A method and related device for distributed new energy aggregation to participate in power market spot transaction

Abstract

The application provides a method and related device for distributed new energy aggregation participating in power market spot transaction, a large-scale clearing model for distributed new energy aggregation participating in spot market transaction is constructed, the output characteristics of the distributed new energy, market demand and grid constraint conditions are comprehensively considered, a reasonable energy dispatching and configuration strategy is formulated, the Shapley value method is used to realize reasonable distribution of the benefits of each unit after aggregation according to the actual contribution rate of each unit to the aggregator, the planning investment incentive of distributed resources is realized, the consumption of renewable energy is promoted, the operation cost of the power system is reduced, the reliability of the power system and the operation efficiency of the energy internet are improved, and the operation and investment benefits are maximized under regional energy sharing.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, and particularly relates to a method for distributed new energy aggregation participating in power market spot trading and a related device. BACKGROUND

[0002] New energy has advantages of green, low carbon, safety and high efficiency compared with traditional thermal power units. Distributed new energy is an effective way to alleviate energy shortage and promote the low-carbon development of power systems. In this context, new energy such as wind and solar energy has been developing rapidly and has gradually become an important source of grid-connected power. Both centralized development and distributed new energy access are important forms of high-proportion new energy grid connection. In 2021, distributed photovoltaic new installations exceeded centralized photovoltaic for the first time, with 29.28 GW of distributed photovoltaic new installations, accounting for about 55% of all new photovoltaic power installations. In 2023, distributed photovoltaic new installations reached 96.29 GW, an increase of 88% year-on-year. The cumulative installed capacity of distributed photovoltaic was 253 GW, accounting for 42% of the total photovoltaic installed capacity. It can be predicted that distributed new energy generation will play an important role in future energy structure.

[0003] Distributed new energy builds power generation equipment at the user end and can operate independently or in parallel with the grid. It mainly includes distributed photovoltaic power generation, distributed wind power and other forms, and has advantages of clean, low carbon, economic and efficient, etc. However, in the case of no light and no wind, distributed new energy changes from "power generation" to "power consumption", and needs power grid to supply power, which has strong uncertainty and weak controllable output. Compared with centralized new energy generation, distributed new energy generation has smaller power and greater fluctuation, and has characteristics of small capacity, wide distribution, large number and complex transaction behavior. However, there is an access threshold in the electricity market, so distributed new energy cannot directly participate in market energy trading.

[0004] Firstly, existing research mainly focuses on multi-agent market transaction mechanism design, and studies the solution of market equilibrium and the clearing algorithm of bidding. The NOBEL project in Spain refers to the securities trading model to design a discrete market transaction mechanism. The buyer and seller match the high-low clearing according to the order book sorting, and the order book table is open to market participants, allowing them to refer to it to modify the bid. However, due to the relatively small and large variation of distributed new energy unit generation capacity, distributed new energy units cannot participate in electricity market energy trading as independent market agents under the existing market mechanism. Therefore, it is necessary to improve the existing electricity market clearing model by considering the characteristics of distributed new energy, so as to adapt to the characteristics of small capacity, wide distribution and large number of distributed new energy units.

[0005] Secondly, the existing research on new energy aggregators mostly focuses on the bidding and optimal scheduling of the aggregators as a whole in the electricity market, and less on the benefit distribution mechanism. Under the condition of electricity market competition, each distributed new energy unit is independent and belongs to different investment subjects. Therefore, how to build a fair, reasonable and transparent benefit distribution mechanism is the key to the cooperation of the units in the new energy aggregator and is related to the integration and expansion of new energy in the power system. SUMMARY

[0006] In view of the problems existing in the prior art, the present application provides a method and related device for distributed new energy aggregation participating in electricity market spot trading, which calculates the upper and lower limits of the aggregated output by comprehensively considering the output characteristics and participation adjustment willingness of the distributed new energy, builds a clearing model for large-scale distributed new energy aggregation participating in spot market trading, and realizes the reasonable distribution of the benefits of each unit according to the actual contribution rate of each unit to the aggregator, thereby perfecting the method for large-scale distributed new energy aggregation participating in the electricity market.

[0007] The present application is realized by the following technical solutions:

[0008] A method for distributed new energy aggregation participating in electricity market spot trading, comprising:

[0009] The clearing model for distributed new energy aggregation participating in electricity market spot trading is solved in combination with the constraint conditions to obtain the active power of each electricity market unit and the node marginal price of the system; and the benefits obtained by the distributed new energy aggregation are distributed based on the Shapley value to complete disaggregation;

[0010] The clearing model for distributed new energy aggregation participating in electricity market spot trading takes the operation cost of the electricity market unit as the objective function and minimizes the operation cost as the optimization target; the constraint conditions include system load balance constraint, line flow constraint, unit operation constraint and unit output increase / decrease rate constraint, and the unit operation constraint includes the output constraint of the distributed new energy after aggregation; the output constraint of the distributed new energy after aggregation is obtained by predicting based on the distributed new energy aggregation model and historical output data using an ARIMA-based time series prediction model.

[0011] Preferably, in the method for distributed new energy aggregation participating in electricity market spot trading, the distributed new energy aggregation model includes a distributed photovoltaic aggregation model, a distributed wind power aggregation model and a distributed energy storage aggregation model.

[0012] Further, in the method for distributed new energy aggregation participating in electricity market spot trading, the distributed photovoltaic aggregation model is expressed as:

[0013]

[0014] wherein p PV,k,t is the actual output of photovoltaic unit k at time t; is the maximum output of photovoltaic unit k in a dispatch cycle T; is the maximum output of photovoltaic cluster aggregated by N p photovoltaic units in a dispatch cycle T; PV,j,h,T , are respectively the actual output and the predicted maximum output of the aggregated photovoltaic cluster managed by aggregator j in a dispatch cycle T at node h.

[0015] Further, in the method for distributed new energy aggregation participating in power market spot trading, the distributed wind power aggregation model is represented as:

[0016]

[0017] wherein p WT,f,t is the actual output of wind power unit f at time t; is the maximum output of wind power unit f in a dispatch cycle T; is the maximum output of wind power cluster of N p wind power units in a dispatch cycle T; WT,j,h,T , are respectively the actual output and the predicted maximum output of the aggregated wind power cluster managed by aggregator j in a dispatch cycle T at node h.

