Power system scheduling optimization method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power system scheduling optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring parameter information of equipment in a power system; the parameter information comprises a first quantity variable of the public building participating in the demand response and a demand response cost coefficient; according to the parameter information, determining an objective function and constraint conditions for performing operation cost optimization on the power system; the objective function comprises an optimization function of the cost of the public building participating in the demand response; the constraint condition comprises a number constraint condition of the public buildings participating in the demand response; constructing an operation cost optimization model of the power system according to the target function and the constraint condition; solving the operation cost optimization model to obtain shadow prices of the public buildings participating in the demand response; and performing scheduling optimization on the power system according to the shadow price of the public building participating in the demand response. By adopting the method, the scheduling optimization efficiency and effectiveness of the power system can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a power system dispatch optimization method and device, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] With the advancement of urbanization, the proportion of public buildings in cities is increasing, and the electricity consumption of public buildings accounts for a high proportion. Public buildings have many flexible resources such as air conditioners, electric vehicles, and energy storage, which can participate in power demand response to alleviate the pressure of power supply and demand balance, so public buildings are good demand response subjects.

[0003] In power system dispatch, adjusting and optimizing the participation of public buildings in response can effectively improve the resource utilization rate of the power system and reduce power supply costs.

[0004] However, the adjustment of the participation of public buildings in response in the related art lacks scientific and effective basis, which affects the efficiency and effectiveness of power system dispatch optimization. SUMMARY

[0005] Therefore, it is necessary to provide a power system dispatch optimization method, device, computer equipment, computer readable storage medium and computer program product capable of improving the efficiency and effectiveness of power system dispatch optimization and reducing power system operation costs.

[0006] In a first aspect, the present application provides a power system dispatch optimization method, comprising:

[0007] obtaining device parameter information of a power system; wherein the device parameter information comprises demand response cost coefficients and response capacities of a plurality of candidate public buildings respectively;

[0008] determining a target function and a constraint condition for operation cost optimization of the power system according to the device parameter information; wherein the target function comprises a response cost optimization function of the candidate public buildings participating in demand response of the power system; and the constraint condition comprises a response capacity constraint condition of the candidate public buildings participating in demand response of the power system;

[0009] constructing an operation cost optimization model of the power system according to the target function and the constraint condition;

[0010] solving the operation cost optimization model to obtain shadow prices of the candidate public buildings participating in demand response of the power system;

[0011] dispatching and optimizing the power system according to the shadow prices.

[0012] In one embodiment, the method further comprises:

[0013] constructing a total demand response cost function of the plurality of candidate public buildings according to the demand response cost coefficients of the respective candidate public buildings and the response capacities of the respective candidate public buildings;

[0014] constructing the response cost optimization function with the function value of the total demand response cost function as the optimization objective.

[0015] In one embodiment, the device parameter information further comprises a capacity threshold of participation of each of the candidate public buildings in the demand response of the power system; and the method further comprises:

[0016] constructing the response capacity constraint condition with the value of the response capacity of each of the candidate public buildings not exceeding the capacity threshold.

[0017] In one embodiment, the method further comprises:

[0018] relaxing the operation cost optimization model to obtain a relaxed model;

[0019] introducing a dual variable corresponding to the response capacity constraint condition in the relaxed model to obtain a dual problem model corresponding to the operation cost optimization model;

[0020] solving the dual problem model to obtain an optimal solution as the shadow price.

[0021] In one embodiment, the device parameter information further comprises a power generation capacity, a start-up cost coefficient, a standby cost coefficient, a power generation power, a switch state, and a standby capacity of a power generator set; the objective function further comprises a power generation cost optimization function of the power generator set and a standby cost optimization function of the power generator set; and the method further comprises:

[0022] constructing the power generation cost optimization function according to the power generation capacity, the start-up cost coefficient, the power generation power, and the switch state of the power generator set;

[0023] constructing the standby cost optimization function according to the standby cost coefficient and the standby capacity.

[0024] In one embodiment, the device parameter information further comprises a power distribution influence of a power transmission line, a transmission capacity condition; and the constraint condition further comprises a power balance constraint; and the method further comprises:

[0025] calculating a power distribution condition of the power transmission line according to the power distribution influence of the power transmission line;

[0026] The line transmission capacity constraint is constructed with the power distribution satisfying the transmission capacity condition as a target.

