the area occupied by the windows, and the area of ​​the thermally inertial building envelope, respectively, in meters. 2 ;R ceiloing R east R south R west R north R window These represent the thermal resistance of the building's ceiling structure, the thermal resistance of the building's east, south, west, and north walls, and the thermal resistance of the building's window structure, respectively (m). 2 ·℃) / W;Q e Q h Let represent the heat released by indoor equipment and the heat released by disturbance of people inside the building, respectively, in kW, both assumed to be constant; s (t) represents solar radiation heat, in kW; C in Indicates the building's indoor heat capacity, kJ / (m³). 2 ·℃); O(h) 2 () represents the truncation error.

[0103] User fee constraints are as follows:

[0104] L·CS≤L fs CS fs

[0105] In the formula, L fs The base state cooling load matrix, CS fs The base state is the cold storage price matrix.

[0106] Cooling load demand response can reshape the cooling load distribution. Meanwhile, considering the cooling energy loss due to building thermal inertia and virtual energy storage, the total cooling load is greater than or equal to the total base-state cooling load that does not participate in demand response. The specific load transfer constraints are as follows:

[0107]

[0108] The maximum load constraint is:

[0109]

[0110] In the formula, α is the preset peak reduction rate.

[0111] In this embodiment, the objective function for the revenue of the cooling station is:

[0112]

[0113] f1 = max(P) profit )

[0114] In the formula, P profit L represents the revenue from cooling sales; CS represents the cooling load matrix; and L represents the cooling price matrix. This refers to the hourly partial power consumption of the cooling unit; PLR x,k,s,t This refers to the hourly partial load power of the refrigeration unit. Hourly partial power consumption of the refrigeration unit and its supporting equipment; ep t This represents the electricity price at time t.

[0115] The cooling station operation model satisfies the constraints of the district cooling system equipment model and the cooling load supply-demand balance constraint. The constraints of the district cooling system equipment model include partial load factor constraints, dual-condition refrigeration unit operating condition constraints, ice storage device status constraints, cold release rate constraints, and cold storage rate constraints. Among them, the partial load factor constraint is:

[0116] 0≤PLR x,k (t)≤1

[0117] PLR x,k (t) represents the partial load rate of the refrigeration unit k under operating condition x.

[0118] The industrial control constraints for the dual-condition refrigeration unit are:

[0119] 0≤PLR re,k (t0)+PLR cha,k (t0)≤1

[0120] In the formula, PLR re,k (t0), PLR cha,k (t0) represent the partial load rate of the refrigeration unit k under refrigeration and cold storage conditions, respectively;

[0121] The state constraints of the ice storage device are:

[0122] V(t)≥0

[0123] V(t) represents the remaining cooling capacity of the cold storage device, in kW.

[0124] The cooling rate is constrained as follows:

[0125]

[0126] In the formula, M(t) represents the amount of cold energy released by the cold storage device, in kW; Cap storage The capacity of the cold storage device is expressed in kW. This indicates the rate of cold release from the cold storage device.

[0127] The cold storage rate constraint is:

[0128]

[0129] This indicates the cold storage efficiency of the cold storage device; The cold storage rate of the cold storage device is represented by 'x', which is the power ratio between the cooling mode and the cold storage mode of the dual-mode refrigeration unit. d This indicates the capacity of the type D dual-mode chiller, in kW; PLR cha,d,s (t) represents the partial load rate of the s-th type d dual-condition refrigeration unit under cold storage condition; This indicates the efficiency of the d-type dual-condition refrigeration unit in cold storage mode;

[0130] The supply and demand balance constraint is:

[0131]

[0132] In the formula, η pipe To improve the transport efficiency of the pipeline system; The cooling capacity provided by the refrigeration unit; The cooling capacity provided to the cold storage device; n (t) represents the cooling load in kW; σ represents the supply-demand error.

[0133] S5: Based on the master-slave game theory, construct a supply and demand side collaborative optimization model between the cooling station and the user-side buildings. The supply and demand side collaborative optimization model between the cooling station and the user-side buildings includes a cooling station operation model and a building energy consumption proxy model. The building energy consumption proxy model is the energy consumption model of the buildings in the cooling area. The supply and demand side collaborative optimization model between the cooling station and the user-side buildings takes maximizing the cooling revenue of the cooling station and minimizing the cooling purchase cost of the user-side buildings as the optimization objective.

