Method, system and medium for coordinated operation of multi-form energy storage and multi-type power sources

Abstract

The present invention discloses a method, system, and medium for the coordinated operation of multi-form energy storage and multi-type power sources. The method obtains multi-form energy storage and source-grid-load data; uses the multi-form energy storage and source-grid-load data to perform electric / heat / hydrogen decoupling calculations to obtain the electric load corresponding to the heat / hydrogen load node; unifies the electric load corresponding to the heat / hydrogen load node with the traditional electric load into an electric-heat-hydrogen combined electric load, and uses the electric-heat-hydrogen combined electric load to perform source-grid-load optimized operation calculations to obtain energy storage peak-shaving quotation leading parameters; uses the energy storage peak-shaving quotation leading parameters to calculate energy storage peak-shaving quotation data; and uses the energy storage peak-shaving quotation data to perform source-grid-load-storage coordinated optimized operation calculations to obtain system operating costs, energy storage operating benefits, and electric / heat / hydrogen energy storage operating power time series data. When applied to power systems containing a high proportion of renewable power sources, the present invention can improve the renewable energy absorption rate and reduce the load loss rate.

Description

Technical Field

[0001] The present invention belongs to the technical field of application of energy storage in power systems, and specifically relates to a method, system and medium for the coordinated operation of multi-form energy storage and multi-type power sources. Background Art

[0002] Energy storage technology is one of the key technologies supporting the new power system dominated by renewable energy. Based on the different forms of energy storage, it can be categorized into electric, thermal, and hydrogen storage technologies. Currently, electric energy storage is in the early stages of commercialization, while thermal and hydrogen storage are still in the experimental and demonstration stages. These technologies are temporally and spatially coupled with the power system, adapting to the multi-timescale electricity, heat, and hydrogen demands at different nodes. Considering the seasonal and regional variations in renewable power output and electricity-heat-hydrogen load demand, especially in power market environments, the coordinated optimization of operational strategies for a high proportion of renewable power, conventional power, and multiple forms of energy storage can, on the one hand, improve the utilization of renewable power, and on the other, enhance the operational efficiency of multiple forms of energy storage. In existing technologies, Hou Hui et al. established a multi-objective optimal scheduling model for energy storage, aiming to maximize the economic benefits of energy storage, minimize energy losses, and minimize power fluctuations between microgrids and the external grid when energy storage is dispatched. Liu Haitao et al. proposed an optimized scheduling scheme for electricity-gas-heat-hydrogen, targeting system operating costs and environmental costs. However, the analysis scenarios of existing technologies are microgrids or integrated energy systems. There is a lack of technology to consider power network constraints in the power system, as well as the coordinated optimization technology between wide-area multi-form energy storage and multi-type power sources in the power market environment.

[0003] [1] Hou Hui, Liu Peng, Liu Zhigang, He Shuyun, Xiao Zhenfeng, Yao Ying. Optimal scheduling method for electrothermal hydrogen multi-element energy storage system[J]. High Voltage Technology, 2022, 48(02): 536-545. DOI: 10.13336 / j.1003-6520.hve.20210009.

[0004] [2] Liu Haitao, Zhu Hainan, Li Fengshuo, Sun Huazhong, Wang Juanjuan, Jiang Xinyi, Chen Jian. Economic operation strategy of electricity-gas-heat-hydrogen integrated energy system considering carbon cost[J]. Electric Power Construction, 2021, 42(12): 21-29. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, system and medium for the coordinated operation of multi-form energy storage and multi-type power sources to overcome the defects of the existing technology. The present invention takes multi-form energy storage and multi-type power sources as the objects, considers the future demand for the absorption of a high proportion of renewable energy, combines the power peak-shaving auxiliary service policy, and forms the actual demand for the comprehensive electric load of electric, thermal and hydrogen through electric, thermal and hydrogen decoupling calculation, establishes the energy storage peak-shaving quotation method, which can improve the renewable energy absorption rate and reduce the load loss rate.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for coordinated operation of multi-form energy storage and multi-type power sources, comprising:

[0008] Obtain multi-form energy storage and source-grid-load data;

[0009] Utilize multi-form energy storage and source-grid load data to perform electricity / heat / hydrogen decoupling calculations to obtain the electrical load corresponding to the heat / hydrogen load node;

[0010] Unify the electric load corresponding to the heat / hydrogen load node and the traditional electric load into the electric heat / hydrogen comprehensive electric load, use the electric heat / hydrogen comprehensive electric load to perform source-grid-load optimization operation calculations, and obtain the leading parameters for energy storage peak regulation quotation;

[0011] Energy storage peak-shaving quotation data is obtained by calculating the energy storage peak-shaving quotation leading parameters;

[0012] Energy storage peak-shaving quotation data is used to perform source-grid-load-storage coordinated optimization operation calculations to obtain electricity / heat / hydrogen energy storage operation power time series data.