[0027] In one embodiment, the method further comprises:

[0028] A reference cost of each of the candidate public buildings is determined according to the shadow price.

[0029] A target public building participating in demand response of the power system and a target demand response capacity of the target public building are determined from the multiple candidate public buildings according to the reference cost.

[0030] In a third aspect, the present application further provides a power system dispatching optimization device, which comprises:

[0031] An acquisition module is configured to acquire device parameter information of a power system, wherein the device parameter information comprises demand response cost coefficients and response capacities of multiple candidate public buildings respectively;

[0032] A determination module is configured to determine a target function and a constraint condition for optimizing operation cost of the power system according to the device parameter information, wherein the target function comprises a response cost optimization function of the candidate public buildings participating in demand response of the power system, and the constraint condition comprises a response capacity constraint condition of the candidate public buildings participating in demand response of the power system;

[0033] A construction module is configured to construct an operation cost optimization model of the power system according to the target function and the constraint condition;

[0034] A solution module is configured to solve the operation cost optimization model to obtain a shadow price of the candidate public buildings participating in demand response of the power system.

[0035] An optimization module is configured to perform dispatching optimization on the power system according to the shadow price.

[0036] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any embodiment of the first aspect when executing the computer program.

[0037] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of any embodiment of the first aspect.

[0038] In a fifth aspect, the present application further provides a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the following steps:

[0039] The power system dispatch optimization method, device, computer equipment, computer readable storage medium and computer program product can provide data support for subsequent introduction of the influence of the public building participating in demand response into the operation cost optimization of the power system by obtaining the device parameter information of the power system, wherein the device parameter information can correspond to the decision variable of the operation cost optimization model of the power system, and specifically, the device parameter information includes the demand response cost coefficient and the response capacity of each candidate public building, thereby providing data support for subsequent introduction of the influence of the public building participating in demand response into the operation cost optimization of the power system; the objective function and the constraint condition of the operation cost optimization of the power system are determined according to the device parameter information, wherein the objective function includes the response cost optimization function of the demand response of the candidate public building participating in the power system, and the constraint condition includes the response capacity constraint condition of the demand response of the candidate public building participating in the power system; the operation cost optimization model of the power system is constructed according to the objective function and the constraint condition; the shadow price of the demand response of the candidate public building participating in the power system is obtained by solving the operation cost optimization model, thereby obtaining the cost reduction of the power system that can be brought by the demand response of the public building, and finally the power system is dispatched and optimized according to the shadow price of the public building participating in the demand response, so that more efficient and intelligent power system operation dispatching can be realized, and the operation cost of the power system is effectively reduced and the resource utilization efficiency of the power system is improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without any creative effort.

[0041] Figure 1 The flowchart of the power system dispatch optimization method in one embodiment;

[0042] Figure 2 The structural block diagram of the power system dispatch optimization device in one embodiment;

[0043] Figure 3 The internal structure diagram of the computer equipment in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0045] In one exemplary embodiment, as Figure 1As shown, a power system dispatch optimization method is provided, which is described by being applied to a preset computer processing device having certain data processing, data storage and communication capabilities, and embodiments of the present application do not limit this. The method includes the following steps 202 to 210. Wherein:

[0046] Step 202, obtaining device parameter information of the power system; wherein the device parameter information includes demand response cost coefficients and response capacities of a plurality of candidate public buildings respectively.

[0047] Wherein, the power system is a system for completing the production, transmission, distribution and consumption of electric energy, which can include a city power grid. Specifically, the devices in the power system can include power generation devices, power transmission devices, power distribution devices and power consumption devices (i.e. loads). Wherein, the power generation device is used to convert other forms of energy (such as fossil fuels, water energy, nuclear energy, wind energy, solar energy, etc.) into electric energy, which can include, for example, thermal power generation devices, hydroelectric power generation devices, nuclear power devices, and renewable energy power generation devices such as wind power and photovoltaic power. The goals of the power system operation can include safety and stability, economic efficiency, optimization of power quality, and environmental sustainability, wherein safety and stability aim to avoid power outages, economic efficiency aims to optimize power generation and transmission costs, optimization of power quality aims to ensure voltage and frequency stability, and environmental sustainability aims to promote clean energy consumption.

