A power storage system for a transformer area

Abstract

The application relates to the technical field of power supply, in particular to a transformer area energy storage system, which comprises a core energy storage unit, an intelligent sensing and control unit and a cloud collaborative management platform, the core energy storage unit comprises a battery cluster, a bidirectional converter and a local controller, the intelligent sensing and control unit comprises a high-precision measurement module, a dynamic boundary identification module and a multi-agent decision module. The application can calculate a safe operation boundary in real time online, fundamentally avoids risks such as overload and voltage out-of-limit, adapts to dynamic changes of transformer area load and topology, significantly enhances the overall flexible adjustment capability of the transformer area, reduces the investment cost of single physical energy storage configuration, simultaneously realizes energy interflow and collaborative optimization across transformer areas, effectively alleviates power distribution network congestion, improves overall operation efficiency, meets the adjustment demand of the transformer area in multiple time scales at the optimal cost, and improves the utilization rate and economy of the energy storage equipment itself.

Description

Technical Field

[0001] This invention relates to the field of power supply technology, specifically to a transformer substation energy storage system. Background Technology

[0002] As the energy structure accelerates its transition towards clean and low-carbon energy, new energy sources, represented by distributed photovoltaic power, and flexible new loads, represented by electric vehicle charging piles, have achieved large-scale and high-penetration integration into distribution networks, especially at the low-voltage distribution area level. While improving energy utilization efficiency, this development trend has also profoundly changed the traditional unidirectional radiation and relatively stable load operation mode of distribution networks, bringing unprecedented and severe challenges to the safe and high-quality power supply of distribution areas.

[0003] First, the distribution area faces multi-dimensional power quality and operational safety issues. Distributed photovoltaic (PV) output exhibits significant intermittency and volatility, and its large-scale integration can easily lead to voltage exceeding limits in the distribution area, especially during peak PV generation periods at midday, often causing voltage spikes or even overvoltages. Electric vehicle charging loads are characterized by spatiotemporal randomness and high power, and concentrated charging can lead to severe overload and voltage drops in the distribution area. Furthermore, the random integration of single-phase loads exacerbates three-phase imbalance, resulting in increased transformer losses and equipment overheating. Of particular note is that when PV output far exceeds the local load, the power flow reverses, causing reverse overload on transformers and lines, threatening the safe lifespan of equipment. The intertwining and superposition of these problems makes traditional "rigid" solutions relying on upgrading lines and expanding transformer capacity costly and slow to respond.

[0004] To address the aforementioned issues, configuring battery energy storage systems in distribution substations has become an effective means of flexible regulation. Through their flexible charging and discharging capabilities, energy storage systems can quickly smooth power fluctuations, regulate voltage, and transfer loads, thereby improving power quality and operational safety.

[0005] However, the distribution area energy storage systems and their control strategies that are currently widely used in actual deployment and research are mostly still in the initial or isolated management and control stage, and their technical limitations are becoming increasingly prominent, mainly in the following four aspects: (1) Rigid regulation capability and low adaptive level: Existing systems mostly adopt simple on-demand or trigger-based control strategies based on fixed thresholds (such as voltage and power upper and lower limits). These strategies fail to deeply integrate the specific network topology, line impedance distribution, and real-time dynamic changes of load and power supply in the transformer area. Their regulation boundaries (such as charging and discharging power limits and allowable voltage regulation range) are usually fixed values ​​set offline. This leads to two main problems: First, during normal operation, the system adopts a conservative strategy for fear of touching the fixed boundaries, failing to make full use of the actual regulation margin of energy storage, resulting in idle resources; second, when the operating state is close to or exceeds the preset boundaries, the rigid control may not be able to make the optimal or safe response, and may even cause malfunctions, posing safety hazards. The system lacks the ability to accurately perceive and dynamically adjust real-time operating boundaries (such as the safety domain considering voltage and current constraints).

