Heat pump coupled phase change energy storage system and control method thereof

Abstract

The application discloses a heat pump coupled phase change energy storage system and a control method thereof. In the heat pump coupled phase change energy storage system, the flow of a deep buried pipe, the heat storage capacity of a phase change energy storage unit and the power of a heat pump in each period are set based on an obtained optimization operation method. The model used by the optimization operation method is a ground source heat pump coupled phase change energy storage device heating model with the minimum operation cost, the maximum system COP and the maximum ground heat utilization coefficient as targets, and a chaotic particle swarm optimization algorithm is used as the solving method. In the technical scheme, the improved chaotic particle swarm optimization algorithm is used to solve the constructed target function model, and the operation scheme of the ground source heat pump coupled energy storage device heating system under different targets can be obtained.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optimization scheduling of ground source heat pump systems, and particularly relates to a heat pump coupled phase change energy storage system and a control method thereof. BACKGROUND

[0002] As a clean and renewable energy, geothermal energy has the characteristics of stable energy supply, large reserves and wide distribution, and has been widely used in the field of building heating. Geothermal energy heating mainly adopts shallow ground source heat pump heating technology, which has the problems of large land occupation of buried pipes and easy occurrence of cold and hot imbalance, which limits the promotion and application of geothermal energy heating to some extent.

[0003] The middle-deep ground source heat pump heating technology drills a hole to the high-temperature rock-soil body 2-3 km deep underground and installs a middle-deep buried pipe heat exchanger in the hole, extracts deep underground geothermal energy by closed circulation, and then supplies heat to the building by high-temperature heat pump. Compared with the shallow ground source heat pump technology, the buried pipe occupies less land, the heat pump has higher energy efficiency, and is not affected by ground climate conditions. Therefore, the middle-deep ground source heat pump heating technology has a broad development prospect, and its popularization and application will help to solve the air pollution problem in northern regions in winter.

[0004] At present, the research on middle-deep ground source heat pump heating systems mainly focuses on numerical simulation in China, mainly focusing on the operation performance, energy efficiency and optimization of the system. The optimization of the system is mainly from two aspects of operation parameters and design parameters, such as circulation flow, operation and stop ratio, and design load ratio.

[0005] With the development of computers and artificial intelligence, particle swarm optimization algorithm (PSO) has been widely used in the optimization operation of integrated energy systems. Specifically, a particle is used to simulate an individual bird, each particle can be regarded as a search individual in an N-dimensional search space, the current position of the particle is a candidate solution of the corresponding optimization problem, the flight process of the particle is the search process of the individual, and the flight speed of the particle can be dynamically adjusted according to the historical optimal position of the particle and the historical optimal position of the population. A particle has only two attributes (i.e. speed and position), speed represents the speed of movement, and position represents the direction of movement. The optimal solution searched by each particle alone is called individual extreme value, and the optimal individual extreme value in the particle swarm is taken as the current global optimal solution. Through continuous iteration and updating of speed and position, the optimal solution meeting the termination condition is finally obtained. The PSO algorithm has good effect when applied to the optimization operation of ground source heat pump coupled energy storage systems, but when the traditional particle swarm optimization algorithm is used to solve the model, the iterative calculation process will fall into a local optimal solution, and the global optimal solution cannot be obtained. SUMMARY

[0006] The heat pump coupling phase change energy storage system and the control method thereof are provided to solve one or more of the above technical problems.

[0007] To achieve the above object, the application adopts the following technical scheme:

[0008] The heat pump coupling phase change energy storage system provided by the application comprises a middle-deep buried pipe, a heat pump unit and a phase change energy storage unit.

[0009] The outlet of the middle-deep buried pipe is connected to the inlet of the middle-deep buried pipe through a ground source side water pump and a first heat exchange channel of the heat pump unit.

[0010] The outlet of the second heat exchange channel of the heat pump unit is divided into two paths after a circulating water pump, one path is connected to the inlet of the second heat exchange channel of the heat pump unit through a heat storage valve, a heat storage water pump and a first heat exchange channel of the phase change energy storage unit, and the other path is connected to the inlet of the second heat exchange channel of the heat pump unit through a user side heat exchange pipeline.

[0011] The outlet of the second heat exchange channel of the phase change energy storage unit is connected to the inlet of the second heat exchange channel of the phase change energy storage unit through a heat release water pump, a heat release valve and a user side heat exchange pipeline.

[0012] The middle-deep buried pipe flow, the phase change energy storage unit heat storage capacity and the heat pump power in each period of the heat pump coupling phase change energy storage system are set based on the pre-obtained optimization operation method, the model used by the optimization operation method is a ground source heat pump coupling phase change energy storage device heating model with the minimum operation cost, the maximum system COP and the maximum ground heat utilization coefficient as the target, and the solving method is a chaotic particle swarm optimization algorithm.

