Electric power resource allocation method of energy storage embedded source-storage-load collaborative main body

Abstract

The invention belongs to the technical field of electric power resource allocation, and relates to an electric power resource allocation method for an energy storage embedded source-storage-load collaborative main body, which comprises the following steps: 1, constructing a multi-time scale resource allocation decision model considering medium and long term scale decision and day-ahead scale resource allocation decision of the source-storage-load collaborative main body, a target function of the decision model is converted into the most balanced resource utilization rate; 2, establishing an electric energy-green evidence combined resource equalization model at a lower layer, and converting a double-layer optimization model into a single-layer optimization model; 3, performing solution by adopting a solver through example simulation; according to the invention, a source-storage-load collaborative main body multi-time scale resource allocation model and an electric energy-green evidence combined resource equalization model are constructed; and the influence of the implementation of the risk aversion index and the renewable energy long-term scale decision scheme on the source-storage-load collaborative main body and the joint resource center is discussed through examples.

Description

Technical Field

[0001] The present invention belongs to the technical field of power resource allocation, and relates to a power resource allocation method for an energy storage embedded source-storage-load collaborative entity. Background Art

[0002] Currently, the research focus on long-term decision-making schemes for renewable energy is on the resource allocation strategy on the power generation side, studying how the structure and resource allocation mode of long-term decision-making schemes for renewable energy can encourage renewable energy developers to provide more reliable power generation forecasts and prompt them to adopt energy storage.

[0003] Existing research on the power energy resource binding strategy of the source-storage-load collaborative entity focuses on multi-time scale decision-making strategies, respectively exploring the influence of power resource allocation parameters on the conditional risk of the source-storage-load collaborative entity under the multi-time scale decision-making scheme with determined output and the multi-time scale decision-making scheme with uncertain output, without considering the scenario where the source-storage-load collaborative entity simultaneously makes long-term decisions for renewable energy and medium- and long-term decisions for traditional energy. In terms of the construction of energy storage in the source-storage-load collaborative entity, existing technologies have proposed an analysis method for the optimal configuration of energy storage in the source-storage-load collaborative entity based on the whole life cycle theory, and established a master-slave game model for the shared energy storage service operation strategy of the source-storage-load collaborative entity.

[0004] However, the current research on long-term decision-making schemes for renewable energy lacks research on the long-term renewable energy resource allocation strategy of the source-storage-load collaborative entity. The research on the power energy resource binding strategy of the source-storage-load collaborative entity focuses on the power energy resource binding strategy under multiple time scales. Since the existing power energy resource binding strategies mainly target the binding of traditional energy power energy resources, they do not consider the influence of the different resource allocation varieties in the long-term decision-making scheme for renewable energy and the medium- and long-term decision-making scheme for traditional energy on the power energy resource binding strategy of the source-storage-load collaborative entity, and cannot be directly used for long-term decision-making schemes for renewable energy. Most of the research on the energy storage investment configuration of the source-storage-load collaborative entity focuses on the shared energy storage mode, and its main gain method only considers short-term strategies. There is a lack of research on the more long-term valuable strategy of the source-storage-load collaborative entity to improve the gain of the long-term decision-making scheme for renewable energy by independently investing in the construction and management of energy storage systems.

[0005] Therefore, a long-term decision-making method for renewable energy of the source-storage-load collaborative entity considering energy storage is needed to solve the above technical problems. Summary of the Invention

[0006] The technical solution adopted by the present invention to solve the technical problems is: a power resource allocation method for an energy storage embedded source-storage-load collaborative entity, including the following steps:

[0007] Step 1: Construct a multi-time scale resource allocation decision model that considers the medium- and long-term scale decision-making of source-storage-load collaborative entities and the day-ahead scale resource allocation decision-making, and use the CVaR (conditional value-at-risk) method to transform the objective function of the multi-time scale resource allocation decision model into the most balanced resource utilization rate;

[0008] Step 2: Establish an equilibrium model for the combined electricity energy-green certificate resource center at the lower layer, and use the KKT (Karush-Kuhn-Tucker) conditions to transform the two-layer optimization model into a single-layer optimization model;

[0009] Step 3: Through example simulations, use the MATLAB solver, YALMIP solver, and Gurobi solver for solution.

