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

Abstract

The invention provides a method for participating in spot transaction of a power market by distributed new energy aggregation and a related device, and the method comprises the steps: constructing a large-scale clearing model for participating in spot market transaction by distributed new energy aggregation, and comprehensively considering the output characteristics, market demands and power grid constraint conditions of distributed new energy; a reasonable energy scheduling and configuration strategy is formulated, and meanwhile, reasonable benefit allocation of each unit after aggregation is realized according to the actual contribution rate of each unit to an aggregator based on a Shapley value method, so that planning investment excitation of distributed resources is realized, consumption of renewable energy sources is promoted, and the operation cost of a power system is reduced. The reliability of the power system and the operation efficiency of the energy internet are improved, the operation under regional energy sharing is realized, and the investment benefit is maximized.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method and related devices for distributed new energy aggregation to participate in spot transactions in power markets. Background Art

[0002] Compared with traditional thermal power units, new energy has the advantages of being green, low-carbon, safe and efficient. Distributed new energy is an effective way to alleviate energy shortages and promote the low-carbon development of power systems. Against this background, new energy sources such as wind and solar energy continue to develop and have gradually become an important source of power for grid connection. Centralized development and distributed new energy access are both important forms of high-proportion new energy grid connection. In 2021, the new installed capacity of distributed photovoltaics exceeded that of centralized photovoltaics for the first time, with 29.28GW of new distributed photovoltaics, accounting for about 55% of all new photovoltaic power generation installed capacity. In 2023, the new installed capacity of distributed photovoltaics was 96.29GW, an increase of 88% year-on-year. The cumulative installed capacity of distributed photovoltaics is 253GW, accounting for 42% of the total installed capacity of photovoltaics. It can be foreseen that distributed new energy power generation will occupy an important position in the future energy structure.

[0003] Distributed new energy builds power generation equipment at the user end, which can be operated independently or connected to the grid. It mainly includes distributed photovoltaic power generation, distributed wind power and other forms, which have the advantages of clean, low-carbon, economical and efficient. However, in the absence of light and wind, distributed new energy changes from "power generation" to "power consumption", and requires the power grid to provide power supply, which has strong uncertainty and weak controllable output. Compared with centralized new energy power generation, distributed new energy power generation has smaller power and greater fluctuations. It has the characteristics of small capacity, wide distribution, large number and complex trading behavior. There are entry barriers to the electricity market. Therefore, distributed new energy cannot directly participate in market energy transactions.

[0004] First, existing research mainly focuses on the design of multi-agent market trading mechanisms, studying the solution of market equilibrium and the quotation clearing algorithm. The Spanish NOBEL project designed a discrete market trading mechanism based on the securities trading model. Buyers and sellers clear according to the order book ranking. At the same time, the order book table is open to market participants, allowing them to refer to modify the quotation. However, due to the relatively small and large changes in the power generation capacity of distributed new energy units, distributed new energy units cannot participate in energy transactions in the power market as independent market entities under the existing market mechanism. Therefore, it is necessary to improve the existing power market clearing model based on the characteristics of distributed new energy to adapt to the characteristics of small capacity, wide distribution and large number of distributed energy units.

[0005] Secondly, most of the existing research on new energy aggregators focuses on their participation in the bidding and optimal dispatch of the power market as a whole, but less on the profit distribution mechanism. Under the competitive conditions of the power market, each distributed new energy unit is independent of each other and belongs to different investment entities. Therefore, how to build a fair, reasonable and transparent profit distribution mechanism is the key to whether the cooperation among the units in the new energy aggregator can be maintained, which is related to the integration and expansion of new energy in the power system. Summary of the invention

[0006] In view of the problems existing in the above-mentioned prior art, the present invention provides a method and related devices for distributed renewable energy aggregation to participate in spot transactions in the electricity market, which calculates the upper and lower limits of the output after aggregation based on the output characteristics of distributed renewable energy and the willingness to participate in regulation, and constructs a clearing model for large-scale aggregation of distributed renewable energy to participate in spot market transactions. At the same time, reasonable distribution of the benefits of each unit is achieved according to the actual contribution rate of each unit to the aggregator, thereby improving the method for distributed renewable energy to participate in the electricity market on a large scale.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for distributed renewable energy aggregation to participate in spot trading in a power market, comprising:

[0009] Combined with the constraints, the clearing model of distributed renewable energy participating in spot trading in the power market is solved to obtain the active power of each power market unit and the node marginal electricity price of the system; the benefits of distributed renewable energy aggregation are distributed based on the Shapley value to complete the de-aggregation;

[0010] The clearing model for the distributed new energy to participate in spot transactions in the electricity market after aggregation takes the operating cost of the power market unit as the objective function and takes minimizing the operating cost as the optimization goal; the constraints include: system load balance constraints, line flow constraints, unit operation constraints and unit output increase and decrease rate constraints, and the unit operation constraints include output constraints after aggregation of distributed new energy; the output constraints after aggregation of distributed new energy are predicted by using an ARIMA-based time series prediction model based on the distributed new energy aggregation model and historical output data.

[0011] Preferably, in the method for distributed new energy aggregation to participate in spot trading in the electricity market, 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] Furthermore, in the method for distributed new energy aggregation to participate in spot trading in the electricity market, the distributed photovoltaic aggregation model is expressed as:

[0013]

[0014] Where: 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 scheduling cycle T period; Because N p The maximum output of the photovoltaic cluster after the aggregation of photovoltaic units in a scheduling cycle T period; PV,j,h,T , They are respectively the actual output and the predicted maximum output allowed by the aggregated PV cluster managed by aggregator j under node h during a scheduling cycle T.

