An energy storage operation optimization method considering local intermittent power benefits

Abstract

The application discloses a kind of energy storage operation optimization methods considering local intermittent power income, steps are as follows: 1) obtain the predicted value of the intermittent power output power of this day and the predicted value of the power load of local consumer, and the predicted data is used to construct mathematical model, 2) construct the mathematical model of energy storage operation optimization model, 3) call mathematical optimization solver to solve mathematical model, obtain the output value of mathematical model, 4) according to the output value, the operation arrangement of energy storage and the total income value of energy storage+intermittent power are obtained.The application solves the energy storage operation optimization problem of distributed energy operator with energy storage and intermittent power participating in electric energy transaction under time-of-use pricing environment, and the optimization target of energy storage operation is the overall income (energy storage+intermittent power) of distributed energy operator in electric energy transaction under the condition of priority to meet the power demand of local consumer.

Description

Technical Field

[0001] This invention belongs to the field of power system automation / energy storage operation optimization technology, and specifically relates to an energy storage operation optimization method that takes into account the benefits of local intermittent power sources. Background Technology

[0002] Distributed energy resources require effective management; otherwise, the unstable output of intermittent power sources such as photovoltaic power generation may lead to a series of grid failures. Configuring energy storage devices for intermittent power sources can effectively alleviate their intermittent power output. Simultaneously, configuring energy storage on the grid can provide peak shaving and frequency regulation services, thereby generating revenue. However, how to configure the charging and discharging operation of energy storage in this source-load-storage-distribution network scenario is a crucial issue. The challenge in optimizing energy storage operation in this scenario lies in how to effectively configure energy storage operation to maximize the overall operational benefits of distributed energy resources, which requires consideration of factors such as revenue calculation and operational constraints. Currently, there is still significant room for improvement in research on energy storage operation optimization. Figure 2 This is a distributed energy scenario diagram, illustrating that the electricity released by energy storage can only supply local users. The arrows on the transmission lines indicate the direction of power transmission; only the transmission lines for energy storage can transmit power bidirectionally. Two related patents have been found in the existing technology. One patent, "A Multi-Mode Optimization Operation Method for Energy Storage Systems in Communication Base Stations," proposes using energy storage systems in communication base stations to participate in the optimization of electricity load and new energy output in the local distribution network. Three operating modes have been developed for this purpose: fluctuation smoothing, time-of-use pricing, and backup supply. The other patent, "A Power System Energy Storage Optimization Operation Method under Multiple Constraints of Source, Grid, and Load," proposes first constructing an energy storage operation optimization model considering different application scenarios on the source, grid, and load sides. Then, after determining the energy storage capacity and configuration location, the energy storage operation optimization model is solved to obtain the optimal operating parameters for energy storage. Both patents' disclosed technical solutions suffer from the difficulties encountered in the aforementioned existing technologies. Summary of the Invention

[0003] To address the aforementioned problems, this invention analyzes the energy storage operation optimization problem in a source-load-storage-distribution network scenario. It considers the time-of-use pricing electricity trading scenario and the unstable output of intermittent power sources, while also taking into account constraints such as transformer capacity. The optimization objective is to maximize the total revenue of energy storage + intermittent power sources. This solves the energy storage operation optimization problem for distributed energy operators with both energy storage and intermittent power sources participating in electricity trading under a time-of-use pricing environment. It is an energy storage operation optimization method that maximizes the overall revenue (energy storage + intermittent power sources) of distributed energy operators in electricity trading while prioritizing the electricity needs of local users.

[0004] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0005] The technical solution of this invention is implemented as follows: an energy storage operation optimization method considering the benefits of local intermittent power sources, comprising the following steps:

[0006] 1) Obtain the predicted value of the intermittent power output and the predicted value of the local electricity load for that day. The predicted data is used to build a mathematical model.

[0007] 2) Construct a mathematical model for the energy storage operation optimization model;

[0008] 3) Call the mathematical optimization solver to solve the mathematical model and obtain the output value of the mathematical model;

[0009] 4) Calculate the energy storage operation schedule and the total revenue of energy storage + intermittent power supply based on the output value.

