Energy management optimization method and system for production and disappearing people

Abstract

The invention provides an energy management optimization method and system for producers and diasters, and the method comprises the steps: constructing an energy storage sharing energy management framework, quantifying the energy storage standby demands, and adjusting and optimizing the energy storage resource configuration; the energy storage sharing energy management framework comprises a production and disappearing person group and an energy management control center; based on an energy storage sharing energy management framework, constructing a target function by taking the lowest operation cost of a producer as a target, and based on energy storage sharing matching, constructing a producer energy management optimization model; a local community clearing model is constructed based on a producer energy management optimization model, distributed clearing solving is carried out by using an alternating direction multiplier method ADMM, and energy sharing and transaction are carried out among producers by solving an energy-reserve sharing problem of producers. The technical problems that an energy management scheme is difficult to popularize on a large scale and the electric energy sharing effect is poor due to low energy management efficiency, insufficient flexibility, high investment cost, extensive management and low utilization rate are solved.

Description

Technical Field

[0001] The present invention relates to the field of energy management data processing, and in particular to a method and system for optimizing energy management of prosumers. Background Art

[0002] With the popularization of household distributed photovoltaics, users have gradually transformed from electricity consumers to prosumers with electricity production capabilities. Prosumer energy management can improve the economy and environmental protection of energy use to a certain extent by reasonably arranging electricity consumption and load adjustment. However, the energy flow in the power grid and the prosumers themselves has low energy management efficiency and insufficient flexibility. Prosumers configuring energy storage is an important method to improve energy management flexibility and improve power supply reliability. However, at this stage, energy storage has problems such as high investment cost, extensive management and low utilization rate, making it difficult to carry out large-scale promotion and application. At the same time, prosumers' energy sharing can achieve the complementarity of power generation and consumption nearby, reducing the demand for shared energy storage and the loss of distributed energy storage of prosumers. However, when the complementarity between power generation and consumption is weak, the role of energy sharing is easily restricted.

[0003] The existing invention patent application document "Multi-microgrid system power sharing mode decision-making method considering risks and coordination costs" with publication number CN114692975A includes: through the system power sharing mode decision-making method, a mathematical model of the multi-microgrid system under different power sharing modes is established, and a power sharing decision-making mechanism is proposed: a multi-microgrid system privacy risk hierarchical assessment system is built based on the fuzzy hierarchical analysis method, the subjective weight value of the risk assessment index is calculated, and the risk cost of the multi-microgrid system under different sharing modes is quantified; considering the energy management form of the multi-microgrid system, a quantitative model of coordination cost under different sharing modes is proposed. However, the aforementioned existing scheme only considers the privacy risk cost of multi-microgrids, and does not consider the power matching problem during actual energy sharing.

[0004] The existing public document "Design of P2P (Point to Point) Trading Framework for Multiple Producers and Consumers in Local Energy Market" adopts three models in terms of the bidding behavior patterns of producers and consumers: the Bertrand model is based on the market quotation set by the producers and consumers for scheduling; the Cournot model is based on bidding for the electricity volume of the producers and consumers; the SFE (Supply Function Equa-lization) model is based on submitting bidding information including electricity prices and electricity volume to the scheduling department. Compared with the first two models, the SFE model can generate a lower pure competition price than the Bertrand model, and is closer to the actual market price than the Cournot model, so that producers and consumers can obtain higher income. The solution algorithm for P2P market transactions includes: Lagrangian relaxation method and alternating direction multiplier method. However, the above existing documents use auctions to clear, without considering the energy reserve sharing of energy storage, and the market transaction varieties are single.

[0005] In summary, the existing technology has technical problems such as low energy management efficiency, insufficient flexibility, high investment cost, extensive management and low utilization rate, which make it difficult to promote energy management solutions on a large scale and lead to poor power sharing effects. Summary of the invention

[0006] The technical problem to be solved by the present invention is: how to solve the technical problem in the prior art that energy management solutions are difficult to promote on a large scale and the power sharing effect is poor due to low energy management efficiency, insufficient flexibility, high investment cost, extensive management and low utilization rate.

[0007] The present invention adopts the following technical solutions to solve the above technical problems: The energy management optimization method for prosumers includes: S1. Construct an energy storage sharing energy management framework, perform quantitative operations on energy storage backup demand based on Gaussian distribution, and optimize energy storage resource configuration; wherein, the energy storage sharing energy management framework includes: prosumer group, energy management control center; S2. Based on the energy storage sharing energy management framework, with the goal of minimizing the operating cost of the prosumer, an objective function is constructed, and based on the energy storage sharing matching, an energy management optimization model for the prosumer is constructed; S3. Based on the energy management optimization model of prosumers, a local community clearing model is constructed, and the alternating direction method of multipliers (ADMM) is used to solve the distributed clearing problem. By solving the energy-reserve sharing problem of multiple prosumers, energy sharing and trading are carried out among prosumers.

[0008] The present invention establishes an energy management framework for energy storage sharing among prosumers, and optimizes the allocation of energy storage resources by quantifying the demand for energy storage backup. With the goal of minimizing the operating cost of all prosumers in the prosumer group, a prosumer energy management optimization model for distributed energy storage sharing among prosumers is established. The alternating direction multiplier method ADMM distributed clearing solution is used to achieve energy sharing and trading among local prosumers, reduce system costs, improve local energy consumption, and optimize the power balancing capability of prosumers.

[0009] In a more specific technical solution, in S1, the prosumer group is connected to the energy management and control center, and the power sharing coordination operation and energy storage capacity allocation operation are performed on the prosumers in the prosumer group to obtain an applicable energy management solution, and the power interaction between the prosumers and the energy management and control center is recorded; the energy storage backup demand of the prosumers is quantified; wherein, t Net demand forecast deviation for the period The probability density function of , represents the net demand forecast deviation, and the cumulative distribution function CDF ( Cumulative distribution function )for ; According to the maximum positive reserve, t The positive reserve demand in the time period is the probability density function Integrate to obtain the preset load loss rate LOLP (Loss of load probability) , using the minimum objective function, calculate the preset load loss rate LOLP The minimum positive reserve required under the load loss ratio is obtained; According to the maximum positive reserve, t The positive reserve demand of the time period is t Net demand forecast deviation for the period and t Load loss rate corresponding to the time period ( Minimum positive reserve The difference between Integrate the product of to find the expected value of the lost load EENS ( Expected energy not served) ; According to the maximum negative reserve, t The minimum negative reserve capacity required to cover the corresponding forecast error during the period is calculated by the probability density function Integrate, defined at the preset light abandonment rate LOPP (Loss of power probability) The minimum negative reserve required under the condition is calculated by using the minimum objective function, according to which the value of the cumulative distribution function CDF is less than or equal to the abandonment rate. ( Minimum negative reserve The probability of , calculate the abandonment rate; According to the maximum negative reserve, t The minimum negative reserve capacity required to cover the corresponding forecast error is met during the period. t Abandonment rate of light in the time period and t Net demand forecast deviation for the period The difference between Integrate the product of to find the expected value of abandoned light EEW (Expected energy wasted); The Cornish-Fisher partial distribution general Gaussian model based on skewness S and kurtosis K is used to describe the distribution characteristics of the deviation and calculate the load loss rate. MPR and light abandonment rate MNR The quotient of, and the expected value of the load loss EENS Expected value of abandonment EEW Business.

[0010] The present invention adopts a prosumer energy management strategy, which can effectively improve the economic benefits of energy management for users, reduce operating costs, and promote local consumption of new energy.

[0011] In a more specific technical solution, the following logic is used, according to the quantile of the Gaussian distribution , the quantile probability of the Gaussian distribution , expresses the quantile function in the general Gaussian model of the Cornish-Fisher partial distribution; Using the product of the quantile function and the standard deviation, plus the average of the forecast deviations, σ is the standard deviation, and the load loss rate is obtained MPR and light abandonment rate MNR The expected value of the load loss EENS Expected value of abandonment EEW Business.

[0012] The present invention sets up an energy management control center to aggregate various prosumers into a whole, and dispatches them with the goal of optimizing the overall interests of the prosumer group. Since it has the right to dispatch the internal resources of each prosumer, the energy management control center can coordinate and optimize the dispatch of various resources, improve the utilization rate of distributed energy storage of prosumers based on shared energy storage and power sharing, effectively utilize new energy, and reduce dependence on the distribution network, thereby reducing operating costs.

