A battery configuration method considering inertia support in an electric-thermal shared energy storage mode

Abstract

This invention discloses a battery configuration method considering inertia support under an electric-thermal shared energy storage mode. The steps are as follows: A planning layer model considering the variable lifespan characteristics of the battery is established, with the optimization objective being to maximize the annual equivalent net revenue; a scheduling layer model considering wind and solar curtailment recovery and system inertia support is established, with the optimization objective being to maximize daily operating revenue; a two-layer optimal battery configuration model is solved, using a discrete particle swarm optimization algorithm to solve the planning layer problem, and the obtained battery rated capacity and rated power are sent down to the scheduling layer. The lower-layer model is solved using CLPEX, and the results are returned to the upper layer. This iterative solution is repeated until the termination condition is met, outputting the optimal battery capacity configuration scheme. This invention fully considers the equivalent energy storage characteristics of generalized energy storage resources and the variable lifespan characteristics of battery devices. It can alleviate the problems of wind and solar curtailment and inertia shortage in the power system through energy storage sharing between different energy systems, effectively reducing the overall cost of energy storage resources and improving the operational economy of the system.

Description

Technical Field

[0001] This invention relates to power systems, and in particular to a battery configuration method considering inertia support under an electric-thermal shared energy storage mode. Background Technology

[0002] In the process of accelerating the construction of a new power system and continuously promoting the low-carbon transformation of energy, the demand for energy storage resources in the power system continues to grow. However, under the influence of market demand, various economic benefits problems exposed by energy storage, such as low technical and economic efficiency, imperfect cost-sharing mechanisms, and poor profitability, have greatly limited the widespread application of energy storage, resulting in an unbalanced and insufficient development of energy storage.

[0003] "Shared energy storage" refers to large-scale centralized energy storage power stations invested and constructed by third parties. These stations, while meeting their own energy storage needs, can provide energy storage services to renewable energy power plants, reducing wind and solar curtailment and increasing the proportion of renewable energy consumption. Shared energy storage achieves centralized management and unified control of distributed power source-side, grid-side, and user-side energy storage through multi-resource integration, which is beneficial for improving the utilization rate of idle energy storage resources and reducing the overall cost of energy storage. The shared energy storage model uses the power grid as a bridge and link, achieving spatial energy storage sharing through energy storage capacity leasing or bilateral transactions.

[0004] Despite the significant application value and development prospects of shared energy storage, and its clear upward trend in recent years, its profitability has been questioned due to the limited availability and high cost of adjustable energy storage, coupled with the difficulty in achieving cost reduction in the short term. In response, domestic and international scholars have proposed a shared energy storage business model based on multi-energy systems. Compared to traditional models, this model allows both thermal and gas systems to serve as "broadly defined energy storage resources" for the power system, possessing enormous adjustable potential. This expands the range of available energy storage resources, effectively reduces the cost of energy storage through cost sharing, and achieves friendly cooperation and mutual benefit among various types of energy systems. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a battery configuration method that considers inertia support under the electric-thermal shared energy storage mode. In the process of planning and configuring battery energy storage resources, the equivalent energy storage characteristics of generalized energy storage resources and the variable lifespan characteristics of battery devices are fully considered. By sharing energy storage among different energy systems, the problem of wind and solar power curtailment and inertia shortage in the power system can be alleviated, thereby effectively reducing the overall cost of energy storage resources and improving the economic efficiency of system operation.

[0006] Technical solution: The battery configuration method considering inertia support under the electric-thermal shared energy storage mode of the present invention includes the following steps:

[0007] (1) Establish a planning layer model that considers the variable life characteristics of the battery. The optimization objective is to maximize the annual equivalent net income, and the constraints are the upper and lower limits of the battery's rated capacity and rated power.

[0008] (1.1) Establish the objective function of the planning layer model; the optimization objective of the planning layer is to maximize the annual equivalent net benefit of the energy storage system, as shown below:

[0009]

[0010]

[0011] In the formula, N Y R represents the total number of days in a year; T represents the number of time periods in a day; R represents the total number of days in a year. net Indicates the annualized net income of the energy storage system; B rec,s,t B ine,s,t C represents the revenue from wind and solar power curtailment and the revenue from inertia replenishment during time period t on day s; BES This indicates the lifespan cost of a battery energy storage device; This represents the annualized cost of investment for the battery. This indicates the annual maintenance cost of the battery; This indicates the cost per unit capacity and cost per unit power of the battery; Indicates the battery's rated capacity and rated power; r represents the discount rate; T BES Indicates the battery's calendar life; This represents the annualized fixed operating and maintenance cost; P represents the annualized variable operating cost; BESd,s,t P BESc,s,t This represents the battery's discharge and charge power during time period t on day s.