[0010] Preferably, step 1) refers to "obtaining the forecast data for that day": obtaining the forecast data required to build the energy storage operation optimization model on the day for which energy storage optimization is to be performed. The required forecast data includes: the forecast value of the intermittent power output of that day and the forecast value of the electricity load of local electricity users on that day.

[0011] Preferably, step 2) refers to "constructing an energy storage operation optimization model": a mathematical model is constructed based on parameters and prediction data obtained from step 1, wherein the parameters include: time granularity of the operating period, time-of-use electricity price, energy storage ratio, charge / discharge depth, total capacity, initial SOC value, rated power, rated capacity of the transformer connected to the external grid, transmission efficiency of the energy storage device line, conversion efficiency of the power converter, historical maximum electricity load value of local electricity users, total installed capacity of intermittent power sources, grid connection price of intermittent power sources, and maximum output ratio; the mathematical model is a mixed integer linear programming model.

[0012] Preferably, the independent variables of the mathematical model are: the energy storage discharge power, charging power, discharge state lock, charging state lock, local power supply power of the intermittent power source, local power supply state lock, and power generation grid connection state lock in each time period; the objective function of the mathematical model is: maximizing the total revenue generated by the energy storage charging and discharging, plus the revenue generated by the intermittent power source supplying local electricity users and power generation grid connection, while considering the transmission efficiency of the energy storage device line and the conversion efficiency of the power converter; the constraints of the mathematical model include: energy storage constraints; local electricity supply and demand constraints; transformer capacity constraints; local historical maximum electricity load constraints; and intermittent power source constraints. The output of the mathematical model is: the energy storage charging and discharging power in each time period and the local power supply power of the intermittent power source in each time period.

[0013] Preferably, the objective function of the mathematical model is calculated using the following formula:

[0014]

[0015] In Equation 1,

[0016]

[0017] Preferably, the formula for calculating the constraints of energy storage is as follows:

[0018]

[0019] In Equation 2,

[0020]

[0021] The first equation in Equation 2 is used to calculate the rated power of energy storage; the second and third inequalities in Equation 2 are used to limit the charging and discharging power values ​​of energy storage; the fourth to sixth equations in Equation 2 are used to limit the charging and discharging states of energy storage, that is, energy storage can only charge or discharge at any given time; the seventh inequality in Equation 2 is used to limit the capacity value of energy storage.

[0022] Preferably, the calculation formula for local power supply and demand constraints is as follows:

[0023] 0≤Pextra t +Pds t +Pc t ≤P′ t (3)

[0024] In Equation 3, P′ t It is the predicted value of the local electricity load at time t, in kW; Equation 3 is used to constrain: the electrical energy released from energy storage and the electrical energy supplied locally by intermittent power sources can only be consumed locally.

[0025] The formula for calculating transformer capacity constraints is as follows:

[0026] -Pc t +P′ t -Pds t -Pextra t ≤TS (4)

[0027] In Equation 4, TS is the rated capacity of the transformer connected to the external grid, in kVA.

[0028] The formula for calculating the local historical maximum electricity load constraint is as follows:

[0029] -Pc t +P′ t -PDs t -Pextra t≤P max (5)

[0030] In Equation 5, P max It is the historical maximum electricity load value of local electricity users, in kW; the reason for setting this constraint is that the power supply to a region cannot exceed the maximum carrying capacity of the local power grid.

[0031] Preferably, only when the local electricity load is fully met by local energy storage and intermittent power supply can excess electrical energy from intermittent power sources be supplied to the grid; the calculation formula for the constraints of intermittent power sources is as follows:

[0032]

[0033] In Equation 6,

[0034]

[0035] The first inequality in Equation 6 directly modifies the predicted data (the predicted power output of the intermittent power source). After obtaining the predicted power output of the intermittent power source, these values ​​are directly judged; if they are greater than srate·E... s The values ​​all need to be reduced to srate·E s The second formula in Equation 6 is used to calculate the power generated by intermittent power sources and fed into the grid. The fourth formula in Equation 6 is used for constraint: only Pds t -P′ t +Pc t +Pextra t When Pnet = 0, meaning the local electricity load is fully met by local energy storage and intermittent power supply, Pnet t Only when the value is greater than 0 can intermittent power generation be connected to the grid;

[0036] Since the fourth equation in Equation 6 is nonlinear, it is linearized; Equation 6 becomes:

[0037]

[0038] In Equation 7,

[0039]

[0040] The model outputs are: 1. The charging and discharging power of the stored energy (Pc) in each time period. t and Pextra t 2. Local power supply capacity (Pds) of intermittent power sources during each time period; t ).

