Power distribution area shared energy storage collaborative charging and discharging control method and storage medium

Abstract

The application discloses a power distribution area shared energy storage collaborative charging and discharging control method, which comprises the following steps: shared energy storage joint scheduling is performed; shared energy storage and multi-power distribution area collaborative scheduling is performed; a shared energy storage and multi-power distribution area collaborative zoning model is built; a dynamic zoning distributed collaborative scheduling optimization strategy is formed by considering the autonomous operation characteristics of the shared energy storage subsystem; a coupling variable is introduced to decompose the shared energy storage layered zoning collaborative charging and discharging scheduling architecture into a double-layer model of an upper-layer scheduling center main problem and a lower-layer SESS i sub-problem; based on a generalized Benders algorithm decoupling optimization solution, optimal distributed charging and discharging decisions of the distributed photovoltaic cluster and the shared energy storage multi-edge collaboration are obtained. The application also discloses a corresponding storage medium. According to the application, the shared energy storage and the multi-power distribution area collaborative charging and discharging power are optimized based on the principle of peak-valley arbitrage and maximum distributed photovoltaic consumption, the advantages of the shared energy storage system are fully brought into play, and the safe and stable operation ability of the low-voltage distribution network is improved.

Description

Technical Field

[0001] This invention relates to the field of shared energy storage and coordinated charging and discharging control in distribution radio stations, specifically to a method and storage medium for shared energy storage and coordinated charging and discharging control in distribution radio stations. Background Technology

[0002] With the development of new energy technologies, a large number of distributed photovoltaic (PV) systems have been connected to low-voltage distribution networks. However, due to the inherent reverse peak-shaving characteristics of their power generation load, the peak-to-valley load difference is constantly increasing, resulting in a large amount of curtailment of solar power. Therefore, it is necessary to improve the system's PV absorption capacity through energy storage devices and corresponding active management strategies to avoid problems such as heavy overload and ensure the safe and stable operation of the low-voltage distribution network.

[0003] Shared energy storage, as a novel energy storage operation model, has developed rapidly in recent years. Its complex interaction with multiple distributed resources presents new challenges to the collaborative management of distribution substations. Currently, source-storage collaborative scheduling models can be divided into centralized optimization scheduling models and distributed optimization scheduling models. The latter can be further divided into consensus algorithms and coordination decomposition algorithms, which allow the demands of individual new energy sources and energy storage systems to be freely expressed through interactive operational status information. However, the aforementioned distribution substation scheduling models focus more on medium- and high-voltage distribution networks and emphasize source-grid-load-storage collaborative optimization to reduce the impact of new energy processing volatility, without considering the impact of distributed photovoltaic consumption and shared energy storage systems (SESS) on source-storage coordination decisions. Therefore, it is necessary to construct a complex physical topology and information interaction topology for shared energy storage and distributed photovoltaic systems in low-voltage distribution substations to enable decomposition and coordination algorithms to reconstruct and optimize the scheduling model. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and storage medium for coordinated charging and discharging of shared energy storage in distribution substations. Based on the principles of peak-valley arbitrage and maximizing the absorption of distributed photovoltaic power, the method optimizes the coordinated charging and discharging power of shared energy storage and multiple distribution substations, fully leverages the advantages of the shared energy storage system, and improves the safe and stable operation capability of low-voltage distribution networks.

[0005] The technical solution adopted in this invention is as follows: One aspect of this invention provides a method for coordinated charging and discharging control of shared energy storage in a distribution substation, comprising the following steps:

[0006] Step S1: Consider the joint scheduling method of peak-valley arbitrage and new energy consumption to obtain shared energy storage;

[0007] Step S2: Obtain a collaborative scheduling method for shared energy storage and multiple distribution stations based on the dynamic capacity leasing model;

[0008] Step S3: Based on the methods proposed in Steps S1 and S2, a collaborative partitioning model for shared energy storage and multiple distribution stations is built with the objective function of maximizing the total revenue of distributed photovoltaic clusters.

[0009] Step S4: Considering the autonomous operation characteristics of the shared energy storage subsystem, obtain the hierarchical and partitioned collaborative charging and discharging scheduling architecture of the shared energy storage, and form a dynamic partitioned distributed collaborative scheduling optimization strategy;

[0010] Step S5 introduces coupling variables to decompose the shared energy storage hierarchical and partitioned collaborative charging and discharging scheduling architecture into the upper-level scheduling center master problem and the lower-level SESS. i A two-layer model of subproblems;

[0011] Step S6: Based on the decoupling optimization solution of the generalized Benders algorithm, the optimal distributed charging and discharging decision of the distributed photovoltaic cluster and the shared energy storage multilateral collaboration is obtained.

[0012] Preferably, step S1 further includes:

[0013] Considering that SESS prioritizes meeting the fluctuation smoothing and auxiliary peak-shaving needs of users in the low-voltage distribution area of ​​new energy, and aims to maximize the total revenue from participating in peak-valley arbitrage and new energy consumption, a shared energy storage dispatch strategy is formulated. The total revenue of SESS participating in the joint dispatch of peak-valley arbitrage and new energy consumption at time t is obtained by the following formula (1). for:

[0014]

[0015] In the formula, and The average charging and discharging power of the shared energy storage system under peak-valley arbitrage mode after completing internal mutual assistance within time period t; The average charging power of the shared energy storage system participating in new energy consumption during time period t; The probability of user reporting volume and new energy power output scenario s; The energy price at time t and in the scenario s of renewable energy output under the peak-valley arbitrage model in the power distribution area; The highest marginal clearing price for the renewable energy consumption scenario s at time t under the renewable energy consumption model in the power generation area; For time-of-use electricity pricing on the power grid; λ tr Δt represents the power grid transmission and distribution price; Δt represents the time interval between peak-valley arbitrage and joint dispatch of new energy consumption.

[0016] Preferably, step S2 further includes:

[0017] Considering the differences in energy storage capacity and power leasing demand among various low-voltage distribution substations, energy storage resources are decomposed into peak-valley arbitrage resources and new energy consumption resources, which are provided to substation users respectively. A shared energy storage and multi-distribution substation collaborative scheduling method based on dynamic capacity leasing model is constructed with the objective function of maximizing multilateral capacity leasing revenue.

[0018] Among them, the subsystem SESS within time period t i Multilateral capacity leasing revenue This includes revenue from capacity leasing and revenue from electricity dispatch, as shown below:

[0019]

[0020]

[0021]

[0022] In the formula, For SESS i The set of users of the new energy low-voltage distribution transformer area in the sub-region; assuming For the subsystem SESS i The negotiated price for energy storage charging capacity resources and the negotiated price for discharging capacity resources reached during time period t′. For the subsystem SESS i Resold to t during period t cl The price per time slot; For users in the new energy low-voltage distribution area, does rs achieve power capacity coordinated scheduling and matching with SESS at time t′? i The Boolean variable indicating when rs matches SESS i ,but on the contrary And the default power capacity for scheduling charging and discharging is For users in the new energy low-voltage distribution area, does rs achieve power capacity coordinated scheduling and matching with SESS at time t? i Indicator Boolean variable; The amount of electricity sold to users in the distribution area due to the use of discharge power capacity; For SESS i According to the negotiated results, the total energy of users in the transformer area will be absorbed during the t-period of the curtailment period. Electricity purchased from new energy power station users due to the use of charging power capacity; Let t be the time-of-use electricity price of the power grid at time t; Let t be the grid purchase price at time t; Δt′ be the multilateral power capacity lease time interval, and Δt=N ms Δt′.

