Networking type novel energy storage multi-objective optimization coordinated scheduling method

Abstract

The invention discloses a network construction type novel energy storage multi-objective optimization coordinated scheduling method, which comprises the following steps: S1, energy storage classification and data acquisition: (1) according to application scene function requirements, dividing energy storage types according to rated continuous discharge duration; (2) data acquisition; s2, calling an energy storage scene and combining with weight parameter setting to construct a multi-objective optimization model: (1) establishing the multi-objective optimization model with the purposes of minimizing the system operation cost, maximizing the energy storage utilization rate and improving the power grid stability; (2) setting constraint conditions; s3, a dynamic optimization algorithm: (1) adopting an improved multi-target particle swarm algorithm or a genetic algorithm, introducing an adaptive weight mechanism, and combining real-time data to dynamically adjust the weight of a target function; (2) aiming at uncertainty factors, embedding a scene analysis method or a robust optimization strategy, and judging whether the comprehensive efficiency is optimal or not; and S4, scheduling execution and monitoring.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, and in particular to a coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system. Background Technology

[0002] 1. Industry Pain Points (1) New energy sources (wind power and photovoltaic) are highly random, volatile and intermittent, which increases the difficulty of peak regulation in the power system. Peak regulation contradictions are prominent during the midday peak photovoltaic power generation period, and there are risks of extreme operating conditions such as "extreme heat and no wind" and "extreme cold and little light".

[0003] (2) Among the existing energy storage technologies, electrochemical energy storage is dominant, but grid-type energy storage (such as hydrogen energy storage, compressed air energy storage, supercapacitors, etc.) has not yet been applied on a large scale, and its regulation potential has not been fully explored.

[0004] (3) Existing scheduling methods rely on power prediction data and spot market clearing data when performing power balancing. If the power prediction data has a large deviation, energy storage needs to be mobilized to balance the power. There is no coordinated scheduling strategy for multiple types of grid-type energy storage, and flexible adjustment and multi-objective optimization scheduling of energy storage for different application scenarios have not been realized.

[0005] 2. Deficiencies in existing technology (1) Technical aspects: Existing scheduling methods do not distinguish the technical characteristics of different energy storage (such as the differences in response speed and duration of power-type and capacity-type energy storage), and cannot bring out their best performance.

[0006] (2) Economic aspect: Single-objective optimization (considering only cost or efficiency) leads to limited overall system benefits.

[0007] (3) Operation level: There is a lack of a real-time control mechanism that can dynamically adapt to the fluctuations of new energy sources and changes in the state of energy storage. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a novel multi-objective optimization and coordinated scheduling method for grid-based energy storage.

[0009] A novel multi-objective optimization coordinated scheduling method for grid-based energy storage includes the following steps: S1. Energy Storage Classification and Data Acquisition: (1) According to the charging and discharging time, the energy storage type is divided into: ≤30min is power type, 1-2h is energy type, and ≥4h is capacity type; (2) Data acquisition: Real-time acquisition of SOC, SOH and charging and discharging power, followed by data preprocessing to determine whether the data is complete and usable. If the data is unusable, data acquisition is performed again. If the data is usable, it is combined with the basic parameters of different types of new energy storage to form an energy storage database. S2. In the scenario of utilizing energy storage, a multi-objective optimization model is constructed by combining weight parameter settings: (1) Establish a multi-objective optimization model with the objectives of minimizing system operating costs, maximizing energy storage utilization, and improving grid stability. The objective function includes: Economic objective function: Minimize the total system cost, i.e., investment cost + operation and maintenance cost + energy loss; Technical objective function: Maximize energy storage utilization rate, i.e., charge / discharge amount / daily equivalent total capacity; Reliability objective function: Minimize voltage / frequency deviation and extend battery life; (2) Set constraints, including: Power balance constraints: in: P storage ( t Let t be the electrical power stored at time t; P renewable ( t Let t be the power of new energy sources connected to the internet at time t; P load ( t Let t be the electrical load at time t; The upper and lower limits of energy storage charging and discharging power, i.e., the state of charge (SOC) limits: SOC min,i ≤ SOC i ( t )≤ SOC max,i in: SOC min,i For the first i Minimum discharge power of energy storage devices; SOC max,i For the first i Maximum charging power of energy storage devices; SOC i ( t ) is the first i The actual charge and discharge power of the energy storage system at time t; Cycle count constraint: State of health (SOH) > 80%; Hierarchical control constraints: A three-tier architecture of regional control, cluster control, and station control is adopted, wherein: the regional control layer allocates total power commands according to regulation needs; the cluster control layer allocates to each energy storage group according to the inter-group allocation strategy; and the station control layer executes intra-group allocation according to SOC priority. S3, Dynamic Optimization Algorithm: (1) An improved multi-objective particle swarm optimization algorithm (MOPSO) or genetic algorithm (GA) is adopted, and an adaptive weighting mechanism is introduced to dynamically adjust the weights of the objective function in combination with real-time data. (2) To address uncertainties, use the Monte Carlo method to simulate and generate new energy output fluctuation scenarios, embed scenario analysis or robust optimization strategies, and determine whether the overall efficiency of energy storage is optimal. If so, output the overall energy storage value; otherwise, proceed to step S2 again. S4. Scheduling Execution and Monitoring: Time-segmented charging and discharging instructions are issued to each energy storage site to monitor the operating status in real time and determine whether the deviation exceeds the allowable value. If so, manual adjustment and optimization are triggered for secondary optimization. If not, an experience database is formed.