[0013] Furthermore, the multi-form energy storage and source-grid-load data include basic data on multi-form energy storage of electricity / heat / hydrogen, basic data on multiple types of power sources, basic data on power grids and electricity / heat / hydrogen loads, electricity market data, and expected rates of return.

[0014] Furthermore, the basic data of the electric / thermal / hydrogen multi-form energy storage includes: the grid connection node, rated power, rated capacity, full life cycle investment and operation and maintenance cost, lifespan, energy conversion efficiency and levelized cost of electricity of the electric / thermal / hydrogen energy storage;

[0015] The multi-type power source basic data includes: grid connection nodes and installed capacity of renewable power sources, theoretical output time series data, renewable power consumption rate boundary value, grid connection nodes and installed capacity of thermal power units, minimum technical output, and power generation cost;

[0016] The basic data of the power grid and electric / heat / hydrogen loads include: bus name, basic data of AC line, parallel / series capacitors and reactances, DC line, transformer, nodes of electric / heat / hydrogen loads and load demand time series data;

[0017] The electricity market data include: peak and valley time-of-use electricity prices, energy storage charging and discharging electricity prices, unit electricity curtailment costs of renewable power sources, unit electricity load loss costs, and deep peak regulation intervals and quotations of thermal power units.

[0018] Furthermore, the electricity / heat / hydrogen decoupling calculation is specifically as follows: with the goal of minimizing electricity cost, considering the satisfaction of heat / hydrogen load demand, rated power of electric hydrogen / heat equipment, capacity of hydrogen / heat storage equipment and capacity state transition constraints, optimizing the operating power time series data of heat / hydrogen energy storage, and obtaining the electrical load corresponding to the heat / hydrogen load node;

[0019] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0020] Furthermore, the source-grid-load optimization operation calculation is specifically as follows: with the goal of minimizing the sum of the power generation cost of thermal power units, the peak-shaving cost of thermal power units, the power curtailment cost of renewable power sources, and the load loss cost, the operating power of thermal power units, the theoretical output of renewable power sources, node power balance, power flow, and load demand constraints are considered to optimize the output of thermal power units and renewable power sources at each node, and obtain the energy storage peak-shaving quotation leading parameters, which include the deep peak-shaving period and peak-shaving price of thermal power units, the actual output and absorption rate of renewable power sources, the time series data of the power curtailment power of renewable power sources, and the load loss period;

[0021] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0022] Furthermore, the energy storage peak shaving quotation data includes energy storage downward peak shaving quotation data and energy storage upward peak shaving quotation data;

[0023] The energy storage downward peak shaving quotation data is calculated specifically as follows: based on the peak shaving period and peak shaving price of the thermal power unit, the energy storage downward peak shaving quotation data is calculated according to the following formula:

[0024]

[0025] Where, is the downward peak-shaving quotation data of energy storage s at time t; is the deep peak-shaving quotation data of thermal power unit i at time t; is the levelized cost of electricity of energy storage s; is the discharge price of energy storage s; is the energy conversion efficiency of energy storage s; T1 is the peak load period of thermal power units;

[0026] The energy storage upward peak shaving quotation data is calculated specifically as follows: based on the load loss period T2 and the unit load loss cost, the energy storage upward peak shaving quotation data is calculated according to the following formula:

[0027]

[0028] Where, is the upward peak-shaving quotation data of energy storage s at time t; is the discharge price of energy storage s; Cl (t) is the unit electricity loss cost at time t; T2 is the load loss period.

[0029] Furthermore, the source-grid-load-storage collaborative optimization operation calculation is specifically as follows: based on the deep peak-shaving period and peak-shaving price of thermal power units, and the energy storage peak-shaving quotation data, with the goal of minimizing the sum of the thermal power unit's power generation cost, the thermal power unit's peak-shaving cost, and the energy storage peak-shaving cost, considering the thermal power unit's operating power, the theoretical output and absorption rate of renewable power sources, node power balance, power flow, and electric load demand constraints, the operation curves of the thermal power units, renewable power sources, and energy storage at each node are optimized to obtain the time series data of the electricity / heat / hydrogen energy storage operating power;

[0030] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0031] A coordinated operation system of multiple forms of energy storage and multiple types of power sources, including:

[0032] Data acquisition module: used to obtain multi-form energy storage and source-grid-load data;

[0033] Electric load calculation module corresponding to heat / hydrogen load nodes: used to perform electricity / heat / hydrogen decoupling calculations using multi-form energy storage and source-grid load data to obtain the electric load corresponding to the heat / hydrogen load nodes;

[0034] Energy storage peak-shaving quotation pilot parameter calculation module: This module is used to unify the electrical load corresponding to the heat / hydrogen load node and the traditional electrical load into a comprehensive electric load of electricity, heat, and hydrogen. This comprehensive electric load is used to perform source-grid-load optimization operation calculations to obtain the energy storage peak-shaving quotation pilot parameters.

