Regional cooling system supply and demand side collaborative optimization method based on master-slave game

Abstract

The invention discloses a regional cooling system supply and demand side collaborative optimization method based on a master-slave game, and the method comprises the following steps: obtaining meteorological data and date attribute data of a climate region where a selected city is located, determining a typical building type, and collecting building information; constructing a building energy consumption agent model of a user side; calculating a cooling load demand based on the building energy consumption agent model, and constructing a target function of the user side cold purchase cost based on the cooling load demand; constructing a cold supply station operation model; a supply and demand side collaborative optimization model between the cold supply station and the user side building is constructed, and the building energy consumption agent model meets the thermal comfort degree constraint; taking the maximum cold supply benefit of the cold supply station and the minimum building cold purchase cost of the user side as optimization objectives, and performing a master-slave game between the cold supply station and the cold users; and solving to obtain a supply-demand side collaborative optimization result. According to the method, the demand side model is incorporated into the optimization problem to form global optimization, the flexibility of the system can be effectively improved, and the operation stability of the regional cooling system in an extreme scene is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of regional cooling optimization, and in particular to a method for collaborative optimization of supply and demand sides of a regional cooling system based on master-slave game. Background Art

[0002] The regional cooling system produces chilled water in a centralized manner by a special cooling station, and delivers it to each cooling building through a distribution network, providing cooling services for large commercial buildings, central business districts, university towns and other public buildings with high building density, large number of users and concentrated cooling load demand. The growing demand for cooling has made cooling one of the most energy-consuming building energy uses. Currently, the cooling electricity load accounts for more than 40% of the total electricity consumption of urban buildings, and in some cities, the air conditioning electricity load has reached 60% of the summer peak load. In particular, in recent years, the frequent occurrence of extreme high temperature weather has led to a surge in cooling load demand, even exceeding the maximum cooling capacity of the cooling system. The cooling demand caused by extreme high temperature has led to a surge in cooling electricity load, which has brought huge pressure on energy supply. Therefore, fully tapping the system's dispatchable potential is of great significance for the regional cooling system to cope with extreme cooling scenarios.

[0003] Reasonable scheduling of the potential of demand-side resources and coordinated optimization of cooling-side and demand-side operation strategies can improve system flexibility and meet the needs of stable system operation. Guiding user interaction by establishing an appropriate demand response mechanism is an important way for regional cooling systems to cope with extreme peak cooling loads. Therefore, research on supply-demand coordinated optimization technology of regional cooling systems based on master-slave game has important practical significance and application value. Summary of the invention

[0004] In order to overcome the defects and shortcomings of the prior art, the present invention provides a supply-demand collaborative optimization method for a regional cooling system based on master-slave game. The present invention fully considers the economic benefits of the cooling station, as well as the cooling purchase cost and comfort of cooling users, and incorporates the demand-side model into the optimization problem to form a global optimization, so as to obtain a scientific and reasonable cooling station operation strategy and user energy use strategy, which can effectively improve the flexibility of the system, effectively balance the interests of multiple parties, achieve a win-win situation for the cooling station and the user end, and fully tap the dispatchable potential of the user side to enable the regional cooling system to operate stably under extreme cooling scenarios.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention provides a method for collaborative optimization of supply and demand sides of a regional cooling system based on master-slave game, comprising the following steps:

[0007] Obtain meteorological data 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] Calculate cooling load demand based on building energy consumption proxy model, and build objective function of cooling cost for users based on cooling load demand;

[0010] Constructing 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, wherein the building energy consumption proxy model satisfies the thermal comfort constraint;

[0012] The supply-demand collaborative optimization model between the cooling station and the user-side buildings takes the maximum cooling revenue of the cooling station and the minimum cooling cost of the user-side buildings as the optimization goals, and the cooling station and the cooling users play a master-slave game.

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

[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 building envelope selection, room thermal disturbance, interior design parameters, and room ventilation conditions.