[0010] Preferably, the specific content in Step 1 includes:

[0011] Step 1-1: In the medium- and long-term scale resource allocation decision-making stage, the source-storage-load collaborative entity decides the medium- and long-term scale power curve of traditional energy, the long-term scale power curve of renewable energy, and the energy storage investment capacity according to the gains of each scenario;

[0012] Step 1-2: In the day-ahead scale resource allocation decision-making stage, the source-storage-load collaborative entity decides the energy storage charge and discharge power and the green certificate resource supply status parameters according to the winning bid quantity of renewable energy obtained from the equilibrium of the combined electricity energy-green certificate, the winning bid quantity of green certificates of the source-storage-load collaborative entity, the green certificate resource parameters, and the electricity energy resource parameters.

[0013] More preferably, in Step 1, the objective function of the medium- and long-term scale resource allocation decision-making stage is constructed based on the gains obtained from the day-ahead scale resource allocation decision-making stage of the source-storage-load collaborative entity under multiple scenarios;

[0014] The power resource allocation parameters in the medium- and long-term scale resource allocation decision-making stage are evenly distributed to each typical scenario, and the part that does not meet the requirements of the electricity quantity ratio of the medium- and long-term scale decision-making scheme and the consumption responsibility weight is used as a penalty term in the objective function;

[0015] The medium- and long-term scale resource allocation decision-making stage and the day-ahead scale resource allocation decision-making stage are combined as the multi-time scale resource allocation decision model of the source-storage-load collaborative entity and placed in the upper layer, and the lower layer is the equilibrium model for the combined electricity energy resource center.

[0016] More preferably, in Step 1, the objective function of the most balanced resource utilization rate is:

[0017] (12)

[0018] In Equation (12): represents the probability of the s scenario occurring, represents the power resource allocation parameters of the source-storage-load collaborative entity in each scenario, represents the risk aversion coefficient, An increase will make the inter-provincial resource dispatcher more averse to risks. When , the source-storage-load collaborative entity does not consider risk control, and CVaR represents the CVaR constraint.

[0019] More preferably, the power resource allocation parameters of the source-storage-load collaborative entity in each scenario are:

[0020] (1)

[0021] In formula (1): represents the power resource allocation parameter of the source-storage-load collaborative entity for the energy storage construction evenly spread over a typical day, , , respectively represent the medium- and long-term excess penalty, medium- and long-term shortage penalty, and unmet consumption responsibility weight penalty of the source-storage-load collaborative entity in scenario s, represents the gain of the source-storage-load collaborative entity by binding to the medium- and long-term technical cooperation mechanism of traditional energy in scenario s, represents the gain of the source-storage-load collaborative entity by binding to the long-term scale decision-making scheme of renewable energy in scenario s, represents the gain of the source-storage-load collaborative entity in the green certificate resource center in scenario s, represents the gain obtained by adjusting the charge and discharge of energy storage in the day-ahead resource center of the source-storage-load collaborative entity in scenario s.

[0022] Preferably, in step 2, the objective function of the joint resource center is the social power resource allocation parameter of electric energy - green certificates to be the lowest:

[0023] (19)

[0024] In formula (19): represents the resource supply status parameter / yuan·MWh of the b-th segment output of traditional energy power generation plant c in scenario s in the electric energy resource center, -1 , represents the winning bid volume / MW of the b-th segment output of traditional energy power generation plant c at time t in scenario s in the electric energy resource center, represents the resource supply status parameter of renewable energy power generation plants in the electric energy resource center, represents the winning bid volume / MW of renewable energy plant h at time t in the electric energy resource center, , Indicates the resource supply status parameter of the source-storage-load collaborative entity binding green certificates in scenario s, / yuan·piece -1 and the winning bid quantity, , Indicates the resource supply status parameter of the renewable energy power plant h unbinding green certificates in scenario s, / yuan·piece -1 and the winning bid quantity / piece; , Indicates the resource supply status parameter of other green certificate resource dispatching entities, / yuan·piece -1 and the winning bid quantity / piece. When the winning bid quantity is greater than 0, the green certificate resource dispatching entity unbinds the green certificate; when the winning bid quantity is less than 0, the green certificate resource dispatching entity binds the green certificate.

