A method for realizing optimal energy storage capacity configuration of a system by using capacity elasticity

Abstract

The application discloses a method for realizing optimal energy storage capacity configuration of a system by using capacity elasticity, and comprises the following steps: firstly, setting the rated output power of a thermal power generation system, limiting the output power of the thermal power generation system, and taking the power variation and capacity variation of an energy storage system as basic design variables of a system model; and then, setting energy storage system operation constraints, system power balance constraints, thermal power generation system constraints and standby power generation system constraints on the three variables to perfect the system model building. The application provides a formula for calculating the energy storage capacity demand threshold in the system by calculating and summarizing the energy storage system capacity demand under multiple groups of different permeability rates, so that the globally optimal energy storage capacity configuration is realized.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology, and specifically relates to a method for achieving optimal energy storage capacity configuration of a system by utilizing capacity flexibility. Background Technology

[0002] Unlike thermal power units, renewable energy output, primarily from wind and solar power, is not as stable and adjustable, exhibiting volatility and uncertainty. Energy storage systems offer a new approach to renewable energy integration and increasing the grid connection rate of wind turbines. These systems can store excess wind power, smoothing out fluctuations and filling peak and valley loads when there is a mismatch between load power and wind turbine power. This not only improves the stability of the power system but also enhances the dispatchability of the energy storage system, ultimately enabling intermittent energy supply to the grid.

[0003] Configuring energy storage systems that meet system requirements at different penetration rates has become a major research focus. Configuring excessively large energy storage capacities offers high efficiency in absorbing renewable energy sources but also incurs high investment costs, resulting in insufficient system economics. Conversely, configuring excessively small energy storage capacities offers higher system economics but also increases the rate of renewable energy curtailment. Therefore, configuring energy storage systems with appropriate capacities to meet the needs of different penetration rates is crucial for microgrid systems.

[0004] For the 14-node medium-voltage microgrid of the CIGRE system, the optimal economic energy storage capacity configuration requirements for different permeability levels were calculated using the particle swarm optimization algorithm, as shown below. Figure 1 As can be seen, the calculation results obtained by the particle swarm algorithm are locally optimal, so the energy storage capacity configuration results fluctuate under different penetration rates. Although the overall energy storage capacity increases with the increase of penetration rate, it is impossible to determine the globally optimal energy storage capacity configuration result of the microgrid system under high penetration rate. Summary of the Invention

[0005] To overcome the above technical problems, the purpose of this invention is to provide a method for achieving optimal energy storage capacity configuration of a system by utilizing capacity elasticity. This method is based on a formula that calculates the energy storage capacity demand threshold of a system by calculating the capacity demand of multiple energy storage systems under different penetration rates, thereby achieving globally optimal energy storage capacity configuration.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for achieving optimal energy storage capacity configuration of a system using capacity elasticity includes the following steps; First, set the rated output power limit for the thermal power generation system. Pt TPGS ) With the power change of the energy storage system and capacity change As basic design variables for the system model; Then, set constraints on the energy storage system operation, system power balance, thermal power generation system, and backup power generation system for the above three variables to improve the system model construction.

[0007] Furthermore, the first step is to constrain the operating state of the energy storage system. The energy storage system can only operate in one state (including charging, discharging, and non-charging / discharging states) within a fixed time interval, using binary variables. and To represent the charging and discharging operation states of the energy storage system respectively, and then... and The charging / discharging power of the energy storage system at time t is represented, and the operating state of the energy storage system is constrained using Big-M constraints, as shown below: ; Where T is the unit time interval calculated by the model. M This is represented by the Big-M constraint, which is an arbitrarily large non-negative number.