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

[0018] The cooling load of the user-side building is calculated based on the building energy consumption simulation software, the factors affecting the cooling load are tested based on the experimental analysis software, the test results of the factors affecting the cooling load are sampled, and the building energy consumption proxy model is fitted. The cooling load influencing factors include outdoor temperature, solar radiation, and indoor temperature at multiple times.

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

[0020] C pc =L·CS

[0021] Among them, L is the matrix composed of cooling load, in which the hourly cooling load demand l of the element is calculated by the outdoor and indoor characteristics through the building energy consumption model, expressed as:

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

[0023] Among them, O in is the matrix of indoor temperature of the building, O out , O S 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:

[0026]

[0027] Where h is the discrete time step, h; l(t) is the building cooling demand, kW; o on (t), o side (t) is the outdoor temperature at the top of the building and the outdoor temperature at the side of the building, °C; in (t) represents the indoor temperature of the building, °C; S east , S south , S west , S north , S ceiling , S window , S are the wall areas of the east, south, west and north sides of the building, the ceiling area of ​​the building, the area occupied by the building windows and the area of ​​the thermally inert enclosure structure, m 2 ; R ceiling , R east , R south , R west , R north , R window They are respectively represented by the thermal resistance of the ceiling structure at the top of the building, the thermal resistance of the east, south, west and north walls of the building, and the thermal resistance of the window structure of the building, (m 2 ℃) / W;Q e , Q h are the heat released by the indoor equipment and the heat released by the disturbance of people in the building, kW; S (t) is the solar radiation heat, kW; C in Indicates the indoor heat capacity of the building, kJ / (m 2 ·℃); O(h 2 ) is the truncation error;

[0028] The user fee constraint is expressed as:

[0029] L·CS≤L fs CS fs

[0030] Among them, L fs is the cooling load matrix of the base state, CS fs is the base state cold selling price matrix;

[0031] The load transfer constraint is expressed as:

[0032]

[0033] Among them, l i represents the cooling load, represents the cooling load of the base state;

[0034] The maximum load constraint is expressed as:

[0035]

[0036] Wherein, α represents the preset peak clipping 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 represents the revenue from selling cold air; L represents the cooling load matrix; CS represents the cooling price matrix; PLR is the hourly power consumption of the refrigeration host; x,k,s,t The hourly partial load power of the refrigeration host; ep is the hourly power consumption of the refrigeration main unit supporting equipment; t represents the electricity price at time t.

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

[0042] The part load rate constraint is expressed as:

[0043] 0≤PLR x,k (t)≤1

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

[0045] The dual-condition refrigeration host operating condition constraint is expressed as:

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

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

[0048] The state constraint of the ice storage device is expressed as:

[0049] V(t)≥0

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

[0051] The cooling rate constraint is expressed as:

[0052]

[0053] Where M(t) represents the cooling capacity released by the cold storage device, kW; Cap storage is the capacity of the cold storage device, kW; Indicates the cooling rate of the cold storage device;

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

[0055]

[0056] in, Indicates the cold storage efficiency of the cold storage device; represents the cold storage rate of the cold storage device; x is the power ratio between the refrigeration condition and the cold storage condition of the dual-condition refrigeration host; cap d Indicates the capacity of the D-type dual-mode refrigeration host, kW; PLR cha,d, s(t) represents the partial load rate of the s-th d-type dual-mode refrigeration host under the cold storage condition; Indicates the efficiency of the cold storage condition of the D-type dual-condition refrigeration host;

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

[0058]

[0059] Among them, η pipe The transmission efficiency of the pipe network system; The cooling capacity provided to the refrigeration host; The cooling capacity provided for the cold storage device; n (t) is cooling load, kW; σ represents the supply-demand error.

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

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

[0062] Among them, H is the master-slave game model, {P scs ∪P user} is the participant set, P scs Represents cooling station, P user Indicates cold users; scs ∪S user} represents the strategy set of the participants, S scs represents the strategy of the cooling station, s user It represents the strategy of the cold users. The strategy of the cooling station includes the price of selling cold to users in each period of the scheduling cycle. The strategy of the cold users participating in the game is the energy consumed by the users.