[0023] Preferably, step S3 further includes:

[0024] Step S30: To distinguish between the main and subsystems, the SESS main system is denoted as SESU, and each sub-region... The SESS subsystem is denoted as {SESS1, SESS2, ..., SESS}. N The main system SESU controls all subsystems, and each subsystem SESS controls the overall system. i Establish an independent control center to be responsible for the internal energy storage battery pack B SESSi While performing data acquisition and power allocation management, it also conducts capacity sharing and collaborative scheduling with nearby new energy power plants;

[0025] Step S31: Establish the objective function for multilateral collaborative scheduling of shared energy storage and new energy low-voltage distribution areas. The overall objective function includes the total revenue of SESS participating in peak-valley arbitrage and joint scheduling of renewable energy consumption, the revenue from multilateral capacity leasing between the shared energy storage subsystem and the renewable low-voltage distribution area, and the cyclic loss cost of each shared energy storage subsystem. The overall objective function is as follows:

[0026]

[0027] In the formula, The total revenue of shared energy storage participating in peak-valley arbitrage and joint dispatch of new energy consumption during time period t; SESS within SESU during time period t i The total revenue from multilateral capacity leasing transactions between independent and new energy low-voltage distribution transformer area users; C bt,t SESS for time period t i This includes the equivalent cycle loss cost generated by the battery pack BT.

[0028] The capacity loss cost of the battery can be obtained from the capacity loss rate of the battery pack BT, specifically:

[0029]

[0030] In the formula, Configuration cost per unit power capacity for battery pack BT; Configuration cost per unit capacity of battery pack BT; This refers to the rated power of the battery pack BT. The initial capacity of the battery pack bt; r SESS The annual cycle loss rate of the SESS battery pack; y bt,t Let t be the operating year of battery pack bt at time t.

[0031] t-moment shared energy storage SESSi The included equivalent cycle loss cost of the battery pack (BT) includes the cost of capacity loss and equivalent operation and maintenance costs incurred during operation within this time interval, specifically:

[0032]

[0033] In the formula, For SESS i The included annual inspection, maintenance, and upkeep costs per unit power of the battery pack;

[0034] Step S32: Determine the constraints of the shared energy storage SESU, including: constraints related to peak-valley arbitrage-new energy consumption joint scheduling, multilateral capacity coordination constraints, subsystem charging and discharging power constraints, and shared energy storage battery pack SOC constraints.

[0035] Preferably, step S4 further includes:

[0036] A hierarchical distributed scheduling method is introduced to decompose the scheduling optimization model established in step S3. By setting up a shared energy storage scheduling center within the shared energy storage SESU, the original shared energy storage SESU collaborative scheduling model is decomposed into a centralized joint scheduling layer with the shared energy storage scheduling center as the main body and a scheduling layer with each SESS within the shared energy storage system as the main body. i The subsystem members are independent entities forming a multilateral collaborative operation layer, further developing into a shared upper-level energy storage dispatch center that controls multiple lower-level SESS systems. i A collaborative distributed operation framework for subsystems;

[0037] In the aforementioned collaborative distributed operation framework, the upper-level shared energy storage dispatch center is responsible for collecting and predicting dispatch information from various entities participating in the joint dispatch of peak-valley arbitrage and new energy consumption within the region; the lower-level subsystems SESS i It is responsible for independently participating in multilateral capacity coordination and scheduling, negotiating with new energy transformer area users matched with contracts, and managing and monitoring the operating status of internal battery packs in real time.

[0038] The upper-level shared energy storage dispatch center and the lower-level individual SESS i The two systems interact in real time through the internal communication network of the energy storage system, exchanging information on power interaction coupling variables related to the joint scheduling strategy.

[0039] During operation, each SESS located in the lower layer i Independently negotiates multilateral capacity leasing plans with users in the new energy distribution area, and formulates power allocation plans for internal energy storage batteries according to the leasing plan requirements, transmitting the necessary dispatch request information to the upper layer; the shared energy storage dispatch center located at the upper layer analyzes the data from each SESS... iThe sent request information, based on the prediction information, coordinates and optimizes the scheduling plans and internal interaction allocation of each subsystem, and then sends it down to the lower-level SESS. i Issue relevant scheduling decision adjustment instructions; each SESS, based on the received scheduling adjustment instructions, negotiates and modifies the original multilateral capacity leasing plan formulated with new energy transformer area users, and adjusts the internal energy storage battery power allocation decision.

[0040] Preferably, step S5 further includes:

[0041] Based on the framework obtained in step S4, the shared energy storage and multi-distribution station area collaborative zoning model in step S3 is decomposed to form the upper-level shared energy storage dispatch center sub-problem F. SEDC With lower-level subproblems Introducing coupling variables SESS represents the subsystem i Information shared with the upper-level energy storage dispatch center, including power strategies for participating in peak-valley arbitrage and renewable energy consumption joint dispatch, and discharge power strategies under power capacity service, is introduced to decouple variables for interaction between the upper and lower layers. And the corresponding decoupling constraints;

[0042] Formula (5) is decomposed into an upper-level scheduling center subproblem and a lower-level SESS subproblem. i Sub-problems.

[0043] Preferably, step S6 further includes:

[0044] Decoupling optimization based on the generalized Benders algorithm enables the lower-level SESS to be optimized. i Limited information exchange with the upper-layer shared energy storage dispatch center is achieved by transmitting Benders cut set constraints containing desired dispatch strategy information. The upper-layer shared energy storage dispatch center then satisfies all SESS (Self-Enhanced Energy Storage) constraints. i The sent Benders cut set constraint realizes local optimization of the charging and discharging strategy that takes into account the expected scheduling of each other.

[0045] Accordingly, another aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method as described above.

[0046] Implementing the embodiments of the present invention has the following beneficial effects:

[0047] This invention provides a method for coordinated charging and discharging control of shared energy storage in distribution substations and a storage medium. By determining a joint scheduling method for shared energy storage that considers peak-valley arbitrage and renewable energy consumption, and a coordinated scheduling method for shared energy storage and multiple distribution substations under a dynamic capacity leasing model, a collaborative partitioning model for shared energy storage and multiple distribution substations is built with the objective function of maximizing the total revenue from distributed photovoltaic cluster consumption.

[0048] In this invention, by further considering the autonomous operation characteristics of the shared energy storage subsystem, a hierarchical and partitioned collaborative charging and discharging scheduling architecture for shared energy storage is proposed, forming a dynamic partitioned distributed collaborative scheduling optimization model for shared energy storage. The upper layer optimizes the overall charging and discharging power of shared energy storage in each time period based on a joint scheduling method under the energy storage capacity retention mechanism, while the lower layer formulates internal energy storage battery pack power allocation decisions based on a multilateral collaborative mechanism of energy storage capacity. Through inter-layer information interaction, the system is continuously adjusted to achieve optimal operation.

[0049] In this invention, the shared energy storage hierarchical and partitioned collaborative charging and discharging scheduling architecture is decomposed into an upper-level scheduling center master problem and a lower-level SESS by introducing coupling variables. i The subproblem is solved by iterative interaction between the upper-level shared energy storage expected power decision and the lower-level Benders cut set constraint to achieve decoupling optimization of the two-level model, thus obtaining the optimal distributed charging and discharging decision for multilateral collaboration between distributed photovoltaic clusters and shared energy storage.

[0050] By implementing this invention, the charging and discharging power of the low-voltage distribution network of new energy is optimized by using a shared energy storage system peak-valley arbitrage-new energy consumption joint scheduling strategy, thereby improving the distributed photovoltaic consumption capacity of the low-voltage distribution network and shortening the cost recovery period of shared energy storage. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0052] Figure 1 This is a schematic diagram of the main flow of an embodiment of a shared energy storage coordinated charging and discharging control method for distribution radio stations provided by the present invention;

[0053] Figure 2 for Figure 1 A schematic diagram of the shared energy storage collaborative distributed operation framework established in China. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0055] like Figure 1 The diagram shown illustrates the main flow of an embodiment of a shared energy storage coordinated charging and discharging control method for distribution substations provided by the present invention; in conjunction with... Figure 2 As shown, in this embodiment, the method includes at least the following steps:

[0056] Step S1: Consider the joint scheduling method of peak-valley arbitrage and new energy consumption to obtain shared energy storage.

