Energy storage multi-cycle coordinated optimization scheduling method, system and equipment of power system and storage medium

Abstract

The invention discloses an energy storage multi-cycle coordinated optimization scheduling method, system and device of a power system and a storage medium, and the method comprises the steps: constructing a day-ahead power grid optimization scheduling model containing energy storage, and solving to obtain a day-ahead optimization scheduling result of the energy storage; according to the day-ahead optimal scheduling result of energy storage, the day-ahead start-stop state of the thermal power generating unit and the day-ahead electric quantity of the energy storage unit in the hourly time period serve as the boundary of intra-day optimal scheduling; in combination with the boundary of the intra-day optimization scheduling, constructing an intra-day power grid optimization scheduling model containing energy storage, and solving to obtain an intra-day optimization scheduling result of the energy storage; and issuing the intra-day optimization scheduling result of the energy storage to the energy storage station, wherein the intra-day optimization scheduling result of the energy storage comprises the charging and discharging state and power of each time period of the energy storage. According to the method, the rolling optimization of the energy storage unit in the day-ahead time scale and the day-intra time scale is dynamically and accurately matched with the balance demand of the power grid, so that the balance adjustment capability of the power grid in peak regulation and peak clipping periods can be improved.

Description

Technical Field

[0001] The present invention relates to power system dispatching automation technology, and in particular to a method, system, equipment and storage medium for energy storage multi-cycle coordinated optimization dispatching of a power system. Background Art

[0002] The random volatility of renewable energy output, coupled with the increasingly peak power load and the impact of extreme climate, poses a huge challenge to the balance of electricity and power. It is necessary to rely on energy storage to change the instantaneous balance characteristics of the power system on a certain time scale to meet the system's balance and regulation needs on different time scales.

[0003] Energy storage can store and release wind and solar energy, which are highly volatile, to turn unstable energy into stable products with higher value, increasing the grid's acceptance of renewable energy. Energy storage can be used to properly adjust the grid load and provide peak-shaving and valley-filling functions. Unlike other resources, energy storage has its own characteristics in terms of physical constraints. Due to its unique energy limitation, the energy storage discharge capacity is not only constrained by the power upper limit, but also by the state of charge, which makes the modeling of energy storage in optimal scheduling special and complex.

[0004] Most provinces use a planned dispatching model for energy storage, and the dispatching agency determines its output curve on the operating day in advance based on experience. At the same time, in the provinces where spot pilot projects are being carried out, energy storage units are usually non-market units, and as a boundary condition for market clearing, optimized dispatching has not been carried out. Therefore, the value of energy storage in the system cannot be quantified, which restricts the optimal scheduling of energy storage unit power and cannot effectively incentivize energy storage to play a role in the system. Therefore, how energy storage resources can be coordinated to participate in the optimized dispatching of the power grid on the day before and during the day has become a technical problem that technicians in this field urgently need to solve. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a method, system, device and storage medium for coordinated optimization and scheduling of energy storage in a power system. The scheme optimizes the charging and discharging plan of the energy storage of the whole network in a unified manner according to the peak-shaving demand of the power grid in the day-ahead stage, and optimizes and adjusts the energy storage charging and discharging plan according to the latest operation status of the power grid in the intraday stage, thereby improving the utilization level of power storage in the power grid, and enhancing the space for absorbing new energy and the power supply capacity of the system.

[0006] Technical solution: A method for multi-cycle coordinated optimization and dispatching of energy storage in a power system of the present invention comprises:

[0007] Construct a day-ahead grid optimization dispatch model with energy storage, and solve the day-ahead optimization dispatch result of energy storage;

[0008] According to the day-ahead optimization dispatch results of energy storage, the start and stop status of thermal power units and the power consumption of energy storage units at the hourly intervals on the day-ahead are used as the boundaries of intra-day optimization dispatch;

[0009] Combined with the boundary of intraday optimal dispatch, a model of intraday power grid optimal dispatch including energy storage is constructed, and the intraday optimal dispatch result of energy storage is obtained;

[0010] The intraday optimization scheduling results of energy storage are sent to the energy storage site, and the intraday optimization scheduling results of energy storage include the charging and discharging status and power of the energy storage in each time period.