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

[0064] The master-slave game process is that the cooling station prioritizes the cooling price, and the cooling users adjust their energy consumption behavior in response to price changes. In the game, the cooling station and the cooling users achieve their maximum benefits through interactive games. When the game between the cooling station and the 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 user obtain the maximum benefit. The Nash equilibrium solution is obtained according to the conditions of the cooling station and the cooling user. The conditions of the cooling station and the cooling user satisfy:

[0066] The strategy sets of cooling stations and cooling users are both 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 strategies of cooling users are known, the cooling station has a unique optimal solution for the strategies of cooling users.

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

[0070] The cooling station operation model will sell the cooling price The building energy consumption proxy model transmitted to the user side is based on the known cooling price. Solve the energy usage plan And the energy plan Return to the cooling station operation model;

[0071] Cooling station operation model according to energy consumption plan Optimize your own interests S1 and apply the new cold selling strategy Building energy consumption proxy model delivered to the user side;

[0072] The building energy consumption proxy model on the user side is based on the known cooling price. Find new energy use solutions And return it to the cooling station operation model, and the cooling station operation model calculates its own benefit S2 according to the energy consumption plan;

[0073] Determine whether the self-interest S2 is greater than the self-interest S1. If so, update the self-interest S1 to the self-interest S2. Otherwise, calculate the self-interest S2 again.

[0074] The game between the cooling station and the cooling user is continuously iterated until a balance is reached between different stakeholders. scs ∪s user}As a balanced solution for the coordinated optimization of the supply and demand sides of the district cooling system.

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

[0076] (1) The present invention effectively solves the problem that the current optimization of the district cooling system 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 a methodological guidance for the coordinated optimization of the supply and demand sides of the district cooling system, and increases the overall dispatch flexibility of the system through the optimized operation strategy and optimized energy use strategy obtained through the master-slave game.

[0077] (2) The present invention fully considers the impact of extreme cooling scenarios on the district cooling system and can effectively reduce the peak cooling load, solving the problem that the prior art only considers a single typical daily cooling load scenario and ignores the difficulty of the district cooling system in maintaining stable operation and economic benefits under extreme cooling scenarios;

[0078] (3) The present invention constructs the supply-demand collaborative optimization of the district cooling system based on the master-slave game, constraints and objective function, fully considers the user side's requirements for thermal comfort, and uses the optimization method 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 for the comfort cooling load in the urban energy system;

[0079] On the basis of optimizing the operation strategy of the district cooling system, the demand-side model is incorporated into the optimization problem to form a coordinated optimization of the cold source side and the load side, taking into account the interests of the supply and demand sides and effectively ensuring the stability of the district cooling system in extreme scenarios. This solves the problem that the existing technology only considers the optimization of the cold source side but fails to tap the dispatchable potential on the user side and cannot ensure system stability and user thermal comfort under extreme conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a flow chart of the collaborative optimization method for supply and demand sides of a regional cooling system based on master-slave game according to the present invention;

[0081] Figure 2 Schematic diagram of the minimum energy consumption unit office building room constructed for the present invention;

[0082] Figure 3 This is a schematic diagram of the time-sharing cold selling price after optimization of the present invention

[0083] Figure 4 This is a schematic diagram of load distribution after user response under the time-sharing cooling price of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0085] Example

[0086] like Figure 1 As shown, this embodiment provides a method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game, 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 climate zone where the corresponding city is located. Meteorological data include dry-bulb temperature, solar radiation, wet-bulb temperature and relative humidity; date attribute data include monthly attribute data and weekly attribute data; building type and building information include building envelope structure selection, room thermal disturbance, interior design parameters, room ventilation conditions, etc.

