Multi-energy scheduling optimization method considering ground source heat pump system

Abstract

The invention relates to a multi-energy scheduling optimization method considering a ground source heat pump system, and the method comprises the steps: carrying out the segmentation approximation linearization of heating / cooling capacity and active power according to a sample data set, so as to obtain a relational expression between the heating / cooling capacity and the active power; day-ahead comprehensive power-heat scheduling is carried out in a power distribution network with a distributed photovoltaic panel, a resident load and a ground source heat pump system, and a target function with the purpose of achieving the minimum power cost is constructed; based on a relational expression between the heating / cooling capacity and the active power, a first constraint condition of the ground source heat pump system and a second constraint condition of the target function are established; and on the basis of the target function, the first constraint condition and the second constraint condition, the optimal solution for realizing the minimum power cost is obtained through optimization solution, so that the advantages of the ground source heat pump are fully played through multi-energy scheduling optimization, the energy utilization rate can be effectively improved, carbon emission is reduced, the system stability is enhanced, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy system optimization, and particularly to a multi-energy scheduling optimization method considering a ground-source heat pump system. Background Art

[0002] With the profound changes in the global energy structure, the traditional power generation mode relying on fossil fuels is gradually transforming towards green and low-carbon renewable energy, especially under the impetus of the carbon peak and carbon neutrality goals. Although the rapid development of renewable energy helps to reduce carbon emissions, its intermittency and instability pose increasing challenges to the power system in terms of scheduling, supply-demand balance, and system stability. Therefore, how to efficiently utilize existing resources and improve the consumption capacity of renewable energy has become the core issue faced by the current power system.

[0003] In traditional air conditioning and heating systems, the direct energy supply using fossil fuels or electricity brings high energy consumption and carbon emission problems. With the increasing global demand for addressing climate change, green, low-carbon, and sustainable energy technologies have gradually become the research focus. As an efficient, energy-saving, and low-carbon heating and cooling technology, the ground-source heat pump has gradually become an important solution for modern building energy conservation and environmental protection due to its high energy conversion efficiency and low environmental burden.

[0004] The ground-source heat pump system utilizes the constant temperature characteristics of underground soil or water bodies and uses a small amount of electric energy to drive the heat pump system to achieve heating, cooling, and domestic hot water supply for buildings. During its operation, the energy conversion efficiency is high, which can greatly reduce the operating cost and greenhouse gas emissions, meeting the requirements of a low-carbon economy. Especially in the context of low-carbon energy conservation, the ground-source heat pump, as a renewable energy utilization technology, is being widely applied in different fields such as industry, commerce, and residential.

[0005] Although there have been many studies on ground-source heat pumps and multi-energy system scheduling, the existing scheduling methods mostly focus on single energy or lack comprehensive optimization of each component of the system, and cannot consider the coordination and optimization among multiple energies simultaneously. Summary of the Invention

[0006] (I) Technical Problems to be Solved

[0007] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a multi-energy scheduling optimization method considering a ground-source heat pump system, which solves the technical problem in the prior art that the coordination and optimization among multiple energies cannot be considered simultaneously.

[0008] (II) Technical Solutions

[0009] To achieve the above object, the main technical solutions adopted by the present invention include:

[0010] In a first aspect, an embodiment of the present invention provides a multi - energy scheduling optimization method considering a ground - source heat pump system, including: obtaining a sample data set composed of the heating / cooling capacity of the ground - source heat pump system corresponding to the electric power of different ground - source heat pump systems, and segmentally approximating and linearizing the heating / cooling capacity and active power according to the sample data set to obtain a relational expression between the heating / cooling capacity and the active power; considering the day - ahead integrated power - heat scheduling in a distribution network with distributed photovoltaic panels, residential loads, and a ground - source heat pump system, and constructing an objective function with the goal of minimizing the power cost; based on the relational expression between the heating / cooling capacity and the active power, establishing a first constraint condition of the ground - source heat pump system and a second constraint condition of the objective function; on the basis of the objective function, the first constraint condition, and the second constraint condition, obtaining the optimal solution for achieving the minimum power cost through optimization and solution.