[0025] Preferably, step 3 specifically includes:

[0026] Converting the calculation of the medium- and long-term excess penalty, medium- and long-term deficit penalty, and non-linear term penalty of the unmet consumption responsibility weight of the source-storage-load collaborative entity in the objective function into linear constraints;

[0027] Replacing the lower-level problem with the KKT conditions of the lower-level problem to convert the two-layer model into a single-layer model.

[0028] The beneficial effects of the present invention are:

[0029] 1. By constructing a multi-time scale resource dispatching strategy model for the source-storage-load collaborative entity and an electric energy-green certificate joint resource equilibrium model, the present invention analyzes the decision-making of the source-storage-load collaborative entity considering the medium- and long-term power curve of traditional energy, the long-term power curve of renewable energy, and the investment capacity of energy storage; through numerical examples, the impacts of the risk aversion index and the implementation of the long-term renewable energy decision-making plan on the source-storage-load collaborative entity and the joint resource center are explored.

[0030] 2. When adopting the long-term renewable energy decision-making plan, the higher the risk aversion degree of the source-storage-load collaborative entity, the more reasonable it can optimize the long-term power curve of renewable energy and the investment capacity of energy storage, and its conditional risk coefficient decreases with the increase of the risk aversion index; when not adopting the long-term renewable energy decision-making plan, the higher the risk aversion degree of the source-storage-load collaborative entity, the more inclined it is to avoid using energy storage to obtain resources by taking advantage of the peak-valley difference of the resource center during the day-ahead, and its conditional risk increases with the increase of the risk aversion index.

[0031] 3. In the case of adopting the long-term scale decision-making scheme of renewable energy, the source-storage-load collaborative entity suppresses the deviation power of the long-term scale decision-making scheme of renewable energy caused by the randomness of renewable energy power generation by building energy storage, and reduces the fluctuation risk of the resource parameters of the day-ahead resource center.

[0032] 4. With the implementation of the long-term scale decision-making scheme of renewable energy, the source-storage-load collaborative entity increases the energy storage capacity, enhancing the system's renewable energy consumption capacity. Compared with the non-implementation of the long-term scale decision-making scheme of renewable energy, after the implementation of the long-term scale decision-making scheme of renewable energy, the peak-valley difference of the system significantly decreases, the winning bid volume of renewable energy in the day-ahead resource center increases, and the winning bid volume of traditional energy decreases. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flowchart of a power resource allocation method for an energy storage embedded source-storage-load collaborative entity of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] Next, the relevant technologies in the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0035] In this embodiment, the source-storage-load collaborative entity performs sub-period optimization during resource allocation decision-making to form a medium- and long-term scale time-sharing power curve for traditional energy and a long-term scale time-sharing power curve for renewable energy.

[0036] In the medium- and long-term scale resource allocation decision-making stage, the source-storage-load collaborative entity decides the medium- and long-term scale power curve of traditional energy, the long-term scale power curve of renewable energy, and the energy storage construction capacity according to the gains of each scenario. In the day-ahead scale resource allocation decision-making stage, the source-storage-load collaborative entity decides the energy storage charge and discharge power and the green certificate resource supply status parameters according to the winning bid volume of renewable energy obtained by the combined resource balance of electric energy and green certificates, the winning bid volume of green certificates of the source-storage-load collaborative entity, the green certificate resource parameters, and the electric energy resource parameters.

[0037] Among them, the decision-making subjects in the medium- and long-term decision-making stage and the day-ahead scale resource allocation decision-making stage are both the source-storage-load collaborative subjects. The objective function in the medium- and long-term decision-making stage is constructed based on the gains obtained in the day-ahead scale resource allocation decision-making stage of the source-storage-load collaborative subjects under multiple scenarios. By optimizing the power curve of the multi-time scale coupling scheme, the source-storage-load collaborative subjects can achieve the efficient connection between medium- and long-term decision-making and day-ahead scale resource allocation decision-making. In view of this, the power resource allocation parameters in the medium- and long-term decision-making stage of the source-storage-load collaborative subjects are evenly distributed to each typical scenario, and the part that does not meet the requirements of the electricity quantity ratio of the medium- and long-term scale decision-making scheme and the consumption responsibility weight is used as a penalty term in the objective function. At this time, the medium- and long-term decision-making stage and the day-ahead scale resource allocation decision-making stage of the source-storage-load collaborative subjects can be combined as the multi-time scale resource allocation decision-making model of the source-storage-load collaborative subjects placed in the upper layer, and the lower layer is the power energy-joint resource center to reach the equilibrium model.