[0057] Specifically, for large-scale shared energy storage systems that can serve multiple distribution substations on the new energy side, in addition to undertaking tasks such as power fluctuation smoothing, predicted power deviation compensation, and curtailment of solar power in the corresponding substations, they can also leverage their scale advantage to maximize the use of the remaining energy storage space after sharing, increase profit margins through peak-valley arbitrage, and improve cost recovery efficiency while ensuring the safe and reliable operation of the low-voltage distribution network.

[0058] SESS prioritizes smoothing out predicted power fluctuations from new energy sources and serves as a peak-shaving resource for low-voltage distribution substations to help them reduce or waive paid peak-shaving sharing fees. The remaining energy storage space participates in peak-valley arbitrage and new energy consumption joint scheduling, and engages in power exchange or provides paid peak-shaving services with other entities in the low-voltage distribution network.

[0059] Considering that SESS prioritizes meeting the fluctuation smoothing and auxiliary peak-shaving needs of users in the low-voltage distribution area of ​​renewable energy, a shared energy storage dispatch strategy is formulated with the goal of maximizing the total revenue from participating in peak-valley arbitrage and renewable energy consumption. The total revenue of SESS participating in the joint dispatch of peak-valley arbitrage and renewable energy consumption at time t is... for:

[0060]

[0061] In the formula, and The average charging and discharging power of the shared energy storage system under peak-valley arbitrage mode after completing internal mutual assistance within time period t; The average charging power of the shared energy storage system participating in new energy consumption during time period t; The probability of user reporting volume and new energy power output scenario s; The energy price at time t and in the scenario s of renewable energy output under the peak-valley arbitrage model in the power distribution area; The highest marginal clearing price for the renewable energy consumption scenario s at time t under the renewable energy consumption model in the power generation area; For time-of-use electricity pricing on the power grid; λ trΔt represents the power grid transmission and distribution price; Δt represents the time interval between peak-valley arbitrage and joint dispatch of new energy consumption.

[0062] Step S2: Obtain a method for coordinated scheduling of shared energy storage and multiple distribution stations based on the dynamic capacity leasing model.

[0063] Specifically, under the traditional supporting leasing model, since SESS itself can only be in a single charging / discharging state at any given time, when it forms a supporting relationship with multiple distribution substations, the distribution substations cannot simultaneously provide diversified charging and discharging services, resulting in each substation not having complete energy storage usage rights. For a low-voltage distribution substation with complete energy storage usage rights, the tasks it completes using energy storage can be summarized into two categories: the "energy time-shifting" task, which involves transferring energy across time periods to achieve peak price excess profits, and the "power support" task, which involves addressing the fluctuations and deviations in renewable energy output during intermittent output periods to reduce assessment costs, corresponding to peak-valley arbitrage and renewable energy consumption, respectively. Therefore, the energy storage usage rights of a distribution substation can be decomposed into the right to use energy capacity and the right to use power capacity. The right to use energy capacity refers to the right of a distribution substation to rent energy storage capacity space to store energy during specific low-price periods and release and sell it during specific peak-price periods; the right to use power capacity refers to the right of renewable energy stations to rent energy storage charging and discharging power capacity space during specific periods and to flexibly control charging and discharging within the rented power capacity space to provide power compensation based on power output fluctuations and prediction deviations.

[0064] To address this, considering the differences in energy storage capacity and power leasing needs among various low-voltage distribution substations, energy storage resources are decomposed into peak-valley arbitrage resources and new energy consumption resources, which are provided to substation users respectively. A shared energy storage and multi-distribution substation collaborative scheduling method based on dynamic capacity leasing model is constructed with the objective function of maximizing multilateral capacity leasing revenue.

[0065] Subsystem SESS within time period t i Multilateral capacity leasing revenue This includes revenue from capacity leasing and revenue from electricity dispatch, as shown below:

[0066]

[0067]

[0068]

[0069] In the formula, For SESS i The set of users of the new energy low-voltage distribution transformer area in the sub-region; assuming For the subsystem SESS iThe negotiated price for energy storage charging capacity resources and the negotiated price for discharging capacity resources reached during time period t′. For the subsystem SESS i The commission rate for reselling from time period t to time period tcl; For users in the new energy low-voltage distribution area, does rs achieve power capacity coordinated scheduling and matching with SESS at time t′? i The Boolean variable indicating when rs matches SESS i ,but on the contrary And the default power capacity for scheduling charging and discharging is For users in the new energy low-voltage distribution area, does rs achieve power capacity coordinated scheduling and matching with SESS at time t? i Indicator Boolean variable; The amount of electricity sold to users in the distribution area due to the use of discharge power capacity; For SESS i According to the negotiated results, the total energy of users in the transformer area will be absorbed during the t-period of the curtailment period. Electricity purchased from new energy power station users due to the use of charging power capacity; Let t be the time-of-use electricity price of the power grid at time t; Let t be the grid purchase price at time t; Δt′ be the multilateral power capacity lease time interval, and Δt=N ms Δt′.

[0070] Step S3: Based on the methods proposed in Steps S1 and S2, a collaborative zoning model for shared energy storage and multiple distribution areas is built with the objective function of maximizing the total revenue of distributed photovoltaic clusters.

[0071] Under this collaborative zoning model, SESS establishes energy storage subsystems near the grid connection points of distributed photovoltaic clusters in different low-voltage distribution substations to provide localized services to the distributed photovoltaic clusters in the region. On the one hand, it signs long-term supporting lease contracts with multiple new energy low-voltage distribution substations, thereby forming a clear supporting relationship to meet the mandatory energy storage configuration requirements of new energy low-voltage distribution substations. These substations have priority access to the leased energy storage capacity space and use this energy storage to achieve their own new energy fluctuation smoothing, deviation compensation, and peak-shaving tasks. On the other hand, it utilizes the remaining energy storage space after sharing to participate in peak-valley arbitrage-new energy consumption joint scheduling, obtain peak price excess revenue, and optimize it to the electricity cost required to provide new energy consumption resource leasing services.

[0072] Step S30: To distinguish between the main and subsystems, the SESS main system is denoted as SESU, and each sub-region... The SESS subsystem is denoted as {SESS1, SESS2, ..., SESS}.N The main system SESU controls all subsystems, and each subsystem is controlled by SESS. i An independent control center will be established to oversee the internal energy storage battery packs. While collecting data and managing power allocation, it also conducts capacity sharing and collaborative scheduling with nearby new energy power plants.

[0073] Step S31: Consider the interaction and linkage of SESS subsystems in different sub-regions to form a shared energy storage and multi-distribution area collaborative zoning model that jointly participates in peak-valley arbitrage-new energy consumption joint scheduling and multilateral capacity collaborative scheduling, with the objective function of maximizing the total revenue of distributed photovoltaic cluster consumption.

[0074] First, establish a multilateral collaborative scheduling objective function for shared energy storage and new energy low-voltage distribution areas. The objective function includes the total revenue from SESS's participation in peak-valley arbitrage and joint dispatch of renewable energy consumption, the revenue from multilateral capacity leasing between the shared energy storage subsystem and the renewable low-voltage distribution area, and the cyclic loss costs of each shared energy storage subsystem. The overall objective function is as follows:

[0075] SESS within SESU i The total revenue from multilateral capacity leasing transactions between independent and new energy low-voltage distribution transformer area users; C bt,t SESS for time period t i It includes the equivalent cycle loss cost generated by the battery pack BT.