[0006] (2) Insufficient resource coordination and limited overall potential: The current control targets are mainly concentrated on centrally configured physical energy storage units. However, there are also a large number of dispatchable distributed resources in modern distribution substations, such as interruptible / transferable loads like smart air conditioners and water heaters, user-built rooftop energy storage, and electric vehicles with vehicle-to-grid interaction potential. These resources are considerable in total, but they are scattered and belong to multiple entities. The existing system lacks an effective aggregation, communication, and collaborative scheduling mechanism, making it impossible to coordinate and optimize these "virtual energy storage" resources with physical energy storage. As a result, the overall flexible adjustment potential of the substation is not fully explored, and its ability to cope with complex operating conditions is limited.

[0007] (3) Isolated operation, lack of regional coordination: Each distribution area's energy storage system is usually regarded as an independent unit, making decisions and controlling only based on local information of its own area. This model lacks a collaborative optimization mechanism between adjacent distribution areas and even with the upper-level distribution network. At the larger distribution network level, it is impossible to optimize power flow distribution, alleviate main line congestion, and support the voltage of upper-level substations through the coordinated cooperation of multiple distribution area energy storage systems. At the same time, the dispersed energy storage resources cannot be effectively aggregated to form an aggregate with a certain scale and reliability, thus losing the opportunity to participate in higher-level power ancillary service markets or demand response projects, limiting the realization of its economic value.

[0008] No solutions have yet been proposed for the relevant technical issues. Summary of the Invention

[0009] To address the problems in related technologies, this invention proposes a distribution area energy storage system to overcome the aforementioned technical issues in existing technologies. The purpose of this invention is to calculate the safe operating boundary in real time online, fundamentally avoiding risks such as overload and voltage exceeding limits. It adapts to the dynamic changes in distribution area load and topology, significantly enhances the overall flexible adjustment capability of the distribution area, reduces the investment cost of a single physical energy storage configuration, and simultaneously achieves energy mutual assistance and collaborative optimization across distribution areas. This effectively alleviates distribution network congestion, improves overall operating efficiency, meets the multi-timescale adjustment needs of the distribution area at the optimal cost, and enhances the utilization rate and economy of the energy storage equipment itself.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a transformer substation energy storage system, comprising a core energy storage unit, an intelligent sensing and control unit, and a cloud-based collaborative management platform, wherein the cloud-based collaborative management platform is communicatively connected to the core energy storage unit and the intelligent sensing and control unit, and the core energy storage unit and the intelligent sensing and control unit are communicatively connected. The core energy storage unit includes: Battery clusters are used to store and release electrical energy; A bidirectional converter is used to achieve bidirectional conversion of AC and DC power. A local controller, which is communicatively connected to the bidirectional converter, is used to execute low-level control commands from the intelligent sensing and control unit; The intelligent sensing and control unit includes: The high-precision measurement module is used to collect voltage, current, power and power quality data in real time at the main entrance of the transformer area, the grid connection point of the core energy storage unit and the key load branch nodes; The dynamic boundary identification module is used to calculate and dynamically update the real-time adjustable capability boundary and safe operation boundary of the transformer area based on the transformer area topological impedance model and real-time measurement data. The multi-agent decision-making module, with an embedded coordination control algorithm, is used to generate a scheduling strategy for the core energy storage unit and local adjustable resources within the boundary given by the dynamic boundary recognition module. The cloud-based collaborative management platform is used to receive data from multiple distribution zones, perform cross-distribution zone collaborative optimization, and issue control instructions and market trading strategies.

[0011] Preferably, the dynamic boundary identification module determines the real-time safety boundary by solving an optimization problem aimed at maximizing the adjustment margin. The constraints include transformer load rate constraints, line capacity constraints, and node voltage dynamic constraints. The node voltage dynamic constraints are calculated based on the power flow of the real-time topology and load distribution.

[0012] Preferably, the coordination control algorithm implemented by the multi-agent decision-making module is based on a hierarchical Stackelberg game model.

[0013] Preferably, the intelligent sensing and control unit further includes a virtual energy storage aggregation module, which is used to aggregate the adjustable potential of dispersed adjustable loads, electric vehicle charging piles and user-side energy storage within the transformer area to form virtual energy storage resources, and to perform joint optimization scheduling with the core energy storage unit.

[0014] Preferably, the cloud-based collaborative management platform is used for: (1) Receive and aggregate data from several of the aforementioned intelligent sensing and control units; (2) Perform cross-regional collaborative optimization calculations; (3) Issue cross-regional regulation instructions and market trading strategies.