[0018] Further, in the method for distributed new energy aggregation participating in power market spot trading, the distributed energy storage aggregation model includes a distributed energy storage unit state of charge and charging / discharging characteristic model and a distributed energy storage unit acceptance probability model of dispatch;

[0019] The distributed energy storage unit state of charge and charging / discharging characteristic model is represented as:

[0020]

[0021] Δt=t-T start , t∈[T start , T end ]

[0022]

[0023] wherein E ES,v,t is the remaining capacity of energy storage unit v at time t, SOC ES,v,t is the state of charge of energy storage unit v at time t, represents the initial time T of energy storage unit v in a dispatch cycle Tstart The state of charge; when the energy storage unit v is fully charged, the SOC ES,v,t The value is 1, when the energy storage unit v is in a fully discharged state, the SOC is... ES,v,t The value is 0. These are the minimum and maximum values ​​of the state of charge of the energy storage unit v within a scheduling cycle T, respectively. Δt represents the charging power and discharging power of the energy storage unit v at time t, respectively; Δt is the time interval for analysis and calculation. These represent the maximum and minimum energy values ​​of energy storage unit v within a scheduling period T, respectively; P ES,v,t The actual charging and discharging power of the energy storage unit v at time t. and These represent the upper and lower limits of the charging and discharging power of the energy storage unit v within a scheduling period T, respectively. dis and η ch These represent the discharge efficiency and charging efficiency of the energy storage unit, respectively. and These represent the discharge power and charging power of the energy storage unit v during a scheduling period T, respectively.

[0024] The probabilistic model for the distributed energy storage unit to be scheduled is expressed as follows:

[0025] y v =β0+β1SOC ES,v,t +β2π v +ε v

[0026]

[0027] In the formula, y v π is the dependent variable of the distributed energy storage unit v. v For the scheduling compensation of distributed energy storage unit v, β0 is the baseline probability coefficient, and β1 and β2 are the explanatory variables SOC. ES,v,t π v The probability coefficient, ε v Represents the random error variable. Let v be the decision variable for energy storage unit v during a certain scheduling period T. This indicates that the system will accept scheduling during this scheduling cycle. This indicates that no scheduling will be accepted during the scheduling period T. This represents the decision probability of the distributed energy storage unit v accepting scheduling, with a value range of (0,1). These represent the charging power and discharging power of the energy storage cluster aggregated over a scheduling period T, respectively. respectively are the discharge power and the charge power of the energy storage cluster managed by the aggregator j at node h in a dispatch period T. ES,j,h,T is the actual power of the energy storage cluster managed by the aggregator j at node h in a dispatch period T.

[0028] Further, in the distributed new energy aggregation method for participating in the power market spot transaction, the objective function and the constraint condition of the clearing model of the distributed new energy aggregation for participating in the power market spot transaction include:

[0029]

[0030]

[0031] η l (t) : P l (t) ≤ P l

[0032]

[0033] e i (t) : p i (t) ≥ P i

[0034]

[0035] δ i (t) : p i (t) - p i (t-1) ≥ -Δ i

[0036]

[0037] Wherein F represents the generation cost based on the bid, including the operation cost, the starting cost and the no-load cost of the unit i participating in the power market, C i (p i (t)) is the operation cost of the unit i participating in the power market at time t, T is the total period considered during system dispatch; I is the total number of units participating in the power market; p i (t) is the active power of the unit i participating in the power market at time t; d j is the load of the node j, J is the number of nodes, is the total load of the system at time t, is the total active power of all units participating in the power market at time t; λ(t) is the shadow price of system operation constraint; P l represents the power flow of line l, P l represents the upper limit and the lower limit of the power flow of line l; a shadow price representing an upper limit of power flow constraint of line l, η l a shadow price representing a lower limit of power flow constraint of line l, and e i (t) are respectively a shadow price of an upper limit of active power constraint of a power market participating unit i, a shadow price of a lower limit of active power constraint of a power market participating unit i; Δ i is a maximum ramp rate of a power market participating unit i per time period, is a shadow price of a maximum ramp rate constraint of a power market participating unit i, δ i (t) is a shadow price of a minimum ramp rate constraint of a power market participating unit i; are respectively an actual output of an aggregated photovoltaic cluster managed by an aggregator j at node h in a T time period and a predicted maximum allowed output, is a predicted maximum allowed power of an aggregated wind power plant cluster managed by an aggregator j at node h; are respectively a discharging power and a charging power of an energy storage cluster managed by an aggregator at node h in a T time period.

[0038] Preferably, in the distributed new energy aggregation participating in power market spot trading method, the Shapley value-based cooperation benefit allocation is specifically:

[0039]

[0040] wherein, R represents that there are n distributed new energy units in the area, R={1, 2, 3,..., n}; x(r) is a benefit obtained by the distributed new energy unit r; s is an aggregator of different new energy unit combinations, s is a subset of R; v(s) represents a characteristic function of the aggregator s, that is, a benefit obtained by the aggregator s in market transaction; v(s / r) represents a remaining benefit after deleting the new energy unit r in the aggregator s, and v(r)-v(s / r) represents an actual contribution of the new energy unit r in the aggregator s.

[0041] The application further provides a distributed new energy aggregation participating in power market spot trading system, comprising:

[0042] a clearing module, used for solving a clearing model of the distributed new energy aggregation participating in power market spot trading in combination with constraint conditions, to obtain active power of each power market unit and a node marginal price of the system;

[0043] a benefit allocation module, used for allocating a benefit obtained by the distributed new energy aggregation based on a Shapley value, to complete disaggregation;

[0044] The clearing model of the distributed new energy aggregation participating in the power market spot transaction takes the operation cost of the power market unit as the objective function and minimizes the operation cost as the optimization target; the constraint conditions include system load balance constraint, line flow constraint, unit operation constraint and unit increase / decrease power output rate constraint, the unit operation constraint includes the distributed new energy aggregation output constraint; the distributed new energy aggregation output constraint is obtained by using the ARIMA-based time series prediction model according to the distributed new energy aggregation model and historical output data.

[0045] Preferably, in the distributed new energy aggregation participating in the power market spot transaction system, the distributed new energy aggregation model includes a distributed photovoltaic aggregation model, a distributed wind power aggregation model and a distributed energy storage aggregation model.

[0046] Further, in the distributed new energy aggregation participating in the power market spot transaction system, the distributed photovoltaic aggregation model is represented as:

[0047]

[0048] In the formula, p PV,k,t is the actual output of the photovoltaic unit k at time t; is the maximum output of the photovoltaic unit k in a dispatching cycle T period; is the maximum output of the photovoltaic cluster aggregated by N p photovoltaic units in a dispatching cycle T period; p PV,j,h,T , are respectively the actual output and the predicted maximum allowed output of the aggregated photovoltaic cluster managed by the aggregator j under the node h in a dispatching cycle T period.

