New energy station group shared energy storage dynamic capacity allocation operation mode and planning method

Abstract

The application discloses a new energy station group shared energy storage dynamic capacity allocation operation mode and planning method, and relates to the technical field of new energy; including: establishing a shared energy storage operation framework, calculating a capacity allocation factor for each station by an energy storage service platform according to the predicted and actual power generation capacity of each station, and dynamically allocating virtual energy storage capacity for each station; the operation platform satisfies the charging and discharging demand reported by the stations through power mutual aid between the stations in priority, adopts a pricing model based on supply and demand ratio for settlement, and fairly allocates the energy storage operation cost through a weighted factor according to the actual use and efficiency of the stations; a double-layer optimization model is constructed with new energy stations as the upper layer and an energy storage service platform as the lower layer, the upper layer station optimizes its own operation under the constraint of virtual capacity, the lower layer platform cooperatively optimizes the final capacity configuration and scheduling scheme of the physical energy storage with the minimum total cost of the system as the target, and updates the total virtual energy storage capacity.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology, specifically relating to the dynamic capacity allocation operation mode and planning method of shared energy storage in new energy power plant clusters. Background Technology

[0002] With the proposal of my country's "dual carbon" goals and the rapid development of the new energy industry, the penetration rate of new energy sources (such as wind power and photovoltaics) in the power system is constantly increasing.

[0003] However, the volatility and uncertainty of new energy power generation pose significant challenges to grid dispatch, operational safety, and the balance of power supply and demand. Energy storage technology, capable of storing and releasing energy, regulating peak and valley loads in the power system, and smoothing new energy output, is considered a crucial technological means to address grid stability issues under high new energy penetration rates. In recent years, shared energy storage has gained widespread attention as a novel energy storage application model. By sharing energy storage facilities among multiple new energy power plants and users, not only can the scale effect of energy storage be fully utilized and resource utilization improved, but the investment and operating costs of individual power plants can also be effectively reduced, enhancing the capacity and economic viability of new energy absorption. However, the business model of shared energy storage is still in the exploratory stage. The profit-sharing mechanism among participating entities is not yet perfect, management methods are relatively extensive, and it is difficult to accurately match the capacity needs and actual benefits of each participant, resulting in the overall benefits of the energy storage system not being maximized.

[0004] Therefore, research on key technologies such as dynamic capacity allocation, revenue settlement, and investment planning is urgently needed for the planning and management of shared energy storage facilities in new energy power plant clusters. Through scientific capacity allocation methods, energy storage resources can be dynamically dispatched according to the actual power generation and energy consumption needs of each power plant, improving the overall efficiency of the system. At the same time, a reasonable and sound revenue distribution mechanism and investment decision-making model can help form a sustainable business development model, promoting the healthy development and large-scale promotion of shared energy storage technology. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art; to this end, this invention proposes a dynamic capacity allocation operation mode and planning method for shared energy storage in new energy power plant clusters, aiming to solve the technical problems of inflexible capacity allocation, unreasonable cost sharing, and uncoordinated planning in existing shared energy storage systems.

[0006] To address the aforementioned problems, this invention provides a dynamic capacity allocation operation mode and planning method for shared energy storage in new energy power plant clusters, comprising the following steps:

[0007] S1: Establish a shared energy storage operation framework for new energy power plant clusters. Through the energy storage service platform, calculate the capacity allocation factor based on the predicted power generation and actual power generation capacity of each new energy power plant under different scenarios, and allocate virtual energy storage capacity to each power plant according to the capacity allocation factor.

[0008] S2: The energy storage service platform prioritizes meeting the charging and discharging power demands reported by each site through power sharing between sites; it uses a pricing model based on the supply-demand ratio to determine the power exchange price between sites; and it calculates the energy storage operating cost to be shared by each site based on the actual dispatched energy storage power and energy storage utilization efficiency of each site, combined with weighting factors.