[0013] The further improvement of the application lies in that, in the ground source heat pump coupling phase change energy storage device heating model,

[0014] (1) the objective function, comprising:

[0015] 1) the operation cost w is the sum of the electricity fees generated by the system operation in each hour, and the expression is,

[0016]

[0017] In the formula, p hp (t) is the heat pump power consumption in the t period; p wp (t) is the water pump power consumption in the t period; and e(t) is the electricity price in the t period.

[0018] 2) System COP is defined as the ratio of system heat supply and system power consumption, and the expression is,

[0019]

[0020] In the formula, p geo (t) is the heat extraction amount of the ground heat pipe in the t period; p wp, (t) is the power consumption of the water pump on the ground source side in the t period;

[0021] 3) The ground heat energy utilization coefficient ξ is defined as the ratio of the heat extraction amount of the ground heat pipe and the system heat supply, and the expression is,

[0022]

[0023] (2) Constraint conditions, including:

[0024] 1) The heat power balance constraint is expressed as, P geo (t) + P hp (t) + P wp,h (t) = P load (t) + P TES (t);

[0025] In the formula, P load (t) is the user heat load in the t period; P TES (t) is the heat storage power of the phase change energy storage unit in the t period;

[0026] 2) The water pump flow constraint is expressed as, Q TES,min ≤ Q TES ≤ Q TES,max ; In the formula, Q TES,min is the minimum value of the water pump flow, Q TES is the water pump flow, and Q TES,max is the maximum value of the water pump flow;

[0027] 3) The phase change energy storage unit capacity constraint is expressed as, E TES,min ≤ E TES ≤ E TES,max ; In the formula, E TES,min is the minimum value of the capacity, E TES is the capacity value, and E TES,max is the maximum value of the capacity.

[0028] Further improvement of the present application is that the chaotic particle swarm optimization algorithm is obtained based on improvement of the particle swarm optimization algorithm;

[0029] In the formula, the chaotic system equation for chaotic initialization of particle position and speed and chaotic optimization of the optimal position is the Logistic equation, and the expression is,

[0030] z n+1 = μzn (1-z n ),n=0,1,2,…,Z n+1 ;

[0031] In the formula: μ is a control variable; the initial value z0 satisfies 0≤z0≤1;

[0032] The speed update formula of the chaotic particle swarm optimization algorithm is,

[0033]

[0034]

[0035] In the formula: And Respectively represent the speed and position of the i-th particle at the k-th iteration; r1 and r2 are random numbers; pbest id Is the individual optimal position of the i-th particle; gbest d Is the population optimal position;

[0036] The formula of the inertia weight and the two learning factors which are dynamically changed according to the iteration number is:

[0037]

[0038]

[0039]

[0040] In the formula: ω max , ω min Respectively are the upper and lower limits of the inertia weight ω; c 1s , c 1e Respectively are the initial value and the final value of the acceleration factor c1; c 2s , c 2e Respectively are the initial value and the final value of the acceleration factor c2; i is the current iteration number, i max Is the maximum iteration number.

[0041] Further improvement of the present application is that the heat pump power includes heat pump unit power, ground source side water pump power and circulating water pump power.

[0042] The control method of the heat pump coupled phase change energy storage system provided by the present application comprises the following steps:

[0043] Based on the pre-acquired optimization operation method, the flow rate of the deep-buried pipe, the heat storage capacity of the phase change energy storage unit and the heat pump power in each period of the heat pump coupled phase change energy storage system are set to realize the control of the heat pump coupled phase change energy storage system;

[0044] The model adopted by the optimization operation method is a ground source heat pump coupled phase change energy storage device heating model with the minimum operation cost, the maximum system COP and the maximum ground heat energy utilization coefficient as targets, and a chaos particle swarm optimization algorithm is adopted for solving.

[0045] Further improvement of the present application is that the construction step of the ground source heat pump coupled phase change energy storage device heating model with the minimum operation cost, the maximum system COP and the maximum ground heat energy utilization coefficient as targets comprises:

[0046] According to the power grid peak-valley time-of-use electricity price policy, the user end heat load demand and the intermittency of new energy storage, a ground source heat pump coupled phase change energy storage system to be controlled is established with the minimum operation cost, the maximum system COP and the maximum ground heat energy utilization coefficient as targets.

[0047] Further improvement of the present application is that in the ground source heat pump coupled phase change energy storage device heating model,

[0048] (1) objective function, comprising:

[0049] 1) the operation cost w is the sum of electricity charges generated by system operation in each hour, and the expression is,

[0050]

[0051] In the formula, P hp (t) is the heat pump power consumption in the t period; P wp (t) is the water pump power consumption in the t period; and e(t) is the electricity price in the t period.

