Electricity consumption community energy management method and system considering price type demand response

Abstract

The invention provides an electricity utilization community energy management method and system considering price type demand response, and belongs to the technical field of electricity utilization cost optimization. According to the method, the required electric quantity is calculated based on the electric quantity-price elasticity coefficient; calculating the photovoltaic generating capacity according to the output curve of the photovoltaic generator set, and calculating the energy storage capacity according to the power condition of the electric energy storage device; calculating the required electric quantity based on the required electric quantity, the photovoltaic generating capacity and the energy storage capacity; calculating the electricity consumption cost of the community resident users based on the electricity purchase quantity and constructing a target function; constructing a state transition matrix based on the real-time electric energy storage amount and the electric quantity purchased from the power grid in each time period; and setting a cost optimization strategy based on the objective function and the state transition matrix, and solving the cost optimization strategy by adopting a Markov decision method to obtain the optimal electricity purchasing quantity and the optimal electricity purchasing moment. According to the method, the influence of the time-of-use electricity price on the electricity consumption of the users in the community can be fully utilized, and the electricity consumption cost of the community resident users under the condition of the time-of-use electricity price is remarkably reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of electricity cost optimization, and in particular relates to an energy management method and system for an electricity community taking price-based demand response into consideration. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As the social demand for electricity increases, there will be obvious peaks and valleys in electricity consumption during the operation of the power grid; when the electricity consumption situation is tense, in addition to increasing the power generation capacity, residential users, as relatively dispersed "small loads", are more susceptible to "passive adjustments", such as power outages, orderly electricity consumption, etc. In order to alleviate the pressure of load peaks and valleys on the power system, the time-of-use electricity price mechanism came into being, that is: through the differentiated price settings in different time periods, guide electricity users to change their electricity consumption behavior, so as to achieve peak shaving and valley filling of the load curve.

[0004] Demand-side management is also one of the methods to reduce electricity costs. It can be combined with demand-side flexible loads, distributed resources, energy storage, etc. to obtain certain benefits and reduce user-side electricity costs. As a price-based demand response method, time-of-use electricity prices will cause changes in user demand, thereby causing load fluctuations in the distribution network. The load transfer rates between peaks and valleys, peak-to-flat, and flat-to-valley will change accordingly. Therefore, it is extremely important to design a user electricity cost optimization method that can respond to price-based demand response.

[0005] However, the inventors have found that there are still some technical problems in the existing user electricity cost optimization method, such as:

[0006] (1) For example, patent application number 202210770517.3 is a residential electricity optimization strategy that considers differentiated demand response schemes. This method matches electricity strategies based on the diversified trends of user electricity consumption behaviors. However, in the strategy calculation process, it only focuses on the behavioral differences between users, but ignores the impact of various devices closely related to life (photovoltaic, energy storage devices, etc.) on user electricity demand, which makes it difficult to fully reflect the relationship between time-of-use electricity prices and user electricity consumption. Therefore, the optimization effect of users' electricity costs under time-of-use electricity prices is not ideal.

[0007] (2) Convolutional neural networks are commonly used in existing technologies to analyze the acquired electricity-related data and then directly provide electricity consumption prediction results or suggestions. However, convolutional neural networks are more suitable for extracting and analyzing data features, but it is difficult to accurately map the relationship between time-of-use electricity prices and user electricity consumption behavior. This makes it difficult to solve the model and the solution accuracy is not high. Summary of the invention

[0008] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides an energy management method and system for electricity consumption communities that takes price-type demand response into consideration. This method and system can significantly reduce the electricity costs of community residents under time-of-use electricity prices on the basis of fully utilizing the impact of time-of-use electricity prices on the electricity consumption of users in the community, and ultimately achieve optimal energy management of centralized electricity consumption communities.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] A first aspect of the present invention provides an energy management method for an electricity consumption community taking into account price-based demand response.

[0011] An energy management method for an electricity consumption community considering price-based demand response, comprising:

[0012] According to the relationship between the electricity consumption of community residents and the time-of-use electricity price, the electricity-price elasticity coefficient is calculated; based on the obtained electricity-price elasticity coefficient, the demand electricity of community residents under the time-of-use electricity price is calculated;

[0013] Calculate the photovoltaic power generation according to the output curve of the photovoltaic power generation group, and calculate the energy storage capacity according to the power condition of the electric energy storage device; calculate the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculate the electricity cost of community residents based on the required amount of electricity purchased, and take the lowest electricity cost as the objective function;

[0014] The real-time electric energy storage amount of the electric energy storage device is used as the state space, and the amount of electricity purchased from the power grid in each period is used as the behavior space; a state transfer matrix is ​​constructed based on the state space and the behavior space;

[0015] A cost optimization strategy is set based on the objective function and the state transfer matrix, and the cost optimization strategy is solved using a Markov decision method to obtain the optimal power purchase amount and power purchase time for community residents.

