Bilateral service area power supply cooperation method, device and system based on energy storage system
By employing a dual-side power supply coordination method using energy storage systems in highway service areas, and utilizing dynamic time warping algorithms and collaborative optimization scheduling strategies, dual-side load balancing is achieved, improving energy utilization and system stability while reducing costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-03-24
AI Technical Summary
The power supply system of highway service areas suffers from uneven load on both sides, resulting in low energy utilization. Traditional expansion and capacity increase solutions are costly and difficult to respond quickly to load changes.
A dual-service area power supply coordination method based on energy storage system is adopted. The load data similarity is calculated by dynamic time warping algorithm, and scheduling information is generated by collaborative optimization scheduling strategy model. The energy storage system is controlled to perform charging and discharging operations to achieve dual-side load balance. The dual-side energy storage systems are connected by DC interconnection cable.
It improves energy efficiency, ensures stable system operation, reduces power supply costs, enables rapid response to load changes, and reduces system upgrade and maintenance costs.
Smart Images
Figure CN121727063A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of energy storage control of power systems, and particularly relates to a double-side service area power supply coordination method, device and system based on an energy storage system. BACKGROUND
[0002] As an important node in the traffic network, the power distribution system of the expressway service area is usually designed based on conventional load statistical data and with a certain margin to meet the daily operation demand. However, with the significant increase in traffic flow in recent years and the increasing popularity of new energy vehicles, the service area as an important charging node in long-distance travel, the use frequency and power demand of its charging facilities are also increasing. Especially during the holiday peak period, the surge in demand for charging new energy vehicles and the asymmetry of passenger flow, such as the dense passenger flow in some periods (such as noon and evening) and the relatively quiet in other periods, further aggravate the load fluctuation of the power distribution system of the service area. This brings unprecedented challenges to the power supply and distribution system of the service area. The traditional expansion and capacity increase scheme not only has high cost, but also is difficult to quickly respond to load changes.
[0003] At present, the traditional service area power supply system has the problem of unbalanced load on both sides, resulting in low energy utilization rate (the load deviation rate of some service areas is even greater than 30%). SUMMARY
[0004] In order to solve the above problems, the application provides a double-side service area power supply coordination method, device and system based on an energy storage system, which can ensure balanced load on both sides of the service area, ensure the stability of the power system of the service area, improve the energy utilization rate, has low cost, fast response, and is suitable for large-area popularization and use.
[0005] In the first aspect, the application provides a double-side service area power supply coordination method based on an energy storage system, and the double-side service area is respectively provided with an energy storage system; the method comprises the following steps: obtaining load data of the double-side service area and operating state parameters of the energy storage system; performing similarity calculation on the load data of the double-side service area by using a dynamic time warping algorithm to obtain a best matching path; determining load difference data of the double-side service area according to the best matching path; inputting the load difference data and the operating state parameters into a pre-constructed collaborative optimization scheduling strategy model to obtain collaborative scheduling information; sending the collaborative scheduling information to local controllers of the double-side service area respectively, so that the local controllers control the energy storage systems on the same side to perform charging or discharging operation according to the collaborative scheduling information.
[0006] In an optional implementation, the load difference data comprises time-aligned bilateral load sequences and / or power difference value sequences determined according to the bilateral load sequences.
[0007] In an optional implementation, the collaborative optimization scheduling strategy model is constructed according to preset charging and discharging rules and energy storage system constraints, and aims to minimize total power supply cost or maximize peak shaving and valley filling effect.
[0008] In an optional implementation, the preset charging and discharging rules comprise: when the load of one side service area is higher than that of the other side, controlling the energy storage system of the low-load service area to discharge to the high-load service area, and / or controlling the energy storage system of the low-load service area to store local redundant power; based on a time-of-use electricity price policy, charging the energy storage system in a low electricity price period and discharging the energy storage system in a high electricity price period.
[0009] In an optional implementation, the collaborative optimization scheduling strategy model is a mixed integer linear programming model or adopts a model predictive control framework for rolling optimization.
[0010] In an optional implementation, the collaborative scheduling information comprises power control information for controlling DC interconnection cable power; the DC interconnection cable connects DC bus bars in bilateral service areas, and the DC interconnection cable is used to realize power complementation of bilateral energy storages.
[0011] In an optional implementation, after the load data of the bilateral service areas and the operating state parameters of the energy storages are acquired, the method further comprises: performing denoising processing, normalization processing and missing value filling processing on the load data and the operating state parameters; wherein the denoising processing adopts a sliding average algorithm and / or a wavelet transform algorithm.
[0012] In a second aspect, the present application provides a bilateral service area power supply collaboration device based on an energy storage system, wherein the bilateral service areas are respectively provided with energy storage systems; the device comprises: a data acquisition module, configured to acquire load data of the bilateral service areas and operating state parameters of the energy storages; a path calculation module, configured to perform similarity calculation on the load data of the bilateral service areas by using a dynamic time warping algorithm to obtain a best matching path; a load difference module, configured to determine load difference data of the bilateral service areas according to the best matching path; a scheduling information module, configured to input the load difference data and the operating state parameters into a pre-constructed collaborative optimization scheduling strategy model to obtain collaborative scheduling information; The information sending module is configured to send the cooperative scheduling information to the local controllers of the two-sided service areas, so that the local controllers control the energy storage systems on the same side to perform charging or discharging operations according to the cooperative scheduling information.
[0013] In a third aspect, the present application provides a two-sided service area power supply cooperative system based on energy storage systems, comprising: The first service area subsystem comprises a first DC bus, a first energy storage system connected to the first DC bus, and a first local controller for controlling the first energy storage system. The second service area subsystem comprises a second DC bus, a second energy storage system connected to the second DC bus, and a second local controller for controlling the second energy storage system. The DC interconnection cable is connected to the first DC bus and the second DC bus, respectively. The server is connected to the first local controller and the second local controller, respectively, and is configured to execute the method of any one of the first aspect, generate system scheduling information, and send the cooperative scheduling instructions to the first local controller and the second local controller, respectively. The first local controller is configured to control the first energy storage system to charge or discharge according to the system scheduling information, the second local controller is configured to control the second energy storage system to charge or discharge according to the system scheduling information, and the first local controller and the second local controller are further configured to cooperatively control the power flowing through the DC interconnection cable.