[0013] In a more specific technical solution, in S2, the objective function is defined; wherein, the scheduling period is assumed to be T , taking the minimum sum of the operating costs of all prosumers as the optimization goal, constructing the objective function, where the operating costs include: energy storage investment cost, electricity purchase cost from the power grid, load adjustment cost, energy storage operation cost, power transmission cost, air conditioning load response cost, power sharing cost and expected utility loss; Define constraints, including: power balance constraints of prosumers, shared energy storage constraints, air conditioning load constraints, indoor temperature constraints, and flexible load adjustment constraints; For prosumers The allowable working period is determined according to the load that can be translated. t Time period prosumer Corresponding 0-1 variables And the rated power consumption, and t The result of determining whether the time period is within the permissible working time period is obtained. t Prosumers The power of the load that can be translated; according to t Time period prosumer The corresponding 0-1 variables and the allowable working time periods are summed to obtain the working time of the shiftable load.

[0014] The energy storage sharing energy management framework provided by the present invention is applicable to the scenario where some prosumers have already configured distributed energy storage, and is used to solve the problems of low utilization rate and extensive management of distributed energy storage of these prosumers. For prosumers with energy storage needs, the repeated investment in distributed energy storage can be reduced. The energy management control center uniformly allocates the distributed energy storage resources of prosumers and coordinates the sharing of electricity among prosumers, which can give play to the complementarity of power generation and consumption of multiple prosumers and reduce the demand for energy storage resources by prosumers.

[0015] The present invention adopts energy storage and backup energy sharing transactions to achieve real-time power matching, thereby avoiding electricity default penalties and further reducing the economic costs of producers and consumers.

[0016] In a more specific technical solution, according to the capacity cost coefficient of energy storage, the power cost coefficient of energy storage, the production and consumption Distributed energy storage capacity, prosumers The maximum charge and discharge power of distributed energy storage and the service life of energy storage are used to calculate the investment cost of energy storage; In the scheduling cycle T In, according to t The grid electricity price during the period, Prosumers i The amount of electricity purchased from the grid, The on-grid electricity price of surplus electricity during the period and Prosumers i The amount of electricity sold to the power grid is summed up to obtain the power purchase cost of the power grid; According to the prosumer i exist t Loads and prosumers transferred into the time period i exist t The load transferred out during the time period is summed up to obtain the load adjustment cost; According to the prosumer i exist t Charging power in different time periods, prosumers i exist t The discharge power of each time period is summed up to obtain the energy storage operation cost; According to the prosumer i exist t The electricity consumed by power sharing during the period, the prosumers i exist t Electricity supplied during the period , perform a summation operation to obtain the power transmission cost; According to the producer and consumer before the response i No. k The power of each air conditioner, the prosumer after the response i No. k The power of each air conditioner is used to calculate the air conditioner load response cost; The cost of electricity sharing is calculated based on the supply price and consumption price of electricity sharing.

[0017] In a more specific technical solution, the power balance constraints of the prosumers are defined. i , according to the prosumer i of new energy output; prosumers i exist t Base load for the time period; Prosumers i The amount of electricity purchased from the grid; Prosumers i of electricity sold to the grid; prosumers i exist t Discharge power in a period; Prosumers i exist t Charging power in the time period; Prosumers i exist t The electricity consumed by power sharing during the period; i exist t Electricity supplied by power sharing during the period; i No. k The power of an air conditioner, definition t Power balance equation for the time period; Define shared energy storage constraints for prosumers i , t The shared energy storage constraints for the time period are as follows: (25) (26) (27) (28) (29) (30) In the formula, For prosumers i exist t The electricity stored by shared energy storage during the period, For prosumers i exist The amount of electricity stored by shared energy storage during the period; For energy storage charging efficiency, Represents electrical energy storage, Indicates charging. is the energy storage discharge efficiency, Indicates discharge; For prosumers i The maximum charging power of shared energy storage, For prosumers i The maximum discharge power of shared energy storage, Indicates power; and To limit the 0-1 variables that appear simultaneously in charge and discharge situations, It means at the same time; For prosumers i The storage capacity of the initial period of the dispatch cycle, For prosumers i Scheduling cycle T The amount of stored electricity at the end of the period; For prosumers i The maximum value of energy storage. represents the number of prosumers; The unit scheduling period.

[0018] In a more specific technical solution, air conditioning load constraints are defined; the first-order equivalent thermal parameter ETP (Equivalent Thermal Parameter) model is used to describe the dynamic changes of air conditioning load; the first-order equivalent thermal parameter ETP model is used to obtain and calculate the equivalent heat capacity of the air-conditioned room, the equivalent thermal resistance of the air-conditioned room, t Indoor temperature during the period, t The outdoor temperature during the period, t Cooling capacity of time-slot air conditioner, t The cooling power of the air conditioner in each period, the rated power of the air conditioner and the energy efficiency ratio are used to establish the first-order differential equation of indoor temperature change; Assume that the outdoor temperature is within the unit scheduling period Keeping the interior constant, solving the first-order differential equation of indoor temperature change, we get t+1 The relationship between indoor temperature and time period; The flexible load adjustment constraints are determined using the following logic: (36) (37) (38) (39) In the formula, for t Time period prosumer i The adjusted transferable load; for t Time period prosumer i The maximum transferable load; for t Time period prosumer i The transferred load, Indicates transfer in. for t The load transferred out during the period, Indicates transfer out. Indicates a load transfer operation; for t Prosumers in the time period i The base load, Indicates the basis; is the ratio of the maximum transferable load to the base load, Indicates the scheduling cycle In t The load transferred out during each period is summed up. Indicates the scheduling cycle In t The sum of the load transferred into the time period.

[0019] In a more specific technical solution, in S3, based on the objectives of maximizing welfare and minimizing clearing costs, a transaction clearing objective function of the prosumer group community is constructed; The alternating direction multiplier method ADMM is used to solve the energy-reserve sharing problem of multiple producers and consumers, and multi-center calculations are performed to obtain local market clearing results. Using the following logic, we can use the Lagrange multiplier corresponding to the P2P transaction constraint: , represents the shadow price of P2P transactions, and uses the following logic to define the augmented Lagrangian function of the optimization problem: (43) in: (44) In the formula, represents the constructed Lagrangian function, represents the coupled decision variables, represents auxiliary variables, represents the Lagrange multiplier, Indicated in i In the sub-question, t The coupled decision variables of the time period, Indicates that in the i sub-problem, t Auxiliary variables for the time period, Indicated in i In the sub-question, t The Lagrange multiplier of the time period, Indicated in i In the sub-question, t The Lagrangian function of the time period, Indicated in i In the sub-question, t The supremum function of the time period, represents the supremum, Indicated in i In the sub-question, t Cost function or constraint function for a time period, represents the regularization parameter in the ADMM algorithm, Indicated in i Sub-questions and j Coupling variables between sub-problems and The square of the Euclidean norm of .

[0020] The trading method of the present invention can promote the economic potential of energy storage resources in the process of energy balance backup service and power balance real-time adjustment. The present invention adopts the alternating direction multiplier method ADMM to solve the energy-backup sharing problem of multiple producers and consumers, reduces the computing power burden through multi-center computing, and obtains local market clearing results under the premise of protecting the privacy of producers and consumers. Compared with the prior art, the present invention focuses on exploring the flexible adjustment capability of energy storage, and adopts the alternating direction multiplier method ADMM to solve the multi-variety joint transaction of energy and backup energy storage, which can improve the flexibility of energy trading and increase the market benefits of electric energy sharing.