[0011] In a possible embodiment, segmentally approximating and linearizing the heating / cooling capacity and the active power according to the sample data set to obtain a relational expression between the heating / cooling capacity and the active power includes: determining the optimal segmentation points of the first piece - wise linear function constructed from the sample data set; where the optimal segmentation points include multiple segmentation points; segmentally approximating and linearizing the heating / cooling capacity and the active power based on the optimal segmentation points to obtain the active power of the ground - source heat pump system between any two adjacent segmentation points among the multiple segmentation points, the heating capacity between any two adjacent segmentation points, and the cooling capacity between any two adjacent segmentation points; constructing a second piece - wise linear function using the active power of the ground - source heat pump system between any two adjacent segmentation points and the heating capacity between any two adjacent segmentation points, and based on the second piece - wise linear function, determining the relational expression between the heating capacity and the active power; constructing a third piece - wise linear function using the active power of the ground - source heat pump system between any two adjacent segmentation points and the cooling capacity between any two adjacent segmentation points, and based on the third piece - wise linear function, determining the relational expression between the cooling capacity and the active power.

[0012] In a possible embodiment, determining the optimal segmentation points of the first piece - wise linear function constructed from the sample data set includes: using a particle swarm optimization algorithm to determine the optimal segmentation points.

[0013] In a possible embodiment, the calculation expression of the objective function is:

[0014]

[0015] In the formula, J represents the objective value of the objective function; L represents the total number of operation periods; λ t represents the electricity price when the ground - source heat pump system exchanges electricity with the transmission network at time t; P tr,t represents the electricity quantity exchanged between the ground - source heat pump system and the transmission network at time t.

[0016] In a possible embodiment, the calculation expression of the first constraint condition is:

[0017]

[0018] In the formula, represents the lower limit of the active power output by the ground-source heat pump at node i; P hp,i,t represents the active power of the ground-source heat pump at node i in period t; represents the upper limit of the active power output by the ground-source heat pump at node i.

[0019] In a possible embodiment, the second constraint condition includes the power flow constraint condition of the distribution network, and the calculation expression of the power flow constraint condition of the distribution network is:

[0020] P i,t = P pv,i,t - P load,i,t - P hp,i,t ;

[0021]

[0022] P 0,t = P tr,t ;

[0023]

[0024]

[0025] In the formula, P i,t represents the active power injected into node i in period t; P pv,i,t represents the active power generated by the photovoltaic power source at node i in period t; P load,i,t represents the active power of the load at node i in period t; P hp,i,t represents the active power of the ground-source heat pump at node i in period t; Q i,t represents the reactive power injected into node i in period t; represents the power factor angle at node i; P 0,t represents the active power injected into the slack node in period t; P tr,t represents the amount of electricity exchanged between the ground-source heat pump system and the transmission network in period t; BR represents the set of branches; p br,ji,t represents the active power on the branch connecting node j and node i in period t; p br,ik,t represents the active power on the branch connecting node i and node k in period t; q br,ji,t represents the reactive power on the branch connecting node j and node i in period t; q br,ik,t represents the reactive power on the branch connecting node i and node k in period t; Represents the square of the voltage at node i at time t; Represents the square of the voltage at node j at time t; r br,ij Represents the resistance on the branch connecting node i and node j; x br,ij Represents the reactance on the branch connecting node i and node j; Represents the lower limit of the voltage at node i; Represents the upper limit of the voltage at node i; Represents the square of the voltage at the slack node at time t; Represents the square value of the reference voltage.

[0026] In a possible embodiment, the second constraint condition further includes a photovoltaic power generation constraint condition, and the calculation expression of the photovoltaic power generation constraint condition is:

[0027]

[0028] In the formula, Represents the predicted value of the active power generated by the photovoltaic power source at node i at time t.