[0076] The battery capacity loss cost can be obtained from the battery capacity loss rate of the battery pack BT. This cost is related to its initial configuration cost, specifically:

[0077]

[0078] In the formula, Configuration cost per unit power capacity for battery pack BT; Configuration cost per unit capacity of battery pack BT; This refers to the rated power of the battery pack BT. The initial capacity of the battery pack bt; r SESS The annual cycle loss rate of the SESS battery pack; y bt,t Let t be the operating year of battery pack bt at time t.

[0079] t-moment shared energy storage SESS i The included equivalent cycle loss cost of the battery pack BT includes the cost of capacity loss and equivalent operation and maintenance costs incurred during operation within this time interval.

[0080]

[0081] In the formula, For SESS i The included annual inspection, maintenance, and upkeep costs per unit power of the battery pack.

[0082] Step S32: Determine the constraints of the shared energy storage SESU, including: constraints related to peak-valley arbitrage-new energy consumption joint scheduling, multilateral capacity coordination constraints, subsystem charging and discharging power constraints, and shared energy storage battery pack SOC constraints.

[0083] 1) Peak-valley arbitrage - Constraints related to the joint dispatch of renewable energy consumption

[0084] In the peak-valley arbitrage mode, the negotiated amount of a shared energy storage SESU is equal to the sum of the net negotiated amounts after all internal members have completed energy sharing. When the sum of the discharge power demands of all subsystems exceeds the charging power, the SESU chooses to participate in peak-valley arbitrage through discharge; otherwise, it participates through charging.

[0085]

[0086]

[0087]

[0088]

[0089] In the formula, and Subsystem SESS i The charging negotiated power demand and discharging negotiated power demand given at time t′; and These represent the net charging negotiated power and net discharging negotiated power of SESU under the peak-valley arbitrage mode at time t′, respectively. For SESS i A Boolean variable indicating whether the device is in a charging state at time t′, where 1 indicates a discharging state and 0 indicates a charging state; M is a maximal positive real number.

[0090] Among them, the actual negotiated amount by which SESU participates in peak-valley arbitrage through discharge during peak electricity price periods is the net discharge power of the internal subsystem and the renewable energy curtailment power of the subsystem in the coordinated scheduling of power capacity. The sum of these factors, under the dynamic leasing model, dictates the power balance constraints for shared energy storage and new energy distribution areas to collaboratively participate in peak-valley arbitrage as follows:

[0091]

[0092] In the formula, G emThis refers to a collection of thermal power units operating under the peak-valley arbitrage model. Let g be the thermal power unit at time t at the le g Segment reporting volume; U em This refers to the collection of users in the transformer area under the peak-valley arbitrage model. For user u in the platform area at time t, at the le u Segment report volume; PV em This refers to a distributed photovoltaic system operating under a peak-valley arbitrage model. Let t be the reported value of distributed photovoltaic PV at time t.

[0093] To prevent improper SESU scheduling from affecting normal system operation, the amount of SESU's negotiated scheduling should be limited to a certain range, namely:

[0094]

[0095]

[0096] In the formula, and These represent the maximum negotiated scheduling quantities during charging and discharging of the shared energy storage main system, respectively. Within the same time interval Δt, the SESU can only participate in peak-valley arbitrage through either charging or discharging, i.e.:

[0097]

[0098]

[0099] In the formula, This is a Boolean variable indicating whether the SESU exhibits discharge behavior under peak-valley arbitrage mode at time t′. A value of 1 indicates that the shared energy storage master system participates in peak-valley arbitrage through discharge at time t′. In this case, the SESU cannot participate through charging during the time period t containing time t′. Let t″ be any sub-time within the time period t. It must be 0, as expressed in equation (16).

[0100] The amount of shared energy storage SESU participating in renewable energy consumption scheduling at time t is equal to the total renewable energy consumption scheduling amount of each internal subsystem at time t′ within time t, that is:

[0101]

[0102] In the formula, For the subsystem SESS i The charging power participating in renewable energy consumption at time t′. Specifically, the actual renewable energy consumption capacity of SESU during peak-shaving periods equals the total dispatch volume of shared energy storage renewable energy consumption and the charging power of each subsystem absorbing renewable energy waste during power capacity negotiation and dispatch. The sum of these factors, under the dynamic leasing model, dictates the power balance constraints for shared energy storage participating in renewable energy consumption as follows: That is:

[0103]

[0104] 2) Multilateral capacity coordination constraints

[0105] In power capacity coordinated scheduling, SESS i According to the negotiated results, the total energy from users in the renewable energy distribution area will be absorbed during the t-period of solar power curtailment. And during the designated peak electricity price period t cl Released within a time period, the charging power at each time t′ within that time period. The range can vary within the range given by the users in the new energy distribution area, which is expressed as equations (19)-(20):

[0106]

[0107]

[0108] In the formula, and These are the original dispatched power capacity for users in the new energy distribution area. Given the upper and lower limits of the allowable power absorption at time t′, the negotiated upper and lower limits vary. Change proportionally.

[0109] In addition, the multilateral capacity coordination constraints also include multiple constraints on the matched new energy distribution area users and the corresponding KKT optimality conditions, namely...

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] In the formula, θ SPd θ SPc θ SE These represent the percentage range that can be adjusted for new energy vehicle users; For new energy transformer area users rs at time t, the dual variable is the discharge power capacity resource negotiation constraint. The dual variable for the negotiation constraint of charging power capacity resources for users rs in the new energy distribution area at time t; For new energy transformer area users rs, the dual variable of the power capacity resource negotiation constraint at time t is rs.

[0116]

[0117]

[0118] Formulas (26) and (27) together constitute the KKT maximum The superiority condition, where equation (26) is the complementary relaxation constraint in the KKT condition, which is composed of the basic form 0≤X⊥Y≥0, meaning X≥0,Y≥0,X·Y=0. In the above equation, both X and Y contain variables, that is, X⊥Y is a nonlinear term, which can be linearized using the classic Big M method.

[0119] 3) Power constraints of shared energy storage subsystem

[0120] From the perspective of power balancing, each SESS within SESU i The total charging power equals the sum of the charging power participating in peak-valley arbitrage, the charging power participating in renewable energy consumption, the charging power called by users of charging power capacity leasing, and the charging power called by users of electricity capacity leasing; the total discharging power equals the sum of the discharging power participating in peak-valley arbitrage, the discharging power participating in renewable energy consumption, and the discharging power called by users of discharging power capacity leasing, that is:

[0121]

[0122]

[0123]

[0124] In the formula, To indicate whether time t′ falls within the user-specified peak electricity price t cl A Boolean variable for a time period, where a value of 1 represents the specified peak electricity price at time t′. cl Within a time period, or conversely, outside of a time period.

[0125] From the perspective of the battery pack within the subsystem, SESS i The total charging and discharging power is equal to the sum of the charging and discharging power of each battery pack within it, that is:

[0126]

[0127] In the formula, and They belong to SESS i The charging and discharging power of the battery pack bt at time t′ should be limited to the upper and lower limits of the allowable range, i.e.:

[0128]

[0129]

[0130] In the formula, and They belong to SESS i The upper limit of the charge and discharge power of the battery pack bt at time t′. For a single SESS i Introducing SESS i Boolean variable of charging state To constrain SESS i At any time t′, it can only be in one state: charging or discharging, that is:

[0131]

[0132]

[0133] 4) SOC constraints of shared energy storage battery packs

[0134] The state of charge (SOC) of each battery pack within the shared energy storage system (SESS) bt，t′ for:

[0135]

[0136] In the formula, η ch,bt η dis,bt SESS i The charging and discharging efficiency of the internal battery pack (BT); The initial state of charge of the battery pack bt; t′0 and t′ end These represent the initial and final times of the battery pack BT scheduling. The state of charge (SOC) of the battery pack BT should be maintained within the allowable range. bt,min ,soc bt,max ], that is:

[0137]

[0138] Step S4: Considering the autonomous operation characteristics of the shared energy storage subsystem, obtain the hierarchical and partitioned collaborative charging and discharging scheduling architecture of the shared energy storage, and form a dynamic partitioned distributed collaborative scheduling optimization strategy.