[0049] Further, in the distributed new energy aggregation participating in the power market spot transaction system, the distributed wind power aggregation model is represented as:

[0050]

[0051] In the formula, p WT,f,t is the actual output of the wind power unit f at time t; is the maximum output of the wind power unit f in a dispatching cycle T period; is the maximum output of the wind power field cluster of N p wind power units in a dispatching cycle T period; p WT,j,h,T , are respectively the actual output and the predicted maximum allowed output of the aggregated wind power field cluster managed by the aggregator j under the node h in a dispatching cycle T period.

[0052] Further, the distributed new energy aggregation participates in the power market spot transaction system, the distributed energy storage aggregation model includes a distributed energy storage unit state of charge and charging and discharging characteristic model and a probability model of distributed energy storage units accepting scheduling;

[0053] The distributed energy storage unit state of charge and charging and discharging characteristic model is represented as:

[0054]

[0055] Δt=t-T start , t∈[T start , T end ]

[0056]

[0057] In the formula, E ES，v，t is the residual capacity of the energy storage unit v at t, SOC ES，v，t is the state of charge of the energy storage unit v at t, represents the state of charge of the energy storage unit v at the initial time T start in a scheduling period T; when the energy storage unit v is in a fully charged state, the value of SOC ES，v，t is 1, and when the energy storage unit v is in a discharged state, the value of SOC ES，v，t is 0, are respectively the minimum value and the maximum value of the state of charge of the energy storage unit v in a scheduling period T; are respectively the charging power and the discharging power of the energy storage unit v at t; and Δt is an analysis and calculation time interval; are respectively the maximum energy and the minimum energy of the energy storage unit v in a scheduling period T; P ES，v，t is the actual charging and discharging power of the energy storage unit v at t, and are respectively the upper limit and the lower limit of the charging and discharging power of the energy storage unit v in a scheduling period T, η dis and η ch respectively represent the discharging efficiency and the charging efficiency of the energy storage unit, and are respectively the discharging power and the charging power of the energy storage unit v in a scheduling period T;

[0058] The probability model of the distributed energy storage unit accepting scheduling is represented as:

[0059] y v = β0+ β1SOC ES，v，t + β2π v + ε v

[0060]

[0061]

[0062] where y v is the dependent variable of the distributed energy storage unit v, π v is the dispatch compensation of the distributed energy storage unit v, β0is the base probability coefficient, β1and β2are the probability coefficients of the explanatory variables SOC ES，v，t , π v , and ε v represents a random error variable, is the decision variable of the energy storage unit v in a certain dispatch period T, represents that the dispatch is accepted in the dispatch period, represents that the dispatch is not accepted in the dispatch period T; represents the decision probability of the distributed energy storage unit v accepting the dispatch, and the value range is (0, 1); are the aggregated charging power and discharging power of the energy storage cluster in a dispatch period T, respectively; are the discharging power and charging power of the energy storage cluster managed by the aggregator j under the node h in a dispatch period T, respectively, and p ES，j，h，T is the actual power of the energy storage cluster managed by the aggregator j under the node h in a dispatch period T.

[0063] Further, in the distributed new energy aggregation participating in the power market spot trading system, the objective function and the constraint condition of the clearing model of the distributed new energy aggregation participating in the power market spot trading include:

[0064]

[0065] η l (t) : P l (t) ≤ P l

[0066]

[0067] e i (t) : p i (t) ≥ P i

[0068]

[0069] δ i (t) : p i (t) - p i (t-1) ≥ -Δ i

[0070]

[0071] Wherein: F represents the generation cost based on the quotation, including the operation cost, start-up cost and no-load cost of the unit i participating in the electricity market, C i (p i (t)) is the operation cost of the unit i participating in the electricity market at time t, T is the total period considered during the system scheduling, I is the total number of units participating in the electricity market, p i (t) is the active power of the unit i participating in the electricity market at time t, d j is the load of the node j, J is the number of nodes, is the total load of the system at time t, is the total active power of all units participating in the electricity market at time t; λ(t) is the shadow price of system operation constraints; P l represents the power flow of line l, P l represents the upper and lower limits of the power flow of line l; represents the shadow price of the upper limit of the power flow constraint of line l, η l (t) represents the shadow price of the lower limit of the power flow constraint of line l; and e i (t) are respectively the shadow price of the upper limit constraint of the active power of the unit i participating in the electricity market, and the shadow price of the lower limit constraint of the active power of the unit i participating in the electricity market; Δ i is the maximum ramp rate of the unit i participating in the electricity market per period, is the shadow price of the maximum ramp rate constraint of the unit i participating in the electricity market, δ i (t) is the shadow price of the minimum ramp rate constraint of the unit i participating in the electricity market; are respectively the actual output and the predicted maximum allowable output of the aggregated photovoltaic cluster managed by the aggregator j at node h within T periods, is the predicted maximum allowable power of the aggregated wind farm cluster managed by the aggregator j at node h; are respectively the discharge power and the charge power of the energy storage cluster managed by the aggregator at node h within T periods.

[0072] Preferably, in the distributed new energy aggregation participating in the electricity market spot trading system, the Shapley value-based distributed new energy aggregation benefit distribution is performed, specifically:

[0073]

[0074] In the formula, R represents the region with n distributed new energy units, R={1, 2, 3,..., n}; x(r) is the income obtained by the distributed new energy unit r; s is an aggregator of different new energy unit combinations, s is a subset of R; v(s) represents a characteristic function of the aggregator s, that is, the income obtained by the aggregator s in market transaction; v(s / r) represents the remaining income after deleting the new energy unit r in the aggregator s, and v(r)-v(s / r) represents the actual contribution of the new energy unit r in the aggregator s.

[0075] The application further provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the distributed new energy aggregation method for participating in the power market spot transaction.

[0076] The application further provides a computer readable storage medium, which stores a computer program, and the processor implements the steps of the distributed new energy aggregation method for participating in the power market spot transaction when the computer program is executed.