[0052] 2) the system COP is defined as the ratio of system heat supply to system power consumption, and the expression is,

[0053]

[0054] In the formula, P geo (t) is the heat extraction amount of the buried pipe in the t period; P wp, (t) is the ground source side water pump power consumption in the t period.

[0055] 3) the ground heat energy utilization coefficient ξ is defined as the ratio of the heat extraction amount of the buried pipe to the system heat supply, and the expression is,

[0056]

[0057] (2) constraint condition, comprising:

[0058] 1) heat power balance constraint, the expression is, P geo (t) is the heat pump power consumption in the t period; P hp (t) is the water pump power consumption in the t period; and e(t) is the electricity price in the t period. wp,h (t) is the heat pump power consumption in the t period; P load(t) + P TES (t) ;

[0059] P (t) = P (t) + P (t) ; wherein: P (t) is the user heat load at t period; P (t) is the heat storage power of the phase change energy storage unit at t period; load (t) is the heat storage power of the phase change energy storage unit at t period; TES (t) is the heat storage power of the phase change energy storage unit at t period;

[0060] 2) Water pump flow constraint, the expression is, Q TES,min ≤ Q TES ≤ Q TES,max ; wherein: Q TES,min is the minimum value of the water pump flow, Q TES is the water pump flow, Q TES,max is the maximum value of the water pump flow;

[0061] 3) Phase change energy storage unit capacity constraint, the expression is, E TES,min ≤ E TES ≤ E TES,max ; wherein: E TES,min is the minimum value of the capacity, E TES is the capacity value, E TES,max is the maximum value of the capacity.

[0062] Further improvement of the application is that the step of solving by using the chaotic particle swarm optimization algorithm comprises:

[0063] S21: Set the population size, iteration number, search space dimension of the particle swarm;

[0064] S22: Chaotic initialization of particle position and velocity;

[0065] S23: Initialization of pbest and gbest;

[0066] S24: Calculate the inertia weight and weighting factor;

[0067] S25: Calculate the velocity and position of the particle;

[0068] S26: Update pbest and gbest;

[0069] S27: Chaotic optimization of the optimal position;

[0070] S28: Randomly replace the position of a particle;

[0071] S29: The particle velocity is in the defined domain, and it is judged whether the iteration number reaches the maximum. If the iteration number reaches the maximum, the optimized result is obtained, and the optimal operation scheme is output, including the ground buried pipe flow, heat storage and heat pump power at each period. If it is not reached, jump to step S24 to continue iteration calculation;

[0072] In steps S22 and S27, the chaotic system equation for initializing particle position and velocity and performing chaotic optimization on the optimal position is the Logistic equation, expressed as follows:

[0073] z n+1 =μz n (1-z n ), n=0,1,2,…,Z n+1 ;

[0074] In the formula: μ is a control parameter; the initial value z0 satisfies 0≤z0≤1;

[0075] In step S25, the speed update formula for the chaotic particle swarm optimization algorithm is:

[0076]

[0077] ;

[0078] In the formula: and These represent the velocity and position of the i-th particle in the k-th iteration, respectively; r1 and r2 are random numbers; pbest id The optimal position for the i-th particle; gbest d The optimal location for the population;

[0079] The formulas for the inertia weight and the two learning factors that change dynamically according to the number of iterations are:

[0080]

[0081]

[0082]

[0083] In the formula: ω max ω min These are the upper and lower limits of the inertia weight ω, respectively; c 1s c 1e These are the initial and final values ​​of the acceleration factor c1, respectively; c 2s c 2e Here, c represents the initial and final values ​​of the acceleration factor c2, respectively; i represents the current iteration number. max This represents the maximum number of iterations.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] The application provides a technical scheme, which considers the influence of heat load on a heating system of a middle-deep ground source heat pump coupled phase change energy storage device, establishes a heating model of the ground source heat pump coupled phase change energy storage device with the minimum operation cost, the maximum system COP (the ratio of system heating capacity to system power consumption) and the maximum ground heat utilization coefficient as target functions, simultaneously improves a traditional PSO algorithm by using a chaotic optimization idea to obtain an improved chaotic particle swarm optimization algorithm, and solves by using the improved chaotic particle swarm optimization algorithm to obtain the operation scheme of the heating system of the ground source heat pump coupled phase change energy storage device under different targets. BRIEF DESCRIPTION OF DRAWINGS

[0086] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following briefly introduces the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0087] Figure 1 is a schematic diagram of a heat pump coupled phase change energy storage system provided by an embodiment of the present application;

[0088] Figure 2 is a flowchart of a control method of a heat pump coupled phase change energy storage system provided by an embodiment of the present application;

[0089] Figure 3 is a calculation flowchart of the improved chaotic particle swarm optimization algorithm in the embodiment of the present application;

[0090] Figure 4 is a diagram of the flow of the buried pipe and the time-of-use electricity price in each period after optimization with the minimum operation cost as the target function in the embodiment of the present application;