[0038] Resource Allocation Decision-Making Model for the Long-Term Scale of Renewable Energy of the Source-Storage-Load Collaborative Subject Considering Energy Storage:

[0039] 1 Multi-Time Scale Resource Allocation Decision-Making Model of the Source-Storage-Load Collaborative Subject:

[0040] 1.1 Objective Function of the Source-Storage-Load Collaborative Subject:

[0041] The source-storage-load collaborative subject makes decisions on the medium- and long-term scale power curve scheme of traditional energy and the long-term scale power curve scheme of renewable energy in the medium- and long-term, and constructs energy storage; in the day-ahead resource center, the electricity quantity bound by the medium- and long-term resources is unbundled, and at the same time, the charge and discharge strategy of the energy storage is adjusted and green certificates are bought and sold to obtain gains. The part of the source-storage-load collaborative subject that does not meet the requirements of the electricity quantity ratio of the medium- and long-term scale decision-making scheme and the consumption responsibility weight is punished. The excess ratio of the total medium- and long-term bound electricity of the source-storage-load collaborative subject exceeding the actual electricity consumption or the difference ratio less than the actual electricity consumption both need to be punished for excess or difference gains.

[0042] Power resource allocation parameters of the source-storage-load collaborative subject in each scenario The calculation method is as follows:

[0043] (1)

[0044] In the formula: represents the power resource allocation parameter evenly distributed to the typical day for the source-storage-load collaborative subject to construct energy storage; , , respectively represent the medium- and long-term excess penalty, medium- and long-term deficit penalty, and penalty for not meeting the consumption responsibility weight of the source-storage-load collaborative subject under scenario s; It represents the gain obtained by the source-storage-load collaborative subject in scenario s by binding the medium- and long-term decision-making plan of traditional energy; represents the gain of the long-term decision-making scheme of binding renewable energy sources under scenario s by the source-storage-load collaborative subject; represents the gain of the source-storage-load collaborative subject in the green certificate resource center under scenario s; It represents the gain obtained by the source-storage-load collaborative entity by adjusting the energy storage charging and discharging in the day-ahead resource center under scenario s.

[0045] The actual settlement of renewable energy long-term scale decision-making power in each period under scenario s The smaller of the initial decision power of the long-term decision-making plan for renewable energy, the power consumption of the source-storage-load coordinated entity, and the power of renewable energy on-grid is taken.

[0046] (2)

[0047] (3)

[0048] Where: Indicates the power consumption of the source-storage-load collaborative entity after proportional reduction; represents the initial decision power at time t of the renewable energy long-term decision-making plan bound by the source-storage-load collaborative entity and h renewable energy generators / W; represents the decision-making power at time t bound by the source-storage-load collaborative entity and the traditional energy power generation company c (MW); It represents the energy storage charging and discharging power of the source-storage-load collaborative entity at load l under scenario s at time t / MW; represents the load power of load l at time t in scenario s / W; represents the actual allocated power consumption of h renewable energy bound power at time t in scenario s / MW; It represents the actual winning bid amount of renewable energy bundled electricity at time t in scenario s / MW.

[0049] The calculation formulas for gain and power resource allocation parameters are as follows:

[0050] (4)

[0051] (5)

[0052] (6)

[0053] (7)

[0054] (8)

[0055] (9)

[0056] (10)

[0057] (11)