[0035] Energy storage peak shaving quotation data calculation module: used to calculate energy storage peak shaving quotation data using energy storage peak shaving quotation leading parameters;

[0036] Source-grid-load-storage collaborative optimization operation calculation module: used to use energy storage peak-shaving quotation data to perform source-grid-load-storage collaborative optimization operation calculations to obtain electricity / heat / hydrogen energy storage operation power time series data.

[0037] Furthermore, in the data acquisition module, the multi-form energy storage and source-grid-load data include basic data of multi-form energy storage of electricity / heat / hydrogen, basic data of multiple types of power sources, basic data of power grid and electricity / heat / hydrogen load, power market data, and expected rate of return;

[0038] The basic data of electric / thermal / hydrogen multi-form energy storage includes: grid connection node, rated power, rated capacity, full life cycle investment and operation and maintenance cost, lifespan, energy conversion efficiency and levelized cost of electricity of electric / thermal / hydrogen energy storage;

[0039] The multi-type power source basic data includes: grid connection nodes and installed capacity of renewable power sources, theoretical output time series data, renewable power consumption rate boundary value, grid connection nodes and installed capacity of thermal power units, minimum technical output, and power generation cost;

[0040] The basic data of the power grid and electric / heat / hydrogen loads include: bus name, basic data of AC line, parallel / series capacitors and reactances, DC line, transformer, nodes of electric / heat / hydrogen loads and load demand time series data;

[0041] The electricity market data include: peak and valley time-of-use electricity prices, energy storage charging and discharging electricity prices, unit electricity curtailment costs of renewable power sources, unit electricity load loss costs, and deep peak regulation intervals and quotations of thermal power units.

[0042] Furthermore, in the electric load calculation module corresponding to the heat / hydrogen load node, the electric / heat / hydrogen decoupling calculation is specifically as follows: with the goal of minimizing electricity cost, considering the satisfaction of heat / hydrogen load demand, rated power of electric hydrogen / heat equipment, capacity of hydrogen / heat storage equipment and capacity state transition constraints, the operating power time series data of heat / hydrogen energy storage is optimized to obtain the electric load corresponding to the heat / hydrogen load node;

[0043] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0044] Furthermore, in the energy storage peak-shaving quotation leading parameter calculation module, the source-grid-load optimization operation calculation is specifically as follows: with the goal of minimizing the sum of the power generation cost of the thermal power unit, the peak-shaving cost of the thermal power unit, the power curtailment cost of the renewable power source, and the load loss cost, the operating power of the thermal power unit, the theoretical output of the renewable power source, the node power balance, the power flow, and the load demand constraints are considered, and the output of the thermal power unit and the renewable power source at each node is optimized to obtain the energy storage peak-shaving quotation leading parameters. The energy storage peak-shaving quotation leading parameters include the deep peak-shaving period and peak-shaving price of the thermal power unit, the actual output and absorption rate of the renewable power source, the time series data of the power curtailment of the renewable power source, and the load loss period;

[0045] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0046] Furthermore, in the energy storage peak shaving quotation data calculation module, the energy storage peak shaving quotation data includes energy storage downward peak shaving quotation data and energy storage upward peak shaving quotation data;

[0047] The energy storage downward peak shaving quotation data is calculated specifically as follows: based on the peak shaving period and peak shaving price of the thermal power unit, the energy storage downward peak shaving quotation data is calculated according to the following formula:

[0048]

[0049] Where, is the downward peak-shaving quotation data of energy storage s at time t; is the deep peak-shaving quotation data of thermal power unit i at time t; is the levelized cost of electricity of energy storage s; is the discharge price of energy storage s; is the energy conversion efficiency of energy storage s; T1 is the peak load period of thermal power units;

[0050] The energy storage upward peak shaving quotation data is calculated specifically as follows: based on the load loss period T2 and the unit load loss cost, the energy storage upward peak shaving quotation data is calculated according to the following formula:

[0051]

[0052] Where, is the upward peak-shaving quotation data of energy storage s at time t; is the discharge price of energy storage s; C l (t) is the unit electricity loss cost at time t; T2 is the load loss period.