[0015] Preferably, the battery cluster adopts a hybrid topology of power-type battery modules and energy-type battery modules, wherein the power-type battery modules are used to provide fast power response and the energy-type battery modules are used to provide continuous energy throughput.

[0016] Preferably, the cloud-based collaborative management platform is also connected to an electricity market trading system, which is used for: (1) Based on the forecast of adjustable capacity of cross-regional aggregation, resources that can participate in market transactions are formed; (2) Participate in electricity spot market or ancillary services market transactions; (3) In accordance with the preset rules, the market revenue obtained will be distributed to the relevant entities in each district.

[0017] Preferably, the intelligent sensing and control unit is deployed in the intelligent converged terminal of the distribution area.

[0018] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention is a distribution area energy storage system. By setting up a core energy storage unit, an intelligent sensing and control unit and a cloud-based collaborative management platform, it realizes the organic combination of management, decision-making and execution. The cloud-based collaborative management platform is responsible for cross-regional collaborative optimization and market transactions. The edge-side intelligent sensing and control unit realizes real-time sensing, boundary identification and collaborative decision-making at the distribution area level. The terminal-side energy storage unit quickly executes precise control. By adopting a hierarchical collaborative mechanism, it realizes a closed loop from global optimization to local rapid response, which significantly improves the automation and intelligence of distribution area operation. (2) This invention is a distribution area energy storage system. By setting up a dynamic boundary identification module, it solves the optimization problem that considers multiple constraints such as transformer, line and node voltage in real time, dynamically calculates and updates the safe operation boundary and adjustable capacity of the distribution area, so that the scheduling strategy for energy storage and adjustable resources always operates within the safe domain, fundamentally preventing risks such as equipment overload and voltage over-limit, and effectively improving the carrying capacity and operational safety of the distribution network for high proportion of distributed energy access. (3) This invention is a distribution area energy storage system. By setting up a virtual energy storage aggregation module, it can uniformly aggregate and model flexible resources such as distributed adjustable loads, electric vehicle charging piles, and user-side energy storage. Combined with a multi-agent decision-making algorithm based on hierarchical Stackelberg game, it can meet the grid regulation objectives while taking into account the individual interests of resource subjects, forming an effective economic incentive, thereby stimulating the regulation potential of massive distributed resources and achieving synergistic optimization that unifies technical and economic aspects. (4) This invention is a transformer area energy storage system. By setting the core energy storage unit to adopt a hybrid topology of power type battery module and energy type battery module, it can simultaneously cope with short-term power quality problems and long-term peak shaving and valley filling needs. It has excellent comprehensive performance. The cloud collaborative management platform aggregates resources from multiple transformer areas to participate in the power market transaction, forming a tradable regulation capability, obtaining market benefits and allocating them reasonably. (5) This invention is a transformer area energy storage system. By setting up an intelligent sensing and control unit, it can be deployed in the transformer area intelligent fusion terminal, making full use of existing hardware resources to achieve localized rapid calculation and control, reducing the absolute dependence on cloud collaborative management platform communication, reducing the investment and deployment complexity of new hardware, and ensuring the rapid and reliable execution of key control commands in abnormal situations such as communication interruption, thus ensuring the real-time performance and robustness of control. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall framework of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0021] Example Please see Figure 1This invention proposes a technical solution for a transformer substation energy storage system: a transformer substation energy storage system includes a core energy storage unit, an intelligent sensing and control unit, and a cloud-based collaborative management platform. The cloud-based collaborative management platform is communicatively connected to both the core energy storage unit and the intelligent sensing and control unit. Specifically, the core energy storage unit, the intelligent sensing and control unit, and the cloud-based collaborative management platform are connected via wired or wireless communication networks (such as optical fiber, 5G, HPLC) to form a layered collaborative architecture of cloud-edge-terminal. The core energy storage unit includes: Battery clusters are used to store and release electrical energy; A bidirectional converter is used to achieve bidirectional conversion of AC and DC power. The local controller, which communicates with the bidirectional converter, is used to execute low-level control commands from the intelligent sensing and control unit. The intelligent sensing and control unit includes: The high-precision measurement module is used to collect voltage, current, power and power quality data in real time at the main entrance of the transformer area, the grid connection point of the core energy storage unit and the key load branch nodes; The dynamic boundary identification module is used to calculate and dynamically update the real-time adjustable capability boundary and safe operation boundary of the transformer area based on the transformer area topological impedance model and real-time measurement data. The multi-agent decision-making module, with an embedded coordination control algorithm, is used to generate scheduling strategies for the core energy storage unit and locally adjustable resources within the boundary given by the dynamic boundary recognition module. The cloud-based collaborative management platform is used to receive data from multiple distribution zones, perform cross-regional collaborative optimization, and issue control instructions and market trading strategies.