[0091] Figure 5 is a diagram of the heat storage capacity and the heat load in each period after optimization with the minimum operation cost as the target function in the embodiment of the present application;

[0092] Figure 6 is a diagram of the power consumption in each period after optimization with the minimum operation cost as the target function in the embodiment of the present application;

[0093] Figure 7 is a diagram of the flow of the buried pipe in each period after optimization with the maximum system COP as the target function in the embodiment of the present application;

[0094] Figure 8 is a diagram of the heat storage capacity and the heat load in each period after optimization with the maximum system COP as the target function in the embodiment of the present application;

[0095] Figure 9 is a schematic diagram of power consumption in each period after optimization of the system COP maximum as the objective function in the embodiment of the present application;

[0096] Figure 10 is a schematic diagram of ground heat pipe flow in each period after optimization of the ground heat utilization coefficient maximum as the objective function in the embodiment of the present application;

[0097] Figure 11 is a schematic diagram of heat storage and heat load in each period after optimization of the ground heat utilization coefficient maximum as the objective function in the embodiment of the present application;

[0098] Figure 12 is a schematic diagram of power consumption in each period after optimization of the ground heat utilization coefficient maximum as the objective function in the embodiment of the present application;

[0099] In the figure, 1 is a ground source side water pump, 2 is a circulating water pump, 3 is a heat storage water pump, 4 is a heat release water pump, 5 is a middle-deep ground buried pipe, 6 is a heat storage valve, 7 is a heat pump unit, 8 is a heat release valve, and 9 is a phase change energy storage unit. DETAILED DESCRIPTION

[0100] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the personnel in the field without creative labor should belong to the protection scope of the present application.

[0101] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0102] The present application will be described in further detail below in combination with the drawings:

[0103] Please refer to Figure 1The heat pump coupling phase change energy storage system provided by the embodiment of the present application comprises a middle-deep buried pipe 5, a heat pump unit 7, a phase change energy storage unit 9, a water pump and a pipeline.

[0104] The outlet of the middle-deep buried pipe 5 is connected to the inlet of the middle-deep buried pipe 5 through a ground source side water pump 1 and a first heat exchange channel of the heat pump unit 7.

[0105] The outlet of the second heat exchange channel of the heat pump unit 7 is divided into two paths through a circulating water pump 2, one path is connected to the inlet of the second heat exchange channel of the heat pump unit 7 through a heat storage valve 6 and a heat storage water pump 3 and a first heat exchange channel of the phase change energy storage unit 9, and the other path is connected to the inlet of the second heat exchange channel of the heat pump unit 7 through a user side heat exchange pipeline.

[0106] The outlet of the second heat exchange channel of the phase change energy storage unit 9 is connected to the inlet of the second heat exchange channel of the phase change energy storage unit 9 through a heat release water pump 4, a heat release valve 8 and a user side heat exchange pipeline.

[0107] The middle-deep buried pipe flow, the heat storage amount of the phase change energy storage unit and the heat pump power in each period of the heat pump coupling phase change energy storage system are set based on the pre-acquired optimization operation method.

[0108] In the embodiment of the present application, the acquisition step of the optimization operation method comprises:

[0109] Based on the heat pump coupling phase change energy storage system, a ground source heat pump coupling phase change energy storage device heating model with the minimum operation cost, the maximum system COP and the maximum geothermal energy utilization coefficient as the target functions is established.

[0110] The middle-deep ground source heat pump coupling phase change energy storage device heating model under each target is solved by using a chaos particle swarm optimization algorithm to obtain the optimization operation method, wherein the chaos particle swarm optimization algorithm is obtained by improving the existing particle swarm optimization algorithm.

[0111] Please refer to Figure 2 The control method of the heat pump coupling phase change energy storage system provided by the embodiment of the present application comprises the following steps:

[0112] S1: According to the peak-valley time-of-use electricity price policy, the user end heat load demand and the intermittency of new energy storage, a ground source heat pump coupling phase change energy storage device heating model of the heat pump coupling phase change energy storage system to be controlled is established, with the minimum operation cost, the maximum system COP and the maximum geothermal energy utilization coefficient as the target functions.

[0113] S2: using the improved chaotic particle swarm optimization algorithm to solve the middle-deep ground source heat pump coupled phase change energy storage device heating model under each target, and obtaining the middle-deep ground source heat pump heating system optimization operation method;

[0114] S3: according to the middle-deep ground source heat pump heating system optimization operation method obtained in step S2, setting the ground source heat pump heating system ground heat pipe flow, heat storage and heat pump power in each period, so as to realize the operation optimization of the middle-deep ground source heat pump coupled phase change energy storage device heating system.