[0041] Preferably, step 3) refers to "calling the solver to solve": calling the mathematical optimization solver to solve the mathematical model constructed in step 2), thereby obtaining the output value of the mathematical model, 1. the charging and discharging power value Pc of the stored energy in each time period. t and Pextra t 2. Local power supply value Pds of intermittent power sources in each time period. t The mathematical optimization solver is a solver that can solve mixed-integer linear programming problems.

[0042] Preferably, step 4) refers to "outputting the energy storage operation schedule and revenue results": based on the output value obtained in step 3), calculate: 1. the energy storage operation schedule, including the power and electricity values ​​in each time period; 2. the total revenue value of energy storage and intermittent power sources; wherein, the revenue value brought by energy storage discharge and local power supply from intermittent power sources is calculated based on time-of-use pricing, and the revenue value brought by intermittent power generation and grid connection is calculated based on the grid connection pricing of intermittent power sources; specifically:

[0043] First, according to Pc t and Pextra t The power values ​​of the energy storage in each time period can be directly obtained, denoted as: p1, p2, p3, ..., p NOP ;

[0044] Secondly, the energy storage capacity is calculated based on the energy storage power value; the algorithm is as follows:

[0045] Algorithm:

[0046]

[0047] The above algorithm can be used to calculate the energy storage capacity (ET) for each time period;

[0048] Next, calculate the total benefit of energy storage and intermittent power sources; the formula is as follows:

[0049]

[0050] In the formula, R is the total revenue from energy storage and intermittent power sources.

[0051] Compared with the prior art, the technical solution provided by this invention has the following advantages:

[0052] This paper analyzes the energy storage operation optimization problem in the context of a power generation, load, storage, and distribution network. It considers the time-of-use electricity trading scenario and the unstable output of intermittent power sources, as well as constraints such as transformer capacity. It also considers the energy storage operation optimization model, the calculation method of power and revenue values, and the optimization objective of maximizing the total revenue of energy storage + intermittent power sources. This ensures that the distributed energy operator maximizes the overall revenue (energy storage + intermittent power sources) in the electricity trading while prioritizing the electricity demand of local users. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the process of the present invention;

[0054] Figure 2 This is a diagram illustrating a distributed energy scenario in the background technology of this invention. Detailed Implementation

[0055] The present invention will now be described in further detail with reference to the accompanying drawings, but this is not intended to limit the scope of protection of the present invention.

[0056] like Figure 1 As shown, an energy storage operation optimization method considering the revenue from local intermittent power sources is proposed. 1. Obtain the predicted output power of the intermittent power source and the predicted electricity load of local users for the day; this predicted data is used to construct a mathematical model. 2. Construct a mathematical model for the energy storage operation optimization model. 3. Call a mathematical optimization solver to solve the mathematical model and obtain its output value. 4. Calculate the energy storage operation schedule and the total revenue value of energy storage + intermittent power sources based on the output value. The implementation process of the scheme is as follows: Figure 2 As shown, the specific steps are as follows:

[0057] Step 1: Obtain prediction data

[0058] This patent obtains the predicted output power of intermittent power sources and the predicted electricity load of local users for a given day during the distributed energy operation period. (Note: This patent does not cover methods for predicting the output power of intermittent power sources or the electricity load of local users.)

