Energy and reserve scheduling method based on ADMM algorithm and reserve inter-zone calling

Abstract

The application discloses an electric energy-backup scheduling method based on an ADMM algorithm and backup cross-zone calling, wherein the method comprises the following steps: constructing a target function according to economic operation data of a power system, and constructing an electric energy-backup joint scheduling model of the power system based on the target function, electric energy constraints and backup constraints, backup partition constraints and cross-zone callability; constructing a solving algorithm of a distributed optimization problem according to an alternating direction multiplier algorithm; iteratively solving the electric energy-backup joint scheduling model of the power system by using the solving algorithm of the distributed optimization problem, and outputting an optimal scheduling scheme of the power system. The consensus variable in the alternating direction multiplier algorithm and the iteration mechanism are modified, applied to the optimization problem of the power system scheduling, and the partition backup is considered. The method is favorable for optimizing the scheduling decision and increasing data security, and meanwhile, the backup partition is considered, so that a flexible scheduling mode is provided for the power generation side to cope with the increasing proportion of renewable energy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system optimal dispatching, and particularly relates to an electric energy-reserve dispatching method and device based on an ADMM algorithm and reserve cross-zone calling, an electronic device and a storage medium. BACKGROUND

[0002] With the increasing proportion of renewable energy in the power system, to ensure the safe and reliable operation of the power system, reserve service is of considerable importance. At the same time, the establishment of regional energy systems also brings the problem of inter-zone dispatching of regional reserves. Considering the cross-zone calling of reserves for reserve optimization is a hot topic at present.

[0003] Unlike centralized optimization methods that optimize one objective function, distributed algorithms can synchronize the calculation of an economic dispatching problem through multiple agents. Each agent obtains regional data and adjacent data without further understanding of global perspective information, which indicates the distribution of the algorithm. Therefore, the distributed optimization method can reduce the computational burden of the central node and increase data security.

[0004] In order to decompose the centralized dispatching problem, two main methods are widely used. One method is optimal condition decomposition (OCD), which decomposes the first-order Karush-Kuhn-Tucker (KKT) of the centralized problem. It is a challenge to approximate the KKT matrix to make the algorithm converge, especially for strongly coupled multi-region problems. The other method is dual decomposition, which alternately updates the original variables and dual variables according to the gradient of the (augmented) Lagrangian function. A simple variant of dual decomposition is Lagrangian relaxation (LR). By adding a quadratic penalty term, the convergence speed of LR is improved, and it is represented as augmented Lagrangian relaxation (ALR). On the basis of ALR, the alternating direction multiplier method is proposed to overcome the decomposition difficulty caused by the quadratic term. SUMMARY

[0005] The present application provides an electric energy-reserve dispatching method based on an ADMM algorithm and reserve cross-zone calling, which is beneficial to optimize dispatching decisions, increase data security, and consider reserve partitioning, thereby providing a flexible dispatching mode for the power generation side to cope with the increasing proportion of renewable energy.

[0006] The first aspect embodiment of the present application provides an electric energy-backup scheduling method based on an ADMM algorithm and backup cross-zone calling, comprising the following steps: constructing a target function according to economic operation data of a power system, and constructing a power system electric energy-backup joint scheduling model based on the target function, electric energy constraints and backup constraints, backup partition constraints, and cross-regional callability; constructing a solving algorithm of a distributed optimization problem according to an alternating direction multiplier algorithm; iteratively solving the power system electric energy-backup joint scheduling model by using the solving algorithm of the distributed optimization problem, and stopping iteration and outputting an optimal scheduling scheme of the power system when an iteration end condition is met.

[0007] Optionally, in an embodiment of the present application, the economic operation data of the power system includes one or more of unit output upper and lower limits, unit ramping rate, unit cost function, backup cost function, and line maximum power flow.

[0008] Optionally, in an embodiment of the present application, the target function constructed according to the economic operation data of the power system is:

[0009]

[0010] W i (p i , r i ) = a i p i 2 +b i p i +c i +d i r i , i = 1, 2, 3, …, N

[0011] wherein N represents the number of generators, W i (p i , r i ) represents the cost of the i th generator, p i represents the generator output at node i, r i represents the generator backup at node i, a i , b i , c i , and d i are coefficients describing the quadratic relationship between the cost of the generator and its output and backup.