[0016] Furthermore, the output curve of the photovoltaic power generation group satisfies the Beta distribution; the shape parameters under the Beta distribution are calculated by introducing the mean value and standard deviation value of the irradiance.

[0017] Furthermore, the power condition of the electric energy storage device includes: the charging and discharging power of the electric energy storage device and the capacity of the electric energy storage device.

[0018] Furthermore, the electricity-price elasticity coefficient represents the relationship between the electricity load of community resident users before and after the implementation of time-of-use electricity prices and the electricity prices before and after the implementation of demand response; if the implementation time of time-of-use electricity prices coincides with that of demand response, the electricity-price elasticity coefficient is a self-elasticity coefficient; otherwise, it is a cross-elasticity coefficient.

[0019] Furthermore, the amount of electricity purchased from the power grid in each time period is used as the behavior space, including: using branch pricing theory to set the purchased amount of electricity to equal parts of the maximum energy storage capacity.

[0020] Furthermore, a cost optimization strategy is set based on the objective function and the state transfer matrix. Specifically, the lower the electricity cost, the better the cost optimization effect. The electricity cost is only related to the electricity purchase price and the required electricity purchase amount.

[0021] Furthermore, the cost optimization strategy is expressed as:

[0022]

[0023] in, and They represent the cost optimization results of community residents at time t and time t+1, Trans() represents the state transfer matrix, S t+1 represents the amount of energy stored in the energy storage device at the t+1th moment, and c(t) represents the electricity cost of community residents at the tth moment.

[0024] A second aspect of the present invention provides an energy management system for an electricity consumption community that takes price-based demand response into consideration.

[0025] An energy management system for an electricity consumption community considering price-based demand response, comprising:

[0026] The power demand calculation module is configured to: calculate the power-price elasticity coefficient according to the relationship between the power consumption of community residents and the time-of-use electricity price; and calculate the power demand of community residents under the time-of-use electricity price based on the obtained power-price elasticity coefficient;

[0027] The objective function building module is configured to: calculate the photovoltaic power generation according to the output curve of the photovoltaic power generation group, and calculate the energy storage capacity according to the power condition of the electric energy storage device; calculate the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculate the electricity cost of community residents based on the required amount of electricity purchased, and take the lowest electricity cost as the objective function;

[0028] The cost optimization module is configured as follows: using the real-time electric energy storage capacity of the electric energy storage device as the state space, and using the amount of electricity purchased from the power grid in each time period as the behavior space; constructing a state transfer matrix based on the state space and the behavior space; setting a cost optimization strategy based on the objective function and the state transfer matrix, and using the Markov decision method to solve the cost optimization strategy to obtain the optimal amount and time of electricity purchase for community residents.

[0029] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a method for energy management of an electricity community taking into account price-based demand response as described in the first aspect of the present invention.

[0030] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a method for energy management of an electricity community that considers price-based demand response as described in the first aspect of the present invention are implemented.

[0031] One or more of the above technical solutions have the following beneficial effects:

[0032] (1) The present invention calculates the user's demand electricity under the time-of-use electricity price based on the electricity-price elasticity coefficient; calculates the photovoltaic power generation according to the output curve of the photovoltaic generator set, and calculates the energy storage capacity according to the power condition of the electric energy storage device; calculates the amount of electricity required by community residents based on the demand electricity, photovoltaic power generation and energy storage capacity, that is: taking the centralized community load as the starting point, considering the impact of the time-of-use electricity price on the user's electricity consumption, and combining flexible resources such as energy storage. Therefore, the present invention can make full use of the impact of the time-of-use electricity price on the electricity consumption of users in the community, significantly reduce the electricity cost of community residents under the time-of-use electricity price, and realize the optimal energy management of the centralized electricity community.