[0059] Step 2: Construct an energy storage operation optimization model

[0060] Obtain the predicted data (data obtained in Step 1) and parameters (parameters include: time granularity of the operating period, time-of-use electricity price, energy storage ratio, charge / discharge depth, total capacity, initial SOC value, rated power, rated capacity of the transformer connected to the external grid, transmission efficiency of the energy storage device line, conversion efficiency of the power converter, historical maximum electricity load of local users, total installed capacity of intermittent power sources, grid connection price of intermittent power sources, and maximum output ratio), and construct a mathematical model. The content of the energy storage operation optimization model is as follows: The objective function of the model is:

[0061] In Equation 1,

[0062]

[0063] The constraints of the model are:

[0064] 1. Constraints of Energy Storage

[0065]

[0066] In Equation 2,

[0067]

[0068] The first equation in Equation 2 is used to calculate the rated power of the energy storage. The second and third inequalities in Equation 2 are used to limit the charging and discharging power values ​​of the energy storage. The fourth to sixth equations in Equation 2 are used to limit the charging and discharging states of the energy storage, that is, the energy storage can only charge or discharge at any given time. The seventh inequality in Equation 2 is used to limit the capacity value of the energy storage.

[0069] 2. Local power supply and demand constraints

[0070] 0≤Prxtra t +Pds t +Pc t ≤P′ t (3)

[0071] In Equation 3, P′ t This is the predicted electricity load of local users at time t, in kW. Equation 3 is used as a constraint: the electrical energy released from energy storage and the electrical energy supplied locally by intermittent power sources can only be consumed locally.

[0072] 3. Transformer capacity constraints

[0073] -Pc t +P′ t -Pds t -Pextra t ≤TS (4)

[0074] In Equation 4, TS is the rated capacity of the transformer connected to the external grid, in kVA.

[0075] 4. Local historical maximum electricity load constraint

[0076] -Pc t +P′ t -Pds t -Pextra t ≤P max (5)

[0077] In Equation 5, P max This is the historical maximum electricity load value of local users, in kW. The reason for setting this constraint is that the power supply to an area cannot exceed the maximum carrying capacity of the local power grid.

[0078] 5. Constraints of intermittent power sources

[0079] This patent stipulates that: only when the local power load is fully met by the local energy storage and intermittent power supply can the excess power from the intermittent power supply be supplied to the grid.

[0080]

[0081] In Equation 6,

[0082]

[0083] The first inequality in Equation 6 directly modifies the predicted data (the predicted power output of the intermittent power source). After obtaining the predicted power output of the intermittent power source, these values ​​are directly judged; if they are greater than srate·E... s The values ​​all need to be reduced to srate·E s The second formula in Equation 6 is used to calculate the power generated by intermittent power sources and fed into the grid. The fourth formula in Equation 6 is used for constraint: only Pds t -P′ t +Pc t +Pextra t When Pnet = 0 (i.e., the local electricity load is fully met by local energy storage and intermittent power supply), t Only then can it be greater than 0 (intermittent power generation connected to the grid).

[0084] Because the fourth equation in Equation 6 is nonlinear, it is linearized in this patent. Equation 6 becomes:

[0085]

[0086] In Equation 7,

[0087]

[0088] The model outputs are: 1. The charging and discharging power of the stored energy in each time period (PC). t and Pextra t 2. Local power supply capacity (Pds) of intermittent power sources during each time period; t ).

[0089] Step 3: Call the solver to solve.

[0090] The mathematical optimization solver is invoked to solve the mathematical model constructed in step two, thereby obtaining the output value of the mathematical model (1. the charging and discharging power value of energy storage in each time period (Pc)). t and Pextra t 2. Local power supply value of intermittent power sources in each time period (Pds) t ).

[0091] Step 4: Output energy storage operation schedule and revenue results

[0092] First, according to Pc t and Pextra t The power values ​​of the energy storage in each time period can be directly obtained, denoted as: p1, p2, p3, ..., p NOP .

[0093] Secondly, the energy storage capacity is calculated based on the energy storage power value. The algorithm is as follows:

[0094] Algorithm:

[0095]

[0096] The above algorithm can be used to calculate the energy storage capacity (ET) for each time period.