[0011] Furthermore, the objective function of the day-ahead grid optimization dispatching model with energy storage is as follows:

[0012]

[0013] Among them, NT D represents the number of time periods included in the day-ahead optimal dispatch; NI represents the total number of thermal power units in the power system; C i represents the unit operating cost of thermal power unit i; P D,i,t represents the active power output of thermal power unit i in the day-ahead period t; S i represents the startup cost of thermal power unit i; y D,i,t Indicates whether the thermal power unit i has a status change from shutdown to startup in the previous period t; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; P D,w,t It represents the abandoned power of the new energy unit w in the day-ahead period t.

[0014] Furthermore, the objective function of the day-ahead grid optimization dispatching model with energy storage sets the following constraints:

[0015]

[0016] P i,min u D,i,t ≤P D,i,t ≤P i,max u D,i,t ;

[0017]

[0018] -Δ i ≤P D,i,t -P D,i,t-1 ≤Δ i ;

[0019]

[0020]

[0021] Among them, NS represents the total number of energy storage units in the power system; and They represent the discharge power and charging power of the energy storage unit s in the day-ahead period t respectively; L D,t represents the load demand of the system in the day-ahead period t; PI D,w,t represents the predicted power of the new energy unit w in the day-ahead period t; P i,max and P i,min Respectively represent the upper and lower limits of the output power of thermal power unit i; u D,i,t represents the start / stop status of thermal power unit i in time period t before the day; z D,i,t Indicates whether the thermal power unit i has changed from startup to shutdown status in the previous day period t; UT i and DT i Respectively represent the minimum start-up time and minimum shutdown time of thermal power unit i; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , P l,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; N represents the set of grid computing nodes; P D,n,t represents the power generation power of the grid computing node n at time t before the day; l D,n,t represents the load power of the grid computing node n at time t before the day; S n,l,t It represents the sensitivity of the power of the grid computing node n to the lth transmission section at time period t; and They represent the upper and lower limits of the discharge power of the energy storage unit s respectively; and They represent the upper and lower limits of the charging power of the energy storage unit s respectively; and They represent the discharge state and charge state of the energy storage unit s in the day-ahead period t respectively; E D,s,t E represents the power consumption of energy storage unit s in the day-ahead period t; D,s,t-1 represents the power consumption of energy storage unit s in the day-ahead period t-1; α s and β s They represent the charging efficiency coefficient and discharging efficiency coefficient of the energy storage unit s respectively; and They respectively represent the upper and lower limits of the energy storage unit s.

[0022] Furthermore, according to the day-ahead optimization dispatch results of energy storage, the day-ahead start and stop status of thermal power units and the day-ahead hourly power consumption of energy storage units are used as the boundaries of intraday optimization dispatch, including:

[0023] u R,i,t =u D,i,t t∈T period

[0024]

[0025] In the formula, u R,i,t represents the start / stop status of thermal power unit i in time period t during the day; u D,i,t Indicates the start and stop status of thermal power unit i in time period t before the day; It represents the power consumption of energy storage unit s at the hourly period t during the day; E D,s,t represents the power consumption of energy storage unit s in the day-ahead period t; T period represents the set of all scheduling periods within the scheduling range; T hour Represents the set of hourly scheduling periods within the scheduling range.

[0026] Furthermore, the objective function of the intraday power grid optimization dispatching model with energy storage is as follows:

[0027]

[0028] Where NT R represents the number of time periods included in the daily optimal dispatch; NI represents the total number of thermal power units in the power system; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; C i represents the unit operating cost of thermal power unit i; P R,i,t represents the active power output of thermal power unit i in time period t during the day; P R,w,t It represents the abandoned power of renewable energy unit w in the day time period t.

