Energy coordination and charging and discharging path planning system of medium-large energy storage system

Abstract

The invention discloses an energy coordination and charging and discharging path planning system of a medium-large energy storage system. The system is composed of a data acquisition module, an energy coordination control module, a charging and discharging path planning module and a communication module. The data acquisition module collects data of an energy storage unit, a power grid and an external environment; the energy coordination control module carries out energy distribution by considering the characteristics of the energy storage unit according to the data, and is realized by establishing a model, evaluating a state, setting a multi-objective optimization function and adopting a distributed collaborative optimization algorithm; the charging and discharging path planning module combines an energy coordination instruction and power grid real-time information, defines a search space and a cost function through power grid modeling, and plans an optimal path by applying an algorithm; the communication module guarantees data transmission. According to the invention, energy can be accurately coordinated, the charging and discharging path is optimized, the overall performance and economic benefits of the energy storage system are improved, and the problems of the existing energy storage system are effectively solved.

Description

Technical Field

[0001] This paper deals with the energy coordination and charge and discharge path planning system for medium and large energy storage systems. Background Art

[0002] As renewable energy generation, such as solar and wind power, continues to expand, the intermittency and volatility of its generation have become increasingly prominent. As a key means of balancing power supply and demand and improving power system stability and reliability, medium and large-scale energy storage systems are becoming increasingly important.

[0003] However, current medium- and large-scale energy storage systems have numerous shortcomings in energy coordination and charge-discharge path planning. For example, existing energy coordination strategies often fail to fully account for the differences in the characteristics of individual storage units within the system, leading to overcharging and overdischarging of some units, impacting the service life and performance of the entire energy storage system.

[0004] In the planning of charging and discharging paths, there is a lack of comprehensive consideration of the real-time status of the power grid, electricity price fluctuations, and changes in load demand, which makes the charging and discharging paths unreasonable and increases energy transmission losses and operating costs.

[0005] Therefore, it is of great practical significance to develop an energy coordination and charge and discharge path planning system for medium and large energy storage systems that can effectively solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide an energy coordination and charge-discharge path planning system for medium- to large-scale energy storage systems. This system can achieve precise energy coordination and control of each energy storage unit in the energy storage system, and dynamically plan the optimal charge-discharge path based on factors such as grid status, electricity prices, and load demand, thereby improving the overall performance and economic benefits of the energy storage system. The following is a specific solution:

[0007] An energy coordination and charge-discharge path planning system for medium and large energy storage systems mainly includes a data acquisition module, an energy coordination control module, a charge-discharge path planning module and a communication module.

[0008] The data acquisition module is responsible for collecting real-time status data of each energy storage unit in the energy storage system, such as power, voltage, current, temperature, etc., and collecting real-time operating parameters of the power grid, including voltage, frequency, power flow, etc., as well as external environmental data such as electricity price information, weather forecast (used to predict renewable energy power generation power) and load demand data.

[0009] The energy coordination and control module distributes and coordinates energy across the energy storage units based on data acquired by the data acquisition module. It takes into account the remaining capacity, charge / discharge efficiency, and health status of each energy storage unit to optimize the overall performance of the energy storage system.

[0010] The charge and discharge path planning module uses a path search algorithm to plan the charge and discharge path according to the instructions of the energy coordination control module and factors such as the real-time status of the power grid, electricity price fluctuations and changes in load demand, so as to reduce energy transmission loss and cost.

[0011] The communication module is responsible for data transmission and communication between modules and between the system and external devices (such as power grid monitoring center, renewable energy power generation equipment, etc.).

[0012] Establish energy storage unit model: Build a detailed mathematical model for each energy storage unit, including charge and discharge characteristic model, capacity decay model, and thermal model, to accurately describe the behavior of the energy storage unit.

[0013] Status assessment: Based on the established model and collected real-time data, the status of each energy storage unit is evaluated, including remaining capacity (SOC), state of health (SOH), and charge and discharge efficiency.

[0014] Objective function setting: To maximize the overall service life of the energy storage system, improve the charging and discharging efficiency and ensure the stability of the system, a multi-objective optimization function is established. The multi-objective optimization function is:

[0015]

[0016] Where n is the number of energy storage units, ΔSOH i is the change in health status of the i-th energy storage unit within unit time Δt, η charge-i and η discharge- are the charging and discharging efficiencies of the i-th energy storage unit, P charge-i and P discharge-i are the charging and discharging powers of the i-th energy storage unit, σsystem is the stability index of the energy storage system, ω1, ω2, ω3, and ω4 are the weight coefficients of each target, which are adjusted according to actual needs.

[0017] Using a distributed collaborative optimization algorithm, each energy storage unit exchanges status information through a communication module, performs local optimization calculations, and feeds the results back to the energy coordination and control module. Based on this feedback, the energy coordination and control module performs global optimization adjustments and ultimately determines the charge and discharge power distribution for each energy storage unit.

