A power resource deployment method of an energy storage embedded source-storage-load collaborative subject

Abstract

The application belongs to the technical field of power resource allocation, and relates to a power resource allocation method of an energy storage embedded source-storage-load collaborative subject, comprising the following steps: 1, a multi-time scale resource allocation decision model considering long-term scale decision and day-ahead scale resource allocation decision of the source-storage-load collaborative subject is constructed, and an objective function of the decision model is converted into a most balanced resource utilization rate; 2, an electric energy-green certificate combined resource balancing model is established in the lower layer, and the double-layer optimization model is converted into a single-layer optimization model; 3, an example simulation is carried out, and a solver is used for solving; the source-storage-load collaborative subject multi-time scale resource allocation model and the electric energy-green certificate combined resource balancing model are constructed; and the influence of a risk-averse index and a renewable energy long-term scale decision scheme on the source-storage-load collaborative subject and the combined resource center is discussed through an example.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power resource allocation, and relates to a power resource allocation method of a storage-embedded source-storage-load collaborative subject. BACKGROUND

[0002] Currently, the research focus of the renewable energy long-term scale decision scheme is the resource allocation strategy of the power generation side, and the structure of the renewable energy long-term scale decision scheme and the resource allocation mode are researched to encourage the renewable energy developer to provide more reliable power generation prediction and to promote the renewable energy developer to adopt storage.

[0003] The existing research on the source-storage-load collaborative subject power energy resource binding strategy focuses on the multi-time scale decision strategy, respectively explores the influence of the power resource allocation parameters on the conditional risk of the source-storage-load collaborative subject under the output-determined multi-time scale decision scheme and the output-uncertain multi-time scale decision scheme, and does not consider the scenario that the source-storage-load collaborative subject simultaneously makes renewable energy long-term scale decision and traditional energy medium and long-term scale decision. In the aspect of the construction of the storage of the source-storage-load collaborative subject, the existing technology proposes a source-storage-load collaborative subject storage optimization configuration analysis method based on the whole life cycle theory, and establishes a master-slave game model of the source-storage-load collaborative subject shared storage service operation strategy.

[0004] However, the existing research on the renewable energy long-term scale decision scheme lacks the research on the renewable energy long-term scale resource allocation strategy of the source-storage-load collaborative subject. The research on the source-storage-load collaborative subject power energy resource binding strategy focuses on the power energy resource binding strategy under the multi-time scale. Since the existing power energy resource binding strategy is mainly aimed at the traditional energy power energy resource binding, the influence of the different resource allocation varieties in the renewable energy long-term scale decision scheme and the traditional energy medium and long-term scale decision scheme on the source-storage-load collaborative subject power energy resource binding strategy is not considered, and the existing power energy resource binding strategy cannot be directly used for the renewable energy long-term scale decision scheme. The research on the storage investment configuration of the source-storage-load collaborative subject mainly focuses on the shared storage mode, and only considers the short-term strategy for the main gain mode. The research on the strategy that the source-storage-load collaborative subject improves the renewable energy long-term scale decision scheme gain by independently investing in and managing the storage system is relatively lacking.

[0005] Therefore, a renewable energy long-term scale decision method of a source-storage-load collaborative subject considering storage is needed to solve the above technical problems. SUMMARY

[0006] The technical scheme adopted by the application to solve the technical problems is: a power resource allocation method of a storage-embedded source-storage-load collaborative subject, comprising the following steps:

[0007] Step 1, constructing a multi-time scale resource allocation decision-making model considering the long-term scale decision-making of the source-storage-load collaborative subject and the day-ahead scale resource allocation decision-making, and using the CVaR (conditional value-at-risk) method to transform the objective function of the multi-time scale resource allocation decision-making model into the most balanced resource utilization;

[0008] Step 2, establishing an electricity-energy certificate joint resource center to achieve a balanced model in the lower layer, and using the KKT (Karush-Kuhn-Tucker) condition to transform the double-layer optimization model into a single-layer optimization model;

[0009] Step 3, through example simulation, using MATLAB solver, YALMIP solver and Gurobi solver for solving.