[0097] Next, calculate the total benefit of energy storage and intermittent power generation. The formula is as follows:

[0098]

[0099] In the formula, R is the total revenue from energy storage and intermittent power sources.

[0100] This invention analyzes the energy storage operation optimization problem in a source-load-storage-distribution network scenario. It considers time-of-use pricing for electricity trading and the unstable output of intermittent power sources, while also taking into account constraints such as transformer capacity. The optimization objective is to maximize the total revenue from energy storage and intermittent power sources to calculate the energy storage operation schedule. In summary, this method considers a wide range of factors.

[0101] The term "energy storage" as used in this invention refers to electrochemical energy storage systems, such as lithium battery energy storage systems.

[0102] "Intermittent power source" refers to a power source whose power generation is intermittent and difficult to control, such as wind turbines and photovoltaic panels.

[0103] "Distributed energy" refers to a comprehensive energy utilization system distributed at the user end.

[0104] "Time-of-use pricing environment" refers to an electricity purchase environment in which the 24 hours of a day are divided into several time periods, and electricity fees are charged for each time period based on the average marginal cost of system operation.

[0105] "Energy storage operation optimization" refers to using optimization methods to optimize the charging and discharging operation of energy storage during operation, thereby achieving the set goals.

Claims

1. A method for optimizing energy storage operation considering the benefits of local intermittent power sources, characterized in that, The steps are as follows: 1) Obtain the predicted power output of intermittent power sources and the predicted electricity load of local users on the target day. The predicted data is used to build a mathematical model. 2) Construct a mathematical model for the energy storage operation optimization model; 3) Call the mathematical optimization solver to solve the mathematical model and obtain the output value of the mathematical model; 4) Calculate the energy storage operation schedule based on the output values, including the power and energy values ​​in each time period, as well as the total revenue value of energy storage and intermittent power sources; The formula for calculating the objective function of the mathematical model is as follows: (1) In equation (1), The time granularity of a runtime segment, i.e. the duration of a time period; with 1 hour as "1"; Number of time periods in a day Equal to the length of a day divided by ; The discharge power of the energy storage at time t is the independent variable, and its unit is kW; Conversion efficiency of power converters in energy storage devices; The power transmission efficiency of the energy storage device's transmission lines; The charging power of energy storage at time t is the independent variable, and its unit is kW; The local power supply of the intermittent power source at time t is the independent variable, and its unit is kW; Electricity price at time t, unit: yuan / kWh; Predicted power generation of intermittent power source at time t, unit: kW; Feed-in tariff for intermittent power sources, unit: yuan / kWh; The formula for calculating the constraints of energy storage is as follows: (2) In equation (2), Rated power of energy storage, unit: kW; Energy storage capacity, unit: kWh; Depth of charge and discharge of energy storage; Energy storage ratio; The charging state lock of energy storage is an independent variable; The discharge state lock of energy storage is an independent variable; The SOC value of the energy storage at 00:00 on the target date; The first formula in Equation (2) is used to calculate the rated power of energy storage; the second and third inequalities in Equation (2) are used to limit the charging and discharging power values ​​of energy storage; the fourth to sixth formulas in Equation (2) are used to limit the charging and discharging states of energy storage, that is, energy storage can only charge or discharge at a given moment; the seventh inequality in Equation (2) is used to limit the capacity value of energy storage.

2. The energy storage operation optimization method considering local intermittent power source revenue according to claim 1, characterized in that, Step 2) includes: constructing a mathematical model based on parameters and prediction data obtained from step 1, wherein the parameters include: time granularity of the operating period, time-of-use electricity price, energy storage ratio, charge / discharge depth, total capacity, initial SOC value, rated power, rated capacity of the transformer connected to the external grid, transmission efficiency of the energy storage device line, conversion efficiency of the power converter, historical maximum electricity load value of local electricity users, total installed capacity of intermittent power sources, grid connection price of intermittent power sources, and maximum output ratio; the mathematical model is a mixed integer linear programming model.