[0010] In step S1, the power type includes supercapacitors and flywheel energy storage; the energy type includes lithium-ion batteries and flow batteries; and the capacity type includes hydrogen energy storage and compressed air energy storage.

[0011] In step S1, real-time monitoring involves collecting the battery state of charge and charge / discharge power in real time through the energy management system of the energy storage power station. The accuracy of the battery state of charge (SOC) is ±1%, and the sampling frequency of the charge / discharge power is ≥1Hz. Data preprocessing: Missing or erroneous data are filled in using linear interpolation or LSTM neural networks, with a prediction error of <2%; Multi-source fusion: Real-time load data and new energy power prediction data collected by the data acquisition and processing system are integrated with the basic parameters of different types of new energy storage to form an energy storage database. Among them, the prediction time of new energy power prediction data is ≥72 hours.

[0012] In step S2, the economic objective function is: in, C inv The unit capacity investment cost C inv =500 yuan / kW P cap For energy storage installed capacity; C ope This is the operation and maintenance cost coefficient. C ope =0.02 yuan / kWh P discharge This refers to the discharge power. C loss This is the power grid loss cost coefficient. P grid The power transmitted through the power grid; T is a calculation period, usually taken as 24 hours. The technical objectives are: in, E total,i For the first i The equivalent capacity of energy storage, in terms of the equivalent capacity of lithium-ion batteries. E total =Based on 100MWh, N is the total number of energy storage types; P charge,t Let be the charging power at time t. P discharge,t Let t be the discharge power at time t; T is a calculation period, usually taken as 24 hours; The reliability target is: Where: △v t Let be the voltage deviation at time t. v t The actual voltage value at time t. v ref The system's rated reference voltage is typically taken as 1.0 per unit; Δf t The frequency deviation at time t f t The actual frequency value at time t. f ref The system's rated frequency is typically taken as 50Hz (per unit); T is a calculation period, typically taken as 24 hours. The constraint condition is: △v t ±1.5%, △f t ≤±0.2Hz.

[0013] Specifically, step S4 involves sending time-sharing charging and discharging commands from the automatic active power control (AGC) system on the dispatch master station to the AGC system of the energy storage power station substation every minute via the IEC 61850 protocol. The operating status of the PCS is monitored in real time. The state of charge (SOC) and charging / discharging power of the energy storage power station are collected in real time through the phasor measurement unit (PMU) in the dispatch technical support system and the energy storage coordination control system. The power deviation and SOC deviation are judged to determine whether they exceed the allowable values. If so, manual adjustment and optimization are triggered for secondary optimization. If not, an experience database is formed.