[0012] Optionally, in an embodiment of the present application, the electric energy constraints and backup constraints include:

[0013] The electric energy constraints are:

[0014]

[0015]

[0016] where L i is the load at node i, T k is the flow of the kth branch, k1 and k2 are the start node and end node of the kth hop, θ i is the voltage phase angle of node i, x k is the line impedance of branch k, N br is the number of branches;

[0017] The output constraint of each generator and the flow constraint of each line are:

[0018] p i min ≤ p i ≤ p i max , i = 1, 2,..., N gen

[0019] -T k max ≤ T k ≤ T k max , k = 1, 2,..., N br

[0020] where p i min is the lower limit of generator output, p i max is the upper limit of generator output, N gen is the number of generators, T k max is the maximum active flow of the line;

[0021] The reserve constraint is:

[0022]

[0023] r i + p i ≤ p i max , i = 1, 2,..., N gen

[0024] 0 ≤ r i ≤ r i max , i = 1, 2,..., N gen

[0025] where p t is the reserve coefficient, r i maxMaximal reserve for the ith generator.

[0026] Optionally, in an embodiment of the present application, the reserve partition constraint and cross-zone callability comprise:

[0027] The line constraint of transmission line blocking between zones is established as:

[0028]

[0029] Where p is a zone, Q p is the total reserve demand in zone p, Br p is a set of transmission lines connected to zone p.

[0030] Optionally, in an embodiment of the present application, the solution algorithm for constructing a distributed optimization problem according to an alternating direction multiplier algorithm comprises: constructing a Lagrangian function with a penalty term using an alternating direction multiplier structure; selecting a consensus variable, introducing the consensus variable into the Lagrangian function; and performing Lagrangian multiplier iteration and consensus variable iteration based on the consensus variable.

[0031] Optionally, in an embodiment of the present application, the iteration end condition comprises: the Lagrangian multiplier and the consensus variable value converge at the same time, and the penalty term of the Lagrangian function is greater than zero.

[0032] The second aspect embodiment of the present application provides an electric energy-reserve scheduling device based on an ADMM algorithm and reserve cross-zone calling, comprising: a model establishing module, configured to construct a target function according to economic operation data of a power system, and construct a power system electric energy-reserve joint scheduling model based on the target function, electric energy constraints and reserve constraints, reserve partition constraints, and cross-zone callability; an algorithm constructing module, configured to construct a solution algorithm for a distributed optimization problem according to an alternating direction multiplier algorithm; and a model solving module, configured to iteratively solve the power system electric energy-reserve joint scheduling model using the solution algorithm for the distributed optimization problem, and stop iteration and output an optimal scheduling scheme of the power system when an iteration end condition is met.

[0033] Optionally, in an embodiment of the present application, the economic operation data of the power system comprises one or more of unit output upper and lower limits, unit ramping rate, unit cost function, reserve cost function, and line maximum power flow.

[0034] Optionally, in an embodiment of the present application, the target function constructed according to the economic operation data of the power system is:

[0035]

[0036] Wi (p i , r i ) = a i p i 2 +b i p i +c i +d i r i , i = 1, 2, 3,..., N

[0037] where N represents the number of generators, W i (p i , r i ) represents the cost of the i-th generator, p i is the generator output at node i, r i is the generator reserve at node i, a i , b i , c i , d i are coefficients describing the quadratic relationship between the cost of the generator and its output and reserve.

[0038] Optionally, in an embodiment of the present application, the electrical energy constraints and reserve constraints include:

[0039] The electrical energy constraints are:

[0040]

[0041]

[0042] where L i is the load at node i, T k is the power flow of the k-th branch, k1 and k2 are the starting node and the ending node of the k-th branch, θ i is the voltage phase angle of node i, x k is the line impedance of branch k, and N br is the number of branches.

[0043] The output constraint of each generator and the power flow constraint of each line are:

[0044] p i min ≤ p i ≤ p i max , i = 1, 2,..., N gen

[0045] -T k max ≤ T k ≤ T k max, k = 1, 2,..., N br

[0046] where p i min is the lower bound of generator output, p i max is the upper bound of generator output, N gen is the number of generators, T k max is the maximum active power flow of the line, and

[0047] The reserve constraint is:

[0048]

[0049] r i +p i ≤ p i max , i = 1, 2,..., N gen

[0050] 0 ≤ r i ≤ r i max , i = 1, 2,..., N gen

[0051] where p t is the reserve coefficient, r i max is the maximum reserve of the i-th generator.