[0048] Specifically, the devices in the power system can include power generating units, power transmission lines, and candidate public buildings with demand response capabilities. Wherein, the traditional power generating unit includes a thermal power generating unit and a wind power generating unit. The thermal power generating unit not only provides power generation capacity for the power system to maintain supply and demand balance, but also provides backup capacity to respond to emergencies. The candidate public building, as a power user, pays for power use when it does not participate in demand response; when it participates in demand response, it provides regulation capacity for the power system and earns demand response subsidies.

[0049] Correspondingly, the device parameter information includes parameter information of a device that has an impact on the operation cost of the power system, wherein the device parameter information can be divided into static parameters and dynamic parameters, the static parameters can be considered as remaining unchanged in the operation cost optimization process of the power system, such as the start-up cost coefficient of the generator set, the standby cost coefficient of the generator set, the response cost coefficient of each candidate public building participating in demand response, etc. Correspondingly, the dynamic parameters are parameters that can be continuously adjusted and changed to approach the operation cost optimization target of the power system in the operation cost optimization process of the power system. Specifically, the dynamic parameters can include the capacity specifically responded by the candidate public building participating in demand response, the switching state of the generator set, the standby capacity of the generator set, etc. It can be understood that the variables that need to be optimized and solved can be determined according to the device parameter information, that is, the decision variables of the operation cost optimization of the power system are obtained.

[0050] In the embodiment of the present application, it is considered that in the power system such as urban power grid, the development of traditional power sources such as thermal power is limited, however, the urban power load is dense, and the peak-valley difference is large, which leads to further difficulty in balancing supply and demand, and the urban power grid needs more flexible resources. With the advancement of urbanization, the proportion of public buildings in the city is increasing, and the electricity consumption of public buildings is high. At the same time, considering that there are many flexible resources in public buildings such as air conditioners, electric vehicles, energy storage, etc., which can participate in power demand response to relieve the pressure of power supply and demand balance, therefore, public buildings are good demand response subjects. Due to the lack of cost evaluation scheme of public buildings participating in demand response in the related art, it is difficult to screen out public buildings with high economic value, that is, buildings that are more beneficial to relieve power supply pressure and reduce operation cost by participating in demand response, thereby affecting the efficiency and effect of public buildings participating in demand response, and thus affecting the resource utilization rate and operation cost of the power system. Therefore, in the embodiment of the present application, the parameter information of the candidate public building participating in the demand response of the power system is obtained, which serves as the basis for subsequent evaluation of the cost benefit of the power system brought by the candidate public building participating in the demand response. Wherein, the parameter information at least includes the demand response cost coefficient of each candidate public building, and the response capacity of each candidate public building participating in demand response. The demand response cost coefficient is used to represent the cost required by the candidate public building participating in unit capacity demand response, which can be regarded as the unit price per capacity of the candidate public building participating in demand response. It can be understood that the cost of different candidate public buildings participating in demand response is different, that is, the corresponding demand response cost coefficients may be different, and the demand response cost coefficients of each candidate public building can also be dynamically changed. Correspondingly, the response capacity corresponding to the candidate public building can be used as one of the decision variables of the operation cost optimization of the power system, that is, the response capacity is used as a dynamic variable, and the optimal solution is solved to optimize the operation cost of the power system.

[0051] In step 204, a target function for optimizing the operation cost of the power system and a constraint condition are determined according to the device parameter information; the target function includes a response cost optimization function of the candidate public buildings participating in the demand response of the power system; and the constraint condition includes a response capacity constraint condition of the candidate public buildings participating in the demand response of the power system.

[0052] The target function is used to represent the target of optimizing the operation cost of the power system, for example, minimizing the operation cost of the power system. The operation cost of the power system can include a generator set power generation cost, a generator set standby cost, a public building demand response cost, and optionally, when the power system includes an energy source such as wind power with intermittency and uncertainty, the operation cost of the power system can further include a renewable energy prediction penalty cost, for example, a wind power prediction penalty cost, based on the characteristics of the energy source. The wind power prediction penalty cost is an additional cost borne by the wind farm due to inaccurate power generation prediction, and the core purpose is to promote prediction accuracy and ensure grid stability.

[0053] In the embodiment of the present application, considering the cost paid by the power system for the candidate public buildings participating in the demand response, the response cost optimization function of the candidate public buildings participating in the demand response of the power system is introduced when optimizing the operation cost of the power system. The operation cost brought by the candidate public buildings participating in the demand response is jointly affected by the number of the candidate public buildings participating in the response, the response amount of each candidate public building participating in the response, and the response unit price.

