A method and system for optimizing the standby capacity of an energy storage device

Abstract

The present invention provides a method and system for optimizing the positive and standby capacity of an energy storage device, including: bringing the predicted output of new energy, load, operating parameters of thermal power units and energy storage devices into a pre-established mixed-integer linear programming model, solving the optimization model, and obtaining the standby capacity provided by the energy storage device. This method provides technical support for the short-term optimal scheduling of a power system containing an energy storage device, can accurately obtain the positive standby capacity provided by the energy storage device, realizes the linearized modeling of the positive and standby capacity of the energy storage device, and reduces the solution difficulty of the optimal scheduling model.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching operation, and relates to a positive reserve capacity optimization method and system considering the real-time stored electricity of energy storage devices. Background Art

[0002] With the increase in the installed capacity of new energy in the power system, it has brought great challenges to the dispatching operation of the power system. The random volatility of new energy output has brought great difficulties to the peak shaving of the power system. In the short-term optimal dispatching of the power system, when the actual output of new energy is lower than the predicted output, reserving the positive reserve of system power generation is an important technical means to cope with the deviation of the predicted results of new energy output. At present, energy storage devices such as pumped storage power stations and chemical energy storage batteries are the most widely used and technically mature peak shaving power sources. Energy storage devices have good "peak shaving and valley filling" capabilities, that is, they can store surplus power during the low-load period of the power system or the peak output period of new energy, and generate electricity during the peak-load period or the low-output period of new energy. Due to the advantages of fast output adjustment speed of energy storage devices, in the existing research on short-term optimal dispatching methods of power systems with energy storage devices, it is also considered to let energy storage devices provide the reserve capacity required by the power system to exert the adjustment ability of energy storage devices and cope with the problem of insufficient power generation capacity caused by the low prediction of new energy output. However, the influence of the real-time stored electricity of energy storage devices on the maximum output power of the reserved system power generation positive reserve is not considered in the existing technologies. In the existing short-term optimal dispatching modeling methods, when the energy storage device provides positive reserve capacity, only its rated maximum output power is considered. When the stored electricity of the energy storage device is insufficient, its actual maximum output power will be lower than the rated maximum output power, resulting in insufficient positive reserve capacity that the energy storage device can actually provide, increasing the risk of load shedding due to insufficient system reserve. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides a positive reserve capacity optimization method and system for energy storage devices, providing technical support for the short-term optimal dispatching of power systems with energy storage devices, capable of accurately obtaining the maximum output power and positive reserve capacity provided by energy storage devices, realizing the linearization modeling of the positive reserve capacity of energy storage devices, and reducing the solution difficulty of the optimal dispatching model.

[0004] The present invention provides a positive reserve capacity optimization method for the real-time stored electricity of energy storage devices, including the following steps:

[0005] Substitute the predicted output of new energy, load, operating parameters of thermal power units and energy storage devices into a pre-established mixed-integer linear programming model;

[0006] Based on the boundary conditions and the mixed-integer linear programming model, solve the optimization model to obtain the reserve capacity provided by the energy storage device;

[0007] Among them, the mixed-integer linear programming model is determined based on the maximum output power of the real-time stored power of the energy storage device.

[0008] Furthermore, the establishment of the mixed-integer linear programming model includes:

[0009] Construct an objective function with the goal of maximizing the total power generation of new energy during all scheduling periods considering energy storage;

[0010] Consider system power balance, system positive reserve capacity, system negative reserve capacity, the theoretical maximum output power of the energy storage device, the real-time stored power state of the energy storage device, the stored power of the energy storage device, the stored power capacity of the energy storage device, the stored power state of the energy storage device, and the operating state of the thermal power unit to construct constraint conditions;

[0011] Based on the objective function and constraint conditions, establish a short-term optimal scheduling model.

[0012] Furthermore, the objective function is shown as the following formula:

[0013]

[0014] In the formula, max obj is the maximum total power generation of new energy during all scheduling periods, p w (t) is the predicted power generation of new energy at time t, Δt is the duration of each optimization period, and T is the total number of scheduling periods.

[0015] Furthermore, the system power balance constraint is shown as the following formula:

[0016]

[0017] In the formula, p w (t) is the predicted power generation of new energy at time t, is the power generation of the i-th thermal power unit at time t, d(t) is the system load at time t, p s (t) represents the actual output power of the energy storage device at time t, p c (t) represents the stored power of the energy storage device at time t, T is the total number of scheduling periods, and I is the number of thermal power units.

