Control method for peak regulation ancillary service market participation of optical storage system considering carbon benefit

Abstract

The application discloses a control method for a photovoltaic energy storage system participating in a peak regulation auxiliary service market considering carbon benefits, and establishes an optimization model with a target of maximum net benefits of a photovoltaic energy storage combined system in a peak regulation market, wherein the net benefits are compensation benefits minus operation costs of the combined system, the compensation benefits mainly include peak regulation benefits and carbon emission reduction benefits, and the operation costs include energy storage calling costs, photovoltaic system operation and maintenance costs and light abandonment punishment of the photovoltaic system. Constraint conditions of the constructed model include three aspects: power flow direction constraints of the photovoltaic energy storage combined system, charge and discharge power value constraints of the photovoltaic energy storage combined system and energy storage equipment state of charge constraints. The constructed model can be converted into a mixed integer linear programming model, and effective results can be solved by relying on a Cplex commercial solving software. The solved operation control mode can guide the photovoltaic energy storage combined system to realize maximum benefits in the peak regulation auxiliary service market.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and specifically to a control method for a photovoltaic-storage system that takes carbon revenue into account to participate in the peak-shaving ancillary service market. Background Technology

[0002] In recent years, with the continuous maturation of photovoltaic (PV) power generation technology, the assembly cost of large-scale PV systems has decreased year by year, leading to a continuous increase in my country's PV power generation capacity. However, due to the strong randomness, intermittency, and uncertainty of PV system output, reverse peak-shaving phenomena occur in the system, resulting in curtailment and energy waste. Therefore, configuring a certain scale of energy storage devices on the renewable energy side to construct a complete PV-storage integrated system can help smooth fluctuations in PV output and deliver stable power to the grid through energy storage. Furthermore, with the continuous increase in the scale of new energy power grid connection, the problem of insufficient power system regulation means has gradually become prominent, and the existing peak-shaving capacity is no longer sufficient to meet the rapid development of new energy. With the increasing diversification of the power ancillary service market players, PV-storage integrated systems can participate in peak-shaving ancillary services as market players, contributing to the further absorption of PV power.

[0003] Existing technology application CN201710377613.0 discloses a distribution network dispatching method that comprehensively considers photovoltaic power output and load demand forecasting intervals. It establishes a photovoltaic power output forecasting interval model based on an approximate Beta distribution of irradiance. Secondly, based on a hierarchical probability forecasting method for electricity load, it combines empirical mode decomposition and sparse Bayesian learning to establish a load demand forecasting interval model. Finally, based on the photovoltaic power output and load demand forecasting intervals, it proposes a dispatching model that balances the reliability and economy of distribution network operation. This solves the problem of power supply unreliability caused by the uncertainty of photovoltaic power generation output and reduces the economic operating costs of the distribution network caused by the difficulty in predicting photovoltaic power generation and load. However, its peak-shaving capacity still has limitations. Summary of the Invention

[0004] 1. The technical problem to be solved:

[0005] To address the aforementioned technical problems, this invention provides a control method for photovoltaic-storage systems that take carbon revenue into account when participating in the peak-shaving ancillary services market, and studies an optimized modeling method for photovoltaic power plants equipped with energy storage systems to participate in grid peak-shaving ancillary services to achieve renewable energy consumption.

[0006] 2. Technical Solution:

[0007] A control method for a photovoltaic-storage system that takes carbon revenue into account when participating in the peak-shaving ancillary services market, characterized by the following steps:

[0008] Step 1: Based on the weather forecast, obtain the predicted sequence of future power output data for the photovoltaic power station;

[0009] Step 2: The power dispatching department issues peak-shaving command signals to photovoltaic power plants based on load forecasts and energy market clearing results;

[0010] Step 3: The power dispatching department recently completed the pre-clearing in the peak-shaving ancillary service market and obtained the unit capacity peak-shaving compensation electricity price for each time period of the day;