[0033] (2) The present invention takes the lowest electricity cost as the objective function and sets a cost optimization strategy, and uses the Markov decision method to solve the cost optimization strategy to obtain the optimal electricity purchase amount and time for community residents. The non-aftereffect and state transition probability characteristics in the Markov characteristic can accurately reflect the relationship between time-of-use electricity prices and user electricity consumption behavior. Therefore, the model solution of the present invention is more concise and has high solution accuracy.

[0034] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 This is a flow chart of an energy management method for an electricity consumption community taking into account price-based demand response in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0037] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0038] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0039] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0040] The overall idea proposed by the present invention is as follows: The present invention provides an energy management method for electricity consumption communities that takes price-based demand response into consideration. First, by considering various types of flexibility devices in the residential community, an operation model of each device is designed. At the same time, combined with the cost situation, a community electricity cost optimization model is proposed with the goal of minimizing the electricity cost of all users in the community. Finally, the community electricity cost optimization model is solved in combination with the Markov decision method to achieve optimal energy management of centralized electricity consumption communities.

[0041] Embodiment 1

[0042] This embodiment discloses an energy management method for an electricity consumption community taking price-based demand response into consideration.

[0043] like Figure 1 As shown, a method for energy management of an electricity consumption community considering price-based demand response includes:

[0044] Step S1, calculating the electricity-price elasticity coefficient according to the relationship between the electricity consumption of community residents and the time-of-use electricity price; and calculating the electricity demand of community residents under the time-of-use electricity price based on the obtained electricity-price elasticity coefficient;

[0045] Step S2, calculating the photovoltaic power generation according to the output curve of the photovoltaic power generation group, and calculating the energy storage capacity according to the power condition of the electric energy storage device; calculating the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculating the electricity cost of community residents based on the required amount of electricity purchased, and taking the lowest electricity cost as the objective function;

[0046] Step S3, using the real-time electric energy storage amount of the electric energy storage device as the state space, and the amount of electricity purchased from the power grid in each time period as the behavior space; constructing a state transfer matrix based on the state space and the behavior space;

[0047] Step S4: setting a cost optimization strategy based on the objective function and the state transfer matrix, and solving the cost optimization strategy using a Markov decision method to obtain the optimal power purchase amount and power purchase time for community residents.

[0048] Based on the above process, the present invention can make full use of the impact of time-of-use electricity prices on the electricity consumption of users in the community, significantly reduce the electricity costs of community residents under time-of-use electricity prices, and ultimately achieve optimal energy management of centralized electricity consumption communities. To facilitate the understanding of the technical solution of the present invention, the specific implementation steps in the technical solution of the present invention are further explained and illustrated below.

[0049] Step S1, according to the relationship between the electricity consumption of community residents and the time-of-use electricity price, the electricity-price elasticity coefficient is calculated; based on the obtained electricity-price elasticity coefficient, the demand electricity of community residents under the time-of-use electricity price is calculated.

[0050] Step S1-1: At the beginning of the calculation in step S1, this embodiment first comprehensively considers the load demand of community residents under normal circumstances, specifically:

[0051] The number of users in the community is represented as m; at the same time, the electricity demand of user i in a period t is represented as L i (t). Then the collective electricity demand of the entire community in time period t can be expressed as:

[0052]

[0053] Where L(t) represents the collective electricity demand of the entire community in time period t, L i (t) represents the electricity demand of user i in a period t.

[0054] Step S1-2: The actual electricity consumption process uses a time-of-use electricity price mechanism, and the electricity consumption of residential users will be affected by the time-of-use electricity price, and the electricity load will actively move toward the low-price period.

[0055] The electricity-price elasticity coefficient is used to express the relationship between the electricity load of community residents before and after the implementation of time-of-use electricity prices and the electricity prices before and after the implementation of demand response. Furthermore, if the time of implementation of time-of-use electricity prices coincides with the time of implementation of demand response, the electricity-price elasticity coefficient is a self-elasticity coefficient; otherwise, it is a cross-elasticity coefficient. Specifically, under time-of-use electricity prices, the electricity-price elasticity coefficient of community users can be expressed as:

[0056]

[0057] in, represents the electricity consumption-price elasticity coefficient of residential users; L m and L' m They represent the power load before and after the implementation of time-of-use electricity price at time m; p n and p' nThey represent the electricity prices before and after the implementation of demand response at time n. m and n both represent time. If m and n coincide, it means that the electricity-price elasticity coefficient of the above community users is a self-elasticity coefficient; if they do not coincide, it means that the electricity-price elasticity coefficient of the above community users is a cross-elasticity coefficient.