[0009] S3: Construct a two-layer optimization model with new energy power plants as the upper layer and energy storage service platform as the lower layer; in the upper layer model, each power plant, under the constraint of the dynamically allocated virtual energy storage capacity, determines the charging and discharging power demand for each time period with the goal of minimizing its own operating cost; in the lower layer model, after aggregating the demand of all power plants, the energy storage service platform determines the optimal capacity configuration and power scheduling scheme of physical energy storage with the goal of minimizing the total cost of the energy storage system throughout its entire life cycle, and updates the total virtual energy storage capacity used for operation based on the optimal configuration.

[0010] Preferably, in step S1, the capacity allocation factor includes the following steps:

[0011] Set scene set New energy power stations In the scene Below, each time period The predicted power generation and the actual power generation are respectively and ;

[0012] The aforementioned new energy power station In the scene In this case, the capacity allocation factor is specifically:

[0013]

[0014] in, For new energy power stations In the scene At that time, the corresponding capacity allocation factor, This represents the total number of new energy power stations. The number of time periods within a scheduling day. Number the scene. Numbering of new energy power stations For time period numbering, For new energy power stations In the scene Time period The predicted power generation capacity, This corresponds to the actual power generation.

[0015] Preferably, in step S1, the virtual energy storage capacity is allocated to each power station according to the capacity allocation factor, specifically by multiplying the capacity allocation factor by the total virtual energy storage capacity to obtain the virtual energy storage capacity of each new energy power station in the corresponding scenario; wherein, the virtual energy storage capacity is set to be greater than the physical energy storage configuration capacity.

[0016] Preferably, in step S2, the inter-station power exchange price is determined using a pricing model based on the supply-demand ratio, including the following steps:

[0017] S201: The energy storage service platform aggregates the charging power demand and discharging power demand reported by all new energy power stations; when the discharging power demand is met, it is matched by the total charging power supply of all power stations.

[0018] S202: If the total charging power demand is greater than the total discharging power demand, then the discharging power demand between stations will be fully met, and the remaining charging power demand will be allocated among the stations according to the proportion of each station's charging power demand to the total demand; if the total discharging power demand is greater than the total charging power demand, then the opposite will be true.

[0019] S203: When calculating the supply-demand ratio, the charging and discharging power demand data used for calculation only includes the power exchanged between power stations achieved through steps S201 and S202, and the power portion satisfied through external energy resource scheduling.

[0020] Preferably, in step S2, the energy storage operating cost to be allocated to each site is calculated based on the actual dispatched energy storage power and energy storage utilization efficiency of each site, combined with a weighting factor, including the following steps:

[0021] Let the probability of each scenario be... New energy power stations In the scene The rated energy storage power obtained by the lower allocation is The actual energy storage needs and energy storage efficiency of the aforementioned new energy power stations The energy storage cost allocation factors are respectively and ;

[0022] Assume the total cost of energy storage is The energy storage cost allocation factor and The weighting indicators are respectively and Then new energy power stations The energy storage operation cost to be borne .

[0023] Preferably, in step S3, the operating cost of the upper-level model includes the cost of power curtailment, the penalty for insufficient output, and the revenue from electricity sales. The method for obtaining the cost of power curtailment is as follows:

[0024] When the actual power generation capacity of a renewable energy power plant exceeds its planned on-grid power generation, the power plant reduces its actual power generation through wind and solar curtailment. The cost of this curtailment... Specifically:

[0025]

[0026] in, For the scene New energy power station In the Power curtailment during the time period Cost per unit of abandoned power For time period numbering, This represents the number of time periods within a single scheduling day.

[0027] Preferably, in step S3, the objective function of the lower-level model is to minimize the total lifecycle cost of the energy storage system, wherein the total cost includes energy storage investment cost, energy storage operation loss cost, and external transaction costs with the energy storage system. Specifically: ;in, and These are the rated capacity and power of the energy storage, respectively. and These represent the unit investment cost corresponding to the rated capacity and power of energy storage, respectively. , As a discount factor for the time value of cost, The discount rate is... This refers to the lifespan of the energy storage system.