[0014] In a fourth aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method of any one of the embodiments of the first aspect.
[0015] In a fifth aspect, the present application provides a computer readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to execute the method of any one of the embodiments of the first aspect.
[0016] The technical scheme provided by the embodiment of the present application has the beneficial effects that: the bilateral service area power supply coordination method, device and system based on the energy storage system provided by the present application firstly acquire the load data of the bilateral service area and the operating state parameters of the energy storage system, accurately master the operating state of the entire power supply system of the bilateral service area in real time, lay a data foundation for subsequent load difference analysis and coordination scheduling information generation; then, the dynamic time warping algorithm is used to perform similarity calculation on the load data of the bilateral service area, so as to obtain the best matching path, accurately identify the load imbalance of the bilateral service area, determine the load imbalance data, identify the best opportunity for power interconnection, and lay a foundation for subsequent specified scheduling strategies; then, the load difference data of the bilateral service area is obtained through the best matching path, and the load difference data and the operating state parameters representing the state of the energy storage system obtained in the foregoing are input into the pre-constructed coordination optimization scheduling strategy model, so as to obtain the coordination scheduling information through the optimization calculation of the model; finally, the coordination scheduling information is issued to the local controllers of the bilateral service area, and the local controllers directly drive the energy storage systems on the same side to perform charging and discharging operations, so as to realize the power scheduling of the bilateral service area by using the local control equipment, successfully solve the bilateral load imbalance problem, effectively improve the energy utilization rate, ensure the stable operation of the system, reduce the power supply cost, and realize the rapid response; the present application makes full use of the existing local control equipment, does not need to reconstruct the control system, and saves the system reconstruction cost and operation and maintenance cost. BRIEF DESCRIPTION OF DRAWINGS
[0017] Figure 1 A flowchart of the bilateral service area power supply coordination method based on the energy storage system is provided for the embodiment of the present application. Figure 2 A circuit principle schematic diagram of the bilateral service area based on an alternating current bus is provided for the embodiment of the present application. Figure 3 A circuit principle schematic diagram of the bilateral service area based on a direct current bus is provided for the embodiment of the present application. Figure 4 Another flowchart of the bilateral service area power supply coordination method based on the energy storage system is provided for the embodiment of the present application. Figure 5 A coordination control strategy system architecture schematic diagram is provided for the embodiment of the present application. Figure 6 A system principle schematic diagram of the bilateral service area power supply coordination device based on the energy storage system is provided for the embodiment of the present application. Figure 7 A system principle schematic diagram of the bilateral service area power supply coordination system based on the energy storage system is provided for the embodiment of the present application. Figure 8 A system principle schematic diagram of an electronic device is provided for the embodiment of the present application.
[0018] In the diagram: 201 - Photovoltaic array; 202 - Service area A energy storage battery; 203 - Inverter; 204 - Service area A bidirectional converter PCS; 205 - Service area A AC bus; 206 - Main grid connection point; 207 - Service area A load; 208 - Wind turbine; 209 - Service area B energy storage battery; 210 - Converter; 211 - Service area B bidirectional converter PCS; 212 - Service area B AC bus; 213 - Service area B load; 301 - Service area A DC bus; 302 - Service area B DC bus; 501 - Cloud coordination layer; 502 - Communication network; 503 - Local controller A; 504 - Service area A - Energy storage system; 505 - Service area B - Energy storage system; 506 - Local controller B; 507 - DC interconnection switch; 601 - Data acquisition module; 602 - Path calculation module; 603 - Load difference module; 604 - Dispatch information module; 605 - Information sending module; 701 - Server; 702 - First local controller; 703 - First energy storage system; 704 - First DC bus; 705 - Second local controller; 706 - Second energy storage system; 707 - Second DC bus; 1000 - Electronic equipment; 1001 - Communication interface; 1002 - Processor; 1003 - Memory; 1004 - Bus. Detailed Implementation
[0019] Currently, traditional service area power supply systems suffer from uneven load distribution, resulting in low energy utilization and a load deviation rate exceeding 30%. Existing energy storage systems mostly employ a single-sided independent control mode, lacking a dual-sided coordination mechanism, making it difficult to achieve optimal peak shaving and valley filling effects. After renewable energy is integrated, output fluctuations occur, and existing control strategies suffer from insufficient dynamic response speed and long response delays (e.g., greater than 5 minutes). Furthermore, the synergistic optimization of electricity price differences during certain periods and battery life is not considered, hindering economic improvement. Therefore, this invention proposes a dual-sided service area power supply coordination method, device, and system based on an energy storage system. It aims to address the power supply pressure caused by increased electricity load in service areas and the uncertainty of charging demand from new energy vehicles through innovative energy storage system interconnection and coordinated control strategies. This will improve energy utilization efficiency, enhance power supply stability, reduce operating costs, and promote sustainable development. The invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0020] Reference Figure 1 A method for coordinated power supply in two service areas based on an energy storage system, wherein energy storage systems are installed in both service areas. The method in this embodiment includes the following steps S100 to S500.
[0021] Step S101: Obtain load data and operating status parameters of the energy storage system from both service areas.
[0022] Preferably, energy storage systems are installed in both service areas, and each energy storage system is connected to its respective local controller. The local controllers on both sides are then connected to a server (such as a cloud server). The method in this embodiment is applied to a server.