[0029] Furthermore, the objective function of the intraday power grid optimization dispatching model with energy storage sets the following constraints:

[0030]

[0031] P i,min u R,i,t ≤P R,i,t ≤P i,max u R,i,t

[0032] -Δ i ≤P R,i,t -P R,i,t-1 ≤Δ i

[0033]

[0034] In the formula, NS represents the total number of energy storage units in the power system; and are the discharge power and charging power of energy storage unit s in the daytime period t respectively; LR,t is the load demand of the system in the intraday period t; PI R,w,t is the predicted power of the new energy unit w in the intraday period t; P i,max and P i,min Respectively represent the upper and lower limits of the output power of thermal power unit i; u R,i,t is the start / stop status of thermal power unit i in time period t during the day, which is the same as the status a day before; P R,i,t-1 represents the active power output of thermal power unit i in the intraday period t-1; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , P l,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; P R,n,t is the power generation power of node n calculated by the power grid at time t within the day; R,n,t S is the load power of the grid calculation node n at time t within the day; n,l,t It represents the sensitivity of the power of the grid computing node n to the lth transmission section at time period t; and are the discharge state and charge state of the energy storage unit s in the intraday period t respectively; and They represent the upper and lower limits of the discharge power of the energy storage unit s respectively; and They respectively represent the upper and lower limits of the charging power of the energy storage unit s.

[0035] Furthermore, the objective function of the intraday power grid optimization dispatching model with energy storage sets the following constraints:

[0036]

[0037] In the formula, E R,s,t E is the power of energy storage unit s in the daytime period t; R,s,t-1 is the power of energy storage unit s in the intraday period t-1; α s and β s They represent the charging efficiency coefficient and discharging efficiency coefficient of the energy storage unit s respectively; and They represent the upper and lower limits of the energy storage unit s respectively; It represents the power consumption of energy storage unit s at the hourly period t during the day; T hour Represents the set of hourly scheduling periods within the scheduling range.

[0038] Based on the same inventive concept, a multi-cycle coordinated optimization dispatching system for energy storage of a power system of the present invention comprises:

[0039] The energy storage day-ahead optimization scheduling module is used to build a day-ahead power grid optimization scheduling model containing energy storage and solve the day-ahead optimization scheduling results of energy storage;

[0040] The day-ahead and intraday scheduling connection module is used to use the day-ahead start and stop status of thermal power units and the day-ahead hourly power consumption of energy storage units as the boundaries of intraday optimization scheduling according to the day-ahead optimization scheduling results of energy storage;

[0041] The energy storage intraday optimization scheduling module is used to combine the boundaries of intraday optimization scheduling, build an intraday power grid optimization scheduling model containing energy storage, and solve the intraday optimization scheduling results of energy storage;

[0042] The energy storage optimization scheduling result sending module is used to send the energy storage's intraday optimization scheduling results to the energy storage site. The energy storage's intraday optimization scheduling results include the charging and discharging status and power of the energy storage in each time period.

[0043] Based on the same inventive concept, a multi-cycle coordinated optimization scheduling device for energy storage in an electric power system of the present invention includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the above-mentioned power load data clustering method.

[0044] Based on the same inventive concept, a computer-readable storage medium of the present invention stores a computer program thereon, and when the program is executed by a processor, the steps of the multi-cycle coordinated optimization scheduling method for energy storage of the above-mentioned power system are implemented.

[0045] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are:

[0046] The present invention fully considers the system load demand at different time scales, establishes a two-layer optimization scheduling model for energy storage day-ahead and day-intraday, designs a multi-period connection strategy that uses the energy storage power consumption at the hourly period of the day-ahead as the boundary of the intraday optimization scheduling, and coordinates the large-scale resource allocation capability of the day-ahead plan and the precise scheduling capability of the intraday plan through rolling optimization of the energy storage units at two time scales, day-ahead and day-intraday, to achieve continuous dynamic optimization of the energy storage units that accurately matches the grid balancing demand, thereby improving the economic benefits of the system while ensuring system balance. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a method for multi-cycle coordinated optimization scheduling of energy storage in a power system disclosed in an embodiment of the present invention;

[0048] Figure 2 It is a structural schematic diagram of a multi-cycle coordinated optimization scheduling system for energy storage in a power system disclosed in an embodiment of the present invention;

[0049] Figure 3 It is a structural schematic diagram of a multi-period coordinated optimization scheduling system device for energy storage in a power system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It will be understood by those skilled in the art that the purposes and advantages that can be achieved with the present invention are not limited to the specific description of the above beneficial effects, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description.