[0028] Preferably, in step S3, the two-layer optimization model includes the following steps:

[0029] The upper level is composed of each new energy power station as an independent decision-maker. Under the constraint of the allocated virtual energy storage capacity, each power station determines and reports its optimal charging and discharging power demand to the platform with the goal of minimizing its own operating costs.

[0030] The lower layer consists of an energy storage service platform that acts as the decision-maker. After aggregating the needs of all upper-level sites, it aims to minimize the total system cost, including investment and operating costs, to determine the optimal capacity and power scheduling scheme for physical energy storage and allocate virtual energy storage capacity to each site.

[0031] The two-level optimization model is solved through iterative interaction between upper-level and lower-level decisions until the optimal solution is obtained.

[0032] The solution employs a genetic algorithm to optimize the operational decisions of each upper-level new energy power station, and nests a Gurobi solver to iteratively solve the capacity configuration and scheduling model of the lower-level energy storage service platform until convergence.

[0033] The beneficial effects of this invention are:

[0034] This invention, through a shared energy storage model for new energy power plant clusters, designs a demand-based virtual energy storage capacity dynamic allocation strategy according to the needs of each power plant at different times, thereby achieving flexible and real-time allocation of energy storage capacity among different power plants, improving resource utilization and overall efficiency.

[0035] This invention designs a power settlement mechanism that meets the needs of each site, clarifies the power source, and uses the supply-to-demand ratio (SDR) for pricing, thereby achieving fair and efficient energy storage power trading and economic settlement between sites and ensuring reasonable returns for all parties.

[0036] This invention innovatively proposes an investment cost sharing method adapted to shared energy storage by comprehensively considering the actual energy storage dispatch power and utilization efficiency of each site. It reasonably shares the energy storage investment expenditure according to the actual usage of each party, promoting fair participation and continuous cooperation among sites.

[0037] This invention establishes a two-layer optimization model for decision support by the energy storage service platform based on the energy storage capacity leasing and sharing mechanism. The upper layer is determined by each site to decide on the power demand for energy storage, while the lower layer is optimized by the service platform to configure physical energy storage capacity and allocate it to each site, thereby synergistically improving the system scheduling capability. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0039] Figure 2 This is a schematic diagram of the shared energy storage operation mode for new energy power stations according to the present invention;

[0040] Figure 3 This is a schematic diagram illustrating the calculation process for the power demand sources of each station in this invention;

[0041] Figure 4 This is a schematic diagram of the two-layer optimization model of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 As shown, this invention provides an operational mode and planning method for shared energy storage dynamic capacity allocation in new energy power plant clusters, comprising the following steps:

[0044] S1: Establish a shared energy storage operation framework for new energy power plant clusters. Through the energy storage service platform, calculate the capacity allocation factor based on the predicted power generation and actual power generation capacity of each new energy power plant under different scenarios, and allocate virtual energy storage capacity to each power plant according to the capacity allocation factor.

[0045] S2: The energy storage service platform prioritizes meeting the charging and discharging power demands reported by each site through power sharing between sites; it uses a pricing model based on the supply-demand ratio to determine the power exchange price between sites; and it calculates the energy storage operating cost to be shared by each site based on the actual dispatched energy storage power and energy storage utilization efficiency of each site, combined with weighting factors.

[0046] S3: Construct a two-layer optimization model with new energy power plants as the upper layer and energy storage service platform as the lower layer; in the upper layer model, each power plant, under the constraint of the dynamically allocated virtual energy storage capacity, determines the charging and discharging power demand for each time period with the goal of minimizing its own operating cost; in the lower layer model, after aggregating the demand of all power plants, the energy storage service platform determines the optimal capacity configuration and power scheduling scheme of physical energy storage with the goal of minimizing the total cost of the energy storage system throughout its entire life cycle, and updates the total virtual energy storage capacity used for operation based on the optimal configuration.