[0023] In an optional embodiment, after acquiring the load data of both service areas and the operating status parameters of the energy storage system, the method further includes: The load data and operating status parameters undergo denoising, normalization, and missing value imputation. Denoising employs a moving average algorithm and / or wavelet transform algorithm. Denoising eliminates noise in the data, normalization eliminates dimensional differences, and missing value imputation improves data integrity.
[0024] Preferably, this embodiment uses db4 wavelet transform for denoising, decomposing the data collected per minute into 6 levels to obtain approximate data and detail data; unbiased likelihood estimation is used to perform soft thresholding on the detail coefficients (high-frequency coefficients) obtained from each level of decomposition; the thresholded detail coefficients and approximate coefficients are then reconstructed using wavelet transform to obtain the denoised data. The denoised data is then normalized using Min-Max to map the data to the [0,1] interval, and missing values are handled using linear interpolation; finally, the preprocessed data is obtained and used in subsequent steps.
[0025] Step S102: The similarity calculation of the load data of the two service areas is performed using the dynamic time warping algorithm to obtain the optimal matching path.
[0026] Dynamic Time Warping (DTW) is an algorithm for measuring the similarity of time series data, based on the concept of dynamic programming. This algorithm constructs a distance matrix between two curves and uses path constraints with slopes ranging from 0.5 to 2 to find the optimal path with the minimum cumulative distance. The basic metric is typically Euclidean distance, commonly used in isolated word speech recognition to effectively solve template matching problems with varying pronunciation lengths. It is also frequently applied in gesture recognition. Compared to Hidden Markov Models (HMMs), this algorithm has the advantage of lower training computation. This embodiment creatively applies DTW to the time warping of load data from both service areas (i.e., matching of power load curves). Compared to the traditional method of simple instantaneous power comparison, this embodiment uses DTW to directly address the potential asynchrony between peak and low load periods on both sides. By finding the optimal alignment path through DTW, it provides unprecedentedly accurate data for subsequent precise analysis of time offsets, calculation of power differences, and determination of optimal charging and discharging timing.
[0027] In an optional embodiment, step S102 includes the following steps (1) to (5).
[0028] (1) Obtain the load data sequences of service area A and service area B. These two sequences may not be perfectly aligned at the time points.
[0029] (2) Determine the Euclidean distance (or other similarity measure) between each data point in service area A sequence and each data point in service area B sequence to obtain a two-dimensional distance matrix that quantifies the load difference between any two time points.
[0030] (3) In this embodiment, a dynamic programming strategy is adopted. Starting from the starting point of the distance matrix (representing the beginning of the two sequences), a path with the minimum cumulative distance to the ending point (representing the end of the two sequences) is gradually found. The extension direction of the path is constrained, for example, it can only move to the right, down or down to the right, to ensure the rationality of the time sequence.
[0031] (4) When the path finding process reaches the end point, the algorithm backtracks from the end point back to the starting point to finally determine the path with the smallest cumulative distance, which is the best matching path.
[0032] (5) The optimal matching path consists of a series of coordinate pairs, each of which precisely indicates which point in the load sequence of service area A is most similar to which point in the load sequence of service area B.
[0033] Through the above steps (1) to (5), this embodiment can accurately align two load curves that are not synchronized on the time axis, thereby providing a key basis for accurately identifying the real load differences and formulating collaborative scheduling strategies.
[0034] The goal of the dynamic time warping algorithm in this embodiment is to calculate the similarity of the load curves on both sides and find the best matching path. It calculates the Euclidean distance between each point in the two sequences by constructing a distance matrix; then it performs dynamic programming to solve the problem, accumulating the minimum distance path from the starting point to the ending point of the matrix; and outputs the alignment path to determine the time correspondence of the loads on both sides (e.g., t1 in region A corresponds to t2 in region B).
[0035] Step S103: Determine the load difference data of the two service areas based on the optimal matching path.
[0036] Specifically, the server (such as Figure 5The cloud coordination layer (shown) uses the optimal matching path calculated in the previous step S102 to rearrange and align the original, time-asynchronous load data sequences of service areas A and B. This alignment process matches the points on the two load curves that are most similar in shape together. For example, it matches the load point of service area A at time t1 with the load point of service area B at time t2, thereby eliminating the comparison error caused by the inconsistency of peak electricity consumption times and obtaining the aligned time points. Based on the aligned time points, the system calculates the load difference between the two service areas point by point. Specifically, for each pair of matching points on the optimal matching path, the power difference between them is calculated, thereby generating an accurate power difference sequence corresponding to each time point.
[0037] In an optional embodiment, the load difference data includes a time-aligned two-sided load sequence and / or a power difference sequence determined based on the two-sided load sequence.
[0038] In this embodiment, the load difference data includes a time-aligned bilateral load sequence and a power difference sequence determined based on the bilateral load sequence. Here, the time-aligned bilateral load sequence is the aligned bilateral load sequence itself. The service area uses the generated power difference sequence and the aligned bilateral load sequence itself as input data to characterize the load difference, providing accurate and reliable input for the collaborative optimization scheduling strategy model in the next stage.
[0039] The load difference analysis in this embodiment includes time difference and amplitude difference. Time difference identifies the time offset between peaks or troughs on both sides (e.g., the peak in region A is 2 hours earlier than that in region B). Amplitude difference determines the energy storage charging and discharging demand window by calculating the aligned power difference.
[0040] Step S104: Input the load difference data and operating status parameters into the pre-built collaborative optimization scheduling strategy model to obtain collaborative scheduling information.
[0041] Specifically, operational status parameters refer to the operational status parameters of the energy storage system, including the system's State of Health (SOC), maximum charge / discharge power, etc. These parameters input real-time constraints and capacity boundaries into the collaborative optimization scheduling strategy model. For example, in service area A, the energy storage SOC (State of Health) is 80% (i.e., the energy storage battery has 80% charge remaining), with a maximum discharge capacity of 100kW; in service area B, the energy storage SOC is 40%, with a maximum charge capacity of 50kW. This set of operational status parameters is a strong guarantee for obtaining accurate, feasible, and safe collaborative scheduling information. If the collaborative optimization scheduling strategy model does not obtain this set of operational status parameters, the model may only issue a dangerous instruction to allow the already controlled batteries to continue discharging.