[0052] Optionally, in an embodiment of the present application, the reserve partition constraint and cross-area invocability include:

[0053] The line constraint of establishing transmission line congestion between areas is:

[0054]

[0055] where p is an area, Q p is the total reserve demand in the area p, and Br p is a set of transmission lines connected to the area p.

[0056] Optionally, in an embodiment of the present application, the algorithm construction module is further configured to: adopt an alternating direction multiplier structure to construct a Lagrangian function with a penalty term, select a consensus variable, introduce the consensus variable into the Lagrangian function, and perform Lagrangian multiplier iteration and consensus variable iteration based on the consensus variable.

[0057] Optionally, in an embodiment of the present application, the iteration end condition comprises: the Lagrange multiplier and the consensus variable value converge at the same time, and the penalty term of the Lagrange function is greater than zero.

[0058] The third aspect embodiment of the present application provides an electronic device, comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to perform the power energy-reserve scheduling method based on the ADMM algorithm and the reserve cross-zone calling as described in the above embodiments.

[0059] The fourth aspect embodiment of the present application provides a computer readable storage medium, which stores a computer program executable by a processor to perform the power energy-reserve scheduling method based on the ADMM algorithm and the reserve cross-zone calling as described in the above embodiments.

[0060] The present application has the following beneficial effects:

[0061] The consensus variable in the ADMM (Alternating Direction Method of Multipliers) algorithm and the iteration mechanism are modified, applied to the optimization problem of power system scheduling, and the reserve partition is considered. Compared with the centralized method, the distributed optimization method can reduce the calculation burden of the center node and increase the data security; at the same time, considering the reserve partition, a flexible scheduling mode is provided for the power generation side to cope with the increasing proportion of renewable energy.

[0062] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0063] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0064] Figure 1 A flowchart of a power energy-reserve scheduling method based on the ADMM algorithm and the reserve cross-zone calling according to an embodiment of the present application is provided;

[0065] Figure 2 A framework schematic diagram of a power energy-reserve scheduling method based on the ADMM algorithm and the reserve cross-zone calling according to an embodiment of the present application is provided;

[0066] Figure 3 An example diagram of a power energy-reserve scheduling device based on the ADMM algorithm and the reserve cross-zone calling according to an embodiment of the present application is provided;

[0067] Figure 4 A schematic diagram of the structure of the electronic device provided in the application embodiment. Detailed Implementation

[0068] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0069] Figure 1 This is a flowchart of an energy-backup scheduling method based on the ADMM algorithm and backup cross-regional dispatch, according to an embodiment of this application.

[0070] like Figure 1 As shown, the power-reserve scheduling method based on the ADMM algorithm and standby inter-regional dispatch includes the following steps:

[0071] In step S101, an objective function is constructed based on the economic operation data of the power system, and a joint dispatch model of power system energy and reserve is constructed based on the objective function, energy constraints and reserve constraints, reserve partition constraints and cross-regional callability.

[0072] It is understood that this application is a power energy-reserve joint dispatch algorithm based on ADMM technology, considering cross-regional reserve callability. First, a power system power energy-reserve joint dispatch model is constructed. For example... Figure 2 As shown, model construction consists of three parts:

[0073] 1-1) Obtain economic operation data of the power system and construct the objective function

[0074] The basic data for the economic operation of the power system in this application embodiment includes the upper and lower limits of unit output, unit ramp rate, unit cost function, reserve cost function, and maximum power flow of the line.

[0075] Construct the objective function, with the following expression:

[0076]

[0077] W i (p i f i ) = a i p i 2 +b i p i +c i +d i f i ,i=1,2,3,...,N (2)

[0078] where N denotes the number of generators, W i (p i , r i ) denotes the cost of the i-th generator. Equation (1) assumes that a node is connected to only one generator. Equation 2 states that the cost of a generator includes the cost of energy and the cost of reserve. In particular, the cost of energy is described as a quadratic function of the output p i , while the cost of reserve is described as a linear function of r i .

[0079] 1-2) Consider the energy and reserve constraints to form equality and inequality constraints

[0080] System constraints ensure that the output meets the demand and the power flow is within the line limits. The formulation is as follows:

[0081]

[0082]

[0083] Equation constraint (4) describes the power flow formula derived from the node voltage phase angle and line impedance, where T k is the power flow of the k-th branch (with direction), k1 and k2 are the start node and end node of the k-th path. θ i is the voltage phase angle of node i, x k is the line impedance of branch k.