[0058] In the formula: represents the power resource allocation coefficient for energy storage / yuan·MW -1 ; represents the energy storage capacity established by the source-storage-load collaborative entity at load l / MW; , represents the penalty resource parameter for the excess penalty of the multi-time scale coupling scheme / yuan·MWh -1 and the excess ratio / %; , represents the penalty resource parameter for the difference penalty of the multi-time scale coupling scheme / yuan·MWh -1 and the difference ratio / %; , represents the penalty resource parameter for the source-storage-load collaborative entity to complete the consumption responsibility weight / yuan·MWh -1 and the assessment ratio; represents the resource parameter for the electric energy to reach equilibrium at node n at time t under scenario s / yuan·MWh -1 ; represents the resource parameter of the multi-time scale coupling scheme at time t when the source-storage-load collaborative entity is bound to the traditional energy power generation manufacturer c / yuan·MWh -1 ; represents the resource parameters of each time period of the long-term scale decision-making scheme of renewable energy when the source-storage-load collaborative entity is bound to the new energy power generation manufacturer h / yuan·MWh -1 ; represents the resource parameter for the green certificate to reach equilibrium under scenario s / piece -1 ; represents the number of green certificates unbound by the source-storage-load collaborative entity under scenario s / piece.

[0059] The CVaR method can control the risk state exceeding the VaR value, so it can better reflect the potential risk loss. Set the confidence level to , and the present invention takes the value corresponding to the quantile of the probability distribution of the power resource allocation parameters of the source-storage-load collaborative entity in each scenario as the VaR value of the power resource allocation parameters of the source-storage-load collaborative entity, so that the probability that the power resource allocation parameters of the source-storage-load collaborative entity are greater than the VaR value is less than . The upper layer takes the most balanced resource utilization rate of the source-storage-load collaborative entity as the objective function:

[0060] (12)

[0061] Wherein: represents the probability of the s scenario occurring; represents the risk aversion coefficient. An increase will make the provincial inter - resource dispatcher more risk - averse. When the source - storage - load collaborative entity does not consider risk control.

[0062] 1.2 Constraints of the source - storage - load collaborative entity:

[0063] 1.2.1 CVaR constraint:

[0064] (13)

[0065] Wherein: The optimization result of the variable is defined as the VaR value of the power resource allocation parameter for the operation of the source - storage - load collaborative entity; is a non - negative auxiliary variable, specifically representing the amount by which the power resource allocation parameter of the source - storage - load collaborative entity exceeds under each typical scenario. The source - storage - load collaborative entity optimizes the risk scenarios where the power resource allocation parameter exceeds the VaR value, and reduces the power resource allocation parameter in these risk scenarios.

[0066] 1.2.2 Energy storage charge - discharge power constraint:

[0067] The source - storage - load collaborative entity builds an energy storage at the l load, and the energy storage charge - discharge power satisfies the upper and lower limits of the charging power constraint.

[0068] (14)

[0069] Wherein: represents the maximum value of the discharge power of the energy storage power station; represents the maximum value of the charging power of the energy storage power station. The maximum value of the energy storage charge - discharge power is generally proportional to the energy storage capacity . When is positive, the energy storage is charging; when is negative, the energy storage is discharging.

[0070] 1.2.3 Energy storage state of charge constraint:

[0071] The energy storage power station satisfies the upper and lower limits of the state of charge constraint and the constraint that the initial and final states of charge of the energy storage are the same, with a time interval of 1h.

[0072] (15)

[0073] (16)

[0074] (17)

[0075] Wherein: represents the state of charge of the energy storage power station established by the source-storage-load collaborative entity at load l at time t, in %; represents the minimum value of the state of charge of the l energy storage power station; represents the maximum value of the state of charge of the l energy storage power station.

[0076] 1.2.4 Upper and lower limit constraints of the green certificate resource supply status parameters:

[0077] The resource unbundling of the green certificate is equivalent to a gain for renewable energy power generation manufacturers. The resource supply status parameter of the source-storage-load collaborative entity on the green certificate resource center will not exceed its penalty resource parameter:

[0078] (18)

[0079] Wherein: represents the government gain resource parameter, in yuan·piece -1 .

[0080] 2 Electric energy-green certificate joint resource center

[0081] 2.1 Joint resource center objective function

[0082] The present invention designs a day-ahead electric energy-green certificate joint resource allocation center. In the joint resource allocation center, renewable energy power generation manufacturers and traditional energy power generation manufacturers report the electric energy volume and the resource supply status parameter in segments. Renewable energy power generation manufacturers, source-storage-load collaborative entities, and other regional green certificate resource allocators unbind or bind the green certificate for the resource supply status parameter of the green certificate volume. Taking the optimal system operation efficiency as the objective function, the electric energy resource parameter and the green certificate resource parameter are settled according to the marginal unity to reach the equilibrium resource parameter.