[0010] Preferably, the step 1 specifically comprises:

[0011] Step 1-1, in the long-term scale resource allocation decision-making stage, the source-storage-load collaborative subject decides the traditional energy long-term scale power curve, the renewable energy long-term scale power curve and the storage capacity according to the gain of each scenario;

[0012] Step 1-2, in the day-ahead scale resource allocation decision-making stage, the source-storage-load collaborative subject decides the storage charging and discharging power and the green certificate resource supply state parameters according to the renewable energy winning amount obtained by the electricity-energy certificate joint resource balance, the green certificate winning amount of the source-storage-load collaborative subject, the green certificate resource parameters and the electricity energy resource parameters.

[0013] More preferably, in the step 1, the objective function of the long-term scale resource allocation decision-making stage is constructed according to the gain obtained by the source-storage-load collaborative subject in the day-ahead scale resource allocation decision-making stage under multiple scenarios;

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

[0015] The long-term scale resource allocation decision-making stage and the day-ahead scale resource allocation decision-making stage are combined as a multi-time scale resource allocation decision-making model of the source-storage-load collaborative subject in the upper layer, and the lower layer is an electricity-energy joint resource center to achieve a balanced model.

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

[0017] (12)

[0018] In formula (12), a probability of occurrence of the s scenario, a power resource allocation parameter of the source-storage-load collaborative subject in each scenario, a risk aversion coefficient, an increase in which causes the inter-provincial resource dispatcher to increase the degree of risk aversion, and when the source-storage-load collaborative subject does not consider the control of risk, and the CVaR represents the CVaR constraint.

[0019] More preferably, the power resource allocation parameter of the source-storage-load collaborative subject in each scenario is:

[0020] (1)

[0021] In formula (1): represents the power resource allocation parameter of the source-storage-load collaborative subject to build energy storage on a typical day, , , respectively represent the medium and long-term excess penalty, the medium and long-term deficiency penalty, and the unmet consumption responsibility weight penalty of the source-storage-load collaborative subject in the scenario s, represents the gain of the source-storage-load collaborative subject through the binding of the traditional energy medium and long-term technology collaboration mechanism in the s scenario, represents the gain of the source-storage-load collaborative subject in the scenario s by binding the renewable energy long-term scale decision scheme, represents the gain of the source-storage-load collaborative subject in the scenario s in the green certificate resource center, represents the gain of the source-storage-load collaborative subject in the scenario s by adjusting the charging and discharging of energy storage in the day-ahead resource center.

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

[0023] (19)

[0024] In formula (19): represents the resource supply state parameter of the bth section of the traditional energy power plant c in the scenario s in the electric energy resource center / MWh·m, -1 , represents the bid amount of the bth section of the traditional energy power plant c in the scenario s at time t in the electric energy resource center / MW, represents the resource supply state parameter of the renewable energy power plant in the electric energy resource center, represents the bid amount of the renewable energy plant h in the electric energy resource center at time t / MW, 、 Parameter representing the resource supply status of the green certificate bound to the resource of the source-storage-load collaborative subject in scenario s / yuan·unit -1 and the winning bid amount, 、 Represents the resource supply status parameter of renewable energy power plant h in scenario s when unbinding green certificates / yuan / unit -1 and winning bid quantity / piece; 、 Indicates the resource supply status parameters of other green certificate resource allocators / yuan·piece -1 When the number of winning bids is greater than 0, the Green Certificate Resource Allocator will unbind the Green Certificates from its resources. When the number of winning bids is less than 0, the Green Certificate Resource Allocator will bind the Green Certificates from its resources.

[0025] Preferably, the step 3 specifically includes:

[0026] The nonlinear penalty calculation of medium- and long-term excess penalty, medium- and long-term shortfall penalty and unsatisfied consumption responsibility weight of the source-storage-load coordination subject in the objective function is converted into linear constraints;

[0027] The KKT condition of the lower-level problem is used to replace the lower-level problem, so that the two-level model is transformed into a single-level model.

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

[0029] 1. The present invention constructs a multi-time-scale resource allocation strategy model for source-storage-load collaborative entities and an electric energy-green certificate joint resource balancing model, and analyzes the decision-making of source-storage-load collaborative entities considering the medium- and long-term power curves of traditional energy, the long-term power curves of renewable energy, and the storage investment capacity; and explores the impact of the risk aversion index and the implementation of the long-term decision-making plan for renewable energy on the source-storage-load collaborative entities and the joint resource center through examples.