[0015] Furthermore, in the method for distributed renewable energy aggregation to participate in spot trading in the electricity market, the distributed wind power aggregation model is expressed as:

[0016]

[0017] Where: p WT,f,t is the actual output of wind turbine f at time t; is the maximum output of wind turbine f in a scheduling cycle T; N p The maximum output of a wind farm cluster with wind turbines in a dispatching cycle T; WT,j,h,T , They are respectively the actual output and the predicted maximum allowable output of the aggregated wind farm cluster managed by aggregator j under node h during a scheduling cycle T.

[0018] Furthermore, in the method for distributed new energy aggregation to participate in spot trading in the electricity market, the distributed energy storage aggregation model includes a distributed energy storage unit charge state and charge-discharge characteristic model and a distributed energy storage unit probability model for accepting scheduling;

[0019] The state of charge and charge-discharge characteristic model of the distributed energy storage unit is expressed as:

[0020]

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

[0022]

[0023] 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 charge state of the energy storage unit v at time t, Represents the initial time T of the energy storage unit v in a scheduling cycle Tstart When the energy storage unit v is fully charged, SOC ES,v,t The value is 1. When the energy storage unit v is fully discharged, SOC ES,v,t The value is 0, are the minimum and maximum values ​​of the state of charge of the energy storage unit v in a scheduling period T respectively; are 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; are the maximum and minimum energy values ​​of the energy storage unit v in a scheduling cycle T period respectively; P ES,v,t is the actual charging and discharging power of the energy storage unit v at time t, and are the upper and lower limits of the charging and discharging power of the energy storage unit v in a scheduling cycle T, respectively. dis and η ch They represent the discharge efficiency and charging efficiency of the energy storage unit respectively. and They are the discharge power and charging power of the energy storage unit v in a scheduling cycle T period respectively;

[0024] The probability model of the distributed energy storage unit accepting dispatch is expressed as:

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

[0026]

[0027] In the formula, y v is the dependent variable of the distributed energy storage unit v, π v is the dispatch compensation of distributed energy storage unit v, β0 is the benchmark probability coefficient, β1 and β2 are the explanatory variables SOC ES,v,t , π v The probability coefficient, ε v represents the random error variable, is the decision variable of the energy storage unit v in a certain scheduling cycle T period, Indicates that the scheduling is accepted in this scheduling cycle. Indicates that scheduling is not accepted during the T period of the scheduling cycle; It represents the decision probability of the distributed energy storage unit v accepting the dispatch, and its value range is (0,1); They are respectively the charging power and discharging power of the energy storage cluster aggregated during a scheduling cycle T period; are the discharge power and charging power of the energy storage cluster managed by aggregator j at node h in a scheduling cycle T period, p ES,j,h,T It is the actual power of the energy storage cluster managed by aggregator j at node h during a scheduling cycle T.

[0028] Furthermore, in the method for distributed renewable energy aggregation to participate in spot trading in the power market, the objective function and constraints of the clearing model for distributed renewable energy aggregation to participate in spot trading in the power market include:

[0029]

[0030]

[0031] or l (t): P l (t)≤ P l

[0032]

[0033] e i (t): p i (t)≥ P i

[0034]

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

[0036]

[0037] Where: F represents the power generation cost based on the quotation, including the operating cost, startup cost and no-load cost of unit i participating in the power market, C i (p i (t)) is the operating cost of unit i participating in the power market at time t, T is the total number of time periods considered during system dispatch; I is the total number of units participating in the power 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 the j-node in the system, J is the number of nodes, is the total system load 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 the system operation constraint; P l represents the power flow of line l, P l Indicates the upper and lower limits of the power flow of line l; represents the shadow price of the power upper limit of line l power flow constraint, or l (t) represents the shadow price of the power lower limit of line l power flow constraint; and e i (t) are the shadow price of the upper limit constraint of the active power of unit i participating in the power market and the shadow price of the lower limit constraint of the active power of unit i participating in the power market; Δ i is the maximum ramp rate of unit i participating in the power market in each period, is the shadow price of the maximum ramp rate constraint of unit i participating in the power market, d i (t) is the shadow price of the minimum glide rate constraint of unit i participating in the electricity market; are the actual output and the predicted maximum allowable output of the aggregated PV cluster managed by aggregator j under node h during period T, The predicted maximum allowed power of the aggregated wind farm cluster managed by aggregator j under node h; They are respectively the discharge power and charging power of the energy storage cluster managed by the aggregator at node h in period T.

[0038] Preferably, in the method for distributed renewable energy aggregation to participate in spot trading in the electricity market, the distribution of cooperative benefits based on Shapley value is specifically as follows:

[0039]

[0040] Where R means that there are n distributed renewable energy units in the region, R = {1, 2, 3, ..., n}; x(r) is the income obtained by the distributed renewable energy unit r; s is the aggregator of different renewable energy unit combinations, s is a subset of R; v(s) represents the characteristic function of aggregator s, that is, the income obtained by aggregator s from participating in market transactions; v(s / r) represents the remaining income after deleting renewable energy unit r from aggregator s, and v(r)-v(s / r) represents the actual contribution of renewable energy unit r in aggregator s.