[0008] The power change of the energy storage system at time t can be calculated using the above formula. : ; Meanwhile, since overcharging and over-discharging of electrochemical energy storage batteries will affect their lifespan, in order to ensure the long-term reliable operation of the energy storage system, the SOC state of the energy storage system is constrained, limiting the stored capacity of the energy storage system to the minimum allowable stored capacity. With maximum allowable storage capacity BESS between: ; Then the power change of the energy storage system With capacity change This results in an energy storage system, as follows: ; After establishing the energy storage system, the energy storage system is constrained in l The equal amount of charge and discharge within a certain period is taken as the charge-discharge cycle of the energy storage system: ; In a microgrid, the power generation unit meets the system load demand at time t. Under the premise of reducing the waste of new energy sources, excess electricity can be injected into energy storage systems, as shown below: ; The penetration rate of renewable energy This can be achieved by setting constraints on the amount of renewable energy generated relative to the total power generation of the system. The ratio is used to obtain the value. In microgrids, renewable energy mainly includes the rated power of wind power systems. and rated power of photovoltaic power generation system Its rated power generation increases with the increase in renewable energy penetration, and the sum of the two should be less than the product of the total rated power generation of the microgrid system and the penetration rate. ; The next step is to set constraint functions for the thermal power generation system to improve the model, and to set ramp constraint functions for the thermal power generation system to better simulate its operating state and give it good inertia. ; in, and These are the upper and lower limits of the rated power generation of a thermal power generation system. and These are the limits on the descent and ascent rates of thermal power generation systems; At the same time, start-up and shutdown time interval constraints should be set for thermal power generation systems to prevent wear and tear on generator sets due to excessively high start-up and shutdown frequency. ; Among them, binary variables This indicates the start-up and shutdown status of a thermal power generation system. When the generator starts, The value is 1, when the generator stops. =0, t on It is the minimum time required to maintain the operating status of a thermal power generation system. t off It is the minimum time required for a thermal power generation system to remain in a shutdown state. The next step is to constrain the standby power generation system to meet the system's load demand at various times and maintain system stability. The standby power generation system mainly consists of thermal power generating units, so the standby power generation system also needs to consider unit start-up and shutdown constraints and ramp-up constraints: ; ; In the formula: and These are the upper and lower limits of the rated power generation of the standby power generation system. and It is the limitation of the ramp-down and ramp-up rates of the backup power generation system. This indicates the start / stop status of the backup power generation system. t standonIt is the minimum time required for the backup power generation system to maintain its operational status. t standoff It is the minimum time that the backup power generation system must remain in a shutdown state.

[0009] Furthermore, using the above constraint formulas, a basic microgrid system model is constructed. Finally, the optimal energy storage capacity configuration, wind power price, and other parameters of the microgrid system are calculated. C wind Annual power generation dispatch curve of wind power system, photovoltaic power generation price C solar Annual power generation dispatch curve of photovoltaic power generation system, time-of-use electricity price of thermal power generation system Operating costs of energy storage systems The price of backup power generation systems C standby Once price-related parameters are incorporated into the system model, and combined with the aforementioned basic variables, the objective function for minimizing the operating cost of the microgrid system can be obtained: ; in, It is the wind power output at time t. It is the photovoltaic power output at time t. It is the output power of the thermal power generation system at time t. It is the output / input power of the energy storage system at time t. It is the output power of the standby power generation system at time t; Therefore, a complete energy storage optimization configuration model is established, and then the energy storage capacity requirements under various renewable energy penetration rates in the microgrid system are calculated using this model.

[0010] Furthermore, by calculating the energy storage capacity demand under various renewable energy penetration rates, the energy storage capacity demand configuration curve of the microgrid system is plotted. Then, the renewable energy penetration rate under the peak energy storage requirement of the system is calculated using the following capacity elasticity formula: ; Capacity Flexibility E d Indicates the system's energy storage capacity requirements P With system renewable energy penetration rate Q The changing dependency relationship reflects the corresponding rate of change in energy storage capacity caused by changes in permeability, that is, the sensitivity of energy storage capacity to permeability and charge / discharge cycle information. The threshold change in the required energy storage capacity of a microgrid system can be obtained from the capacity elasticity formula, if and only if E dWhen the penetration rate is 0, the required energy storage capacity of the system reaches its peak. Therefore, the energy storage capacity requirement under this penetration rate can meet the energy storage capacity required for various renewable energy penetration rate changes. After obtaining the optimal penetration rate of energy storage configuration in the system through capacity elasticity, the energy storage optimization configuration model can be used to calculate the annual operating cost of the microgrid system under different charge-discharge cycles at this penetration rate. A weighted formula is then used to calculate the annual operating cost SC of the system relative to the charge-discharge cycle of the energy storage system. With the required capacity E of the energy storage system BESS By weighting, the final economic result TC under different microgrid penetration rates can be obtained, from which the optimal economic energy storage system capacity configuration result can be obtained; ; After weighted calculation and sorting of the operating cost results of microgrid systems with different charge-discharge cycles under the optimal penetration rate, the optimal energy storage capacity result is recorded. Then, the energy storage capacity configuration requirements under different renewable energy penetration rates are calculated again using the energy storage optimization configuration model. The results are then compared with the previous calculation results to determine whether the results converge. If they converge, the optimal energy storage capacity configuration results obtained multiple times are taken as the optimal energy storage capacity result for the microgrid system. If they do not converge, the previous result is replaced and the convergence of the results is determined again using the energy storage optimization configuration model until the optimal energy storage capacity configuration result at the convergence point is obtained.