[0077] Compared with the prior art, the application has the following beneficial effects:

[0078] The application realizes the operation and investment benefit maximization under the regional energy sharing by constructing a large-scale clearing model of the distributed new energy aggregation participating in the spot market transaction, comprehensively considering the output characteristics of the distributed new energy, market demand, grid constraint conditions, formulating reasonable energy scheduling and configuration strategies, realizing the reasonable distribution of the benefits of each unit after aggregation based on the Shapley value method according to the actual contribution rate of each unit to the aggregator, realizing the planning investment incentive of the distributed resources, promoting the consumption of renewable energy, reducing the operation cost of the power system, improving the reliability of the power system and the operation efficiency of the energy internet, and realizing the operation and investment benefit maximization under the regional energy sharing.

[0079] Further, the distributed energy storage aggregation model of the application considers the user participation willingness of the energy storage unit, introduces a logit-based regression model to obtain the probability of the energy storage user participating in the grid scheduling, and uses the probability as the actual output, thereby improving the accuracy of the distributed new energy aggregation participating in the power market spot transaction. BRIEF DESCRIPTION OF DRAWINGS

[0080] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0081] Figure 1 A schematic diagram of a distributed new energy aggregation participating in the electricity market spot transaction overall framework according to an embodiment of the present application is shown in FIG. 1.

[0082] Figure 2 An ARIMA-based time series prediction model flowchart for the distributed new energy aggregation participating in the electricity market spot transaction according to an embodiment of the present application is shown in FIG. 2.

[0083] Figure 3 A simplified model diagram of a 5-node system for the distributed new energy aggregation participating in the electricity market spot transaction according to an embodiment of the present application is shown in FIG. 3.

[0084] Figure 4 A distributed new energy aggregation output broken line graph and a broken line graph after difference processing for the distributed new energy aggregation participating in the electricity market spot transaction according to an embodiment of the present application are shown in FIG. 4.

[0085] Figure 5 An output prediction result graph of a distributed new energy aggregator for the distributed new energy aggregation participating in the electricity market spot transaction according to an embodiment of the present application is shown in FIG. 5. DETAILED DESCRIPTION

[0086] The present application is described and explained more fully with reference to the following detailed description. Other advantages of the present application will be more fully understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0087] It should be noted that the process equipment or devices not specifically mentioned in the following examples are all conventional equipment or devices in the field.

[0088] It should be noted that the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. In addition, unless otherwise specified, the numbering of each method step is only a convenient tool for identifying each method step, and is not a limitation on the arrangement order of each method step or a limitation on the scope of the present application. Changes or adjustments of the relative relationship, without substantial changes in the technical content, are also considered as the scope of the present application.

[0089] Reference Figure 1 and Figure 2 A method for a distributed new energy aggregation participating in the electricity market spot transaction according to the present application comprises:

[0090] S1, collecting data, synthesizing the output characteristics of the wind and light distributed new energy and the user participation willingness characteristics, constructing a distributed new energy aggregation model, and determining the upper and lower limits of the output of the aggregated distributed new energy as the constraint condition for participating in power market clearing.

[0091] The distributed new energy aggregation model includes a distributed photovoltaic aggregation model, a distributed wind power aggregation model, and a distributed energy storage aggregation model.

[0092] The construction process of the distributed new energy aggregation model includes the following steps:

[0093] S1.1, constructing a distributed photovoltaic aggregation model

[0094] In this step, a lumped model is used to describe the distributed photovoltaic aggregation model, and the aggregated photovoltaic cluster is regarded as being composed of multiple distributed photovoltaic units. The daily maximum output of the photovoltaic unit can be estimated according to the predicted value of the light intensity and the light resource coefficient.

[0095] The photovoltaic can not be fully utilized, and the light can be abandoned. The formula of the distributed photovoltaic aggregation model is:

[0096]

[0097] In the formula, p PV,k,t is the actual output of the photovoltaic unit k at time t; is the maximum output of the photovoltaic unit k in a dispatching period T; is the maximum output of the photovoltaic cluster aggregated by N p photovoltaic units in a dispatching period T; p PV,j,h,T , are respectively the actual output and the predicted maximum output allowed of the aggregated photovoltaic cluster managed by the aggregator j under the node h in a dispatching period T.

[0098] S1.2, constructing a distributed wind power aggregation model

[0099] In this step, a lumped model is used to describe the distributed wind power aggregation model, and the aggregated wind power cluster is regarded as being composed of multiple distributed wind power units. The daily maximum output of the distributed wind power unit can be estimated according to the wind speed characteristics and the wind speed resource coefficient.

[0100] The wind can not be fully utilized, and the wind can be abandoned. The formula of the distributed wind power aggregation model is:

[0101]

[0102]

[0103] wherein: p WT，f，t is the actual output of the wind turbine f at time t; is the maximum output of the wind turbine f in a dispatch cycle T; is the maximum output of the N p wind turbine cluster in a dispatch cycle T; p WT，j，h，T , is the actual output and the predicted maximum allowable output of the aggregated wind turbine cluster managed by the aggregator j at node h in a dispatch cycle T, respectively.

[0104] S1.3, constructing a distributed energy storage aggregation model

[0105] In this step, the lumped model is used to describe the distributed energy storage aggregation model, and the aggregated energy storage cluster is regarded as being composed of multiple distributed energy storage units. The distributed energy storage aggregation model takes into account the user's willingness to participate, introduces a logit-based regression model to obtain the probability of the energy storage unit participating in the grid dispatch, and calculates the actual available power of the energy storage cluster.

[0106] The state of charge of the distributed energy storage unit and the charge-discharge characteristic model of the distributed energy storage unit:

[0107]

[0108] Δt = t - T start t ∈ [T start , T end ]

[0109]

[0110] In the formula, E ES，v，t is the remaining capacity of the energy storage unit v at time t, SOC ES，v，t is the state of charge of the energy storage unit v at time t, represents the state of charge of the energy storage unit v at the initial time T start in a dispatch cycle T; when the energy storage unit v is in a fully charged state, the SOC ES，v，t value is considered to be 1, and when the energy storage unit v is in a discharged state, the SOC ES，v，t value is considered to be 0, are the minimum value and the maximum value of the state of charge of the energy storage unit v in a dispatch cycle T, respectively; are the charging power and the discharging power of the energy storage unit v at time t, respectively; and Δt is the analysis and calculation time interval; are the maximum energy and the minimum energy of the energy storage unit v in a dispatch cycle T, respectively; and P ES，v，tThe actual charging and discharging power of the energy storage unit v at time t, With The upper limit and the lower limit of the charging and discharging power of the energy storage unit v in a scheduling period T, respectively, dis And η ch Represent the discharging efficiency and charging efficiency of the energy storage unit, And The discharging power and charging power of the energy storage unit v in a scheduling period T;

[0111] A probability model of the distributed energy storage unit accepting scheduling is established by considering the state of charge of the distributed energy storage unit and the compensation price of the energy storage user, and the schedulable capacity of the distributed energy storage unit in the aggregation area is calculated by combining the probability of the distributed energy storage unit accepting scheduling.