[0051] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software or a combination of the two. Whether it is implemented in hardware or software depends on the specific application and design of the technical solution. Professional and technical personnel can use different methods to implement the functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0052] Reference to "embodiments" in the present invention means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] Example 1

[0054] See also Figure 1 , Figure 1 The present invention discloses a flow chart of a method for multi-cycle coordinated optimization scheduling of energy storage in a power system. Figure 1 The described energy storage multi-period coordinated optimization scheduling method is applied to the power system, such as for the day-ahead and intra-day optimization scheduling of energy storage resources, etc., which is not limited in the embodiments of the present invention. Figure 1 As shown, the energy storage multi-period coordinated optimization scheduling method of the power system may include the following operations:

[0055] S1. Construct a day-ahead grid optimization dispatching model with energy storage and solve it to obtain the day-ahead optimization dispatching result of energy storage.

[0056] In this embodiment, the objective function of the day-ahead grid optimization dispatching model with energy storage is as follows:

[0057]

[0058] Among them, NT Drepresents the number of time periods included in the day-ahead optimal dispatch; NI represents the total number of thermal power units in the power system; C i represents the unit operating cost of thermal power unit i; P D,i,t represents the active power output of thermal power unit i in the day-ahead period t; S i represents the startup cost of thermal power unit i; y D,i,t Indicates whether the thermal power unit i has a status change from shutdown to startup in the previous period t; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; P D,w,t It represents the abandoned power of the new energy unit w in the day-ahead period t.

[0059] For the objective function of the day-ahead grid optimization dispatch model with energy storage, the following constraints are set:

[0060]

[0061] P i,min u D,i,t ≤P D,i,t ≤P i,max u D,i,t ;

[0062]

[0063] -Δ i ≤P D,i,t -P D,i,t-1 ≤Δ i ;

[0064]

[0065] Among them, NS represents the total number of energy storage units in the power system; and They represent the discharge power and charging power of the energy storage unit s in the day-ahead period t respectively; L D,t represents the load demand of the system in the day-ahead period t; PI D,w,t represents the predicted power of the new energy unit w in the day-ahead period t; P i,max and P i,min Respectively represent the upper and lower limits of the output power of thermal power unit i; u D,i,t represents the start / stop status of thermal power unit i in time period t before the day; z D,i,t Indicates whether the thermal power unit i has changed from startup to shutdown status in the previous day period t; UT i and DT i Respectively represent the minimum start-up time and minimum shutdown time of thermal power unit i; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , Pl,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; N represents the set of grid computing nodes; P D,n,t represents the power generation power of the grid computing node n at time t before the day; l D,n,t represents the load power of the grid computing node n at time t before the day; S n,l,t It represents the sensitivity of the power of the grid computing node n to the lth transmission section at time period t; and They represent the upper and lower limits of the discharge power of the energy storage unit s respectively; and They represent the upper and lower limits of the charging power of the energy storage unit s respectively; and They represent the discharge state and charge state of the energy storage unit s in the day-ahead period t respectively; E D,s,t E represents the power consumption of energy storage unit s in the day-ahead period t; D,s,t-1 represents the power consumption of energy storage unit s in the day-ahead period t-1; α s and β s They represent the charging efficiency coefficient and discharging efficiency coefficient of the energy storage unit s respectively; and They respectively represent the upper and lower limits of the energy storage unit s.