[0011] Step 4: Establish an optimized scheduling model for the participation of the photovoltaic-storage joint system in peak shaving ancillary services, taking into account carbon revenue, considering the photovoltaic-storage interaction mode within the joint system and the power transfer mode between the photovoltaic-storage joint system and the grid;

[0012] Step 5: Based on the photovoltaic forecast data and market disclosure information obtained in Steps 1, 2 and 3, solve the optimization model established in Step 4 to obtain the power transfer values ​​within the photovoltaic-storage combined system and between the combined system and the grid, which is the optimal control result of the photovoltaic-storage combined system.

[0013] Furthermore, the optimization objective of the optimized scheduling model is to maximize the net revenue of the photovoltaic-storage joint system; the net revenue includes peak-shaving market revenue plus carbon emission reduction revenue, minus energy storage scheduling costs, photovoltaic system operation and maintenance costs, and curtailment penalties.

[0014] Furthermore, the constraints of the optimized model consider the physical constraints of the photovoltaic-storage combined system, as well as the constraints imposed on market participants by the peak-shaving ancillary service market; the specific implementation process is as follows:

[0015] The output data prediction sequence is represented as P. PV (t), with a sampling interval of Δt, a total sampling period of T, and a total number of sampling points N = T / Δt;

[0016] The signal for issuing peak-shaving instructions for photovoltaic power plants is represented as P. peak (t), whose signal interval is Δt, the total time span is T, and the total number of discrete points is N=T / Δt;

[0017] The unit capacity peak-shaving compensation electricity price is expressed as ρ peak (t);

[0018] S51: Establish the objective function:

[0019] The objective function of the optimization model is to maximize the net revenue of the photovoltaic-storage combined system in the peak-shaving ancillary services market, and its expression is as follows:

[0020] maxObj = R peak +R c -C B-C PV -C quitPV (1)

[0021] The calculation expressions for each component are as follows:

[0022] 1)R peak Compensation revenue obtained by the photovoltaic-storage combined system in peak shaving ancillary services:

[0023]

[0024]

[0025] (2) In the formula, ρ peak (t) represents the price of peak-shaving ancillary services per unit capacity within each settlement period. P represents the total peak-shaving grid-connected power of the photovoltaic-storage combined system; PV-G (t) and P B-G (t) represents the magnitude of the power injected into the grid by the photovoltaic system and the energy storage device at time t, respectively; B PV-G (t) and B B-G (t) is a Boolean variable, representing whether the photovoltaic system and energy storage device inject power into the grid at time t, with 0 indicating no injection and 1 indicating injection;

[0026] 2)R c Carbon gains for photovoltaic-storage combined systems:

[0027]

[0028]

[0029] (3) In the formula, α represents the historical performance of the photovoltaic-storage combined system in the carbon trading market; This represents the total daily on-grid electricity generated by the photovoltaic-storage combined system; φ c Represents the carbon emission factor; ρ c This indicates the transaction price of carbon emission reductions;

[0030] 3)C B Average daily operating cost for energy storage:

[0031]

[0032]

[0033]

[0034]

[0035] (4) In the formula, This represents the average annual cost of deploying energy storage devices; This indicates the operation and maintenance costs of energy storage; Indicates the initial investment cost of energy storage; i r represents the discount rate; m represents the total lifespan of the energy storage. Indicates the rated power of the energy storage device; Indicates the rated capacity of the energy storage device; The energy storage operation and maintenance cost coefficient representing the unit power; This represents the unit capacity cost of configuring energy storage; This indicates the unit power cost of configuring energy storage; This indicates the price of auxiliary facilities per unit capacity of energy storage.

[0036] 4) Operating and maintenance costs of photovoltaic systems:

[0037]

[0038] In the formula, This indicates the operation and maintenance coefficient of the photovoltaic system.