[0022] Furthermore, the dynamic boundary identification module determines the real-time safety boundary by solving an optimization problem aimed at maximizing the regulation margin. The constraints include transformer load rate constraints, line capacity constraints, and node voltage dynamic constraints. The node voltage dynamic constraints are calculated based on the power flow of the real-time topology and load distribution.

[0023] In this embodiment, the optimization problem can be formally represented as: Objective function: Max(∑Maximum available power of adjustable resources) Constraints: Transformer load factor: P_tr≤P_tr_max, Q_tr≤Q_tr_max; Line capacity constraint: |I_ij|≤I_ij_max, for all lines (i, j); Node voltage dynamic constraints: U_min≤U_i≤U_max, for all nodes i, where U_i is obtained through real-time power flow calculation.

[0024] The dynamic boundary identification module repeatedly solves such optimization problems and outputs boundary parameters such as the safe charging and discharging power range and voltage / reactive power adjustment range of the core energy storage unit and aggregated resources in real time.

[0025] Furthermore, the coordinated control algorithm implemented by the multi-agent decision-making module is based on a hierarchical Stackelberg game model.

[0026] In this embodiment, in the hierarchical Stackelberg game model, the intelligent sensing and control unit formulates electricity prices or incentive signals based on cloud instructions and dynamic boundaries; the core energy storage unit and the virtual energy storage aggregation module adjust their own operating strategies according to the signals to optimize their own benefits; the intelligent sensing and control unit then adjusts its strategies according to the reactions of the core energy storage unit and the virtual energy storage aggregation module, ultimately reaching an equilibrium state, thereby coordinating the overall optimization goals (such as minimizing network losses and maximizing voltage stability) with individual interests.

[0027] Furthermore, the intelligent sensing and control unit also includes a virtual energy storage aggregation module. The virtual energy storage aggregation module is used to aggregate the adjustable loads (such as air conditioners and water heaters), electric vehicle charging piles and user-side energy storage adjustable potential (such as interruptible power and transferable electricity) distributed within the transformer area to form virtual energy storage resources, and to perform joint optimization scheduling with the core energy storage unit.

[0028] Furthermore, the cloud-based collaborative management platform is used for: (1) Receive and aggregate data from several intelligent sensing and control units; (2) Perform cross-regional collaborative optimization calculations; (3) Issue cross-regional regulation instructions and market trading strategies.

[0029] In this embodiment, real-time operating data, adjustable capability data, and boundary information from several intelligent sensing and control units are received and aggregated; based on global optimization objectives (such as minimizing the total network loss of the regional distribution network, maximizing the voltage qualification rate, minimizing the electricity purchase cost, or maximizing market revenue), cross-regional collaborative optimization calculations are performed to generate coordinated control instructions for each distribution unit; and cross-regional control instructions and electricity market trading strategies are issued to the intelligent sensing and control units of each distribution unit.

[0030] Furthermore, the battery cluster adopts a hybrid topology of power-type battery modules and energy-type battery modules, with the power-type battery modules providing fast power response and the energy-type battery modules providing continuous energy throughput.

[0031] In this embodiment, the battery cluster adopts a hybrid topology of power-type battery modules (such as supercapacitors and lithium titanate batteries) and energy-type battery modules (such as lithium iron phosphate batteries), and is coordinated and managed by a battery management system. The power-type battery modules are used to provide fast power response to address power quality issues such as instantaneous voltage drops and flicker; the energy-type battery modules are used to provide continuous energy throughput to enable long-term applications such as peak shaving and valley filling, and energy time shifting. This hybrid architecture balances power response speed and energy storage capacity, improving the overall performance of the system.