[0014] In step S1, the grid-type energy storage is combined with two energy storage technologies: (1) Parallel architecture: Hydrogen energy storage is connected in parallel with supercapacitors, with the former handling base load fluctuations and the latter smoothing out high-frequency oscillations; Control logic: Set the power allocation coefficient k hydro=0.7, k SC =0.3; Where k hydro k is the weighting coefficient for hydrogen energy storage. hydro This is the weighting coefficient for supercapacitor energy storage; (2) Shared energy storage mode: The grid-side lithium-ion battery and compressed air energy storage combination is used. The former responds to frequency regulation commands with a regulation accuracy of ≤0.1Hz, while the latter performs intraday peak regulation with a charge / discharge duration of ≥4h.

[0015] In step S1, the data preprocessing method includes: (1) Abnormal data detection: noise is removed based on sliding window mean filtering, where the window length is 5 min; (2) Missing data imputation: For short-term data with a collection resolution of <1 hour, linear interpolation was used to fill in the missing data. For long-term missing data with a collection resolution > 1 hour, an LSTM neural network is used to predict the missing data, with a prediction error R² > 0.95.

[0016] In step S3, the particle encoding is a real number encoding, and each particle contains the charging and discharging power instructions of each energy storage power station. The adaptive weighting is based on the entropy weighting method to dynamically adjust the weights of the objective function, and the formula is as follows: in, d j Let be the dispersion of the j-th target. σ j The variance of the objective function value; Uncertainty handling: Embedded scenario analysis method to generate new energy output fluctuation scenarios, specifically Monte Carlo simulation, with ≥10 scenarios.

[0017] In step S3, the improved multi-objective particle swarm optimization algorithm includes: (1) Particle encoding: Each particle has a dimension of 1. N storage ×2 (charge and discharge power command); (2) Adaptive weights: The initial weight w = 0.5, and the weight increases with the number of iterations. W new = W old × e -0.01t attenuation; (3) Constraint handling: Apply penalty functions to variables that exceed the bounds. Ppenalty =10×( violation ) 2 .

[0018] In step S4, the real-time monitoring is specifically a status monitoring module: deploying a PMU measurement unit with a synchronous phasor measurement accuracy of ≤100μs, and collecting real-time data on the battery state of charge (SOC), the charging and discharging power of the energy storage power station, and the grid frequency / voltage data, with a sampling period of ≤1 second. Emergency power support strategy: Triggering conditions: System frequency deviation > 0.5 Hz or reserve capacity < 5%.

[0019] Execution logic: One-button charging mode: all stations switch to rated charging power; One-button discharging mode: all stations switch to rated discharging power; Exit conditions: Frequency recovers to ±0.2Hz or reserve capacity >10%.

[0020] Safety interlocking constraints: SOC interlocking: Charging command is disabled when SOC ≥ 95%; Discharge commands are disabled when SOC ≤ 20%.

[0021] Cross-section over-limit blocking: If the control command causes the cross-section power flow to exceed the limit, the power is reduced proportionally according to the safety margin; Frequency lockout: Upward adjustment commands are prohibited when f > 50.5Hz; downward adjustment commands are prohibited when f < 49.5Hz.

[0022] Deviation corrections include: Triggering conditions: Energy storage charging and discharging power deviation >15% or battery state of charge (SOC) deviation >5%; system frequency deviation >0.5Hz or reserve capacity <5%.

[0023] Execution logic: Based on the energy storage model, closed-loop optimization control MPC predicts the energy storage response trajectory for the next 5 minutes and dynamically adjusts the charging and discharging power; One-click charging mode: all stations switch to rated charging power; One-click discharging mode: all stations switch to rated discharging power; Exit conditions: Frequency recovers to ±0.2Hz or reserve capacity >10%.

[0024] Safety interlocking constraints: SOC interlocking: Charging command is disabled when SOC ≥ 95%; Discharge commands are disabled when SOC ≤ 20%.

[0025] Cross-section over-limit blocking: If the control command causes the cross-section power flow to exceed the limit, the power is reduced proportionally according to the safety margin; Frequency lockout: Upward adjustment commands are prohibited when f > 50.5Hz; downward adjustment commands are prohibited when f < 49.5Hz.

[0026] It also includes device protection strategies: Forced disconnection threshold: When the battery state of charge (SOC) is less than 20% or the battery state of health (SOH) is less than or equal to 80%, the battery will switch to the backup power source. Cold start protection: Power-type energy storage is prohibited from discharging when the ambient temperature is <-20℃.