[0134] Based on the established collaborative optimization model, the optimized operation strategy and optimized energy consumption strategy are obtained.

[0135] In this embodiment, based on the master-slave game theory, a supply-demand collaborative optimization model is constructed, involving the district cooling system and user-side buildings. The cooling station is designated as the leader, and the cooling users as followers. This establishes a Nash-Stackelberg Game optimization problem for the district cooling system's supply and demand sides. The master-slave game model is specifically represented as follows:

[0136] H = {{P} scs ∪UP user}{S scs US user}}

[0137] In the formula, H represents the master-slave game model, {P}scs ∪P user Let P be the set of participants. scs This represents the cooling station, P user This represents users who use cold services; {S scs ∪S user} is the strategy set of the participants, S scs S represents the strategy of the cooling station. user The strategy of the cooling user is represented by the price at which the cooling station sells cooling to the user during each time period within the scheduling cycle; the strategy of the cooling user participating in the game is the energy consumed by the user.

[0138] In this embodiment, the master-slave game process involves the upper-level leader prioritizing the setting of cooling prices, while the lower-level followers adjust their energy consumption behavior in response to price changes. Solving the master-slave game model can output optimized operating strategies and optimized energy consumption strategies. Specifically, the cooling station, acting as the upper-level leader, engages in a master-slave game with the lower-level cooling users, who act as followers. In the game, the cooling station and the cooling users achieve their maximum benefit through interactive game. When the cooling station and the cooling users can no longer obtain greater benefits by changing their strategies, the game reaches a Nash equilibrium, and the unique solution of the game model is obtained.

[0139] When the game reaches Nash equilibrium, both the cooling station and the cooling user can obtain the maximum benefit, and a Nash equilibrium solution is obtained according to the conditions of the leader and the follower; the conditions of the leader and the follower are: the policy sets of the leader and the follower are both non-empty compact convex sets; when the leader's policy is known, the follower has a unique optimal solution; when the follower's policy is known, the leader has a unique optimal policy for the follower.

[0140] In this embodiment, the optimization results output by combining the master-slave game model include the following steps:

[0141] Step 1. The upper-level cooling station operation model will set the cooling price. The building energy consumption proxy model is passed to the lower-level user side, and the lower-level user side building energy consumption proxy model is based on the known cooling price. The energy consumption scheme is obtained by solving the problem. This data is then returned to the upper-level cooling station operation model.

[0142] Step 2. The upper-level cooling station operation model is based on the energy consumption plan. Optimize its own interests S1 and implement a new cold chain sales strategy A building energy consumption proxy model is passed to the lower-level user side;

[0143] Step 3. The building energy consumption proxy model on the lower-level user side is based on the known cooling price. The solution yields a new energy consumption scheme. This is then returned to the upper-level cooling station operation model, which calculates its own benefit S2 based on the energy consumption plan.

[0144] Step 4. Determine whether self-interest S2 is greater than self-interest S1. If so, update self-interest S1 to self-interest S2; otherwise, execute Step 3 again.

[0145] Step 5. Determine if the output conditions are met. If yes, output the optimized result; otherwise, execute Step 3.

[0146] The cooling station and the cooling user depend on each other's decisions, and the process iterates continuously in the game until an equilibrium is reached among the different stakeholders. At this point, {S} scs ∪S user} represents the equilibrium solution for supply and demand coordination optimization of the district cooling system.

[0147] In this embodiment, the leader uses the cooling station operation model to construct the operation, denoted as maxP. profit Followers use a building energy consumption proxy model to construct an energy consumption strategy optimization problem, denoted as minC. pc The followers calculate cooling load demand based on a building energy consumption surrogate model, and this model satisfies thermal comfort constraints. The thermal comfort type of the minimum energy consumption unit is used to characterize the thermal comfort level in the region. For the upper-level problem, a multi-objective genetic algorithm is used to generate time-sharing cooling prices, including: a population size of 10, 500 generations, a crossover coefficient of 0.9, and a mutation coefficient of 0.2. The lower-level problem is solved using the mixed-integer linear programming (MILP) solver GUROBI. The solution steps are detailed below:

[0148] Step 1. Input the specific parameters and data involved in the optimization problem and initialize the population CS.