[0053] Furthermore, the source-grid-load-storage collaborative optimization operation calculation is specifically as follows: based on the deep peak-shaving period and peak-shaving price of thermal power units, and the energy storage peak-shaving quotation data, with the goal of minimizing the sum of the thermal power unit's power generation cost, the thermal power unit's peak-shaving cost, and the energy storage peak-shaving cost, considering the thermal power unit's operating power, the theoretical output and absorption rate of renewable power sources, node power balance, power flow, and electric load demand constraints, the operation curves of the thermal power units, renewable power sources, and energy storage at each node are optimized to obtain the time series data of the electricity / heat / hydrogen energy storage operating power;

[0054] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0055] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for coordinated operation of multi-form energy storage and multi-type power supplies.

[0056] Compared with the prior art, the present invention has the following beneficial technical effects:

[0057] The present invention is a method for the coordinated operation of multiple forms of energy storage and multiple types of power sources that takes into account the demand for electric, thermal and hydrogen loads. Based on the loaded and set basic data information of multiple forms of energy storage and source, grid and load, it performs electric, thermal and hydrogen decoupling calculations, optimizes the source, grid and load operation results without energy storage, sets energy storage peak-shaving quotations, and ultimately achieves coordinated and optimized operation of source, grid, load and storage. When facing power systems with a high proportion of renewable power sources, it can improve the renewable energy absorption rate and reduce the load loss rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0059] Figure 1 This is a flow chart of the method for coordinated operation of multi-form energy storage and multi-type power sources of the present invention;

[0060] Figure 2 This is a structural diagram of the collaborative operation system of multi-form energy storage and multi-type power sources of the present invention. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0062] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0063] Example 1

[0064] like Figure 1 As shown, the present invention provides a method for the coordinated operation of multi-form energy storage and multi-type power sources. First, multi-form energy storage and source-grid-load data are obtained; secondly, electricity / heat / hydrogen decoupling calculation and source-grid-load optimization operation are performed; then, energy storage peak-shaving quotation is set; finally, source-grid-load-storage coordinated optimization operation calculation and result output are performed.

[0065] (1) Obtaining multi-form energy storage and source-grid-load data

[0066] Multi-form energy storage and source-grid-load data include but are not limited to basic data on loading and setting multi-form energy storage, basic data on multiple types of power sources, basic data on power grids and electricity / heat / hydrogen loads, electricity market data, expected rates of return, etc.

[0067] Basic data on multi-form energy storage include but are not limited to: grid-connected nodes and rated power and capacity of electric / thermal / hydrogen energy storage, full life cycle investment and operation and maintenance costs, lifespan, energy conversion efficiency, levelized electricity cost, etc.

[0068] Basic data on multiple types of power sources include but are not limited to the grid-connected nodes and installed capacity of renewable power sources, theoretical output time series data, boundary values ​​of renewable power consumption rates, grid-connected nodes and installed capacity of thermal power units, minimum technical output, and power generation costs.

[0069] The basic data of the power grid and electric / heat / hydrogen loads include but are not limited to basic data of components such as busbar names, AC lines, parallel / series capacitors and reactances, DC lines, transformers, and the nodes and load demand time series data of electric / heat / hydrogen loads.

[0070] Electricity market data includes but is not limited to peak-valley time-of-use electricity prices, energy storage charging and discharging electricity prices, unit electricity curtailment costs of renewable power sources, unit electricity load loss costs, deep peak-shaving intervals and quotations of thermal power units. The quotations of thermal power units at different load rate levels must be set based on local peak-shaving auxiliary service policies and the minimum technical output of thermal power units at different nodes.

[0071] (2) Electricity / heat / hydrogen decoupling calculation

[0072] The decoupling calculation method for electricity / heat / hydrogen is: with the goal of minimizing electricity costs, considering the satisfaction of heat / hydrogen load demand, rated power of electric hydrogen / heat equipment, capacity of hydrogen / heat storage equipment and capacity state conversion constraints, optimize the operating power time series data of heat / hydrogen energy storage, and the optimization algorithm includes but is not limited to nonlinear programming, genetic algorithm, particle swarm algorithm, etc., to obtain the electric load corresponding to the heat / hydrogen load node, and unify it with the traditional electric load into the electric, heat and hydrogen comprehensive electric load, which is used for the coordinated optimization operation of source, grid and load.

[0073] (3) Source-grid-load optimization operation calculation

[0074] The calculation method for source-grid-load optimization operation is: with the goal of minimizing the sum of the power generation cost of thermal power units, the peak-shaving cost of thermal power units, the power curtailment cost of renewable power sources, and the load loss cost, considering the operating power of thermal power units, the theoretical output of renewable power sources, node power balance, power flow, and electric load demand constraints, optimize the output of thermal power units and renewable power sources at each node. The optimization algorithm includes but is not limited to nonlinear programming, genetic algorithm, particle swarm algorithm, etc., and obtains parameters such as the deep peak-shaving period T1 of thermal power units and the peak-shaving price, the actual output and absorption rate of renewable power sources, the time series data of the power curtailment power of renewable power sources, and the load loss period T2.