[0084] p i min ≤ p i ≤ p i max , i = 1, 2,..., N gen (5)

[0085] -T k max ≤ T k ≤ T k max , k = 1, 2,..., N br (6)

[0086] Constraints (5) and (6) describe the output constraints of each generator and the power flow constraints of each line.

[0087]

[0088] r i +p i ≤ p i max , i = 1, 2,..., Ngen (8)

[0089] 0≤r i ≤r i max , i = 1, 2, …, N gen (9)

[0090] 1-3) Consider the backup partition constraints and cross-area invocability

[0091] Constraints (7), (8), (9) are backup constraints introduced by backup r. The following introduces line constraints considering the transmission line blocking between areas:

[0092]

[0093] Constraint (10) shows that when considering the mutual invocation of backup r between areas, the allowed range of transmission lines between areas needs to be considered, and backup cannot be directly allocated in different areas: Br p represents a set of transmission lines connected to area p, and Q p indicates the total backup demand in area p.

[0094] In step S102, a solution algorithm for the distributed optimization problem is constructed according to the alternating direction multiplier algorithm.

[0095] After constructing the power energy- backup joint scheduling model of the power system, a solution algorithm for the distributed optimization problem is established.

[0096] Optionally, in an embodiment of the present application, the solution algorithm for the distributed optimization problem is constructed according to the alternating direction multiplier algorithm, including: using the alternating direction multiplier structure to construct a Lagrangian function with a penalty term; selecting a consensus variable, introducing the consensus variable into the Lagrangian function; and performing Lagrangian multiplier iteration and consensus variable iteration based on the consensus variable.

[0097] Specifically, as shown in Figure 2 , the solution algorithm for the distributed optimization problem includes three steps:

[0098] 2-1) Use the ADMM structure to design a Lagrangian function with a penalty term:

[0099]

[0100] Under the KKT condition, the local optimal solution is the global optimal solution. When the penalty term of the objective function is zero, it means that the solution is within the constraint range. At the same time, for the distributed optimization problem, iteration needs to be performed for each sub-area respectively, so the iteration result needs to ensure that some shared variable values are the same, otherwise the solution will not converge. For this purpose, a consensus variable needs to be introduced to ensure that all solutions can converge.

[0101] 2-2) Select consensus variable, design consensus variable iteration

[0102] As a specific example, the consensus variable is selected as the phase angle of the voltage at the two ends of the transmission line (branch connecting between the link areas).

[0103] z1 k = (θ i k + θ 12 k + θ 13 k ) / 3 (12)

[0104] z2 k = (θ2 k + θ 21 k ) / 2 (13)

[0105] z3 k = (θ3 k + θ 31 k ) / 2 (14)

[0106] Equations (12), (13), and (14) are the mapping relationship between the phase angle and the consensus variable z. Among them, 1, 2, and 3 are three voltage nodes, and the connection mode is: 1 and 2, 3 are connected, 2 and 3 are not connected, then the mapping relationship is as shown above. Among them, θ ij k represents the voltage phase angle difference between node i and node j after the kth iteration.

[0107] Introducing the consensus variable into the function gives:

[0108]

[0109] Similarly, the consensus variable is selected as the backup transmission between areas, so:

[0110]

[0111]

[0112]

[0113] Introducing the consensus variable into the function gives:

[0114]

[0115] 2-3) Establish Lagrange multiplier iteration considering consensus variable

[0116] The Lagrange multiplier needs to be affected by the consensus variable, otherwise it cannot be judged to converge through the consensus variable. The iteration result of the Lagrange multiplier affected by the consensus variable is as follows:

[0117]

[0118] Similarly, the consensus variable itself also iterates, and the iteration process is as follows: (take the backup cross-zone transmission amount as an example):

[0119]

[0120]

[0121] In step S103, the distributed optimization problem solving algorithm is used to iteratively solve the power system electric energy-backup joint scheduling model, and when the iteration end condition is met, the iteration is stopped, and the optimal scheduling scheme of the power system is output.

[0122] After obtaining the power system electric energy-backup joint scheduling model and the distributed optimization problem solving algorithm, iteration is performed according to the solving algorithm.