[0042] In an optional embodiment, the collaborative optimization scheduling strategy model is constructed based on preset charging and discharging rules and energy storage system constraints, with the goal of minimizing total power supply cost or maximizing peak shaving and valley filling effect.
[0043] Specifically, the collaborative optimization scheduling strategy model is a multi-timescale optimization model. In some possible embodiments, this collaborative optimization scheduling strategy model includes a day-ahead scheduling layer and a real-time control layer. The day-ahead scheduling layer is an economic optimization that considers electricity price fluctuations, aiming to minimize the total power supply cost; the real-time control layer is based on a model predictive control framework (MPC) for rolling optimization, and its core task is to smooth out fluctuations, achieve precise and maximized peak shaving and valley filling, solve the problem of load imbalance on both sides, and improve energy utilization.
[0044] In optional embodiments, the preset charging and discharging rules include strategy one and strategy two.
[0045] Strategy 1 is: when the load of one service area is higher than the load of the other, control the energy storage system of the low-load service area to discharge to the high-load service area, and / or control the energy storage system of the low-load service area to store local redundant energy. In other words, when the load on one side of a dual-service area is higher than that on the other side, one of the following options (A-C) can be selected for execution: Option A: External support, energy storage in the low-load area discharges to the high-load area to supplement the high-load area; Option B: Internal absorption, energy storage in the low-load area stores local redundant electricity; Option C: Collaborative operation, options A and B are performed simultaneously. The core of this rule is to address spatial load asymmetry and ensure power supply reliability and energy utilization.
[0046] Strategy two involves charging the energy storage system during periods of low electricity prices and discharging it during periods of high electricity prices, based on the time-of-use pricing policy. The time-of-use pricing policy is a continuously effective and independent rule, its core being to address time-based electricity price fluctuations, with the goal of achieving the economic benefits of buying low and selling high. This strategy serves as one of the decision bases for the optimization model at all times.
[0047] When both strategies are in effect simultaneously, the collaborative optimization scheduling strategy model will perform comprehensive calculations to arrive at a globally optimal solution that satisfies load balancing while achieving economic optimization as much as possible.
[0048] In some possible embodiments, energy storage system constraints include a battery health constraint model, as well as other constraints. The battery health constraint model can be a cycle life guarantee strategy where the State of Charge (SOH) is greater than a preset threshold (e.g., 80%). Other implicit constraints can be upper and lower limits of the energy storage system's State of Charge (e.g., 20%~90%), charge / discharge power limits (e.g., charge / discharge efficiency η=85%), etc. Here, an SOH of 80% indicates that the battery has degraded, and its current maximum capacity is only 80% of its original capacity.
[0049] In optional embodiments, the collaborative optimization scheduling strategy model is a mixed integer linear programming model (MILP) or a rolling optimization using a model predictive control framework (MPC).
[0050] Specifically, the mixed-integer linear programming model can simultaneously handle continuous variables such as charging and discharging power and discrete integer variables such as energy storage system start-up and shutdown, thus transforming complex scheduling problems into computable mathematical problems. This model constructs standard optimization problems by combining objectives such as minimizing total cost or maximizing peak shaving and valley filling effects with preset rules such as energy storage state of charge (SOC) constraints, power balance constraints, and equipment action logic. Then, a mathematical programming solver calculates globally optimal or near-optimal deterministic scheduling schemes.
[0051] The model predictive control framework employs a sliding window operation. Every fixed time interval (e.g., 15 minutes), the system re-executes dynamic load alignment and strategy optimization, starting with the latest system state (e.g., real-time load, energy storage SOC), to adapt to dynamic load changes and generate an optimal scheduling sequence for a finite future time domain. However, the system only executes the optimal command for the current time period. When the next cycle arrives, it immediately performs optimization again based on the latest measured data, and this process is repeated cyclically. This mechanism enables the system to continuously adapt to dynamic changes in load and renewable energy output, effectively overcoming prediction errors and improving the robustness and real-time performance of the scheduling strategy.
[0052] In practical implementation, the mixed-integer linear programming model and the model predictive control framework can work together. The model predictive control framework is responsible for the overall process control of rolling optimization, while the optimization problem to be solved in each rolling cycle can be constructed as a mixed-integer linear programming model for computation. The mixed-integer linear programming model can be used as a one-time global optimization tool, or it can be embedded within the model predictive control framework as a solver within a rolling cycle.
[0053] For rolling optimization, this embodiment organically combines DWT alignment, difference analysis, optimization (MILP / MPC), and real-time rolling updates through a periodic rolling optimization process of data acquisition (step S100), alignment (step S200), analysis (step S300), optimization (step S400), and rolling. This forms a highly adaptive control framework that considers the dynamic characteristics of the time dimension. Compared with the traditional method of formulating a strategy for the whole day at once, the rolling update mechanism of this embodiment effectively ensures that the control strategy can keep up with the actual changes in load, thereby improving the robustness and economy of the system.