[0047] Specifically, Figure 2 This framework establishes an operational framework for the joint construction and sharing of energy storage at new energy power plants. Participants include new energy power plants, shared energy storage equipment, and an energy storage service platform. Each new power plant is interconnected with the jointly invested and constructed centralized energy storage power station. Considering that when multiple new energy power plants jointly invest in and construct energy storage power stations, without fully sharing energy storage demand information, it is impossible for any single new energy power plant to independently decide on energy storage capacity. Therefore, based on practical engineering considerations, a third-party energy storage service platform is introduced as an auxiliary decision-maker to aggregate power demand information from power plants and optimize energy storage capacity and power scheduling, providing technical support for the joint construction and sharing of energy storage operation mechanism across multiple new energy power plants.

[0048] In this context, the centralized shared energy storage equipment of the new energy power station cluster can be "managed" by the energy storage service platform, which will then realize the actual scheduling and management of the energy storage resources. The energy storage service platform will allocate the virtual capacity of energy storage to each power station according to the power adjustment needs of each power station. Each power station will independently decide on the scheduling power of the virtual energy storage based on its own revenue and publish it to the service platform. After aggregating the power needs of each power station, the platform will meet the power adjustment needs of each power station by coordinating energy trading, physical energy storage charging and discharging, and calling on external energy resources among the new energy power stations.

[0049] In one embodiment of the present invention, step S1, the capacity allocation factor, includes the following steps:

[0050] Set scene set New energy power stations In the scene Below, each time period The predicted power generation and the actual power generation are respectively and ;

[0051] The aforementioned new energy power station In the scene In this case, the capacity allocation factor is specifically:

[0052]

[0053] in, For new energy power stations In the scene At that time, the corresponding capacity allocation factor, This represents the total number of new energy power stations. The number of time periods within a scheduling day. Number the scene. Numbering of new energy power stations For time period numbering, For new energy power stations In the scene Time period The predicted power generation capacity, This corresponds to the actual power generation.

[0054] Specifically, among them, The number of time periods within a scheduling day; in this embodiment, 1 hour is taken as the duration of a unit scheduling time period. ,but .

[0055] In one embodiment of the present invention, in step S1, virtual energy storage capacity is allocated to each power station according to the capacity allocation factor, specifically by multiplying the capacity allocation factor by the total virtual energy storage capacity to obtain the virtual energy storage capacity of each new energy power station in the corresponding scenario; wherein, the virtual energy storage capacity is set to be greater than the physical energy storage configuration capacity.

[0056] Specifically, let the total capacity of virtual energy storage be... Then the station In the scene Energy storage capacity allocated through the energy storage service platform for: ;

[0057] Due to the differences and complementarity in power generation among various new energy power plants, physical energy storage only schedules charging or discharging based on the net power demand aggregated from all plants. Therefore, the configured capacity of physical energy storage will be less than the sum of the virtual energy storage capacities allocated to each plant. This is precisely the advantage of the energy storage capacity sharing model. With virtual energy storage capacity The following relationship must be satisfied: ,in, This is the rate factor between physical energy storage and virtual energy storage. This refers to physical energy storage capacity.

[0058] In one embodiment of the present invention, step S2, which uses a pricing model based on the supply-demand ratio to determine the inter-station power exchange price, includes the following steps:

[0059] S201: The energy storage service platform aggregates the charging power demand and discharging power demand reported by all new energy power stations; when the discharging power demand is met, it is matched by the total charging power supply of all power stations.

[0060] S202: If the total charging power demand is greater than the total discharging power demand, then the discharging power demand between stations will be fully met, and the remaining charging power demand will be allocated among the stations according to the proportion of each station's charging power demand to the total demand; if the total discharging power demand is greater than the total charging power demand, then the opposite will be true.

[0061] S203: When calculating the supply-demand ratio, the charging and discharging power demand data used for calculation only includes the power exchanged between power stations achieved through steps S201 and S202, and the power portion satisfied through external energy resource scheduling.