[0115] The process of establishing the ground source heat pump coupled phase change energy storage device heating model in step S1 of the embodiment of the application includes the following steps:

[0116] S11: establishing a target function, for the evaluation of the middle-deep ground source heat pump heating system, which can be carried out from multiple angles, including economy, energy efficiency, clean energy utilization, etc. Therefore, three target functions are designed for optimization in the embodiment of the application, which are operation cost w, system COP and geothermal energy utilization coefficient ξ respectively;

[0117] The operation cost w is the sum of the electricity charges generated by the system operation in each hour:

[0118]

[0119] In the formula, P hp (t) is the heat pump power consumption in t period, kW; P wp (t) is the water pump power consumption in t period, kW; e(t) is the electricity price in t period, yuan·kWh -1 ;

[0120] The system COP is defined as the ratio of the system heat supply to the system power consumption:

[0121]

[0122] In the formula, P geo (t) is the heat extraction amount of the ground heat pipe in t period, kW; P wp,h (t) is the ground source side water pump power consumption in t period, kW;

[0123] The geothermal energy utilization coefficient ξ is defined as the ratio of the heat extraction amount of the ground heat pipe to the system heat supply:

[0124]

[0125] S12: obtaining the constraint condition, specifically including:

[0126] S121: heat power balance constraint:

[0127] The system heat supply part is used to meet the user heat load, and the remaining part is stored in the phase change energy storage unit. If the calculated heat storage power is negative, the phase change energy storage unit is in the heat release state, and the specific constraint expression is,

[0128] P geo (t) + P hp (t) + P wp,h (t) = P load (t) + P TES (t);

[0129] In the formula, P load (t) is the user heat load at t period, kW; P TES (t) is the heat storage power of the phase change energy storage unit at t period, kW;

[0130] S122: water pump flow constraint:

[0131] The flow of the water pump should be greater than the specified minimum flow, and at the same time should not exceed the rated flow:

[0132] Q TES,min ≤ Q TES ≤ Q TES,max ;

[0133] S123: phase change energy storage unit capacity constraint:

[0134] The heat energy stored in the phase change energy storage unit cannot exceed its heat storage capacity, and at the same time cannot be lower than the specified minimum value:

[0135] E TES,min ≤ E TES ≤ E TES,max .

[0136] Please refer to Figure 3 , in step S2 of the embodiment of the application, the improved chaotic particle swarm optimization algorithm is used to solve the model solving process of the middle-deep layer ground source heat pump heating system as follows:

[0137] S21: set the population size, iteration number, and search space dimension of the particle swarm;

[0138] S22: chaotic initialization of particle position and velocity;

[0139] S23: initialization of pbest and gbest;

[0140] S24: calculation of inertia weight and weighting factor;

[0141] S25: calculation of particle speed and position;

[0142] S26: update pbest and gbest;

[0143] S27: Chaotic optimization is performed on the optimal position;

[0144] S28: The position of a particle is randomly replaced;

[0145] S29: The particle velocity is within the defined domain, and it is determined whether the iteration number reaches the maximum. If the iteration number reaches the maximum, the optimized result is obtained, and the optimal operation scheme is output, including the flow of the buried pipe, the heat storage capacity and the heat pump power in each period. If not, it is jumped to step S24 for continuous iteration calculation;

[0146] In step S22 and S27, the chaotic system equation for chaotic initialization of the particle position and velocity and chaotic optimization of the optimal position is the Logistic equation:

[0147] z n+1 =μz n (1-z n ),n=0,1,2,…,Z n+1 ;

[0148] In the formula, μ is a control parameter, and μ=4 is taken; the initial value z0 satisfies 0≤z0≤1;

[0149] In step S25, the velocity update formula of the chaotic particle swarm optimization algorithm is:

[0150]

[0151]

[0152] In the formula, vi(k) and xi(k) represent the velocity and position of the i-th particle at the k-th iteration, respectively; r1 and r2 are random numbers; pbest id is the individual optimal position of the i-th particle; gbest d is the population optimal position; The formula for calculating the inertia weight and the two learning factors that dynamically change according to the iteration number used in step S25 for calculating the particle velocity is:

[0153]

[0154]

[0155]

[0156]

[0157] In the formula, ω max , ω max ω min are the upper and lower limits of the inertia weight; c 1s , c​​1e respectively are the initial and final values of the acceleration factor c1; c 2s , c 2e respectively are the initial and final values of the acceleration factor c2; i is the current iteration number; i max is the maximum iteration number.