[0139] The model proposed in step S3 involves all SESS i The matching new energy distribution area user multi-sided capacity coordination constraints and internal battery pack operation constraints will integrate the distributed SESS iThe subsystem scheduling problem was transformed into a unified scheduling problem for optimization, neglecting the autonomous operation characteristics of the subsystems within SESU. This resulted in the need to collect and exchange massive amounts of information, such as energy storage battery parameters and operating status, within each subsystem during the scheduling optimization process. Meanwhile, the subsystem SESS... i Multilateral capacity collaborative scheduling negotiations need to be conducted by energy storage zones within sub-regions. Therefore, centralized scheduling restricts the autonomous operation of new energy distribution area users among participating sub-regions. To address this, a hierarchical distributed scheduling method is introduced to decompose the scheduling optimization model established in step S3. By setting up a shared energy storage SESU with an internal shared energy storage scheduling center, the original shared energy storage SESU collaborative scheduling model is decomposed into a centralized joint scheduling layer with the shared energy storage scheduling center as the main body and a layer with each SESS within the shared energy storage system as the main body. i The subsystem members are independent entities forming a multilateral collaborative operation layer, further developing into a shared upper-level energy storage dispatch center that controls multiple lower-level SESS systems. i The collaborative distributed operation framework of the subsystem is as follows: Figure 2 As shown. The specific operating mechanism of this layered distributed runtime framework is as follows:

[0140] In this framework, the upper-level shared energy storage dispatch center is responsible for collecting and forecasting dispatch information from various entities participating in the joint dispatch of peak-valley arbitrage and new energy consumption within the region; the lower-level subsystems, SESS... i It is responsible for independently participating in multilateral capacity collaborative scheduling, negotiating with new energy distribution area users matched with contracts, and managing and monitoring the operating status of internal battery packs in real time; the upper layer shares the energy storage dispatch center and the lower layer is various SESS i The energy storage systems communicate in real-time via their internal communication network, exchanging information about power interaction coupling variables related to the joint scheduling strategy. During operation, each SESS located at the lower level... i Independently negotiates multilateral capacity leasing plans with users in the new energy distribution area, and formulates power allocation plans for internal energy storage batteries according to the leasing plan requirements, transmitting the necessary dispatch request information to the upper layer; the shared energy storage dispatch center located at the upper layer analyzes the data from each SESS... i The sent request information, based on the prediction information, coordinates and optimizes the scheduling plans and internal interaction allocation of each subsystem, and then sends it down to the lower-level SESS. i Issue relevant scheduling decision adjustment instructions to achieve optimal overall operational efficiency of shared energy storage; each SESS, based on the received scheduling adjustment instructions, negotiates and modifies the original multilateral capacity leasing plan formulated with new energy transformer area users, and adjusts the internal energy storage battery power allocation decision to achieve optimal operational efficiency of the SESS subsystem.

[0141] Therefore, the upper-level shared energy storage dispatch center, as the coordinator and manager for the joint dispatch of peak-valley arbitrage and new energy consumption by shared energy storage in the region, communicates with the various subsystems (SESS) within the region. i Coordination and interaction among them enable the formulation of joint scheduling decisions for peak-valley arbitrage and renewable energy consumption, as well as the internal power interaction management within the system, ensuring the smooth operation of all SESS within the overall system. i The independence of multilateral collaboration and battery pack power allocation scheduling improves the overall operational profitability of SESS, forming a dynamic partitioned distributed collaborative scheduling optimization strategy. Under this strategy, SESS in each sub-region... i With equal status, their scheduling decisions are uniformly managed by the superior shared energy storage scheduling center, and the multilateral capacity coordination and battery pack power allocation autonomy within the sub-region are enhanced.

[0142] Step S5 introduces coupling variables to decompose the shared energy storage hierarchical and partitioned collaborative charging and discharging scheduling architecture into the upper-level scheduling center master problem and the lower-level SESS. i A two-layer model of subproblems.

[0143] Based on the framework obtained in step S4, the shared energy storage and multi-distribution station area collaborative zoning model in step S3 is decomposed to form the upper-level shared energy storage dispatch center sub-problem F. SEDC With lower-level subproblems Introducing coupling variables SESS (subsystem) i Information shared with the upper-level energy storage dispatch center, including power strategies for participating in peak-valley arbitrage and renewable energy consumption joint dispatch, and discharge power strategies under power capacity service, is introduced to decouple variables for interaction between the upper and lower layers. And the corresponding decoupling constraint is:

[0144]

[0145]

[0146] Formula (5) is decomposed into an upper-level scheduling center subproblem and a lower-level SESS subproblem. i Sub-problems. Among them, the lower-level SESS is determined by KKT conditions. i The subproblem is transformed into a concave quadratic objective function:

[0147]

[0148] Lower layer SESS i The constraints of the subproblems include equations (19)-(38). The total optimization variables of the lower-level subproblems involved in the above constraints include continuous variables of multilateral capacity collaborative scheduling and battery pack internal processes, Boolean variables of energy storage charging and discharging states, and introduced Boolean variables, specifically:

[0149]

[0150] The objective function of the main problem of the upper-level scheduling center is given by equation (1), which contains nonlinear products. Applying the linear relaxation method, slack variables are introduced. The new objective function of the upper-level scheduling center subproblem is obtained as follows:

[0151]

[0152] In the above formula, the relationship between slack variables and the scheduling amount of SESS is as follows:

[0153]

[0154]

[0155] In the formula, variables related to new energy consumption are expressed in the form of superscript d. Let these be auxiliary real variables for the segmented intervals where shared energy storage participates in the optimization of new energy consumption power. The sum of these two variables equals the corresponding segmented Boolean variable. To optimize the power consumption of new energy sources without the participation of shared energy storage at times s and t, i.e., the amount of load reduction by the unit under paid peak shaving without the participation of energy storage. To optimize the power consumption of new energy sources when energy storage is involved. In addition to the above equation, the mixed integer linear constraints corresponding to each slack variable are as follows (45).

[0156]

[0157] In addition, constraints (8)-(17) are also included, where For the uncoupled optimization variables involved in these constraints, This is the overall optimization variable for the upper layer.

[0158] Step S6: Based on the decoupling optimization solution of the generalized Benders algorithm, obtain the optimal distributed charging and discharging decision for multilateral collaboration between distributed photovoltaic clusters and shared energy storage.

[0159] The large number of logical variables involved in the upper-level scheduling model makes it impossible to solve the problem using the conventional ADMM distributed method, while the Generalized Benders Algorithm (GBD Algorithm) can be used to solve mixed-integer nonlinear distributed problems. The basic idea of ​​GBD decomposition is as follows: A complex mixed-integer maximization problem is decomposed into a main problem with mixed-integer variables and convex subproblems containing only continuous variables. By fixing the complex variables of the main problem, a convex optimization problem with continuous variables in the subproblems is obtained. When a subproblem has an optimal solution, the obtained optimal solution is a feasible solution to the original problem. The objective function value of the original problem corresponding to this feasible solution is less than or equal to the optimal objective function value of the original maximization problem, and can be used as a lower bound for the solution of the original problem. An optimal cut set is then passed to the main problem. When the subproblem is unbounded, the constraints of the original problem are relaxed, and a feasible cut set is passed to the main problem. The main problem obtains the cut set information from the subproblem feedback and optimizes to obtain the optimal solution for the complex variables of the main problem. Since a series of constraints within the subproblem are ignored, the objective function value corresponding to this optimal solution is greater than or equal to the optimal objective function value of the original problem, and can be used as an upper bound for the solution of the original problem. The optimal solution of the main problem in this iteration is fixed for the next subproblem optimization cycle. When the upper and lower bounds approximately coincide, the optimal solution of the main and subproblems in this cycle is the optimal solution of the original problem.