[0062] Specifically, the platform prioritizes utilizing power complementarity transactions between power stations, the charging or discharging power of physical energy storage, and the discharging or charging power of external energy resources to meet the power station's power requirements, i.e.:

[0063] ;

[0064] ;

[0065] in, and Scenes Next, station In the The charging and discharging power requirements during different time periods , and In the aforementioned scenarios, the site In the The electricity sold to other renewable energy power plants, the charging power supplied to physical energy storage facilities, and the charging power sold to external energy suppliers during the specified time period. , and In the aforementioned scenarios, the site In the The power purchased from other renewable energy power plants, the power discharged from physical energy storage, and the power discharged from external energy suppliers during the period;

[0066] The costs or benefits of power sharing between power stations will be settled directly based on the inter-station electricity price. To this end, this invention first establishes a trading rule for determining the inter-station power based on the power supply-demand ratio of the power stations: taking the case where the charging power demand of a power station cluster is greater than the discharging power demand as an example, in this case, for power stations with discharging power demand, their demand can be fully met through power sharing between power stations; while for power stations with charging demand, the charging power obtained through inter-station transactions is allocated according to the proportion relative to the total charging power demand. The specific calculation process is detailed below. Figure 3 ;

[0067] In this invention, after determining the mutual power between power plants, the Supply-Demand Ratio (SDR) model is used to determine the power interaction price between power plants. The essence of this model is to use the price to reflect the supply and demand relationship of energy, thereby reflecting the value difference of energy at different times. It should be noted that, since each new energy power plant does not need to pay costs when using physical energy storage (based on the form of investment cost amortization) under the co-construction and sharing model set by this invention, the power demand met by physical energy storage is not considered when calculating the SDR value.

[0068] As an auxiliary operating entity, the energy storage service platform does not have a profit-making purpose. Therefore, under the operating framework of co-construction and sharing of energy storage, the investment and construction costs and loss costs of energy storage are ultimately borne by each new energy power station. The energy storage service platform will comprehensively consider the actual use needs of the power station for energy storage and the efficiency of use to allocate the energy storage costs among the power stations.

[0069] In one embodiment of the present invention, step S2 involves calculating the energy storage operating cost to be allocated to each site based on the actual dispatched energy storage power and energy storage utilization efficiency of each site, combined with a weighting factor. This includes the following steps:

[0070] Let the probability of each scenario be... New energy power stations In the scene The rated energy storage power obtained by the lower allocation is The actual energy storage needs and energy storage efficiency of the aforementioned new energy power stations The energy storage cost allocation factors are respectively and ;

[0071] Assume the total cost of energy storage is The energy storage cost allocation factor and The weighting indicators are respectively and Then new energy power stations The energy storage operation cost to be borne .

[0072] Specifically, ; ; ;in, and In the aforementioned scenarios, the site In the The charging power supplied to physical energy storage and the charging power sold to external energy suppliers during a given period. and Scenes Next, station In the The charging and discharging power requirements during different time periods For new energy power stations In the scene The rated power of energy storage obtained by the allocation.

[0073] In one embodiment of the present invention, in step S3, the operating cost of the upper-level model includes the cost of power curtailment, the penalty for insufficient output, and the revenue from electricity sales. The method for obtaining the cost of power curtailment is as follows:

[0074] When the actual power generation capacity of a renewable energy power plant exceeds its planned on-grid power generation, the power plant reduces its actual power generation through wind and solar curtailment. The cost of this curtailment... Specifically:

[0075]

[0076] in, For the scene New energy power station In the Power curtailment during the time period Cost per unit of abandoned power For time period numbering, This represents the number of time periods within a single scheduling day.