[0012] Among them, the calendar life T of the battery energy storage device BES For float charge life T f and cycle life T c The smaller value in the expression is as follows:

[0013] T BES =min{T f ,T c}

[0014] The number of cycles N at the end of the battery's lifespan Life for:

[0015]

[0016] In the formula, N0 represents the number of charge-discharge cycles when the battery is at 100% discharge depth; d c The depth of battery cycle discharge can be obtained using the rainflow counting method; k pThis represents a constant obtained by fitting a power function.

[0017] Therefore, the total charge and discharge capacity of the battery over its entire lifespan when charged and discharged at 100% depth of discharge can be obtained. The expression:

[0018]

[0019] The daily charge / discharge cycle depth is d. c In this case, we can obtain The relationship between battery charge / discharge power and battery cycle life is as follows:

[0020]

[0021] In the formula, Δt represents the unit time interval.

[0022] This yields the total annual charge / discharge capacity at 100% depth of discharge. As shown below:

[0023]

[0024] Therefore, the cycle life T of the battery c for:

[0025]

[0026] (1.2) Establish the constraints of the planning layer model.

[0027] (1.2.1) Establish upper and lower limits for battery rated capacity constraints:

[0028]

[0029] In the formula, This indicates the upper and lower limits of the battery's rated capacity.

[0030] (1.2.2) Establish upper and lower limits of battery rated power constraints:

[0031]

[0032] In the formula, This indicates the upper and lower limits of the battery's rated power.

[0033] (2) Establish a scheduling layer model that considers wind and solar curtailment recovery and system inertia support. The optimization objective is to maximize daily operating revenue. The constraints include wind and solar curtailment recovery constraints, compressed air energy storage operation constraints, cogeneration unit operation constraints, heating network constraints, and battery energy storage operation constraints.

[0034] (2.1) Establish the objective function of the scheduling layer model; the optimization objective of the scheduling layer is to maximize the daily operating revenue of the energy storage system, as shown below:

[0035]

[0036]

[0037] In the formula, P represents the reduced electrical power of a combined heat and power (CHP) unit during time period t in order to share its own energy storage capacity; that is, the equivalent discharge power. CAESd,s,t H represents the discharge power of compressed air energy storage during time period t; BES,s,t H CAES,s,t ΔH represents the inertia that battery energy storage and compressed air energy storage can provide during time period t on day s; s,t θ represents the inertia deficit of the system at different time periods; θ represents the on-grid electricity price of new energy sources; π represents the unit inertia compensation price.

[0038] The expression for the inertia level of compressed air energy storage is:

[0039]

[0040] In the formula, H CAESc H CAESd These represent the inertial time constants of the compressor and expander, respectively. This indicates the rated charging power and rated discharging power of compressed air energy storage; u CAESc,s,t u CAESd,s,t This indicates the charging and discharging states of compressed air energy storage.

[0041] When the actual inertia level of the system in the current period is lower than the minimum inertia requirement, the system can make up for the inertia deficit by allocating a certain amount of energy storage resources. The inertia deficit ΔH s,t The expression is as follows:

[0042] ΔH s,t = H re,s,t -H sys,s,t

[0043] In the formula, H re,s,t H sys,s,t This represents the system's minimum inertia requirement and actual inertia level.

[0044] (2.2) Establish the constraints of the scheduling layer model.

[0045] (2.2.1) Establish constraints for wind and solar power curtailment recovery, namely, the sum of the charging power of energy storage resources shall not exceed the total amount of curtailed wind and solar power that can be utilized, as shown below:

[0046]

[0047] In the formula, u BESc,s,t P represents the state of charge of the battery during time period t; CAESc,s,t This indicates the charging power of compressed air energy storage; w t v t This indicates the amount of wind and solar power that has been curtailed.

[0048] (2.2.2) Establish operational constraints for compressed air energy storage:

[0049]

[0050] In the formula, P CAESc , P CAESd This indicates the lower limit of the charging and discharging power of compressed air energy storage; p CAES,s,t Indicates the gas pressure in the storage tank; k c k d This represents a coefficient reflecting the relationship between the gas pressure in the storage tank and the charging / discharging power. p CAES This indicates the upper and lower pressure limits of the gas storage tank.