[0011] The beneficial effects of the present invention.

[0012] Compared to most energy storage system capacity configuration calculations, this invention focuses on energy storage system capacity configuration for microgrid systems with different renewable energy penetration rates. It also derives an energy storage capacity elasticity formula to calculate the elasticity range of energy storage system capacity configuration at various penetration rates and the renewable energy penetration rate at which the maximum capacity is achieved. Furthermore, the energy storage system capacity demand calculated based on the energy storage configuration optimization model changes with variations in various parameters, and still meets the energy storage system requirements under these conditions. By limiting the SOC state of the energy storage system, the calculated energy storage system capacity results are guaranteed to contain a margin to accommodate changes in energy storage system capacity demand caused by parameter variations. Attached image description: Figure 1 A schematic diagram showing the results of calculating energy storage capacity configuration requirements using the particle swarm optimization algorithm under different permeability.

[0013] Figure 2 A schematic diagram showing the results of calculating energy storage capacity configuration requirements for capacity elasticity under different penetration rates.

[0014] Figure 3 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figure 3 As shown: A method for achieving optimal energy storage capacity configuration of a system using capacity elasticity includes the following steps; First, set the rated output power limit for the thermal power generation system. With the power change of the energy storage system and capacity change As basic design variables for the system model; Then, set constraints on the energy storage system operation, system power balance, thermal power generation system, and backup power generation system for the above three variables to improve the system model construction.

[0017] Furthermore, the first step is to constrain the operating state of the energy storage system. The energy storage system can only operate in one state (including charging, discharging, and non-charging / discharging states) within a fixed time interval, using binary variables. and To represent the charging and discharging operation states of the energy storage system respectively, and then... and The charging / discharging power of the energy storage system at time t is represented, and the operating state of the energy storage system is constrained using Big-M constraints, as shown below: ; Where T is the unit time interval calculated by the model. M This is represented by the Big-M constraint, which is an arbitrarily large non-negative number.

[0018] The power change of the energy storage system at time t can be calculated using the above formula. : ; Meanwhile, since overcharging and over-discharging of electrochemical energy storage batteries will affect their lifespan, in order to ensure the long-term reliable operation of the energy storage system, the SOC state of the energy storage system is constrained, limiting the stored capacity of the energy storage system to the minimum allowable stored capacity. With maximum allowable storage capacity between: ; Then the power change of the energy storage system With capacity change This results in an energy storage system, as follows: ; After establishing an energy storage system, the energy storage system is constrained in its operation. The equal amount of charge and discharge within a certain period is taken as the charge-discharge cycle of the energy storage system: ; In a microgrid, the power generation unit meets the system load demand at time t. Under the premise of reducing the waste of new energy sources, excess electricity can be injected into energy storage systems, as shown below: ; The penetration rate of renewable energy This can be achieved by setting constraints on the amount of renewable energy generated and the total power generation of the system. The ratio is used to obtain the value. In microgrids, renewable energy mainly includes the rated power of wind power systems. and rated power of photovoltaic power generation system Its rated power generation increases with the increase in renewable energy penetration, and the sum of the two should be less than the product of the total rated power generation of the microgrid system and the penetration rate: ; The next step is to set constraint functions for the thermal power generation system to improve the model, and to set ramp constraint functions for the thermal power generation system to better simulate its operating state and give it good inertia. ; in, and These are the upper and lower limits of the rated power generation of a thermal power generation system. and These are the limits on the rate of descent and ascent of thermal power generation systems; At the same time, start-up and shutdown time interval constraints should be set for thermal power generation systems to prevent generator set wear caused by excessive start-up and shutdown frequency. ; Among them, binary variables This indicates the start-up and shutdown status of a thermal power generation system. When the generator starts, The value is 1, when the generator stops. =0, t on It is the minimum time required to maintain the operating status of a thermal power generation system. t off It is the minimum time required for a thermal power generation system to remain in a shutdown state. The next step is to constrain the standby power generation system to meet the system's load demand at various times and maintain system stability. The standby power generation system mainly consists of thermal power generating units, so the standby power generation system also needs to consider unit start-up and shutdown constraints and ramp-up constraints: ; ; In the formula: and These are the upper and lower limits of the rated power generation of the standby power generation system. and It is the limitation of the ramp-down and ramp-up rates of the backup power generation system. This indicates the start / stop status of the backup power generation system. t standon It is the minimum time required for the backup power generation system to maintain its operational status. t standoff It is the minimum time that the backup power generation system must remain in a shutdown state.