[0032] Furthermore, the cloud-based collaborative management platform is also connected to the electricity market trading system, which is used for: (1) Based on the forecast of adjustable capacity of cross-regional aggregation, resources that can participate in market transactions are formed; (2) Participate in electricity spot market or ancillary services market transactions; (3) In accordance with the preset rules, the market revenue obtained will be distributed to the relevant entities in each district.

[0033] In this embodiment, based on the forecast data of adjustable capacity of multiple transformer substations aggregated by the cloud-based collaborative management platform, a combination of adjustable resources of a certain scale that meets market access requirements is formed; the aggregated resources participate in the electricity spot market (such as the energy market) or ancillary service market (such as frequency regulation and reserve market) transactions; according to the preset revenue distribution rules (such as based on the adjustment contribution), the obtained market revenue is distributed to each participating transformer substation and related entities (such as energy storage owners and users who provide adjustable loads), forming an effective economic incentive.

[0034] Furthermore, the intelligent sensing and control unit is deployed in the intelligent converged terminal of the distribution area.

[0035] In this embodiment, the hardware resources of existing power distribution automation equipment are fully utilized to achieve localized rapid sensing, calculation and control, reduce reliance on cloud communication and ensure real-time control response.

[0036] The core energy storage unit of this invention is the physical execution entity of the system. The battery cluster adopts a topology in which power modules (such as supercapacitor modules) and energy modules (such as lithium iron phosphate battery modules) are connected in a DC bus. The bidirectional converter (PCS) is responsible for power conversion between the DC bus and the AC grid. The local controller receives instructions from the intelligent sensing and control unit, converts them into specific control signals for the PCS (such as constant power charging and discharging, constant voltage support, etc.), and monitors the battery status (SOC, temperature, etc.).

[0037] The intelligent sensing and control unit serves as the edge brain of this system and is preferably deployed within the intelligent converged terminal of the distribution area. The high-precision measurement module acquires real-time data through smart meters or sensors connected to the main outgoing line of the low-voltage side of the distribution area transformer, the energy storage grid connection point, and important branch nodes. The dynamic boundary recognition module incorporates the distribution area's topological impedance parameters and, combined with real-time measurement data, executes the following process: establishing an optimization model aimed at maximizing the regulation margin, considering constraints such as transformers, lines, and voltage, and quickly solving the problem through online power flow calculations and optimization algorithms (such as linear programming and interior-point methods), outputting the safe power limits (P_min, P_max) and voltage regulation range of the energy storage and virtual resources at the current moment. Within this boundary, the multi-agent decision-making module publishes internal electricity prices or incentive signals λ to the core energy storage and virtual energy storage (aggregated air conditioning clusters, EV charging piles, etc.) that serve as core energy storage units and virtual energy storage aggregation modules. The core energy storage units and virtual energy storage aggregation modules respond to λ with the goal of minimizing their own costs or maximizing their benefits, adjusting their power plans. The intelligent sensing and control unit adjusts λ based on the deviation between the total response and the target value, iterating until equilibrium is reached, forming the final local scheduling strategy. The virtual energy storage aggregation module is responsible for assessing the adjustable potential of various distributed resources, performing unified parameterized modeling (equivalent to energy storage with parameters such as charging and discharging power, capacity, and ramp rate), and providing this information to the multi-agent decision-making module.

[0038] The cloud-based collaborative management platform, acting as a super brain, aggregates boundary, capacity, and status information uploaded by each distribution area. With the overall goal of minimizing the total operating cost of the distribution network or maximizing market revenue, and considering network flow constraints, it performs centralized or distributed optimization calculations to derive the optimal power exchange plan or regulation target for each distribution area. Simultaneously, the cloud-based collaborative management platform connects to the electricity market trading system, using the aggregated adjustable capacity as bidding parameters to participate in market clearing. After obtaining market instructions or revenue, the platform decomposes the instructions and settles the revenue based on the actual regulation contribution of each distribution area, and then distributes the instructions to each distribution area. Upon receiving the cloud-based instructions, the intelligent sensing and control units of each distribution area further verify and fine-tune them based on the latest local dynamic boundaries to ensure the feasibility and security of the instructions before finally distributing them to the execution unit.