[0075] (4) Setting energy storage peak shaving quotation

[0076] The energy storage peak shaving quotation setting method includes but is not limited to setting energy storage downward peak shaving quotation data and energy storage upward peak shaving quotation data.

[0077] Energy storage downward peak shaving quotation data setting method: Based on the peak shaving period T1 of the thermal power unit and the peak shaving price, the energy storage downward peak shaving quotation data is set according to the following method

[0078]

[0079] Where, is the downward peak-shaving quotation data of energy storage s at time t; is the deep peak-shaving quotation data of thermal power unit i at time t; is the levelized cost of electricity of energy storage s; is the discharge price of energy storage s; is the energy conversion efficiency of the energy storage s.

[0080] Energy storage upward peak shaving quotation data setting method: Based on the load loss period T2 and the unit power load loss cost, the energy storage upward peak shaving quotation data is set according to the following method

[0081]

[0082] Where, is the upward peak-shaving quotation data of energy storage s at time t; C l (t) is the unit power loss cost at time t.

[0083] (5) Coordinated optimization of power generation, grid, load and storage.

[0084] The calculation method for the coordinated optimization operation of source, grid, load and storage is as follows: based on the deep peak-shaving period and peak-shaving price of thermal power units and the energy storage peak-shaving quotation data, with the goal of minimizing the sum of the thermal power unit's power generation cost, thermal power unit peak-shaving cost and energy storage peak-shaving cost, considering the thermal power unit's operating power, the theoretical output and absorption rate of renewable power sources, node power balance, power flow, and electric load demand constraints, optimize the operating curves of thermal power units, renewable power sources and energy storage at each node. The optimization algorithms include but are not limited to nonlinear programming, genetic algorithm, particle swarm algorithm, etc., and obtain the time series data of the electric / heat / hydrogen energy storage operating power.

[0085] Preferably, in addition to the time series data of the electric / thermal / hydrogen energy storage operating power, the system operating cost and energy storage operating benefits can also be obtained;

[0086] Calculation method for annual operating economic benefits of energy storage:

[0087]

[0088] Where, The annual benefit of downward peak regulation for energy storage s; The annual revenue of energy storage s is upward peak regulation; The annual profit from arbitrage of the charge-discharge price difference of energy storage s; is the investment and operation cost of energy storage s throughout its life cycle; i0 is the expected rate of return; and n is the lifespan.

[0089] (6)Result output.

[0090] The output results include but are not limited to system operating costs and energy storage operating benefits, and time series data on electricity / heat / hydrogen energy storage operating power.

[0091] At present, electrochemical energy storage has entered the early stage of commercial application, while hydrogen and thermal energy storage are still in the project demonstration construction stage. The coexistence of multi-form energy storage of electricity, heat and hydrogen is the future direction of energy development. This invention proposes a method for the coordinated operation of multi-form energy storage and multi-type power sources to solve the problems of safe, stable and economical operation of the power grid.

[0092] Example 2

[0093] A coordinated operation system of multiple forms of energy storage and multiple types of power sources, including:

[0094] Data acquisition module: used to obtain multi-form energy storage and source-grid-load data; the multi-form energy storage and source-grid-load data include basic data of multi-form energy storage of electricity / heat / hydrogen, basic data of multiple types of power sources, basic data of power grid and electricity / heat / hydrogen load, power market data, and expected rate of return;

[0095] The basic data of electric / thermal / hydrogen multi-form energy storage includes: grid connection node, rated power, rated capacity, full life cycle investment and operation and maintenance cost, lifespan, energy conversion efficiency and levelized cost of electricity of electric / thermal / hydrogen energy storage;

[0096] The multi-type power source basic data includes: grid connection nodes and installed capacity of renewable power sources, theoretical output time series data, renewable power consumption rate boundary value, grid connection nodes and installed capacity of thermal power units, minimum technical output, and power generation cost;

[0097] The basic data of the power grid and electric / heat / hydrogen loads include: bus name, basic data of AC line, parallel / series capacitors and reactances, DC line, transformer, nodes of electric / heat / hydrogen loads and load demand time series data;

[0098] The electricity market data include: peak and valley time-of-use electricity prices, energy storage charging and discharging electricity prices, unit electricity curtailment costs of renewable power sources, unit electricity load loss costs, and deep peak regulation intervals and quotations of thermal power units.