[0029] In a possible embodiment, the second constraint condition further includes an indoor temperature constraint condition of a building, and the calculation expression of the indoor temperature constraint condition of the building is:

[0030]

[0031] In the formula, C th,i Represents the specific heat capacity of the building at node i; T build,i (t) represents the indoor temperature of the building at node i at time t; H heat,i (t) represents the heating capacity of the ground source heat pump at the building at node i at time t; T env (t) represents the ambient temperature at time t; R th,i Represents the thermal resistance of the building at node i; H cool,i (t) represents the cooling capacity of the ground source heat pump at the building at node i at time t; T build,i,t+1 Represents the indoor temperature of the building at node i at time t + 1; Δt represents the time interval; Represents the lower limit of the indoor temperature of the building at node i; Represents the upper limit of the indoor temperature of the building at node i.

[0032] In a second aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the method described in the first aspect or any optional implementation manner of the first aspect.

[0033] In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the method described in the first aspect or any optional implementation manner of the first aspect is executed.

[0034] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on a computer, the computer is caused to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0035] (III) Beneficial effects

[0036] The beneficial effects of the present invention are as follows:

[0037] An embodiment of the present application provides a multi-energy scheduling optimization method considering a ground-source heat pump system. Through multi-energy scheduling optimization, the advantages of the ground-source heat pump are fully utilized, the energy utilization rate can be effectively improved, carbon emissions can be reduced, the system stability can be enhanced, the operating cost can be lowered, and effective technical support can be provided for achieving the goals of green and low-carbon and sustainable development.

[0038] In addition, through optimizing the scheduling strategy, the present application can achieve more precise energy allocation, improve the flexibility and stability of equipment operation, and ultimately minimize resource consumption and environmental burden while ensuring the comfort requirements of users, providing a practical solution for promoting low-carbon and green development.

[0039] To make the above objects, features, and advantages of the embodiments of the present application more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. Description of the drawings

[0040] To more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative efforts.

[0041] Figure 1 The flowchart of a multi-energy scheduling optimization method considering a ground-source heat pump system provided by an embodiment of the present application is shown. Detailed implementation manners

[0042] To better explain the present invention for easy understanding, the following makes a detailed description of the present invention through specific implementation manners in conjunction with the accompanying drawings.

[0043] There is still much room for improvement in the existing methods for dealing with the coordination, load balance, and resource consumption between ground source heat pumps and other energy devices in large-scale systems.

[0044] To address the deficiencies of the existing technology, the embodiments of the present application provide a multi-energy scheduling optimization method considering a ground source heat pump system. Through multi-energy scheduling optimization, the advantages of the ground source heat pump can be fully utilized, effectively improving energy utilization efficiency, reducing carbon emissions, enhancing system stability, reducing operating costs, and providing effective technical support for achieving green and low-carbon goals and sustainable development.

[0045] To better understand the above technical solutions, the exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0046] Please refer to Figure 1 , Figure 1 which shows a flowchart of a multi-energy scheduling optimization method considering a ground source heat pump system provided by the embodiments of the present application. It should be understood that this multi-energy scheduling optimization method can be executed by an electronic device, and the specific device of the electronic device can be set according to actual needs, and the embodiments of the present application are not limited thereto. For example, the electronic device can be a computer or a server, etc. Specifically, this multi-energy scheduling optimization method includes:

[0047] Step S110, obtaining a sample data set composed of the heating / cooling capacity of the ground source heat pump system corresponding to the electric power of different ground source heat pump systems, and performing piecewise approximate linearization on the heating / cooling capacity and active power according to the sample data set to obtain a relationship formula between the heating / cooling capacity and the active power.