[0039] The present invention also provides a management method for the above-mentioned system, comprising the following steps: S1: Real-time collection of electrical measurement data of each transformer area through intelligent sensing and control units deployed in each transformer area; S2: Based on the topology model and real-time data, dynamically calculate and update the real-time safe operation boundary and adjustable capability boundary of each transformer area; S3: At the distribution area level, aggregate local virtual energy storage resources and, within the dynamic boundary, use a multi-agent decision-making algorithm to generate a preliminary scheduling strategy for core energy storage and virtual resources. S4: Each transformer area will upload its boundary information, adjustability, and operational status to the cloud-based collaborative management platform; S5: The cloud-based collaborative management platform performs cross-regional collaborative optimization calculations and integrates price or demand signals from the electricity market to generate global optimization instructions and market trading strategies, which are then distributed to each region. S6: The intelligent sensing and control unit of the transformer area receives instructions from the cloud, performs verification and fine-tuning in combination with local dynamic boundaries, generates the final executable control instructions, and sends them to the local controller and adjustable load controller of the core energy storage unit for execution. S7: Repeat steps S1-S6 to achieve closed-loop operation.

[0040] Working principle of the invention: The system's operation begins with real-time monitoring by the intelligent sensing and control unit (deployed in the intelligent converged terminal of the distribution area). The high-precision measurement module continuously collects voltage, current, power, and power quality data from the main transformer inlet, energy storage grid connection point, and key load nodes within the distribution area. Based on this real-time data and the built-in distribution area topology impedance model, the dynamic boundary identification module initiates online safety calculations. It constructs and solves an optimization problem aimed at maximizing the system's adjustability margin, with strict constraints covering: Equipment safety constraints: transformer load rate, current carrying capacity of each line.

[0041] Power grid quality constraints: Voltage dynamic range of all nodes (obtained through fast power flow calculations integrating real-time measurement data).

[0042] By repeatedly solving this optimization problem, the module dynamically outputs the safety adjustment boundaries of the core energy storage unit and various aggregated resources at the current moment, under the premise of ensuring the safe operation of the distribution area, including the charging and discharging power limit range (P_min, P_max), voltage / reactive power adjustable range, etc.

[0043] Within the dynamically identified safety boundary, the multi-agent decision-making module initiates a coordination control algorithm based on hierarchical Stackelberg game theory to formulate the optimal local scheduling strategy: The intelligent sensing and control unit generates and publishes internal electricity prices or incentive signals (λ) to local resources based on the control targets (such as peak shaving power values) sent from the cloud and local boundaries.

[0044] The core energy storage unit (through its local controller) and distributed resources (such as adjustable loads and electric vehicle charging stations) aggregated by the virtual energy storage aggregation module report their respective power adjustment plans in response to signals and with the goal of optimizing their own operating costs or benefits.

[0045] Iterative Equilibrium: The intelligent sensing and control unit dynamically adjusts the excitation signal λ based on the deviation between the total response of all core energy storage units and virtual energy storage aggregation modules and the expected target. After multiple rounds of game iteration, an equilibrium state is finally reached. This state satisfies both system-level regulation requirements (such as power balance and network loss reduction) and coordinates the interests of individual resources, forming feasible local scheduling instructions.

[0046] The cloud-based collaborative management platform aggregates real-time operational data, adjustability information, and security boundary information from various distribution areas, enabling optimization at a broader level. Cross-regional collaborative optimization: The platform takes the minimum total network loss, the highest voltage qualification rate, or the lowest total operating cost of the regional distribution network as the global goal, takes into account network power flow constraints, performs centralized optimization calculations, and generates and issues the optimal power exchange plan or collaborative control instructions for each distribution area.

[0047] Electricity Market Trading: The platform aggregates the adjustable capacity of various distribution areas (especially the large-scale resources formed through virtual energy storage aggregation modules) as a whole, participates in electricity spot market or ancillary service market trading, and obtains economic benefits. According to preset rules (such as based on actual adjustment contribution), the platform distributes market revenue to each participating distribution area and related entities, forming an economic incentive closed loop.