[0027] The beneficial effects of this invention are: 1. Improved economic efficiency: By coordinating multiple types of energy storage, peak-shaving costs can be reduced and the frequency of peak-shaving by coal-fired power units can be decreased, which is expected to reduce system operating costs by 15%-25%.

[0028] 2. Enhanced regulation capability: Power-type energy storage can quickly respond to peak loads, while capacity-type energy storage can smooth out long-cycle fluctuations, thus comprehensively improving the stability of the power grid frequency / voltage.

[0029] 3. Extended equipment lifespan: Charge and discharge strategies based on lifespan prediction can slow down the rate of decline in the health status of energy storage and extend cycle life by approximately 20%-30%. Attached Figure Description

[0030] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the invention.

[0032] like Figure 1 As shown, a novel multi-objective optimization coordinated scheduling method for grid-based energy storage includes the following steps: S1. Energy Storage Classification and Data Acquisition: (1) According to the charging and discharging time, the energy storage type is divided into: ≤30min is power type, 1-2h is energy type, and ≥4h is capacity type; (2) Data acquisition: Real-time acquisition of SOC, SOH and charging and discharging power, followed by data preprocessing to determine whether the data is complete and usable. If the data is unusable, data acquisition is performed again. If the data is usable, it is combined with the basic parameters of different types of new energy storage to form an energy storage database. S2. In the scenario of utilizing energy storage, a multi-objective optimization model is constructed by combining weight parameter settings: (1) Establish a multi-objective optimization model with the objectives of minimizing system operating costs, maximizing energy storage utilization, and improving grid stability. The objective function includes: Economic objective function: Minimize the total system cost, i.e., investment cost + operation and maintenance cost + energy loss; Technical objective function: Maximize energy storage utilization rate, i.e., charge / discharge amount / daily equivalent total capacity; Reliability objective function: Minimize voltage / frequency deviation and extend battery life; (2) Set constraints, including: Power balance constraints: Let be the electrical power stored at time t; P renewable ( t Let t be the power of new energy sources connected to the internet at time t; P load ( t Let t be the electrical load at time t; The upper and lower limits of energy storage charging and discharging power, i.e., the state of charge (SOC) limits: SOC min,i ≤ SOC i ( t )≤ SOC max,i in: SOC min,i For the first i Minimum discharge power of energy storage devices; SOC max,i For the first i Maximum charging power of energy storage devices; SOC i ( t ) is the first i The actual charge and discharge power of the energy storage system at time t; Cycle count constraint: State of health (SOH) > 80%; Hierarchical control constraints: A three-tier architecture of regional control, cluster control, and station control is adopted, wherein: the regional control layer allocates total power commands according to regulation needs; the cluster control layer allocates to each energy storage group according to the inter-group allocation strategy; and the station control layer executes intra-group allocation according to SOC priority. S3, Dynamic Optimization Algorithm: (2) An improved multi-objective particle swarm optimization algorithm (MOPSO) or genetic algorithm (GA) is adopted, and an adaptive weighting mechanism is introduced to dynamically adjust the weights of the objective function in combination with real-time data. Among them, the SOC operating range division and adjustment limit correction: set the threshold value of the energy storage SOC operating range, and the minimum operating lower limit SOC.min (Default 20%), Ideal operating minimum SOC L-idea (Default 30%), Ideal operating limit SOC H-idea (Default 90%) Maximum operating capacity (SOC) max (Default 95%). When the State of Charge (SOC) is in the low-limit prohibition range, the upper limit of adjustment is 0; when the SOC is in the low-limit warning range, the upper limit of adjustment is proportional to the deviation of the ideal lower limit of SOC; when the SOC is in other ranges, the upper limit of adjustment is the rated discharge power. The correction logic for the lower limit of energy storage regulation is derived by analogy.