[0030] 2. When the present invention adopts a long-term renewable energy decision-making scheme, the higher the risk aversion of the source-storage-load collaborative subject, the more reasonably it can optimize the long-term renewable energy power curve and energy storage investment and construction capacity, and its conditional risk coefficient decreases as the risk aversion index increases; when the long-term renewable energy decision-making scheme is not adopted, the higher the risk aversion of the source-storage-load collaborative subject, the more it tends to avoid using energy storage for day-ahead resource center utilization of peak-valley difference for resource acquisition, and its conditional risk increases as the risk aversion index increases.

[0031] 3、The application is in the case of renewable energy long-term scale decision scheme, the source-storage-load collaborative subject suppresses the renewable energy long-term scale decision scheme deviation power caused by the randomness of renewable energy power generation by constructing energy storage, and reduces the fluctuation risk of day-ahead resource center resource parameters.

[0032] 4、With the implementation of renewable energy long-term scale decision scheme, the source-storage-load collaborative subject increases the amount of energy storage, and increases the renewable energy consumption capacity of the system. Compared with not implementing renewable energy long-term scale decision scheme, the peak-valley difference of the system is significantly reduced after implementing renewable energy long-term scale decision scheme, the renewable energy winning amount in the day-ahead resource center is increased, and the traditional energy winning amount is decreased. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of a power resource deployment method of a source-storage-load collaborative subject with energy storage embedded. DETAILED DESCRIPTION

[0034] The related technologies in the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0035] In the present embodiment, the source-storage-load collaborative subject performs time period optimization when making resource deployment decisions, forming a traditional energy medium and long-term scale time period power curve and a renewable energy long-term scale time period power curve.

[0036] In the medium and long-term scale resource deployment decision stage, the source-storage-load collaborative subject decides the traditional energy medium and long-term scale power curve, the renewable energy long-term scale power curve and the energy storage construction capacity according to the gain of each scene. In the day-ahead scale resource deployment decision stage, the source-storage-load collaborative subject decides the energy storage charging and discharging power and the green certificate resource supply state parameter according to the renewable energy winning amount obtained by the energy-greencertificate joint resource balancing, the source-storage-load collaborative subject green certificate winning amount, the green certificate resource parameter and the energy resource parameter.

[0037] In the medium and long-term decision stage and the day-ahead scale resource allocation decision stage, the decision subject is the source-storage-load collaborative subject. The objective function of the medium and long-term decision stage is constructed according to the gain obtained by the source-storage-load collaborative subject in the day-ahead scale resource allocation decision stage. The source-storage-load collaborative subject can realize efficient connection between the medium and long-term decision and the day-ahead scale resource allocation decision by optimizing the power curve of the multi-time scale coupling scheme. Therefore, the power resource allocation parameters of the source-storage-load collaborative subject in the medium and long-term decision stage are distributed to each typical scenario, and the part that does not meet the proportion requirement of the medium and long-term scale decision scheme and the responsibility weight of consumption is taken as the penalty term in the objective function. At this time, the medium and long-term decision stage and the day-ahead scale resource allocation decision stage of the source-storage-load collaborative subject can be combined as a multi-time scale resource allocation decision model of the source-storage-load collaborative subject in the upper layer, and the lower layer is the power-energy-joint resource center equilibrium model.

[0038] The source-storage-load collaborative subject renewable energy long-term scale decision scheme resource allocation decision model considering energy storage:

[0039] 1. The multi-time scale resource allocation decision 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 the traditional energy medium and long-term scale power curve scheme decision and the renewable energy long-term scale power curve scheme decision in the medium and long-term, and builds energy storage; in the day-ahead resource center, the source-storage-load collaborative subject unbinds the power bound by the medium and long-term resources, adjusts the energy storage charging and discharging strategy, and buys and sells green certificates to obtain gain. The part of the source-storage-load collaborative subject that does not meet the proportion requirement of the medium and long-term scale decision scheme and the responsibility weight of consumption is punished. The excess proportion of the total power bound by the medium and long-term of the source-storage-load collaborative subject exceeding the actual power consumption or the difference proportion less than the actual power consumption needs to be punished for excess or difference gain.