[0048] Specifically, determine the optimal segmentation points of the first piecewise linear function constructed from the sample data set; wherein, the optimal segmentation points include multiple segmentation points; based on the optimal segmentation points, perform piecewise approximate linearization on the heating / cooling capacity and active power, and obtain the active power of the ground source heat pump system between any two adjacent segmentation points among the multiple segmentation points, the heating capacity between any two adjacent segmentation points, and the cooling capacity between any two adjacent segmentation points; construct a second piecewise linear function using the active power of the ground source heat pump system between any two adjacent segmentation points and the heating capacity between any two adjacent segmentation points, and based on the second piecewise linear function, determine the relationship between the heating capacity and the active power; construct a third piecewise linear function using the active power of the ground source heat pump system between any two adjacent segmentation points and the cooling capacity between any two adjacent segmentation points, and based on the third piecewise linear function, determine the relationship between the cooling capacity and the active power.

[0049] To facilitate the understanding of step S110, the following will be described through specific embodiments.

[0050] Specifically, to more accurately describe the operating characteristics of the ground source heat pump, the present application may assume that the heating / cooling capacity of the ground source heat pump is a functional relationship with the electric power, so that the corresponding heating / cooling capacity can be directly derived from the electric power.

[0051] Based on the above, through experimental measurement of the ground source heat pump equipment, the corresponding heating / cooling capacity at different electric powers is obtained, forming a set of sample data sets. wherein, N represents the total number of samples collected; x n represents the nth electric power; y n represents the heating / cooling capacity corresponding to the nth electric power collected.

[0052] Moreover, in order to improve the fitting accuracy of the piecewise linear model, the present application optimizes the segmentation point position through a non-linear optimization algorithm, significantly reducing the fitting error, thereby constructing a more accurate first piecewise linear function, and the fitting error evaluation function of the first piecewise linear function can be defined by the following formula (1), specifically:

[0053]

[0054] In the formula, E represents the total fitting error; f(x n ) represents the heating / cooling capacity of the ground source heat pump corresponding to the linear function fitted when the segmentation point is x n .

[0055] In addition, since the particle swarm algorithm has high computational efficiency, strong global search ability and adaptability, the present application can use the particle swarm optimization algorithm to determine the optimal segmentation points of the first piecewise linear function, specifically:

[0056] Define the current iteration number as k, initialize k = 0, set ζ initial particles to be randomly generated, and use the following formulas (2) to (3) to establish the position X of the a-th particle in the k-th iteration a k and update the velocity v at each segmentation point a k :

[0057]

[0058]

[0059] In the formula, represents the segmentation point of the a-th particle in the k-th iteration, and M < N; represents the velocity of each segmentation point update of the a-th particle in the k-th iteration;

[0060] Subsequently, perform the execution steps of the fitness value in the k-th iteration: Substitute X a k into the above formula (1) to obtain the fitness value of the a-th particle in the k-th iteration

[0061] Subsequently, update the velocity and position of the a-th particle according to at least formula (4) to formula (5) to obtain the update velocity of each segmentation point of the a-th particle in the (k + 1)-th iteration and the position X a k+1 :

[0062]

[0063] In the formula, g represents the inertia weight; c1 represents the individual learning factor, which is used to control the degree to which the particle approaches its own historical optimal position; r1 represents a random number within the range of [0, 1]; X a,best represents the optimal position of the a-th particle during the iteration process; c2 represents the social learning factor, which is used to control the degree to which the particle approaches the global historical optimal position of the group; r2 represents a random number within the range of [0, 1]; g k represents the global optimal solution of all particles in the k-th iteration; represents the position of the a-th particle at the (k + 1)-th iteration;

[0064] Subsequently, perform the steps to update the optimal solution of a single particle:

[0065] Substitute the updated position of the a-th particle into the above formula (1) to obtain the particle fitness of the a-th particle in the (k + 1)-th iteration, and compare it with the particle fitness in the k-th iteration. If there is Then update the optimal position of the ath particle

[0066] Subsequently, perform the steps to update the global optimal solution:

[0067] Select the particle with the best fitness from all particles as the global optimal solution g k+1 , and substitute g k+1 into the above formula (1) to update the global optimal fitness;

[0068] If the number of iterations reaches the maximum number of iterations K max or the change in the global optimal solution is less than the set threshold, that is, |g k +1 -g k | < e, where e is the set threshold, then stop the iteration, and the optimal solution is Conversely, after assigning k + 1 to k, return to execute the execution steps of the fitness value in the kth iteration above.