[0077] Specifically, new energy power stations In a typical scheduling scenario The operating costs are covered by the cost of curtailment. Punishment for insufficient effort Revenue from electricity sales It consists of three parts;

[0078] Among them, the operating cost of the upper-level model ;

[0079] The curtailment cost is as follows: New energy power plants upload their day-ahead forecasted output to the grid. The grid determines the day-ahead on-grid power generation of each new energy power plant based on all power generation outputs and user load forecasts. To simplify the problem, this paper assumes that the forecasted output of each plant is the on-grid power generation agreed in the power purchase agreement with the grid. Due to output deviations in wind and solar power, when the actual power generation capacity of new energy exceeds the day-ahead planned on-grid power generation, the plant needs to adjust the operating status of wind and solar power equipment to reduce the actual power generation. The equipment loss costs and additional labor costs incurred during the adjustment process are modeled as curtailment costs. Obtain the cost of abandoned electricity;

[0080] The aforementioned insufficient power output penalty: When the actual grid-connected power of a renewable energy power plant is lower than the planned grid-connected power, the power grid will pay an insufficient power output penalty to the power grid based on the difference between the actual grid-connected power and the predicted power generation. ,in, For time period The actual power output to the grid is assumed here to be less than the planned power output from the power plants. Therefore, the actual power output to the grid must not exceed the planned power output. The penalty cost for power deviation per unit output. For new energy power stations In the scene Time period The predicted power generation capacity;

[0081] Wherein, the electricity sales revenue is: assuming the unit price of electricity sold from grid-connected renewable energy is... Then new energy power stations In the scene Revenue from grid-connected electricity sales ,in, For new energy power stations In the scene Time period The actual power sold to the grid;

[0082] The constraints include power balance constraints and virtual energy storage constraints.

[0083] The power balance constraint is specifically as follows: ; ; ;in, Describe the station The output power balance situation, the actual grid-connected power is obtained by the actual power generation capacity after calling energy storage charging and discharging and / or partially generating wind and solar power curtailment; and These are non-negative constraints on grid-connected power and curtailed power, respectively, while requiring that the actual grid-connected power not exceed the planned grid-connected power;

[0084] The virtual energy storage constraint is specifically: Equation 1). ; ; ;

[0085] Formula 2): ; ; where, Equation 1) represents the station The charging and discharging power constraints of virtual energy storage are invoked, where... and These are Boolean variables representing charging and discharging behaviors, respectively. For station Virtual energy storage and its energy state at different times. and These represent the charging and discharging efficiencies of energy storage, respectively. and Stations The maximum and minimum limits of the virtual energy storage state are defined to ensure the sustainable dispatch of energy storage at the end of the dispatch cycle. Requires initial value Maintain consistency.

[0086] In one embodiment of the present invention, in step S3, the objective function of the lower-level model is to minimize the total lifecycle cost of the energy storage system, wherein the total cost includes energy storage investment cost, energy storage operation loss cost, and external transaction costs with energy storage: the energy storage investment cost Specifically: ,in, and These are the rated capacity and power of the energy storage, respectively. and These represent the unit investment cost corresponding to the rated capacity and power of energy storage, respectively. , As a discount factor for the time value of cost, The discount rate is... This refers to the lifespan of the energy storage system.

[0087] Specifically, the energy storage service platform aggregates the energy storage power demand information of various new energy power plants, and optimizes the jointly invested and constructed energy storage capacity for the new energy power plant cluster with the goal of minimizing energy storage investment costs and operating costs; the objective function of the energy storage platform is cost minimization: 1) Energy storage investment cost Specifically: ,in, and These are the rated capacity and power of the energy storage, respectively. and These represent the unit investment cost corresponding to the rated capacity and power of energy storage, respectively. , As a discount factor for the time value of cost, The discount rate is... 1) Energy storage lifespan; 2) Energy storage operation loss cost ,in, and Scenes Under these circumstances, physical energy storage during the period Internal charging and discharging power, 3) External transaction costs for energy storage: Transaction costs between the energy storage service platform and external energy suppliers. ,in, and For external energy suppliers during the time period The power generation capacity of electricity sales and purchase transactions within the region. and This refers to the corresponding unit power trading price;

[0088] Therefore, the cost function of an energy storage service platform can be expressed as: ;

[0089] Among them, the constraints of the energy storage platform include the demand balance constraint of the site, the energy storage energy-power ratio constraint and the energy storage charging and discharging constraint;

[0090] The demand balance constraint of the station: Equation 1): ; Equation 2): ; In the formula: After aggregating the power demands of each lower-level new energy power station, the energy storage service platform obtains the net charging demand for energy storage through formula 1). or net discharge demand Equation 2) indicates that the power required to meet the needs of the lower layer consists of the charging and discharging power of the physical energy storage and the power traded with external energy suppliers.