[0058] Step S1-3: After implementing the time-of-use electricity price, the electricity demand of community residents can be expressed as:

[0059]

[0060] Among them, L n and L' n They respectively represent the electricity load conditions before and after the implementation of time-of-use electricity prices at time n.

[0061] Step S2: Calculate the photovoltaic power generation according to the output curve of the photovoltaic power generation group, calculate the energy storage capacity according to the power condition of the electric energy storage device; calculate the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculate the electricity cost of community residents based on the required amount of electricity purchased, and take the lowest electricity cost as the objective function. This can be achieved by the following steps:

[0062] Step S2-1, calculating the photovoltaic power generation according to the output curve of the photovoltaic power generation group.

[0063] The output curve of the photovoltaic generator set satisfies the Beta distribution, and the shape parameters under the Beta distribution can be calculated by introducing the mean value and standard deviation of the irradiance. Specifically, the output curve of the photovoltaic generator set is expressed as:

[0064]

[0065] Among them, α and β are the shape parameters of Beta distribution, and θ is the radiance correlation coefficient. The shape parameters of Beta distribution are calculated by introducing the mean value and standard deviation of irradiance, that is:

[0066]

[0067] Among them, μ and δ represent the average value and standard deviation of solar radiation respectively; further, we can get:

[0068]

[0069] Where P(θ) represents the probability function of the photovoltaic module; θ c ,θ d They represent the upper and lower limits of solar radiation θ respectively.

[0070] Furthermore, under the time-of-use electricity price, the electric energy that distributed photovoltaic power generation can provide to the community can be expressed as:

[0071]

[0072] Among them, g PV (t) represents the total amount of electricity that can be provided by the distributed photovoltaic units in the community during period t. It represents the power generation of photovoltaic generator h in period t, W h,t represents the factor affecting the weather on the photovoltaic generator h, g h represents the conversion efficiency of the generator set h; H represents the number of photovoltaics contained in the photovoltaic generator set.

[0073] Step S2-2: Calculate the energy storage capacity according to the power condition of the electric energy storage device.

[0074] The power of an electric energy storage device is generally related to the charging and discharging power and capacity. Therefore, the power condition of the electric energy storage device in this embodiment includes: the charging and discharging power of the electric energy storage device and the capacity of the electric energy storage device. Specifically:

[0075]

[0076] Among them, ES t+1 and ES t Respectively represent the capacity of the energy storage device at time t+1 and time t; δ chr and δ disc Respectively represent the charging and discharging efficiency of the energy storage device; Δt represents the time interval between time t+1 and time t. chr and P disc are the charging and discharging power of the energy storage device respectively; and since the energy storage device cannot be charged and discharged at the same time, there is P chr ·P disc =0 means: when one term is a non-zero positive number, the other term must be 0.

[0077] Furthermore, the operating constraints of the electric energy storage device are expressed as:

[0078]

[0079]

[0080] ES min ≤ES t ≤ES max ;

[0081] in, and Respectively represent the minimum and maximum values ​​of the charging power of the electric energy storage device; and Respectively represent the minimum and maximum discharge power of the electric energy storage device; ES min and ES max They respectively represent the lower and upper limits of the capacity of the electric energy storage device.

[0082] Step S2-3, calculate the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculate the electricity cost of community residents based on the required electricity purchase amount, and take the lowest electricity cost as the objective function.

[0083] Generally speaking, the amount of electricity generated by renewable energy is generally greater than the electricity demand. However, for the community model, there are a large number of residential users and a large electricity demand. In most cases, it is difficult to fully rely on distributed photovoltaic power generation to meet user needs. Therefore, it is still necessary to purchase electricity from the grid to meet electricity demand. It is understandable that the amount of electricity purchased by community residents = the amount of electricity required by community residents - photovoltaic power generation - energy storage capacity.

[0084] The electricity cost of community residents is calculated based on the required amount of electricity purchased. Common sense says that the electricity cost is the product of the amount of electricity purchased and the electricity price. The lowest electricity cost is used as the objective function. Specifically, the objective function is expressed as:

[0085]

[0086] Among them, c(t) represents the electricity cost of community residents at the tth moment, p t represents the electricity price at time t, P t It represents the amount of electricity purchased by the community at time t. Since the electricity cost function of community users has obvious Markov properties, the Markov decision method is applied to solve it. The specific solution process is shown in the following steps.