[0051] (2.2.3) Establish operating constraints for combined heat and power units:

[0052]

[0053] In the formula, h CHP,t This represents the thermal power of a combined heat and power unit during time period t; This indicates the upper limit of the thermal power of a combined heat and power unit; P CHP This indicates the maximum and minimum electrical power that a combined heat and power (CHP) unit can output; F CHP Indicates the upper and lower limits of oil consumption for combined heat and power units; k CHP This represents the electrothermal ratio of a combined heat and power (CHP) unit, which is the slope of boundary BC in the CHP unit's thermoelectric characteristic curve. These are fuel consumption per unit of electrical power and fuel consumption per unit of thermal power, respectively. Let be the slope of the boundaries AB and DE in the thermoelectric characteristic curve; This represents the equivalent charging power of the combined heat and power unit during time period t on day s; P CHP,s,t This indicates the electrical output of the combined heat and power unit before and after the optimized configuration of battery energy storage; This indicates the equivalent charging state and equivalent discharging state of a combined heat and power (CHP) unit.

[0054] (2.2.4) Establishing heating network constraints:

[0055]

[0056] In the formula, Ω represents the set of load nodes of the thermal system; These represent the water supply temperatures of the heat source node and node k during time period t, respectively. γ represents the return water temperature at heat source node and node k in time period t, respectively; k q represents the equivalent insulation coefficient of the heating pipe at node k; amb Indicates ambient temperature; m0, m k These represent the mass flow rates of the heating pipes passing through the heat source node and node k, respectively. This represents the heat load of node k during time period t; c w This indicates the specific heat capacity of water; Indicates the upper and lower limits of the water supply temperature; This indicates the upper and lower limits of the return water temperature.

[0057] (2.2.5) Establish battery energy storage operation constraints:

[0058]

[0059] In the formula, E BES,s,t Indicates the state of charge of the battery; E BES Indicates the upper and lower limits of the battery's charge capacity; η c Indicates battery energy storage cycle efficiency; u BESd,s,t This indicates the battery's discharge state during time period t.

[0060] (3) Solve the battery two-layer optimization configuration model. Use the discrete particle swarm algorithm to solve the planning layer problem. Send the obtained battery rated capacity and rated power to the scheduling layer. Use CLPEX to solve the lower layer model and return the result to the upper layer. Iterate continuously until the termination condition is met and output the optimal battery capacity configuration scheme.

[0061] (3.1) Solve the two-layer optimal configuration model for battery energy storage. The specific steps are as follows:

[0062] (3.1.1) Obtain system parameter information, including the technical parameters of each device and the system's wind and solar curtailment amount and minimum inertia requirements.

[0063] (3.1.2) Set the parameters of the discrete particle swarm algorithm for solving planning layer problems, including the population size of the particle swarm, the number of iterations, the learning factor, and the inertia weight.

[0064] (3.1.3) Initialize each particle, that is, initialize the upper-level decision variables.

[0065] (3.1.4) Perform a feasibility analysis based on the constraints in step (1). If the constraints are met, the result is directly passed to the scheduling layer. If the limit is exceeded, the particle value is corrected to the limit before being passed to the scheduling layer.

[0066] (3.1.5) Determine the battery energy storage operation control variables based on the upper-level results, and then solve the scheduling layer optimization problem.

[0067] (3.1.6) The daily operating revenue of battery energy storage obtained from the lower layer solution is sent to the planning layer to solve the annual equivalent net revenue of the system, i.e. the fitness function value of the discrete particle swarm.

[0068] (3.1.7) Update the individual optimal value and the overall optimal value of each particle, and update the inertia weight.

[0069] (3.1.8) Update the velocity and position of each particle.

[0070] (3.1.9) Determine whether the iteration termination condition is met. If not, jump to step (3.1.4); if it is met, output the result as the optimal battery configuration scheme.

[0071] (3.2) Perform calculation example analysis and verification based on the above solution steps.

[0072] A computer storage medium storing a computer program that, when executed by a processor, implements a battery configuration method considering inertia support under an electric-thermal shared energy storage mode, as described above.

[0073] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described battery configuration method considering inertia support under an electric-thermal shared energy storage mode.

[0074] Beneficial effects: Compared with the prior art, the present invention has the following advantages: The present invention fully considers the equivalent energy storage characteristics of generalized energy storage resources and the variable lifespan characteristics of battery devices, and can alleviate the problems of wind and solar power curtailment and inertia shortage in the power system through energy storage sharing among different energy systems, thereby effectively reducing the overall cost of energy storage resources and improving the economic efficiency of system operation. Attached Figure Description

[0075] Figure 1 This is a flowchart of the steps of the method described in this invention;

[0076] Figure 2 The thermoelectric characteristic curves of the extraction cogeneration unit;

[0077] Figure 3This is a heat map showing the daily operating revenue of an energy storage system under a shared energy storage model. Detailed Implementation

[0078] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0079] A battery configuration method considering inertia support in an electric-thermal shared energy storage mode includes the following steps:

[0080] like Figure 1 As shown, (1) a planning layer model considering the variable lifespan characteristics of the battery is established. The optimization objective is to maximize the annual equivalent net income, and the constraints are the upper and lower limits of the battery's rated capacity and rated power.