[0019] Furthermore, using the above constraint formulas, a basic microgrid system model is constructed. Finally, the optimal energy storage capacity configuration, wind power price, and other parameters of the microgrid system are calculated. C wind Annual power generation dispatch curve of wind power system, photovoltaic power generation price C solar Annual power generation dispatch curve of photovoltaic power generation system, time-of-use electricity price of thermal power generation system Operating costs of energy storage systems The price of backup power generation systems C standby Once price-related parameters are written into the system model, and combined with the aforementioned basic variables, the objective function for minimizing the operating cost of the microgrid system can be obtained: ; in, It is the wind power output at time t. It is the photovoltaic power output at time t. It is the output power of the thermal power generation system at time t. It is the output / input power of the energy storage system at time t. It is the output power of the standby power generation system at time t; Therefore, a complete energy storage optimization configuration model is established, and then the energy storage capacity requirements under various renewable energy penetration rates in the microgrid system are calculated using this model.

[0020] Furthermore, by calculating the energy storage capacity demand under various renewable energy penetration rates, the energy storage capacity demand configuration curve of the microgrid system is plotted. Then, the renewable energy penetration rate under the peak energy storage requirement of the system is calculated using the following capacity elasticity formula: ; Capacity Flexibility E d Indicates the system's energy storage capacity requirements P With system renewable energy penetration rate Q The changing dependency relationship reflects the corresponding rate of change in energy storage capacity caused by changes in permeability, that is, the sensitivity of energy storage capacity to permeability and charge / discharge cycle information. The threshold change in the required energy storage capacity of a microgrid system can be obtained from the capacity elasticity formula, if and only if E d When the penetration rate is 0, the required energy storage capacity of the system reaches its peak. Therefore, the energy storage capacity requirement under this penetration rate can meet the energy storage capacity required for various renewable energy penetration rate changes. After obtaining the optimal penetration rate of energy storage configuration in the system through capacity elasticity, the energy storage optimization configuration model can be used to calculate the annual operating cost of the microgrid system under different charge-discharge cycles at this penetration rate. A weighted formula is then used to calculate the annual operating cost SC of the system and the charge-discharge cycle of the energy storage system. With the required capacity E of the energy storage system BESS By weighting, the final economic result TC under different microgrid penetration rates can be obtained, from which the optimal economic energy storage system capacity configuration result can be obtained; ; After weighted calculation and sorting of the operating cost results of microgrid systems with different charge-discharge cycles under the optimal penetration rate, the optimal energy storage capacity result is recorded. Then, the energy storage capacity configuration requirements under different renewable energy penetration rates are calculated again using the energy storage optimization configuration model. The results are then compared with the previous calculation results to determine whether the results converge. If they converge, the optimal energy storage capacity configuration results obtained multiple times are taken as the optimal energy storage capacity result for the microgrid system. If they do not converge, the previous result is replaced and the convergence of the results is determined again using the energy storage optimization configuration model until the optimal energy storage capacity configuration result at the convergence point is obtained.

