Reactive efficiency optimization method and system considering voltage safety multi-cycle

Abstract

The present invention relates to a reactive power benefit optimization method and system considering multi-period voltage security. The optimization method includes: First step, establishing a reactive power benefit optimization model with the objectives of minimizing network loss, minimizing voltage deviation, and minimizing the reactive power output of generators. The reactive power benefit optimization model includes distribution network power flow constraints, operating constraints of reactive power regulation equipment such as on-load tap-changing branch switches and shunt capacitors, and node voltage constraints; Second step, modifying the objective function of the reactive power benefit optimization model in the first step by replacing some constraints with discrete variables restricted by time constraints for subsequent solution by a hierarchical algorithm; Third step, solving for y t and z t within the entire cycle by fixing the discrete variable d t ; Fourth step, outputting the final reactive power benefit optimization result after iterative operations of the inner and outer layer algorithms. The present invention can seek preventive setting points for static reactive power resources and regulating the bus voltage amplitude so that the system has sufficient dynamic reactive power reserve to operate safely under various emergency conditions.

Description

Technical Field

[0001] The invention relates to a reactive power optimization method, and more specifically to a reactive power benefit optimization method and system taking voltage safety multi-cycle into consideration. Background Art

[0002] Reactive power resources can be divided into two major categories: dynamic and static. Dynamic reactive resources are automatically controlled to change their reactive output quickly and continuously in response to voltage deviations, regardless of their terminal voltage level. Dynamic reactive resources include synchronous generators / capacitors and flexible AC transmission system (FACTS) equipment. Inverter-based generation resources have a rapid drop in reactive output when the terminal voltage drops, which greatly limits their ability to prevent voltage instability. Since dynamic reactive resources are fast-responding and voltage-independent, they are crucial in emergency situations, so the power system needs to reserve more dynamic reactive power. In other words, during normal operation, the reactive power output of dynamic reactive resources should be minimized so that there is enough dynamic reactive power available to respond to emergencies. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a reactive power benefit optimization method taking into account multiple cycles of voltage safety, which can seek static reactive power resources and adjust preventive setting points of bus voltage amplitude so that the system has sufficient dynamic reactive power reserves to operate safely in various emergency situations.

[0004] The technical solution adopted by the present invention to solve the technical problem is: constructing a reactive power benefit optimization method considering voltage safety multi-cycle, comprising the following steps:

[0005] The first step is to establish a reactive power benefit optimization model with the goal of minimizing network loss, voltage deviation, and generator output reactive power. The reactive power benefit optimization model includes distribution network flow constraints, on-load tap-changing branch switches, reactive power regulation equipment operation constraints of shunt capacitors, and node voltage constraints.

[0006] The second step is to modify the objective function of the reactive power benefit optimization model in the first step by replacing some constraints with discrete variables subject to time constraints, and obtain a modified objective function;

[0007] The third step is to decompose the problem from the full cycle T into each time period t in the inner algorithm, by fixing the discrete variable d t To solve the control variable y in the modified objective function within the whole cycle t and consistent control variable z t ;

[0008] Step 4: The algorithm collects the solutions to the t subproblems in step 3 and updates the discrete variable d in the outer algorithm.t and the penalty parameter ξ in the modified objective function t The value of is used to meet the time constraint, and the final reactive power benefit optimization result is output after iteration of the inner and outer layers of the algorithm.

[0009] According to the above scheme, the objective function of the reactive power benefit optimization model is:

[0010]

[0011] In the formula, s = [1, 2, 3, ..., S] is the set of emergency events, s = 0 means that no emergency event occurs, T is the time period set, is the state variable set at time t for event s, which includes current and voltage; is the control variable set at time t of event s, which includes transformer taps and reactors; L is the branch set in the system, X l represents the line impedance of branch l, is the current on branch l at time t for event s; NG is the set of generator nodes in the system, is the reactive power generated by generator node i at time t during event s; N is the set of nodes in the system, is the voltage on node i at time t of event s, is the expected voltage on node n; a1, a2, a3 are weight coefficients.