[0083] The objective function of the joint resource center is the social electric power resource allocation parameter of the electric energy-green certificate Minimum:

[0084] (19)

[0085] Wherein: represents the resource supply status parameter of the output of the traditional energy power generation manufacturer c in the b-th segment in the electric energy resource center in scenario s, in yuan·MWh -1 ; Denote the winning bid scalar / MW of the b-th output of the traditional energy power generation plant c at time t in scenario s at the electric energy resource center; Denote the resource supply status parameter of the renewable energy power generation plant at the electric energy resource center; Denote the winning bid scalar / MW of the renewable energy plant h at time t at the electric energy resource center; 、 Denote the resource supply status parameter / yuan·piece of the source-storage-load collaborative entity binding green certificates in scenario s -1 and the winning bid scalar; 、 Denote the resource supply status parameter / yuan·piece of the renewable energy power plant h unbinding green certificates in scenario s -1 and the winning bid scalar / piece; 、 Denote the resource supply status parameter / yuan·piece of other green certificate resource dispatching merchants -1 and the winning bid scalar / piece. When the winning bid scalar is greater than 0, the green certificate resource dispatching merchant unbinds the green certificate. When the winning bid scalar is less than 0, the green certificate resource dispatching merchant binds the green certificate.

[0086] 2.2 Constraints of the joint resource center:

[0087] 2.2.1 Power supply and demand balance constraint:

[0088] (20)

[0089] (21)

[0090] In the formula: Denote the total output of the traditional energy generator set c at time t in scenario s; Denote the admittance of line nm / S; Denote the voltage phase angle of node n at time t in scenario s / rad, Denote the voltage phase angle of node m at time t in scenario s / rad. Denote the power consumption / MW at load l at time t, Denote the charging and discharging power of the energy storage power station established by the source-storage-load collaborative entity at l at time t, Denote the connection set of the load at the node

[0091] 2.2.2 Winning bid scalar constraint of power:

[0092] (22)

[0093] In the formula: 、 Denote the upper and lower limits / MW of the b-th output of the traditional energy generator set c, , respectively represent the upper and lower limits of the output of renewable energy.

[0094] 2.2.3 Traditional energy unit ramp constraint:

[0095] (23)

[0096] In the formula: represents the ramp-up limit of traditional energy generating unit c / MW.

[0097] 2.2.4 Node power constraint:

[0098] (24)

[0099] 2.2.5 Line power flow upper and lower limit constraints:

[0100] (25)

[0101] In the formula: represents the set of nodes connected to node n; represents the upper limit of the power flow of line nm.

[0102] 2.2.6 Green certificate balance constraint:

[0103] The quantity of green certificate resources bound is equal to the quantity of green certificate resources unbound.

[0104] (26)

[0105] 2.2.7 Green certificate winning bid quantity constraint:

[0106] The upper limit of the green certificates that renewable energy power generation manufacturers can unbind is the winning bid quantity minus the green certificates bound in the long-term scale decision-making plan for renewable energy; the upper limit of the quantity of green certificates that the source-storage-load collaborative entity can unbind is the number of green certificates obtained by binding the long-term scale decision-making plan for renewable energy, and the upper limit of the quantity of green certificates that the source-storage-load collaborative entity needs to bind is the number of green certificates required by its quota.

[0107] (27)

[0108] In the formula: , represent the upper and lower limits of the green certificate resource allocation of green certificate resource allocator m in scenario s.

[0109] 3 Solution method

[0110] The medium- and long-term excess penalty of the source-storage-load collaborative entity in the objective function , and the medium- and long-term shortage penalty and the penalty for unmet consumption responsibility weights The calculation of is a non - linear term, which is transformed into a linear constraint:

[0111] (28)

[0112] (29)

[0113] (30)

[0114] Replace equations (5) - (7) with equations (31) - (33):

[0115] (31)

[0116] (32)

[0117] (33)

[0118] In the objective function 、 contains bilinear terms. For 、 perform binary discretization:

[0119] (34)

[0120] (35)

[0121] In the formula, 、 , 、 are 0 - 1 variables.