[0091] The energy storage energy-power ratio constraint: ,in, The rate factor represents the physical energy storage capacity.

[0092] The energy storage charging and discharging constraints: ; ; ; ; .

[0093] In one embodiment of the present invention, step S3 of the two-layer optimization model includes the following steps:

[0094] The upper level is composed of each new energy power station as an independent decision-maker. Under the constraint of the allocated virtual energy storage capacity, each power station determines and reports its optimal charging and discharging power demand to the platform with the goal of minimizing its own operating costs.

[0095] The lower layer consists of an energy storage service platform that acts as the decision-maker. After aggregating the needs of all upper-level sites, it aims to minimize the total system cost, including investment and operating costs, to determine the optimal capacity and power scheduling scheme for physical energy storage and allocate virtual energy storage capacity to each site.

[0096] The two-level optimization model is solved through iterative interaction between upper-level and lower-level decisions until the optimal solution is obtained.

[0097] The solution employs a genetic algorithm to optimize the operational decisions of each upper-level new energy power station, and nests a Gurobi solver to iteratively solve the capacity configuration and scheduling model of the lower-level energy storage service platform until convergence.

[0098] Specifically, the two-layer optimization model is as follows: New energy power plants, after comprehensively considering day-ahead output deviation costs, wind and solar curtailment costs, and grid-connected electricity sales revenue, determine the power demand for energy storage in each scenario and time period, and upload their respective charging and discharging power demands for energy storage to the energy storage service platform. Upon receiving the demand information from each power plant, the energy storage service platform determines the physical energy storage capacity with the goal of minimizing energy storage investment and operating costs, and allocates virtual energy storage capacity for each scenario to each power plant according to the capacity allocation strategy described above. This invention uses a two-layer optimization model to model and solve the problem, with each new energy power plant as the upper layer and the energy storage service platform as the lower layer. The two-layer optimization model can then be expressed as follows: Figure 4 As shown, the solution can be obtained by nesting a genetic algorithm with the commercial Gurobi solver.

[0099] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters, characterized in that, Includes the following steps: S1: Establish a shared energy storage operation framework for new energy power plant clusters. Through the energy storage service platform, calculate the capacity allocation factor based on the predicted power generation and actual power generation capacity of each new energy power plant under different scenarios, and allocate virtual energy storage capacity to each power plant according to the capacity allocation factor. S2: The energy storage service platform prioritizes meeting the charging and discharging power demands reported by each site through power sharing between sites; it uses a pricing model based on the supply-demand ratio to determine the power exchange price between sites; and it calculates the energy storage operating cost to be shared by each site based on the actual dispatched energy storage power and energy storage utilization efficiency of each site, combined with weighting factors. S3: Construct a two-layer optimization model with new energy power plants as the upper layer and energy storage service platform as the lower layer; in the upper layer model, each power plant, under the constraint of the dynamically allocated virtual energy storage capacity, determines the charging and discharging power demand for each time period with the goal of minimizing its own operating cost; in the lower layer model, after aggregating the demand of all power plants, the energy storage service platform determines the optimal capacity configuration and power scheduling scheme of physical energy storage with the goal of minimizing the total cost of the energy storage system throughout its entire life cycle, and updates the total virtual energy storage capacity used for operation based on the optimal configuration.

2. The shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters according to claim 1, characterized in that, In step S1, the capacity allocation factor includes the following steps: Set scene set New energy power stations In the scene Below, each time period The predicted power generation and the actual power generation are respectively and ; The aforementioned new energy power station In the scene In this case, the capacity allocation factor is specifically: in, For new energy power stations In the scene At that time, the corresponding capacity allocation factor, This represents the total number of new energy power stations. The number of time periods within a scheduling day. Number the scene. Numbering of new energy power stations For time period numbering, For new energy power stations In the scene Time period The predicted power generation capacity, This corresponds to the actual power generation.