[0021] In a more specific technical solution, in S3, during the solution process of the alternating direction multiplier method ADMM, the local decision variables are updated: the producer and consumer i According to the previous iteration , Solve the local optimization decision problem: (45) In the formula, is the number of iterations, Indicated in i In the sub-question, t The coupling decision variables of the time period are v+1 The value at the iteration, Indicated in i In the sub-question, t The coupled decision variables of the time period, It means that the Lagrangian function i In the sub-question, t The value of the coupled decision variables in each time period, express Time period, represents the scheduling period, Indicated in i In the sub-question, t The auxiliary variables of the period are v The value at the iteration, Indicated in i In the sub-question, tThe Lagrange multiplier of the time period is v The value at the iteration; Will update the obtained Prosumers ; Solve the global optimization problem and update the auxiliary variables according to the Lagrangian function; Separate and derive the coupled part of the optimization problem to obtain the coupled variables Y ij Analytical solution to achieve fully distributed solution; Update Lagrange multipliers; Among them, the following logic is used to calculate the original residual and dual residual of P2P transaction volume, and an adaptive step size mechanism is used to adjust the step size according to the residual state so that the original variable is consistent with the auxiliary variable; when the original residual is relatively small, the step size is shortened; The following logic is used to express the energy transaction relationship: (48) (49) (50) In the formula, Indicated in v The original residual at iteration , represents the set of all subproblems, Indicated in v The regularization parameter at the iteration, Indicated in The regularization parameter at the iteration, Indicated in i Sub-questions and j Coupling variables between sub-problems In the v The value at the iteration, represents the step size adjustment factor in the adaptive step size mechanism, Indicated in v The dual residual at the iteration, Represents the dual residual The norm of Indicates v The original residual at iteration The norm of ; Prosumers The Lagrange multipliers are updated independently to transmit the price signals to the corresponding prosumers until the iteration of the alternating direction multiplier method ADMM converges.

[0022] The present invention calculates the original residual and dual residual of the P2P transaction volume. In order to solve the problem of uneven residual convergence speed, an adaptive step size mechanism is adopted to flexibly adjust the step size according to the residual state. When the original residual is relatively large, the step size is increased to promote the original variable and the auxiliary variable to reach a consensus and accelerate the convergence speed; when the original residual is relatively small, the step size is shortened to reduce the oscillation of the objective function.

[0023] In a more specific technical solution, the prosumer energy management optimization method and system include: The shared framework building module is used to build a shared energy management framework for energy storage, quantify the energy storage backup demand based on Gaussian distribution, and optimize the energy storage resource configuration; the shared energy management framework for energy storage includes: a prosumer group and an energy management control center; A management optimization model construction module is used to construct an objective function based on the energy storage sharing energy management framework with the goal of minimizing the operating cost of the prosumer, and to construct a prosumer energy management optimization model based on energy storage sharing matching. The management optimization model construction module is connected to the sharing framework construction module; The distributed clearing solution and shared trading module is used to build a local community clearing model based on the energy management optimization model of producers and consumers, and use the alternating direction multiplier method ADMM to perform distributed clearing solutions. By solving the energy-spare sharing problem of multiple producers and consumers, energy sharing and trading are carried out among producers and consumers. The distributed clearing solution and shared trading module is connected with the management optimization model construction module.

[0024] Compared with the prior art, the present invention has the following advantages: The present invention establishes an energy management framework for energy storage sharing among prosumers, and optimizes the allocation of energy storage resources by quantifying the demand for energy storage backup. With the goal of minimizing the operating cost of all prosumers in the prosumer group, a prosumer energy management optimization model for distributed energy storage sharing among prosumers is established. The alternating direction multiplier method ADMM distributed clearing solution is used to achieve energy sharing and trading among local prosumers, reduce system costs, improve local energy consumption, and optimize the power balancing capability of prosumers.

[0025] The present invention adopts a prosumer energy management strategy, which can effectively improve the economic benefits of energy management for users, reduce operating costs, and promote local consumption of new energy.

[0026] The present invention sets up an energy management control center to aggregate various prosumers into a whole, and dispatches them with the goal of optimizing the overall interests of the prosumer group. Since it has the right to dispatch the internal resources of each prosumer, the energy management control center can coordinate and optimize the dispatch of various resources, improve the utilization rate of distributed energy storage of prosumers based on shared energy storage and power sharing, effectively utilize new energy, and reduce dependence on the distribution network, thereby reducing operating costs.

[0027] The energy storage sharing energy management framework provided by the present invention is applicable to the scenario where some prosumers have already configured distributed energy storage, and is used to solve the problems of low utilization rate and extensive management of distributed energy storage of these prosumers. For prosumers with energy storage needs, the repeated investment in distributed energy storage can be reduced. The energy management control center uniformly allocates the distributed energy storage resources of prosumers and coordinates the sharing of electricity among prosumers, which can give play to the complementarity of power generation and consumption of multiple prosumers and reduce the demand for energy storage resources by prosumers.

[0028] The present invention adopts energy storage and backup energy sharing transactions to achieve real-time power matching, thereby avoiding electricity default penalties and further reducing the economic costs of producers and consumers.

[0029] The trading method of the present invention can promote the economic potential of energy storage resources in the process of energy balance backup service and power balance real-time adjustment. The present invention adopts the alternating direction multiplier method ADMM to solve the energy-backup sharing problem of multiple producers and consumers, reduces the computing power burden through multi-center computing, and obtains local market clearing results under the premise of protecting the privacy of producers and consumers. Compared with the prior art, the present invention focuses on exploring the flexible adjustment capability of energy storage, and adopts the alternating direction multiplier method ADMM to solve the multi-variety joint transaction of energy and backup energy storage, which can improve the flexibility of energy trading and increase the market benefits of electric energy sharing.

[0030] The present invention calculates the original residual and dual residual of the P2P transaction volume. In order to solve the problem of uneven residual convergence speed, an adaptive step size mechanism is adopted to flexibly adjust the step size according to the residual state. When the original residual is relatively large, the step size is increased to promote the original variable and the auxiliary variable to reach a consensus and accelerate the convergence speed; when the original residual is relatively small, the step size is shortened to reduce the oscillation of the objective function.

[0031] The present invention solves the technical problems existing in the prior art that energy management solutions are difficult to promote on a large scale and the power sharing effect is poor due to low energy management efficiency, insufficient flexibility, high investment cost, extensive management and low utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of basic steps of the prosumer energy management optimization method according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of an energy management framework for distributed energy storage sharing by prosumers according to Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of photovoltaic prediction deviation distribution and risk quantification according to Example 1 of the present invention; Figure 4 This is a schematic diagram of a first-order ETP model of air conditioning load according to Embodiment 1 of the present invention; Figure 5This is a schematic diagram of time-of-use electricity prices and outdoor temperature changes in Example 1 of the present invention; Figure 6a This is a schematic diagram of the operation of the transferable load and the air conditioning load under the first solution of the second embodiment of the present invention; Figure 6b This is a schematic diagram of the operation of the transferable load and the air conditioning load under the second solution of Example 2 of the present invention; Figure 7a This is a schematic diagram of energy storage changes of the prosumer under the first solution of Example 2 of the present invention; Figure 7b This is a schematic diagram of energy storage changes of the prosumer under the second solution of Example 2 of the present invention; Figure 8a This is a schematic diagram of the amount of electric energy shared by producers and consumers in the first solution of embodiment 2 of the present invention; Figure 8b This is a schematic diagram of the amount of electricity shared by producers and consumers in the second solution of Example 2 of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Example 1 like Figure 1 As shown, the prosumer energy management optimization method provided by the present invention includes the following basic steps: S1. Construct an energy storage sharing energy management framework, perform quantitative operations on energy storage backup demand based on Gaussian distribution, and optimize energy storage resource configuration; wherein, the energy storage sharing energy management framework includes: prosumer group, energy management control center; like Figure 2 As shown, in this embodiment, the distributed energy storage sharing energy management framework of producers and consumers includes but is not limited to: producer-consumer groups and energy management control centers. Each producer and consumer cooperates to establish the aforementioned energy management control center, which has the scheduling rights of the distributed resources of each producer and consumer and provides energy management services for the producer-consumer group. The distributed energy storage of each producer and consumer is interconnected through the power transmission network. The energy management control center uniformly manages the distributed energy storage and allocates energy storage capacity to each producer and consumer. There are two-way power transmission lines between the energy management control center and each producer and consumer to coordinate the sharing of electricity among the producers and consumers. Smart meters are installed between producers and consumers and between producers and consumers and the energy management control center to record the interaction of electricity for subsequent settlement.