[0054] In practical implementation, the collaborative optimization scheduling strategy model can incorporate a cross-regional power support triggering mechanism as the core preset charging and discharging rule. This mechanism stipulates that only when the difference in power between the two loads after alignment based on the dynamic time warping algorithm exceeds a set threshold (e.g., 30%) can the optimization model be triggered to generate scheduling instructions that promote power mutual assistance, thereby ensuring the accuracy and necessity of coordinated actions. Then, the collaborative optimization scheduling strategy model can ensure the reliable realization of the above-mentioned goals of minimizing total power supply costs or maximizing peak shaving and valley filling effects by considering robust optimization methods for photovoltaic / wind power prediction errors. The robust optimization method for photovoltaic / wind power prediction errors can set the predicted value of new energy output to an uncertainty range (e.g., a fluctuation tolerance of ±15%) and solve for the optimal strategy that ensures that system constraints are satisfied in all cases within this range, thereby significantly improving the robustness of the scheduling scheme in the face of intermittent renewable energy fluctuations. Finally, the collaborative optimization scheduling strategy model can use the dynamic balancing method of dual-side energy storage SOC as a key constraint condition for the energy storage system. In the optimization solution, it is required that the difference in the state of charge of the dual-side energy storage be maintained within a narrow range (e.g., less than 5%). This not only prevents overcharging and over-discharging of individual energy storage, but also ensures the balanced use of energy storage resources and the sustainability of the overall system in the long-term operation from the system level.
[0055] In an optional embodiment, the coordinated scheduling information includes power control information for controlling the power of the DC interconnect cable; the DC interconnect cable connects the DC bus in both service areas and is used to realize power mutual assistance between the two energy storage facilities.
[0056] Currently, the power grids of most service areas are AC buses, with energy storage devices, loads, photovoltaic, and wind power equipment on both sides of the service area connected to the AC bus. To achieve energy storage synergy between the two service areas, a common solution is to interconnect them via the AC bus, such as... Figure 2As shown, the photovoltaic array 201 of service area A is connected to the AC bus 205 of service area A via inverter 203. The energy storage battery 202 of service area A is connected to the AC bus 205 of service area A via bidirectional converter PCS 204 of service area A. The loads 207 of service area A (such as charging piles, air conditioners, lighting, etc.) are directly connected to the AC bus 205 of service area A, which is then connected to the main grid connection point 206. Similarly, the wind turbine 208 of service area B is connected to the AC bus 212 of service area B via converter. The energy storage battery 209 of service area B is connected to the AC bus 212 of service area B via bidirectional converter PCS 211 of service area B. The loads 213 of service area B (such as charging piles, air conditioners, lighting, etc.) are directly connected to the AC bus 212 of service area B, which is then connected to the main grid connection point 206. The AC bus 206 of service area A and the AC bus 212 of service area B are connected via an AC interconnection bus, achieving power sharing through the interconnection of the AC buses of the two service areas. However, this scheme requires the energy storage systems on both sides to be connected to the AC bus via their respective converters (PCS), and then connected via AC tie lines. This scheme has problems such as complex structure, the need for additional grid-connected converter equipment, high cost, and multiple AC-DC conversions during power sharing between the two energy storage systems, leading to efficiency losses.
[0057] Therefore, this embodiment improves the power distribution system. Based on the existing conventional energy storage system in the service area, a DC cable is used to connect the DC buses of the energy storage systems in both service areas together. That is, the energy storage systems in both service areas are directly connected to their respective DC buses, and the DC buses on both sides are directly connected through a DC interconnection cable to form a unified DC energy pool. Each service area's DC bus is connected to the local AC bus and load through a bidirectional converter (PCS). A cloud-based collaborative control platform on the server executes dynamic load alignment and optimization algorithms, and generates system scheduling information for the dual-side energy storage systems and the DC interconnection line, realizing energy storage scheduling. In this way, with minimal modification costs, the load of both service areas can be balanced, the power supply capacity utilization rate can be improved, and the cost of expanding the power distribution system can be reduced. Previous conventional solutions required the purchase of dedicated dual-port energy routers to interconnect different power distribution systems, increasing the construction cost of the service areas.
[0058] Specifically, such as Figure 3 As shown, the DC transformation of the service areas on both sides is carried out through the following measures (1) to (3).
[0059] (1) For service area A: the photovoltaic array 201 and the energy storage battery 202 of service area A are connected to the DC bus 301 of service area A. The DC bus 301 of service area A is connected to the bidirectional converter PCS 204 of service area A through a DC cable. The bidirectional converter PCS 204 of service area A is then connected to the AC bus 205 of service area A through an AC cable. At the same time, the AC bus 205 of service area A is connected to the load 207 of service area A.
[0060] (2) For Service Area B: Service Area B fan 208 and Service Area B energy storage battery 209 are connected to Service Area B DC bus 302. Service Area B DC bus 302 is connected to Service Area B bidirectional converter PCS 211 through DC line. Service Area B bidirectional converter PCS 211 is then connected to Service Area B AC bus 212 through AC cable. At the same time, Service Area B AC bus 212 is connected to Service Area B load 213.
[0061] (3) Connect the DC bus 301 of service area A and the DC bus 302 of service area B through the DC interconnection bus.
[0062] The advantage of the DC interconnection scheme in this embodiment stems from the fundamental simplification of the system's energy conversion process. Specifically, the core of the scheme is to construct a local photovoltaic-storage microgrid with a DC bus as its backbone. Under this architecture, when DC loads (such as DC charging piles and LED lighting) exist within the service area, the DC power generated by the photovoltaic system or the DC power stored in the energy storage batteries can be directly supplied to the loads, achieving the highest efficiency in energy utilization. For unavoidable AC loads, the electrical energy only requires one inversion (DC / AC) process, which is a basic requirement. Compared to the AC interconnection scheme, the key improvement of this embodiment lies in eliminating the additional conversion stage required to achieve two-way interconnection. In the AC interconnection scheme ( Figure 2 In this scenario, to achieve power exchange, the path of power from energy storage in area A to the load in area B is typically: energy storage DC - inverter DC / AC - (AC interconnection line) - rectifier AC / DC - load DC, or a more complex conversion, which includes an unnecessary additional AC / DC conversion step designed for the interconnection itself. This DC interconnection scheme (… Figure 3 This simplifies to: energy storage DC - DC interconnection line - load DC or an inverter that only needs to supply AC load once. This subtractive design of the core interconnection path directly reduces the number of required converter equipment and system losses, thereby significantly reducing initial modification costs and long-term operating costs.