[0041] The present invention also provides a distributed new energy aggregation participating in the electricity market spot trading system, comprising:

[0042] The clearing module is used to solve the clearing model of distributed renewable energy participating in spot trading in the power market after aggregation, combining constraints, and obtain the active power of each power market unit and the node marginal electricity price of the system;

[0043] The benefit allocation module is used to allocate the benefits of distributed new energy aggregation based on Shapley value and complete de-aggregation;

[0044] Among them, the clearing model for the distributed new energy participating in the spot transaction in the electricity market after aggregation takes the operating cost of the power market unit as the objective function and the minimum operating cost as the optimization goal; the constraints include: system load balance constraints, line flow constraints, unit operation constraints and unit output increase and decrease rate constraints, and the unit operation constraints include the output constraints after the aggregation of distributed new energy; the output constraints after the aggregation of distributed new energy are predicted according to the distributed new energy aggregation model and historical output data using the ARIMA-based time series prediction model.

[0045] Preferably, in the distributed new energy aggregation participating in the electricity 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.

[0046] Furthermore, in the distributed new energy aggregation participating in the power market spot trading system, the distributed photovoltaic aggregation model is expressed as:

[0047]

[0048] Where: 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 scheduling cycle T period; Because N p The maximum output of the photovoltaic cluster after the aggregation of photovoltaic units in a scheduling cycle T period; PV,j,h,T , They are respectively the actual output and the predicted maximum output allowed by the aggregated PV cluster managed by aggregator j under node h during a scheduling cycle T.

[0049] Furthermore, in the distributed new energy aggregation participating in the power market spot trading system, the distributed wind power aggregation model is expressed as:

[0050]

[0051] Where: p WT,f,t is the actual output of wind turbine f at time t; is the maximum output of wind turbine f in a scheduling cycle T; N p The maximum output of a wind farm cluster with wind turbines in a dispatching cycle T; WT,j,h,T , They are respectively the actual output and the predicted maximum allowable output of the aggregated wind farm cluster managed by aggregator j under node h during a scheduling cycle T.

[0052] Furthermore, in the distributed new energy aggregation participating in the spot trading system of the power market, the distributed energy storage aggregation model includes a distributed energy storage unit charge state and charge-discharge characteristic model and a distributed energy storage unit acceptance probability model;

[0053] The state of charge and charge-discharge characteristic model of the distributed energy storage unit is expressed as:

[0054]

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

[0056]

[0057] 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 charge state of the energy storage unit v at time t, Represents the initial time T of the energy storage unit v in a scheduling cycle T start When the energy storage unit v is fully charged, SOC ES，v，t The value is 1. When the energy storage unit v is fully discharged, SOC ES，v，t The value is 0, are the minimum and maximum values ​​of the state of charge of the energy storage unit v in a scheduling period T respectively; are 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; are the maximum and minimum energy values ​​of the energy storage unit v in a scheduling cycle T period respectively; P ES，v，t is the actual charging and discharging power of the energy storage unit v at time t, and are the upper and lower limits of the charging and discharging power of the energy storage unit v in a scheduling cycle T, respectively. dis and η ch They represent the discharge efficiency and charging efficiency of the energy storage unit respectively. and They are the discharge power and charging power of the energy storage unit v in a scheduling cycle T period respectively;

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

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

[0060]

[0061]

[0062] In the formula, y v is the dependent variable of the distributed energy storage unit v, π v is the dispatch compensation of distributed energy storage unit v, β0 is the benchmark probability coefficient, β1 and β2 are the explanatory variables SOC ES，v，t , π v The probability coefficient, ε v represents the random error variable, is the decision variable of the energy storage unit v in a certain scheduling cycle T period, Indicates that the scheduling is accepted in this scheduling cycle. Indicates that scheduling is not accepted during the T period of the scheduling cycle; It represents the decision probability of the distributed energy storage unit v accepting the dispatch, and its value range is (0, 1); They are respectively the charging power and discharging power of the energy storage cluster aggregated during a scheduling cycle T period; are the discharge power and charging power of the energy storage cluster managed by aggregator j at node h in a scheduling cycle T period, p ES，j，h，T It is the actual power of the energy storage cluster managed by aggregator j at node h during a scheduling cycle T.

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

[0064]

[0065] or l (t): P l (t)≤ P l

[0066]

[0067] e i (t): p i (t)≥ P i

[0068]

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

[0070]

[0071] Where: F represents the power generation cost based on the quotation, including the operating cost, startup cost and no-load cost of unit i participating in the power market, C i (p i (t)) is the operating cost of unit i participating in the power market at time t, T is the total number of time periods considered during system dispatch; I is the total number of units participating in the power 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 the j-node in the system, J is the number of nodes, is the total system load 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 the system operation constraint; P l represents the power flow of line l, P l Indicates the upper and lower limits of the power flow of line l; represents the shadow price of the power upper limit of line l power flow constraint, or l (t) represents the shadow price of the power lower limit of line l power flow constraint; and e i (t) are the shadow price of the upper limit constraint of the active power of unit i participating in the power market and the shadow price of the lower limit constraint of the active power of unit i participating in the power market; Δ i is the maximum ramp rate of unit i participating in the power market in each period, is the shadow price of the maximum ramp rate constraint of unit i participating in the power market, d i (t) is the shadow price of the minimum glide rate constraint of unit i participating in the electricity market; are the actual output and the predicted maximum allowable output of the aggregated PV cluster managed by aggregator j under node h during period T, The predicted maximum allowed power of the aggregated wind farm cluster managed by aggregator j under node h; They are respectively the discharge power and charging power of the energy storage cluster managed by the aggregator at node h in period T.