[0160] The fact that the lower-level subproblem is a convex problem is a prerequisite for ensuring the effective convergence of the algorithm. However, the lower-level problem of the model proposed in step S5 contains a large M-factor Boolean variable. The charging and discharging state variables in equations (35)-(36) This results in the lower-level problems exhibiting a non-convex form. To ensure that the lower-level problems in this model are convex, the following assumptions are made:

[0161] (1)SESS i Priority should be given to participating in multilateral collaborative scheduling, followed by participation in peak-valley arbitrage-new energy consumption joint scheduling;

[0162] (2) Users in the new energy distribution area can report their capacity according to their own wishes, and the capacity demand will not be reduced to the minimum limit;

[0163] (3) During the multilateral capacity coordination phase, SESSi quoted a price higher than its own energy storage loss cost.

[0164] Assume (1) guarantees the charging and discharging state of SESSi under multilateral capacity coordinated scheduling. The certainty of SESS during time interval t′. i The known indicator Boolean parameters for participating in charge / discharge power capacity scheduling, energy capacity scheduling, and renewable energy consumption scheduling are as follows: A value of 1 indicates participation in the corresponding transaction, while a value of 0 indicates non-participation. Therefore, equations (35)-(36) can be rewritten as:

[0165]

[0166]

[0167] Assume (2) guarantees the large M-type Boolean variable. It can remain at 1. Meanwhile, to avoid the impact of pricing, assumption (3) is introduced based on assumption (2) to ensure that energy storage remains profitable under shared trading. right The constraints can be ignored.

[0168]

[0169] Under the above assumptions, the lower-level problem only needs to deal with continuous variables. Optimization is performed, i.e., the total number of optimization variables. The shared energy storage distributed scheduling model proposed in the previous section is rewritten in GBD decomposition form, and the original distributed scheduling optimization problem is decomposed into the upper-level scheduling center main problem and the lower-level SESS problem. i The subproblem, the main subproblem, achieves distributed scheduling through the exchange of scheduling power information and Benders cut set information, as detailed below:

[0170] (1) Lower-level shared energy storage member SESS i Sub-problems:

[0171] Suppose that in the igth cycle, the upper-layer shared energy storage dispatch center sends data to the lower-layer SESS. i Send the previously optimized shared energy storage joint dispatch power information as follows: The lower-level subproblem is:

[0172]

[0173] In the formula, For SESS i Inequality constraints within the subproblem, i.e. all constraints involved in the lower-level subproblem proposed in step S5; The upper and lower layers of the distributed problem are decoupled by the equality constraint, i.e., equation (40). The lower layer SESS... i The sub-problem first examines the scheduling power information sent down from the upper layer. Whether it is feasible can be verified using the following feasibility test model:

[0174]

[0175] In the formula, These are non-negative slack variables used to decouple equality constraints; The target value for feasibility testing is the sum of all elements of the relaxation variables. A value of 0 indicates that the atomic problem does not require relaxation and the scheduling power strategy issued by the main problem is feasible; otherwise, it is not feasible. These are the dual multipliers corresponding to the relaxation constraints. If the lower-level SESS... i The subproblem is infeasible, therefore we get optimal value and dual multipliers with relaxed constraints (Where k represents the number of times the subproblem has no feasible solution during the iterative solution process). Based on the weak duality of the feasibility model, the lower-level SESS... i The feasibility cut set constraint for feeding the subproblem back to the upper-level shared energy storage dispatch center is:

[0176]

[0177] If the lower-level subproblem is solvable, and we obtain The corresponding optimal solution and dual multipliers of decoupling equality constraints (where j represents the number of times a feasible solution appears during the ig iterations), then the optimal value of the lower-level objective function That is, the main question about SESS i Effective lower bound At this time, each SESS i The main problem of effective lower bounds: effective lower bound LB ig The update process, and the lower-level SESS in this cycle i The optimal cut set constraint of the main problem, which involves feeding back subproblems to the upper-level shared energy storage dispatch center, is:

[0178]

[0179]

[0180] In the formula, UBD i The main question is about SESS i Effective upper bound of the augmented Lagrange function.

[0181] (2) Main problem of upper-level shared energy storage dispatch center:

[0182] The main problem of the upper-level shared energy storage dispatch center is to achieve optimal decision-making for charging and discharging of shared energy storage only through a series of optimal cut sets, feasible cut sets fed back from the lower-level sub-problems, and its own internal constraints. This achieves distributed collaborative scheduling without involving the internal systems of the lower-level shared energy storage subsystems. The main problem is as follows:

[0183]

[0184] In the formula, G SEDC (N SEDC The internal inequality constraints of the main problem for the shared energy storage dispatch center are given. From the objective function of the main problem, it can be seen that regarding the lower-level SESS... i The subproblem is partly due to the UBD associated with the optimal cut set constraint. i In this alternative approach, the optimization constraints of lower-level problems are replaced by increasingly more optimal and feasible cut set constraints. Therefore, the GBD distributed algorithm effectively reduces communication information between subproblems and the main problem, ensuring internal independence within lower-level problems. Each optimization of the main problem... The problem will be assigned to a lower-level subproblem to initiate the next iteration of the solution, and the optimal value of the objective function will be generated. Both serve as effective upper bounds for distributed collaborative scheduling (UB). ig :

[0185]

[0186] The effective upper bound (UB) obtained in each iteration ig and effective lower bound LB ig The aggregation results all affect the termination of the iterative solution:

[0187]

[0188] In the formula, δ GBD This is a pre-determined convergence threshold for distributed solution.

[0189] Based on the GBD master-subproblem decomposition description of hierarchical distributed scheduling of shared energy storage, the lower-level SESS i Limited information exchange with the upper-layer shared energy storage dispatch center is achieved by transmitting Benders cut set constraints containing desired dispatch strategy information. The upper-layer shared energy storage dispatch center then satisfies all SESS (Self-Enhanced Energy Storage) constraints. i The transmitted Benders cut set constraints enable local optimization of the charging and discharging strategy while accommodating the desired scheduling of both layers. The upper layer shares a common energy storage scheduling center with the lower layer SESS. i Limited information exchange between them ensures that parallel computing is achieved while maintaining the efficiency of each lower-level SESS. i The independence of internal multilateral collaborative scheduling ultimately achieves the optimal distributed operation benefits of shared energy storage within the region.

[0190] It is understood that the embodiments of the present invention have the following key improvements:

[0191] 1. Establish a collaborative zoning model for shared energy storage in distribution substations with the goal of maximizing the absorption of distributed photovoltaic clusters. This invention proposes a joint scheduling method for shared energy storage that considers peak-valley arbitrage and renewable energy absorption, as well as a collaborative scheduling method for shared energy storage and multiple distribution substations under a dynamic capacity leasing model. Based on this, a collaborative zoning model for shared energy storage and multiple distribution substations is established with the objective function of maximizing the total revenue from the absorption of distributed photovoltaic clusters.