[0012] According to the above scheme, the constraints of the reactive power benefit optimization model include equality constraints and inequality constraints, specifically:

[0013] 1) Equality constraints

[0014]

[0015] In the formula, and They represent the active and reactive power generated by the generator node i respectively; and represent the active load and reactive load of generator node i respectively; is the voltage on node i at time t during event s; is the voltage on node j at time t of event s; G ij is the conductance between node i and node j; B ij is the susceptance between node i and node j; θ ij is the voltage phase angle difference between node i and node j;

[0016] 2) Inequality constraints

[0017]

[0018] In the formula, is the voltage on node i at time t of event s, u i-min and u i-max are the minimum and maximum values ​​of the voltage at node i, respectively; represents the reactive power generated by the generator node i, Q Gi-min and Q Gi-max are the minimum and maximum values ​​of reactive power output of generator node i respectively; is the position of the upper tap of transformer node i at time t in event s, Tap i-min and Tap i-max are the minimum and maximum values ​​of the tap position on transformer node i, respectively; is the reactive power compensation capacity at node i at time t of event s, Q Ci-min and Q Ci-max are the minimum and maximum values ​​of reactive power compensation capacity at node i; When there is no emergency event, the value of the i-th control variable at time t, y i max is the maximum number of actions of the ith control variable in one cycle.

[0019] According to the above scheme, the corrected objective function is:

[0020]

[0021] In the formula, z t is a consistent control variable between the base case and all emergency events at time t; d t represents a discrete control variable subject to time constraints; Y represents a set of control indices corresponding to the discrete variables; is the control variable y t and consistent control variable z t The penalty parameter between t Penalty parameter to make consistent control close to effective discrete control.

[0022] According to the above scheme, the objective function (4) is solved by adopting a hierarchical solution method.

[0023] According to the above scheme, the inner algorithm decomposes the problem from the full cycle T into each time period t, and then solves it step by step. After the problem is decomposed into each time period t, the objective function changes to:

[0024]

[0025] First initialize the variables and penalty parameters and t , variable yt and z t Initialized as some temporary control plans, and then updated according to formula (6) to obtain a new set of control variables

[0026]

[0027] Secondly, according to A new set of consistent control variables is updated as follows

[0028]

[0029] In the above formula, the penalty parameter is then updated as follows

[0030]

[0031] In the formula, the parameters is the control variable y t and consistent control variable z t The penalty parameter between .

[0032] According to the above scheme, in the fourth step, an intermediate variable h is introduced, assuming:

[0033]

[0034] Where, parameter k>1; if h>0, the new generation of discrete variables is updated as follows: and penalty parameters

[0035]

[0036] If h<0, then the discrete variable d t and penalty parameter ξ t Remain unchanged; when the error is less than the pre-specified value ε r The proposed external algorithm stops when , where the error is defined as:

[0037]

[0038] In the above formula, ε is the defined error; T is the time period set; Y represents a set of control indices corresponding to discrete variables; S is the emergency event set.

[0039] The present invention also provides a reactive power benefit optimization system considering multiple cycles of voltage safety, including a reactive power benefit optimization module, which is used to establish a reactive power benefit optimization model with the goal of minimizing network loss, voltage deviation, and generator output reactive power; the reactive power benefit optimization model includes power flow constraints of the distribution network, operation constraints of reactive power regulation equipment of on-load tap-changing branch switches and shunt capacitors, and node voltage constraints;

[0040] The objective function correction module of the reactive power benefit optimization model is used to correct the objective function of the reactive power benefit optimization model by replacing part of the constraints with discrete variables subject to time constraints to obtain a corrected objective function;

[0041] The variable solving module is used to decompose the problem from the full cycle T into each time period t in the inner algorithm, by fixing the discrete variable d t To solve the control variable y in the modified objective function within the whole cycle t and consistent control variable z t ;

[0042] The reactive power optimization result module is used to collect the solutions of t sub-problems and update the discrete variable d in the outer algorithm. t and the penalty parameter ξ in the modified objective function t The value of is used to meet the time constraint, and the final reactive power benefit optimization result is output after iteration of the inner and outer layers of the algorithm.

[0043] The present invention also provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0044] A computer program is stored in the memory, and when the program is executed by the processor, the processor is caused to perform the steps of the method.

[0045] The present invention also provides a computer-readable storage medium on which executable instructions are stored. When the instructions are executed by a processor, the processor implements the above method.