[0063] This embodiment innovatively employs DC cables to directly connect the DC buses of the energy storage systems on both sides of the service area, i.e., DC bus interconnection technology. This design not only simplifies the system structure and reduces implementation difficulty, but also significantly reduces retrofit costs. Through the interconnection of the DC buses, the energy storage systems on both sides of the service area can form a unified energy storage pool, realizing energy sharing and dispatch.
[0064] Step S105: Send the coordinated scheduling information to the local controllers of the two service areas respectively, so that the local controllers can control the energy storage system on the same side to perform charging or discharging operations according to the coordinated scheduling information.
[0065] Specifically, the system architecture is as follows: Figure 5 As shown, the system adopts a collaborative control mode of centralized optimization in the cloud and distributed execution by local controllers. The cloud coordination layer 501 (hosted in the server) acts as the intelligent hub, dynamically aligning the load curves on both sides based on the dynamic time warping algorithm, and generating globally optimal scheduling instructions using optimization models such as mixed integer linear programming (MILP) or model predictive control framework (MPC). The instructions are distributed through the communication network 502 (5G / fiber). Local controller A 503 is responsible for data acquisition and instruction execution, driving the energy storage converter (PCS) in local service area A to control the charging and discharging of energy storage system 504 in service area A. Local controller B 506 is also responsible for data acquisition and instruction execution, driving the energy storage converter (PCS) in local service area B to control the charging and discharging of energy storage system 505 in service area B. The DC buses of the two service areas are directly connected through the DC interconnection switch 507 (or power regulation unit) to form a physical channel for efficient power exchange. This allows the local controller to coordinate and control the power flowing through this channel according to cloud instructions, ultimately achieving precise load balancing on both sides and an overall improvement in energy utilization efficiency.
[0066] In a possible embodiment, refer to Figure 4 The method in this embodiment may include the following steps S401 to S412. Specifically, the server executes steps S401 to S404, local controller A of service area A executes steps S407 to S408, local controller B of service area B executes steps S410 to S412, and local controller A of service area A and local controller B of service area B collaboratively execute step S413: controlling the power of the interconnecting lines.
[0067] Step S401, Data Acquisition; Step S402, preprocessing aligned with DTW; Step S403, optimize model solution (MILP / MPC); Step S404: Generate the service area control instruction set; Step S405: Send the instruction set of service area A to the local controller A of service area A; Step S406: Send the service area B instruction set to the service area B local controller B; Step S407: Receive instruction; Step S408: Parse and drive PCS_A; Step S409: Control the charging or discharging of the energy storage battery in service area A; Step S410: Receive instruction; Step S411: Parse and drive PCS_B; Step S412: Control the charging or discharging of the energy storage battery in service area B; Step S413: Control the power of the interconnecting lines.
[0068] In optional embodiments, the following four collaborative power supply strategies are used to achieve collaborative control and energy mutual assistance of energy storage systems in both service areas through flexible scheduling strategies.
[0069] Strategy one involves installing sensors and data acquisition devices for real-time monitoring and data analysis, specifically monitoring key parameters such as voltage, current, and capacity of the energy storage systems in both service areas. Then, data analytics are used to analyze and process these parameters to obtain the real-time status and demand of the energy storage systems.
[0070] Strategy Two: Achieve flexible scheduling based on real-time monitoring data. The charging and discharging strategies of the energy storage system are dynamically adjusted according to factors such as the actual load demand of the service areas on both sides, the status of the energy storage system, and weather forecasts.
[0071] Strategy three involves achieving energy exchange through efficient energy transfer and conversion mechanisms. By selecting appropriate DC cables, converters, and other equipment, energy loss during transmission is minimized.
[0072] Strategy Four: To ensure the safety and reliability of the coordinated power supply strategy, a series of safety measures are taken, including setting up electrical safety devices such as overcurrent protection and short-circuit protection, as well as developing fault early warning and emergency handling systems to deal with possible abnormal situations.
[0073] The method in this embodiment has the following beneficial effects: (1) This embodiment can make more effective use of electrical energy resources and reduce energy waste by realizing the coordinated control and energy mutual assistance of the energy storage systems in both service areas.
[0074] (2) In this embodiment, during peak electricity consumption periods or in case of emergencies, power can be allocated from the energy storage system on the other side to provide support, thereby enhancing the power supply stability of the service area and reducing the risk of power outages.
[0075] (3) This embodiment can reduce the power operation cost of the service area by optimizing the utilization efficiency of the energy storage system and reducing the expansion needs.
[0076] (4) This embodiment helps to promote the green and low-carbon development of highway service areas, which is in line with the national policy orientation of energy conservation, emission reduction and sustainable development.
[0077] The following specific examples will provide further explanation.
[0078] This embodiment applies to a microgrid system in service areas on both sides of a highway. Both service areas (taking the east and west service areas as examples) are equipped with distributed photovoltaic and energy storage systems. The system hardware configuration is as follows: each service area deploys a 500kWh / 250kW energy storage system and a 200kWp photovoltaic power generation unit. The loads in the two service areas exhibit typical time-complementary characteristics. The peak load in the east service area is approximately 1.2MW, concentrated in the morning period from 07:00 to 09:00; while the peak load in the west service area is approximately 0.8MW, concentrated in the evening period from 18:00 to 20:00.
[0079] First, the data acquisition system in the server continuously acquires real-time load power, photovoltaic output, and real-time status parameters (such as state of charge, SOC) of the energy storage system in both service areas with extremely high real-time performance (sampling interval ≤ 1 second). Based on this, the server executes a two-tier optimization strategy: In the day-ahead phase, based on accurate load and photovoltaic forecast data, and a significant peak-valley electricity price difference (e.g., 0.8 yuan / kWh), a day-ahead scheduling plan is formulated with optimal economic efficiency as the goal. In the real-time rolling optimization phase, the server initiates a Model Predictive Control (MPC) algorithm, with a fixed rolling cycle of 15 minutes. Every 15 minutes, based on the latest measured data, it re-executes the optimization calculation for the future short time domain, dynamically generating the latest scheduling instructions. When the server identifies, through the Dynamic Time Warping (DTW) algorithm, that the power deviation between the two loads exceeds a preset threshold of 30% for 5 consecutive minutes, it automatically triggers a cross-regional power support instruction. Throughout the process, battery health management strategies are strictly enforced as hard constraints, such as controlling the upper limit of the energy storage charging rate to within 0.5C to effectively ensure equipment lifespan.