[0149] Step 2. The initial cooling price is transmitted from the upper-level cooling station model to the lower-level user-side model, and the parent population is solved using the GUROBI solver. objective function value Offspring populations are generated based on parent population characteristics using crossover and mutation operations. Calculate the sub-generation objective function The parent and offspring populations merge, and elite individuals are selected to form the next generation. The upper-level cooling station model is passed to the lower-level user-side model, and the results are returned.

[0150] Step 3. Repeat the game in the m-th round, using the parent population of the time-sharing cooling price scheme for the upper-level cooling station model. Solving the parent objective function value using the solver GUROBI Based on the characteristics of the parent population, crossover and mutation are used to generate the offspring population. Calculate the sub-generation objective function The parent and offspring populations merge to form a new population, and elite individuals are selected to form the next generation.

[0151] Step 4. Determine if the iteration count meets the stopping condition; otherwise, increment the iteration count by 1 and repeat the previous step.

[0152] Step 5. Obtain the Pareto front and reach Nash equilibrium for the multi-objective optimization problem. Output the optimized time-sharing refrigeration price scheme as the result of game theory and supply-demand side co-optimization.

[0153] Taking into account the introduction of a user-side model for supply-demand coordinated optimization of the district cooling system, after the game between the cooling station and the user, such as Figure 3 As shown, the optimized refrigeration price of the cooling station is obtained, such as... Figure 4 As shown, the cooling load response curve after user energy optimization is obtained. Figure 4 It can be seen that after optimization, users will shift the cooling load from 10:00-14:00 to 6:00-10:00, and the cooling load from 16:00-17:00 to 15:00-16:00. According to... Figure 3 Optimizing time-of-use (TOU) cooling prices shows that users shift their energy demand from periods with higher cooling prices to periods with lower prices, achieving a "peak shaving and valley filling" effect and validating the model's effectiveness. Simultaneously, effectively reducing peak cooling load helps the district cooling system maintain stable operation in extreme scenarios.

[0154] This embodiment first addresses the issue of existing technologies paying insufficient attention to the enormous dispatchable potential on the user side. Based on the optimization of district cooling system operation strategies, it incorporates the user-side model into the optimization problem, forming a collaborative optimization of the cold source and load sides based on a master-slave game model to coordinate the interests between cooling stations and users. Secondly, addressing the issue of existing technologies neglecting user thermal comfort, to consider user energy demand, it uses the minimum energy consumption unit to expand and construct the regional demand-side energy characteristics, employing the thermal comfort type of the minimum energy consumption unit to characterize the thermal comfort level within the region. Then, to address the problem of the large number of decision variables and nonlinear factors introduced by the demand-side model, making the model difficult to solve, it uses leader and follower modules to decompose the game model, forming an operation strategy optimization problem and an energy consumption strategy optimization problem. These two problems are linked through time-of-use cooling prices and optimized energy consumption strategies, and are solved using the NSGA-II+GUROBI nested optimization method, accelerating convergence and improving optimization quality.

[0155] Example 2

[0156] This embodiment provides a supply-demand side collaborative optimization system for a district cooling system based on master-slave game theory, used to implement the supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory in Embodiment 1 above. The system includes: a user model construction module, a district cooling system equipment selection module, a master-slave game model establishment module, and an optimization solution module.

[0157] In this embodiment, the user model building module is used to build a building energy consumption proxy model for cooling users based on the RC thermal network model;

[0158] In this embodiment, the district cooling system equipment selection module is used to select a district cooling system configuration scheme;

[0159] In this embodiment, the master-slave game model building module is used to establish a master-slave game model for collaborative transactions between the cooling station and the cooling user. The master-slave game model includes an optimization model with the cooling station as the leader and an optimization model with the cooling user as the follower. The optimization objective of the leader optimization model is to maximize the revenue of the cooling station, and the constraints are the equipment model constraints of the district cooling system and the cooling load supply and demand balance constraints. The optimization objective of the follower optimization model is to minimize the energy cost of the cooling user, and the constraints are thermal comfort constraints, user cost constraints, load transfer constraints, and maximum load constraints.

[0160] In this embodiment, the optimization solution module is used to solve the master-slave game model to obtain the optimized operation strategy of the cooling station and the optimized energy consumption strategy of the cooling users.