[0089] like Figure 2 As shown, this embodiment takes a single room in an office building in a southern city as an example, firstly collects meteorological data and building envelope structure information, and the collected building parameter information is shown in the following Table 1:

[0090] Table 1 Case building parameters

[0091]

[0092]

[0093] S2: In this embodiment, the building energy consumption proxy model on the user side based on the RC thermal network model is constructed using the building energy consumption simulation software according to the building type and building information, and 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 combined to perform a multi-factor and multi-level response surface test design for cooling load. The multi-levels include outdoor temperature, solar radiation, indoor temperature at the current moment, indoor temperature at the previous moment, and indoor temperature at the previous two moments. The test data of the factors affecting the cooling load are sampled, and finally the building energy consumption proxy model is fitted, 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 cold selling price to formulate a strategy and constructs a cooling station operation model. The configuration combination scheme obtained according to the regional cooling system configuration method has an ice storage device capacity of 47344kWh as the research object of the case cooling side.

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

[0098] C pc =L·CS

[0099] Where L is the matrix of cooling load, where the hourly cooling load demand l is calculated by outdoor and indoor characteristics through the building energy consumption model: l(O in ,O out ,O S ,A),O in is the matrix composed of indoor temperature of the building; out , O S 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 the thermal comfort constraint, user cost constraint, load transfer constraint, and maximum load constraint, where the thermal comfort constraint is:

[0101]

[0102] Where h is the discrete time step, h; l(t) is the building cooling demand, kW; o on (t), o side(t) is the outdoor temperature at the top of the building and the outdoor temperature at the side of the building, °C; in (t) represents the indoor temperature of the building, °C; S east , S south , S west , S north , S ceiling , S window , S is the wall area of ​​the east, south, west and north sides of the building, the ceiling area of ​​the building, the area occupied by the building window and the area of ​​the thermally inert enclosure structure, m 2 ; R ceiloing , R east , R south , R west , R north , R window They are respectively represented by the thermal resistance of the ceiling structure at the top of the building, the thermal resistance of the east, south, west and north walls of the building, and the thermal resistance of the window structure of the building, (m 2 ℃) / W;Q e , Q h are the heat released by the indoor equipment and the heat released by the disturbance of the indoor personnel, kW, both assumed to be constants; s (t) is the solar radiation heat, kW; C in Indicates the indoor heat capacity of the building, kJ / (m 2 ·℃); O(h 2 ) is the truncation error.

[0103] The user fee constraint is:

[0104] L·CS≤L fs CS fs

[0105] Where, L fs is the cooling load matrix of the base state, CS fs is the base state cold selling price matrix.

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

[0107]

[0108] The maximum load constraint is:

[0109]

[0110] Where α is the preset peak clipping rate.

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

[0112]

[0113] f1=max(P profit )

[0114] Where P profit represents the revenue from selling cold air; L represents the cooling load matrix; CS represents the cooling price matrix; PLR is the hourly power consumption of the refrigeration host; x,k,s,t The hourly partial load power of the refrigeration host; ep is the hourly power consumption of the refrigeration main unit supporting equipment; t represents the electricity price at time t.

[0115] The cooling station operation model meets the constraints of the regional cooling system equipment model and the cooling load supply and demand balance constraints; the regional cooling system equipment model constraints include partial load rate constraints, dual-condition refrigeration host condition constraints, ice storage device state constraints, cold release rate constraints, and cold storage rate constraints, among which the partial load rate constraints are:

[0116] 0≤PLR x,k (t)≤1

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

[0118] The industrial control constraints of the dual-mode refrigeration host are:

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

[0120] Where PLR re,k (t0), PLR cha,k (t0) represents the partial load rate of the refrigeration host k under the refrigeration and cold storage conditions respectively;

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

[0122] V(t)≥0

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

[0124] The cooling rate constraint is:

[0125]

[0126] Where M(t) represents the cooling capacity released by the cold storage device, kW; Cap storage is the capacity of the cold storage device, kW; Indicates the cool release rate of the cool storage device.