[0035] In this embodiment, the energy management optimization operation includes but is not limited to: Each producer and consumer will upload energy arrangement information and new energy output forecast information to the energy management and control center; The energy management control center sets the electricity sharing price and allocates the shared energy storage capacity. By scheduling the charging and discharging of shared energy storage, adjusting the load and coordinating the electricity sharing, the energy management plan is decided with the goal of minimizing the sum of the operating costs of all producers and consumers. In this embodiment, in order to ensure fairness, the cooperative operation costs are shared.

[0036] During the operation of this embodiment, the prosumer can conduct electricity purchase and sale transactions with the power grid, and can also share power among prosumers through the energy management control center. Prosumers with idle energy storage capacity can provide part of the energy storage capacity to prosumers with energy storage needs, and obtain corresponding service fees. The power grid provides the prosumers with supply and demand balancing services, provides the prosumers with electricity, and purchases the prosumers' surplus electricity.

[0037] In this embodiment, the energy storage backup demand of the prosumer is quantified; Specifically, the products traded between producers and consumers include: electric energy and backup services provided by energy storage. It is necessary to clarify the amount of energy storage purchasing power required by producers and consumers. Therefore, considering the uncertainty perspective, quantify the energy storage backup needs of producers and consumers. The forecast deviations of photovoltaics and loads include but are not limited to: positive deviations and negative deviations, which can be regarded as the amount of load loss and the amount of abandoned light, which are reflected in the positive and negative backup amounts of producers and consumers. Assumptions t Net demand forecast deviation for the period The probability density function of , the cumulative distribution function ( Cumulative distribution function, CDF )for .

[0038] In this embodiment, it is determined that at a preset load loss rate LOLP The minimum positive reserve required under load loss is obtained; specifically, the load loss rate ( Loss of load probability, LOLP) It indicates the probability that the system power generation cannot meet the load demand, which is defined as a certain load loss rate. LOLP The minimum positive reserve required under Minimum positive reserve, MPR) : (1) (2) in, is the maximum positive reserve, express t The positive reserve demand of the time period is the minimum positive reserve capacity required to cover the corresponding forecast error. express t The preset load loss rate for the time period, express t The minimum positive reserve corresponding to the preset load loss rate of the time period , Indicates that the cumulative distribution function CDF is greater than the minimum positive reserve The probability of represents the minimum objective function, R represents the resource of backup power; Using the following logic, we can find the expected value of load loss ( Expected energy not served, EENS) : (3) In the formula, express t Expected value of load loss during the period EENS , express t Load loss rate corresponding to the time period ; In this embodiment, the preset light abandonment rate is defined LOPP The minimum negative reserve required under the condition is used to determine the abandonment rate. Specifically, the abandonment rate LOPP (Loss of power probability) It indicates the probability that the photovoltaic power generation is greater than the sum of the sales and the load consumption, which is defined as the probability of photovoltaic power generation exceeding the sum of the sales and the load consumption. LOPP Minimum negative reserve required MNR ( Minimum negative reserve ): (4) (5) In the formula, express t The time period meets the minimum negative reserve capacity required to cover the corresponding forecast error, is the maximum negative reserve, express t Preset load loss rate for the time period LOPP The corresponding abandonment rate , express t Preset abandoned light rate for the time period LOPP , Indicates that the value of the cumulative distribution function CDF is less than or equal to the abandonment rate probability; Using the following logic, we can find the expected value of abandoned light: EEW (Expected energy wasted): (6) In the formula, expresst Expected value of abandoned light in the time period EEW , express t Abandonment rate of light in the time period ; The Cornish-Fisher partial distribution general Gaussian model based on skewness S and kurtosis K is used to describe the distribution characteristics of the deviation and calculate the load loss rate. MPR and light abandonment rate MNR The quotient of, and the expected value of the load loss EENS Expected value of abandonment EEW commerce; like Figure 3 As shown, in this embodiment, in order to fully describe the distribution characteristics of the deviation and obtain more accurate prediction results, the Cornish-Fisher partial distribution general Gaussian model based on skewness S and kurtosis K is used to calculate MPR / MNR and EENS / EEW, and its quantile function The first four stages of are expanded as follows: (7) in, is the quantile of the Gaussian distribution, and the quantile probability of the Gaussian distribution is , corresponding to the risk attitude information of the prosumer, represents the quantile, where, according to the quantile function , calculate the load loss rate MPR and light abandonment rate MNR commerce; express t The preset load loss rate for the time period, express t Abandonment rate of light in the time period , Based on skewness S and Kurtosis K The quantile function of the general Gaussian model of the Cornish-Fisher partial distribution is, represents the inverse cumulative distribution function of the Gaussian distribution, also known as the quantile function; In this embodiment, the risk attitude of the prosumer can be expressed by the aforementioned cumulative distribution function ( Cumulative distribution function, CDF ) is calculated by inverse calculation. According to the quantile function , calculate the load loss rate MPR and light abandonment rate MNR Business: (8) (9) In the formula, is the average of the forecast deviations, σis the standard deviation, Represents the probability inverse function of the cumulative distribution function CDF with respect to the quantile of the Gaussian distribution; In this embodiment, according to the quantile function , calculate the expected value of the load loss EENS Expected value of abandonment EEW Business: (10) (11) S2. Based on the energy storage sharing energy management framework, with the goal of minimizing the operating cost of the prosumer, an objective function is constructed, and based on the energy storage sharing matching, an energy management optimization model for the prosumer is constructed; In this embodiment, the energy management of prosumers includes but is not limited to: the purchase and sale of electricity between prosumers and the power grid, shared energy storage charging and discharging, power sharing between prosumers, and load adjustment of prosumers, which are dispatched by the energy management control center to achieve energy management optimization of prosumers. Based on the distributed energy storage sharing energy management framework of prosumers, with the goal of minimizing the operating cost of the prosumer group, an energy management optimization model of distributed energy storage sharing prosumers is established.

[0039] In this embodiment, the objective function is defined; wherein, the scheduling period is assumed to be T , taking the minimum sum of the operating costs of producers and consumers as the optimization goal, construct the objective function: + (12) (13) In the formula, Prosumer The running cost, For prosumers The energy storage investment cost is It means investment; For prosumers The cost of purchasing electricity from the grid, Represents the power grid; For prosumers The load adjustment cost, Indicates load; For prosumers The energy storage operating cost, It represents electrical energy storage; For prosumers The cost of electricity transmission, Indicates transmission; For prosumers The air conditioning load response cost is Indicates air conditioning, represents the optimization objective; For prosumers The cost of electricity sharing, represents the internal power sharing transaction among the prosumer group, Prosumer In the period t The expected utility loss that may occur due to insufficient reserves, Rloss Indicates resource loss, Indicates the value of unit loss load, Lloss represents the load loss, Represents the benefit loss coefficient per unit of abandoned light, Indicates the amount of abandoned light per unit; Specifically, the distributed resources of prosumers are coordinated and managed by the energy management and control center to achieve energy complementarity among prosumers. The distributed resources of prosumers include but are not limited to: distributed new energy, distributed energy storage, air conditioning load and flexible load. Suppose the scheduling period is T , with the optimization goal of minimizing the sum of the operating costs of producers and consumers.

[0040] In this embodiment, the energy storage investment cost is calculated , energy storage investment cost Includes: Power cost and capacity cost: (17) In the formula, is the capacity cost coefficient of energy storage, is the power cost coefficient of energy storage; For prosumers The capacity of distributed energy storage, For prosumers Maximum charging and discharging power of distributed energy storage; The service life of the energy storage; In this embodiment, the cost of purchasing electricity from the power grid is determined : (18) In the formula, for t The grid electricity price for the time period, Represents the power grid, for Prosumers i The amount of electricity purchased from the grid, It means that the grid transmits electricity to prosumers; yes The on-grid electricity price of surplus electricity during the period, Indicates the remaining power in the grid. yes Prosumers i of electricity sold to the grid, It means that the prosumer transmits electricity to the grid. T is the scheduling period; In this embodiment, the load adjustment cost is determined : (19) In the formula, For prosumers i exist t The load transferred into the period, For prosumers i exist t The load transferred out during the period, is the cost coefficient for flexible load adjustment, Indicates the current load, Indicates load adjustment, Indicates load transfer, Indicates load transfer, Indicates load transfer out; specifically, , All are non-negative and at least one of them is 0.