[0046] The method and system for optimizing reactive power efficiency considering multiple cycles of voltage safety of the present invention have the following features:

[0047] Beneficial effects:

[0048] 1. The reactive power benefit optimization method considering voltage safety multi-cycle proposed in the present invention establishes a reactive power benefit optimization model with the goal of minimizing network loss, voltage deviation and generator output reactive power under the constraints of distribution network flow, operation constraints of reactive power regulation equipment such as on-load voltage regulating branch switches and shunt capacitors, node voltage constraints and the number of switch actions of each discrete control device, thereby optimizing the economic benefits of distribution network operation;

[0049] 2. The present invention has certain theoretical value and practical significance in providing a preventive reactive power dispatch control plan. It can reasonably seek static reactive resources and adjust the preventive setting point of bus voltage amplitude so that the system has sufficient dynamic reactive reserves to operate safely in various emergency situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0051] Figure 1 It is a flow chart of the reactive power benefit optimization method considering voltage safety multi-cycle of the present invention;

[0052] Figure 2 It is the inner algorithm flow chart;

[0053] Figure 3 It is the outer algorithm flow chart. DETAILED DESCRIPTION

[0054] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0055] like Figure 1 As shown, the reactive power benefit optimization method considering voltage safety multi-cycle of the present invention comprises the following steps:

[0056] The first step is to establish a reactive power benefit optimization model with the goal of minimizing network loss, voltage deviation, and generator output reactive power. The reactive power benefit optimization model includes distribution network flow constraints, on-load tap-changing branch switches, reactive power regulation equipment operation constraints of shunt capacitors, and node voltage constraints.

[0057] The objective function of the reactive power benefit optimization model is:

[0058]

[0059] In the formula, s = [1, 2, 3, ..., S] is the set of emergency events, s = 0 means that no emergency event occurs, T is the time period set, is the state variable set at time t for event s, which includes current and voltage; is the control variable set at time t of event s, which includes transformer taps and reactors; L is the branch set in the system, X l represents the line impedance of branch l, is the current on branch l at time t for event s; NG is the set of generator nodes in the system, is the reactive power generated by generator node i at time t during event s; N is the set of nodes in the system, is the voltage on node i at time t of event s, is the expected voltage on node n; a1, a2, a3 are weight coefficients.

[0060] The constraints of the reactive power efficiency optimization model include equality constraints and inequality constraints, specifically:

[0061] 1) Equality constraints

[0062]

[0063] In the formula, and They represent the active and reactive power generated by the generator node i respectively; and represent the active load and reactive load of generator node i respectively; is the voltage on node i at time t during event s; is the voltage on node j at time t of event s; G ij is the conductance between node i and node j; B ij is the susceptance between node i and node j; θ ij is the voltage phase angle difference between node i and node j;

[0064] 2) Inequality constraints

[0065]

[0066] In the formula, is the voltage on node i at time t of event s, u i-min and u i-max are the minimum and maximum values ​​of the voltage at node i, respectively; represents the reactive power generated by the generator node i, Q Gi-min and Q Gi-max are the minimum and maximum values ​​of reactive power output of generator node i respectively; is the position of the upper tap of transformer node i at time t in event s, Tap i-min and Tap i-max are the minimum and maximum values ​​of the tap position on transformer node i, respectively; is the reactive power compensation capacity at node i at time t of event s, Q Ci-min and Q Ci-max are the minimum and maximum values ​​of reactive power compensation capacity at node i; y 0 t,i When there is no emergency event, the value of the i-th control variable at time t, y i max is the maximum number of actions of the ith control variable in one cycle.

[0067] The second step is to modify the objective function of the reactive power optimization model in the first step by replacing some constraints with discrete variables subject to time constraints, so as to facilitate the subsequent hierarchical algorithm solution;

[0068] The corrected objective function is:

[0069]

[0070] In the formula, z t is a consistent control variable between the base case and all emergency events at time t; d t represents a discrete control variable subject to a time constraint; Y represents a set of control indices corresponding to discrete variables (e.g., transformer tap ratio); is the control variable y t and consistent control variable z t The penalty parameter between t In order to make the consistent control close to the penalty parameter of effective discrete control, the objective function (4) is solved by using the hierarchical solution method.

[0071] Step 3: Initialize variables and penalty parameters and t , variable y t and z t is initialized as a temporary control plan, and in the inner algorithm the problem is decomposed from the full cycle T into each time period t, by fixing the discrete variable d t To solve for y in the full cycle t and z t ;

[0072] The inner algorithm decomposes the problem from the full cycle T into each time period t, and then solves it step by step. After the problem is decomposed into each time period t, the objective function changes to:

[0073]

[0074] First initialize the variables and penalty parameters and t , variable y t and z tInitialized as some temporary control plans, and then updated according to formula (6) to obtain a new set of control variables

[0075]

[0076] Secondly, according to A new set of consistent control variables is updated as follows

[0077]

[0078] In the above formula, the penalty parameter is then updated as follows

[0079]

[0080] In the formula, the parameters is the control variable y t and consistent control variable z t The penalty parameter between .