[0033] (2) To address uncertainties, use the Monte Carlo method to simulate and generate new energy output fluctuation scenarios, embed scenario analysis or robust optimization strategies, and determine whether the overall efficiency of energy storage is optimal. If so, output the overall energy storage value; otherwise, proceed to step S2 again. S4. Scheduling Execution and Monitoring: Time-segmented charging and discharging instructions are issued to each energy storage site to monitor the operating status in real time and determine whether the deviation exceeds the allowable value. If so, manual adjustment and optimization are triggered for secondary optimization. If not, an experience database is formed.

[0034] In step S1, the power type includes supercapacitors and flywheel energy storage; the energy type includes lithium-ion batteries and flow batteries; and the capacity type includes hydrogen energy storage and compressed air energy storage.

[0035] In step S1, real-time monitoring involves collecting the battery state of charge and charge / discharge power in real time through the energy management system of the energy storage power station. The accuracy of the battery state of charge (SOC) is ±1%, and the sampling frequency of the charge / discharge power is ≥1Hz. Data preprocessing: Missing or erroneous data are filled in using linear interpolation or LSTM neural networks, with a prediction error of <2%; Multi-source fusion: Real-time load data and new energy power prediction data collected by the data acquisition and processing system are integrated with the basic parameters of different types of new energy storage to form an energy storage database. Among them, the prediction time of new energy power prediction data is ≥72 hours.

[0036] In step S2, the economic objective function is: in, C inv The unit capacity investment cost C inv =500 yuan / kW P cap For energy storage installed capacity; C ope This is the operation and maintenance cost coefficient.C ope =0.02 yuan / kWh P discharge This refers to the discharge power. C loss This is the power grid loss cost coefficient. P grid The power transmitted through the power grid; T is a calculation period, usually taken as 24 hours. The technical objectives are: in, E total,i For the first i The equivalent capacity of energy storage, in terms of the equivalent capacity of lithium-ion batteries. E total =Based on 100MWh, N is the total number of energy storage types; P charge,t Let be the charging power at time t. P discharge,t Let t be the discharge power at time t; T is a calculation period, usually taken as 24 hours; The reliability target is: Where: △v t Let be the voltage deviation at time t. v t The actual voltage value at time t. v ref The system's rated reference voltage is typically taken as 1.0 per unit; Δf t The frequency deviation at time t f t The actual frequency value at time t. f ref The system's rated frequency is typically taken as 50Hz (per unit); T is a calculation period, typically taken as 24 hours. The constraint condition is: △v t ±1.5%, △f t ≤±0.2Hz.

[0037] Specifically, step S4 involves sending time-sharing charging and discharging commands from the automatic active power control (AGC) system on the dispatch master station to the AGC system of the energy storage power station substation every minute via the IEC 61850 protocol. The operating status of the PCS is monitored in real time. The state of charge (SOC) and charging / discharging power of the energy storage power station are collected in real time through the phasor measurement unit (PMU) in the dispatch technical support system and the energy storage coordination control system. The power deviation and SOC deviation are judged to determine whether they exceed the allowable values. If so, manual adjustment and optimization are triggered for secondary optimization. If not, an experience database is formed.

[0038] In step S1, the grid-type energy storage is combined with two energy storage technologies: (1) Parallel architecture: Hydrogen energy storage is connected in parallel with supercapacitors, with the former handling base load fluctuations and the latter smoothing out high-frequency oscillations; Control logic: Set the power allocation coefficient k hydro =0.7, k SC =0.3; Where k hydro k is the weighting coefficient for hydrogen energy storage. hydro This is the weighting coefficient for supercapacitor energy storage; (2) Shared energy storage mode: The grid-side lithium-ion battery and compressed air energy storage combination is used. The former responds to frequency regulation commands with a regulation accuracy of ≤0.1Hz, while the latter performs intraday peak regulation with a charge / discharge duration of ≥4h.

[0039] In step S1, the data preprocessing method includes: (1) Abnormal data detection: noise is removed based on sliding window mean filtering, where the window length is 5 min; (2) Missing data imputation: For short-term data with a collection resolution of <1 hour, linear interpolation was used to fill in the missing data. For long-term missing data with a collection resolution > 1 hour, an LSTM neural network is used to predict the missing data, where the prediction error R² > 0.95. (3) Normalization: Min-Max is normalized to the [0,1] interval, the formula is as follows In step S3, the particle encoding is a real number encoding, and each particle contains the charging and discharging power instructions of each energy storage power station. The adaptive weighting is based on the entropy weighting method to dynamically adjust the weights of the objective function, and the formula is as follows: in, d j Let be the dispersion of the j-th target. σ j The variance of the objective function value; Uncertainty handling: Embedded scenario analysis method to generate new energy output fluctuation scenarios, specifically Monte Carlo simulation, with ≥10 scenarios.