[0041] Specifically, flexible loads include transferable loads and shiftable loads. Transferable loads can transfer part of the load from the peak electricity price period to the valley electricity price period, reducing the electricity consumption during the peak electricity price period, but it needs to bear the load adjustment cost. Shiftable loads, including but not limited to: dishwashers, washing machines, refer to the actual working period of the shiftable load within the allowed working time interval, which can be scheduled, but the load is not allowed to be interrupted during the working period of the shiftable load. Since the shiftable load has a smaller impact on the prosumer within the working time interval, the load adjustment cost in this embodiment takes into account the adjustment cost of the transferable load.

[0042] In this embodiment, the energy storage operation cost is determined ; (20) Specifically, the battery of distributed energy storage will decay during operation, which is related to the charge and discharge amount of energy storage; where, is the depreciation cost coefficient of charging and discharging electric energy storage; For prosumers i exist t The charging power of the time period, For prosumers i exist t The discharge power of the time period, Indicates charging. Indicates discharge, represents power; specifically, , are all non-negative values. In this embodiment, in order to reduce the energy storage operation cost, , At least one item is set to 0.

[0043] In this embodiment, the power transmission cost is determined : (twenty one) In the formula, For prosumers i exist t The electricity consumed by energy sharing during the period, Indicates shared consumption, For prosumers i exist t The amount of electricity supplied during the period, Indicates supply; is the unit power transmission cost; Specifically, producers and consumers share electricity through the energy management and control center, which requires them to bear the cost of electricity transmission. , All are non-negative and at least one is set to 0.

[0044] In this embodiment, the air conditioning load response cost is determined : (twenty two) In the formula, Before the response t Prosumers i No. k The power of an air conditioner, After the response t Prosumers i No. k The power of an air conditioner, is the adjustment cost coefficient of air conditioning load; The cost of electricity sharing is determined using the following logic: : (twenty three) In the formula, for The supply price of electricity sharing in the time period, for The consumption price of electricity sharing in the time period, For prosumers i exist t The power supplied by power sharing during the period, Indicates supply.

[0045] In this embodiment, the constraints are defined; Specifically, the power balance constraints of the prosumers are defined. , The power balance equation for the time period is as follows: (twenty four) In the formula, For prosumers i of new energy output, It means new energy; For prosumers i exist t Base load for the time period; For prosumers i No. k The power of the air conditioner, Indicates air conditioning, express t Prosumers The power of the load that can be translated; Define shared energy storage constraints for prosumers i , t The shared energy storage constraints for the time period are as follows: (25) (26) (27) (28) (29) (30) In the formula, For prosumers i exist t The electricity stored by shared energy storage during the period, For prosumers i exist The electric energy stored by shared energy storage during the period; For energy storage charging efficiency, Represents electrical energy storage, Indicates charging. is the energy storage discharge efficiency, Indicates discharge; For prosumers i The maximum charging power of shared energy storage, For prosumers i The maximum discharge power of shared energy storage, Indicates power; and To limit the 0-1 variables that appear simultaneously in charge and discharge situations, Indicates that at the same time, Indicates charging. Indicates discharge; is the storage capacity in the initial period of the dispatch cycle, Scheduling cycle T The amount of stored electricity at the end of the period; For prosumers i The maximum value of energy storage. represents the number of prosumers; For unit scheduling period, , Keep them equal to leave energy storage margin for the next dispatch cycle.

[0046] like Figure 4 As shown, in this embodiment, the air conditioning load constraint is defined; specifically, the first-order equivalent thermal parameter ETP model is used to describe the dynamic change of the air conditioning load; according to the first-order equivalent thermal parameter ETP (Equivalent Thermal Parameter) model, the first-order differential equation of indoor temperature change is obtained. Figure 4 .

[0047] In this embodiment, the first-order differential equation describing the indoor temperature change is obtained from the first-order equivalent thermal parameter ETP model of the air conditioning load shown in the figure: (31) (32) (33) In the formula, is the equivalent heat capacity of the air-conditioned room, is the equivalent thermal resistance of the air-conditioned room, for t The indoor temperature during the period, for t The outdoor temperature during the time period; for t Cooling capacity of time-slot air conditioner; for t The cooling power of the air conditioner during the period, is the rated power of the air conditioner, is the energy efficiency ratio; In this embodiment, the outdoor temperature is set to Keeping the interior constant, solving the first-order differential equation of indoor temperature change, we get the following equation: (34) In the formula, for t+1 The indoor temperature during the period, Indicates indoors; In this embodiment, the premise of air conditioning load participation in response is not to sacrifice user comfort. This paper does not consider the differences in air-conditioned rooms, but considers indoor temperature constraints. The equivalent heat capacity of the air-conditioned room , equivalent thermal resistance Corresponding power is the air conditioning equivalent parameter ,but: (35) In the formula, The best room temperature for air-conditioned rooms. Indicates a setting operation; The allowable deviation between the actual room temperature and the set temperature; for Time period prosumer i No. k The power of the air conditioner, for Time period prosumer i No. k The temperature of the room where the air conditioner is located; In this embodiment, the flexible load adjustment constraint is determined, and the flexible load adjustment constraint of the transferable load is expressed using the following formulas (31) to (34): (36) (37) (38) (39) In the formula, for t Time period prosumer i The adjusted transferable load, Indicates a transfer operation; for t The maximum load that can be transferred out during the period; for t Time period prosumer i The transferred load, Indicates transfer in. for t Time period prosumer i The transferred load, Indicates transfer out. Indicates a load transfer operation; for t Prosumers in the time period i The base load, Indicates the basis; is the ratio of the maximum transferable load to the base load, Indicates the scheduling cycle In t The load transferred out during each period is summed up. Indicates the scheduling cycle In t The sum of the load transferred into the time period.

[0048] In this embodiment, for the prosumer The movable load determines the permissible working period , using the following logic, determine t Prosumers Power of the load that can be translated : (14) In the formula, shift represents translation, for t Time period prosumer The corresponding 0-1 variable is in working state when it is 1; is the rated power consumption, Indicates rated value, Indicates the start time of the permitted working period. Indicates the end time of the permissible working period; During the scheduling period, the working duration of the movable load is fixed and continuous. The working duration of the movable load is expressed using the following logic: (15) (16) In the formula, For continuous working time, Indicates the permissible working hours Prosumers The corresponding 0-1 variables are summed. k Indicates the time sequence number. Indicates k Moment Prosumer The corresponding 0-1 variable, Indicates Moment Prosumer The corresponding 0-1 variable, Indicates the continuous working time Prosumers The corresponding 0-1 variables are summed, and equations (15) and (16) are used to constrain the working time and work continuity.

[0049] S3. Based on the energy management optimization model of prosumers, a local community clearing model is constructed, and the alternating direction multiplier method ADMM is used to solve the distributed clearing problem. By solving the energy-spare sharing problem of multiple prosumers, energy sharing and trading are carried out among prosumers.

[0050] In this embodiment, based on the welfare maximization goal, a transaction clearing objective function of the producer-consumer group community is constructed: (40) In the formula, represents the clearing cost, L Represents a set of variables, represents the objective function of minimum clearing cost; The alternating direction multiplier method ADMM is used to solve the energy-reserve sharing problem of multiple producers and consumers, and multi-center calculations are performed to obtain the local market clearing results; in which the auxiliary variable is introduced The coupling constraints are rewritten to make them conform to the solution form of the alternating direction multiplier method ADMM:

[0051] (41) (42) In the formula, Y ij Prosumer For prosumers The auxiliary variable component of represents the coupled decision variables, Prosumer Prosumers The power of energy trading, Indicates a transaction, Prosumer Prosumers Positive reserve for conducting transactions, Indicates dosage, Indicates that it is in standby mode. Prosumer Prosumers Negative reserve for trading, Indicates negative reserve, represents all decision variables, Prosumer For prosumers The auxiliary variable component of express Time period, represents the scheduling period, represents the set of prosumers; In this embodiment, the ADMM algorithm is used to solve the energy-spare sharing problem of multiple producers and consumers, and the computing power burden is reduced through multi-center computing, so as to obtain the local market clearing result under the premise of protecting the privacy of producers and consumers.

[0052] Among all the constraints, except for the P2P transaction constraint, which is a global coupling constraint, the remaining constraints are only related to the decision variables of each producer and consumer. The coupling constraints are rewritten to make them conform to the solution form of the alternating direction multiplier method ADMM.