[0018] Furthermore, the system positive reserve capacity constraint is shown as the following formula:

[0019]

[0020] In the formula, p w (t) is the predicted power generation of new energy at time t; is the maximum technical output of the i-th thermal power unit; The state variable indicating whether the i-th thermal power unit is in operation at time t, where when it indicates that the i-th thermal power unit is in operation, otherwise it indicates shutdown; r s (t) represents the theoretical maximum output power of the energy storage device at time t; d(t) is the system load at time t; R + represents the system's positive reserve capacity requirement; T is the total number of scheduling periods; I is the number of thermal power units.

[0021] Furthermore, the real-time stored electricity state constraint of the energy storage device is shown as follows:

[0022]

[0023] In the formula, is the rated maximum output power of the energy storage device; E s (t) is the stored electricity of the energy storage device at time t; Δt is the duration of each optimization period; v(t) represents the minimum stored electricity state of the energy storage device at time t, where when v(t) = 1, it means that the stored electricity of the energy storage device at time t is not enough to achieve full power generation within 1 scheduling period, and when v(t) = 0, it means that the stored electricity of the energy storage device at time t is enough to achieve full power generation within 1 scheduling period; m is the first constant, M is the second constant, m < M; T is the total number of scheduling periods.

[0024] Furthermore, the theoretical maximum output power constraint of the energy storage device at time t is shown as follows:

[0025]

[0026] In the above formula, z(t) is an intermediate calculation variable; is the rated maximum power generation of the energy storage device; v(t) represents the minimum stored electricity state of the energy storage device at time t. When v(t) = 1, it means that the stored electricity of the energy storage device at time t is not enough to achieve full power generation within 1 scheduling period, and when v(t) = 0, it means that the stored electricity of the energy storage device at time t is enough to achieve full power generation within 1 scheduling period; among them, the value range of the intermediate calculation variable z(t) is determined by the following constraint conditions:

[0027]

[0028] In the formula, E s (t) is the stored electricity of the energy storage device at time t; Δt is the duration of each optimization period; M is a constant.

[0029] Furthermore, the system's negative reserve capacity constraint is shown as follows:

[0030]

[0031] In the formula, Let \(u_{i}(t)\) be a 0-1 state variable indicating whether the \(i\)-th thermal power unit is on at time \(t\). If \(u_{i}(t)=1\), it means the \(i\)-th thermal power unit is on; otherwise, it means the unit is off. Let \(P_{i,\min}\) be the minimum technical output of the \(i\)-th thermal power unit; \(d(t)\) be the system load at time \(t\); \(R\) _ represents the system's negative reserve capacity requirement; \(T\) is the total number of scheduling periods; \(I\) is the number of thermal power units.

[0032] Based on the same inventive concept, the present invention also provides an optimization system for the positive reserve capacity of an energy storage device, which is characterized by including: a prediction input module and a solution optimization module;

[0033] The prediction input module is used to input the predicted output of new energy, load, operating parameters of thermal power units and energy storage devices into a pre-established mixed-integer linear programming model;

[0034] The solution optimization module is used to solve the optimization model based on the boundary conditions and the mixed-integer linear programming model to obtain the reserve capacity provided by the energy storage device;

[0035] Among them, the mixed-integer linear programming model is determined based on the maximum output power of the energy storage device's real-time stored electricity.

[0036] Furthermore, the optimization system for the positive reserve capacity of an energy storage device is characterized by further including a model establishment module, which is used to establish a short-term scheduling model based on the determination of the maximum output power of the energy storage device's real-time stored electricity.

[0037] Compared with the closest prior art, the beneficial effects of the present invention are as follows:

[0038] (1) The optimization method and system for the positive reserve capacity of an energy storage device provided by the present invention can consider the influence of the energy storage device's real-time stored electricity on its maximum power generation, input the predicted output of new energy, load, operating parameters of thermal power units and energy storage devices into a pre-established mixed-integer linear programming model; solve the optimization model to obtain the reserve capacity provided by the energy storage device, and solve the problem of insufficient power generation capacity caused by the low prediction of new energy output when the energy storage device exerts its regulation ability, providing technical support for the short-term optimal scheduling of a power system containing an energy storage device.

[0039] (2) The optimization method and system for the positive reserve capacity of an energy storage device provided by the present invention do not increase the difficulty of solving the optimization scheduling model. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 FIG. is a flowchart of an optimization method and system for the positive reserve capacity of an energy storage device provided by the present invention;

[0041] Figure 2 The curve of the stored electricity and the maximum power generation capacity of the reservoir of Pumped - storage Power Station 1 within 5 weeks in a certain regional power grid;

[0042] Figure 3 The curve of the stored electricity and the maximum power generation capacity of the reservoir of Pumped - storage Power Station 2 within 5 weeks in a certain regional power grid;

[0043] Figure 4 The schematic diagram of the basic structure of the system provided by the present invention;

[0044] Figure 5 The schematic diagram of the detailed structure of the system provided by the present invention. Specific implementation manners

[0045] The following further elaborates on the specific implementation manners of the present invention with reference to the accompanying drawings.