[0072] Preferably, in the distributed new energy aggregation participating in the spot trading system of the power market, the benefit distribution obtained by the distributed new energy aggregation based on the Shapley value is specifically as follows:

[0073]

[0074] Where R means that there are n distributed renewable energy units in the region, R = {1, 2, 3, ..., n}; x(r) is the income obtained by the distributed renewable energy unit r; s is the aggregator of different renewable energy unit combinations, s is a subset of R; v(s) represents the characteristic function of aggregator s, that is, the income obtained by aggregator s from participating in market transactions; v(s / r) represents the remaining income after deleting renewable energy unit r from aggregator s, and v(r)-v(s / r) represents the actual contribution of renewable energy unit r in aggregator s.

[0075] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for distributed new energy aggregation to participate in spot trading in the electricity market as described above are implemented.

[0076] The present invention also provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of the above-mentioned method for distributed new energy aggregation to participate in spot trading in the electricity market.

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

[0078] The present invention constructs a large-scale clearing model for distributed renewable energy aggregation to participate in spot market transactions, comprehensively considers the output characteristics, market demand, and grid constraints of distributed renewable energy, formulates reasonable energy scheduling and configuration strategies, and at the same time, based on the Shapley value method, realizes reasonable distribution of the benefits of each unit after aggregation according to the actual contribution rate of each unit to the aggregator, realizes planning and investment incentives for distributed resources, promotes the consumption of renewable energy, reduces the operating cost of the power system, improves the reliability of the power system and the operating efficiency of the energy Internet, and realizes the maximization of operation and investment benefits under regional energy sharing.

[0079] Furthermore, the distributed energy storage aggregation model of the present invention takes into account the user participation willingness of the energy storage unit, introduces a logit-based regression model, and obtains the probability of energy storage users participating in grid dispatching, which is used as their actual output, thereby improving the accuracy of distributed new energy aggregation participating in spot transactions in the electricity market. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0081] Figure 1 This is a schematic diagram of the overall framework of distributed new energy aggregation participating in spot trading in the power market according to an embodiment of the present invention;

[0082] Figure 2 A flow chart of a time series forecasting model based on ARIMA for distributed renewable energy aggregation participating in spot trading in the power market according to an embodiment of the present invention;

[0083] Figure 3 This is a simplified model diagram of a 5-node system for distributed renewable energy aggregation participating in spot trading in the power market according to an embodiment of the present invention;

[0084] Figure 4 It is a line graph of the output of distributed new energy aggregated and participating in spot trading in the power market according to an embodiment of the present invention and a line graph after difference processing;

[0085] Figure 5 This is a graph of output forecast results of a distributed new energy aggregator that participates in spot transactions in the electricity market according to an embodiment of the present invention. DETAILED DESCRIPTION

[0086] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0087] It should be noted that the process equipment or devices not specifically specified in the following embodiments are all conventional equipment or devices in the art.

[0088] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. Moreover, unless otherwise specified, the numbering of each method step is only a convenient tool for identifying each method step, and is not intended to limit the order of arrangement of each method step or to define the scope of the present invention. Changes or adjustments in their relative relationships should also be regarded as the scope of the present invention without substantially changing the technical content.

[0089] Reference Figure 1 and Figure 2 , which is a method for distributed renewable energy aggregation to participate in spot trading in the electricity market of the present invention, comprising:

[0090] S1, collect data, integrate the output characteristics of wind and solar distributed renewable energy and the characteristics of user participation willingness, build a distributed renewable energy aggregation model, and determine the upper and lower limits of the output after distributed renewable energy aggregation as a constraint condition for participating in the clearing of the electricity market.

[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, Building a distributed photovoltaic aggregation model

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

[0095] Photovoltaic power can be incompletely utilized, and abandoned power is allowed. The formula of the distributed photovoltaic aggregation model is expressed as:

[0096]

[0097] Where: 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 scheduling cycle T period; Because N p The maximum output of the photovoltaic cluster after the aggregation of photovoltaic units in a scheduling cycle T period; PV,j,h,T , They are respectively the actual output and the predicted maximum output allowed by the aggregated PV cluster managed by aggregator j under node h during a scheduling cycle T.

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

[0099] In this step, a lumped model is used to describe the distributed wind power aggregation model. The aggregated wind farm cluster is considered to be composed of multiple distributed wind turbines. The daily maximum output of the distributed wind turbines can be estimated based on the wind speed characteristics and wind speed resource coefficient.

[0100] Wind power may not be fully utilized, and wind abandonment is allowed. The formula of the distributed wind power aggregation model is expressed as:

[0101]

[0102]

[0103] Where: p WT，f，t is the actual output of wind turbine f at time t; is the maximum output of wind turbine f in a scheduling cycle T; N p The maximum output of a wind farm cluster with wind turbines in a dispatching cycle T; WT，j，h，T , They are respectively the actual output and the predicted maximum allowable output of the aggregated wind farm cluster managed by aggregator j under node h during a scheduling cycle T.

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

[0105] In this step, a lumped model is used to describe the distributed energy storage aggregation model, and the aggregated energy storage cluster is considered to be composed of multiple distributed energy storage units. The distributed energy storage aggregation model takes into account the user participation willingness of the energy storage unit, introduces a logit-based regression model, and obtains the probability of the energy storage unit participating in the grid dispatch, thereby calculating the actual available power of the energy storage cluster.