[0081] (1.1) Establish the objective function of the planning layer model; the optimization objective of the planning layer is to maximize the annual equivalent net benefit of the energy storage system, as shown below:

[0082]

[0083]

[0084] In the formula, N Y R represents the total number of days in a year; T represents the number of time periods in a day; R represents the total number of days in a year. net Indicates the annualized net income of the energy storage system; B rec,s,t B ine,s,t C represents the revenue from wind and solar power curtailment and the revenue from inertia replenishment during time period t on day s; BES This indicates the lifespan cost of a battery energy storage device; This represents the annualized cost of investment for the battery. This indicates the annual maintenance cost of the battery; This indicates the cost per unit capacity and cost per unit power of the battery; Indicates the battery's rated capacity and rated power; r represents the discount rate; T BES Indicates the battery's calendar life; This represents the annualized fixed operating and maintenance cost; P represents the annualized variable operating cost; BESd,s,t P BESc,s,t This represents the battery's discharge and charge power during time period t on day s.

[0085] Among them, the calendar life T of the battery energy storage device BES For float charge life T f and cycle life T c The smaller value in the expression is as follows:

[0086] T BES =min{T f ,T c}

[0087] The number of cycles N at the end of the battery's lifespan Life for:

[0088]

[0089] In the formula, N0 represents the number of charge-discharge cycles when the battery is at 100% discharge depth; d c The depth of battery cycle discharge can be obtained using the rainflow counting method; k p This represents a constant obtained by fitting a power function.

[0090] Therefore, the total charge and discharge capacity of the battery over its entire lifespan when charged and discharged at 100% depth of discharge can be obtained. The expression:

[0091]

[0092] The daily charge / discharge cycle depth is d. c In this case, we can obtain The relationship between battery charge / discharge power and battery cycle life is as follows:

[0093]

[0094] In the formula, Δt represents the unit time interval.

[0095] This yields the total annual charge / discharge capacity at 100% depth of discharge. As shown below:

[0096]

[0097] Therefore, the cycle life T of the battery c for:

[0098]

[0099] (1.2) Establish the constraints of the planning layer model.

[0100] (1.2.1) Establish upper and lower limits for battery rated capacity constraints:

[0101]

[0102] In the formula, This indicates the upper and lower limits of the battery's rated capacity.

[0103] (1.2.2) Establish upper and lower limits of battery rated power constraints:

[0104]

[0105] In the formula, This indicates the upper and lower limits of the battery's rated power.

[0106] (2) Establish a scheduling layer model that considers wind and solar curtailment recovery and system inertia support. The optimization objective is to maximize daily operating revenue. The constraints include wind and solar curtailment recovery constraints, compressed air energy storage operation constraints, cogeneration unit operation constraints, heating network constraints, and battery energy storage operation constraints.

[0107] (2.1) Establish the objective function of the scheduling layer model; the optimization objective of the scheduling layer is to maximize the daily operating revenue of the energy storage system, as shown below:

[0108]

[0109]

[0110] In the formula, P represents the reduced electrical power of a combined heat and power (CHP) unit during time period t in order to share its own energy storage capacity; that is, the equivalent discharge power. CAESd,s,t H represents the discharge power of compressed air energy storage during time period t; BES,s,t H CAES,s,t ΔH represents the inertia that battery energy storage and compressed air energy storage can provide during time period t on day s; s,t θ represents the inertia deficit of the system at different time periods; θ represents the on-grid electricity price of new energy sources; π represents the unit inertia compensation price.

[0111] The expression for the inertia level of compressed air energy storage is:

[0112]

[0113] In the formula, H CAESc H CAESd These represent the inertial time constants of the compressor and expander, respectively. This indicates the rated charging power and rated discharging power of compressed air energy storage; u CAESc,s,t u CAESd,s,t This indicates the charging and discharging states of compressed air energy storage.

[0114] When the actual inertia level of the system in the current period is lower than the minimum inertia requirement, the system can make up for the inertia deficit by allocating a certain amount of energy storage resources. The inertia deficit ΔH s,t The expression is as follows:

[0115] ΔH s,t = H re,s,t -H sys,s,t

[0116] In the formula, H re,s,t H sys,s,tThis represents the system's minimum inertia requirement and actual inertia level.

[0117] (2.2) Establish the constraints of the scheduling layer model.