[0127] The cold storage rate constraint is:

[0128]

[0129] Indicates the cold storage efficiency of the cold storage device; represents the cold storage rate of the cold storage device; x is the power ratio between the refrigeration condition and the cold storage condition of the dual-condition refrigeration host; cap d Indicates the capacity of the D-type dual-mode refrigeration host, kW; PLR cha,d,s (t) represents the partial load rate of the sth d-type dual-mode refrigeration host under the cold storage condition; Indicates the efficiency of the cold storage condition of the D-type dual-condition refrigeration host;

[0130] The supply and demand balance constraint is:

[0131]

[0132] Where η pipe The transmission efficiency of the pipe network system; The cooling capacity provided to the refrigeration host; The cooling capacity provided for the cold storage device; n (t) is cooling load, kW; σ represents the supply-demand error.

[0133] S5: Based on the master-slave game theory, a supply-demand collaborative optimization model between the cooling station and the user-side buildings is constructed. The supply-demand 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 an energy consumption model of buildings in the cooling area. The supply-demand collaborative optimization model between the cooling station and the user-side buildings takes the maximum cooling revenue of the cooling station and the minimum cooling cost of the user-side buildings as the optimization objectives;

[0134] Based on the established collaborative optimization model, the optimal operation strategy and the optimal energy utilization strategy are solved.

[0135] In this embodiment, based on the master-slave game theory, a supply-demand collaborative optimization model between the district cooling system and the user-side buildings is constructed. The cooling station is taken as the leader and the cooling user is taken as the follower. The Nash-Stackelberg Game optimization problem on the supply-demand side of the district cooling system is established. The master-slave game model is specifically expressed as follows:

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

[0137] Where H is the master-slave game model, {Pscs ∪P user} is the participant set, P scs represents the cooling station, P user Represents cold users; scs ∪S user} is the strategy set of the participants, S scs represents the strategy of the cooling station, S user It represents the strategy of the cold users. The strategy of the cooling station includes the price of selling cold to users in each period of the scheduling cycle. The strategy of the cold users participating in the game is the energy consumed by the users.

[0138] In this embodiment, the master-slave game process is that the upper-level leader prioritizes the setting of the cold selling price and the lower-level followers adjust their energy consumption behavior in response to price changes. Solving the master-slave game model can output optimized operation strategies and optimized energy consumption strategies, which specifically include: the cooling station as the upper-level leader and the lower-level cold users as followers play a master-slave game. In the game, the cooling station and the cold users achieve their maximum benefits through interactive games. When the cooling station and the cold users can no longer obtain greater benefits through changes in strategies, the game between the two reaches a Nash equilibrium, and the unique solution to the game model is obtained.

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

[0140] In this embodiment, the following steps are performed to output the optimization results in combination with the master-slave game model:

[0141] Step 1. The upper cooling station runs the model to sell the cooling price The building energy consumption proxy model transmitted to the lower user side is based on the known cooling price. Solve the energy usage plan And return this data to the upper cooling station operation model;

[0142] Step 2. The upper cooling station operation model is based on the energy consumption plan Optimize your own interests S1 and apply the new cold selling strategy Building energy consumption proxy model transmitted to the lower user side;

[0143] Step 3. The building energy consumption proxy model on the lower user side is based on the known cooling price. Find new energy use solutions And return it to the upper cooling station operation model, and the upper cooling station operation model calculates its own benefit S2 according to the energy consumption plan;

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

[0145] Step 5. Determine whether the output conditions are met. If yes, output the optimization result. Otherwise, execute Step 3.

[0146] The cooling station and the cooling user rely on each other's decisions, and the game is iterated continuously until a balance is reached between different stakeholders. scs ∪S user} is a balanced solution for the coordinated optimization of the supply and demand sides of the district cooling system.