[0042] The 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 of the source-storage-load collaborative subject building energy storage distributed to the typical day; , , respectively represent the medium and long-term excess penalty, medium and long-term deficiency penalty and the part that does not meet the responsibility weight of consumption of the source-storage-load collaborative subject in scenario s; Gain of source-storage-load coordination subject in scenario s through binding long-term decision scheme of traditional energy Gain of source-storage-load coordination subject in scenario s through binding long-term decision scheme of renewable energy Gain of source-storage-load coordination subject in scenario s in green certificate resource center Gain of source-storage-load coordination subject in scenario s in day-ahead resource center through adjusting storage charging and discharging

[0045] Actual renewable energy long-term scale decision period power in scenario s Take the minimum of initial decision power, power consumption of source-storage-load coordination subject and renewable energy on-grid power for renewable energy long-term scale decision scheme

[0046] (2)

[0047] (3)

[0048] In the formula: Power consumption of source-storage-load coordination subject after proportional reduction Initial decision power of renewable energy long-term scale decision scheme of source-storage-load coordination subject and h renewable energy generator at time t / W Decision power of source-storage-load coordination subject and traditional energy generator c at time t / MW Charging and discharging power of storage established by l load of source-storage-load coordination subject at time t in scenario s / MW Load power of l load at time t in scenario s / W Actual distribution power of h renewable energy binding power at time t in scenario s / MW Actual bid power of h renewable energy binding power at time t in scenario s / MW

[0049] Gain and power resource allocation parameter calculation formula 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 energy storage power resource allocation coefficient / unit·MW -1 ; represents the energy storage capacity established by the source- storage-load collaborative subject at the l load / MW; , represents the penalty resource parameter / unit·MWh of the excess penalty of the multi-time scale coupling scheme and the excess ratio / %; -1 , represents the penalty resource parameter / unit·MWh of the difference penalty of the multi-time scale coupling scheme and the difference ratio / %; -1 , represents the penalty resource parameter / unit·MWh of the responsibility weight of the source-storage-load collaborative subject for completing the consumption and the evaluation ratio; -1 represents the resource parameter / unit·MWh of the energy balance at the n node at the time t under the scenario s; -1 represents the resource parameter / unit·MWh of the multi-time scale coupling scheme of the source-storage-load collaborative subject and the traditional energy power plant c at the time t; -1 represents the resource parameter / unit·MWh of the renewable energy long-time scale decision scheme of the source-storage-load collaborative subject and the new energy power plant h in each period; -1 represents the resource parameter / unit·individual of the green certificate balance under the scenario s; -1 represents the number of green certificates of the source-storage-load collaborative subject under the scenario s.

[0059] The CVaR method can control the risk state exceeding the VaR value, and thus can better reflect the potential risk loss. By setting the confidence level as , the value corresponding to the quantile of the probability distribution of the power resource allocation parameter of the source-storage-load collaborative subject in each scenario is taken as the VaR value of the power resource allocation parameter of the source-storage-load collaborative subject, so that the probability of the power resource allocation parameter of the source-storage-load collaborative subject being greater than the VaR value is less than . The upper layer takes the most balanced resource utilization rate of the source-storage-load collaborative subject as the objective function: ​​​​​​​

[0060] (12)

[0061] In the formula: represents the probability of s scenario occurring; represents the risk aversion coefficient. An increase will make the inter-provincial resource dispatcher's aversion to risk increase. When , the source- storage-load collaborative subject does not consider the control of risk.

[0062] 1.2 Source- storage-load collaborative subject constraint conditions:

[0063] 1.2.1 CVaR constraint condition:

[0064] (13)

[0065] In the formula: variable The optimization result of the variable is defined as the VaR value of the source- storage-load collaborative subject operation power resource allocation parameter; is a non-negative auxiliary variable, which specifically represents the amount of the source- storage-load collaborative subject power resource allocation parameter exceeding The source- storage-load collaborative subject optimizes the risk scenarios in which 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 charging and discharging power constraint:

[0067] The source- storage-load collaborative subject constructs energy storage at l load, and the energy storage charging and discharging power satisfies the upper and lower limit constraints of charging power.