[0046] Example 1:

[0047] An optimization method for the positive reserve capacity of an energy storage device provided in this example, Figure 1 is the flowchart provided by the present invention, Figure 2 and Figure 3 is the effect diagram obtained by the present invention to verify the effectiveness of this method by conducting tests on the data relationship between the stored electricity of the reservoirs of two pumped - storage power stations and their maximum power generation capacities within 5 weeks in a certain regional power grid.

[0048] The implementation process of the method of the present invention is as follows:

[0049] 1. For simplicity of description, only three types of power sources, namely thermal power, new energy, and energy storage devices, are considered. The predicted output of new energy, load, operating parameters of thermal power units, and boundary conditions of energy storage devices are input into a pre - established mixed - integer linear programming model;

[0050] 2. A short - term optimal scheduling model including an energy storage device is established to solve the problem of insufficient power generation capacity caused by the low prediction of new energy output when the energy storage device exerts its regulation ability. This model consists of an objective function and constraint conditions. The mathematical form is as follows:

[0051] (1) Objective function

[0052] The objective function is to maximize the total power generation of new energy during all scheduling periods. The mathematical expression is as follows:

[0053]

[0054] In the formula, p w (t) is the predicted power generation of new energy at time t, Δt is the duration of each optimization period, and T is the total number of scheduling periods.

[0055] (2) Constraint conditions

[0056] The main constraints of the model are as follows:

[0057] 1) System power balance constraint

[0058]

[0059] In the above formula, is the power generation of the i-th thermal power unit at time t, I is the number of thermal power units, d(t) is the system load at time t, p s (t) represents the actual output power of the energy storage device at time t, p c (t) represents the stored electric power of the energy storage device at time t.

[0060] 2) System spinning reserve capacity constraint

[0061]

[0062] In the above formula, is the maximum technical output of the i-th thermal power unit; represents the status variable indicating whether the i-th thermal power unit is turned on at time t, where when it means the i-th thermal power unit is turned on, otherwise it means it is turned off; r s (t) represents the theoretical maximum output power of the energy storage device at time t; R + represents the system spinning reserve capacity requirement.

[0063] 3) Real-time stored electricity state constraint of the energy storage device

[0064]

[0065] In the above formula, is the rated maximum output power of the energy storage device, E s (t) is the stored electricity of the energy storage device at time t; v(t) represents the minimum stored electricity state of the energy storage device at time t, where when v(t) = 1, it means the stored electricity of the energy storage device at time t is not enough to achieve full power generation within 1 scheduling period, and when v(t) = 0, it means the stored electricity of the energy storage device at time t is enough to achieve full power generation within 1 scheduling period; m is the first constant, and its value needs to be small enough (such as less than 10 -3 ), M is the second constant, and its value needs to be large enough (such as greater than 10 8 ).

[0066] 4) Theoretical maximum output power constraint of the energy storage device at time t

[0067]

[0068] In the above formula, z(t) is an intermediate calculation variable. From the above formula, it can be seen that when v(t) = 1, r sy(t) = z(t); when v(t) = 0, wherein, the value range of the intermediate calculation variable z(t) is determined by the following constraint conditions:

[0069]

[0070] It can be seen from the above formula that when v(t) = 1, that is, the stored power of the energy storage device at time t is not enough to achieve full power generation within one scheduling period, z(t) = E s (t) / Δt, combined with the formula it can be known that r s (t) = E s (t) / Δt, that is, the theoretical maximum output power of the energy storage device at time t is the current stored power divided by one scheduling period; when v(t) = 0, z(t) = 0, combined with the formula it can be known that that is, the theoretical maximum output power of the energy storage device at time t is its rated maximum power generation.

[0071] 5) System negative reserve capacity constraint

[0072]

[0073] In the above formula, is the minimum technical output of the i-th thermal power unit; R_ represents the system negative reserve capacity demand. In addition to the above constraints, this model also includes the following constraint conditions: energy storage device stored power constraint, which restricts the upper and lower limits of the stored power of the energy storage device; energy storage device stored capacity constraint, which indicates that the stored capacity of the energy storage device needs to be between the minimum and maximum power ranges; energy storage device stored state constraint, which describes the conversion relationship between the stored power of the energy storage device at the current moment, the stored power at the previous moment, and the current stored and generated power; thermal power unit operation constraint, which describes the output limit of the thermal power unit, the on-off state relationship, the ramp-up ability limit, and the minimum start-stop time limit.