[0069] In addition, after determining the optimal segmentation points of the first piecewise linear function, use the piecewise linear function to fit and describe the relationship between the heating / cooling capacity and the electric power, and introduce a non - negative auxiliary variable θ x,j,t , and use the following formulas (6) to (11) to approximately linearize the power and heating / cooling capacity of the ground - source heat pump in segments:

[0070]

[0071] θ x,j ≥0 (9);

[0072]

[0073] {θ x,j |j = 1, 2, …W x} ∈ SOS2 (11);

[0074] In the formula, represents the active power of the ground - source heat pump between the xth segmentation point and the jth segmentation point after linearization; W x represents the number of segments; θ x,j represents the weight coefficient of the ground - source heat pump power between the xth segmentation point and the jth segmentation point; P hp,x,j represents the original active power (which is also the electric power) of the ground - source heat pump between the xth segmentation point and the jth segmentation point; represents the heating capacity of the ground - source heat pump between the xth segmentation point and the jth segmentation point after linearization; H heat,x,j represents the original heating capacity of the ground - source heat pump between the xth segmentation point and the jth segmentation point; Denote the cooling capacity of the ground source heat pump between the \(x\)-th and \(j\)-th segmentation points after linearization; \(H\) cool,x,j Denote the original cooling capacity of the ground source heat pump between the \(x\)-th and \(j\)-th segmentation points; \(SOS2\) means that in an ordered set, there are at most two non-zero values and the two non-zero values are adjacent.

[0075] Moreover, construct a second piecewise linear function by using the active power of the ground source heat pump system between any two adjacent segmentation points and the heating capacity between any two adjacent segmentation points, and take the active power as the abscissa of the coordinate system where the second piecewise linear function is located, and at the same time take the heating capacity as the ordinate of the coordinate system where the second piecewise linear function is located, so as to determine the relationship between the heating capacity and the active power based on the second piecewise linear function:

[0076]

[0077] In the formula, \(H\) heat Denote the heating capacity of the ground source heat pump; Denote the intercept in the heating mode of the ground source heat pump; Denote the slope in the heating mode of the ground source heat pump; \(P\) hp Denote the power of the ground source heat pump; \(P\) h0 , \(P\) h1 ,..., \(P\) hM Denote the optimal segmentation points in the heating mode obtained by using the particle swarm optimization algorithm;

[0078] Moreover, construct a third piecewise linear function by using the active power of the ground source heat pump system between any two adjacent segmentation points and the cooling capacity between any two adjacent segmentation points, and take the active power as the abscissa of the coordinate system where the third piecewise linear function is located, and at the same time take the cooling capacity as the ordinate of the coordinate system where the third piecewise linear function is located, so as to determine the relationship between the cooling capacity and the active power based on the third piecewise linear function:

[0079]

[0080] In the formula, \(H\) cool Denote the cooling capacity of the ground source heat pump; Denote the intercept in the cooling mode of the ground source heat pump; Denote the slope in the cooling mode of the ground source heat pump; \(P\) c0 , \(P\) c1 ,..., \(P\) cM Denote the optimal segmentation points in the cooling mode obtained by using the particle swarm optimization algorithm.

[0081] Step S120, consider performing day-ahead integrated power-thermal dispatch in a distribution network with distributed photovoltaic panels, residential loads and a ground source heat pump system, and construct an objective function aiming to minimize the power cost.

[0082] Specifically, this application considers performing day-ahead integrated power-thermal scheduling in a distribution network with distributed photovoltaic panels, residential loads, and a ground-source heat pump for building heating and cooling. The following formula (14) can be used to construct an objective function J with the goal of minimizing the power cost:

[0083]

[0084] In the formula, J represents the objective value of the objective function; L represents the total number of operating periods; λ t represents the electricity price when the ground-source heat pump system exchanges electricity with the transmission network at time t; P tr,t represents the amount of electricity exchanged between the ground-source heat pump system and the transmission network at time t.