[0161] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for supply-demand side collaborative optimization of a district cooling system based on master-slave game theory, characterized in that, Includes the following steps: Obtain meteorological and date attribute data for the climate zone of the selected city, determine typical building types, and collect building information; Construct a user-side building energy consumption proxy model based on building type and building information; Cooling load demand is calculated based on a building energy consumption proxy model. An objective function for user-side cooling cost is then constructed based on this demand, specifically expressed as follows: C pc =L·CS Where L is a matrix composed of cooling loads, and the hourly cooling load demand l of each element is calculated from outdoor and indoor characteristics through a building energy consumption model, expressed as: l(o in ,the out ,THE S ,THE) Among them, o in A matrix representing the indoor temperature of a building, O out O S These are the outdoor temperature matrix and the solar radiation matrix, respectively. A represents other inputs, and CS is the cooling price matrix. Construct a cooling station operation model; The objective function for the revenue of the cooling station is expressed as follows: f1=max(P profit ) Among them, P profit L represents the revenue from cooling sales; CS represents the cooling load matrix; and L represents the cooling price matrix. This refers to the hourly partial power consumption of the cooling unit; PLR x,k,s,t This refers to the hourly partial load power of the refrigeration unit. Hourly partial power consumption of the refrigeration unit and its supporting equipment; ep t Indicates the electricity price at time t; A supply-demand collaborative optimization model between the cooling station and the user-side building is constructed based on the cooling station operation model and the building energy consumption proxy model. The building energy consumption proxy model satisfies thermal comfort constraints. The supply-demand collaborative optimization model between cooling stations and user-side buildings aims to maximize the cooling revenue of the cooling station and minimize the cooling purchase cost of the user-side buildings. The cooling station and cooling users engage in a master-slave game, specifically including: The master-slave game process involves the cooling station prioritizing the setting of cooling prices, while cooling users adjust their energy consumption behavior in response to price changes. In the game, the cooling station and cooling users achieve their maximum benefits through interactive game. When the game between the cooling station and cooling users reaches Nash equilibrium, the unique solution of the game model is obtained. When the game reaches Nash equilibrium, both the cooling station and the cooling users obtain their maximum profit. The Nash equilibrium solution is obtained based on the conditions of the cooling station and the cooling users, which satisfy the following conditions: Both the strategy sets of the cooling station and the cooling users are non-empty compact convex sets; When the strategy of the cooling station is known, the cooling user has a unique optimal solution; When the cooling user's strategy is known, the cooling station has a unique optimal solution for the cooling user's strategy; When the game reaches Nash equilibrium, the optimized time-sharing cold selling price scheme is output as the result of the game problem and the collaborative optimization of supply and demand.

2. The supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory as described in claim 1, characterized in that, The meteorological data includes dry-bulb temperature, solar radiation, wet-bulb temperature, and relative humidity; The date attribute data includes month attribute data and week attribute data; The building information includes the building envelope selection, room thermal disturbance, interior design parameters, and room ventilation status.

3. The supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory as described in claim 1, characterized in that, Based on building type and building information, a user-side building energy consumption proxy model is constructed, specifically including: The cooling load of user-side buildings is calculated using building energy consumption simulation software. Experiments are conducted on the factors affecting the cooling load using experimental analysis software. The experimental results of the factors affecting the cooling load are sampled and fitted to obtain a building energy consumption proxy model. The factors affecting the cooling load include outdoor temperature, solar radiation, and indoor temperature at multiple times.

4. The supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory as described in claim 1, characterized in that, The building energy consumption proxy model also satisfies user cost constraints, load transfer constraints, and maximum load constraints. The thermal comfort constraint is expressed as follows: Where h is the discrete time step, h; l(t) is the building cooling demand, kW; o on (t), o side (t) represents the outdoor temperature at the top of the building and the outdoor temperature on the side of the building, in °C; in (t) represents the indoor temperature of the building, in °C; S east S south S west S north S ceiling S window S represents the area of ​​the east, south, west, and north walls of the building, the area of ​​the ceiling at the top of the building, the area occupied by the windows, and the area of ​​the thermally inertial building envelope, respectively, in meters. 2 ;R ceiling R east R south R west R north R window These represent the thermal resistance of the building's ceiling structure, the thermal resistance of the building's east, south, west, and north walls, and the thermal resistance of the building's window structure, respectively (m). 2 ·℃) / W;Q e Q h These represent the heat released by indoor equipment and the heat released by disturbance of people inside the building, respectively, in kW; s (t) represents solar radiation heat, in kW; C in Indicates the building's indoor heat capacity, kJ / (m³). 2 ·℃); O(h) 2 ) represents the truncation error; The user fee constraint is expressed as follows: L·CS≤L fs ·CS fs Among them, L fs The base state cooling load matrix, CS fs The base state cold chain price matrix; The load transfer constraint is expressed as follows: Among them, l i Indicates cooling load. This represents the cooling load in the ground state. The maximum load constraint is expressed as follows: Where α represents the preset peak reduction rate.