[0040] In step S3, the improved multi-objective particle swarm optimization algorithm includes: (1) Particle encoding: Each particle has a dimension of 1. Nstorage ×2 (charge and discharge power command); (2) Adaptive weights: The initial weight w = 0.5, and the weight increases with the number of iterations. W new = W old × e -0.01t attenuation; (3) Constraint handling: Apply penalty functions to variables that exceed the bounds. P penalty =10×( violation ) 2 .

[0041] In step S4, the real-time monitoring is specifically a status monitoring module: deploying a PMU measurement unit with a synchronous phasor measurement accuracy of ≤100μs, and collecting real-time data on the battery state of charge (SOC), the charging and discharging power of the energy storage power station, and the grid frequency / voltage data, with a sampling period of ≤1 second. Emergency power support strategy: Triggering conditions: System frequency deviation > 0.5 Hz or reserve capacity < 5%.

[0042] Execution logic: One-button charging mode: all stations switch to rated charging power; One-button discharging mode: all stations switch to rated discharging power; Exit conditions: Frequency recovers to ±0.2Hz or reserve capacity >10%.

[0043] Safety interlocking constraints: SOC interlocking: Charging command is disabled when SOC ≥ 95%; Discharge commands are disabled when SOC ≤ 20%.

[0044] Cross-section over-limit blocking: If the control command causes the cross-section power flow to exceed the limit, the power is reduced proportionally according to the safety margin; Frequency lockout: Upward adjustment commands are prohibited when f > 50.5Hz; downward adjustment commands are prohibited when f < 49.5Hz.

[0045] Deviation corrections include: Triggering conditions: Energy storage charging and discharging power deviation >15% or battery state of charge (SOC) deviation >5%; system frequency deviation >0.5Hz or reserve capacity <5%.

[0046] Execution logic: Based on the energy storage model, closed-loop optimization control MPC predicts the energy storage response trajectory for the next 5 minutes and dynamically adjusts the charging and discharging power; One-click charging mode: all stations switch to rated charging power; One-click discharging mode: all stations switch to rated discharging power; Exit conditions: Frequency recovers to ±0.2Hz or reserve capacity >10%.

[0047] Safety interlocking constraints: SOC interlocking: Charging command is disabled when SOC ≥ 95%; Discharge commands are disabled when SOC ≤ 20%.

[0048] Cross-section over-limit blocking: If the control command causes the cross-section power flow to exceed the limit, the power is reduced proportionally according to the safety margin; Frequency lockout: Upward adjustment commands are prohibited when f > 50.5Hz; downward adjustment commands are prohibited when f < 49.5Hz.

[0049] It also includes device protection strategies: Forced disconnection threshold: When the battery state of charge (SOC) is less than 20% or the battery state of health (SOH) is less than or equal to 80%, the battery will switch to the backup power source. Cold start protection: Power-type energy storage is prohibited from discharging when the ambient temperature is <-20℃.

[0050] The power grid dispatching scenarios include, but are not limited to: (1) Peak shaving scenario: Triggering condition: Solar power output at midday exceeds load demand by 15%; Dispatch strategy: Power-type energy storage responds first, followed by capacity-type energy storage.

[0051] (2) Emergency backup scenario: Triggering condition: Power grid failure results in reserve capacity being less than 5%; Response requirement: Provide ≥20% of rated power support within 15 minutes.

[0052] Example 1: Peak shaving scheduling during midday peak photovoltaic power generation 1. Scenario input: It is predicted that the photovoltaic output will surge to 1.5 times the load demand at noon, and the excess power needs to be reduced.

[0053] 2. Scheduling process: (1) Energy storage call method: Power type supercapacitor (response time <1s) is called first to absorb instantaneous excess power, and then the capacity type hydrogen energy storage is switched to discharge for a long time.