[0068] (14)

[0069] In the formula: represents the maximum value of the discharging 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 charging and discharging power is generally proportional to the energy storage capacity When is positive, the energy storage charges; when is negative, the energy storage discharges.

[0070] 1.2.3 Energy storage state of charge constraint:

[0071] The energy storage power station satisfies the upper and lower limit constraints of the state of charge and the consistency constraint of the initial and final state of charge, and the time interval is 1h.

[0072] (15)

[0073] (16)

[0074] (17)

[0075] In the formula: Soc l, t represents the state of charge of the energy storage power station established by the source-storage-load collaborative subject at the l load at time t / %; Soc l, min represents the minimum value of the state of charge of the l energy storage power station; Soc l, max 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 green certificate resource supply state parameters:

[0077] The resource unbundling of the green certificate is equivalent to a gain for the renewable energy power generator. The resource supply state parameter of the source-storage-load collaborative subject at the green certificate resource center will not exceed its penalty resource parameter:

[0078] (18)

[0079] In the formula: G represents the government gain resource parameter / yuan·unit -1 .

[0080] 2 Energy-greencertificate joint resource center

[0081] 2.1 Objective function of joint resource center

[0082] The present application designs a day-ahead energy-greencertificate joint resource dispatching center. In the joint resource dispatching center, the renewable energy power generator and the traditional energy power generator report the time-division electric energy and the resource supply state parameter. The renewable energy power generator, the source-storage-load collaborative subject and other regional green certificate resource dispatchers report the green certificate resource supply state parameter for unbundling or bundling the green certificate. The system operation efficiency optimization is taken as the objective function, and the marginal uniformity is used to achieve the balanced resource parameter settlement of the electric energy resource parameter and the green certificate resource parameter.

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

[0084] (19)

[0085] In the formula: Soc c, b, s represents the resource supply state parameter of the bth segment output of the traditional energy power generator c under the scenario s at the electric energy resource center / yuan·MWh -1 ; represents the winning amount of the b-th segment of the conventional energy power plant c at time t in scenario s / MW; represents the resource supply state parameter of the renewable energy power plant in the electric energy resource center; represents the winning amount of the renewable energy plant h at time t in the electric energy resource center / MW; 、 represents the resource supply state parameter of the resource binding green certificate of the source-storage-load collaborative subject in scenario s / yuan·person -1 and the winning amount; 、 represents the resource supply state parameter of the resource unbinding green certificate of the renewable energy power plant h in scenario s / yuan·person -1 and the winning amount / person; 、 represents the resource supply state parameter of the other green certificate resource dispatcher / yuan·person -1 and the winning amount / person. When the winning amount is greater than 0, the green certificate resource dispatcher unbinds the green certificate, and when the winning amount is less than 0, the green certificate resource dispatcher binds the green certificate.

[0086] 2.2 Joint resource center constraint conditions:

[0087] 2.2.1 Electric quantity supply and demand balance constraint:

[0088] (20)

[0089] (21)

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

[0091] 2.2.2 Electric quantity winning amount constraint:

[0092] (22)

[0093] In the formula: 、 represents the upper and lower limits of the b-th segment of the conventional energy power unit c / MW, , respectively represent the upper and lower limits of the renewable energy output.

[0094] 2.2.3 Conventional energy unit ramping constraint:

[0095] (23)

[0096] wherein: represents the upper limit of the ramping of the conventional energy generator unit c / MW.

[0097] 2.2.4 Node power constraint:

[0098] (24)

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

[0100] (25)

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

[0102] 2.2.6 Green certificate balance constraint:

[0103] The amount of green certificate resources bound and the amount of green certificate resources unbound are equal.

[0104] (26)

[0105] 2.2.7 Green certificate winning amount constraint:

[0106] The upper limit of the green certificate that can be unbound by the renewable energy power plant is the winning amount of the green certificate minus the green certificate bound in the renewable energy long-term scale decision scheme; the upper limit of the green certificate that can be unbound by the source- storage-load collaborative subject is the number of green certificates obtained by binding the renewable energy long-term scale decision scheme, and the upper limit of the green certificate that needs to be bound by the source- storage-load collaborative subject is the number of green certificates required by its quota.