[0066] S2. Based on the day-ahead optimization dispatch results of energy storage, the start and stop status of thermal power units and the hourly power consumption of energy storage units are used as the boundaries of intraday optimization dispatch. Specifically, it includes:

[0067] u R,i,t =u D,i,t t∈T period

[0068]

[0069] In the formula, u R,i,t represents the start / stop status of thermal power unit i in time period t during the day; u D,i,t Indicates the start and stop status of thermal power unit i in time period t before the day; It represents the power consumption of energy storage unit s at the hourly time period t during the day; E D,s,t represents the power consumption of energy storage unit s in the day-ahead period t; T period represents the set of all scheduling periods within the scheduling range; T hour Represents the set of hourly scheduling periods within the scheduling range.

[0070] S3. Combined with the boundary of intraday optimal dispatch, a model of intraday power grid optimal dispatch including energy storage is constructed, and the result of intraday optimal dispatch of energy storage is obtained by solving it.

[0071] In this embodiment, the objective function of the intraday power grid optimization dispatch model with energy storage is as follows:

[0072]

[0073] Where NT R represents the number of time periods included in the daily optimal dispatch; NI represents the total number of thermal power units in the power system; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; C i represents the unit operating cost of thermal power unit i; P R,i,t represents the active power output of thermal power unit i in time period t during the day; P R,w,t It represents the abandoned power of renewable energy unit w in the day time period t.

[0074] For the objective function of the intraday power grid optimization dispatch model with energy storage, the following constraints are set:

[0075]

[0076] P i,min u R,i,t ≤P R,i,t ≤P i,max u R,i,t

[0077] -Δ i ≤P R,i,t -P R,i,t-1 ≤Δ i

[0078]

[0079] In the formula, and are the discharge power and charging power of energy storage unit s in the daytime period t respectively; L R,t is the load demand of the system in the intraday period t; PI R,w,t is the predicted power of the new energy unit w in the intraday period t; u R,i,t is the start / stop status of thermal power unit i in time period t during the day, which is the same as the status a day before; P R,n,t is the power generation power of node n calculated by the power grid at time t within the day; R,n,t The load power of the grid node n is calculated at time t within the day; and are the discharge state and charge state of the energy storage unit s in the daytime period t, respectively; E R,s,t is the power of energy storage unit s in the daytime period t; P R,i,t-1 represents the active power output of thermal power unit i in the intraday period t-1; E R,s,t-1is the electricity consumption of energy storage unit s in the intraday period t-1;.

[0080] S4. Send the daily optimization scheduling results of energy storage to the energy storage site. The daily optimization scheduling results of energy storage include the charging and discharging status and power of energy storage in each time period.

[0081] The present invention realizes rolling optimization of energy storage units on two time scales, day-ahead and day-intraday, and dynamically and accurately matches the balancing demand of the power grid, which helps to improve the balancing and regulating capabilities of the power grid during peak load regulation and peak shaving periods.

[0082] The following is a preferred implementation case of the present invention, which includes the calculation process of the day-ahead and day-intraday coordinated optimization scheduling of energy storage resources in a provincial power grid using the method of the present invention. Its characteristics, purposes and advantages can be seen from the description of the embodiment.

[0083] First, in the day-ahead planning module, the power control center constructs a day-ahead grid optimization dispatching model including energy storage based on the short-term load forecast, new energy forecast and other boundary data for 96 time periods of the next day, and calculates the day-ahead optimization dispatching results of energy storage, including the charging and discharging status, charging and discharging power of the energy storage units in each time period of the day-ahead, and the energy storage charge in each time period.

[0084] Secondly, the day-ahead optimization scheduling results of the energy storage are sent to the energy storage station for charging and discharging guidance, and the energy storage charge at the whole hour, for example, the charge state of a certain energy storage station at point 1 is 15MWh, the charge state at point 2 is 30MWh, the charge state at point 3 is 45MWh, and the charge state at point 4 is 60MWh, is transmitted to the intraday planning module as the boundary for the energy storage intraday planning.