[0074] 3. By calling the commercial optimization software Cplex to solve the optimization model, the reserve capacity provided by the energy storage device can be obtained, and at the same time, the power generation plan of each type of power source can also be obtained.

[0075] Embodiment 2:

[0076] Based on the same inventive concept, the present invention also provides an energy storage device positive reserve capacity optimization system. Since the principles of these devices for solving technical problems are similar to those of an energy storage device positive reserve capacity optimization method, the repeated parts will not be described in detail.

[0077] The basic structural schematic diagram of the system is as Figure 4 shown, including: a prediction input module and a solution optimization module;

[0078] A prediction input module, configured to input the predicted output of new energy, load, operating parameters of thermal power units, and energy storage devices into a pre-established mixed-integer linear programming model;

[0079] An optimization solution module, configured to solve the optimization model based on the boundary conditions and the mixed-integer linear programming model to obtain the reserve capacity provided by the energy storage device;

[0080] Wherein, the mixed-integer linear programming model is determined based on the maximum output power of the real-time stored power of the energy storage device.

[0081] An optimization system for the positive reserve capacity of an energy storage device, characterized in that it further includes a model establishment module, configured to establish a short-term scheduling model based on the determination of the maximum output power of the real-time stored power of the energy storage device;

[0082] The model establishment module includes: an objective function unit, a constraint condition unit, and a short-term optimization scheduling model unit;

[0083] The objective function unit is configured to consider the objective of maximizing the total power generation of new energy during all scheduling periods after considering energy storage;

[0084] The constraint condition unit is configured to construct constraint conditions by considering system power balance, system positive reserve capacity, system negative reserve capacity, the theoretical maximum output power of the energy storage device, the real-time stored power state of the energy storage device, the stored power of the energy storage device, the stored capacity of the energy storage device, the stored power state of the energy storage device, and the operating state of the thermal power unit;

[0085] The short-term optimization scheduling model unit is configured to establish a short-term optimization scheduling model unit based on the objective function unit and the constraint condition unit.

[0086] Wherein, the constraint condition unit includes: a system power balance subsection, a system positive reserve capacity subsection, a system negative reserve capacity subsection, a theoretical maximum output power subsection of the energy storage device, a real-time stored power state subsection of the energy storage device, a stored power subsection of the energy storage device, a stored capacity subsection of the energy storage device, a stored power state subsection of the energy storage device, and an operating state subsection of the thermal power unit;

[0087] A detailed structural schematic diagram of an optimization system for the positive reserve capacity of an energy storage device is as Figure 5 shown;

[0088] The system power balance subsection is configured to constraint and consider the balance between the predicted new energy and the power generation power of the thermal power unit, and the balance between the system load and the actual output power of the energy storage device;

[0089] The system positive reserve capacity subsection is used to constrain and consider the relationship between the maximum technical output of thermal power units, the on / off state of thermal power units, the theoretical maximum output power of energy storage devices, and the system positive reserve capacity demand and system load;

[0090] The system negative reserve capacity subsection is used to constrain and consider the relationship between the minimum technical output of thermal power units, the on / off state of thermal power units, and the system load and negative reserve capacity demand;

[0091] The theoretical maximum output power subsection of the energy storage device is used to constrain and consider whether the real-time stored power of the energy storage device meets the condition of full power generation within one dispatching period;

[0092] The real-time stored power state subsection of the energy storage device is used to constrain and consider the conditional relationship among the rated maximum output power of the energy storage device, the real-time stored power of the energy storage device, and the real-time minimum stored power state of the energy storage device that can meet the condition of full power generation within one dispatching period;

[0093] The stored power subsection of the energy storage device is used to constrain the upper and lower limit ranges of the stored power of the energy storage device;

[0094] The stored capacity subsection of the energy storage device is used to constrain that the stored capacity of the energy storage device needs to be between the minimum and maximum power ranges;

[0095] The stored power state subsection of the energy storage device is used to constrain and describe the conversion relationship among the stored power at the current moment, the stored power at the previous moment, and the stored power and output power at the current moment of the energy storage device;

[0096] The operating state subsection of the thermal power unit is used to constrain and describe the output power limit, on / off state relationship, ramp-up capacity limit, and minimum start-up and shutdown time limit of the thermal power unit.