3. The shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters according to claim 1, characterized in that, In step S1, virtual energy storage capacity is allocated to each power station according to the capacity allocation factor. Specifically, the capacity allocation factor is multiplied by the total virtual energy storage capacity to obtain the virtual energy storage capacity of each new energy power station in the corresponding scenario. The virtual energy storage capacity is set to be greater than the physical energy storage configuration capacity.

4. The shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters according to claim 1, characterized in that, In step S2, the power exchange price between power plants is determined using a pricing model based on the supply-demand ratio, including the following steps: S201: The energy storage service platform aggregates the charging power demand and discharging power demand reported by all new energy power stations; when the discharging power demand is met, it is matched by the total charging power supply of all power stations. S202: If the total charging power demand is greater than the total discharging power demand, then the discharging power demand between stations will be fully met, and the remaining charging power demand will be allocated among the stations according to the proportion of each station's charging power demand to the total demand; if the total discharging power demand is greater than the total charging power demand, then the opposite will be true. S203: When calculating the supply-demand ratio, the charging and discharging power demand data used for calculation only includes the power exchanged between power stations achieved through steps S201 and S202, and the power portion satisfied through external energy resource scheduling.

5. The shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters according to claim 1, characterized in that, In step S2, based on the actual dispatched energy storage power and energy storage utilization efficiency of each site, and combined with weighting factors, the energy storage operating cost to be allocated to each site is calculated, including the following steps: Let the probability of each scenario be... New energy power stations In the scene The rated energy storage power obtained by the lower allocation is The actual energy storage needs and energy storage efficiency of the aforementioned new energy power stations The energy storage cost allocation factors are respectively and ; Assume the total cost of energy storage is The energy storage cost allocation factor and The weighting indicators are respectively and Then new energy power stations The energy storage operation cost to be borne .

6. The shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters according to claim 1, characterized in that, In step S3, the operating cost of the upper-level model includes the cost of power curtailment, the penalty for insufficient output, and the revenue from electricity sales. The method for obtaining the cost of power curtailment is as follows: When the actual power generation capacity of a renewable energy power plant exceeds its planned on-grid power generation, the power plant reduces its actual power generation through wind and solar curtailment. The cost of this curtailment... Specifically: in, For the scene New energy power station In the Power curtailment during the time period Cost per unit of abandoned power For time period numbering, This represents the number of time periods within a single scheduling day.

7. The shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters according to claim 1, characterized in that, In step S3, the objective function of the lower-level model is to minimize the total lifecycle cost of the energy storage system. This total cost includes energy storage investment cost, energy storage operation loss cost, and external transaction costs related to energy storage. The energy storage investment cost... Specifically: ,in, and These are the rated capacity and power of the energy storage, respectively. and These represent the unit investment cost corresponding to the rated capacity and power of energy storage, respectively. , As a discount factor for the time value of cost, The discount rate is... This refers to the lifespan of the energy storage system.

8. The shared energy storage dynamic capacity allocation operation mode and planning method for new energy power station clusters according to claim 1, characterized in that, In step S3, the two-layer optimization model includes the following steps: The upper level is composed of each new energy power station as an independent decision-maker. Under the constraint of the allocated virtual energy storage capacity, each power station determines and reports its optimal charging and discharging power demand to the platform with the goal of minimizing its own operating costs. The lower layer consists of an energy storage service platform that acts as the decision-maker. After aggregating the needs of all upper-level sites, it aims to minimize the total system cost, including investment and operating costs, to determine the optimal capacity and power scheduling scheme for physical energy storage and allocate virtual energy storage capacity to each site. The two-level optimization model is solved through iterative interaction between upper-level and lower-level decisions until the optimal solution is obtained. The solution employs a genetic algorithm to optimize the operational decisions of each upper-level new energy power station, and nests a Gurobi solver to iteratively solve the capacity configuration and scheduling model of the lower-level energy storage service platform until convergence.

Images