[0021] Example: Step 1: Load the curve parameters and cost parameters of each system in the microgrid; Step 2: By setting system operation constraints on basic variables, an energy storage optimization configuration model considering renewable energy penetration rate is built, and the energy storage capacity configuration results under each energy storage system charge-discharge cycle under each renewable energy penetration rate are calculated with minimizing the annual operating cost of the microgrid system as the objective function.

[0022] Step 3: Calculate the optimal penetration rate for energy storage configuration using the capacity elasticity formula; Step 4: Weight the system operating costs under each charge-discharge cycle under the optimal penetration rate to obtain the optimal energy storage system charge-discharge cycle result.

[0023] Step 5: Combine the results of Steps 3 and 4 to obtain the optimal capacity configuration of the energy storage system. To ensure the accuracy of the calculation results, this application sets a convergence interval. The termination condition can be that the set fitness value reaches a certain value or the loop is repeated a certain number of times. In this application, the termination condition is 20 iterations.

[0024] like Figure 2 As shown, compared to the particle swarm optimization algorithm for calculating the energy storage capacity configuration in a microgrid system, the capacity elasticity method for achieving the optimal energy storage capacity configuration in a microgrid system can estimate the system energy storage capacity demand curve under different penetration rates based on different system configuration parameters. The optimal energy storage capacity configuration demand result is obtained by calculating the capacity elasticity result based on the curve, making the calculation result real-time, observable, and clear. Furthermore, in the case of high penetration rate, the maximum energy storage capacity demand point of the microgrid system can be obtained, ensuring that the energy storage system capacity under this condition can meet the energy storage capacity demand of the microgrid under different penetration rates.

Claims

1. A method for achieving optimal energy storage capacity configuration of a system using capacity flexibility, characterized in that, By calculating the energy storage capacity demand under various renewable energy penetration rates, a demand configuration curve for energy storage capacity in the microgrid system is plotted. Then, the renewable energy penetration rate for the peak energy storage required by the system is calculated using the following capacity elasticity formula: ； Capacity elasticity E d This indicates the relationship between the system's energy storage capacity demand P and the system's renewable energy penetration rate Q, reflecting the corresponding rate of change in energy storage capacity caused by changes in penetration rate, i.e., the sensitivity of energy storage capacity to penetration rate and charge / discharge cycle information. The threshold change in the required energy storage capacity of a microgrid system can be obtained from the capacity elasticity formula, if and only if E d When the penetration rate is 0, the required energy storage capacity of the system reaches its peak. Therefore, the energy storage capacity requirement under this penetration rate can meet the energy storage capacity required for various renewable energy penetration rate changes. After obtaining the optimal penetration rate for energy storage configuration through capacity elasticity, the energy storage optimization configuration model can be used to calculate the annual operating cost of the microgrid system under different charge-discharge cycles at this penetration rate. A weighted formula is then used to combine the annual operating cost SC with the charge-discharge cycle λ of the energy storage system and the required capacity E of the energy storage system. BESS By weighting, the final economic result TC under different microgrid penetration rates can be obtained, from which the optimal economic energy storage system capacity configuration result can be obtained; ； After weighted calculation and sorting of the operating cost results of microgrid systems with different charge-discharge cycles under the optimal penetration rate, the optimal energy storage capacity result is recorded. Then, the energy storage capacity configuration requirements under different renewable energy penetration rates are calculated again using the energy storage optimization configuration model. The results are then compared with the previous calculation results to determine whether the results converge. If they converge, the optimal energy storage capacity configuration results obtained multiple times are taken as the optimal energy storage capacity result for the microgrid system. If they do not converge, the previous result is replaced and the convergence of the results is determined again using the energy storage optimization configuration model until the optimal energy storage capacity configuration result at the convergence point is obtained.

2. The method for achieving optimal energy storage capacity configuration of a system using capacity flexibility according to claim 1, characterized in that, Includes the following steps; First, set the rated output power limit for the thermal power generation system. With the power change of the energy storage system and capacity change As basic design variables for the system model; Then, set constraints on the energy storage system operation, system power balance, thermal power generation system, and backup power generation system for the above three variables to improve the system model construction.