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

[0107]

[0108] Δt=tT 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 charge state of the energy storage unit v at time t, Represents the initial time T of the energy storage unit v in a scheduling cycle T start When the energy storage unit v is fully charged, SOC ES，v，t The value is considered to be 1. When the energy storage unit v is in a fully discharged state, its SOC ES，v，t The value is considered to be 0. are the minimum and maximum values ​​of the state of charge of the energy storage unit v in a scheduling period T respectively; are 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; are the maximum and minimum energy values ​​of the energy storage unit v in a scheduling cycle T period respectively; P ES，v，tis the actual charging and discharging power of the energy storage unit v at time t, and are the upper and lower limits of the charging and discharging power of the energy storage unit v in a scheduling cycle T period, η dis and η ch They represent the discharge efficiency and charging efficiency of the energy storage unit respectively. and They are the discharge power and charging power of the energy storage unit v in the T period within a scheduling cycle;

[0111] Considering the two factors of the state of charge of the distributed energy storage units and the compensation electricity price for energy storage users, a probability model for the distributed energy storage units to accept dispatch is established. Combined with the probability of the distributed energy storage units accepting dispatch, the dispatchable capacity of the distributed energy storage units in the aggregation area is calculated.

[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 dispatch compensation of distributed energy storage unit v, β0 is the benchmark probability coefficient, β1 and β2 are the explanatory variables SOC ES，v，t , π v The probability coefficient, ε v represents the random error variable, is the decision variable of the energy storage unit v in a certain scheduling period T, Indicates that the scheduling is accepted in this scheduling cycle. Indicates that scheduling is not accepted during this scheduling cycle; It represents the decision probability of the distributed energy storage unit v accepting the dispatch, and its value range is (0, 1); They are respectively the charging power and discharging power of the energy storage cluster aggregated during a scheduling cycle T period. They are the discharge power and charging power of the energy storage cluster managed by the aggregator at node h in a scheduling cycle T period, pES,j,h, and T are the actual power of the energy storage cluster managed by the aggregator j at node h in a scheduling cycle T period.

[0115] S2, combines the distributed photovoltaic aggregation model, distributed photovoltaic aggregation model, and distributed energy storage aggregation model to collect historical output data, builds an ARIMA-based time series prediction model, performs short-term power forecasting, and obtains the output of distributed new energy after aggregation, which serves as the upper and lower limits of the actual output of new energy units participating in spot market transactions.

[0116] The construction of the ARIMA-based time series forecasting model includes: testing the stability of the input time series; selecting model parameters; and testing the model validity.

[0117] (1) The stability test of the input time series includes: testing the stationarity of the input time series by using a graphical test method or a unit root test method. If the input time series is not stationary, the input time series needs to be stabilized by using a difference method.

[0118] (2) Model parameter selection of the time series prediction model includes: finding the minimum AIC or BIC value through the AIC criterion or the BIC criterion to obtain the optimal p and q values, and determining the model constant parameters.

[0119] Table 1. Model establishment conditions

[0120]

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

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

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

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

[0125] (3) The validity test of the time series prediction model includes: judging the accuracy and reliability of the prediction results by performing a normality test on the residual term of the time series prediction model.

[0126] S3, construct a clearing model for distributed renewable energy to participate in spot transactions in the electricity market after aggregation, and design a corresponding algorithm to solve the clearing model.

[0127] Specifically, a clearing model for distributed renewable energy participating in spot transactions in the electricity market after aggregation is constructed, including: considering the minimum operating cost as the optimization goal; considering system load balance constraints, line flow constraints, unit operation constraints and unit output increase and decrease rate constraints; unit operation constraints include output constraints after distributed renewable energy aggregation; and solving the clearing model by constructing an extended Lagrangian function.

[0128] Among them, the objective function and constraints of the clearing model for distributed renewable energy aggregation participating in spot transactions in the electricity market include:

[0129]

[0130] 1) System load balancing constraints:

[0131]

[0132] 2) Line flow constraints:

[0133]

[0134] or l (t):P l (t)≤ P l

[0135] 3) Unit operation constraints:

[0136]

[0137] e i (t):p i (t)≥ P i

[0138] For distributed renewable energy unit aggregators (i.e., output constraints after distributed renewable energy aggregation):

[0139]

[0140] 4) Rate constraints for increasing or decreasing the unit output (including climbing rate and landslide rate constraints)

[0141]

[0142] d i (t):p i (t)-p i (t-1)≥-Δ i

[0143] Where: F represents the power generation cost based on the quotation, including the operating cost, startup cost and no-load cost of unit i participating in the power market, C i (p i (t)) is the operating cost of unit i participating in the power market at time t, T is the total number of time periods considered during system dispatch; I is the total number of units participating in the power 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 the j-node in the system, J is the number of nodes, is the total system load 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 the system operation constraint; P l represents the power flow of line l, P l Indicates the upper and lower limits of the power flow of line l; represents the shadow price of the power upper limit of line l power flow constraint, or l (t) represents the shadow price of the power lower limit of line l power flow constraint; e i (t) are the shadow prices of the upper limit constraint of active power of unit i participating in the power market and the shadow prices of the lower limit constraint of active power of unit i participating in the power market. i is the maximum ramp rate of unit i participating in the power market in each period, is the shadow price of the maximum ramp rate constraint of unit i, d i (t) is the shadow price of the minimum slippage rate constraint for unit i participating in the electricity market.

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

[0145] Among them: constructing an extended Lagrangian function to solve the clearing model, including:

[0146]

[0147]

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

[0149] S4, distribute the cooperative benefits based on Shapley value, distribute the benefits according to the actual contribution rate of each new energy unit to the aggregator, and complete the deaggregation.

[0150] Among them, for any aggregator s∈I of new energy units, 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 include any new energy units or combinations of new energy units, it cannot obtain benefits.

[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 cooperative benefits of aggregator s1 and aggregator s2 are greater than the benefits of independent transactions.

[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 proved that when x(r) satisfies the above three conditions, the aggregator's allocation plan for distributed renewable energy units has a unique Shapley value.