[0085] Then, in the intraday planning module, the power control center builds an intraday power grid optimization dispatch model including energy storage based on the ultra-short-term load forecast for the next 4 hours, new energy forecast and other boundary data, as well as the day-ahead energy storage decision results, and calculates the intraday optimization dispatch results of energy storage, including the charge and discharge status, charge and discharge power of the energy storage unit in each period of the day, and the energy storage charge in each period. For example, for a certain energy storage station, after intraday decision-making, the charge status of the four full hours from 1:00 to 4:00 remains consistent with the day-ahead, which are still 15MWh, 30MWh, 45MWh, and 60MWh respectively, but the charge and discharge power of the energy storage station will be newly optimized and adjusted in each period of each hour.

[0086] Finally, the daily optimization scheduling results of energy storage, including the charging and discharging status and power of energy storage in each time period, are sent to the energy storage station for scheduling and execution.

[0087] The analysis is based on the results of energy storage optimization scheduling based on multi-period coordination of the day-ahead and day-intraday. According to the short-term supply and consumption balance needs, the operators make energy storage charging and discharging plan arrangements and mobilize dispatchable resources in advance, which reduces the cost of resource scheduling and organization. According to the latest supply and consumption balance needs, the charging and discharging plans are optimized and issued during the day, realizing the consumption and peak regulation of surplus electricity in the day by energy storage.

[0088] The present invention can realize the multi-cycle coordinated optimization scheduling calculation of energy storage in the power system, does not require the participation of a large amount of manpower, and the calculation speed can meet the needs of practical applications. It effectively solves the problems that traditional energy storage charging and discharging plan arrangements are difficult to adapt to the uncertainty characteristics of new energy, rely on experience, and have low efficiency. It has broad promotion prospects.

[0089] Example 2

[0090] See also Figure 2 , Figure 2 1 is a schematic diagram of the structure of a multi-period coordinated optimization dispatching system for energy storage in a power system disclosed in an embodiment of the present invention. The system can realize the day-ahead and intra-day optimization dispatching of energy storage resources, and specifically includes:

[0091] The energy storage day-ahead optimization scheduling module is used to build a day-ahead power grid optimization scheduling model containing energy storage and solve the day-ahead optimization scheduling results of energy storage;

[0092] The day-ahead and intraday scheduling connection module is used to use the day-ahead start and stop status of thermal power units and the day-ahead hourly power consumption of energy storage units as the boundaries of intraday optimization scheduling according to the day-ahead optimization scheduling results of energy storage;

[0093] The energy storage intraday optimization scheduling module is used to combine the boundaries of intraday optimization scheduling, build an intraday power grid optimization scheduling model containing energy storage, and solve the intraday optimization scheduling results of energy storage;

[0094] The energy storage optimization scheduling result sending module is used to send the energy storage's intraday optimization scheduling results to the energy storage site. The energy storage's intraday optimization scheduling results include the charging and discharging status and power of the energy storage in each time period.

[0095] In an optional embodiment, the energy storage multi-period coordinated optimization scheduling method of the power system includes: a) constructing a day-ahead grid optimization scheduling model containing energy storage, and solving to obtain the day-ahead optimization scheduling result of the energy storage; b) according to the day-ahead optimization scheduling result of the energy storage, taking the day-ahead start and stop status of the thermal power unit and the day-ahead hourly power consumption of the energy storage unit as the boundary of intraday optimization scheduling; c) combining the boundary of intraday optimization scheduling, constructing the day-ahead grid optimization scheduling model containing energy storage, and solving to obtain the day-ahead optimization scheduling result of the energy storage; d) sending the day-ahead optimization scheduling result of the energy storage to the energy storage site, and the day-ahead optimization scheduling result of the energy storage includes the charging and discharging status and power of the energy storage in each time period.

[0096] Example 3

[0097] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a multi-cycle coordinated optimization dispatching system for energy storage in a power system disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to the power system, such as for the day-ahead and intra-day optimization scheduling of energy storage resources, and the embodiments of the present invention are not limited thereto.

[0098] like Figure 3 As shown, the device may include a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described in the above embodiment and can achieve the technical effect consistent with the above method.

[0099] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The program modules typically perform the functions and / or methods in the embodiments described herein.

[0100] The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention.

[0101] Example 4

[0102] Embodiment 4 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in the above embodiment are implemented and the technical effect consistent with the above method can be achieved.