[0107] (27)

[0108] wherein: , represents the upper and lower limits of the green certificate resource allocation by the m green certificate resource allocator under the s scenario.

[0109] 3. Solution method

[0110] Medium and long-term excess penalty of the source- storage-load collaborative subject in the objective function , medium and long-term shortage penalty and a penalty for not fulfilling the responsibility weight of accommodation The calculation of the non-linear term 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 a bilinear term, and , discretization of binary:

[0119] (34)

[0120] (35)

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

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

[0123] (36)

[0124] (37)

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

[0126] (38)

[0127] The lower layer established electric energy-green certificate joint resource center model is a linear programming problem with convex function property, and the KKT condition of the lower layer problem can be used to replace the lower layer problem, so that the double-layer model is converted into a single-layer model.

[0128] After the total power resource allocation parameter of the source-storage-load collaborative subject is corrected, the minimum condition risk value of the source-storage-load collaborative subject represented by formula (12) is taken as the objective function, and the KKT conditions of the lower layer electric energy-green certificate resource center joint equilibrium represented by formula (13), formula (14), formula (18), formula (28)-formula (30), formula (36)-formula (38) are taken as constraint conditions, and a source-storage-load collaborative subject investment portfolio optimization decision model is established. The model is a non-convex nonlinear mixed integer optimization model, and the embodiment adopts MATLAB+YALMIP+Gurobi solver for solving.

[0129] The key technical point of the embodiment is:

[0130] 1. The resource allocation mechanism of the renewable energy long-term scale decision scheme is deeply studied, covering the electric quantity settlement mechanism and the risk hedging strategy. The decision behavior of the source-storage-load collaborative subject under different risk aversion degrees is analyzed, and the optimized multi-time scale coupled scheme power resource allocation curve is provided for the source-storage-load collaborative subject 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 center resource allocation strategy support for the source-storage-load collaborative subject, helps to optimize the electric energy resource binding power resource allocation coefficient, and improves the competitiveness.

[0131] 2. The model of electric energy-green certificate joint resource equilibrium is constructed, the synergy of renewable energy electric energy bid amount and green certificate bid amount is considered, and the resource equilibrium mechanism is optimized to reduce the social electric energy resource binding power resource allocation coefficient and the green certificate resource allocation coefficient.

[0132] 3. The randomness of renewable energy power generation is suppressed by the energy storage to ensure the implementation of the renewable energy long-term scale decision scheme, the synergy of the renewable energy long-term scale decision scheme and the energy storage is analyzed, and the optimization configuration of the energy storage capacity, the development of the charging and discharging strategy and the influence of the energy storage on the renewable energy consumption capacity of the source-storage-load collaborative subject binding the renewable energy long-term scale decision scheme are studied.

[0133] To sum up, the application analyzes the source-storage-load collaborative subject decision by constructing a source-storage-load collaborative subject multi-time scale resource allocation strategy model and an electric energy-green certificate combined resource balancing model, considering the traditional energy medium and long term scale power curve of the source-storage-load collaborative subject, the renewable energy long term scale power curve and the storage capacity of the source-storage-load collaborative subject; and the influence of the risk aversion index and the implementation of the renewable energy long term scale decision scheme on the source-storage-load collaborative subject and the combined resource center is discussed through an example.

[0134] It should be emphasized that the above is only a preferred embodiment of the present application, and does not limit the present application in any form. Any simple modification, equivalent change and modification of the above embodiment according to the technical essence of the present application still belongs to the scope of the technical solution of the present application.