[0122] Since and in constraints (15) and (16) are both optimization variables, introduce variable and so that it is transformed into a linear constraint: ,

[0123] (36)

[0124] (37)

[0125] The actual settlement of the long - term scale decision - making scheme of renewable energy in equation (3) is the minimum of the three, which is a non - linear term and can be transformed into a constraint:

[0126] (38)

[0127] The electricity energy - green certificate joint resource center model established at the lower layer is a linear programming problem with the property of a convex function. The KKT conditions of the lower - layer problem can be used to replace the lower - layer problem, transforming the bilevel model into a single - level model.

[0128] After modifying the total power resource allocation parameters of the source - storage - load collaborative entity, with the minimum conditional value - at - risk of the source - storage - load collaborative entity represented by Equation (12) as the objective function, and Equations (13), (14), (18), (28) - (30), (36) - (38) and the KKT conditions for the joint equilibrium of the lower - layer electricity energy - green certificate resource center as the constraint conditions, an optimal decision - making model for the source - storage - load collaborative entity's investment portfolio is established. This model is a non - convex, non - linear mixed - integer optimization model, and in this embodiment, MATLAB + YALMIP + Gurobi solver is used for solving.

[0129] The key technical points of this specific embodiment are as follows:

[0130] 1. Deeply studied the resource allocation mechanism of the long - term scale decision - making scheme for renewable energy, covering the electricity settlement mechanism and risk hedging strategy. Analyzed the decision - making behaviors of the source - storage - load collaborative entity under different degrees of risk aversion, provided an optimized multi - time - scale coupled scheme power resource allocation curve for the source - storage - load collaborative entity and the power grid operator, which helps the efficient connection between the medium - and long - term resource center and the day - ahead resource center, provides resource allocation strategy support for the source - storage - load collaborative entity, helps it optimize the power resource allocation coefficient of the electricity energy resource binding, and enhances its competitiveness.

[0131] 2. Constructed a model for the joint equilibrium of electricity energy - green certificates, considered the synergy between the winning bid quantity of renewable energy electricity energy and the winning bid quantity of green certificates, and optimized the resource equilibrium mechanism to reduce the power resource allocation coefficient and green certificate resource allocation coefficient of the social electricity energy resource binding.

[0132] 3. Ensured the implementation of the long - term scale decision - making scheme for renewable energy by suppressing the randomness of renewable energy power generation through energy storage, analyzed the synergy between the long - term scale decision - making scheme for renewable energy and energy storage, and studied the optimal configuration of energy storage capacity, the formulation of charge - discharge strategies, and the impact of energy storage on the consumption capacity of renewable energy under the condition that the source - storage - load collaborative entity binds the long - term scale decision - making scheme for renewable energy.

[0133] In summary, the present invention analyzes the decision-making of the source-storage-load collaborative entity by constructing a multi-time scale resource allocation strategy model for the source-storage-load collaborative entity and a combined resource equilibrium model of electric energy and green certificates, aiming at the medium- and long-term power curves of traditional energy, the long-term power curve of renewable energy, and the installed capacity of energy storage in the source-storage-load collaborative entity; through an example, it explores the impacts of the risk aversion index and the implementation of the long-term decision-making scheme of renewable energy on the source-storage-load collaborative entity and the combined resource center.

[0134] It should be emphasized that the above are only the preferred embodiments of the present invention, and there is no restriction on the present invention in any form. Any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A power resource allocation method for an energy storage embedded source-storage-load collaborative entity, characterized in that It includes the following steps: Step 1: Construct a multi-time scale resource allocation decision model considering the medium- and long-term scale decision-making of source-storage-load collaborative entities and the day-ahead scale resource allocation decision-making, and use the CVaR method to transform the objective function of the multi-time scale resource allocation decision model into balanced resource utilization rate; Step 2: Establish an electric energy-green certificate joint resource balance model in the lower layer, and use the KKT conditions to transform the two-layer optimization model into a single-layer optimization model; Step 3: Through case simulation, use the MATLAB solver, YALMIP solver, and Gurobi solver for solution.