[0080] The above methods address the issues of large timing differences in loads on both sides and low energy utilization, achieving significant economic benefits. Actual operational data shows that compared to traditional independent operation, this collaborative system improves the overall efficiency of peak shaving and valley filling by approximately 40%. By optimizing the spatial and temporal distribution of electricity and fully utilizing peak-valley electricity price differences, the overall operating cost of the system is reduced by approximately 25%. Simultaneously, by avoiding overcharging and over-discharging of energy storage and implementing smooth control, the battery cycle life is expected to be extended by more than 30%, greatly improving the long-term economic viability of the investment.
[0081] This embodiment, based on the existing conventional energy storage system in the service area, uses DC cables to connect the DC buses of the energy storage systems on both sides of the service area together. This allows the energy storage systems on both sides of the service area to form a unified energy reserve, enabling energy sharing and dispatch. This design not only simplifies the system structure and reduces implementation difficulty, but also requires minimal modification costs to balance the load on both sides of the service area, improve power supply capacity utilization, and reduce the cost of expanding the power distribution system. In contrast, conventional solutions require the purchase of dedicated dual-port energy routers to interconnect different power distribution systems, increasing the construction cost of the service area.
[0082] Based on the DC bus interconnection, this embodiment also proposes an intelligent scheduling strategy that can dynamically adjust the charging and discharging strategy of the energy storage system according to the actual power demand and energy storage status of both service areas. This not only effectively balances the load of both service areas and avoids power shortages caused by excessive load in a single service area, but also significantly improves the utilization rate of power supply capacity, enabling a more rational allocation and utilization of electrical resources.
[0083] Compared to traditional dual-port power router solutions, the DC bus interconnection technology proposed in this embodiment not only significantly reduces procurement and installation costs but also minimizes additional expenses associated with capacity expansion. Simultaneously, by improving power supply capacity utilization and load balancing, it further enhances the overall operational efficiency and user experience of the service area.
[0084] This embodiment is applicable to scenarios with dual-sided power supply needs, such as highway service areas and distributed energy parks, and is particularly aimed at optimizing and improving the power distribution system of highway service areas. This embodiment aims to address the power supply pressure caused by increased electricity load in service areas and the uncertainty of charging demand from new energy vehicles through innovative energy storage system interconnection and collaborative control strategies.
[0085] Referring to Figure 6, an embodiment of the present invention provides a dual-service area power supply coordination device based on an energy storage system, comprising a data acquisition module 100, a path calculation module 200, a load difference module 300, a scheduling information module 400, and an information sending module 500. The data acquisition module 100 acquires load data from both service areas and operating status parameters of the energy storage system. The path calculation module 200 uses a dynamic time warping algorithm to perform similarity calculations on the load data of both service areas to obtain the optimal matching path. The load difference module 300 determines the load difference data between the two service areas based on the optimal matching path. The scheduling information module 400 inputs the load difference data and operating status parameters into a pre-constructed collaborative optimization scheduling strategy model to obtain collaborative scheduling information. The information sending module 500 sends the collaborative scheduling information to the local controllers of each of the two service areas, so that the local controllers control the energy storage system on the same side to perform charging or discharging operations according to the collaborative scheduling information.
[0086] In an optional embodiment, the load difference data includes a time-aligned two-sided load sequence and / or a power difference sequence determined based on the two-sided load sequence.
[0087] In an optional embodiment, the collaborative optimization scheduling strategy model is constructed based on preset charging and discharging rules and energy storage system constraints, with the goal of minimizing total power supply cost or maximizing peak shaving and valley filling effect.
[0088] In an optional embodiment, the scheduling information module 400 includes a preset charging and discharging rule module, which is used to: control the energy storage system of the low-load service area to discharge to the high-load service area when the load of one service area is higher than that of the other side, and / or control the energy storage system of the low-load service area to store local redundant energy; and based on the time-of-use pricing policy, charge the energy storage system during low-price periods and discharge the energy storage system during high-price periods.
[0089] In optional embodiments, the collaborative optimization scheduling strategy model is a mixed-integer linear programming model or a rolling optimization using a model predictive control framework.
[0090] In an optional embodiment, the coordinated scheduling information includes power control information for controlling the power of the DC interconnect cable; the DC interconnect cable connects the DC bus in both service areas and is used to realize power mutual assistance between the two energy storage facilities.
[0091] In an optional embodiment, the apparatus further includes a preprocessing module, which is used to perform denoising, normalization, and missing value filling on the load data and operating status parameters; wherein the denoising process employs a moving average algorithm and / or a wavelet transform algorithm.
[0092] The apparatus provided in the embodiments of this application has the same inventive concept as the method provided in the embodiments of this application. As long as the method can solve the technical problem, the apparatus can also solve the technical problem. This will not be elaborated here.