[0192] 2. Constructing a hierarchical and zoned collaborative charging and discharging scheduling architecture for shared energy storage under high-proportion distributed photovoltaic access. This invention further considers the autonomous operation characteristics of the shared energy storage subsystem and proposes a hierarchical and zoned collaborative charging and discharging scheduling architecture for shared energy storage, forming a dynamic zoned distributed collaborative scheduling optimization model for shared energy storage. The upper layer optimizes the overall charging and discharging power of shared energy storage in each time period based on a joint scheduling method under the energy storage capacity retention mechanism, while the lower layer formulates internal energy storage battery pack power allocation decisions based on a multilateral collaborative mechanism of energy storage capacity. Through inter-layer information interaction, continuous adjustments are made to achieve optimal system operation.

[0193] This invention proposes a distributed charging and discharging decision-making solution method for distributed photovoltaic clusters and shared energy storage in a multilateral collaborative manner. By introducing coupling variables, this invention decomposes the hierarchical and partitioned collaborative charging and discharging scheduling architecture of shared energy storage into an upper-level scheduling center master problem and a lower-level SESS (Search and Discharge System) problem. i The subproblem is solved by iterative interaction between the upper-level shared energy storage expected power decision and the lower-level Benders cut set constraint to achieve decoupling optimization of the two-level model, thus obtaining the optimal distributed charging and discharging decision for multilateral collaboration between distributed photovoltaic clusters and shared energy storage.

[0194] The following specific examples illustrate the beneficial effects obtained by using the method of the present invention in practice.

[0195] In a specific example, the pseudocode for the shared energy storage hierarchical distributed scheduling solution algorithm based on the GBD decomposition idea is as follows:

[0196] Algorithm for solving hierarchical distributed scheduling of shared energy storage:

[0197]

[0198]

[0199] Simulation example:

[0200] Suppose that SESS establishes three shared energy storage subsystems (SESS1, SESS2, and SESS3) in three different low-voltage distribution substations to serve nearby distributed photovoltaic clusters. Each shared energy storage subsystem consists of four types of energy storage battery packs, labeled bt1, bt2, bt3, and bt4. The specific energy storage parameters are shown in Table 1 below, with reference to standard depth of charge / discharge and state of charge (DOD). ref=soc ref =0.8. Let the multilateral power capacity leasing time interval Δt′ be 15 min, and the peak-valley arbitrage-new energy consumption joint scheduling time interval Δt be 1 h. This invention is programmed using the YALMIP toolbox on the MATLAB software platform and solved using the commercial Gurobi solver. All tests were completed on a desktop PC with a 2.90 GHz Intel(R) Core(TM) i5-9400 CPU and 16.0 GB RAM.

[0201] Table 1 Equipment Parameters of Shared Energy Storage System

[0202]

[0203]

[0204] To verify the superiority of the proposed method, the following five scheduling and operation schemes were designed for comparative analysis:

[0205] (1) Scheme 1: Using the method proposed in this invention, three SESS subsystems work together to participate in peak-valley arbitrage-new energy consumption joint scheduling, while each SESS independently negotiates with multilateral capacity collaboration users and manages the internal management of the energy storage battery pack.

[0206] (2) Scheme 2: Compared with Scheme 1, the three SESS subsystems do not consider the impact of their respective scheduling behavior on the overall joint scheduling of the system and participate in joint scheduling independently;

[0207] (3) Option 3: Compared with Option 1, the SEDC in the main system does not consider peak-valley arbitrage and new energy consumption;

[0208] (4) Option 4: Compared with Option 1, the SESS subsystem does not participate in dynamic capacity leasing;

[0209] (5) Scheme 5: Compared with Scheme 1, the dynamic cycle loss of the energy storage battery pack in the subsystem is not considered.

[0210] Table 2 shows the comparison results of the operational efficiency indicators of shared energy storage under schemes 1-5. Compared with schemes 2-5, the overall revenue of SESS in scheme 1 is increased by at least 7.86%, and the total revenue of distributed photovoltaic clusters of subsystems SESS1, SESS2, and SESS3 is increased by at least 8.62%, 9.78%, and 10.04%, respectively, proving the effectiveness of the method proposed in this invention.

[0211] Table 2 Comparison of Operational Efficiency Indicators for Shared Energy Storage under Schemes 1-5

[0212]

[0213]

[0214] Implementing the embodiments of the present invention has the following beneficial effects:

[0215] This invention provides a method for coordinated charging and discharging control of shared energy storage in distribution substations and a storage medium. By determining a joint scheduling method for shared energy storage that considers peak-valley arbitrage and renewable energy consumption, and a coordinated scheduling method for shared energy storage and multiple distribution substations under a dynamic capacity leasing model, a collaborative partitioning model for shared energy storage and multiple distribution substations is built with the objective function of maximizing the total revenue from distributed photovoltaic cluster consumption.

[0216] In this invention, by further considering the autonomous operation characteristics of the shared energy storage subsystem, a hierarchical and partitioned collaborative charging and discharging scheduling architecture for shared energy storage is proposed, forming a dynamic partitioned distributed collaborative scheduling optimization model for shared energy storage. The upper layer optimizes the overall charging and discharging power of shared energy storage in each time period based on a joint scheduling method under the energy storage capacity retention mechanism, while the lower layer formulates internal energy storage battery pack power allocation decisions based on a multilateral collaborative mechanism of energy storage capacity. Through inter-layer information interaction, the system is continuously adjusted to achieve optimal operation.

[0217] In this invention, the shared energy storage hierarchical and partitioned collaborative charging and discharging scheduling architecture is decomposed into an upper-level scheduling center master problem and a lower-level SESS by introducing coupling variables. i The subproblem is solved by iterative interaction between the upper-level shared energy storage expected power decision and the lower-level Benders cut set constraint to achieve decoupling optimization of the two-level model, thus obtaining the optimal distributed charging and discharging decision for multilateral collaboration between distributed photovoltaic clusters and shared energy storage.

[0218] By implementing this invention, the charging and discharging power of the low-voltage distribution network of new energy is optimized by using a shared energy storage system peak-valley arbitrage-new energy consumption joint scheduling strategy, thereby improving the distributed photovoltaic consumption capacity of the low-voltage distribution network and shortening the cost recovery period of shared energy storage.

[0219] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0220] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0221] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for coordinated charging and discharging control of shared energy storage in a distribution network, characterized in that, Includes the following steps: Step S1: Consider the joint scheduling method of peak-valley arbitrage and new energy consumption to obtain shared energy storage; Step S2: Obtain a collaborative scheduling method for shared energy storage and multiple distribution stations based on the dynamic capacity leasing model; Step S3: Based on the methods proposed in Steps S1 and S2, a collaborative partitioning model for shared energy storage and multiple distribution stations is built with the objective function of maximizing the total revenue of distributed photovoltaic clusters. Step S4: Considering the autonomous operation characteristics of the shared energy storage subsystem, obtain the hierarchical and partitioned collaborative charging and discharging scheduling architecture of the shared energy storage, and form a dynamic partitioned distributed collaborative scheduling optimization strategy; Step S5 introduces coupling variables to decompose the shared energy storage hierarchical and partitioned collaborative charging and discharging scheduling architecture into the upper-level scheduling center master problem and the lower-level SESS. i A two-layer model of sub-problems; Step S6: Based on the decoupling optimization solution of the generalized Benders algorithm, the optimal distributed charging and discharging decision of the distributed photovoltaic cluster and the shared energy storage multilateral collaboration is obtained. Step S4 further includes: A hierarchical distributed scheduling method is introduced to decompose the scheduling optimization model established in step S3. By setting up an internal shared energy storage scheduling center through the shared energy storage SESU, the original shared energy storage SESU collaborative scheduling model is decomposed into a centralized joint scheduling layer with the shared energy storage scheduling center as the main body and a scheduling layer with each SESS within the shared energy storage system as the main body. i The subsystem members are independent entities forming a multilateral collaborative operation layer, which further develops into a shared upper-level energy storage dispatch center that controls multiple lower-level SESS systems. i The collaborative distributed operation framework of the subsystem; SESU is the SESS main system; In the aforementioned collaborative distributed operation framework, the upper-level shared energy storage dispatch center is responsible for collecting and predicting dispatch information from various entities participating in the joint dispatch of peak-valley arbitrage and new energy consumption within the region; the lower-level subsystems SESS i Responsible for independently participating in multilateral capacity collaborative scheduling, negotiating with new energy transformer area users matched with contracts, and managing and monitoring the operating status of internal battery packs in real time; The upper-level shared energy storage dispatch center and the lower-level individual SESS i The two systems interact in real time through the internal communication network of the energy storage system, exchanging information on power interaction coupling variables related to the joint scheduling strategy. During operation, each SESS located in the lower layer i Independently negotiates multilateral capacity leasing plans with users in the new energy distribution area, and formulates power allocation plans for internal energy storage batteries according to the leasing plan requirements, transmitting the necessary dispatch request information to the upper layer; the shared energy storage dispatch center located at the upper layer analyzes the data from each SESS... i The sent request information, based on the prediction information, coordinates and optimizes the scheduling plans and internal interaction allocation of each subsystem, and then sends it down to the lower-level SESS. i Issue relevant scheduling decision adjustment instructions; each SESS i Based on the received dispatch decision adjustment instructions, the original multilateral capacity leasing plan formulated with new energy transformer area users was negotiated and modified, and the internal energy storage battery power allocation decision was adjusted.