[0103] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0104] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0105] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0106] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] Of course, the storage medium containing computer executable instructions provided by an embodiment of the present invention, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the method provided by any embodiment of the present invention.

[0108] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for multi-period coordinated optimization scheduling of energy storage in a power system, characterized in that: include: Construct a day-ahead grid optimization dispatch model with energy storage, and solve the day-ahead optimization dispatch result of energy storage; According to the day-ahead optimization dispatch results of energy storage, the start and stop status of thermal power units and the power consumption of energy storage units at the hourly intervals on the day-ahead are used as the boundaries of intra-day optimization dispatch; Combined with the boundary of intraday optimal dispatch, a model of intraday power grid optimal dispatch including energy storage is constructed, and the intraday optimal dispatch result of energy storage is obtained; The intraday optimization scheduling results of energy storage are sent to the energy storage site, and the intraday optimization scheduling results of energy storage include the charging and discharging status and power of the energy storage in each time period.

2. The energy storage multi-cycle coordinated optimization scheduling method for the power system according to claim 1 is characterized in that: The objective function of the day-ahead grid optimization dispatching model with energy storage is as follows: Among them, NT D represents the number of time periods included in the day-ahead optimal dispatch; NI represents the total number of thermal power units in the power system; C i represents the unit operating cost of thermal power unit i; P D,i,t represents the active power output of thermal power unit i in the day-ahead period t; S i represents the startup cost of thermal power unit i; y D,i,t Indicates whether the thermal power unit i has a status change from shutdown to startup in the previous period t; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; P D,w,t It represents the abandoned power of the new energy unit w in the day-ahead period t.

3. The energy storage multi-cycle coordinated optimization scheduling method for the power system according to claim 2 is characterized in that: The objective function of the day-ahead grid optimization dispatch model with energy storage sets the following constraints: P i,min in D,i,t ≤P D,i,t ≤P i,max in D,i,t -D i ≤P D,i,t -P D,i,t-1 ≤Δ i ; Among them, NS represents the total number of energy storage units in the power system; and They represent the discharge power and charging power of the energy storage unit s in the day-ahead period t respectively; L D,t represents the load demand of the system in the day-ahead period t; PI D,w,t represents the predicted power of the new energy unit w in the day-ahead period t; P i,max and P i,min Respectively represent the upper and lower limits of the output power of thermal power unit i; u D,i,t represents the start / stop status of thermal power unit i in time period t before the day; z D,i,τ Indicates whether the thermal power unit i has changed from startup to shutdown status in the previous day period t; UT i and DT i Respectively represent the minimum start-up time and minimum shutdown time of thermal power unit i; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , P l,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; N represents the set of grid computing nodes; P D,n,t represents the power generation power of the grid computing node n at time t before the day; l D,n,t represents the load power of the grid computing node n at time t before the day; S n,l,t It represents the sensitivity of the power of the grid computing node n to the lth transmission section at time period t; and They represent the upper and lower limits of the discharge power of the energy storage unit s respectively; and They represent the upper and lower limits of the charging power of the energy storage unit s respectively; and They represent the discharge state and charge state of the energy storage unit s in the day-ahead period t respectively; E D,s,t E represents the power consumption of energy storage unit s in the day-ahead period t; D,s,t-1 represents the power consumption of energy storage unit s in the day-ahead period t-1; α s and β s They represent the charging efficiency coefficient and discharging efficiency coefficient of the energy storage unit s respectively; and They respectively represent the upper and lower limits of the energy storage unit s.

4. The method for multi-cycle coordinated optimization and dispatching of energy storage in a power system according to claim 1, characterized in that: According to the day-ahead optimization dispatch results of energy storage, the start and stop status of thermal power units and the power consumption of energy storage units at the hourly intervals on the day-ahead are used as the boundaries of intra-day optimization dispatch. include: in R,i,t =in D,i,t t∈T period In the formula, u R,i,t represents the start / stop status of thermal power unit i in time period t during the day; u D,i,t Indicates the start and stop status of thermal power unit i in time period t before the day; It represents the power consumption of energy storage unit s at the hourly period t during the day; E D,s,t represents the power consumption of energy storage unit s in the day-ahead period t; T period represents the set of all scheduling periods within the scheduling range; T hour Represents the set of hourly scheduling periods within the scheduling range.