[0039] 5)C quitPV Cost of curtailment for photovoltaic systems:

[0040] C quitPV =k quitPV ×[P PV (t)-P PV-G (t)·B PV-G (t)-P PV-B (t)·B PV-B (t)] (6)

[0041] In the formula, k quitPV P represents the curtailment penalty coefficient of a photovoltaic system. PV-B B represents the magnitude of the power injected by the photovoltaic system into the energy storage device at time t; PV-B (t) is a Boolean variable, representing whether the photovoltaic system injects power into the energy storage device at time t, where 0 indicates no injection and 1 indicates injection;

[0042] S52: Constraints

[0043] 1) Power flow constraints of photovoltaic-storage combined systems:

[0044] Constraint 1 requires that, at any given time, photovoltaic power cannot simultaneously supply power to the grid and energy storage; energy storage devices cannot interact with both the photovoltaic system and the grid; and energy storage devices cannot simultaneously charge and discharge. In other words:

[0045]

[0046] (7) In the formula, B PV-G (t),B PV-B (t),B B-G (t),B G-B (t) All four variables are Boolean variables, and their physical meaning is whether the former transmits power to the latter at time t. The superscript PV represents the photovoltaic system, the superscript B represents energy storage, the superscript G represents the power grid, 0 represents no injection, and 1 represents injection.

[0047] 2) Constraints on the charging and discharging power of the photovoltaic-storage combined system:

[0048] Constraint 2 requires that, at any given time, the discharge power of the photovoltaic system must be less than the maximum power emitted by the photovoltaic system; and the charging and discharging power of the energy storage device at any given time should be less than its rated power, i.e.:

[0049]

[0050]

[0051] 3) State of charge constraints for energy storage devices:

[0052] Constraint 3 requires that the state of charge of the energy storage system at any given time must be between the upper and lower limits of the energy storage state of charge.

[0053]

[0054] η ch / η dis SOC represents the energy storage charge / discharge efficiency; SOC(i) represents the real-time state of charge of the stored energy; SOC max The maximum state of charge (SOC) allowed for energy storage devices. min This is the minimum state of charge allowed for an energy storage device.

[0055] Furthermore, effective results can be obtained by using the Cplex commercial solver software to solve the optimization model.

[0056] 3. Beneficial effects:

[0057] Although the model constructed in this invention has nonlinear constraints, the existing mixed-integer nonlinear constraints can be directly transformed into a mixed-integer linear programming model using the "Big M method." The optimized model results can be quickly obtained using the mature commercial Cplex software. The resulting operational control method can guide the photovoltaic-storage combined system to maximize revenue in the peak-shaving ancillary services market. Attached Figure Description

[0058] Figure 1 The architecture diagram of the photovoltaic-storage combined system that takes into account carbon emission reduction benefits and participates in the power peak shaving ancillary service market provided by the present invention. Detailed Implementation

[0059] The present invention will now be described in detail with reference to the accompanying drawings.

[0060] As attached Figure 1 The diagram shows the architecture of a photovoltaic-storage integrated system that takes into account carbon emission reduction benefits participating in the power peak shaving ancillary services market.

[0061] A control method for a photovoltaic-storage system that takes carbon revenue into account when participating in the peak-shaving ancillary services market, characterized by the following steps:

[0062] Step 1: Based on the weather forecast, obtain the predicted sequence of future power output data for the photovoltaic power station;

[0063] Step 2: The power dispatching department issues peak-shaving command signals to photovoltaic power plants based on load forecasts and energy market clearing results;

[0064] Step 3: The power dispatching department recently completed the pre-clearing in the peak-shaving ancillary service market and obtained the unit capacity peak-shaving compensation electricity price for each time period of the day;

[0065] Step 4: Establish an optimized scheduling model for the participation of the photovoltaic-storage joint system in peak shaving ancillary services, taking into account carbon revenue, considering the photovoltaic-storage interaction mode within the joint system and the power transfer mode between the photovoltaic-storage joint system and the grid;

[0066] Step 5: Based on the photovoltaic forecast data and market disclosure information obtained in Steps 1, 2 and 3, solve the optimization model established in Step 4 to obtain the power transfer values ​​within the photovoltaic-storage combined system and between the combined system and the grid, which is the optimal control result of the photovoltaic-storage combined system.