[0054] (2) Optimization results: The supercapacitor handles 80% of the high-frequency fluctuation power, and hydrogen storage provides the remaining 20%, resulting in a total cost reduction of 18% compared to the traditional solution.

[0055] Example 2: Supply Dispatch During Extreme Cold Weather 1. Scenario input: Wind power output drops sharply at night during winter, while load demand surges.

[0056] 2. Scheduling process: (1) Energy storage and dispatch method: Compressed air energy storage (large capacity, long discharge) is the main power supply, supplemented by lithium-ion batteries to make up for high frequency fluctuations.

[0057] (2) Optimization results: the voltage deviation was reduced from ±5% to ±1.5%, and the equipment life loss was reduced by 22%.

[0058] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system, characterized in that, Includes the following steps: S1. Energy Storage Classification and Data Acquisition: (1) According to the charging and discharging time, the energy storage type is divided into: ≤30min is power type, 1-2h is energy type, and ≥4h is capacity type; (2) Data acquisition: Real-time acquisition of SOC, SOH and charging and discharging power, followed by data preprocessing to determine whether the data is complete and usable. If the data is unusable, data acquisition is performed again. If the data is usable, it is combined with the basic parameters of different types of new energy storage to form an energy storage database. S2. In the scenario of utilizing energy storage, a multi-objective optimization model is constructed by combining weight parameter settings: (1) Establish a multi-objective optimization model with the objectives of minimizing system operating costs, maximizing energy storage utilization, and improving grid stability. The objective function includes: Economic objective function: Minimize the total system cost, i.e., investment cost + operation and maintenance cost + energy loss; Technical objective function: Maximize energy storage utilization rate, i.e., charge / discharge amount / daily equivalent total capacity; Reliability objective function: Minimize voltage / frequency deviation and extend battery life; (2) Set constraints, including: Power balance constraints: ,in: P storage ( t Let t be the electrical power stored at time t; P renewable ( t Let t be the power of new energy sources connected to the internet at time t; P load ( t Let t be the electrical load at time t; The upper and lower limits of energy storage charging and discharging power, i.e., the state of charge (SOC) limits: SOC min,i ≤ SOC i ( t )≤ SOC max,i in: SOC min,i For the first i Minimum discharge power of energy storage devices; SOC max,i For the first i Maximum charging power of energy storage devices; SOC i ( t ) is the first i The actual charge and discharge power of the energy storage device at time t; Cycle count constraint: State of health (SOH) > 80%; Hierarchical control constraints: A three-tier architecture of regional control, cluster control, and station control is adopted, wherein: the regional control layer allocates total power commands according to regulation needs; the cluster control layer allocates to each energy storage group according to the inter-group allocation strategy; and the station control layer executes intra-group allocation according to SOC priority. S3, Dynamic Optimization Algorithm: An improved multi-objective particle swarm optimization algorithm (MOPSO) or a genetic algorithm (GA) is adopted, and an adaptive weighting mechanism is introduced to dynamically adjust the weights of the objective function based on real-time data. (2) To address uncertainties, use the Monte Carlo method to simulate and generate new energy output fluctuation scenarios, embed scenario analysis or robust optimization strategies, and determine whether the overall efficiency of energy storage is optimal. If so, output the overall energy storage value; otherwise, proceed to step S2 again. S4. Scheduling Execution and Monitoring: Time-segmented charging and discharging instructions are issued to each energy storage site to monitor the operating status in real time and determine whether the deviation exceeds the allowable value. If so, manual adjustment and optimization are triggered for secondary optimization. If not, an experience database is formed.

2. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, In step S1, the power type includes supercapacitors and flywheel energy storage; Energy-type batteries include lithium-ion batteries and flow batteries; Capacity-type energy storage includes hydrogen energy storage and compressed air energy storage.

3. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, In step S1, real-time monitoring is performed by collecting the battery state of charge and charging / discharging power in real time through the energy management system of the energy storage power station. The accuracy of the battery state of charge (SOC) is ±1%, and the sampling frequency of the charging / discharging power is ≥1Hz. Data preprocessing: Missing or erroneous data are filled in using linear interpolation or LSTM neural networks, with a prediction error of <2%; Multi-source fusion: Real-time load data and new energy power prediction data collected by the data acquisition and processing system are integrated with the basic parameters of different types of new energy storage to form an energy storage database. Among them, the prediction time of new energy power prediction data is ≥72 hours.

4. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, In step S2, the economic objective function is: in, C inv The unit capacity investment cost C inv =500 yuan / kW P cap For energy storage installed capacity; C ope This is the operation and maintenance cost coefficient. C ope =0.02 yuan / kWh P discharge This refers to the discharge power. C loss This is the power grid loss cost coefficient. P grid The power transmitted through the power grid; T is a calculation period, usually taken as 24 hours. The technical objectives are: in, E total,i For the first i The equivalent capacity of energy storage, in terms of the equivalent capacity of lithium-ion batteries. E total =Based on 100MWh, N is the total number of energy storage types; P charge,t Let be the charging power at time t. P discharge,t Let t be the discharge power at time t; T is a calculation period, usually taken as 24 hours; The reliability target is: Where: △v t Let be the voltage deviation at time t. v t The actual voltage value at time t. v ref The system's rated reference voltage is typically taken as 1.0 per unit; Δf t The frequency deviation at time t f t The actual frequency value at time t. f ref The system's rated frequency is typically taken as 50Hz (per unit); T is a calculation period, typically taken as 24 hours. The constraint condition is: △v t ±1.5%, △f t ≤±0.2Hz.

5. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, Specifically, step S4 involves sending time-sharing charging and discharging commands from the automatic active power control (AGC) system on the dispatch master station to the AGC system of the energy storage power station substation every minute via the IEC 61850 protocol. The operating status of the PCS is monitored in real time. The state of charge (SOC) and charging / discharging power of the energy storage power station are collected in real time through the phasor measurement unit (PMU) in the dispatch technical support system and the energy storage coordination control system. The power deviation and SOC deviation are then judged to determine whether they exceed the allowable values. If so, manual adjustment and optimization are triggered for secondary optimization. If not, an experience database is formed.

6. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, In step S1, the grid-type energy storage is combined with two energy storage technologies: (1) Parallel architecture: Hydrogen energy storage is connected in parallel with supercapacitors, with the former handling base load fluctuations and the latter smoothing out high-frequency oscillations; Control logic: Set the power allocation coefficient k hydro =0.7, k SC =0.3; Where k hydro k is the weighting coefficient for hydrogen energy storage. hydro This is the weighting coefficient for supercapacitor energy storage; (2) Shared energy storage mode: The grid-side lithium-ion battery and compressed air energy storage combination is used. The former responds to frequency regulation commands with a regulation accuracy of ≤0.1Hz, while the latter performs intraday peak regulation with a charge / discharge duration of ≥4h.

7. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, In step S1, the data preprocessing method includes: (1) Abnormal data detection: noise is removed based on sliding window mean filtering, where the window length is 5 min; (2) Missing data imputation: For short-term data with a collection resolution of <1 hour, linear interpolation was used to fill in the missing data. For long-term missing data with a collection resolution > 1 hour, an LSTM neural network is used to predict the missing data, with a prediction error R² > 0.

95.

8. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, In step S3, the particle encoding involved is real number encoding, and each particle contains the charging and discharging power instructions of each energy storage power station. The adaptive weighting is based on the entropy weighting method to dynamically adjust the weights of the objective function, and the formula is as follows: in, d j Let be the dispersion of the j-th target. σ j The variance of the objective function value; Uncertainty handling: Embedded scenario analysis method to generate new energy output fluctuation scenarios, specifically Monte Carlo simulation, with ≥10 scenarios.

9. The coordinated scheduling method for multi-objective optimization of a novel grid-type energy storage system according to claim 1, characterized in that, In step S3, the improved multi-objective particle swarm optimization algorithm includes: (1) Particle encoding: Each particle has a dimension of 1. N storage ×2 (charge and discharge power command); (2) Adaptive weights: The initial weight w = 0.5, and the weight increases with the number of iterations. W new = W old × e -0.01t attenuation; (3) Constraint handling: Apply penalty functions to variables that exceed the bounds. P penalty =10×( violation ) 2 。

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