[0118] (2.2.1) Establish constraints for wind and solar power curtailment recovery, namely, the sum of the charging power of energy storage resources shall not exceed the total amount of curtailed wind and solar power that can be utilized, as shown below:

[0119]

[0120] In the formula, u BESc,s,t P represents the state of charge of the battery during time period t; CAESc,s,t This indicates the charging power of compressed air energy storage; w t v t This indicates the amount of wind and solar power that has been curtailed.

[0121] (2.2.2) Establish operational constraints for compressed air energy storage:

[0122]

[0123] In the formula, P CAESc , P CAESd This indicates the lower limit of the charging and discharging power of compressed air energy storage; p CAES,s,t Indicates the gas pressure in the storage tank; k c k d This represents a coefficient reflecting the relationship between the gas pressure in the storage tank and the charging / discharging power. p CAES This indicates the upper and lower pressure limits of the gas storage tank.

[0124] (2.2.3) Establish operating constraints for combined heat and power units:

[0125]

[0126] In the formula, h CHP,t This represents the thermal power of a combined heat and power unit during time period t; This indicates the upper limit of the thermal power of a combined heat and power unit; P CHP This indicates the maximum and minimum electrical power that a combined heat and power (CHP) unit can output; F CHP Indicates the upper and lower limits of oil consumption for combined heat and power units; k CHP This indicates the electro-thermal ratio of a combined heat and power (CHP) unit. Figure 2 The slope of boundary BC in the thermoelectric characteristic curve of a combined heat and power unit; These are fuel consumption per unit of electrical power and fuel consumption per unit of thermal power, respectively. Let be the slope of the boundaries AB and DE in the thermoelectric characteristic curve; This represents the equivalent charging power of the combined heat and power unit during time period t on day s; This indicates the electrical output of the combined heat and power unit before and after the optimized configuration of battery energy storage; This indicates the equivalent charging state and equivalent discharging state of a combined heat and power (CHP) unit.

[0127] (2.2.4) Establishing heating network constraints:

[0128]

[0129] In the formula, Ω represents the set of load nodes of the thermal system; These represent the water supply temperatures of the heat source node and node k during time period t, respectively. γ represents the return water temperature at heat source node and node k in time period t, respectively; k q represents the equivalent insulation coefficient of the heating pipe at node k; amb Indicates ambient temperature; m0, m k These represent the mass flow rates of the heating pipes passing through the heat source node and node k, respectively. This represents the heat load of node k during time period t; c w This indicates the specific heat capacity of water; Indicates the upper and lower limits of the water supply temperature; This indicates the upper and lower limits of the return water temperature.

[0130] (2.2.5) Establish battery energy storage operation constraints:

[0131]

[0132] In the formula, E BES,s,t Indicates the state of charge of the battery; E BES Indicates the upper and lower limits of the battery's charge capacity; η c Indicates battery energy storage cycle efficiency; u BESd,s,t This indicates the battery's discharge state during time period t.

[0133] (3) Solve the battery two-layer optimization configuration model. Use the discrete particle swarm algorithm to solve the planning layer problem. Send the obtained battery rated capacity and rated power to the scheduling layer. Use CLPEX to solve the lower layer model and return the result to the upper layer. Iterate continuously until the termination condition is met and output the optimal battery capacity configuration scheme.

[0134] (3.1) Solve the two-layer optimal configuration model for battery energy storage. The specific steps are as follows:

[0135] (3.1.1) Obtain system parameter information, including the technical parameters of each device and the system's wind and solar curtailment amount and minimum inertia requirements.

[0136] (3.1.2) Set the parameters of the discrete particle swarm algorithm for solving planning layer problems, including the population size of the particle swarm, the number of iterations, the learning factor, and the inertia weight.

[0137] (3.1.3) Initialize each particle, that is, initialize the upper-level decision variables.

[0138] (3.1.4) Perform a feasibility analysis based on the constraints in step (1). If the constraints are met, the result is directly passed to the scheduling layer. If the limit is exceeded, the particle value is corrected to the limit before being passed to the scheduling layer.

[0139] (3.1.5) Determine the battery energy storage operation control variables based on the upper-level results, and then solve the scheduling layer optimization problem.

[0140] (3.1.6) The daily operating revenue of battery energy storage obtained from the lower layer solution is sent to the planning layer to solve the annual equivalent net revenue of the system, i.e. the fitness function value of the discrete particle swarm.

[0141] (3.1.7) Update the individual optimal value and the overall optimal value of each particle, and update the inertia weight.

[0142] (3.1.8) Update the velocity and position of each particle.

[0143] (3.1.9) Determine whether the iteration termination condition is met. If not, jump to step (3.1.4); if it is met, output the result as the optimal battery configuration scheme.

[0144] (3.2) Based on the above solution steps, a case study analysis and verification were carried out. Taking the actual system data of Tacheng, Xinjiang in 2009 as an example, a case study analysis was conducted.