5. The method for multi-cycle coordinated optimization and dispatching of energy storage in a power system according to claim 1, characterized in that: The objective function of the intraday power grid optimization dispatch model with energy storage is as follows: Where NT R represents the number of time periods included in the daily optimal dispatch; NI represents the total number of thermal power units in the power system; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; C i represents the unit operating cost of thermal power unit i; P R,i,t represents the active power output of thermal power unit i in time period t during the day; P R,w,t It represents the abandoned power of renewable energy unit w in the day time period t.

6. The method for multi-cycle coordinated optimization and dispatching of energy storage in a power system according to claim 5, characterized in that: The objective function of the intraday power grid optimization dispatch model with energy storage sets the following constraints: P i,min in R,i,t ≤P R,i,t ≤P i,max in R,i,t -D i ≤P R,i,t -P R,i,t-1 ≤Δ i In the formula, NS represents the total number of energy storage units in the power system; and are the discharge power and charging power of energy storage unit s in the daytime period t respectively; L R,t is the load demand of the system in the intraday period t; PI R,w,t is the predicted power of the new energy unit w in the intraday period t; P i,max and P i,min Respectively represent the upper and lower limits of the output power of thermal power unit i; u R,i,t is the start / stop status of thermal power unit i in time period t during the day, which is the same as the status a day before; P R,i,t-1 represents the active power output of thermal power unit i in the intraday period t-1; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , P l,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; P R,n,t is the power generation power of node n calculated by the power grid at time t within the day; R,n,t S is the load power of the grid calculation node n at time t within the day; n,l,t It represents the sensitivity of the power of the grid computing node n to the lth transmission section at time period t; and are the discharge state and charge state of the energy storage unit s in the intraday period t respectively; and They represent the upper and lower limits of the discharge power of the energy storage unit s respectively; and They respectively represent the upper and lower limits of the charging power of the energy storage unit s.

7. The method for multi-cycle coordinated optimization and dispatching of energy storage in a power system according to claim 6, characterized in that: The objective function of the intraday power grid optimization dispatch model with energy storage sets the following constraints: In the formula, E R,s,t E is the power of energy storage unit s in the daytime period t; R,s,t-1 is the power of energy storage unit s in the intraday period t-1; α s and β s They represent the charging efficiency coefficient and discharging efficiency coefficient of the energy storage unit s respectively; and They represent the upper and lower limits of the energy storage unit s respectively; It represents the power consumption of energy storage unit s at the hourly period t during the day; T hour Represents the set of hourly scheduling periods within the scheduling range.

8. A multi-cycle coordinated optimization dispatching system for energy storage in a power system, characterized in that: include: The energy storage day-ahead optimization scheduling module is used to build a day-ahead power grid optimization scheduling model containing energy storage and solve the day-ahead optimization scheduling results of energy storage; The day-ahead and intraday scheduling connection module is used to use the day-ahead start and stop status of thermal power units and the day-ahead hourly power consumption of energy storage units as the boundaries of intraday optimization scheduling according to the day-ahead optimization scheduling results of energy storage; The energy storage intraday optimization scheduling module is used to combine the boundaries of intraday optimization scheduling, build an intraday power grid optimization scheduling model containing energy storage, and solve the intraday optimization scheduling results of energy storage; The energy storage optimization scheduling result sending module is used to send the energy storage's intraday optimization scheduling results to the energy storage site. The energy storage's intraday optimization scheduling results include the charging and discharging status and power of the energy storage in each time period.

9. A multi-cycle coordinated optimization dispatching device for energy storage in a power system, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the multi-cycle coordinated optimization scheduling method for energy storage in the power system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the multi-cycle coordinated optimization scheduling method for energy storage in a power system as claimed in any one of claims 1 to 7.

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