[0087] Step S3: Using the real-time electric energy storage capacity of the electric energy storage device as the state space and the amount of electricity purchased from the power grid in each time period as the behavior space; constructing a state transfer matrix based on the state space and the behavior space.

[0088] Step S3-1: Using the real-time electric energy storage capacity of the electric energy storage device as the state space.

[0089] Combined with the storage characteristics of the electric energy storage device, the real-time electric energy storage capacity in the electric energy storage device is considered as the system state space S(t), that is:

[0090]

[0091] Wherein, SQ(t) and SQ(t-1) represent the real-time energy of the energy storage device at time t and time t-1 respectively; P(t-1) represents the amount of electricity purchased at time t-1; λ represents the power loss coefficient.

[0092] Step S3-2: The amount of electricity purchased from the power grid in each period is used as the behavior space, that is:

[0093] A(t) = [P(t)];

[0094] Among them, A(t) represents the purchase of electricity from the power grid at time t. Then, the branch pricing theory is used to set the purchased electricity to equal the maximum energy storage capacity, that is:

[0095]

[0096] Among them, S max represents the maximum area of ​​the energy storage component; y represents the efficiency coefficient.

[0097] Step S3-3, construct a state transfer matrix based on the state space and behavior space. Consider the flow of electric energy in the system, including the power generation of distributed photovoltaic components and load demand information. SQ(t) is related to the load demand and the energy purchased from the grid. The state transfer matrix is ​​expressed as:

[0098]

[0099] Among them, Trans() represents the state transfer matrix, S t+1 It represents the amount of energy stored in the energy storage device at the time t+1.

[0100] Step S4: setting a cost optimization strategy based on the obtained objective function and state transfer matrix, and solving the cost optimization strategy using the Markov decision method to obtain the optimal power purchase amount and power purchase time for community residents.

[0101] The objective function is to reduce the electricity cost of community users, so the lower the electricity cost, the greater the return brought by the Markov decision process. The cost optimization strategy is set based on the obtained objective function and state transfer matrix. Specifically, the lower the electricity cost, the better the cost optimization effect; among them, the electricity cost is only related to the electricity purchase price and the required electricity purchase amount. Therefore, the optimization strategy can be further expressed as:

[0102]

[0103] in, and They represent the cost optimization results of community residents at time t and time t+1, Trans() represents the state transfer matrix, S t+1represents the amount of energy stored in the energy storage device at time t+1, and c(t) represents the electricity cost of community residents at time t. On this basis, after inputting specific data, a set of optimal solutions can be found from different power purchase plans through the Markov decision algorithm. The data at this time will correspond to the optimal amount of power purchased and the time of power purchase, and the distributed photovoltaic power generation in the community at this time can be obtained through calculation.

[0104] Based on the above steps, the impact of time-of-use electricity prices on the electricity consumption of users in the community can be fully utilized, and the electricity costs of community residents under time-of-use electricity prices can be significantly reduced.

[0105] Embodiment 2

[0106] This embodiment discloses an energy management system for an electricity-using community that takes price-based demand response into consideration.

[0107] An energy management system for an electricity consumption community considering price-based demand response, comprising:

[0108] The power demand calculation module is configured to: calculate the power-price elasticity coefficient according to the relationship between the power consumption of community residents and the time-of-use electricity price; and calculate the power demand of community residents under the time-of-use electricity price based on the obtained power-price elasticity coefficient;

[0109] The objective function building module is configured to: calculate the photovoltaic power generation according to the output curve of the photovoltaic power generation group, and calculate the energy storage capacity according to the power condition of the electric energy storage device; calculate the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculate the electricity cost of community residents based on the required amount of electricity purchased, and take the lowest electricity cost as the objective function;

[0110] The cost optimization module is configured as follows: using the real-time electric energy storage capacity of the electric energy storage device as the state space, and using the amount of electricity purchased from the power grid in each time period as the behavior space; constructing a state transfer matrix based on the state space and the behavior space; setting a cost optimization strategy based on the objective function and the state transfer matrix, and using the Markov decision method to solve the cost optimization strategy to obtain the optimal amount and time of electricity purchase for community residents.

[0111] Embodiment 3

[0112] The purpose of this embodiment is to provide a computer-readable storage medium.

[0113] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a method for energy management of an electricity community taking into account price-type demand response as described in the first embodiment of the present disclosure.