[0048] After receiving cross-regional instructions from the cloud-based collaborative management platform, the intelligent sensing and control unit of the transformer area will immediately combine the latest local measurement data with the boundary calculated in real time by the dynamic boundary recognition module to perform safety verification and feasibility fine-tuning, ensuring that the instructions do not cross the boundary.

[0049] The final control command, once verified, is sent to the core energy storage unit. Its local controller parses the command into specific control signals for the bidirectional converter (such as constant power charging / discharging, constant voltage support, etc.). Based on the command, the battery cluster performs AC / DC conversion via the bidirectional converter, thereby precisely storing or releasing electrical energy. Among these, power-type battery modules (such as supercapacitors) are responsible for responding to high-frequency, rapid power demands to address power quality issues; while energy-type battery modules (such as lithium iron phosphate batteries) undertake long-term energy dispatching tasks such as peak shaving and valley filling.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A transformer substation energy storage system, characterized in that, It includes a core energy storage unit, an intelligent sensing and control unit, and a cloud-based collaborative management platform. The cloud-based collaborative management platform is communicatively connected to both the core energy storage unit and the intelligent sensing and control unit. The core energy storage unit includes: Battery clusters are used to store and release electrical energy; A bidirectional converter is used to achieve bidirectional conversion of AC and DC power. A local controller, which is communicatively connected to the bidirectional converter, is used to execute low-level control commands from the intelligent sensing and control unit; The intelligent sensing and control unit includes: The high-precision measurement module is used to collect voltage, current, power and power quality data in real time at the main entrance of the transformer area, the grid connection point of the core energy storage unit and the key load branch nodes; The dynamic boundary identification module is used to calculate and dynamically update the real-time adjustable capability boundary and safe operation boundary of the transformer area based on the transformer area topological impedance model and real-time measurement data. The multi-agent decision-making module, with an embedded coordination control algorithm, is used to generate a scheduling strategy for the core energy storage unit and local adjustable resources within the boundary given by the dynamic boundary recognition module. The cloud-based collaborative management platform is used to receive data from multiple distribution zones, perform cross-distribution zone collaborative optimization, and issue control instructions and market trading strategies.

2. The distribution area energy storage system according to claim 1, characterized in that, The dynamic boundary identification module determines the real-time safety boundary by solving an optimization problem aimed at maximizing the adjustment margin. The constraints include transformer load rate constraints, line capacity constraints, and node voltage dynamic constraints. The node voltage dynamic constraints are calculated based on the power flow of the real-time topology and load distribution.

3. The transformer substation energy storage system according to claim 1, characterized in that, The coordination control algorithm implemented by the multi-agent decision-making module is based on a hierarchical Stackelberg game model.

4. The transformer substation energy storage system according to claim 3, characterized in that, The intelligent sensing and control unit also includes a virtual energy storage aggregation module, which is used to aggregate the adjustable loads, electric vehicle charging piles and user-side energy storage potential scattered within the transformer area to form virtual energy storage resources, and to perform joint optimization scheduling with the core energy storage unit.

5. The transformer substation energy storage system according to claim 1, characterized in that, The cloud-based collaborative management platform is used for: (1) Receive and aggregate data from several of the aforementioned intelligent sensing and control units; (2) Perform cross-regional collaborative optimization calculations; (3) Issue cross-regional regulation instructions and market trading strategies.

6. The transformer substation energy storage system according to claim 1, characterized in that, The battery cluster adopts a hybrid topology of power-type battery modules and energy-type battery modules. The power-type battery modules are used to provide fast power response, and the energy-type battery modules are used to provide continuous energy throughput.

7. A transformer substation energy storage system according to claim 5, characterized in that, The cloud-based collaborative management platform is also connected to the electricity market trading system, which is used for: (1) Based on the forecast of adjustable capacity of cross-regional aggregation, resources that can participate in market transactions are formed; (2) Participate in electricity spot market or ancillary services market transactions; (3) In accordance with the preset rules, the market revenue obtained will be distributed to the relevant entities in each district.

8. A transformer substation energy storage system according to claim 1, characterized in that, The intelligent sensing and control unit is deployed in the intelligent converged terminal of the transformer substation.

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