[0145] The system has a thermal power capacity of 1997MW, a wind power capacity of 2119MW, and a photovoltaic capacity of 1199MW, with a peak load of approximately 640MW. 10% of the wind and photovoltaic capacity is used locally, and 90% is exported to other regions. The heating season runs from October 10th to April 10th of the following year. The technical parameters of the thermal power units, combined heat and power units, and compressed air energy storage are shown in Tables 1, 2, and 3, respectively. The governor droop coefficient for the thermal power units is 0.05, and the governor time constant and steam box time constant are 0.25 and 0.35, respectively. The inertial time constant for the wind turbine units is 3s, and the primary frequency regulation droop coefficient and virtual inertia constant are 0.05 and 0.6, respectively. The renewable energy feed-in tariff is $100 / MWh, and the government subsidy is $30 / MWh. The unit inertia compensation cost is set at $0.714 / (MW.s). The technical parameters of the battery energy storage device are shown in Table 4.

[0146] Table 1 Technical parameters of thermal power units

[0147]

[0148] Table 2 Technical parameters of cogeneration units

[0149] Cogeneration unit parameters unit Electricity-to-heat ratio 0.85 / Fuel consumption upper and lower limits 250 / 30 MW Maximum thermal power 70 MW upper and lower limits of electrical power 100 / 8.5 MW fuel consumption coefficient per unit power 2.4 / fuel consumption coefficient per unit heat power 0.95 / Climbing speed 2 MW / min Minimum start-stop time 6 h Unit start-up and shutdown costs 500 $ Inertial time constant 5.8 s

[0150] Table 3 Technical parameters of compressed air energy storage

[0151] Compressed air energy storage parameters unit Charging power upper and lower limits 60 / 24 MW upper and lower limits of discharge power 80 / 12 MW upper and lower pressure limits of gas storage tank 40～55 bar Gas storage tank capacity 180,000 <![CDATA[m 3 ]]> compressor inertial time constant 4.06 s Expander inertial time constant 5.8 s Coefficient reflecting the relationship between gas pressure in the gas storage tank and charging power 0.02625 / Coefficient reflecting the relationship between gas pressure in the gas storage tank and discharge power 0.0375 /

[0152] Table 4 Battery Energy Storage Technical Parameters

[0153] Battery technical parameters unit float life 15 a Discount rate 0.05 / Mean depth of cycle discharge 0.8 / Number of cycles when charged and discharged at 100% depth of discharge 5000 / Annualized fixed operation and maintenance costs 2000 $ / MWh Annualized variable operation and maintenance costs 1.89 $ / MW Unit capacity cost 171.43 $ / kWh Unit power cost 71.43 $ / kW

[0154] Based on the above parameters, the system's 365-day wind and solar curtailment volume and minimum inertia requirement can be obtained through optimized scheduling. Table 5 lists the wind and solar curtailment situation and minimum inertia requirement on January 31, 2009.

[0155] Table 5. Wind and Solar Curtailment Status and Minimum Inertia Requirements of Xinjiang Tacheng System as of January 31, 2009

[0156]

[0157]

[0158] With the above data obtained, the particle swarm optimization (PSO) algorithm parameters were set as follows: population size 500, iterations 200, learning factors c1 = 1.53, c2 = 1.47, and initial inertia weight 0.9. The discrete particle swarm optimization algorithm was used to solve the two-layer optimal battery capacity configuration model. Two sets of comparative experiments were set up to analyze the impact of the equivalent energy storage characteristics of the thermal system on the optimal battery energy storage configuration results. Scenario 1: optimal battery configuration with cogeneration unit participation; Scenario 2: optimal battery configuration without cogeneration unit participation. The optimal battery energy storage configuration schemes for the two scenarios are shown in Table 6. Furthermore, based on Scenario 1, a daily operating revenue heatmap for the shared energy storage mode can be generated, as shown below. Figure 3 As shown.

[0159] Table 6. Battery energy storage configuration results with and without CHP units.

[0160]

[0161]

[0162] As shown in Table 6, compared to Scenario 2, the optimal battery capacity in Scenario 1 is reduced by 1MW, and the annual charge / discharge power of the battery is reduced by 9171.75MW, resulting in a reduction of $152,921 in battery life costs and a final annualized net benefit of $6,461,763, an increase of 6.17% compared to Scenario 2. Therefore, the participation of combined heat and power (CHP) units is more conducive to the rational allocation of energy storage resources and the absorption of curtailed wind and solar power, thereby obtaining greater operational economic benefits and achieving mutual benefit and win-win results across energy systems.