[0085] Step S130: Based on the relationship between heating / cooling capacity and active power, establish the first constraint condition of the ground-source heat pump system and the second constraint condition of the objective function.

[0086] It should be understood that the specific constraint conditions included in the first constraint condition and the specific constraint conditions included in the second constraint condition can both be set according to actual needs, and the embodiments of this application are not limited thereto.

[0087] Optionally, the calculation expression of this first constraint condition is:

[0088]

[0089] In the formula, represents the lower limit of the active power output by the ground-source heat pump at node i; P hp,i,t represents the active power of the ground-source heat pump at node i at time t; represents the upper limit of the active power output by the ground-source heat pump at node i.

[0090] Optionally, this second constraint condition includes the power flow constraint condition of the distribution network, and this application uses a linear branch power flow equation to describe power balance and voltage constraints. The calculation expression of the power flow constraint condition of this distribution network is:

[0091] P i,t = P pv,i,t - P load,i,t - P hp,i,t (16);

[0092]

[0093] P 0,t = P tr,t (18);

[0094]

[0095]

[0096] Wherein, P i,t represents the active power injected into node i at time t; P pv,i,t represents the active power generated by the photovoltaic power source at node i at time t; P load,i,t represents the active power of the load at node i at time t; P hp,i,t represents the active power of the ground source heat pump at node i at time t; Q i,t represents the reactive power injected into node i at time t; represents the power factor angle at node i; P 0,t represents the active power injected into the slack node at time t; P tr,t represents the amount of electricity exchanged between the ground source heat pump system and the transmission network at time t; BR represents the set of branches; p br,ji,t represents the active power on the branch connecting node j and node i at time t; p br,ik,t represents the active power on the branch connecting node i and node k at time t; q br,ji,t represents the reactive power on the branch connecting node j and node i at time t; q br,ik,t represents the reactive power on the branch connecting node i and node k at time t; represents the square of the voltage at node i at time t; represents the square of the voltage at node j at time t; r br,ij represents the resistance on the branch connecting node i and node j; x br,ij represents the reactance on the branch connecting node i and node j; represents the lower limit of the voltage at node i; represents the upper limit of the voltage at node i; represents the square of the voltage at the slack node at time t; represents the square value of the reference voltage.

[0097] Furthermore, the second constraint condition further includes a photovoltaic power generation constraint condition, and the calculation expression of the photovoltaic power generation constraint condition is:

[0098]

[0099] Wherein, represents the predicted value of the active power generated by the photovoltaic power source at node i at time t. That is to say, the planned output of photovoltaic power generation should be less than or equal to the predicted value and non - negative.

[0100] Furthermore, the second constraint condition further includes the indoor temperature constraint condition of the building, and this application uses a first-order thermal resistance model to approximate the dynamic thermal behavior of the building, regarding the building as a thermal system, where the thermal resistance characterizes the heat insulation property of the building, and the heat capacity represents the heat storage capacity of the building. The indoor temperature varies with the balance between the heat input from the ground source heat pump and the heat loss caused by the temperature difference between the building and the outdoor environment. The calculation expression for the indoor temperature constraint condition of the building is as follows:

[0101]

[0102] In the formula, C th,i represents the specific heat capacity of the building at node i; T build,i (t) represents the indoor temperature of the building at node i at time t; H heat,i (t) represents the heating capacity of the ground source heat pump at the building at node i at time t; T env (t) represents the ambient temperature at time t; R th,i represents the thermal resistance of the building at node i; H cool,i (t) represents the cooling capacity of the ground source heat pump at the building at node i at time t; T build,i,t+1 represents the indoor temperature of the building at node i at time t + 1; Δt represents the time interval; represents the lower limit of the indoor temperature of the building at node i; represents the upper limit of the indoor temperature of the building at node i.