[0123]

[0124]

[0125]

[0126]

[0127] Further, according to formula (16) to formula (20), new consensus variables and Lagrange multipliers λ i , λ ri are obtained. This is called one iteration.

[0128] After multiple iterations, it is judged whether to converge to an optimal solution. As a specific implementation, the iteration end condition includes: the values of the Lagrange multiplier and the consensus variable converge at the same time, and the penalty term of the Lagrange function is greater than zero.

[0129] Specifically, it is checked whether the Lagrange multipliers λ i , λ ri , node voltage phase angle θ i , backup capacity r i and output p i converge before and after iteration, and if they converge and the penalty term is greater than zero, the optimal solution is reached, otherwise iteration is continued. After iteration, the optimal solution is obtained, which is the optimal scheduling scheme of the power system.

[0130] ​The power energy-reserve scheduling method based on the ADMM algorithm and the reserve cross-zone calling according to the embodiment of the application comprises constructing a power system electric energy-reserve joint scheduling model, establishing a solving algorithm of a distributed optimization problem, and constructing an iterative solving process. The consensus variable in the ADMM algorithm and the iteration mechanism are modified, applied to the optimization problem of power system scheduling, and the reserve partition is considered. Compared with the centralized method, the distributed optimization method can reduce the calculation burden of the center node and increase data security; at the same time, the reserve partition is considered, and a flexible scheduling mode is provided for the power generation side to cope with the increasing proportion of renewable energy.

[0131] Secondly, the power energy-reserve scheduling device based on the ADMM algorithm and the reserve cross-zone calling according to the embodiment of the application is described with reference to the accompanying drawings.

[0132] Figure 3 The power energy-reserve scheduling device based on the ADMM algorithm and the reserve cross-zone calling according to the embodiment of the application is described with reference to the accompanying drawings.

[0133] As Figure 3 shown, the power energy-reserve scheduling device 10 based on the ADMM algorithm and the reserve cross-zone calling comprises a model establishing module 100, an algorithm constructing module 200, and a model solving module 300.

[0134] The model establishing module 100 is configured to construct a target function according to power system economic operation data, and construct a power system electric energy-reserve joint scheduling model based on the target function, electric energy constraints and reserve constraints, reserve partition constraints, and cross-zone callability. The algorithm constructing module 200 is configured to construct a solving algorithm of a distributed optimization problem according to an alternating direction multiplier algorithm. The model solving module 300 is configured to perform iterative solving on the power system electric energy-reserve joint scheduling model by using the solving algorithm of the distributed optimization problem, and stop the iteration and output an optimal scheduling scheme of the power system when an iteration end condition is met.

[0135] Optionally, in an embodiment of the application, the power system economic operation data comprises one or more of unit output upper and lower limits, unit ramping rate, unit cost function, reserve cost function, and line maximum power flow.

[0136] Optionally, in an embodiment of the application, the target function constructed according to the power system economic operation data is:

[0137]

[0138] W i (p i , r i ) = a i p i2 +b i p i +c i +d i r i , i = 1, 2, 3, …, N

[0139] where N represents the number of generators, W i (p i , r i ) represents the cost of the i-th generator, p i is the generator output at node i, r i is the generator reserve at node i, a i , b i , c i , d i are coefficients describing the quadratic relationship between the cost of the generator and its output and reserve.

[0140] Optionally, in an embodiment of the present application, the power energy constraint and the reserve constraint include:

[0141] The power energy constraint is:

[0142]

[0143]

[0144] where L i is the load at node i, T k is the power flow of the k-th branch, k1 and k2 are the starting node and the ending node of the k-th branch, θ i is the voltage phase angle of node i, x k is the line impedance of branch k, and N br is the number of branches.

[0145] The output constraint of each generator and the power flow constraint of each line are:

[0146] p i mln ≤ p i ≤ p i max , i = 1, 2, …, N gen

[0147] -T k max ≤ T k ≤ T k max , k = 1, 2, …, N br

[0148] where p i minis the lower limit of generator output, p i max is the upper limit of generator output, N gen is the number of generators, T k max is the maximum active power flow of the line, P

[0149] The reserve constraint is:

[0150]

[0151] r i +p i ≤p i max , i = 1, 2,..., N gen

[0152] 0≤r i ≤r i max , i = 1, 2,..., N gen

[0153] wherein, p t is the reserve coefficient, r i max is the maximum reserve of the ith generator.