[0067] Furthermore, the optimization objective of the optimized scheduling model is to maximize the net revenue of the photovoltaic-storage joint system; the net revenue includes peak-shaving market revenue plus carbon emission reduction revenue, minus energy storage scheduling costs, photovoltaic system operation and maintenance costs, and curtailment penalties.

[0068] Furthermore, the constraints of the optimized model consider the physical constraints of the photovoltaic-storage combined system, as well as the constraints imposed on market participants by the peak-shaving ancillary service market; the specific implementation process is as follows:

[0069] The output data prediction sequence is represented as P. PV (t), with a sampling interval of Δt, a total sampling period of T, and a total number of sampling points N = T / Δt;

[0070] The signal for issuing peak-shaving instructions for photovoltaic power plants is represented as P. peak (t), whose signal interval is Δt, the total time span is T, and the total number of discrete points is N=T / Δt;

[0071] The unit capacity peak-shaving compensation electricity price is expressed as ρ peak (t);

[0072] S51: Establish the objective function:

[0073] The objective function of the optimization model is to maximize the net revenue of the photovoltaic-storage combined system in the peak-shaving ancillary services market, and its expression is as follows:

[0074] maxObj = R peak +R c -C B -C PV -C quitPV (1)

[0075] The calculation expressions for each component are as follows:

[0076] 1)R peak Compensation revenue obtained by the photovoltaic-storage combined system in peak shaving ancillary services:

[0077]

[0078]

[0079] (2) In the formula, ρ peak (t) represents the price of peak-shaving ancillary services per unit capacity within each settlement period. P represents the total peak-shaving grid-connected power of the photovoltaic-storage combined system; PV-G (t) and P B-G (t) represents the magnitude of the power injected into the grid by the photovoltaic system and the energy storage device at time t, respectively; B PV-G (t) and B B-G (t) is a Boolean variable, representing whether the photovoltaic system and energy storage device inject power into the grid at time t, with 0 indicating no injection and 1 indicating injection;

[0080] 2)R c Carbon gains for photovoltaic-storage combined systems:

[0081]

[0082]

[0083] (3) In the formula, α represents the historical performance of the photovoltaic-storage combined system in the carbon trading market; This represents the total daily on-grid electricity generated by the photovoltaic-storage combined system; φ c Represents the carbon emission factor; ρ c This indicates the transaction price of carbon emission reductions;

[0084] 3)CB Average daily operating cost for energy storage:

[0085]

[0086]

[0087]

[0088]

[0089] (4) In the formula, This represents the average annual cost of deploying energy storage devices; This indicates the operation and maintenance costs of energy storage; Indicates the initial investment cost of energy storage; i r represents the discount rate; m represents the total lifespan of the energy storage. Indicates the rated power of the energy storage device; Indicates the rated capacity of the energy storage device; The energy storage operation and maintenance cost coefficient representing the unit power; This represents the unit capacity cost of configuring energy storage; This indicates the unit power cost of configuring energy storage; This indicates the price of auxiliary facilities per unit capacity of energy storage.

[0090] 4) Operating and maintenance costs of photovoltaic systems:

[0091]

[0092] In the formula, This indicates the operation and maintenance coefficient of the photovoltaic system.