[0103] It should be noted here that H heat,i (t) and H cool,i (t) in step S130 can both be determined by the relational expressions in step S110.

[0104] Step S140, based on the objective function, the first constraint condition, and the second constraint condition, obtains the optimal solution that realizes the minimum electricity cost through optimization and solution.

[0105] Therefore, with the help of the above technical solution, this application can effectively solve the scheduling complexity caused by the interaction of different energy types in a multi-energy environment, overcome the problem of low efficiency of traditional methods in dealing with complex constraints and dynamically changing system loads. Through a refined scheduling strategy, not only the operating efficiency of the ground source heat pump system is optimized, but also while ensuring heating and cooling demands, energy waste is minimized to the greatest extent, the overall system energy consumption is reduced, thereby significantly improving the energy utilization rate in practical applications, reducing carbon emissions, and providing reliable technical support for achieving the low-carbon energy-saving goal.

[0106] Moreover, the main advantage of this application is that by optimizing the dispatching strategies of multiple energy sources, fully considering the operating characteristics of the ground-source heat pump, and combining linearized constraints for accurate calculation, it effectively solves the problem of low efficiency of existing methods in a multi-energy environment. By comprehensively considering the coordinated dispatching of different energy forms, this application can flexibly adjust the system operation according to actual demands and load fluctuations, maximizing the energy utilization efficiency. At the same time, while ensuring the heating and cooling demands of the system, the invention reduces the energy consumption and carbon emissions of the system, improves the overall operating efficiency and sustainability, and has significant economic and environmental benefits.

[0107] It should be understood that the above multi-energy dispatching optimization method considering the ground-source heat pump system is only exemplary, and those skilled in the art can make various deformations according to the above method, and the deformed solutions also fall within the protection scope of this application.

[0108] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0109] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0110] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present invention can be implemented by means of hardware including several different components and by means of a suitably programmed computer. In the claims listing several devices, several of these devices can be embodied by the same hardware. The use of the words first, second, third, etc. is only for convenience of expression and does not indicate any order. These words can be understood as part of the component name.

[0111] In addition, it should be noted that in the description of this specification, the descriptions of terms such as "one embodiment", "some embodiments", "embodiment", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0112] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concepts. Therefore, the claims should be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0113] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention should also include these modifications and variations.

Claims

1. A multi - energy scheduling optimization method considering a ground - source heat pump system, characterized in that, Including: Obtain a sample data set composed of the heating / cooling capacity of the ground source heat pump system corresponding to the electric power of different ground source heat pump systems, and perform piecewise approximate linearization on the heating / cooling capacity and the active power according to the sample data set to obtain a relationship between the heating / cooling capacity and the active power; Consider performing day-ahead integrated power-thermal dispatch in a distribution network with distributed photovoltaic panels, residential loads, and the ground source heat pump system, and construct an objective function aiming to minimize the power cost; Based on the relationship between the heating / cooling capacity and the active power, establish the first constraint condition of the ground source heat pump system and the second constraint condition of the objective function; Based on the objective function, the first constraint condition, and the second constraint condition, obtain the optimal solution for minimizing the power cost through optimization and solution.

2. The multi-energy scheduling optimization method according to claim 1, wherein The step of performing piecewise approximate linearization on the heating / cooling capacity and the active power according to the sample data set to obtain a relationship between the heating / cooling capacity and the active power includes: Determine the optimal segmentation points of the first piecewise linear function constructed from the sample data set; where the optimal segmentation points include multiple segmentation points; Based on the optimal segmentation points, perform piecewise approximate linearization on the heating / cooling capacity and the active power to obtain the active power of the ground source heat pump system between any two adjacent segmentation points among the multiple segmentation points, the heating capacity between any two adjacent segmentation points, and the cooling capacity between any two adjacent segmentation points; Construct a second piecewise linear function using the active power of the ground source heat pump system between any two adjacent segmentation points and the heating capacity between any two adjacent segmentation points, and based on the second piecewise linear function, determine the relationship between the heating capacity and the active power; Construct a third piecewise linear function using the active power of the ground source heat pump system between any two adjacent segmentation points and the cooling capacity between any two adjacent segmentation points, and based on the third piecewise linear function, determine the relationship between the cooling capacity and the active power.