[0154] Optionally, in an embodiment of the present application, the reserve partition constraint and the cross-region callability include:

[0155] The line constraint of establishing the transmission line blockage between the regions is:

[0156]

[0157] wherein, p is the region, Q p is the total reserve demand in the region p, Br p is a set of transmission lines connected to the region p.

[0158] Optionally, in an embodiment of the present application, the algorithm construction module is further configured to: adopt an alternating direction multiplier structure to construct a Lagrangian function with a penalty term, select a consensus variable, introduce the consensus variable into the Lagrangian function, and perform Lagrangian multiplier iteration and consensus variable iteration based on the consensus variable.

[0159] Optionally, in an embodiment of the present application, the iteration end condition includes: the Lagrangian multiplier and the consensus variable value converge at the same time, and the penalty term of the Lagrangian function is greater than zero.

[0160] It should be noted that the foregoing explanation and description of the embodiment of the electric energy-reserve joint dispatching method also apply to the electric energy-reserve joint dispatching device of the embodiment, which will not be described here again.

[0161] The power energy-reserve scheduling device based on the ADMM algorithm and the reserve cross-zone calling according to the embodiments of the application modifies the consensus variable and the iteration mechanism in the ADMM algorithm, applies the ADMM algorithm to the optimization problem of power system scheduling, and considers the reserve partition. Compared with the centralized method, the distributed optimization method can reduce the calculation burden of the center node and increase data security. Meanwhile, considering the reserve partition, the distributed optimization method provides a flexible scheduling mode for the power generation side to cope with the increasing proportion of renewable energy.

[0162] Figure 4 The structural schematic diagram of the electronic device provided by the embodiments of the application is provided. The electronic device can include:

[0163] The memory 401, the processor 402, and the computer program stored in the memory 401 and executable on the processor 402.

[0164] The processor 402 implements the power energy-reserve scheduling method based on the ADMM algorithm and the reserve cross-zone calling provided in the above embodiments when executing the program.

[0165] Further, the electronic device further includes:

[0166] The communication interface 403 is used for communication between the memory 401 and the processor 402.

[0167] The memory 401 is used to store the computer program executable on the processor 402.

[0168] The memory 401 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0169] If the memory 401, the processor 402, and the communication interface 403 are independently implemented, the communication interface 403, the memory 401, and the processor 402 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 4 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0170] Optionally, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can complete the communication among each other through an internal interface.

[0171] The processor 402 can be a central processing unit (CPU) or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the application.

[0172] The embodiment further provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the energy- reserve scheduling method based on the ADMM algorithm and the reserve cross-zone calling as described above.

[0173] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the description of the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0174] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0175] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing the specified logic functions (or steps) and / or can be implemented by one or more hardware or software components, by other physical components, or by combinations thereof. In some embodiments, the preferred embodiments of the application can be implemented in one or more computer programs or software modules that are executed on one or more programmable computers or processors. Generally, computer programs include a routine that is executed by the processor using the computer program to perform desired functions. The execution of the routine can be spread over several computers or processors, which can be physically located close together, or which can be remotely located, and which can span several locations utilizing, for example, a communications network. Each program can be implemented in a high-level procedural or object-oriented programming and / or scripting languages, and / or in assembly or machine languages. Each such program can be stored in a computer readable storage medium, which can be any device or medium that can store and / or transfer data.

[0176] It should be understood that portions of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0177] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