3. The energy storage operation optimization method considering local intermittent power source revenue according to claim 2, characterized in that, The independent variables of the mathematical model include: the energy storage discharge power, charging power, discharge state lock, charging state lock, local power supply power of the intermittent power source, local power supply state lock, and power generation grid connection state lock in each time period; the objective function of the mathematical model is: to maximize the total revenue generated by the energy storage charging and discharging, and the revenue generated by the intermittent power source supplying local electricity users and power generation grid connection in the target day, considering the transmission efficiency of the energy storage device line and the conversion efficiency of the power converter; the constraints of the mathematical model include constraints on energy storage, local power supply and demand, transformer capacity, local historical maximum electricity load, and intermittent power source; the output of the mathematical model includes the energy storage charging and discharging power in each time period and the local power supply power of the intermittent power source in each time period.

4. The energy storage operation optimization method considering local intermittent power source revenue according to claim 3, characterized in that, The formula for calculating local power supply and demand constraints is as follows: (3) In equation (3), It is the predicted value of local electricity load at time t, in kW; Equation (3) is used to constrain the following: the electrical energy released from the energy storage and the electrical energy supplied locally by the intermittent power source can only be consumed locally; The formula for calculating the local historical maximum electricity load constraint is as follows: (5) In equation (5), It is the historical maximum electricity load value of local electricity users, in kW; the reason for setting the local historical maximum electricity load constraint is that the power supply to a region cannot exceed the maximum carrying capacity of the local power grid.

5. The energy storage operation optimization method considering local intermittent power source revenue according to claim 1, characterized in that, Only when the local electricity load is fully met by local energy storage and intermittent power sources can excess electricity from intermittent power sources be supplied to the grid; the calculation formula for the constraints of intermittent power sources is as follows: (6) In equation (6), In reality, the ratio of the maximum output power of intermittent power sources is... It equals the actual maximum output power of the intermittent power source divided by its total installed capacity; Total installed capacity of intermittent power sources, unit: kW; The power generated and fed into the grid by an intermittent power source during time period t, in kW; The first inequality in equation (6) directly modifies the predicted data (the predicted value of the intermittent power source output). After obtaining the predicted value of the intermittent power source output, these values ​​are directly judged. If the value is greater than the predicted value, the inequality is correct. The values ​​all need to be reduced to The second formula in equation (6) is used to calculate the power generated by intermittent power sources and fed into the grid, and the fourth formula in equation (6) is used for constraint: only In this case, the local electricity load is fully met by local energy storage and intermittent power supply. Only when the value is greater than 0 can intermittent power generation be connected to the grid; Since the fourth equation in equation (6) is nonlinear, it is linearized; equation (6) becomes: (7) In equation (7), The local power supply state lock of an intermittent power supply is the independent variable; The grid connection status lock of intermittent power sources is an independent variable; A very large positive real number is used as the boundary for the changes in the third and fourth inequalities in equation (7); The charging and discharging power of energy storage in each time period are respectively and The local power supply capacity of the intermittent power source during each time period is: .

6. The energy storage operation optimization method considering local intermittent power source revenue according to claim 1, characterized in that, Step 3) includes: calling a mathematical optimization solver to solve the mathematical model constructed in step 2), thereby obtaining the output value of the mathematical model and the charging and discharging power value of the stored energy in each time period. and Local power supply value of intermittent power sources during each time period The mathematical optimization solver is a solver that can solve mixed-integer linear programming problems.

7. The energy storage operation optimization method considering local intermittent power source revenue according to claim 6, characterized in that, Step 4) includes: calculating the total revenue value of energy storage and intermittent power sources based on the output value obtained in step 3); wherein, the revenue value from energy storage discharge and local power supply from intermittent power sources is calculated based on time-of-use pricing, and the revenue value from power generation and grid connection of intermittent power sources is calculated based on the grid connection pricing of intermittent power sources; specifically: First, according to and The power value of energy storage in each time period can be directly obtained, denoted as: ; Secondly, the energy storage capacity is calculated based on the energy storage power value; the energy storage capacity is then calculated for each time period. Next, calculate the total benefit of energy storage and intermittent power sources; the formula is as follows: ； In the formula, It represents the total revenue from energy storage and intermittent power sources.

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