[0112] y v = β0+ β1SOC ES，v，t + β2π v + ε v

[0113]

[0114] In the formula, y v is the dependent variable of the distributed energy storage unit v, π v is the scheduling compensation of the distributed energy storage unit v, β0is the reference probability coefficient, β1and β2are the probability coefficients of the explanatory variables SOC ES，v，t and π v , and ε v represents a random error variable, is the decision variable of the energy storage unit v in a scheduling period T, indicates that the scheduling is accepted in the scheduling period, indicates that the scheduling is not accepted in the scheduling period; indicates the decision probability of the distributed energy storage unit v accepting scheduling, and the value range is (0, 1); are the charging power and discharging power of the energy storage cluster after aggregation in a scheduling period T, are the discharging power and charging power of the energy storage cluster managed by the aggregator j under the node h in a scheduling period T, and pES,j,h,T is the actual power of the energy storage cluster managed by the aggregator j under the node h in a scheduling period T.

[0115] S2, combined with the distributed photovoltaic aggregation model, the distributed photovoltaic aggregation model, and the distributed energy storage aggregation model, collects historical output data, constructs an ARIMA-based time series prediction model, performs short-term power prediction, and obtains the aggregated output of distributed new energy, which is used as the upper and lower limits of the actual output of new energy units participating in spot market transactions.

[0116] The ARIMA-based time series prediction model is constructed, including: testing stability of the input time series; model parameter selection; model validity test.

[0117] (1) The stability of the input time series is tested, including: testing the stationarity of the input time series by the graph test method or the unit root test method, and if the input time series is not stationary, the input time series is stationary by using the difference method.

[0118] (2) The model parameter selection of the time series prediction model includes: finding the optimal p and q values by AIC criterion or BIC criterion to determine the model constant parameters.

[0119] Table 1 Model establishment condition

[0120]

[0121] The AIC criterion or BIC criterion for model parameter selection includes:

[0122] AIC = -2ln(L) + 2K

[0123] BIC = -2ln(L) + Kln(m)

[0124] Wherein, L represents the estimated value of the maximum likelihood function of the time series prediction model, K represents the number of time series prediction model parameters, and m represents the sample size.

[0125] (3) The time series prediction model validity test includes: judging the accuracy and reliability of the prediction result by normal item test on the residual term of the time series prediction model.

[0126] S3, constructing a clearing model of the distributed new energy aggregation participating in the power market spot transaction, and designing an algorithm for solving the clearing model.

[0127] Specifically, the clearing model of the distributed new energy aggregation participating in the power market spot transaction is constructed, including: considering the minimum operation cost as the optimization target; considering the system load balance constraint, the line flow constraint, the unit operation constraint and the unit increase and decrease power rate constraint; the unit operation constraint includes the distributed new energy aggregation output constraint; the clearing model is solved by constructing an extended Lagrange function.

[0128] The objective function and constraint condition of the clearing model of the distributed new energy aggregation participating in the power market spot transaction include:

[0129]

[0130] 1) System load balance constraint:

[0131]

[0132] 2) Line power flow constraint:

[0133]

[0134] η l (t):P l (t)≤ P l

[0135] 3) Unit operation constraint:

[0136]

[0137] e i (t):P i (t)≥ P i

[0138] For distributed new energy unit aggregators (i.e. distributed new energy aggregation output constraint):

[0139]

[0140] 4) Rate constraint of unit power increase and decrease (including ramp rate and slide rate constraint)

[0141]

[0142] δ i (t):P i (t)-p i (t-1)≥-Δ i

[0143] Where F represents the generation cost based on the bid, including the operation cost, start-up cost and no-load cost of the unit i participating in the electricity market, C i (p i (t)) is the operation cost of unit i participating in the electricity market at time t, T is the total period considered during system dispatch; I is the total number of units participating in the electricity market; p i (t) is the active power of unit i participating in the electricity market at time t; d j is the load of node j, J is the number of nodes, is the total load of the system at time t, is the total active power of all units participating in the electricity market at time t; λ(t) is the shadow price of system operation constraint; P l represents the power flow of line l, P l represents the upper and lower limits of the power flow of line l; shadow price of the upper limit of power flow constraint of line l, η l (t) shadow price of the lower limit of power flow constraint of line l; e i (t) are respectively the shadow price of the upper limit of active power constraint of power market participating unit i and the shadow price of the lower limit of active power constraint of power market participating unit i.Δ i is the maximum ramp rate of power market participating unit i per period, is the shadow price of the maximum ramp rate constraint of unit i, δ i (t) is the shadow price of the minimum ramp rate constraint of power market participating unit i.

[0144] The power market participating units include new energy unit aggregators, thermal power units and hydropower units. The new energy unit aggregators include aggregated photovoltaic clusters, wind farm clusters and energy storage clusters.

[0145] Wherein: the extended Lagrange function is constructed to solve the clearing model, including:

[0146]

[0147]

[0148] By solving the extended Lagrange function to solve the clearing model, the active power p of each power market participating unit is obtained i (t) and the node marginal price of the system.

[0149] S4, cooperation benefit allocation based on Shapley value is carried out, the benefits are allocated according to the actual contribution rate of each new energy unit to the aggregator, and the disaggregation is completed.

[0150] Wherein, for any new energy unit cooperative aggregator s∈I, the revenue v(s) of the aggregator s needs to meet the following three conditions, including:

[0151] (1) That is, when the aggregator s does not contain any new energy unit or new energy unit combination, the aggregator s cannot obtain the revenue.

[0152] (2) v(s1∪s2)≥v(s1)+ν(s2), superadditivity, that is, for any new energy unit aggregator s1, s2, and At this time, the cooperation revenue of the aggregator s1 and the aggregator s2 is greater than the revenue when trading independently.

[0153] (3) The sum of the profits allocated to each new energy unit is equal to the profit of the aggregator after cooperation.

[0154] It can be proven that when x(r) satisfies the above three conditions, there exists a unique Shapley value for the aggregator's allocation scheme for distributed new energy units.