[0081] Step 4: The algorithm collects solutions to t subproblems and updates the discrete variable d in the outer algorithm. t and penalty parameter ξ t The value of is used to meet the time constraint, and the final reactive power benefit optimization result is output after iteration of the inner and outer layers of the algorithm.

[0082] Introduce the intermediate variable h, and assume:

[0083]

[0084] Where, parameter k>1; if h>0, the new generation of discrete variables is updated as follows: and penalty parameters

[0085]

[0086] If h<0, then the discrete variable d t and penalty parameter ξ t Remain unchanged; when the error is less than the pre-specified value ε r The proposed external algorithm stops when , where the error is defined as:

[0087]

[0088] In the above formula, ε is the defined error; T is the time period set; Y represents a set of control indices corresponding to discrete variables; S is the set of emergency events. Figure 2-3 The figure shows the overall process of the hierarchical algorithm described in steps 3 and 4.

[0089] The present invention also provides a reactive power benefit optimization system considering multiple cycles of voltage safety, including a reactive power benefit optimization module, which is used to establish a reactive power benefit optimization model with the goal of minimizing network loss, voltage deviation, and generator output reactive power; the reactive power benefit optimization model includes power flow constraints of the distribution network, operation constraints of reactive power regulation equipment of on-load tap-changing branch switches and shunt capacitors, and node voltage constraints;

[0090] The objective function correction module of the reactive power benefit optimization model is used to correct the objective function of the reactive power benefit optimization model by replacing some constraints with discrete variables subject to time constraints, so as to facilitate the subsequent hierarchical algorithm solution;

[0091] y in the whole cycle t and z t Module, used to initialize variables and penalty parameters and t , variable y t and z t is initialized as a temporary control plan, and in the inner algorithm the problem is decomposed from the full cycle T into each time period t, by fixing the discrete variable d t To solve for y in the full cycle t and z t ;

[0092] The reactive power optimization result module is used to collect the solutions of t sub-problems and update the discrete variable d in the outer algorithm. t and penalty parameter ξ t The value of is used to meet the time constraint, and the final reactive power benefit optimization result is output after iteration of the inner and outer layers of the algorithm.

[0093] Based on the same inventive concept, the present invention also provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0094] A computer program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of the reactive power benefit optimization method considering voltage safety multiple cycles.

[0095] Based on the same inventive concept, the present invention also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the above-mentioned reactive power benefit optimization method considering multiple cycles of voltage safety.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A reactive power benefit optimization method considering voltage safety multi-cycle, characterized in that: The following steps are involved: The first step is to establish a reactive power benefit optimization model with the goal of minimizing network loss, voltage deviation, and generator output reactive power. The reactive power benefit optimization model includes distribution network flow constraints, on-load tap-changing branch switches, reactive power regulation equipment operation constraints of shunt capacitors, and node voltage constraints. The second step is to modify the objective function of the reactive power benefit optimization model in the first step by replacing some constraints with discrete variables subject to time constraints, and obtain a modified objective function; The third step is to decompose the problem from the full cycle T into each time period t in the inner algorithm, by fixing the discrete variable d t To solve the control variable y in the modified objective function within the whole cycle t and consistent control variable z t ; Step 4: The algorithm collects the solutions to the t subproblems in step 3 and updates the discrete variable d in the outer algorithm. t and the penalty parameter ξ in the modified objective function t The value of is used to meet the time constraint, and the final reactive power benefit optimization result is output after iteration of the inner and outer layers of the algorithm.

2. The reactive power benefit optimization method considering voltage safety multi-cycle according to claim 1 is characterized in that: The objective function of the reactive power benefit optimization model is: In the formula, s = [1, 2, 3, ..., S] is the set of emergency events, s = 0 means that no emergency event occurs, T is the time period set, is the state variable set at time t for event s, which includes current and voltage; is the control variable set at time t of event s, which includes transformer taps and reactors; L is the branch set in the system, X l represents the line impedance of branch l, is the current on branch l at time t for event s; NG is the set of generator nodes in the system, is the reactive power generated by generator node i at time t during event s; N is the set of nodes in the system, is the voltage on node i at time t of event s, is the expected voltage on node n; a1, a2, a3 are weight coefficients.