[0099] Electric load calculation module corresponding to heat / hydrogen load nodes: used to perform electricity / heat / hydrogen decoupling calculations using multi-form energy storage and source-grid load data to obtain the electric load corresponding to the heat / hydrogen load nodes;

[0100] The electricity / heat / hydrogen decoupling calculation is specifically as follows: with the goal of minimizing electricity cost, considering the satisfaction of heat / hydrogen load demand, rated power of electric hydrogen / heat equipment, capacity of hydrogen / heat storage equipment and capacity state transition constraints, optimizing the operating power time series data of heat / hydrogen energy storage, and obtaining the electrical load corresponding to the heat / hydrogen load node;

[0101] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0102] Energy storage peak-shaving quotation pilot parameter calculation module: This module is used to unify the electrical load corresponding to the heat / hydrogen load node and the traditional electrical load into a comprehensive electric load of electricity, heat, and hydrogen. This comprehensive electric load is used to perform source-grid-load optimization operation calculations to obtain the energy storage peak-shaving quotation pilot parameters.

[0103] The source-grid-load optimization operation calculation is specifically as follows: taking the sum of the power generation cost of thermal power units, the peak-shaving cost of thermal power units, the power curtailment cost of renewable power sources, and the load loss cost as the minimum, considering the operating power of thermal power units, the theoretical output of renewable power sources, node power balance, power flow, and load demand constraints, optimizing the output of thermal power units and renewable power sources at each node, and obtaining the energy storage peak-shaving quotation leading parameters. The energy storage peak-shaving quotation leading parameters include the deep peak-shaving period and peak-shaving price of thermal power units, the actual output and absorption rate of renewable power sources, the time series data of the power curtailment of renewable power sources, and the load loss period;

[0104] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0105] Energy storage peak shaving quotation data calculation module: used to calculate energy storage peak shaving quotation data using energy storage peak shaving quotation leading parameters;

[0106] The calculation of the energy storage downward peak-shaving quotation data is specifically as follows: based on the peak-shaving period and peak-shaving price of the thermal power unit, the energy storage downward peak-shaving quotation data is calculated according to the following formula:

[0107]

[0108] Where, is the downward peak-shaving quotation data of energy storage s at time t; is the deep peak-shaving quotation data of thermal power unit i at time t; is the levelized cost of electricity of energy storage s; is the discharge price of energy storage s; is the energy conversion efficiency of energy storage s; T1 is the peak load period of thermal power units.

[0109] The calculation of the energy storage upward peak-shaving quotation data is as follows: Based on the load loss period T2 and the load loss cost per unit electricity, the energy storage upward peak-shaving quotation data is calculated according to the following formula:

[0110]

[0111] Where, is the upward peak-shaving quotation data of energy storage s at time t; is the discharge price of energy storage s; C l (t) is the unit electricity loss cost at time t; T2 is the load loss period.

[0112] Source-grid-load-storage coordinated optimization operation calculation module: used to use energy storage peak-shaving quotation data to perform source-grid-load-storage coordinated optimization operation calculations to obtain system operating costs and energy storage operating benefits, as well as electricity / heat / hydrogen energy storage operating power time series data.

[0113] The source-grid-load-storage coordinated optimization operation calculation is specifically as follows: based on the deep peak-shaving period and peak-shaving price of thermal power units, and the energy storage peak-shaving quotation data, with the goal of minimizing the sum of the thermal power unit's power generation cost, the thermal power unit's peak-shaving cost, and the energy storage peak-shaving cost, the operation curves of the thermal power units, renewable power sources, and energy storage at each node are optimized considering the operating power of the thermal power units, the theoretical output and absorption rate of renewable power sources, node power balance, power flow, and electric load demand constraints to obtain the time series data of the electric / heat / hydrogen energy storage operation power;

[0114] The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

[0115] Preferably, the system operating costs and energy storage operating benefits can also be obtained.

[0116] The calculation formula for the annual operating economic benefits of energy storage is as follows:

[0117]

[0118] Where, The annual benefit of downward peak regulation for energy storage s; The annual revenue of energy storage s is upward peak regulation; The annual profit from arbitrage of the charge-discharge price difference of energy storage s; is the investment and operation cost of energy storage s throughout its life cycle; i0 is the expected rate of return; and n is the lifespan.