[0155] Among them, the cooperative benefit distribution scheme based on Shapley value includes:

[0156]

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

[0158] Example 1

[0159] Reference Figures 3 to 5 , is an embodiment of the present invention, which provides a method for distributed new energy aggregation to participate in spot trading in the power market, including:

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

[0161] Taking unit 1 as the reference node, the basic information of the system sets node 3 where unit 1 is located as the reference node, and the maximum transmission capacity of the line is 500MW. Table 2 shows the physical parameter information of the unit. The example simulates a high proportion of renewable energy system, and the upper and lower limit values ​​of the distribution network node voltage are 1.05 and 0.95 respectively; a total of four conventional generator sets and one equivalent unit after distributed new energy aggregation are set. Distributed new energy aggregators aggregate all distributed new energy to participate in the clearing of the power market. The total installed capacity is 1530MW, of which the installed capacity of distributed new energy units is 500MW, accounting for 33%, and the capacity of conventional generator sets is 1030MW, accounting for 67%.

[0162] Table 2 Physical parameter information of the unit

[0163]

[0164] Through the method of the present invention, according to the above parameter settings, it is specifically implemented to obtain the output of distributed new energy after aggregation and participating in the spot transaction of the power market after clearing as shown in Table 3.

[0165] Table 3 Output of conventional units and distributed renewable energy aggregators

[0166]

[0167] Table 4 System electricity price

[0168]

[0169] From Table 3, we can see that the output of distributed renewable energy unit aggregators when participating in the clearing of spot transactions in the electricity market is based on the output upper limit of their predicted values. This is because the bids for distributed renewable energy are relatively low, so the electricity market will give priority to absorbing renewable energy with lower bids.

[0170] It can be seen from Table 4 that after the distributed new energy aggregation participates in the clearing of spot transactions in the electricity market, while reducing the total cost, it significantly reduces the operating costs of all users and effectively improves the distribution network's ability to absorb renewable energy.

[0171] Example 2

[0172] Benefit allocation of distributed renewable energy units based on Shapley value

[0173] Taking the regional distributed renewable energy aggregator system including wind and solar as an example, a simulation analysis is carried out to verify the effectiveness of the Shapley value method. The set of distributed renewable energy units participating in power trading is N = {1, 2, 3,}, including 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 represent wind turbines, photovoltaic units and energy storage units respectively.

[0175] Table 5 Basic Situation Income Statement

[0176]

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

[0178] Table 6 Initial revenue distribution of wind turbines within the aggregator

[0179]

[0180]

[0181] Table 7 Initial revenue distribution of PV units within aggregators

[0182]

[0183] Table 8 Initial revenue distribution of energy storage within aggregators

[0184]

[0185]

[0186] From this, we can see that when distributed new energy sources are aggregated and the benefits obtained from participating in electricity trading are greater than the sum of the benefits of each unit participating in the transaction independently, and the benefits allocated to any party through system transactions are higher than its independent transaction benefits, it can promote the enthusiasm of all parties in the system to participate in distributed energy aggregation transactions.

[0187] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0188] In another embodiment of the present invention, a distributed renewable energy aggregation system for participating in a spot transaction in a power market is provided, comprising:

[0189] The clearing module is used to solve the clearing model of distributed renewable energy participating in spot trading in the power market after aggregation, combining constraints, and obtain the active power of each power market unit and the node marginal electricity price of the system;

[0190] The benefit allocation module is used to allocate the benefits of distributed new energy aggregation based on Shapley value and complete de-aggregation;

[0191] Among them, the clearing model for the distributed new energy participating in the spot transaction in the electricity market after aggregation takes the operating cost of the power market unit as the objective function and the minimum operating cost as the optimization goal; the constraints include: system load balance constraints, line flow constraints, unit operation constraints and unit output increase and decrease rate constraints, and the unit operation constraints include the output constraints after the aggregation of distributed new energy; the output constraints after the aggregation of distributed new energy are predicted according to the distributed new energy aggregation model and historical output data using the ARIMA-based time series prediction model.

[0192] In the distributed new energy aggregation participating in the electricity market spot trading system described in the present invention, 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] Where: p PV,k,tis the actual output of photovoltaic unit k at time t; is the maximum output of photovoltaic unit k in a scheduling cycle T period; Because N p The maximum output of the photovoltaic cluster after the aggregation of photovoltaic units in a scheduling cycle T period; PV,j,h,T , They are respectively the actual output and the predicted maximum output allowed by the aggregated PV cluster managed by aggregator j under node h during a scheduling cycle T.

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

[0197]

[0198] Where: p WT,f,t is the actual output of wind turbine f at time t; is the maximum output of wind turbine f in a scheduling cycle T; N p The maximum output of a wind farm cluster with wind turbines in a dispatching cycle T; WT,j,h,T , They are respectively the actual output and the predicted maximum allowable output of the aggregated wind farm cluster managed by aggregator j under node h during a scheduling cycle T.