[0097] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0098] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0099] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the scope of its protection. Although the present application has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present application, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications, or equivalent replacements are all within the scope of the protection of the pending claims of the application.

Claims

1. A method for optimizing the positive reserve capacity of an energy storage device, characterized in that, Including: Input the predicted output of new energy, load, operating parameters of thermal power units and energy storage devices into a pre-established mixed-integer linear programming model; Based on the boundary conditions and the mixed-integer linear programming model, solve the optimization model to obtain the reserve capacity provided by the energy storage device; Among them, the mixed-integer linear programming model is determined based on the maximum output power of the real-time stored electricity of the energy storage device; The establishment of the mixed-integer linear programming model includes: Construct an objective function with the goal of maximizing the total power generation of new energy during all scheduling periods considering energy storage; Consider system power balance, system positive reserve capacity, system negative reserve capacity, the theoretical maximum output power of the energy storage device, the real-time stored electricity state of the energy storage device, the power storage power of the energy storage device, the energy storage capacity of the energy storage device, the energy storage state of the energy storage device and the operating state of thermal power units to construct constraint conditions; Based on the objective function and constraint conditions, establish a short-term optimization scheduling model; The energy storage device Theoretical maximum output power at a given moment The calculation formula is as follows: In the above formula, is an intermediate calculation variable; is the rated maximum power generation of the energy storage device; represents the minimum state of charge of the energy storage device at time . When , it means that the state of charge of the energy storage device at time is not enough to achieve full power generation within one scheduling period. When , it means that the state of charge of the energy storage device at time is sufficient to achieve full power generation within one scheduling period. Among them, the value range of the intermediate calculation variable is determined by the following constraint conditions: In the formula, is the stored power of the energy storage device at moment; is the duration of each optimization period, and M is a constant.

2. The method according to claim 1, characterized in that, The objective function is shown as the following formula: where max obj is the maximum total power generation of new energy during all scheduling periods, is the predicted power generation of new energy at time is the duration of each optimization period, is the total number of scheduling periods.

3. The method according to claim 1, characterized in that, The system power balance constraint is shown as the following formula: Wherein, is the predicted power generation of new energy at time the th thermal power unit's power generation at time , is the system load at time represents the actual output power of the energy storage device at time , represents the stored electric power of the energy storage device at time , is the total number of scheduling periods, is the number of thermal power units.

4. The method according to claim 1, characterized in that, The system positive reserve capacity constraint is shown as the following formula: In the formula, is the predicted power generation of new energy at time the maximum technical output of the -th thermal power unit; represents the state variable indicating whether the -th thermal power unit is started at time where when it means the -th thermal power unit is started, otherwise it means shutdown; when it means the -th thermal power unit is started, otherwise it means shutdown; represents the theoretical maximum output power of the energy storage device at time ; is the system load at time represents the system's positive reserve capacity requirement; is the total number of scheduling periods; is the number of thermal power units.

5. The method according to claim 4, characterized in that, The real-time stored electricity state constraint of the energy storage device is shown as the following formula: Wherein, is the rated maximum power generation of the energy storage device; is the stored electricity of the energy storage device at time; is the duration of each optimization period; represents the minimum stored electricity state of the energy storage device at time, where when it means that the stored electricity of the energy storage device at is not enough to achieve full power generation within one scheduling period, and when it means that the stored electricity of the energy storage device at is enough to achieve full power generation within one scheduling period; is the first constant, is the second constant, m < M; is the total number of scheduling periods.

6. The method according to claim 1, characterized in that, The system negative reserve capacity constraint is shown as the following formula: In the formula, represents the th thermal power unit's status variable indicating whether it is in operation at time. When it indicates that the th thermal power unit is in operation; otherwise, it indicates shutdown; is the th thermal power unit's minimum technical output; is the time system load; represents the system negative reserve capacity requirement; is the total number of dispatching periods; is the number of thermal power units.

7. An energy storage device positive reserve capacity optimization system for implementing the method according to claim 1, characterized in that, Including: A prediction input module and a solution optimization module; The prediction input module is used to input the predicted output of new energy, load, operating parameters of thermal power units and energy storage devices into a pre-established mixed-integer linear programming model; The solution optimization module is used to solve the optimization model based on the boundary conditions and the mixed-integer linear programming model to obtain the reserve capacity provided by the energy storage device; Among them, the mixed-integer linear programming model is determined based on the maximum output power of the real-time stored electricity of the energy storage device.

8. The system according to claim 7, wherein It also includes a model establishment module for establishing a short-term scheduling model based on the determination of the maximum output power of the real-time stored electricity of the energy storage device.

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