[0093] See Figure 7 The present invention also provides a dual-service area power supply coordination system based on an energy storage system, comprising: The first service area subsystem includes a first DC bus 704, a first energy storage system 703 connected to the first DC bus 704, and a first local controller 702 for controlling the first energy storage system 703. The second service area subsystem includes a second DC bus 707, a second energy storage system 706 connected to the second DC bus 707, and a second local controller 705 for controlling the second energy storage system 706. DC interconnecting cables are connected to the first DC bus 704 and the second DC bus 705 respectively; Server 701 is connected to the first local controller 702 and the second local controller 705 respectively, and is used to execute the aforementioned dual-side service area power supply coordination method based on energy storage system, generate system scheduling information, and send coordination scheduling instructions to the first local controller 702 and the second local controller 705 respectively. The first local controller 702 is used to control the charging or discharging of the first energy storage system 703 according to the system scheduling information, and the second local controller 705 is used to control the charging or discharging of the second energy storage system 706 according to the system scheduling information. The first local controller 705 and the second local controller 705 are also used to coordinate the control of the power flowing through the DC interconnect cable.
[0094] In this embodiment, the server executes a collaborative optimization method to generate system scheduling information and sends it to the first local controller 702 and the second local controller 705 respectively. The first local controller 702 controls the charging or discharging of the first energy storage system 703 according to the instructions; the second local controller 705 controls the charging or discharging of the second energy storage system 706 according to the instructions. To achieve precise power scheduling, the first local controller 702 and the second local controller 705 also need to cooperate to jointly control the power flowing through the DC interconnect cable.
[0095] Reference Figure 8The present invention also provides an electronic device 1000, including a communication interface 1001, a processor 1002, a memory 1003, and a bus 1004. The processor 1002, the communication interface 1001, and the memory 1003 are connected via the bus 1004. The memory 1003 is used to store a computer program that supports the processor 1002 in executing the above-mentioned dual-service area power supply coordination method based on an energy storage system. The processor 1002 is configured to execute the program stored in the memory 1003.
[0096] Optionally, embodiments of the present invention also provide a computer-readable medium having non-volatile program code executable by a processor 1002, the program code causing the processor 1002 to execute the dual-service area power supply coordination method based on an energy storage system as described in the above embodiments.
[0097] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or equivalent to the scope of this invention are included in this invention.
Claims
1. A method for coordinated power supply in two service areas based on an energy storage system, characterized in that, Each of the two service areas is equipped with an energy storage system; the method includes: Obtain load data from the two service areas and operating status parameters of the energy storage system; The similarity of the load data of the two service areas is calculated using the dynamic time warping algorithm to obtain the optimal matching path; The load difference data between the two service areas is determined based on the optimal matching path. The load difference data and the operating status parameters are input into a pre-built collaborative optimization scheduling strategy model to obtain collaborative scheduling information; The coordinated scheduling information is sent to the local controllers of the respective service areas on both sides, so that the local controllers control the energy storage systems on the same side to perform charging or discharging operations according to the coordinated scheduling information.
2. The dual-service area power supply coordination method based on an energy storage system according to claim 1, characterized in that, The load difference data includes time-aligned two-sided load sequences and / or power difference sequences determined based on the two-sided load sequences.
3. The dual-service area power supply coordination method based on an energy storage system according to claim 1, characterized in that, The collaborative optimization scheduling strategy model aims to minimize the total power supply cost or maximize the peak shaving and valley filling effect, and is constructed based on preset charging and discharging rules and energy storage system constraints.
4. The dual-service area power supply coordination method based on an energy storage system according to claim 3, characterized in that, The preset charging and discharging rules include: When the load on one service area is higher than the load on the other, the energy storage system in the low-load service area is controlled to discharge to the high-load service area, and / or the energy storage system in the low-load service area is controlled to store local redundant energy; based on the time-of-use pricing policy, the energy storage system is charged during low-price periods and discharged during high-price periods.
5. The dual-service area power supply coordination method based on an energy storage system according to claim 1 or 3, characterized in that, The collaborative optimization scheduling strategy model is a mixed-integer linear programming model or a rolling optimization using a model predictive control framework.
6. The dual-service area power supply coordination method based on an energy storage system according to claim 1, characterized in that, The coordinated scheduling information includes power control information for controlling the power of the DC interconnect cable; the DC interconnect cable connects the DC bus in both service areas and is used to realize energy exchange between the two energy storage systems.
7. The dual-side service area power supply coordination method based on an energy storage system according to claim 1, characterized in that, After acquiring the load data of the dual service areas and the operating status parameters of the energy storage system, the method further includes: The load data and the operating status parameters are subjected to denoising, normalization, and missing value filling; wherein the denoising process employs a moving average algorithm and / or a wavelet transform algorithm.
8. A dual-service area power supply coordination device based on an energy storage system, characterized in that, Each of the two service areas is equipped with an energy storage system; the device includes: The data acquisition module is used to acquire load data of the dual service areas and operating status parameters of the energy storage system; The path calculation module is used to perform similarity calculation on the load data of the two service areas using a dynamic time warping algorithm to obtain the best matching path; The load difference module is used to determine the load difference data between the two service areas based on the optimal matching path. The scheduling information module is used to input the load difference data and the operating status parameters into a pre-built collaborative optimization scheduling strategy model to obtain collaborative scheduling information; The information sending module is used to send the coordinated scheduling information to the local controllers of the two service areas respectively, so that the local controllers control the energy storage system on the same side to perform charging or discharging operations according to the coordinated scheduling information.
9. A dual-service area power supply coordination system based on an energy storage system, characterized in that, include: The first service area subsystem includes a first DC bus, a first energy storage system connected to the first DC bus, and a first local controller for controlling the first energy storage system. The second service area subsystem includes a second DC bus, a second energy storage system connected to the second DC bus, and a second local controller for controlling the second energy storage system; DC interconnecting cables are respectively connected to the first DC bus and the second DC bus; The server is connected to the first local controller and the second local controller respectively, and is used to execute the method according to any one of claims 1-7, generate system scheduling information, and send the cooperative scheduling instructions to the first local controller and the second local controller respectively; The first local controller is used to control the charging or discharging of the first energy storage system according to the system scheduling information, and the second local controller is used to control the charging or discharging of the second energy storage system according to the system scheduling information. The first local controller and the second local controller are also used to coordinately control the power flowing through the DC interconnect cable.
10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1-7.