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

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

[0201]

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

[0203]

[0204] 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 charge state of the energy storage unit v at time t, Represents the initial time T of the energy storage unit v in a scheduling cycle T start When the energy storage unit v is fully charged, SOC ES，v，t The value is 1. When the energy storage unit v is fully discharged, SOC ES，v，t The value is 0, are the minimum and maximum values ​​of the state of charge of the energy storage unit v in a scheduling period T respectively; are 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; are the maximum and minimum energy values ​​of the energy storage unit v in a scheduling cycle T period respectively; P ES，v，t is the actual charging and discharging power of the energy storage unit v at time t, and are the upper and lower limits of the charging and discharging power of the energy storage unit v in a scheduling cycle T, respectively. dis and η ch They represent the discharge efficiency and charging efficiency of the energy storage unit respectively. and They are the discharge power and charging power of the energy storage unit v in a scheduling cycle T period respectively;

[0205] The probability model of the distributed energy storage unit accepting dispatch is expressed 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 distributed energy storage unit v, β0 is the benchmark probability coefficient, β1 and β2 are the explanatory variables SOC ES，v，t , π v The probability coefficient, ε v represents the random error variable, is the decision variable of the energy storage unit v in a certain scheduling cycle T period, Indicates that the scheduling is accepted in this scheduling cycle. Indicates that scheduling is not accepted during the T period of the scheduling cycle; It represents the decision probability of the distributed energy storage unit v accepting the dispatch, and its value range is (0, 1); They are respectively the charging power and discharging power of the energy storage cluster aggregated during a scheduling cycle T period; are the discharge power and charging power of the energy storage cluster managed by aggregator j at node h in a scheduling cycle T period, p ES，j，h，T It is the actual power of the energy storage cluster managed by aggregator j at node h during a scheduling cycle T.

[0210] In the distributed renewable energy aggregation participating in the spot trading system of the power market, the objective function and constraint conditions of the clearing model of the distributed renewable energy aggregation participating in the spot trading of the power market include:

[0211]

[0212] or l (t): P l (t)≤ P l

[0213]

[0214] e i (t): p i (t)≥ P i

[0215]

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

[0217]

[0218] Where: F represents the power generation cost based on the quotation, including the operating cost, startup cost and no-load cost of unit i participating in the power market, C i (p i (t)) is the operating cost of unit i participating in the power market at time t, T is the total number of time periods considered during system dispatch; I is the total number of units participating in the power 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 the j-node in the system, J is the number of nodes, is the total system load 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 the system operation constraint; P l represents the power flow of line l, P l Indicates the upper and lower limits of the power flow of line l; represents the shadow price of the power upper limit of line l power flow constraint, or l (t) represents the shadow price of the power lower limit of line l power flow constraint; and e i (t) are the shadow price of the upper limit constraint of the active power of unit i participating in the power market and the shadow price of the lower limit constraint of the active power of unit i participating in the power market; Δi is the maximum ramp rate of unit i participating in the power market in each period, is the shadow price of the maximum ramp rate constraint of unit i participating in the power market, d i (t) is the shadow price of the minimum glide rate constraint of unit i participating in the electricity market; are the actual output and the predicted maximum allowable output of the aggregated PV cluster managed by aggregator j under node h during period T, The predicted maximum allowed power of the aggregated wind farm cluster managed by aggregator j under node h; They are respectively the discharge power and charging power of the energy storage cluster managed by the aggregator at node h in period T.

[0219] In the benefit distribution module, the benefit distribution obtained by aggregating distributed new energy based on Shapley value is specifically as follows:

[0220]

[0221] In the formula, R represents that there are n distributed renewable energy units in the area, R = {1, 2, 3, ..., n}; x(r)

[0222] is the profit obtained by distributing the distributed renewable energy unit r; s is the aggregator of different renewable energy unit combinations, and s is a subset of R; v(s) represents the characteristic function of aggregator s, that is, the profit obtained by aggregator s from participating in market transactions; v(s / r) represents the remaining profit after deleting the renewable energy unit r from aggregator s, and v(r)-v(s / r) represents the actual contribution of the renewable energy unit r in aggregator s.

[0223] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which 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 the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the method for distributed new energy aggregation to participate in the spot trading of the power market.

[0224] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a 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 here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for distributed new energy aggregation to participate in spot trading in the power market in the above embodiment.

[0225] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0226] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0227] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0228] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for distributed renewable energy aggregation to participate in spot trading in the electricity market, characterized in that: include: Combined with the constraints, the clearing model of distributed renewable energy participating in spot trading in the power market is solved to obtain the active power of each power market unit and the node marginal electricity price of the system; the benefits of distributed renewable energy aggregation are distributed based on the Shapley value to complete the de-aggregation; The clearing model takes the operating cost of the power market unit as the objective function and the minimization of the operating cost as the optimization goal; the constraints include: system load balance constraints, line flow constraints, unit operation constraints and unit output increase and decrease rate constraints, and the unit operation constraints include distributed new energy output constraints after aggregation; the distributed new energy output constraints after aggregation are predicted according to the distributed new energy aggregation model and historical output data using an ARIMA-based time series prediction model.

2. The method for distributed new energy aggregation to participate in spot trading in the power market according to claim 1 is characterized in that: 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.

3. The method for distributed new energy aggregation to participate in spot trading in the power market according to claim 2 is characterized in that: The distributed photovoltaic aggregation model is expressed as: Where: 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 scheduling cycle T period; Because N p The maximum output of the photovoltaic cluster after the aggregation of photovoltaic units in a scheduling cycle T period; PV,j,h,T , They are respectively the actual output and the predicted maximum output allowed by the aggregated PV cluster managed by aggregator j under node h during a scheduling cycle T.

4. The method for distributed new energy aggregation to participate in spot trading in the power market according to claim 2 is characterized in that: The distributed wind power aggregation model is expressed as: Where: p WT,f,t is the actual output of wind turbine f at time t; is the maximum output of wind turbine f in a scheduling cycle T; N p The maximum output of a wind farm cluster with wind turbines in a dispatching cycle T; WT,j,h,T , They are respectively the actual output and the predicted maximum allowable output of the aggregated wind farm cluster managed by aggregator j under node h during a scheduling cycle T.