[0053] In this embodiment, from an economic perspective, the Lagrange multiplier corresponding to the P2P transaction constraint is used: Representing the shadow price of P2P transactions, the augmented Lagrangian function of the optimization problem is defined using the following logic: (43) in: (44) In the formula, represents the constructed Lagrangian function, represents the coupled decision variables, represents auxiliary variables, represents the Lagrange multiplier, Indicated in i In the sub-question, t The coupled decision variables of the time period, Indicates that in the i sub-problem, t Auxiliary variables for the time period, Indicated in i In the sub-question, t The Lagrange multiplier of the time period, Indicated in i In the sub-question, t The Lagrangian function of the time period, Indicated in i In the sub-question, t The supremum function of the time period, represents the supremum, Indicated in i In the sub-question, t Cost function or constraint function for a time period, represents the regularization parameter in the ADMM algorithm, Indicated in i Sub-questions and j Coupling variables between sub-problems and The square of the Euclidean norm of .

[0054] make is the number of iterations, then in the ADMM solution process, the local decision variables are updated: i According to the previous iteration , Solve the local optimization decision problem: (45) In the formula, is the number of iterations, Indicated in i In the sub-question, t The coupling decision variables of the time period are v+1 The value at the iteration, Indicated in i In the sub-question, t The coupled decision variables of the time period, It means that the Lagrangian function i In the sub-question, t The value of the coupled decision variables in each time period, express Time period, represents the scheduling period, Indicated in i In the sub-question, t The auxiliary variables of the period are v The value at the iteration, Indicated in i In the sub-question, t The Lagrange multiplier of the time period is v The value at the iteration; Will update the obtained Prosumers .

[0055] Using the following logic, solve the following global optimization problem and update the auxiliary variables: (46) In the formula, represents auxiliary variables, Indicated in v+1 The value of the coupled decision variable at the iteration, Indicated in v The value of the Lagrange multiplier at the iteration, Indicates the use of v+1 The coupled decision variables of the iteration x(v +1） and v The Lagrange multiplier of the iteration The Lagrangian function of Represents the minimization of the Lagrangian function About auxiliary variables y process; Separate and differentiate the coupled part in the optimization problem (45) to obtain Y ij , achieving fully distributed solution: (47) In the formula, Indicated in i Sub-questions and j Coupling variables between sub-problems In the The value at the iteration, Indicated in j Sub-questions and i Coupling variables between sub-problems In the The value at the iteration, Indicated in i Sub-questions and j Coupling variables between sub-problems In the The value at the iteration, Indicated in j Sub-questions and i Coupling variables between sub-problems In the The value at the iteration, Indicated in j Sub-questions and i The Lagrange multipliers between the subproblems are v The value at the iteration, Indicated in i Sub-questions and j The Lagrange multipliers between the subproblems are v The value at the iteration; In this embodiment, in order to realize decentralized transactions between producers and consumers and take into account both solution speed and privacy protection, the coupled part in the above optimization problem (45) is separated and derived to obtain Y ij Analytical solution of . Therefore, the prosumer i Ability to update auxiliary variables independently without relying on a central platform for global optimization, achieving fully distributed solving.

[0056] In this embodiment, the Lagrange multiplier is updated, wherein the original residual and the dual residual of the P2P transaction volume are calculated using the following logic, and an adaptive step size mechanism is adopted to adjust the step size according to the residual state so that the original variable is consistent with the auxiliary variable; when the original residual is relatively small, the value range of the convergence threshold includes [0.001, 0.01], and the threshold can be taken as, for example: 0.5 to shorten the step size; The following logic is used to express the energy transaction relationship: (48) (49) (50) In the formula, Indicated in v The original residual at iteration , represents the set of all subproblems, Indicated in v The regularization parameter at the iteration, Indicated in The regularization parameter at the iteration, Indicated in i Sub-questions and j Coupling variables between sub-problems In the v The value at the iteration, represents the step size adjustment factor in the adaptive step size mechanism, Indicated in v The dual residual at the iteration, Represents the dual residual The norm of Indicates v The original residual at iteration The norm of ; Specifically, the P2P transaction volume calculates the original residual and the dual residual. In order to solve the uneven convergence speed of the residuals, an adaptive step size mechanism is adopted to flexibly adjust the step size according to the residual state. When the original residual is relatively large, the step size is increased to promote the consistency between the original variable and the auxiliary variable and accelerate the convergence speed; when the original residual is relatively small, the step size is shortened to reduce the oscillation of the objective function.

[0057] Using the following logic, the prosumer Independently update the Lagrange multiplier to transform the price signal Delivered to the corresponding producer and consumer j : (51) In the formula, Indicated in j Sub-questions andi The Lagrange multipliers between the subproblems are v+1 The value at the iteration; The following logic is used to express the iterative convergence criterion of the alternating direction multiplier method ADMM: (52) In the formula, is the set error accuracy.

[0058] Example 2 In this embodiment, in order to verify the energy management effect of the prosumer through power sharing and distributed energy storage capacity sharing, two different schemes are set for analysis according to whether shared energy storage and power sharing are performed: In the first scheme, each prosumer forms a prosumer group, and the energy management and control center coordinates the power sharing among prosumers, optimizing the overall operation cost of the prosumer group to the minimum; In the second option, each prosumer forms a prosumer group, and the energy management and control center allocates distributed energy storage capacity and coordinates the sharing of electricity among prosumers, with the optimization goal of minimizing the overall operating cost of the prosumer group.

[0059] In this embodiment, specific parameter settings are performed; specifically, three prosumers are used as research objects for case analysis, and the distributed resources of the prosumers include distributed photovoltaic new energy, electric energy storage, air conditioning load, flexible load and basic load. The optimization period can be set to, for example, 24h, and the time interval can be, for example, 1h. 0:00 a.m. to 1:00 a.m. is set as the first period, and 23:00 p.m. to 24:00 p.m. is set as the twenty-fourth period. Flexible loads include: transferable loads and shiftable loads. The ratio of the transferable load of each prosumer to the basic load of the same period is set to 20%, and the adjustment cost is 0.15 yuan / kWh; the shiftable load is divided into three periods: morning and evening, and its parameters are shown in Table 1 below. The power grid purchase and sale price adopts the time-of-use electricity price. The outdoor temperature and the power grid purchase and sale price are as follows: Figure 5 shown.

[0060] Table 1. Translable load parameters

[0061] In this embodiment, the depreciation cost of electric energy storage for each prosumer is 0.25 yuan / kWh, and the energy storage charging and discharging efficiency is 0.95. The energy storage capacity of the first prosumer, the second prosumer, and the third prosumer are 5000 kWh, 3000 kWh, and 2000 kWh, respectively. The maximum charging and discharging power is 20% of the capacity value, the initial stored energy is 25% of the capacity, and the power limit of electricity trading between prosumers is 200 kW. The air conditioner runs uninterruptedly, the operating power range of a single air conditioner is 0.2~2.5 kW, the optimal room temperature is 26 ℃, and the allowable temperature deviation is ±2 ℃. The air conditioning load parameters of each prosumer are shown in Table 2 below: Table 2. Air conditioning load parameters

[0062] In this embodiment, a case analysis is performed. For details, see Figure 6a , Figure 6b , Figure 7a , Figure 7b , Figure 8a as well as Figure 8b In this embodiment, the first solution is that each prosumer forms a prosumer group and optimizes the group by sharing power. Figure 6a and Figure 8a It can be seen that the first prosumer charges during the early morning electricity price valley period and the daytime period when photovoltaic power output is sufficient, and releases during the peak electricity price period from 18:00 to 22:00 at night. Its surplus new energy during the day is supplied to the second and third prosumers, and the stored electricity is sold to the second and third prosumers at night. The second prosumer has insufficient photovoltaic output and a double peak load. Its energy storage capacity is fully charged during the early morning electricity price valley period and released from 9:00 to 13:00, and then charged again during the normal electricity price period from 15:00 to 18:00 and released during the evening electricity price peak period.