Claims

1. An energy reserve scheduling method based on ADMM algorithm and backup cross-zone calling, characterized in that, The method comprises the following steps: a target function is constructed according to economic operation data of the power system, and a power energy-reserve joint scheduling model of the power system is constructed based on the target function, power energy constraints and reserve constraints, reserve partition constraints and cross-regional callability; a solving algorithm of a distributed optimization problem is constructed according to an alternating direction multiplier algorithm; the power energy-reserve joint scheduling model of the power system is iteratively solved by using the solving algorithm of the distributed optimization problem, and the iteration is stopped when an iteration end condition is met, and an optimal scheduling scheme of the power system is output; the target function constructed according to the economic operation data of the power system is as follows: where N denotes the number of generators, represents the i cost of the generators at bus i, is the node power output of the generators at bus i, is the node generator reserve at bus i, , , , is the coefficient describing the quadratic relationship between the cost of the generators and their power output and reserve. the power energy constraints and the reserve constraints comprise: the power energy constraints are as follows: in, For nodes The load at the location, It is the first k The trend of a side road and It is the first k The starting and ending nodes of the jump path, It is a node i voltage phase angle, It is the line impedance of branch k. This represents the number of branch roads; the output constraints of each generator and the power flow constraints of each line are as follows: wherein, is a lower limit of generator output, is an upper limit of generator output, is a number of generators, is a maximum active power flow of the line; the reserve constraints are as follows: wherein, is a reserve factor, is the first maximum reserve of the generator of the station; the reserve partition constraints and the cross-regional callability comprise: the line constraints of the transmission line blockage between regions are established as follows: wherein, is a region, is a region total backup demand within, is a set of transmission lines connected to the region ; the solving algorithm of the distributed optimization problem constructed according to the alternating direction multiplier algorithm comprises: a Lagrange function with a penalty term is constructed by using an alternating direction multiplier structure; a consensus variable is selected, and the consensus variable is introduced into the Lagrange function; Lagrange multiplier iteration and consensus variable iteration based on the consensus variable are performed; the iteration end condition comprises: the Lagrange multiplier and the consensus variable value are converged at the same time, and the penalty term of the Lagrange function is greater than zero.

2. The method of claim 1, wherein, The economic operation data of the power system comprises one or more of the following: upper and lower limits of unit output, unit ramp rate, unit cost function, reserve cost function and maximum power flow of a line.

3. An electric energy - reserve scheduling device based on ADMM algorithm and reserve inter-zone calling, characterized in that, It comprises: a model establishing module, configured to construct a target function according to economic operation data of a power system, and construct a power energy-reserve joint scheduling model of the power system based on the target function, power energy constraints and reserve constraints, reserve partition constraints and cross-regional callability; an algorithm constructing module, configured to construct a solving algorithm of a distributed optimization problem according to an alternating direction multiplier algorithm; a model solving module, configured to iteratively solve the power energy-reserve joint scheduling model of the power system by using the solving algorithm of the distributed optimization problem, and stop the iteration when an iteration end condition is met, and output an optimal scheduling scheme of the power system; the target function constructed according to the economic operation data of the power system is as follows: where N denotes the number of generators, represents the cost of the i generator at bus i, represents the generator output at bus i, represents the generator reserve at bus i, represents the generator output at bus i, represents the generator reserve at bus i, , , , represents the coefficient describing the quadratic relationship between the cost of the generator and its output and reserve. the power energy constraints and the reserve constraints comprise: the power energy constraints are as follows: in, For nodes The load at the location, It is the first k The trend of a side road and It is the first k The starting and ending nodes of the jump path, It is a node i voltage phase angle, It is the line impedance of branch k. This represents the number of branch roads; the output constraints of each generator and the power flow constraints of each line are as follows: wherein, is the lower limit of the generator output, is the upper limit of the generator output, is the number of generators, is the maximum active power flow of the line; the reserve constraints are as follows: wherein, is a reserve factor, is the maximum reserve of the generator of the nth bus, is the maximum reserve of the generator of the nth bus, the reserve partition constraints and the cross-regional callability comprise: the line constraints of the transmission line blockage between regions are established as follows: wherein, is a region, is a region total backup demand within, is a set of transmission lines connected to the region consisting of transmission lines. the algorithm constructing module is further configured to: construct a Lagrange function with a penalty term by using an alternating direction multiplier structure, select a consensus variable, introduce the consensus variable into the Lagrange function, and perform Lagrange multiplier iteration and consensus variable iteration based on the consensus variable; the iteration end condition comprises: the Lagrange multiplier and the consensus variable value are converged at the same time, and the penalty term of the Lagrange function is greater than zero.

4. The apparatus of claim 3, wherein, The economic operation data of the power system comprises one or more of unit output upper and lower limits, unit ramp rate, unit cost function, reserve cost function, and line maximum power flow.

5. An electronic device, comprising: The computer program is stored in the memory and executable in the processor, and the processor executes the program to realize the method for electric energy-reserve scheduling based on the ADMM algorithm and reserve cross-zone calling according to any one of claims 1-2. The computer program is stored in the memory and executable in the processor, and the processor executes the program to realize the method for electric energy-reserve scheduling based on the ADMM algorithm and reserve cross-zone calling according to any one of claims 1-2.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, ​

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