[0093] 5)C quitPV Cost of curtailment for photovoltaic systems:

[0094] C quitPV =k quitPV ×[P PV (t)-P PV-G (t)·B PV-G (t)-P PV-B (t)·B PV-B (t)] (6)

[0095] (6) In the formula, k quitPV P represents the curtailment penalty coefficient of a photovoltaic system. PV-B B represents the magnitude of the power injected by the photovoltaic system into the energy storage device at time t; PV-B (t) is a Boolean variable, representing whether the photovoltaic system injects power into the energy storage device at time t, where 0 indicates no injection and 1 indicates injection;

[0096] S52: Constraints

[0097] 1) Power flow constraints of photovoltaic-storage combined systems:

[0098] Constraint 1 requires that, at any given time, photovoltaic power cannot simultaneously supply power to the grid and energy storage; energy storage devices cannot interact with both the photovoltaic system and the grid; and energy storage devices cannot simultaneously charge and discharge. In other words:

[0099]

[0100] (7) In the formula, B PV-G (t),B PV-B (t),B B-G (t),B G-B (t) All four variables are Boolean variables, and their physical meaning is whether the former transmits power to the latter at time t. The superscript PV represents the photovoltaic system, the superscript B represents energy storage, the superscript G represents the power grid, 0 represents no injection, and 1 represents injection.

[0101] 2) Constraints on the charging and discharging power of the photovoltaic-storage combined system:

[0102] Constraint 2 requires that, at any given time, the discharge power of the photovoltaic system must be less than the maximum power emitted by the photovoltaic system; the charging and discharging power of the energy storage device at any given time should be less than its rated power, as shown in the following formula:

[0103]

[0104]

[0105] 3) State of charge constraints for energy storage devices:

[0106] Constraint 3 requires that, at any given time, the state of charge (SBC) of the energy storage system must be between the upper and lower limits of the SBC, i.e.:

[0107]

[0108] (9) where η ch / η dis SOC represents the energy storage charge / discharge efficiency; SOC(i) represents the real-time state of charge of the stored energy; SOC max The maximum state of charge (SOC) allowed for energy storage devices. min This is the minimum state of charge allowed for an energy storage device.

[0109] Furthermore, effective results can be obtained by using the Cplex commercial solver software to solve the optimization model.

[0110] Although the present invention has been disclosed above with reference to preferred embodiments, these are not intended to limit the invention. Any person skilled in the art can make various changes or modifications without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention should be defined by the scope of the claims of this application.