5. The method for distributed new energy aggregation to participate in spot trading in the power market according to claim 2 is characterized in that: The distributed energy storage aggregation model includes a distributed energy storage unit charge state and charge-discharge characteristic model and a distributed energy storage unit probability model for accepting dispatch; The state of charge and charge-discharge characteristic model of the distributed energy storage unit is expressed as: Δt=t-T start ,t∈[T start ,T end ] 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 charge state of the energy storage unit v at time t, Represents the initial time T of the energy storage unit v in a scheduling cycle T start When the energy storage unit v is fully charged, SOC ES,v,t The value is 1. When the energy storage unit v is fully discharged, SOC ES,v,t The value is 0, are the minimum and maximum values ​​of the state of charge of the energy storage unit v in a scheduling period T respectively; are 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; are the maximum and minimum energy values ​​of the energy storage unit v in a scheduling cycle T period respectively; P ES,v,t is the actual charging and discharging power of the energy storage unit v at time t, and are the upper and lower limits of the charging and discharging power of the energy storage unit v in a scheduling cycle T, respectively. dis and η ch They represent the discharge efficiency and charging efficiency of the energy storage unit respectively. and They are the discharge power and charging power of the energy storage unit v in a scheduling cycle T period respectively; The probability model of the distributed energy storage unit accepting dispatch is expressed as: y v =β0+β1SOC ES,v,t +β2π v +e v In the formula, y v is the dependent variable of the distributed energy storage unit v, π v is the dispatch compensation of distributed energy storage unit v, β0 is the benchmark probability coefficient, β1 and β2 are the explanatory variables SOC ES,v,t , π v The probability coefficient, ε v represents the random error variable, is the decision variable of the energy storage unit v in a certain scheduling cycle T period, Indicates that the scheduling is accepted in this scheduling cycle. Indicates that scheduling is not accepted during the T period of the scheduling cycle; It represents the decision probability of the distributed energy storage unit v accepting the dispatch, and its value range is (0,1); They are respectively the charging power and discharging power of the energy storage cluster aggregated during a scheduling cycle T period; are the discharge power and charging power of the energy storage cluster managed by aggregator j at node h in a scheduling cycle T period, p ES,j,h,T It is the actual power of the energy storage cluster managed by aggregator j at node h during a scheduling cycle T.

6. The method for distributed new energy aggregation to participate in spot trading in the power market according to claim 2 is characterized in that: The objective function and constraints of the clearing model for distributed renewable energy aggregation participating in spot transactions in the electricity market include: or l (t):Pl(t)≤ P l e i (t):pi(t)≥ P i d i (t):p i (t)-p i (t-1)≥-Δ i Where: F represents the power generation cost based on the quotation, including the operating cost, startup cost and no-load cost of unit i participating in the power market, C i (p i (t)) is the operating cost of unit i participating in the power market at time t, T is the total number of time periods considered during system dispatch; I is the total number of units participating in the power 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 the j-node in the system, J is the number of nodes, is the total system load 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 the system operation constraint; P l represents the power flow of line l, P l Indicates the upper and lower limits of the power flow of line l; represents the shadow price of the power upper limit of line l power flow constraint, or l (t) represents the shadow price of the power lower limit of line l power flow constraint; and e i (t) are the shadow price of the upper limit constraint of the active power of unit i participating in the power market and the shadow price of the lower limit constraint of the active power of unit i participating in the power market; Δ i is the maximum ramp rate of unit i participating in the power market in each period, is the shadow price of the maximum ramp rate constraint of unit i participating in the power market, d i (t) is the shadow price of the minimum glide rate constraint of unit i participating in the electricity market; are the actual output and the predicted maximum allowable output of the aggregated PV cluster managed by aggregator h under node h during period T, The predicted maximum allowed power of the aggregated wind farm cluster managed by aggregator j under node h; They are respectively the discharge power and charging power of the energy storage cluster managed by aggregator j at node h in period T.

7. The method for distributed new energy aggregation to participate in spot trading in the power market according to claim 1 is characterized in that: The distribution of benefits obtained by aggregating distributed new energy sources based on Shapley values ​​is specifically as follows: In the formula, R indicates that there are n distributed renewable energy units in the region, R = {1, 2, 3, …, n}; x(r) is the income obtained by the distributed renewable energy unit r; s is the aggregator of different renewable energy unit combinations, s is a subset of R; v(s) represents the characteristic function of aggregator s, that is, the income obtained by aggregator s from participating in market transactions; v(s / r) represents the remaining income after deleting renewable energy unit r from aggregator s, and v(r)-v(s / r) represents the actual contribution of renewable energy unit r in aggregator s.

8. A distributed new energy aggregation participating in the electricity market spot trading system, characterized in that: include: The clearing module is used to solve the clearing model of distributed renewable energy participating in spot trading in the power market after aggregation, combining constraints, and obtain the active power of each power market unit and the node marginal electricity price of the system; The benefit allocation module is used to allocate the benefits of distributed new energy aggregation based on Shapley value and complete de-aggregation; Among them, the clearing model for the distributed new energy participating in the spot transaction in the electricity market after aggregation takes the operating cost of the power market unit as the objective function and the minimum operating cost as the optimization goal; the constraints include: system load balance constraints, line flow constraints, unit operation constraints and unit output increase and decrease rate constraints, and the unit operation constraints include the output constraints after the aggregation of distributed new energy; the output constraints after the aggregation of distributed new energy are predicted according to the distributed new energy aggregation model and historical output data using the ARIMA-based time series prediction model.

9. 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, the steps of the method for distributed new energy aggregation to participate in spot trading in the electricity market are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the method for distributed new energy aggregation to participate in spot trading in the electricity market are implemented as described in any one of claims 1 to 7.

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