3. The method for achieving optimal energy storage capacity configuration of a system using capacity flexibility according to claim 1, characterized in that, First, the operating state of the energy storage system is constrained. The energy storage system can only operate in one state within a fixed time interval, using binary variables. and To represent the charging and discharging operation states of the energy storage system respectively, and then... and The charging / discharging power of the energy storage system at time t is represented, and the operating state of the energy storage system is constrained using Big-M constraints, as shown below: ； ； ； Where T is the unit time interval for model calculation, and M represents the Big-M constraint, which is an arbitrarily large non-negative number; The power change of the energy storage system at time t can be calculated using the above formula. : Meanwhile, since overcharging and over-discharging of electrochemical energy storage batteries will affect their lifespan, in order to ensure the long-term reliable operation of the energy storage system, the SOC state of the energy storage system is constrained, limiting the stored capacity of the energy storage system to the minimum allowable stored capacity. With maximum allowable storage capacity between: ； Then the power change of the energy storage system With capacity change This results in an energy storage system, as follows: ； After establishing an energy storage system, the energy storage system's charge and discharge cycles are defined as follows: The energy storage system's charge and discharge cycles are defined as the sum of the charge and discharge times within a given time period λ. ； In a microgrid, the power generation unit meets the system load demand at time t. Under the premise of reducing the waste of new energy sources, excess electricity can be injected into energy storage systems, as shown below: ； The penetration rate β of renewable energy can be determined by setting constraints on the amount of renewable energy generated and the total power generation of the system. The ratio is used to obtain the value. In microgrids, renewable energy mainly includes the rated power of wind power systems. and rated power of photovoltaic power generation system Its rated power generation increases with the increase in renewable energy penetration, and the sum of the two should be less than the product of the total rated power generation of the microgrid system and the penetration rate. ； The next step is to set constraint functions for the thermal power generation system to improve the model, and to set ramp constraint functions for the thermal power generation system to better simulate its operating state and give it good inertia. ； in, and These are the upper and lower limits of the rated power generation of a thermal power generation system. and These are the limits on the descent and ascent rates of thermal power generation systems; At the same time, start-up and shutdown time interval constraints should be set for thermal power generation systems to prevent wear and tear on generator sets due to excessively high start-up and shutdown frequency. ； Among them, binary variables This indicates the start-up and shutdown status of a thermal power generation system. When the generator starts, The value is 1, when the generator stops. τ is 0 on It is the minimum time that a thermal power generation system must maintain its operating state. off It is the minimum time required for a thermal power generation system to remain in a shutdown state. The next step is to constrain the standby power generation system to meet the system's load demand at various times and maintain system stability. The standby power generation system mainly consists of thermal power generating units, so the standby power generation system also needs to consider unit start-up and shutdown constraints and ramp-up constraints: ； ； In the formula: and These are the upper and lower limits of the rated power generation of the standby power generation system. and It is the limitation of the ramp-down and ramp-up rates of the backup power generation system. This refers to the start / stop status of the backup power generation system, τ standon It is the minimum time that the backup power generation system must maintain its operating state, τ. standoff It is the minimum time that the backup power generation system must remain in a shutdown state.

4. The method for achieving optimal energy storage capacity configuration of a system using capacity flexibility according to claim 3, characterized in that, Based on the above constraint formulas, a basic microgrid system model is constructed. Finally, the optimal energy storage capacity configuration, wind power price C, and other parameters of the microgrid system are calculated. wind Annual power generation dispatch curve of wind power system, photovoltaic power generation price C solar Annual power generation dispatch curve of photovoltaic power generation system, time-of-use electricity price of thermal power generation system Operating costs of energy storage systems The price of the backup power generation system C standby Price-related parameters are incorporated into the system model; combined with the basic variables, the objective function for minimizing the operating cost of the microgrid system can be obtained: ； in, It is the wind power output at time t. It is the photovoltaic power output at time t. It is the output power of the thermal power generation system at time t. It is the output / input power of the energy storage system at time t. It is the output power of the standby power generation system at time t; Therefore, a complete energy storage optimization configuration model is established, and then the energy storage capacity requirements under various renewable energy penetration rates in the microgrid system are calculated using this model.

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