[0018] Grid modeling: Modeling the power grid, including elements such as transmission lines, substations, nodes, and the electrical connection relationships and parameters between them, such as line resistance, reactance, capacitance, etc.

[0019] Status update: Based on the real-time operating parameters of the power grid obtained by the data acquisition module, the status of the power grid model is updated in real time, including node voltage, power flow, etc.

[0020] Path search space definition: All possible charging and discharging paths in the power grid are used as the search space, and each path consists of a series of transmission lines and nodes.

[0021] Cost function definition: In order to find the optimal charging and discharging path, a cost function is defined, which comprehensively considers factors such as energy transmission loss, electricity price cost, and load demand satisfaction. The cost function is expressed as:

[0022]

[0023] Where m is the number of line segments in the path, R j is the resistance of the jth line segment, I j is the current passing through the jth line segment, t j is the distribution of charging and discharging time on the jth line segment, P price is the real-time electricity price when charging and discharging on the jth line segment, P flow-j is the power passing through the jth line segment, l is the number of load nodes, P demand-k is the required power of the kth load node, P supply-k is the power provided to the kth load node through the planned path, and λ is the weight coefficient of the degree of load demand satisfaction.

[0024] The cost function is used to search for the path with the minimum cost function in the defined path search space, that is, the optimal charging and discharging path. During the search process, the search strategy is dynamically adjusted based on the real-time updated grid status and electricity price information.

[0025] Beneficial effects:

[0026] By establishing a storage unit model and status assessment system, and fully considering the differences in the characteristics of each storage unit, precise energy coordination control is achieved, effectively preventing overcharging and discharging of some storage units, and extending the overall service life of the energy storage system. At the same time, factors such as the real-time state of the power grid, electricity price fluctuations, and changes in load demand are comprehensively considered to plan charging and discharging paths, significantly reducing energy transmission losses and operating costs, and improving the economic benefits of the energy storage system.

[0027] By leveraging multi-objective optimization functions and distributed collaborative optimization algorithms, the system ensures the stability of the energy storage system under various operating conditions and improves the reliability of the power system. Furthermore, the system can update data in real time and dynamically adjust strategies, adapting to changes in grid status and the external environment. This makes it widely applicable to medium and large energy storage systems of various sizes and types. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a sub-layout of the energy coordination and charge and discharge path planning system for medium and large energy storage systems. DETAILED DESCRIPTION

[0029] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to embodiments and drawings. The embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0030] Example:

[0031] The industrial park is equipped with a medium-to-large energy storage system based on the present invention, comprising 100 energy storage units of various models. This system is used to balance peak and valley variations in industrial electricity consumption within the park and absorb the electricity generated by the park's distributed photovoltaic power generation. The park's electrical equipment is complex and diverse, with large fluctuations in load demand, and the local power grid uses a time-of-use pricing model.

[0032] On a certain day in summer, the peak period of photovoltaic power generation is from 8 to 10 in the morning. It is also the startup period of some industrial equipment, and the load demand gradually increases. At this time:

[0033] Data acquisition module operation: The data acquisition module collects the status data of each energy storage unit, such as power, voltage, current, and temperature, in real time at intervals of 5 minutes. It also simultaneously collects the operating parameters of the power grid, such as voltage, frequency, and power flow. It also obtains external environmental data, such as real-time electricity price information (at this time, it is a low-price period), load demand data within the park, and photovoltaic power generation power forecast data (estimated based on weather forecasts and historical data of photovoltaic power stations).

[0034] Energy Coordination Control Module Operation: Based on the collected data, the Energy Coordination Control Module first evaluates the status of each energy storage unit. Using established charge and discharge characteristic models, capacity decay models, and thermal models, combined with real-time data, it calculates the remaining capacity (SOC), state of health (SOH), and charge and discharge efficiency of each energy storage unit. Then, based on the multi-objective optimization function:

[0035]

[0036] Here, ω1 = 0.4, ω2 = 0.2, ω3 = 0.3, and ω4 = 0.2. Each energy storage unit exchanges status information through the communication module, performs local optimization calculations, and feeds the results back to the energy coordination control module. After global optimization, this module determines that most energy storage units should be charged at medium power to balance charging efficiency and the impact on energy storage unit lifespan.

[0037] The discharge path planning module begins planning the charging path based on instructions from the energy coordination control module. First, it updates the grid model state based on the real-time grid operating parameters acquired by the data acquisition module, clarifying the real-time status of elements such as transmission lines, substations, and nodes. Next, it uses all possible paths from the PV power station to the energy storage unit in the grid as a search space. According to the cost function:

[0038]

[0039] Set λ = 0.5, m is the number of line segments in the path, R j is the resistance of the jth line segment, I j is the current passing through the jth line segment, t j is the distribution of charging and discharging time on the jth line segment, P price-j is the real-time electricity price when charging and discharging on the jth line segment, P flow-j is the power passing through the jth line segment, l is the number of load nodes, P demand-k is the required power of the kth load node, P supply-k In order to obtain the power provided to the kth load node by planning the path, we search in the search space and finally obtain a charging path that has low energy transmission loss and can meet the demand of part of the load considering the current low electricity price.