[0063] In this embodiment, the second solution is that prosumers form a prosumer group and optimize the group by sharing energy storage and electricity. Figure 7b and Figure 8b It can be seen that the second prosumer and the third prosumer obtain the right to use part of the energy storage of the first prosumer. In terms of energy storage charging and discharging, the first prosumer charges during the early morning electricity price valley period and the daytime photovoltaic output sufficient period, and releases during the night electricity price peak period; the second prosumer charges during the early morning electricity price valley period, and releases during the daytime electricity price peak period and the night electricity price peak period; the third prosumer charges during the early morning electricity price valley period, and releases during the night electricity price peak period. Figure 7a , Figure 7b It can be seen that the energy storage energy change curve of the second solution of this embodiment is smoother than that of the first solution. This is because the energy sharing between producers and consumers achieves complementarity and reduces the charging and discharging of energy storage. Figure 8a , Figure 8bIt can be seen that compared with the first option, the second option does not conduct electricity trading at night. The reason is that the night electricity trading in the first option is based on the stored electricity of the first producer and consumer. The producer and consumer needs to bear the storage loss cost, electricity transmission cost and internal transaction cost, and the operating cost is relatively high.

[0064] In summary, the present invention establishes an energy management framework for energy storage sharing among prosumers, and optimizes the allocation of energy storage resources by quantifying the demand for energy storage backup. With the goal of minimizing the operating cost of all prosumers in the prosumer group, a prosumer energy management optimization model for distributed energy storage sharing among prosumers is established. The alternating direction multiplier method ADMM distributed clearing solution is used to achieve energy sharing and trading among local prosumers, reduce system costs, improve local energy consumption, and optimize the power balancing capability of prosumers.

[0065] The present invention adopts a prosumer energy management strategy, which can effectively improve the economic benefits of energy management for users, reduce operating costs, and promote local consumption of new energy.

[0066] The present invention sets up an energy management control center to aggregate various prosumers into a whole, and dispatches them with the goal of optimizing the overall interests of the prosumer group. Since it has the right to dispatch the internal resources of each prosumer, the energy management control center can coordinate and optimize the dispatch of various resources, improve the utilization rate of distributed energy storage of prosumers based on shared energy storage and power sharing, effectively utilize new energy, and reduce dependence on the distribution network, thereby reducing operating costs.

[0067] The energy storage sharing energy management framework provided by the present invention is applicable to the scenario where some prosumers have already configured distributed energy storage, and is used to solve the problems of low utilization rate and extensive management of distributed energy storage of these prosumers. For prosumers with energy storage needs, the repeated investment in distributed energy storage can be reduced. The energy management control center uniformly allocates the distributed energy storage resources of prosumers and coordinates the sharing of electricity among prosumers, which can give play to the complementarity of power generation and consumption of multiple prosumers and reduce the demand for energy storage resources by prosumers.

[0068] The present invention adopts energy storage and backup energy sharing transactions to achieve real-time power matching, thereby avoiding electricity default penalties and further reducing the economic costs of producers and consumers.

[0069] The trading method of the present invention can promote the economic potential of energy storage resources in the process of energy balance backup service and power balance real-time adjustment. The present invention adopts the alternating direction multiplier method ADMM to solve the energy-backup sharing problem of multiple producers and consumers, reduces the computing power burden through multi-center computing, and obtains local market clearing results under the premise of protecting the privacy of producers and consumers. Compared with the prior art, the present invention focuses on exploring the flexible adjustment capability of energy storage, and adopts the alternating direction multiplier method ADMM to solve the multi-variety joint transaction of energy and backup energy storage, which can improve the flexibility of energy trading and increase the market benefits of electric energy sharing.

[0070] The present invention calculates the original residual and dual residual of the P2P transaction volume. In order to solve the problem of uneven residual convergence speed, an adaptive step size mechanism is adopted to flexibly adjust the step size according to the residual state. When the original residual is relatively large, the step size is increased to promote the original variable and the auxiliary variable to reach a consensus and accelerate the convergence speed; when the original residual is relatively small, the step size is shortened to reduce the oscillation of the objective function.

[0071] The present invention solves the technical problems existing in the prior art that energy management solutions are difficult to promote on a large scale and the power sharing effect is poor due to low energy management efficiency, insufficient flexibility, high investment cost, extensive management and low utilization rate.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy management of prosumers, characterized in that: The method comprises: S1. Constructing an energy storage sharing energy management framework, performing quantification operations on energy storage backup demand based on Gaussian distribution, and optimizing energy storage resource configuration; wherein the energy storage sharing energy management framework includes: a prosumer group and an energy management control center; S2. Based on the energy storage sharing energy management framework, with the goal of minimizing the operating cost of all prosumers in the prosumer group, construct an objective function, and based on energy storage sharing matching, construct a prosumer energy management optimization model; S3. Based on the energy management optimization model of the prosumers, a local community clearing model is constructed, and the alternating direction multiplier method ADMM is used to perform distributed clearing solutions. By solving the energy-reserve sharing problem of multiple prosumers, energy sharing and trading are performed among the prosumers.

2. The prosumer energy management optimization method according to claim 1, characterized in that: In S1, the prosumer group is connected to the energy management control center, and the power sharing coordination operation and energy storage capacity allocation operation are performed on the prosumers in the prosumer group to obtain an applicable energy management plan, and the power interaction between the prosumers and the energy management control center is recorded; the quantification operation is performed on the energy storage backup demand of the prosumers; wherein, t Net demand forecast deviation for the period The probability density function of , It represents the net demand forecast deviation, and the cumulative distribution function CDF is ; According to the maximum positive reserve, t The positive reserve demand in the time period is given by the probability density function Integrate to obtain the preset load loss rate LOLP , using the minimum objective function, calculate the preset load loss rate LOLP The minimum positive reserve required under the load loss ratio is obtained; According to the maximum positive reserve, t The positive reserve demand during the period is t Net demand forecast deviation for the period With the t The load loss rate corresponding to the time period The difference between Integrate the product of to find the expected value of the lost load EENS ; According to the maximum negative reserve, t The minimum negative reserve capacity required to cover the corresponding forecast error during the period is given by the probability density function Integrate, defined at the preset light abandonment rate LOPP The minimum negative reserve required under the condition is calculated by using the minimum objective function, according to which the value of the cumulative distribution function CDF is less than or equal to the abandonment rate. Probability, find the light abandonment rate; According to the maximum negative reserve, t The minimum negative reserve capacity required to cover the corresponding forecast error is met during the period. t Abandonment rate of light in the time period With the t Net demand forecast deviation for the period The difference between Integrate the product of to find the expected value of abandoned light EEW ; The Cornish-Fisher partial distribution general Gaussian model based on skewness S and kurtosis K is used to describe the distribution characteristics of the deviation and calculate the load loss rate. MPR The light abandonment rate MNR quotient of, and the expected value of the load loss EENS The expected value of the abandoned light EEW Business.

3. The prosumer energy management optimization method according to claim 2, characterized in that: Using the following logic, according to the quantile of the Gaussian distribution , the quantile probability of the Gaussian distribution , expressing the quantile function in the general Gaussian model of the Cornish-Fisher partial distribution; Using the product of the quantile function and the standard deviation, plus the average of the forecast deviations, σ is the standard deviation, and the load loss rate is obtained MPR The light abandonment rate MNR The expected value of the load loss EENS The expected value of the abandoned light EEW Business.

4. The prosumer energy management optimization method according to claim 1, characterized in that: In S2, the objective function is defined; wherein, the scheduling period is assumed to be T , taking the minimum sum of the operating costs of all the prosumers as the optimization goal, constructing the objective function, wherein the operating costs include: energy storage investment cost, power purchase cost from the power grid, load adjustment cost, energy storage operation cost, power transmission cost, air conditioning load response cost, power sharing cost and expected utility loss; Define constraints, the constraints including: power balance constraints of the prosumer, shared energy storage constraints, air conditioning load constraints, indoor temperature constraints, and flexible load adjustment constraints; For the prosumer The allowable working period is determined according to the load that can be translated. t Prosumers Corresponding 0-1 variables And rated power ,as well as t The result of determining whether the time period belongs to the permissible working time period is obtained. t The prosumer in the period Power of the load that can be translated ; According to the t Prosumers Corresponding 0-1 variables , the allowable working time periods are summed up to obtain the working time of the movable load.