[0114] Embodiment 4

[0115] The purpose of this embodiment is to provide an electronic device.

[0116] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for energy management of an electricity community taking into account price-based demand response as described in the first embodiment of the present disclosure are implemented.

[0117] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0118] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0119] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for energy management of an electricity consumption community considering price-based demand response, characterized in that: include: According to the relationship between the electricity consumption of community residents and the time-of-use electricity price, the electricity-price elasticity coefficient is calculated; Based on the obtained electricity-price elasticity coefficient, calculate the electricity demand of community residents under time-of-use electricity prices; Calculate the photovoltaic power generation according to the output curve of the photovoltaic power generation group, and calculate the energy storage capacity according to the power condition of the electric energy storage device; calculate the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculate the electricity cost of community residents based on the required amount of electricity purchased, and take the lowest electricity cost as the objective function; The real-time electric energy storage amount of the electric energy storage device is used as the state space, and the amount of electricity purchased from the power grid in each period is used as the behavior space; Constructing a state transfer matrix based on the state space and the behavior space; A cost optimization strategy is set based on the objective function and the state transfer matrix, and the cost optimization strategy is solved using a Markov decision method to obtain the optimal power purchase amount and power purchase time for community residents.

2. The method for energy management of a power consumption community considering price-based demand response according to claim 1, characterized in that: The output curve of the photovoltaic generator set satisfies the Beta distribution; the shape parameters under the Beta distribution are calculated by introducing the mean value and standard deviation value of the irradiance.

3. The method for energy management of a power consumption community considering price-based demand response according to claim 1, characterized in that: The power conditions of the electric energy storage device include: the charging and discharging power of the electric energy storage device and the capacity of the electric energy storage device.

4. The method for energy management of a power consumption community considering price-based demand response according to claim 1, characterized in that: The electricity-price elasticity coefficient represents the relationship between the electricity load of community residents before and after the implementation of time-of-use electricity prices and the electricity prices before and after the implementation of demand response; if the implementation time of time-of-use electricity prices coincides with that of demand response, the electricity-price elasticity coefficient is a self-elasticity coefficient; otherwise, it is a cross-elasticity coefficient.

5. The method for energy management of a power consumption community considering price-based demand response according to claim 1, characterized in that: The amount of electricity purchased from the grid in each period is used as the behavior space, including: using branch pricing theory to set the purchased electricity to equal parts of the maximum energy storage capacity.

6. The method for energy management of a power consumption community considering price-based demand response according to claim 1, characterized in that: A cost optimization strategy is set based on the objective function and the state transfer matrix. Specifically, the lower the electricity cost, the better the cost optimization effect. The electricity cost is only related to the electricity purchase price and the required electricity purchase amount.

7. The method for energy management of a power consumption community considering price-based demand response according to claim 6, characterized in that: The cost optimization strategy is expressed as: in, and They represent the cost optimization results of community residents at time t and time t+1, Trans() represents the state transfer matrix, S t+1 represents the amount of energy stored in the energy storage device at the t+1th moment, and c(t) represents the electricity cost of community residents at the tth moment.

8. An energy management system for an electricity-using community considering price-based demand response, characterized in that: include: The power demand calculation module is configured to: calculate the power-price elasticity coefficient based on the relationship between the power consumption of community residents and the time-of-use electricity price; Based on the obtained electricity-price elasticity coefficient, calculate the electricity demand of community residents under time-of-use electricity prices; The objective function building module is configured to: calculate the photovoltaic power generation according to the output curve of the photovoltaic power generation group, and calculate the energy storage capacity according to the power condition of the electric energy storage device; calculate the amount of electricity required by community residents based on the required electricity, photovoltaic power generation and energy storage capacity; calculate the electricity cost of community residents based on the required amount of electricity purchased, and take the lowest electricity cost as the objective function; The cost optimization module is configured to: use the real-time electric energy storage amount of the electric energy storage device as the state space, and use the amount of electricity purchased from the power grid in each period as the behavior space; Constructing a state transfer matrix based on the state space and the behavior space; A cost optimization strategy is set based on the objective function and the state transfer matrix, and the cost optimization strategy is solved using a Markov decision method to obtain the optimal power purchase amount and power purchase time for community residents.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for energy management of an electricity community taking into account price-type demand response as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for energy management of an electricity community considering price-type demand response as described in any one of claims 1-7 are implemented.

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