[0155] Among them, the cooperation benefit allocation scheme based on Shapley value includes:

[0156]

[0157] In the formula, R represents the number of n distributed new energy generator units in the region, R = {1, 2, 3, ..., n}; x(r) is the revenue obtained by the distributed new energy generator unit r; s is the aggregator of different combinations of new energy generator units, and s is a subset of R; v(s) represents the characteristic function of aggregator s, that is, the revenue obtained by aggregator s from participating in market transactions; v(s / r) represents the remaining revenue after deleting new energy generator unit r from aggregator s, and v(r) - v(s / r) represents the actual contribution of new energy generator unit r within aggregator s.

[0158] Example 1

[0159] Reference Figures 3 to 5 This embodiment of the invention provides a method for distributed renewable energy aggregation to participate in spot electricity market transactions, including:

[0160] This embodiment uses the IEEE 5-node distribution network test system as the implementation flow of the method of the present invention. The test system diagram is shown below. Figure 3 As shown:

[0161] Using unit 1 as the reference node, and node 3 (where unit 1 is located) as the reference node, the maximum transmission capacity of all lines is 500MW. Table 2 shows the physical parameters of the units. The example simulates a high-proportion renewable energy system, with upper and lower voltage limits of 1.05 and 0.95 for distribution network nodes, respectively. A total of four conventional generating units and one equivalent unit after distributed renewable energy aggregation are set. The distributed renewable energy aggregator aggregates all distributed renewable energy to participate in the electricity market clearing process. The total installed capacity is 1530MW, of which distributed renewable energy units account for 500MW (33%), and conventional generating units account for 1030MW (67%).

[0162] Table 2 Physical Parameter Information of the Unit

[0163]

[0164] By implementing the method of this invention according to the above parameter settings, the output of distributed renewable energy after participating in the spot market transaction clearing after aggregation can be obtained, as shown in Table 3.

[0165] Table 3 Power Output of Conventional Units and Distributed New Energy Aggregators

[0166]

[0167] Table 4 System electricity price

[0168]

[0169] As can be seen from Table 3, the output of the distributed new energy unit aggregator participating in the power market spot transaction is the upper limit of the predicted output, because the distributed new energy quotation is low, so the power market will prefer to choose the new energy with lower quotation.

[0170] As can be seen from Table 4, after the distributed new energy aggregation participates in the power market spot transaction, the total cost is reduced, the operation cost of all users is significantly reduced, and the renewable energy consumption capacity in the distribution network is effectively improved.

[0171] Embodiment 2

[0172] Distributed new energy unit benefit allocation based on Shapley value

[0173] Take the distributed new energy aggregator system containing wind and light in the region as an example for simulation analysis to verify the effectiveness of the Shapley value method. Let the set of distributed new energy units participating in power transaction be N={1,2,3,}, and the subset

[0174] s{1}, s{2}, s{3}, s{1,2}, s{1,3}, s{2,3}, s{1,2,3}, 1, 2, 3 respectively represent wind turbine units, photovoltaic units and energy storage units.

[0175] Table 5 Basic situation benefit table

[0176]

[0177] The calculation process and results of the unit benefit allocation in the aggregator based on the Shapley value method are shown in the table. The benefit allocation of wind turbine unit 1 is x(1)=0.2133 million yuan, the benefit allocation of wind turbine unit 2 is x(2)=0.2683 million yuan, and the benefit allocation of photovoltaic unit is x(3)=0.3683 million yuan.

[0178] Table 6 Initial benefit allocation of wind turbine units in the aggregator

[0179]

[0180]

[0181] Table 7 Initial benefit allocation of photovoltaic units in the aggregator

[0182]

[0183] Table 8 Initial income distribution of energy storage in the aggregator

[0184]

[0185]

[0186] Therefore, when the distributed new energy is aggregated and participates in power trading, the income obtained is greater than the sum of the incomes of independent trading of each unit, and the income distributed by the system trading of any party is higher than the independent trading income, which can promote the enthusiasm of each party in the system to participate in the distributed energy aggregation trading.

[0187] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not described in the apparatus embodiment, please refer to the method embodiment of the present application.

[0188] In another embodiment of the present application, a distributed new energy aggregation participates in a power market spot trading system, comprising:

[0189] A clearing module is configured to solve a clearing model of the distributed new energy aggregation participating in the power market spot trading in combination with a constraint condition, to obtain active power of each power market unit and a node marginal price of the system;

[0190] An income distribution module is configured to distribute the income obtained by the distributed new energy aggregation based on a Shapley value, to complete disaggregation.

[0191] The clearing model of the distributed new energy aggregation participating in the power market spot trading takes the operating cost of the power market unit as an objective function, and takes the minimum operating cost as an optimization target. The constraint condition includes a system load balance constraint, a line flow constraint, a unit operation constraint and a unit power increase / decrease rate constraint. The unit operation constraint includes a distributed new energy aggregation output constraint. The distributed new energy aggregation output constraint is obtained by using an ARIMA-based time series prediction model based on a distributed new energy aggregation model and historical output data.

[0192] In the distributed new energy aggregation participating in the power market spot trading system, the distributed new energy aggregation model includes a distributed photovoltaic aggregation model, a distributed wind power aggregation model and a distributed energy storage aggregation model.

[0193] The distributed photovoltaic aggregation model is expressed as:

[0194]

[0195] In the formula, p PV,k,tis the actual output of photovoltaic unit k at time t; is the maximum output of photovoltaic unit k in a dispatch cycle T; is the maximum output of photovoltaic cluster aggregated by N p photovoltaic units in a dispatch cycle T; PV,j,h,T , are respectively the actual output and the predicted maximum output of the aggregated photovoltaic cluster managed by aggregator j in a dispatch cycle T at node h.

[0196] The distributed wind power aggregation model is represented as:

[0197]

[0198] wherein p WT,f,t is the actual output of wind power unit f at time t; is the maximum output of wind power unit f in a dispatch cycle T; is the maximum output of wind power cluster of N p wind power units in a dispatch cycle T; p WT,j,h,T , are respectively the actual output and the predicted maximum output of the aggregated wind power cluster managed by aggregator j in a dispatch cycle T at node h.