5. The prosumer energy management optimization method according to claim 4, characterized in that: According to the capacity cost coefficient of energy storage, the power cost coefficient of energy storage, the prosumer Distributed energy storage capacity, the prosumers The maximum charge and discharge power of the distributed energy storage and the service life of the energy storage are used to calculate the investment cost of the energy storage; In the scheduling cycle T In, according to t The grid electricity price during the period, The prosumer in the period i The amount of electricity purchased from the grid, The on-grid electricity price of surplus electricity during the period and The prosumer in the period i The amount of electricity sold to the power grid is summed up to obtain the power purchase cost of the power grid; According to the prosumer i exist t The load transferred in during the period, the prosumers i exist t The load transferred out during the time period is summed up to obtain the load adjustment cost; According to the prosumer i exist t The charging power of the time period, the prosumer i exist t The discharge power of each time period is summed up to obtain the energy storage operation cost; According to the prosumer i exist t The electric energy consumed by the electric energy sharing during the period, the prosumer i exist t Electricity supplied during the period , perform a summation operation to obtain the electric energy transmission cost; According to the producer and consumer before the response i No. k The power of the air conditioner, the consumer after the response i No. k The power of an air conditioner is calculated to obtain the air conditioner load response cost; The power sharing cost is calculated based on the supply price and consumption price of the power sharing.

6. The prosumer energy management optimization method according to claim 4, characterized in that: Define the power balance constraints of the prosumer. i , according to the prosumer i of new energy output; Prosumers i In the t Base load for the time period; Prosumers i of electricity purchased from the grid ; Prosumers i of electricity sold to the grid ; Prosumers i exist t Discharge power in the time period ; Prosumers i exist t Charging power during the period ; Prosumers i exist t Electricity consumed by energy sharing during the period ; Prosumers i exist t Electricity supplied by power sharing during the period ; Prosumers i No. k Power of air conditioner , defined as t Power balance equation for the time period; Define the shared energy storage constraints for the prosumers i , t The shared energy storage constraints for the time period are as follows: (25) (26) (27) (28) (29) (30) In the formula, For the prosumer i exist t The electricity stored by shared energy storage during the period, For prosumers i exist The amount of electricity stored by shared energy storage during the period; For energy storage charging efficiency, Represents electrical energy storage, Indicates charging. is the energy storage discharge efficiency, Indicates discharge; For the prosumer i The maximum charging power of shared energy storage, For the prosumer i The maximum discharge power of shared energy storage, Indicates power; and To limit the 0-1 variables that appear simultaneously in charge and discharge situations, It means at the same time; For prosumers i The storage capacity of the initial period of the dispatch cycle, For prosumers i Scheduling cycle T The amount of stored electricity at the end of the period; For the prosumer i The maximum value of energy storage. represents the number of said prosumers; The unit scheduling period.

7. The prosumer energy management optimization method according to claim 4, characterized in that: Define the air conditioning load constraint; wherein, a first-order equivalent thermal parameter ETP model is used to describe the dynamic change of the air conditioning load; wherein, the first-order equivalent thermal parameter ETP model is used to obtain and calculate the equivalent heat capacity of the air-conditioned room, the equivalent thermal resistance of the air-conditioned room, and the t The indoor temperature during the period, t The outdoor temperature during the period, t The cooling capacity of the time-slot air conditioner, t The cooling power of the air conditioner in each period, the rated power of the air conditioner and the energy efficiency ratio are used to establish the first-order differential equation of indoor temperature change; Assume that the outdoor temperature is within the unit scheduling period Keeping the interior constant, solving the first-order differential equation of indoor temperature change, we get t+1 The relationship between indoor temperature and time period; The flexible load adjustment constraint is determined using the following logic: (36) (37) (38) (39) In the formula, For the t Time period prosumer i The adjusted transferable load; For the t Time period prosumer i The maximum transferable load; For the t Time period prosumer i The transferred load, Indicates transfer in. For the t The load transferred out during the period, Indicates transfer out. Indicates a load transfer operation; For the t Prosumers in the time period i The base load, Indicates the basis; is the ratio of the maximum transferable load to the base load, Indicates the scheduling cycle In t The load transferred out during each period is summed up. Indicates the scheduling cycle In t The sum of the load transferred into the time period.

8. The prosumer energy management optimization method according to claim 1, characterized in that: In S3, based on the objectives of maximizing welfare and minimizing clearing costs, a transaction clearing objective function for the producer-consumer group community is constructed; The alternating direction multiplier method ADMM is used to solve the multi-producer-consumer energy-reserve sharing problem, perform multi-center calculations, and obtain local market clearing results; Using the following logic, we can use the Lagrange multiplier corresponding to the P2P transaction constraint: , represents the shadow price of P2P transactions, and uses the following logic to define the augmented Lagrangian function of the optimization problem: (43) in: (44) In the formula, represents the constructed Lagrangian function, represents the coupled decision variables, represents auxiliary variables, represents the Lagrange multiplier, Indicated in i In the sub-question, t The coupled decision variables of the time period, Indicates that among the i sub-problems, t Auxiliary variables for the time period, Indicated in i In the sub-question, t The Lagrange multiplier of the time period, Indicated in i In the sub-question, t The Lagrangian function of the time period, Indicated in i In the sub-question, t The supremum function of the time period, represents the supremum, Indicated in i In the sub-question, t Cost function or constraint function for a time period, represents the regularization parameter in the ADMM algorithm, Indicated in i Sub-questions and j Coupling variables between sub-problems and The square of the Euclidean norm of .

9. The prosumer energy management optimization method according to claim 1, characterized in that: In S3, during the ADMM solution process, the local decision variables are updated: i According to the previous iteration , Solve the local optimization decision problem: (45) In the formula, is the number of iterations, Indicated in i In the sub-question, t The coupling decision variables of the time period are v+1 The value at the iteration, Indicated in i In the sub-question, t The coupled decision variables of the time period, It means that the Lagrangian function i In the sub-question, t The value of the coupled decision variables in each time period, express Time period, represents the scheduling period, Indicated in i In the sub-question, t The auxiliary variables of the period are v The value at the iteration, Indicated in i In the sub-question, t The Lagrange multiplier of the time period is v The value at the iteration; Will update the obtained Prosumers ; Solve the global optimization problem and update the auxiliary variables according to the Lagrangian function; Separate and derive the coupled part in the optimization problem to obtain the coupled variables Y ij Analytical solution to achieve fully distributed solution; updating the Lagrange multiplier; The following logic is used to calculate the original residual and dual residual of the P2P transaction volume, and an adaptive step size mechanism is used to adjust the step size according to the residual state so that the original variable is consistent with the auxiliary variable; when the original residual is relatively small, the step size is shortened; The following logic is used to express the energy transaction relationship: (48) (49) (50) In the formula, Indicated in v The original residual at iteration , represents the set of all subproblems, Indicated in v The regularization parameter at the iteration, Indicated in The regularization parameter at the iteration, Indicated in i Sub-questions and j Coupling variables between sub-problems In the v The value at the iteration, represents the step size adjustment factor in the adaptive step size mechanism, Indicated in v The dual residual at the iteration, Represents the dual residual The norm of Indicates v The original residual at iteration The norm of ; Prosumers The Lagrange multipliers are updated independently to transmit price signals to the corresponding prosumers until the iteration of the alternating direction multiplier method ADMM converges.

10. The prosumer energy management optimization system is characterized by: The system comprises: A shared framework building module is used to build an energy storage shared energy management framework, quantify energy storage backup demand based on Gaussian distribution, and optimize energy storage resource configuration; wherein, the energy storage shared energy management framework includes: a prosumer group and an energy management control center; A management optimization model construction module is used to construct an objective function based on the energy storage sharing energy management framework, with the goal of minimizing the operating cost of all prosumers in the prosumer group, and to construct a prosumer energy management optimization model based on energy storage sharing matching. The management optimization model construction module is connected to the sharing framework construction module; The distributed clearing solution and shared trading module is used to construct a local community clearing model based on the energy management optimization model of the prosumer, and use the alternating direction multiplier method ADMM to perform distributed clearing solution. By solving the multi-prosumer energy-reserve sharing problem, energy sharing and trading are performed among the prosumers. The distributed clearing solution and shared trading module is connected to the management optimization model construction module.

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