2. The power resource allocation method of an energy storage embedded source-storage-load collaborative entity according to claim 1, characterized in that, Specifically included in the above Step 1 are: Step 1-1: In the medium- and long-term scale resource allocation decision-making stage, the source-storage-load collaborative entity decides the medium- and long-term scale power curve of traditional energy, the long-term scale power curve of renewable energy, and the storage construction capacity according to the gains of each scenario; Step 1-2: In the day-ahead scale resource allocation decision-making stage, the source-storage-load collaborative entity decides the charge and discharge power of the energy storage and the green certificate resource supply status parameters according to the winning bid quantity of renewable energy obtained from the electric energy-green certificate joint resource balance, the winning bid quantity of green certificates of the source-storage-load collaborative entity, the green certificate resource parameters, and the electric energy resource parameters.

3. A power resource allocation method for an energy storage embedded source-storage-load collaborative entity according to claim 2, wherein In the above Step 1, the objective function of the medium- and long-term scale resource allocation decision-making stage is constructed based on the gains obtained from the day-ahead scale resource allocation decision-making stage of the source-storage-load collaborative entity under multiple scenarios; The power resource allocation parameters in the medium- and long-term scale resource allocation decision-making stage are evenly distributed to each typical scenario, and the part that does not meet the requirements of the electricity quantity ratio of the medium- and long-term scale decision-making plan and the consumption responsibility weight is used as a penalty term in the objective function; The medium- and long-term scale resource allocation decision-making stage and the day-ahead scale resource allocation decision-making stage are combined as the multi-time scale resource allocation decision model of the source-storage-load collaborative entity in the upper layer, and the lower layer is the electric energy-joint resource balance model.

4. The power resource allocation method of a storage-embedded source-storage-load collaborative entity according to claim 3, characterized in that In the above Step 1, the objective function of the most balanced resource utilization rate is: （12） In formula (12): represents the probability of scenario s occurring, represents the power resource allocation parameters of the source-storage-load collaborative entity in each scenario, represents the risk aversion coefficient, and CVaR represents the CVaR constraint.

5. A method for power resource allocation of an energy storage embedded source-storage-load collaborative entity according to claim 4, characterized in that, The power resource allocation parameters of the source-storage-load collaborative entity in each scenario are as follows: （1） In formula (1): represents the power resource allocation parameter of the source-storage-load collaborative entity's construction of energy storage evenly distributed to a typical day, , , respectively represent the medium- and long-term excess penalty, medium- and long-term deficit penalty, and unfulfilled consumption responsibility weight penalty of the source-storage-load collaborative entity under scenario s, represents the gain of the source-storage-load collaborative entity by binding to the medium- and long-term technical cooperation mechanism of traditional energy under scenario s, represents the gain of the source-storage-load collaborative entity by binding to the long-term scale decision-making scheme of renewable energy under scenario s, represents the gain of the source-storage-load collaborative entity in the green certificate resource center under scenario s, represents the gain obtained by the source-storage-load collaborative entity through adjusting the charge and discharge of energy storage in the day-ahead resource center under scenario s.

6. A power resource allocation method for an energy storage embedded source-storage-load collaborative entity according to claim 1, characterized in that In step 2, the objective function of the joint resource center is the social power resource allocation parameter of electricity energy - green certificates Minimum: （19） In formula (19): represents the resource supply status parameter of the b-th output of the traditional energy power generation company c in scenario s at the electric energy resource center; represents the winning bid amount of the b-th output of the traditional energy power generation company c in scenario s at time t at the electric energy resource center; represents the resource supply status parameter of the renewable energy power generation company at the electric energy resource center; represents the winning bid amount of the renewable energy company h at time t at the electric energy resource center; 、 represents the resource supply status parameter and winning bid amount of the source-storage-load collaborative entity binding green certificates in scenario s; 、 represents the resource supply status parameter and winning bid amount of the renewable energy power plant h unbinding green certificates in scenario s; 、 represents the resource supply status parameter and winning bid amount of other green certificate resource allocators.

7. A power resource allocation method for an energy storage embedded source-storage-load collaborative entity according to claim 1, characterized in that, Specifically included in the above Step 3 are: Convert the calculation of the medium- and long-term excess penalty, medium- and long-term deficit penalty, and non-linear term penalty for not meeting the consumption responsibility weight of the source-storage-load collaborative entity in the objective function into linear constraints; Replace the lower layer problem with the KKT conditions of the lower layer problem to transform the two-layer model into a single-layer model.

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