[0199] The distributed energy storage aggregation model includes a distributed energy storage unit state of charge and charge-discharge characteristic model and a distributed energy storage unit probability model of accepting dispatch;

[0200] The distributed energy storage unit state of charge and charge-discharge characteristic model is represented as:

[0201]

[0202] Δt = t-T start , t ∈ [T start , T end ]

[0203]

[0204] In the formula, E ES，v，t is the residual capacity of energy storage unit v at time t, SOC ES，v，t is the state of charge of energy storage unit v at time t, represents the state of charge of energy storage unit v at initial time T start in a dispatch cycle T; when the energy storage unit v is in a fully charged state, the value of SOC ES，v，t is 1, and when the energy storage unit v is in a discharged state, the value of SOC ES，v，t is 0, respectively the minimum and maximum of the state of charge of the energy storage unit v in a dispatch cycle T; respectively the charging power and discharging power of the energy storage unit v at time t; Δt is the time interval of analysis and calculation; respectively the maximum and minimum of the energy of the energy storage unit v in a dispatch cycle T period; P ES，v，t is the actual charging and discharging power of the energy storage unit v at time t, and respectively the upper limit and lower limit of the charging and discharging power of the energy storage unit v in a dispatch cycle T period, η dis and η ch represent the discharging efficiency and charging efficiency of the energy storage unit, respectively, and respectively the discharging power and charging power of the energy storage unit v in a dispatch cycle T period;

[0205] The probability model of the distributed energy storage unit accepting dispatch is represented as:

[0206] y v = β0+ β1SOC ES，v,t + β2π v + ε v

[0207]

[0208]

[0209] In the formula, y v is the dependent variable of the distributed energy storage unit v, π v is the dispatch compensation of the distributed energy storage unit v, β0is the base probability coefficient, β1, β2are the probability coefficients of the explanatory variables SOC ES，v，t , π v , and ε v represents a random error variable, is the decision variable of the energy storage unit v in a dispatch cycle T period, represents that the dispatch is accepted in the dispatch cycle, represents that the dispatch is not accepted in the dispatch cycle T period; represents the decision probability of the distributed energy storage unit v accepting dispatch, with a value range of (0, 1); respectively the charging power and discharging power of the energy storage cluster aggregated in a dispatch cycle T period; respectively the discharging power and charging power of the energy storage cluster managed by the aggregator j under the node h in a dispatch cycle T period, p ES，j，h，T is the actual power of the energy storage cluster managed by the aggregator j under the node h in a dispatch cycle T period.

[0210] The distributed new energy aggregation participates in the clearing model of the power market spot transaction system, and the objective function and constraint condition of the distributed new energy aggregation participating in the power market spot transaction include:

[0211]

[0212] η l (t) : P l (t) ≤ P l

[0213]

[0214] e i (t) : p i (t) ≥ P i

[0215]

[0216] δ i (t) : p i (t) - p i (t-1) ≥ -Δ i

[0217]

[0218] Wherein F represents the generation cost based on the offer, including the operation cost, the starting cost and the no-load cost of the unit i participating in the power market, C i (p i (t)) is the operation cost of the unit i participating in the power market at time t, T is the total period number considered during system dispatch; I is the total number of units participating in the power market; p i (t) is the active power of the unit i participating in the power market at time t; d j is the load of the node j, J is the number of nodes, is the total load of the system at time t, is the total active power of all units participating in the power market at time t; λ(t) is the shadow price of system operation constraint; P l represents the power flow of line l, P l represents the upper and lower limits of the power flow of line l; represents the shadow price of the upper limit of the power flow constraint of line l, η l (t) represents the shadow price of the lower limit of the power flow constraint of line l; and e i (t) are the shadow prices of the upper limit constraint of the active power of the unit i participating in the power market and the lower limit constraint of the active power of the unit i participating in the power market, respectively; Δi the maximum ramp rate of the unit i participating in the electricity market per time period, the shadow price of the maximum ramp rate constraint of the unit i participating in the electricity market, δ i the shadow price of the minimum ramp rate constraint of the unit i participating in the electricity market; respectively, the actual output and the predicted maximum allowed output of the aggregated photovoltaic cluster managed by the aggregator j at node h in the T period, the predicted maximum allowed power of the aggregated wind farm cluster managed by the aggregator j at node h; respectively, the discharge power and the charge power of the energy storage cluster managed by the aggregator at node h in the T period.

[0219] In the benefit distribution module, the distributed new energy aggregation benefit distribution based on the Shapley value is performed, and specifically:

[0220]

[0221] wherein R represents that there are n distributed new energy units in the region, R = {1, 2, 3,..., n}; x(r)

[0222] is the benefit obtained by the distributed new energy unit r; s is an aggregator of different new energy unit combinations, s is a subset of R; v(s) represents a characteristic function of the aggregator s, that is, the benefit obtained by the aggregator s in market transaction; v(s / r) represents the remaining benefit after deleting the new energy unit r in the aggregator s, and v(r)-v(s / r) represents the actual contribution of the new energy unit r in the aggregator s.

[0223] In another embodiment of the present application, a computer device is provided, which comprises a processor and a memory for storing a computer program, the computer program comprising program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method process or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the distributed new energy aggregation participating in the power market spot transaction method.

[0224] In another embodiment of the present application, the present application further provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer device, and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium in the computer device, and of course can also include an expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores an operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the distributed new energy aggregation participating in the power market spot transaction method in the above embodiments.

[0225] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0226] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0227] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0228] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0229] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing the technical solutions of the present application, but not for limiting it. Although the present application is described in detail with reference to the above embodiments, those skilled in the field should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for distributed renewable energy aggregation to participate in spot electricity market transactions, characterized in that, include: Based on the constraints, the clearing model for distributed renewable energy aggregation participating in the spot electricity market is solved to obtain the active power of each electricity market unit and the nodal marginal electricity price of the system; the benefits obtained from distributed renewable energy aggregation are allocated based on the Shapley value to complete the deaggregation. The clearing model uses the operating cost of power market units as the objective function, with the goal of minimizing operating cost. The constraints include system load balance constraints, line power flow constraints, unit operation constraints, and rate constraints on unit output increases and decreases. The unit operation constraints include output constraints after distributed renewable energy aggregation. These output constraints are predicted using an ARIMA-based time-series forecasting model based on the distributed renewable energy aggregation model and historical output data. The distributed renewable energy aggregation model includes distributed photovoltaic aggregation, distributed wind power aggregation, and distributed energy storage aggregation. The distributed energy storage aggregation model considers user participation willingness for energy storage units, introducing a logit-based regression model to obtain the probability of energy storage units participating in grid dispatch, thereby calculating the actual callable power of the energy storage cluster.