5. The supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory as described in claim 1, characterized in that, The cooling station operation model satisfies the constraints of the district cooling system equipment model and the cooling load supply and demand balance constraints. The constraints of the district cooling system equipment model include partial load factor constraints, dual-condition refrigeration unit condition constraints, ice storage device status constraints, cold release rate constraints, and cold storage rate constraints. The partial load factor constraint is expressed as follows: 0≤PLR x,k (t)≤1 Among them, PLR x,k (t) represents the partial load rate of the refrigeration unit k under operating condition x; The operating condition constraints of the dual-condition refrigeration unit are expressed as follows: 0≤PLR re,k (t0)+PLR cha,k (t0)≤1 Among them, PLR re,k (t0), PLR cha,k (t0) represent the partial load rate of the refrigeration unit k under refrigeration and cold storage conditions, respectively; The state constraints of the ice storage device are expressed as follows: V(t)≥0 Where V(t) is the remaining cooling capacity of the cold storage device, in kW; The cooling rate constraint is expressed as follows: Where M(t) represents the amount of cold energy released by the cold storage device, in kW; Cap storage The capacity of the cold storage device is expressed in kW. Indicates the cold release rate of the cold storage device; The cold storage rate constraint is expressed as follows: in, This indicates the cold storage efficiency of the cold storage device; The cold storage rate of the cold storage device is represented by 'x', which is the power ratio between the cooling mode and the cold storage mode of the dual-mode refrigeration unit. d This indicates the capacity of the type D dual-mode chiller, in kW; PLR cha,d,s (t) represents the partial load rate of the s-th type d dual-condition refrigeration unit under cold storage condition; This indicates the efficiency of the d-type dual-condition refrigeration unit in cold storage mode; The cooling load supply and demand balance constraint is expressed as follows: Where, η pipe To improve the transport efficiency of the pipeline system; The cooling capacity provided by the refrigeration unit; The cooling capacity provided to the cold storage device; n (t) represents the cooling load in kW; σ represents the supply-demand error.

6. The supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory as described in claim 1, characterized in that, The cooling station and the cooling users engage in a master-slave game, and the master-slave game model is specifically represented as follows: H={{P scs ∪P user },{S scs ∪S user }} Among them, H is a master-slave game model, {P} scs ∪P user Let P be the set of participants. scs P represents a cooling station. user Indicates a cold user; {S scs ∪S user } represents the strategy set of the participants, S scs S represents the strategy of the cooling station. user The strategy of the cooling user is represented by the price at which the cooling station sells cooling to the user during each time period within the scheduling cycle; the strategy of the cooling user participating in the game is the energy consumed by the user.

7. The supply-demand side collaborative optimization method for a district cooling system based on master-slave game theory as described in claim 6, characterized in that, When the game reaches Nash equilibrium, the optimized time-sharing cold storage price scheme is output as the result of the game problem and the collaborative optimization of supply and demand, specifically expressed as follows: The cooling station operation model will determine the cooling price. The building energy consumption proxy model is passed to the user side, and the user-side building energy consumption proxy model is based on the known cooling price. The energy consumption scheme is obtained by solving the problem. and energy consumption plan Return to the cooling station operation model; The cooling station operation model is based on the energy consumption plan. Optimize its own interests S1 and implement a new cold chain sales strategy Building energy consumption proxy model transmitted to the user side; The user-side building energy consumption proxy model is based on the known cooling price. The solution yields a new energy consumption scheme. This is then returned to the cooling station operation model, which calculates its own benefit S2 based on the energy consumption plan. Determine whether self-interest S2 is greater than self-interest S1. If so, update self-interest S1 to self-interest S2; otherwise, recalculate self-interest S2. The game between the cooling station and the cooling user involves continuous iteration until an equilibrium is reached among the different stakeholders, and the strategy set {S} is then determined. scs ∪S user This serves as the equilibrium solution for the coordinated optimization of supply and demand in a district cooling system.

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