[0147] In this embodiment, the leader uses the cooling station operation model to build the operation, represented by maxP profit The followers use the building energy consumption proxy model to construct the energy strategy optimization problem, expressed as minC pc , the followers calculate the cooling load demand based on the building energy consumption proxy model, and its building energy consumption proxy model satisfies the thermal comfort constraint, and the thermal comfort type of the minimum energy consumption unit is used to characterize the thermal comfort level in the area. For the upper problem, a multi-objective genetic algorithm is used to generate time-sharing cooling prices, including: population size 10, evolutionary generations 500, crossover coefficient 0.9, and coefficient of variation 0.2. The lower problem uses the mixed integer linear programming (MILP) solver GUROBI to solve the model, and the specific solution steps are as follows:

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

[0149] Step 2. The upper cooling station model passes the initial cooling price to the lower user side model, and uses the solver GUROBI to solve the parent population Objective function value Based on the characteristics of the parent population, the offspring population is generated using crossover mutation operations Calculate the offspring objective function The parent and offspring populations merge, and elite individuals are selected to form the next generation population The result is passed to the lower user side model through the upper cooling station model and returned.

[0150] Step 3. Repeat the game in the mth round, the parent population of the upper cooling station model time-sharing cooling price plan Use the solver GUROBI to solve the parent generation objective function value Based on the characteristics of the parent population, crossover mutation generates the offspring population Calculate the offspring objective function The parent population and the offspring population merge to form a new population, and the elite individuals are selected to form the next generation population.

[0151] Step 4. Determine whether the number of iterations meets the conditions for stopping iterations. Otherwise, increase the number of iterations by 1 and repeat the previous step.

[0152] Step 5. Obtain the Pareto frontier of the multi-objective optimization problem and reach the Nash equilibrium. Output the optimized time-sharing cold selling price plan as the result of the game problem and the coordinated optimization of the supply and demand sides.

[0153] Comprehensively considering the introduction of user-side model for coordinated optimization of supply and demand side of district cooling system, after the game between cooling station and users, such as Figure 3 As shown in the figure, the cooling price of the cooling station after optimization is obtained, such as Figure 4 As shown in the figure, the cooling load response curve after the user's energy consumption is optimized is obtained. Figure 4 It can be seen that after optimization, the user transfers 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. Figure 3 The optimization of the time-sharing cooling price shows that users shifted their energy demand from the period with higher cooling prices to the period with lower cooling prices, achieving the effect of "peak shaving and valley filling", which verifies the effectiveness of the model. At the same time, the effective reduction of peak cooling load helps the district cooling system maintain stable operation in extreme scenarios.

[0154] This embodiment firstly addresses the problem that the existing technology pays less attention to the huge dispatchable potential on the user side. On the basis of optimizing the operation strategy of the regional cooling system, the user side model is incorporated into the optimization problem, forming a collaborative optimization of the cold source side and the load side based on the master-slave game model to coordinate the interests between the cooling station and the user. Secondly, in order to take into account the energy demand of the user, the minimum energy consumption unit is expanded to construct the demand side energy consumption characteristics of the region, and the thermal comfort type of the minimum energy consumption unit is used to characterize the thermal comfort level in the region. Then, in order to solve the problem that the demand side model introduces a large number of decision variables and nonlinear factors that make the model difficult to solve, the leader module and the follower module are used to disassemble the game model to form an operation strategy optimization problem and an energy strategy optimization problem. The two are connected through the time-sharing cooling price and the optimized energy strategy, and the NSGA-II+GUROBI nested optimization method is used to solve it, which accelerates the convergence speed and improves the optimization quality.

[0155] Example 2

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

[0157] In this embodiment, the user model construction module is used to construct a cold user building energy consumption proxy model based on the RC heat 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 establishment module is used to establish a master-slave game model for collaborative transactions between cooling stations and cooling users; the master-slave game model includes an optimization model in which the cooling station is a leader and an optimization model in which the cooling user is a follower; the optimization goal of the leader optimization model is to maximize the revenue of the cooling station, and the constraints are the regional cooling system equipment model constraints and the cooling load supply and demand balance constraints; the optimization goal 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 optimal operation strategy of the cooling station and the optimal energy use strategy of the cooling user.