2. The method as described in claim 1, characterized in that, Step S1 further includes: Considering that SESS prioritizes meeting the fluctuation smoothing and auxiliary peak-shaving needs of users in the low-voltage distribution area of ​​new energy, and aims to maximize the total revenue from participating in peak-valley arbitrage and new energy consumption, a shared energy storage dispatch strategy is formulated, which is obtained by the following formula (1). Total revenue from SESS's participation in peak-valley arbitrage and joint dispatch of renewable energy consumption for: (1) In the formula, and For shared energy storage as a whole After completing internal mutual assistance within the time period, the average charging power and average discharging power are executed under the peak-valley arbitrage mode. For shared energy storage as a whole The average charging power participating in the consumption of new energy during the time period; User reporting and new energy contribution scenarios s The probability of; For peak-valley arbitrage model in the Taiwan area t Moment, New Energy Power Output Scenarios s Energy prices; For the new energy consumption model in the Taiwan area t Moment, New Energy Power Output Scenarios s The highest marginal clearing price; The time-of-use electricity price of the power grid; For power grid transmission and distribution prices; The time interval for peak-valley arbitrage and joint scheduling of new energy consumption.

3. The method as described in claim 2, characterized in that, Step S2 further includes: Considering the differences in energy storage capacity and power leasing demand among various low-voltage distribution substations, energy storage resources are decomposed into peak-valley arbitrage resources and new energy consumption resources, which are provided to substation users respectively. A shared energy storage and multi-distribution substation collaborative scheduling method based on dynamic capacity leasing model is constructed with the objective function of maximizing multilateral capacity leasing revenue. Among them, time period Internal subsystem SESS i Multilateral capacity leasing revenue This includes revenue from capacity leasing and revenue from electricity dispatch, as shown below: (2) (3) (4) In the formula, For SESS i The set of users of the new energy low-voltage distribution transformer area in the sub-region; assuming , For the subsystem SESS i exist The negotiated prices for energy storage charging capacity and discharging capacity resources are agreed upon within the specified time period. For the subsystem SESS i During the period t Resale to The price per time slot; For users of new energy low-voltage distribution transformer areas rs Is it in Power capacity is coordinated and matched with SESS at all times. i The Boolean variable that indicates when rs Matched with SESS i ,but ,on the contrary And the default power capacity for scheduling charging and discharging is ; For users of new energy low-voltage distribution transformer areas rs Is it in Constantly coordinate and match power capacity with SESS i Indicator Boolean variable; Sold to distribution station users due to the use of discharge power capacity rs The amount of electricity; For SESS i According to the agreement, in the case of abandoning light t Absorbs the total energy of users in the transformer area during the time period; This refers to the electricity purchased from new energy power station users due to the use of charging power capacity; for t Time-of-use electricity pricing on the power grid; for t The electricity purchase price of the power grid at any time; For the multilateral power capacity leasing time interval, and .

4. The method as described in claim 3, characterized in that, Step S3 further includes: Step S30: To distinguish between the main and subsystems, the SESS main system is denoted as SESU, and each sub-region... The SESS subsystem is denoted as The main system SESU controls all subsystems, and each subsystem SESS controls the overall system. i An independent control center will be established to oversee the internal energy storage battery packs. While performing data acquisition and power allocation management, it also conducts capacity sharing and collaborative scheduling with nearby new energy power plants; Step S31: Establish the objective function for multilateral collaborative scheduling of shared energy storage and new energy low-voltage distribution areas. The objective function of the multilateral collaborative scheduling includes the total revenue of SESS participating in peak-valley arbitrage-new energy consumption joint scheduling, the revenue of multilateral capacity leasing between the shared energy storage subsystem and the new energy low-voltage distribution area, and the cyclic loss cost of each shared energy storage subsystem. The objective function of the multilateral collaborative scheduling is as follows: (5) In the formula, for t The total revenue from time-sharing energy storage participating in peak-valley arbitrage and joint dispatch of new energy consumption; for t SESU within the time period i Total revenue from multilateral capacity leasing transactions between independent and new energy low-voltage distribution transformer area users; for t SESS i Includes battery pack bt The resulting equivalent cycle loss cost; Battery pack bt The battery capacity loss rate can be used to calculate the battery capacity loss cost, specifically: (6) In the formula, For battery pack bt The unit power capacity configuration cost; For battery pack bt The unit power capacity configuration cost; For battery pack bt Rated power; For battery pack bt The initial battery capacity; The annual cycle loss rate of the SESS battery pack; For battery pack bt exist t The year in which the time is located; t SESS (Search for Energy Storage) i Included battery pack bt The equivalent cycle loss cost includes the cost of power capacity loss and the equivalent operation and maintenance cost incurred during operation within this time interval. Specifically: (7) In the formula, For SESS i Included battery pack bt Average annual inspection, maintenance and upkeep costs per unit power; Step S32: Determine the constraints of the shared energy storage SESU, including: constraints related to peak-valley arbitrage-new energy consumption joint scheduling, multilateral capacity coordination constraints, subsystem charging and discharging power constraints, and shared energy storage battery pack SOC constraints.

5. The method as described in claim 4, characterized in that, Step S5 further includes: Based on the framework obtained in step S4, the shared energy storage and multi-distribution station area collaborative zoning model in step S3 is decomposed to form the upper-level shared energy storage dispatch center sub-problem. With lower-level subproblems Introducing coupling variables , indicating the subsystem SESS i Information shared with the upper-level energy storage dispatch center, including power strategies for participating in peak-valley arbitrage and renewable energy consumption joint dispatch, and discharge power strategies under power capacity service, is introduced to decouple variables for interaction between the upper and lower layers. And the corresponding decoupling constraints; Formula (5) is decomposed into an upper-level shared energy storage dispatch center subproblem and a lower-level subproblem.

6. The method as described in claim 5, characterized in that, Step S6 further includes: Decoupling optimization based on the generalized Benders algorithm enables the lower-level SESS to be optimized. i Limited information exchange with the upper-layer shared energy storage dispatch center is achieved by transmitting Benders cut set constraints containing desired dispatch strategy information. The upper-layer shared energy storage dispatch center then satisfies all SESS (Self-Enhanced Energy Storage) constraints. i The sent Benders cut set constraint realizes local optimization of the charging and discharging strategy that takes into account the expected scheduling of each other.

7. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method as described in any one of claims 1 to 6.

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