2. The method for distributed renewable energy aggregation to participate in spot electricity market transactions according to claim 1, characterized in that, The distributed photovoltaic aggregation model is represented as follows: In the formula: For photovoltaic units exist t Actual output at any given moment; For photovoltaic units In a scheduling cycle T Maximum output during the time period; For the reason A photovoltaic cluster, formed by aggregating individual photovoltaic units, is in a scheduling cycle T Maximum output during the time period; , They are nodes Lower Aggregator The aggregated photovoltaic clusters under management are managed within a scheduling cycle. T The actual output during the time period and the maximum output that is predicted to be allowed.

3. The method for distributed renewable energy aggregation to participate in spot electricity market transactions according to claim 1, characterized in that, The distributed wind power aggregation model is represented as follows: in: For wind turbines In a scheduling cycle T Maximum output during the time period; for A cluster of wind turbine units in a scheduling cycle T Maximum output during the time period; , They are nodes Lower Aggregator The aggregated wind farm cluster under management in a scheduling cycle T The actual output during the time period compared to the predicted maximum allowable output.

4. The method for distributed renewable energy aggregation to participate in spot electricity market transactions according to claim 1, characterized in that, The distributed energy storage aggregation model includes a distributed energy storage unit state of charge and charge / discharge characteristic model and a distributed energy storage unit acceptance scheduling probability model. The state of charge and charge / discharge characteristics model of the distributed energy storage unit is expressed as follows: In the formula, For energy storage units t Remaining capacity at any given time Energy storage units The state of charge at time t, Represents energy storage units In a scheduling cycle T Initial time within State of charge; when the energy storage unit When fully charged, The value is 1, when the energy storage unit When fully discharged, The value is 0. Energy storage units In a scheduling cycle T Minimum and maximum values ​​of internal charge state; Energy storage units The charging power and discharging power at time t; The time interval for analysis and calculation; , Energy storage units In a scheduling cycle T The maximum and minimum energy values ​​within a given time period; For energy storage units exist t The actual charge and discharge power at any given time. and Energy storage units In a scheduling cycle T The upper and lower limits of charging and discharging power within a given time period. and These represent the discharge efficiency and charging efficiency of the energy storage unit, respectively. and Energy storage units v In a scheduling cycle T Discharge power and charging power during the time period; The probabilistic model for the distributed energy storage unit to be scheduled is expressed as follows: In the formula, For distributed energy storage units The dependent variable, For distributed energy storage units Dispatch compensation, As the baseline probability coefficient, For explanatory variables The probability coefficient, Represents the random error variable. For energy storage units cycle T During the period 1 indicates that the system accepts scheduling within this scheduling cycle. 0 indicates that in this scheduling period T No scheduling will be accepted during the specified time period; Distributed energy storage units The decision probability of accepting the scheduling, with a value range of 1. Each is a scheduling cycle T The charging and discharging power of the energy storage cluster after time-segment aggregation; Each is a scheduling cycle T Time period nodes h Lower Aggregator j The managed energy storage cluster's discharge power and charging power, For a scheduling cycle T Time period nodes h Lower Aggregator j The actual power of the managed energy storage cluster.

5. The method for distributed renewable energy aggregation to participate in spot electricity market transactions according to claim 1, characterized in that, The objective function and constraints of the clearing model for distributed renewable energy aggregation participating in the electricity market spot trading include: in: F This represents the cost of generating electricity based on quoted prices, including units participating in the electricity market. i The costs consist of three parts: operating costs, start-up costs, and no-load costs. For units participating in the electricity market i exist t The operating cost at any given time T I represents the total number of time periods considered during system scheduling; I represents the total number of generating units participating in the electricity market. For units participating in the electricity market i exist t Active power at any given moment; For the system j Node load, J The number of nodes Let be the total system load at time t. For all participating power units in the electricity market t Total active power at any given moment; Shadow prices for system operating constraints; Indicates the line Current power, , Indicates the line Upper and lower limits of power flow; Indicates the line The shadow price of power limit constrained by power flow. Indicates the line The shadow price of the power flow constraint lower limit; and For each participating power unit i Shadow prices constrained by active power caps, and generating units participating in the electricity market i Shadow price of active power lower limit constraint; For units participating in the electricity market i Maximum climbing rate per hour For units participating in the electricity market i Shadow price of maximum gradeability constraint For units participating in the electricity market i Shadow price of minimum landslide rate constraint; They are nodes Lower Aggregator The managed aggregated photovoltaic clusters in T The actual output and the predicted maximum allowable output during the time period. For nodes Lower Aggregator The maximum allowable power predicted by the aggregated wind farm cluster under management; They are respectively T Time period nodes h Lower Aggregator j The discharge power and charging power of the managed energy storage cluster.

6. The method for distributed renewable energy aggregation to participate in spot electricity market transactions according to claim 1, characterized in that, The distribution of benefits obtained from distributed new energy aggregation based on Shapley values ​​is specifically as follows: In the formula, R represents the number of n distributed renewable energy units in the region. ; The revenue obtained from the distribution of new energy generating units r; An aggregator for different combinations of new energy generating units. yes A subset of; Indicates aggregator The characteristic function, i.e., the aggregator quotient Profits gained from participating in market transactions; Indicates aggregator Remove new energy units The remaining profits after that, express r At the aggregator s The actual contribution within.

7. A distributed renewable energy aggregation system for participating in the spot electricity market, characterized in that, include: The clearing module is used to solve the clearing model for distributed renewable energy aggregation participating in the spot electricity market, taking into account constraints, and to obtain the active power of each electricity market unit and the nodal marginal electricity price of the system. The benefit allocation module is used to allocate the benefits obtained from distributed new energy aggregation based on Shapley values ​​and to complete the deaggregation. The clearing model for distributed renewable energy aggregation participating in the electricity market spot trading uses the operating cost of electricity market units as the objective function, with the minimum operating cost as the optimization objective. The constraints include: system load balance constraints, line power flow constraints, unit operation constraints, and rate constraints on unit output increases and decreases. The unit operation constraints include output constraints after distributed renewable energy aggregation. These output constraints are predicted using an ARIMA-based time-series forecasting model based on the distributed renewable energy aggregation model and historical output data. The distributed renewable energy aggregation model includes distributed photovoltaic aggregation, distributed wind power aggregation, and distributed energy storage aggregation. The distributed energy storage aggregation model considers user participation willingness for energy storage units, introducing a logit-based regression model to obtain the probability of energy storage units participating in grid dispatch, thereby calculating the actual callable power of the energy storage cluster.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for distributed new energy aggregation to participate in spot trading in the electricity market as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for distributed new energy aggregation to participate in spot trading in the electricity market as described in any one of claims 1 to 6.

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