[0119] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for the coordinated operation of multi-form energy storage and multi-type power sources, characterized in that: include: Obtain multi-form energy storage and source-grid-load data; The multi-form energy storage and source-grid load data include basic data of electric / thermal / hydrogen multi-form energy storage, basic data of multiple types of power sources, basic data of power grid and electric / thermal / hydrogen load, power market data, and expected rate of return; the basic data of electric / thermal / hydrogen multi-form energy storage include: grid connection nodes, rated power, rated capacity, full life cycle investment and operation and maintenance costs, lifespan, energy conversion efficiency, and levelized electricity cost of electric / thermal / hydrogen energy storage; the basic data of multiple types of power sources include: grid connection nodes and installed capacity of renewable power sources, theoretical output time, The basic data of the power grid and electric / heat / hydrogen load include: busbar name, basic data of AC line, parallel / series capacitor and reactance, DC line, transformer, nodes of electric / heat / hydrogen load and load demand time series data; the power market data include: peak and valley time-of-use electricity price, energy storage charging and discharging price, unit electricity cost of renewable power source, unit electricity loss cost, deep peak regulation interval and quotation of thermal power unit. Utilizing multi-form energy storage and source-grid load data, electric / thermal / hydrogen decoupling calculations are performed to obtain the electric load corresponding to the heat / hydrogen load node. Specifically, the electric / thermal / hydrogen decoupling calculations are performed by optimizing the operating power time series data of the heat / hydrogen energy storage with the goal of minimizing electricity cost, taking into account the heat / hydrogen load demand, the rated power of the electric hydrogen / heat equipment, the capacity of the hydrogen / heat equipment, and the capacity state transition constraints, to obtain the electric load corresponding to the heat / hydrogen load node. The optimization algorithm used is a nonlinear programming algorithm, a genetic algorithm, or a particle swarm algorithm. Unify the electric load corresponding to the heat / hydrogen load node and the traditional electric load into a comprehensive electric load of electricity, heat and hydrogen, and use the comprehensive electric load to perform source-grid-load optimization operation calculations to obtain leading parameters for energy storage peak-shaving quotation. The leading parameters for energy storage peak-shaving quotation include the deep peak-shaving period and peak-shaving price of thermal power units, the actual output and absorption rate of renewable power sources, the time series data of the abandoned power of renewable power sources, and the load loss period; Energy storage peak-shaving quotation data is calculated using energy storage peak-shaving quotation leading parameters; the energy storage peak-shaving quotation data includes energy storage downward peak-shaving quotation data and energy storage upward peak-shaving quotation data; The energy storage downward peak shaving quotation data is calculated specifically as follows: based on the peak shaving period and peak shaving price of the thermal power unit, the energy storage downward peak shaving quotation data is calculated according to the following formula: Where, is the downward peak-shaving quotation data of energy storage s at time t; is the deep peak-shaving quotation data of thermal power unit i at time t; is the levelized cost of electricity of energy storage s; is the discharge price of energy storage s; is the energy conversion efficiency of energy storage s; T1 is the peak load period of thermal power units; The energy storage upward peak shaving quotation data is calculated specifically as follows: based on the load loss period T2 and the unit load loss cost, the energy storage upward peak shaving quotation data is calculated according to the following formula: Where, is the upward peak-shaving quotation data of energy storage s at time t; is the discharge price of energy storage s; C l (t) is the unit power loss cost at time t; T2 is the load loss period; Energy storage peak-shaving quotation data is used to perform source-grid-load-storage coordinated optimization operation calculations to obtain electricity / heat / hydrogen energy storage operation power time series data.

2. The method for coordinated operation of multi-form energy storage and multi-type power sources according to claim 1, characterized in that: The source-grid-load optimization operation calculation specifically includes: taking the sum of the power generation cost of thermal power units, the peak-shaving cost of thermal power units, the power curtailment cost of renewable power sources, and the load loss cost as the minimum, considering the operating power of thermal power units, the theoretical output of renewable power sources, node power balance, power flow, and load demand constraints, optimizing the output of thermal power units and renewable power sources at each node, and deriving the leading parameters of energy storage peak-shaving quotation; The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

3. The method for coordinated operation of multi-form energy storage and multi-type power sources according to claim 1, characterized in that: The source-grid-load-storage collaborative optimization operation calculation is specifically as follows: based on the deep peak-shaving period and peak-shaving price of thermal power units, and the peak-shaving quotation data of energy storage, with the goal of minimizing the sum of the thermal power unit's power generation cost, the thermal power unit's peak-shaving cost, and the energy storage's peak-shaving cost, considering the thermal power unit's operating power, the theoretical output and absorption rate of renewable power sources, node power balance, power flow, and electric load demand constraints, the operation curves of the thermal power units, renewable power sources, and energy storage at each node are optimized to obtain the time series data of the electric / heat / hydrogen energy storage operating power; The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