[0163] observe Figure 3 The revenue data for the 12 months shows that the economic benefits of the system under the shared energy storage model are mainly concentrated in the spring and summer quarters, with May's monthly revenue ranking first at $1,301,692. This is because, ignoring the revenue from inertia replenishment, daily operating revenue is positively correlated with the amount of wind and solar power curtailment recovered. Since the amount of wind and solar power curtailment is relatively large in spring and summer, the revenue from curtailment recovery in these two quarters is also relatively high. This is consistent with the results shown in the heat map, verifying the feasibility and effectiveness of the method proposed in this invention.

[0164] In summary, the battery capacity configuration method under the electric-thermal shared energy storage mode proposed in this invention, which considers system inertia support, fully considers the equivalent energy storage characteristics of generalized energy storage resources and the variable lifespan characteristics of battery devices during the planning and configuration of battery energy storage resources. It can alleviate the problems of wind and solar power curtailment and inertia shortage in the power system through energy storage sharing among different energy systems, thereby effectively reducing the overall cost of energy storage resources and improving the economic efficiency of system operation.

Claims

1. A battery configuration method considering inertia support under an electric-thermal shared energy storage mode, characterized in that, Includes the following steps: (1) Establish a planning layer model that considers the variable life characteristics of the battery, with the optimization objective being to maximize the annual equivalent net income and the constraints being the upper and lower limits of the battery's rated capacity and rated power. (2) Establish a scheduling layer model that considers wind and solar curtailment recovery and system inertia support. The optimization objective is to maximize daily operating revenue. The constraints include wind and solar curtailment recovery constraints, compressed air energy storage operation constraints, cogeneration unit operation constraints, heating network constraints, and battery energy storage operation constraints. (3) Solve the battery two-layer optimal configuration model. Use the discrete particle swarm optimization algorithm to solve the planning layer problem. Send the obtained battery rated capacity and rated power to the scheduling layer. Use CLPEX to solve the lower layer model and return the result to the upper layer. Iterate until the termination condition is met and output the optimal battery capacity configuration scheme. The specific steps (1) are as follows: (1.1) Establish the objective function of the planning layer model; the optimization objective of the planning layer is to maximize the annual equivalent net revenue of the energy storage system, as shown below: In the formula, Indicates the total number of days in a year; Indicates the number of time periods in a day; This indicates the annualized net income of the energy storage system; , Indicates the first Daytime The revenue from abandoned wind and solar power and the revenue from inertia replenishment; This indicates the lifespan cost of a battery energy storage device; This represents the annualized cost of investment for the battery. This indicates the annual maintenance cost of the battery; , This indicates the cost per unit capacity and cost per unit power of the battery; , This indicates the battery's rated capacity and rated power; Indicates the discount rate; Indicates the battery's calendar life; This represents the annualized fixed operating and maintenance cost; This indicates the annualized change in operating and maintenance costs; , Indicates the battery at the first Daytime The discharge and charge power; The calendar life of battery energy storage devices For float charge life and cycle life The smaller value in the expression is as follows: Number of cycles at the end of battery life for: In the formula, This indicates the number of charge-discharge cycles the battery can complete at 100% discharge depth. The depth of battery cycle discharge can be obtained using the rainflow counting method; This represents a constant obtained by fitting a power function; Therefore, the total charge and discharge capacity of the battery over its entire lifespan when charged and discharged at 100% depth of discharge can be obtained. The expression: The daily charge / discharge cycle depth is In this case, we can obtain The relationship between battery charge / discharge power and battery cycle life is as follows: In the formula, Indicates a unit time interval; This yields the total annual charge / discharge capacity at 100% depth of discharge. As shown below: Therefore, the cycle life of the battery for: (1.2) Establish the constraints of the planning layer model; (1.2.1) Establish upper and lower limits for battery rated capacity constraints: In the formula, , This indicates the upper and lower limits of the battery's rated capacity; (1.2.2) Establish upper and lower limits of battery rated power constraints: In the formula, , This indicates the upper and lower limits of the battery's rated power. Step (2) specifically involves: (2.1) Establish the objective function of the scheduling layer model; the optimization objective of the scheduling layer is to maximize the daily operating revenue of the energy storage system, as shown below: In the formula, Indicates the time period of the combined heat and power unit The reduced electrical power in order to share its own energy storage capacity, i.e., the equivalent discharge power; Indicates the time period of compressed air energy storage The discharge power; , This indicates that battery energy storage and compressed air energy storage are in the first... Daytime The inertia that can be provided; This indicates the inertia deficit of the system at different time periods; Indicates the on-grid tariff for renewable energy; This indicates the price per unit of inertia compensation. The expression for the