3. The reactive power benefit optimization method considering voltage safety multi-cycle according to claim 2 is characterized in that: The constraints of the reactive power efficiency optimization model include equality constraints and inequality constraints, specifically: 1) Equality constraints In the formula, and They represent the active and reactive power generated by the generator node i respectively; and represent the active load and reactive load of generator node i respectively; is the voltage on node i at time t during event s; is the voltage on node j at time t of event s; G ij is the conductance between node i and node j; B ij is the susceptance between node i and node j; θ ij is the voltage phase angle difference between node i and node j; 2) Inequality constraints In the formula, is the voltage on node i at time t of event s, u i-min and u i-max are the minimum and maximum values ​​of the voltage at node i, respectively; represents the reactive power generated by the generator node i, Q Gi-min and Q Gi-max are the minimum and maximum values ​​of reactive power output of generator node i respectively; is the position of the upper tap of transformer node i at time t in event s, Tap i-min and Tap i-max are the minimum and maximum values ​​of the tap position on transformer node i, respectively; is the reactive power compensation capacity at node i at time t of event s, Q Ci-min and Q Ci-max are the minimum and maximum values ​​of reactive power compensation capacity at node i; When there is no emergency event, the value of the i-th control variable at time t, y i max is the maximum number of actions of the ith control variable in one cycle.

4. The reactive power benefit optimization method considering voltage safety multi-cycle according to claim 1 is characterized in that: The corrected objective function is: In the formula, z t is a consistent control variable between the base case and all emergency events at time t; d t represents a discrete control variable subject to time constraints; Y represents a set of control indices corresponding to the discrete variables; is the control variable y t and consistent control variable z t The penalty parameter between t Penalty parameter to make consistent control close to effective discrete control.

5. The reactive power benefit optimization method considering voltage safety multi-cycle according to claim 4 is characterized in that: The objective function (4) is solved by adopting a hierarchical solution method.

6. The reactive power benefit optimization method considering voltage safety multi-cycle according to claim 1 is characterized in that: The inner algorithm decomposes the problem from the full cycle T into each time period t, and then solves it step by step. After the problem is decomposed into each time period t, the objective function changes to: First initialize the variables and penalty parameters and t , variable y t and z t Initialized as some temporary control plans, and then updated according to formula (6) to obtain a new set of control variables Secondly, according to A new set of consistent control variables is updated as follows In the above formula, the penalty parameter is then updated as follows In the formula, the parameters is the control variable y t and consistent control variable z t The penalty parameter between .

7. The reactive power benefit optimization method considering voltage safety multi-cycle according to claim 1 is characterized in that: In the fourth step, an intermediate variable h is introduced, and it is assumed that: Where, parameter k>1; if h>0, the new generation of discrete variables is updated as follows: and penalty parameters If h<0, then the discrete variable d t and penalty parameter ξ t Remain unchanged; when the error is less than the pre-specified value ε r The proposed external algorithm stops when , where the error is defined as: In the above formula, ε is the defined error; T is the time period set; Y represents a set of control indices corresponding to discrete variables; S is the emergency event set.

8. A reactive power benefit optimization system considering voltage safety multi-cycle, characterized in that: include: The reactive power benefit optimization module is used to establish a reactive power benefit optimization model with the goal of minimizing network loss, voltage deviation, and generator output reactive power; the reactive power benefit optimization model includes distribution network flow constraints, on-load tap-changing branch switches, reactive power regulation equipment operation constraints of shunt capacitors, and node voltage constraints; The objective function correction module of the reactive power benefit optimization model is used to correct the objective function of the reactive power benefit optimization model by replacing part of the constraints with discrete variables subject to time constraints, thereby obtaining a corrected objective function; The variable solving module is used to decompose the problem from the full cycle T into each time period t in the inner algorithm, by fixing the discrete variable d t To solve the control variable y in the modified objective function within the whole cycle t and consistent control variable z t ; The reactive power optimization result module is used to collect the solutions of t sub-problems and update the discrete variable d in the outer algorithm. t and the penalty parameter ξ in the modified objective function t The value of is used to meet the time constraint, and the final reactive power benefit optimization result is output after iteration of the inner and outer layers of the algorithm.

9. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; A computer program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 7.

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