[0158] In the technical scheme provided by the embodiment of the application, the influence of the heat load on the operation of the middle-deep ground source heat pump coupled phase change energy storage device heating system is considered, a heating model of the ground source heat pump coupled phase change energy storage device is established, the target functions of which are the minimum operation cost, the maximum system COP and the maximum geothermal energy utilization coefficient, the traditional PSO algorithm is improved by using the chaos optimization idea, the improved chaos particle swarm optimization algorithm is used for solving, and thus the operation scheme of the ground source heat pump coupled phase change energy storage device heating system under different targets is obtained. In further specific explanation, the existing optimization method has the problem of premature convergence in the iteration calculation process, and when the target function is a multi-peak function, it is easy to fall into a local optimal solution and cannot obtain a global optimal solution; the traditional PSO algorithm is improved by using the chaos optimization idea in the embodiment of the application, and in order to avoid prematureness, ω is reduced according to the cosine function law, c1 and c2 are linearly reduced respectively, the global search ability of the algorithm is enhanced in the early iteration stage, and the local search ability is enhanced in the late iteration stage; finally, the improved chaos particle swarm optimization algorithm can jump out of the local optimum and find the global optimal solution. Specific embodiment 1

[0160] Taking an office building with an area of 4000 m 2 in Xi'an City and a maximum heat load of 230 kW as an example for analysis, the middle-deep ground source heat pump coupled phase change energy storage device heating system includes a middle-deep ground pipe, a heat pump unit, a phase change energy storage unit, a water pump and a pipeline; the minimum operation cost, the maximum system COP and the maximum geothermal energy utilization coefficient are respectively taken as the target functions, table 1 gives the evaluation indexes of the system after optimization of different target functions, and it can be seen that the change trend of the operation cost and the system COP is opposite, one reaches the optimal and the other reaches the worst. The change trend of the geothermal energy utilization coefficient is the same as that of the system COP, but the change range is smaller.

[0161] Table 1. Evaluation indexes of the system after optimization of different target functions

[0162]

[0163] Please refer to Figures 4 to 12 , Figure 4The buried pipe flow of each period after optimization is compared with the time-of-use electricity price. It can be seen from the figure that, taking 18:00 as the dividing point, the buried pipe flow presents a trend of first decreasing and then increasing over time; the buried pipe flow is larger when the electricity price is lower, and the buried pipe flow is smaller when the electricity price is higher.

[0164] Figure 5 The heat storage amount of each period after optimization is shown. According to the change curve of the heat storage amount over time, the working condition of the heat storage tank is mainly divided into three stages, 2:00-10:00 and 20:00-24:00 are heat release modes, and 12:00-20:00 is a heat storage mode. By comparing the heat load curve, the heat load is smaller during the heat storage period, and the heat load is basically at a high level during the heat release period. By comparing the time-of-use electricity price curve, the heat storage period is basically a valley electricity period, and the heat release period is basically a peak electricity period. Therefore, it can be considered that the working mode of the phase change energy storage unit is determined by the user heat load and the electricity price.

[0165] Figure 6 The power consumption of each period after optimization is given. It can be seen that the heat pump power consumption changes very little, and the change trend of the total power consumption is mainly determined by the water pump power consumption. 21:00-10:00 is a high power consumption period of the system, which is mainly due to the large buried pipe flow at this time, resulting in high water pump power consumption on the ground source side. In general, the system shifts the power consumption in time by increasing the heating capacity and storing heat during the valley electricity period, and releasing heat during the peak electricity period, so as to ultimately achieve the purpose of reducing the operating cost.

[0166] Figure 7 The buried pipe flow of each period after optimization is shown. It can be seen from the figure that the buried pipe flow is in constant fluctuation, but always remains at a high level.

[0167] Figure 8 The heat storage amount of each period after optimization is compared with the user heat load. It can be seen from the figure that, with the passage of time, the change trend of the heat storage amount is basically first decreasing and then increasing, so the working condition of the phase change energy storage unit is mainly divided into two stages, 1:00-10:00 is a heat release mode, and 10:00-20:00 is a heat storage mode. The user heat load is large and continuously increasing during 1:00-10:00, and the heat load is small and continuously decreasing during 10:00-20:00, indicating that the working mode of the phase change energy storage unit is determined by the size of the user heat load.

[0168] Figure 9 The power consumption of each period after optimization is given. It can be seen that the heat pump power consumption changes very little, and the total power consumption and the water pump power consumption fluctuate in a small range. Therefore, to obtain a higher system COP, the buried pipe flow should be maintained at a high level, and the phase change energy storage unit should store or release heat according to the size of the user heat load.

[0169] When the optimization objective is the maximum coefficient of geothermal energy utilization, the simulation results are shown in Figures 10 to 12 It can be seen that the results are close to those when the objective function is the system COP, which shows that the optimization effects of the system COP and the coefficient of geothermal energy utilization are similar.