inertia level of compressed air energy storage is: In the formula, , These represent the inertial time constants of the compressor and expander, respectively. , Indicates the rated charging power and rated discharging power of compressed air energy storage; , This indicates the charging and discharging states of compressed air energy storage; When the actual inertia level of the system in the current period is lower than the minimum inertia requirement, the system can make up for the inertia deficit by allocating a certain amount of energy storage resources. The expression is as follows: In the formula, , This represents the system's minimum inertia requirement and actual inertia level. (2.2) Establish the constraints for the scheduling layer model; (2.2.1) Establish constraints for wind and solar power curtailment recovery, namely, the sum of the charging power of energy storage resources shall not exceed the total amount of wind and solar power that can be utilized, as shown below: In the formula, Indicates the battery during the time period The charging status; This indicates the charging power of compressed air energy storage; , This indicates the amount of wind and solar power that has been forcibly abandoned. (2.2.2) Establish operational constraints for compressed air energy storage: In the formula, , This indicates the lower limit of the charging and discharging power of compressed air energy storage; Indicates the gas pressure in the storage tank; , This represents a coefficient reflecting the relationship between the gas pressure in the storage tank and the charging / discharging power. , Indicates the upper and lower limits of the gas storage tank pressure; (2.2.3) Establish operating constraints for combined heat and power units: In the formula, Indicates the time period of the combined heat and power unit Thermal power; This indicates the upper limit of the thermal power of a combined heat and power unit; , This indicates the maximum and minimum electrical power that a combined heat and power (CHP) unit can output; , This indicates the upper and lower limits of oil consumption for combined heat and power units; This represents the electrothermal ratio of a combined heat and power (CHP) unit, which is the slope of boundary BC in the CHP unit's thermoelectric characteristic curve. , These are fuel consumption per unit of electrical power and fuel consumption per unit of thermal power, respectively. Let be the slope of the boundaries AB and DE in the thermoelectric characteristic curve; This indicates that the combined heat and power unit is in the first Daytime The equivalent charging power; , This indicates the electrical output of the combined heat and power unit before and after the optimized configuration of battery energy storage; , This indicates the equivalent charging state and equivalent discharging state of a combined heat and power (CHP) unit. (2.2.4) Establishing heating network constraints: In the formula, This represents the set of load nodes in a thermal system. , Representing the heat source node and node respectively During the period The water supply temperature; , Representing the heat source node and node respectively During the period The return water temperature; Represents a node The equivalent thermal insulation coefficient of the heating pipeline; Indicates ambient temperature; , These represent the flow through the heat source node and the node, respectively. Mass flow rate of heating pipelines; Represents a node During the period The heat load; This indicates the specific heat capacity of water; , Indicates the upper and lower limits of the water supply temperature; , Indicates the upper and lower limits of the return water temperature; (2.2.5) Establish battery energy storage operation constraints: In the formula, Indicates the state of charge of the battery; , Indicates the upper and lower limits of the battery's charge capacity; Indicates battery energy storage cycle efficiency; Indicates the battery during the time period The discharge state; Step (3) specifically involves: (3.1) Solve the battery energy storage two-layer optimal configuration model. The specific steps are as follows: (3.1.1) Obtain system parameter information, including the technical parameters of each device and the system's wind and solar curtailment amount and minimum inertia requirements; (3.1.2) Set the parameters of the discrete particle swarm algorithm for solving the planning layer problem, including the population size of the particle swarm, the number of iterations, the learning factor and the inertia weight; (3.1.3) Initialize each particle, that is, initialize the upper-level decision variables; (3.1.4) Perform a feasibility analysis based on the constraints in step (1). If the constraints are met, the result is directly passed to the scheduling layer. If the limit is exceeded, the particle value is corrected to the limit before being passed to the scheduling layer. (3.1.5) Determine the battery energy storage operation control variables based on the upper-level results, and then solve the scheduling layer optimization problem; (3.1.6) Upload the daily operating revenue results of the battery energy storage obtained from the lower layer to the planning layer to solve the annual equivalent net revenue of the system, i.e. the fitness function value of the discrete particle swarm. (3.1.7) Update the individual optimal value and the overall optimal value of each particle, and update the inertia weight; (3.1.8) Update the velocity and position of each particle; (3.1.9) Determine whether the iteration termination condition is met. If not, jump to step (3.1.4); if it is met, output the result as the optimal battery configuration scheme. (3.2) Perform calculation example analysis and verification based on the above solution steps.

2. A computer storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a battery configuration method considering inertia support under an electric-thermal shared energy storage mode as described in claim 1.

3. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a battery configuration method considering inertia support under an electric-thermal shared energy storage mode as described in claim 1.

Images