4. A coordinated operation system of multiple forms of energy storage and multiple types of power sources, characterized in that: include: Data acquisition module: used to obtain multi-form energy storage and source-grid-load data; Multi-form energy storage and source-grid load data include basic data on electric / thermal / hydrogen multi-form energy storage, basic data on multiple types of power sources, basic data on power grids and electric / thermal / hydrogen loads, power market data, and expected rates of return; the basic data on electric / thermal / hydrogen multi-form energy storage include: grid-connected nodes, rated power, rated capacity, life-cycle investment and operation and maintenance costs, lifespan, energy conversion efficiency, and levelized electricity costs of electric / thermal / hydrogen energy storage; the basic data on multiple types of power sources include: grid-connected nodes and installed capacity of renewable power sources, theoretical output timing Data, renewable power consumption rate boundary value, grid connection node and installed capacity, minimum technical output, and power generation cost of thermal power units; the basic data of the power grid and electric / thermal / hydrogen load include: bus name, basic data of AC line, parallel / series capacitors and reactance, DC line, transformer, nodes of electric / thermal / hydrogen load and load demand time series data; the power market data include: peak and valley time-of-use electricity price, energy storage charging and discharging electricity price, unit electricity curtailment cost of renewable power source, unit electricity loss cost, deep peak regulation interval and quotation of thermal power units The electric load calculation module corresponding to the heat / hydrogen load node is used to perform electric / heat / hydrogen decoupling calculations using multi-form energy storage and source grid load data to obtain the electric load corresponding to the heat / hydrogen load node. The electric / heat / hydrogen decoupling calculation is specifically as follows: with the goal of minimizing electricity cost, considering the satisfaction of heat / hydrogen load demand, rated power of electric hydrogen / heat equipment, capacity of hydrogen / heat equipment and capacity state transition constraints, the operating power time series data of heat / hydrogen energy storage is optimized to obtain the electric load corresponding to the heat / hydrogen load node. The algorithm used for optimization is nonlinear programming algorithm, genetic algorithm or particle swarm algorithm. Energy storage peak-shaving quotation leading parameter calculation module: used to unify the electric load corresponding to the thermal / hydrogen load node and the traditional electric load into the electric heat hydrogen comprehensive electric load, use the electric heat hydrogen comprehensive electric load to perform source-grid-load optimization operation calculation, and obtain the energy storage peak-shaving quotation leading parameters; the energy storage peak-shaving quotation leading parameters include the deep peak-shaving period and peak-shaving price of the thermal power unit, the actual output and absorption rate of the renewable power source, the renewable power source abandoned power time series data, and the load loss period; Energy storage peak shaving quotation data calculation module: used to calculate energy storage peak shaving quotation data using energy storage peak shaving quotation leading parameters; the energy storage peak shaving quotation data includes energy storage downward peak shaving quotation data and energy storage upward peak shaving quotation data; The energy storage downward peak shaving quotation data is calculated specifically as follows: based on the peak shaving period and peak shaving price of the thermal power unit, the energy storage downward peak shaving quotation data is calculated according to the following formula: Where, is the downward peak-shaving quotation data of energy storage s at time t; is the deep peak-shaving quotation data of thermal power unit i at time t; is the levelized cost of electricity of energy storage s; is the discharge price of energy storage s; is the energy conversion efficiency of energy storage s; T1 is the peak load period of thermal power units; The energy storage upward peak shaving quotation data is calculated specifically as follows: based on the load loss period T2 and the unit load loss cost, the energy storage upward peak shaving quotation data is calculated according to the following formula: Where, is the upward peak-shaving quotation data of energy storage s at time t; is the discharge price of energy storage s; C l (t) is the unit power loss cost at time t; T2 is the load loss period; Source-grid-load-storage collaborative optimization operation calculation module: used to use energy storage peak-shaving quotation data to perform source-grid-load-storage collaborative optimization operation calculations to obtain electricity / heat / hydrogen energy storage operation power time series data.

5. The coordinated operation system of multi-form energy storage and multi-type power sources according to claim 4 is characterized in that: In the energy storage peak-shaving quotation pilot parameter calculation module, the source-grid-load optimization operation calculation is specifically as follows: with the goal of minimizing the sum of the thermal power unit generation cost, the thermal power unit peak-shaving cost, the renewable power source curtailment cost, and the load loss cost, the output of the thermal power unit and renewable power source at each node is optimized considering the thermal power unit operating power, the theoretical output of the renewable power source, the node power balance, the power flow, and the load demand constraints, to obtain the energy storage peak-shaving quotation pilot parameters; The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

6. The coordinated operation system of multi-form energy storage and multi-type power sources according to claim 4, characterized in that: The source-grid-load-storage collaborative optimization operation calculation is specifically as follows: based on the deep peak-shaving period and peak-shaving price of thermal power units, and the peak-shaving quotation data of energy storage, with the goal of minimizing the sum of the thermal power unit's power generation cost, the thermal power unit's peak-shaving cost, and the energy storage's peak-shaving cost, considering the thermal power unit's operating power, the theoretical output and absorption rate of renewable power sources, node power balance, power flow, and electric load demand constraints, the operation curves of the thermal power units, renewable power sources, and energy storage at each node are optimized to obtain the time series data of the electric / heat / hydrogen energy storage operating power; The algorithms used for optimization are nonlinear programming algorithm, genetic algorithm or particle swarm algorithm.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for coordinated operation of multi-form energy storage and multi-type power supplies as claimed in any one of claims 1 to 3 are implemented.

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