[0170] Based on the above analysis, the following optimization operation schemes can be obtained:

[0171] (1) When the minimum operation cost is the objective function, the ground pipe flow decreases first and then increases with time as the dividing point at 18:00, and the system has a high power consumption from 21:00 to 10:00. The working condition of the phase change energy storage unit mainly includes three stages, i.e., heat release mode from 2:00 to 10:00 and from 20:00 to 24:00, and heat storage mode from 12:00 to 20:00. Through the switching of the heat storage and release modes of the phase change energy storage unit, part of the power consumption is transferred from the peak electricity period to the valley and flat electricity periods;

[0172] (2) When the maximum system COP is the objective function, the ground pipe flow fluctuates slightly at a high level, and the system power consumption changes little with time. The working condition of the phase change energy storage unit mainly includes two stages, i.e., heat release mode from 1:00 to 10:00 and heat storage mode from 10:00 to 20:00. Through the switching of the heat storage and release modes of the phase change energy storage unit, the heat supply of the system in each period is maintained at a relatively stable level;

[0173] (3) When the maximum coefficient of geothermal energy utilization is the objective function, the optimization effect is similar to that when the objective function is the system COP;

[0174] (4) By comparing the three objective functions, it can be concluded that the optimization effects of the operation cost and the system COP are opposite, while the coefficient of geothermal energy utilization has little difference under different objective functions.

[0175] In summary, the technical scheme provided by the embodiment of the present application belongs to the technical field of optimal operation of ground source heat pump systems, and particularly relates to a heat pump coupled phase change energy storage system and a control method. The method comprises the following specific steps: according to a peak-valley time-of-use electricity price policy, a user end heat load demand and intermittency of new energy storage, a ground source heat pump coupled phase change energy storage device heating model is established, with the lowest operation cost, the maximum system COP and the maximum ground heat utilization coefficient as objective functions; an improved chaotic particle swarm optimization algorithm is used to solve the ground source heat pump coupled phase change energy storage device heating model under each objective, to obtain an optimal operation method of the middle-deep ground source heat pump; according to the obtained optimal operation method of the middle-deep ground source heat pump, the ground source heat pump heating system is set for ground heat pipe flow, heat storage capacity and heat pump power in each period, so as to realize operation optimization of the middle-deep ground source heat pump coupled phase change energy storage device heating system. The present application improves the traditional PSO algorithm by using the chaotic optimization idea, and uses the improved chaotic particle swarm optimization algorithm to solve, so as to obtain the optimal operation scheme of the ground source heat pump coupled phase change energy storage device heating system under different objectives.

[0176] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: the specific embodiments of the present application can still be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered in the protection scope of the claims of the present application.

Claims

1. A heat pump coupled phase change thermal storage system, characterized by, include: Medium-deep underground pipe (5), heat pump unit (7) and phase change energy storage unit (9); The outlet of the medium-deep underground pipe (5) is connected to the inlet of the medium-deep underground pipe (5) via the first heat exchange channel of the ground source side water pump (1) and heat pump unit (7); The outlet of the second heat exchange channel of the heat pump unit (7) is divided into two paths after passing through the circulating water pump (2). One path is connected to the inlet of the second heat exchange channel of the heat pump unit (7) via the heat storage valve (6), the hot water storage pump (3), the first heat exchange channel of the phase change energy storage unit (9), and the user-side heat exchange pipeline. The outlet of the second heat exchange channel of the phase change energy storage unit (9) is connected to the inlet of the second heat exchange channel of the phase change energy storage unit (9) via a hot water pump (4), a heat release valve (8), and a user-side heat exchange pipeline. In this system, the flow rate of the deep buried pipe, the heat storage capacity of the phase change energy storage unit, and the heat pump power in each time period are set based on a pre-acquired optimized operation method. The optimized operation method uses a model that minimizes operating costs and optimizes the system. COP A heating model for a ground-source heat pump coupled with a phase change energy storage device with the goal of maximizing the geothermal energy utilization coefficient is solved using a chaotic particle swarm optimization algorithm. In the heating model of the ground source heat pump coupled with phase change energy storage device (1) Objective function, including: 1) Operating expenses w The sum of the electricity bills for each hour of system operation, expressed as, ； In the formula: is t periodic heat pump power consumption; is t periodic water pump power consumption; is t periodic electricity price; 2) System COP defined as the ratio of the system heat supply to the system power consumption, expressed as, ； In the formula: is t Heat extraction by ground loop for the time period; is t Ground source side water pump power consumption for the time period; 3) Geothermal energy utilization coefficient defined as the ratio of the heat extracted by the ground heat exchanger to the heat supplied by the system, expressed as ； (2) Constraints, including: 1) a thermal power balance constraint, expressed as, ; In the formula: for t User heat load during specific time periods; for t The thermal storage capacity of the phase change energy storage unit over time; 2) Pump flow constraint, the expression is: In the formula: This represents the minimum flow rate of the water pump. For water pump flow rate, This represents the maximum flow rate of the water pump. 3) Capacity constraint of phase change energy storage unit, the expression is, In the formula: This represents the minimum capacity. This is the capacity value. This represents the maximum capacity. The chaotic particle swarm optimization algorithm is obtained by improving the particle swarm optimization algorithm. The chaotic system equation for initializing particle position and velocity and performing chaotic optimization on the optimal position is the Logistic equation, expressed as follows: ； In the formula: To control parameters; initial values satisfy ; The speed update formula for the chaotic particle swarm optimization algorithm is: ； ； In the formula: and Representing the first i The first particle k Velocity and position at the next iteration; and It is a random number; For the first i The optimal position of each individual particle; The optimal location for the population; The formulas for the inertia weight and the two learning factors that change dynamically according to the number of iterations are: ； ； ； In the formula: , These are the inertia weights. The upper and lower limits; , Acceleration factor The initial value and the final value; , Acceleration factor The initial value and the final value; i This represents the current iteration number. This represents the maximum number of iterations.