3. The multi-energy scheduling optimization method according to claim 2, characterized in that The step of determining the optimal segmentation points of the first piecewise linear function constructed from the sample data set includes: Use the particle swarm optimization algorithm to determine the optimal segmentation points.

4. The multi - energy scheduling optimization method according to claim 1, characterized in that, The calculation expression of the objective function is: Where J represents the objective value of the objective function; L represents the total number of operation periods; λ t represents the electricity price when the ground source heat pump system exchanges electricity with the power transmission network at time t; P tr,t represents the amount of electricity exchanged between the ground source heat pump system and the power transmission network at time t.

5. The multi-energy scheduling optimization method according to claim 1, wherein The calculation expression of the first constraint condition is: In the formula, represents the lower limit of the active power output by the ground source heat pump at node i; P hp,i,t represents the active power of the ground source heat pump at node i in period t; represents the upper limit of the active power output by the ground source heat pump at node i.

6. The multi - energy scheduling optimization method according to claim 1, wherein, The second constraint condition includes the power flow constraint condition of the distribution network, and the calculation expression of the power flow constraint condition of the distribution network is: P i,t = P pv,i,t - P load,i,t - P hp,i,t ; P 0,t = P tr,t ; Where, P i,t represents the active power injected into node i at time t; P pv,i,t represents the active power generated by the photovoltaic power source at node i during the said time t; P load,i,t represents the active power of the load at node i during the said time t; P hp,i,t represents the active power of the ground source heat pump at node i during the said time t; Q i,t represents the reactive power injected into node i during the said time t; represents the power factor angle at node i; P 0,t represents the active power injected into the slack node during the said time t; P tr,t represents the amount of electricity exchanged between the ground source heat pump system and the transmission network during the said time t; BR represents the set of branches; p br,ji,t represents the active power on the branch connecting node j and node i during the said time t; p br,ik,t represents the active power on the branch connecting node i and node k during the said time t; q br,ji,t represents the reactive power on the branch connecting node j and node i during the said time t; q br,ik,t represents the reactive power on the branch connecting node i and node k during the said time t; represents the square of the voltage at node i during the said time t; represents the square of the voltage at node j during the said time t; r br,ij represents the resistance on the branch connecting node i and node j; x br,ij represents the reactance on the branch connecting node i and node j; represents the lower limit of the voltage at node i; represents the upper limit of the voltage at node i; represents the square of the voltage at the slack node during the said time t; represents the squared value of the reference voltage.

7. The multi - energy scheduling optimization method according to claim 6, wherein, The second constraint condition further includes the photovoltaic power generation constraint condition, and the calculation expression of the photovoltaic power generation constraint condition is: In the formula, represents the predicted value of the active power generated by the photovoltaic power source at the i-th node during the t-th period.

8. The multi - energy scheduling optimization method according to claim 6, wherein The second constraint condition further includes the indoor temperature constraint condition of the building, and the calculation expression of the indoor temperature constraint condition of the building is: where C th,i represents the specific heat capacity of the building on the i-th node; T build,i (t) represents the indoor temperature of the building on the i-th node during the t-th period; H heat,i (t) represents the heat output of the ground source heat pump at the building on node i during the t period; T env (t) represents the ambient temperature in the t period; R th,i represents the thermal resistance of the building at node i; H cool,i (t) represents the cooling capacity of the ground source heat pump at the building at node i in the t period; T build,i,t+1 represents the indoor temperature of the building at node i in the (t + 1) period; Δt represents the time interval; represents the lower limit of the indoor temperature of the building at node i; represents the upper limit of the indoor temperature of the building at node i.

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