[0040] Throughout operation, the communication module continuously and stably transmits data, ensuring information exchange between modules and between the system and external devices (such as the photovoltaic power plant controller and grid monitoring system). Over time, the system continuously collects new data and repeats the energy coordination and path planning process. Based on real-time changes in grid conditions, electricity prices, load demand, and energy storage unit status, it dynamically adjusts charging and discharging strategies and path planning, ensuring efficient and stable operation of the energy storage system and reducing the park's electricity costs by approximately 15%.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Energy coordination and charge-discharge path planning system for medium and large energy storage systems, characterized by: include: The data acquisition module is used to collect real-time status data of each energy storage unit in the energy storage system, real-time operating parameters of the power grid, and external environmental data; An energy coordination control module, which distributes and coordinates energy among the energy storage units based on the data acquired by the data acquisition module and taking into account the remaining power, charge and discharge efficiency, and health status of each energy storage unit; A charge and discharge path planning module, which plans the charge and discharge path according to the instructions of the energy coordination control module, combined with the real-time status of the power grid, electricity price fluctuations and load demand changes; The communication module is used to realize data transmission and communication between modules and between the system and external devices.

2. The energy coordination and charge-discharge path planning system for medium and large energy storage systems according to claim 1 is characterized in that: The data acquisition module collects real-time status data of each energy storage unit, including power, voltage, current, and temperature; real-time operating parameters of the power grid include voltage, frequency, and power flow; and external environmental data includes electricity price information, weather forecast, and load demand data.

3. The energy coordination and charge-discharge path planning system for medium and large energy storage systems according to claim 1, characterized in that: The working process of the energy coordination control module includes: Establish a mathematical model for each energy storage unit, including charge and discharge characteristic model, capacity decay model, and thermal model; Based on the established model and collected real-time data, the remaining capacity (SOC), health status (SOH), and charge and discharge efficiency of each energy storage unit are evaluated; Establish a multi-objective optimization function to maximize the overall service life of the energy storage system, improve charging and discharging efficiency, and ensure system stability; Using a distributed collaborative optimization algorithm, each energy storage unit exchanges status information through a communication module, performs optimization calculations locally, and feeds the results back to the energy coordination control module, which performs global optimization adjustments to determine the charging and discharging power distribution of each energy storage unit.

4. The energy coordination and charge-discharge path planning system for medium and large energy storage systems according to claim 3, characterized in that: The multi-objective optimization function is: Where n is the number of energy storage units, ΔSOH i is the change in health status of the i-th energy storage unit within unit time Δt, η charge-i and η discharge- are the charging and discharging efficiencies of the i-th energy storage unit, P charge-i and P discharge-i are the charging and discharging powers of the i-th energy storage unit, σ system is the stability index of the energy storage system, ω1, ω2, ω3, and ω4 are the weight coefficients of each target, which are adjusted according to actual needs.

5. The energy coordination and charge-discharge path planning system for medium and large energy storage systems according to claim 2, characterized in that: The working process of the charge and discharge path planning module includes: Modeling of power grids, including elements such as transmission lines, substations, nodes, and the electrical connection relationships and parameters between them; Update the state of the power grid model based on the real-time operating parameters of the power grid obtained by the data acquisition module; Define the path search space and take all possible charging and discharging paths in the power grid as the search space; Define a cost function that comprehensively considers energy transmission loss, electricity price cost, and load demand satisfaction; An algorithm is used to search for the path with the minimum cost function in the path search space as the optimal charging and discharging path, and the search strategy is dynamically adjusted according to the real-time updated grid status and electricity price information.

6. The energy coordination and charge-discharge path planning system for medium and large energy storage systems according to claim 5, characterized in that: The cost function is expressed as: Where m is the number of line segments in the path, R j is the resistance of the jth line segment, I j is the current passing through the jth line segment, t j is the distribution of charging and discharging time on the jth line segment, P price-j is the real-time electricity price when charging and discharging on the jth line segment, P flow- is the power passing through the jth line segment, l is the number of load nodes, P demand-k is the required power of the kth load node, P supply-k is the power provided to the kth load node through the planned path, and λ is the weight coefficient of the degree of load demand satisfaction.

7. The energy coordination and charge-discharge path planning system for medium and large energy storage systems according to claim 5, characterized in that: The communication module adopts wired communication or wireless communication.

8. The control method of the energy coordination and charge-discharge path planning system of a medium-to-large energy storage system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1, data acquisition module collects relevant data; S2, the energy coordination control module performs energy storage unit status assessment and energy distribution based on the collected data; S3, the charge and discharge path planning module plans the charge and discharge path according to the energy allocation results and real-time data; S4, the communication module realizes data interaction between modules and external devices; S5. The system dynamically adjusts energy distribution and charging and discharging paths based on real-time data.

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