2. The heat pump coupled phase change energy storage system according to claim 1, characterized in that, The heat pump power includes the power of the heat pump unit, the power of the ground source side water pump, and the power of the circulating water pump.

3. A control method for a heat pump coupled phase change energy storage system, characterized in that, Includes the following steps: Based on the pre-acquisition optimization operation method, the deep buried pipe flow rate, phase change energy storage unit heat storage heat and heat pump power of the heat pump coupled phase change energy storage system are set in each time period to realize the control of the heat pump coupled phase change energy storage system. The optimized operation method employs a model that minimizes operating costs and optimizes the system. COP A heating model for a ground-source heat pump coupled with a phase change energy storage device, with the goal of maximizing the geothermal energy utilization coefficient, is solved using a chaotic particle swarm optimization algorithm. In the heating model of the ground source heat pump coupled with phase change energy storage device (1) Objective function, including: 1) Operating costs w The sum of electricity costs generated by the system in each hour is expressed as: ； In the formula: for t Heat pump power consumption during different time periods; for t Pump power consumption during different time periods; for t Time-of-use electricity pricing; 2) System COP Defined as the ratio of system heat supply to system power consumption, expressed as: ； In the formula: for t Heat extraction via underground pipes during specific time periods; for t Power consumption of the ground source side water pump during the time period; 3) Geothermal energy utilization coefficient Defined as the ratio of heat extracted by the buried pipe to the heat supplied by the system, expressed as follows: ； (2) Constraints, including: 1) Thermal power balance constraint, the expression is: ; In the formula: for t User heat load during specific time periods; for t The thermal storage capacity of the phase change energy storage unit over time; 2) Pump flow constraint, the expression is: In the formula: This represents the minimum flow rate of the water pump. For water pump flow rate, This represents the maximum flow rate of the water pump. 3) Capacity constraint of phase change energy storage unit, the expression is, In the formula: This represents the minimum capacity. This is the capacity value. This represents the maximum capacity. The steps for solving the problem using the chaotic particle swarm optimization algorithm include: S21: Set the population size, number of iterations, and search space dimension of the particle swarm; S22: Chaotic initialization of particle position and velocity; S23: Initialization and ; S24: Calculate the inertia weight and weighting factor; S25: Calculate the velocity and position of the particles; S26: Update and ; S27: Perform chaotic optimization on the optimal position; S28: Randomly replace the position of a particle; S29: If the particle velocity is within the defined domain, determine whether the number of iterations has reached the maximum. If the number of iterations has reached the maximum, obtain the optimized result and output the optimal operation plan, including the underground pipe flow rate, heat storage capacity, and heat pump power for each time period. If not, jump to step S24 to continue iterative calculation. In steps S22 and S27, the chaotic system equation for initializing particle position and velocity and performing chaotic optimization on the optimal position is the Logistic equation, expressed as follows: ； In the formula: To control parameters; initial values satisfy ; In step S25, the speed update formula for the chaotic particle swarm optimization algorithm is: ； ； In the formula: and Representing the first i The first particle k Velocity and position at the next iteration; and It is a random number; For the first i The optimal position of each individual particle; The optimal location for the population; The formulas for the inertia weight and the two learning factors that change dynamically according to the number of iterations are: ； ； ； In the formula: , These are the inertia weights. The upper and lower limits; , Acceleration factor The initial value and the final value; , Acceleration factor The initial value and the final value; i This represents the current iteration number. This represents the maximum number of iterations.

4. The control method according to claim 3, characterized in that, The system with the lowest operating cost. COP The steps for constructing a heating model for a ground-source heat pump coupled with a phase change energy storage device, with the goal of maximizing the geothermal energy utilization coefficient, include: Based on the peak-valley time-of-use pricing policy of the power grid, the heat load demand of users, and the intermittency of renewable energy storage, a heat pump coupled phase change energy storage system to be controlled is established with the minimum operating cost and system efficiency. COP A heating model for a ground-source heat pump coupled with a phase change energy storage device with the goal of maximizing the utilization coefficient of geothermal energy.

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