Claims

1. A power resource dispatching method of an energy storage embedded source-storage-load collaborative subject, characterized in that, The method comprises the following steps: Step 1, constructing a multi-time scale resource allocation decision model considering the long-term scale decision of the source-storage-load coordination subject and the day-ahead scale resource allocation decision, and converting the objective function of the multi-time scale resource allocation decision model into resource utilization balance by using the CVaR method; Step 2, establishing an electric energy-green certificate joint resource balance model in the lower layer, and converting the double-layer optimization model into a single-layer optimization model by using the KKT condition; Step 3, through example simulation, the MATLAB solver, the YALMIP solver and the Gurobi solver are used for solving; The step 1 specifically comprises: Step 1-1, in the long-term scale resource allocation decision stage, the source-storage-load coordination subject decides the long-term scale power curve of the traditional energy, the long-term scale power curve of the renewable energy and the storage capacity according to the gain of each scene; Step 1-2, in the day-ahead scale resource allocation decision stage, the source-storage-load coordination subject decides the storage charging and discharging power and the green certificate resource supply state parameter according to the renewable energy winning amount, the green certificate winning amount of the source-storage-load coordination subject, the green certificate resource parameter and the electric energy resource parameter obtained by the electric energy-green certificate joint resource balance; In the step 1, the objective function of the long-term scale resource allocation decision stage is constructed according to the gain obtained by the source-storage-load coordination subject in the day-ahead scale resource allocation decision stage under multiple scenes; The power resource allocation parameter of the long-term scale resource allocation decision stage is evenly distributed to each typical scene, and the part that does not meet the power proportion requirement and the consumption responsibility weight of the long-term scale decision scheme is taken as a penalty term in the objective function; The long-term scale resource allocation decision stage and the day-ahead scale resource allocation decision stage are combined as the multi-time scale resource allocation decision model of the source-storage-load coordination subject in the upper layer, and the lower layer is the electric energy-joint resource balance model.

2. The energy storage embedded source-storage-load collaborative subject power resource dispatching method according to claim 1, characterized in that, In the step 1, the objective function of the most balanced resource utilization is: （12） In formula (12): represents the probability of s scenario occurring, represents the power resource allocation parameter of the source-storage-load collaborative subject in each scenario, represents the risk aversion coefficient, and CVaR represents the CVaR constraint.

3. The energy storage embedded source-storage-load collaborative subject power resource dispatching method according to claim 2, characterized in that, The source-storage-load collaborative subject in each scene power resource allocation parameter Is: （1） In formula (1): represents the power resource allocation parameter of the source-storage-load collaborative subject to the typical day, , , respectively represent the medium and long term excess penalty, the medium and long term deficiency penalty and the unmet consumption responsibility weight penalty of the source-storage-load collaborative subject under the scene s, represents the gain of the source-storage-load collaborative subject through the binding of the medium and long term technical collaboration mechanism of the traditional energy under the scene s, represents the gain of the source-storage-load collaborative subject in binding the long term scale decision scheme of the renewable energy under the scene s, represents the gain of the source-storage-load collaborative subject in the green certificate resource center under the scene s, represents the gain of the source-storage-load collaborative subject in adjusting the storage charge and discharge in the day-ahead resource center under the scene s.

4. The energy storage embedded source-storage-load collaborative body power resource dispatching method according to claim 3, characterized in that, The step 3 specifically comprises: The nonlinear item penalty calculation of the long-term excess penalty, the long-term shortage penalty and the non-compliance of the consumption responsibility weight of the source-storage-load coordination subject in the objective function is converted into a linear constraint; The KKT condition of the lower layer problem is used to replace the lower layer problem, so that the double-layer model is converted into a single-layer model.

5. The energy storage embedded source-storage-load collaborative body power resource dispatching method according to claim 1, characterized in that, In step 2, the objective function of the joint resource center is the social electric power resource allocation parameter of the electric energy-green certificate Lowest: （19） In formula (19): represents the resource supply state parameter of the bth segment of the traditional energy power plant c in the scenario s at the electric energy resource center, represents the winning amount of the bth segment of the traditional energy power plant c in the scenario s at the electric energy resource center at the moment t, represents the resource supply state parameter of the renewable energy power plant at the electric energy resource center, represents the winning amount of the renewable energy plant h at the electric energy resource center at the moment t, , represents the resource supply state parameter and the winning amount of the resource binding green certificate of the source-storage-load collaborative subject in the scenario s, , represents the resource supply state parameter and the winning amount of the resource unbinding green certificate of the renewable energy power plant h in the scenario s; , represents the resource supply state parameter and the winning amount of other green certificate resource dispatchers.

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