[0161] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A collaborative optimization method for supply and demand sides of a regional cooling system based on master-slave game, characterized in that: The steps include: Obtain meteorological data 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; Calculate cooling load demand based on building energy consumption proxy model, and build objective function of cooling cost for users based on cooling load demand; Constructing cooling station operation model; 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, wherein the building energy consumption proxy model satisfies the thermal comfort constraint; The supply-demand collaborative optimization model between the cooling station and the user-side buildings takes the maximum cooling revenue of the cooling station and the minimum cooling cost of the user-side buildings as the optimization goals, and the cooling station and the cooling users play a master-slave game. When the game reaches Nash equilibrium, the output optimized time-sharing cold selling price plan is the result of the coordinated optimization of the game problem and the supply and demand sides.

2. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 1 is characterized in that: The meteorological data include 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 building envelope selection, room thermal disturbance, interior design parameters, and room ventilation conditions.

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

4. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 1 is characterized in that: The objective function of the user-side cooling cost based on the cooling load demand is specifically expressed as: C pc =L·CS Among them, L is the matrix composed of cooling load, in which the hourly cooling load demand l of the element is calculated by the outdoor and indoor characteristics through the building energy consumption model, expressed as: l(The in ,THE out ,THE S ,THE) Among them, O in is the matrix of indoor temperature of the building, O out , O S are the outdoor temperature matrix and the solar radiation matrix respectively, A represents other inputs, and CS is the cooling price matrix.

5. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 1 is 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: Where h is the discrete time step, h; l(t) is the building cooling demand, kW; o on (t), o side (t) is the outdoor temperature at the top of the building and the outdoor temperature at the side of the building, °C; in (t) represents the indoor temperature of the building, °C; S east , S south , S west , S north , S ceiling , S window , S are the wall areas of the east, south, west and north sides of the building, the ceiling area of ​​the building, the area occupied by the building windows and the area of ​​the thermally inert enclosure structure, m 2 ; R ceiling , R east , R south , R west , R north , R window They are respectively represented by the thermal resistance of the ceiling structure at the top of the building, the thermal resistance of the east, south, west and north walls of the building, and the thermal resistance of the window structure of the building, (m 2 ℃) / W;Q e , Q h are the heat released by the indoor equipment and the heat released by the disturbance of people in the building, kW; s (t) is the solar radiation heat, kW; C in Indicates the indoor heat capacity of the building, kJ / (m 2 ℃); O(h 2 ) is the truncation error; The user fee constraint is expressed as: L·CS≤L fs ·CS fs Among them, L fs is the cooling load matrix of the base state, CS fs is the base state cold selling price matrix; The load transfer constraint is expressed as: Among them, l i represents the cooling load, represents the cooling load of the base state; The maximum load constraint is expressed as: Wherein, α represents the preset peak clipping rate.

6. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 1 is characterized in that: The revenue objective function of the cooling station is expressed as: f1=max(P profit ) Among them, P profit represents the revenue from selling cold air; L represents the cooling load matrix; CS represents the cooling price matrix; PLR is the hourly power consumption of the refrigeration host; x,k,s,t The hourly partial load power of the refrigeration host; ep is the hourly power consumption of the refrigeration main unit supporting equipment; t represents the electricity price at time t.

7. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 1 is characterized in that: The cooling station operation model satisfies the regional cooling system equipment model constraints and the cooling load supply and demand balance constraints. The regional cooling system equipment model constraints include partial load rate constraints, dual-condition refrigeration host operating condition constraints, ice storage device state constraints, cold release rate constraints, and cold storage rate constraints. The part load rate constraint is expressed as: 0≤PLR x,k (t)≤1 Among them, PLR x,k (t) represents the partial load rate of refrigeration unit k under operating condition x; The dual-condition refrigeration host operating condition constraint is expressed as: 0≤PLR re,k (t0)+PLR cha,k (t0)≤1 Among them, PLR re,k (t0), PLR cha,k (t0) represents the partial load rate of the refrigeration host k under the refrigeration and cold storage conditions respectively; The state constraint of the ice storage device is expressed as: V(t)≥0 Wherein, V(t) is the remaining cooling capacity of the cold storage device, kW; The cooling rate constraint is expressed as: Where M(t) represents the cooling capacity released by the cold storage device, kW; Cap storage is the capacity of the cold storage device, kW; Indicates the cooling rate of the cold storage device; The cold storage rate constraint is expressed as: in, Indicates the cold storage efficiency of the cold storage device; represents the cold storage rate of the cold storage device; x is the power ratio between the refrigeration condition and the cold storage condition of the dual-condition refrigeration host; cap d Indicates the capacity of the D-type dual-mode refrigeration host, kW; PLR cha,d,s (t) represents the partial load rate of the sth d-type dual-mode refrigeration host under the cold storage condition; Indicates the efficiency of the cold storage condition of the D-type dual-condition refrigeration host; The cooling load supply and demand balance constraint is expressed as: Among them, η pipe The transmission efficiency of the pipe network system; The cooling capacity provided to the refrigeration host; The cooling capacity provided for the cold storage device; n (t) is cooling load, kW; σ represents the supply-demand error.

8. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 1, characterized in that: The cooling station and the cooling user play a master-slave game, and the master-slave game model is specifically expressed as follows: H={{P scs ∪P user },{S scs ∪S user }} Among them, H is the master-slave game model, {P scs ∪P user } is the participant set, P scs Represents cooling station, P user Indicates cold users; scs ∪S user } represents the strategy set of the participants, S scs represents the strategy of the cooling station, s user It represents the strategy of the cold users. The strategy of the cooling station includes the price of selling cold to users in each period of the scheduling cycle. The strategy of the cold users participating in the game is the energy consumed by the users.

9. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 1, characterized in that: The cooling station and the cooling user play a master-slave game, which includes: The master-slave game process is that the cooling station prioritizes the cooling price, and the cooling users adjust their energy consumption behavior in response to price changes. In the game, the cooling station and the cooling users achieve their maximum benefits through interactive games. When the game between the cooling station and the 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 user obtain the maximum benefit. The Nash equilibrium solution is obtained according to the conditions of the cooling station and the cooling user. The conditions of the cooling station and the cooling user satisfy: The strategy sets of cooling stations and cooling users are both non-empty compact convex sets; When the strategy of the cooling station is known, the cooling user has a unique optimal solution; When the strategies of cooling users are known, the cooling station has a unique optimal solution for the strategies of cooling users.

10. The method for collaborative optimization of supply and demand sides of a district cooling system based on master-slave game according to claim 8, characterized in that: When the game reaches Nash equilibrium, the optimized time-sharing cold selling price scheme is output as the result of the coordinated optimization of the game problem and the supply and demand side, which is specifically expressed as: The cooling station operation model will sell the cooling price The building energy consumption proxy model transmitted to the user side is based on the known cooling price. Solve the energy usage plan And the energy plan Return to the cooling station operation model; Cooling station operation model according to energy consumption plan Optimize your own interests S1 and apply the new cold selling strategy Building energy consumption proxy model delivered to the user side; The building energy consumption proxy model on the user side is based on the known cooling price. Find new energy use solutions And return it to the cooling station operation model, and the cooling station operation model calculates its own benefit S2 according to the energy consumption plan; Determine whether the self-interest S2 is greater than the self-interest S1. If so, update the self-interest S1 to the self-interest S2. Otherwise, calculate the self-interest S2 again. The game between the cooling station and the cooling user is continuously iterated until a balance is reached between different stakeholders. scs ∪S user }As a balanced solution for the coordinated optimization of the supply and demand sides of the district cooling system.

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