Claims

1. A control method for photovoltaic-storage systems that take carbon revenue into account when participating in the peak-shaving ancillary services market, characterized in that: Includes the following steps: Step 1: Based on the weather forecast, obtain the predicted sequence of future power output data for the photovoltaic power station; Step 2: The power dispatching department issues peak-shaving command signals to photovoltaic power plants based on load forecasts and energy market clearing results; Step 3: The power dispatching department recently completed the pre-clearing in the peak-shaving ancillary service market and obtained the unit capacity peak-shaving compensation electricity price for each time period of the day; Step 4: Establish an optimized scheduling model for the participation of the photovoltaic-storage joint system in peak shaving ancillary services, taking into account carbon revenue, considering the photovoltaic-storage interaction mode within the joint system and the power transfer mode between the photovoltaic-storage joint system and the grid; Step 5: Based on the photovoltaic forecast data and market disclosure information obtained in Steps 1, 2 and 3, solve the optimization model established in Step 4 to obtain the power transfer values ​​within the photovoltaic-storage combined system and between the combined system and the grid, which is the optimal control result of the photovoltaic-storage combined system. The optimization objective of the optimized scheduling model is to maximize the net revenue of the photovoltaic-storage joint system; the net revenue includes peak-shaving market revenue plus carbon emission reduction revenue, minus energy storage scheduling costs, photovoltaic system operation and maintenance costs, and curtailment penalties; The constraints of the optimization model consider the physical constraints of the photovoltaic-storage combined system, as well as the constraints imposed on market participants by the peak-shaving ancillary service market; the specific implementation process is as follows: The output data prediction sequence is represented as P. PV (t), whose sampling interval is The total sampling period is T, and the total number of sampling points is... ; The signal for issuing peak-shaving instructions for photovoltaic power plants is represented as P. peak (t), whose signal interval is The total time span is The total number of discrete points is ; The unit capacity peak-shaving compensation electricity price is expressed as ρ peak (t); S51: Establish the objective function for the optimization model. The objective function of the optimization model is to maximize the net revenue of the photovoltaic-storage combined system in the peak-shaving ancillary services market, and its expression is as follows: in, R peak Compensation revenue obtained by the photovoltaic-storage combined system in peak shaving ancillary services: In the above formula, This indicates the price of peak-shaving ancillary services per unit capacity within each settlement period. This represents the total peak-shaving grid-connected power of the photovoltaic-storage combined system; and These represent the magnitudes of the power injected into the grid by the photovoltaic system and the energy storage device at time t, respectively. and These are Boolean variables, representing whether the photovoltaic system and energy storage device inject power into the grid at time t, with 0 indicating no injection and 1 indicating injection. Carbon gains for photovoltaic-storage combined systems: In the above formula, α represents an indicator of the historical compliance of the photovoltaic-storage integrated system in the carbon trading market; This indicates the total on-grid electricity generated by the photovoltaic-storage combined system during the day; Indicates carbon emission factor; This indicates the transaction price of carbon emission reductions; Average daily operating cost for energy storage: ； In the above formula, This represents the average annual cost of deploying energy storage devices; This indicates the operation and maintenance costs of energy storage; This indicates the initial investment cost of energy storage; represents the discount rate; m represents the total lifespan of the energy storage. Indicates the rated power of the energy storage device; Indicates the rated capacity of the energy storage device; The energy storage operation and maintenance cost coefficient representing the unit power; This represents the unit capacity cost of configuring energy storage; This indicates the unit power cost of configuring energy storage; This indicates the price of auxiliary facilities per unit capacity of energy storage; Operating and maintenance costs of photovoltaic systems: In the above formula, Indicates the operation and maintenance factor of the photovoltaic system; Cost of curtailment for photovoltaic systems: In the above formula, This represents the curtailment penalty coefficient for a photovoltaic system; This represents the magnitude of the power injected by the photovoltaic system into the energy storage device at time t; This is a Boolean variable, indicating whether the photovoltaic system injects power into the energy storage device at time t, where 0 indicates no injection and 1 indicates injection. S52: Constraints Power flow constraints of photovoltaic-storage combined systems: Constraint 1 requires that, at any given time, photovoltaic power cannot simultaneously supply power to the grid and energy storage; energy storage devices cannot interact with both the photovoltaic system and the grid; and energy storage devices cannot simultaneously charge and discharge. In other words: In the above formula, All four variables are Boolean variables, and their physical meanings indicate whether the former transmits power to the latter at time t. PV represents the photovoltaic system, B represents energy storage, G represents the power grid, 0 represents no injection, and 1 represents injection. Charge and discharge power constraints of photovoltaic-storage combined systems: Constraint 2 requires that, at any given time, the discharge power of the photovoltaic system must be less than the maximum power emitted by the photovoltaic system; the charging and discharging power of the energy storage device at any given time should be less than its rated power, as shown in the following formula: Energy storage device state of charge constraints: Constraint 3 requires that, at any given time, the state of charge (SBC) of the energy storage system must be between the upper and lower limits of the SBC, i.e.: η ch To improve energy storage charging efficiency; η dis SOC represents the energy storage discharge efficiency; SOC(i) represents the real-time state of charge of the stored energy; SOC max The maximum state of charge (SOC) allowed for energy storage devices. min This is the minimum state of charge allowed for an energy storage device.

2. The control method for a photovoltaic-storage system taking carbon revenue into account to participate in the peak-shaving ancillary services market according to claim 1, characterized in that: Effective results can be obtained by using the commercial Cplex solver software to solve the optimization model.

Images