[0054] Correspondingly, when optimizing the cost of the power system, the safety, stability, and economy of the power system need to be considered on the basis of pursuing the minimization of the operation cost, and when introducing the demand response of the public buildings into the power system, considering that the public buildings usually have large power consumption, if they are allowed to participate in the demand response without limitation (such as large-scale load reduction), it may cause local grid voltage fluctuation, affect the power supply quality of surrounding users, or cause the risk of overloading of power distribution equipment (such as transformers and lines), especially when the response actions are concentrated. Therefore, the response capacity of the candidate public buildings participating in the demand response needs to be constrained, and specifically, the response capacity of each candidate public building participating in the demand response can be limited within a certain range.

[0055] Preferably, in an embodiment of the present application, the target function for optimizing the operation cost of the power system is established, wherein the total operation cost of the power system can include a generator set cost, a generator set standby cost, and a wind power generator prediction penalty cost. Specifically as follows:

[0056] ; ; ;

[0057] ;

[0058] where, , , and are the generation cost of generator g, the spinning reserve cost of generator g, the demand response cost of candidate public building i and the wind power prediction penalty cost, respectively. , , and are the on / off cost coefficient of generator g, the spinning reserve cost coefficient of generator g, the demand response cost coefficient of candidate public building i and the wind power prediction penalty cost coefficient, respectively. , , and are the number of dispatching periods, the number of generators, the number of candidate public buildings participating in demand response and the number of wind power generators, respectively. is the on / off state variable of generator g at time t, which is a binary variable. is the output of generator g at time t. a, b and c are the quadratic coefficient, the linear coefficient and the constant coefficient of generator g, respectively. is the spinning reserve capacity of generator g at time t. is the capacity of candidate public building i participating in demand response at time t. is the power output ratio of wind power generator at time t. and are the maximum wind power output and the actual wind power output at time t, respectively.

[0059] Correspondingly, the constraint conditions for optimizing the operation cost of the power system are established, which include power balance constraint, unit capacity constraint, unit on / off constraint, unit ramp rate constraint, line transmission capacity constraint, unit spinning reserve constraint and public building capacity constraint, as follows:

[0060] a) Power balance constraint:

[0061] ;

[0062] where, represents the total load of the power system.

[0063] b) Unit capacity constraint:

[0064] ;

[0065] where, and These represent the minimum and maximum power of generator set g, respectively.

[0066] c) Unit start-up and shutdown constraints:

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] in, and These represent the start-up and shutdown states of generator set g at time t, respectively. and These represent the minimum start-up time and minimum shutdown time of generator set g, respectively.

[0073] d) Unit ramp rate constraint:

[0074] ;

[0075] in, and These are the downward and upward ramp rates of generator set g, respectively. This is the scheduling time interval.

[0076] e) Line transmission capacity constraints:

[0077] ;

[0078] ;

[0079] in, This refers to the power flow of transmission line l. and These are the phase angles at the beginning and end of the transmission line at time t, respectively. and These represent the minimum and maximum transmission capacities of the power transmission line, respectively. It is the susceptance of the power transmission line.

[0080] f) Unit standby constraints:

[0081] ;

[0082] in, It is the standby capacity of generator set g at time t.

[0083] g) Capacity constraint of public buildings participating in demand response;

[0084] ;

[0085] wherein, is the maximum capacity of the candidate public building i participating in the demand response of the power system.

[0086] Step 206, constructing an operation cost optimization model of the power system according to the objective function and the constraint condition.

[0087] wherein, the optimization model is constructed, the objective function is the optimization target of the optimization model, the constraint condition is the constraint that needs to be met in the optimization process of the optimization model, according to the parameter type and the value characteristics of the equipment parameter information, the optimization model can be a mixed integer linear programming model, and the embodiments of the present application do not limit this.

[0088] Step 208, solving the operation cost optimization model to obtain the shadow price of the candidate public building participating in the demand response of the power system.

[0089] wherein, the shadow price represents the amount of the total operation cost of the power system caused by the increase of the unit capacity of the candidate public building, that is, the marginal cost of the candidate public building participating in the demand response. It can be understood that,

[0090] In order to improve the efficiency of solving the operation cost optimization model, the operation cost optimization model can be linearly relaxed, and the relaxed model is solved. Correspondingly, in order to solve the shadow price related to the demand response, the dual variable corresponding to the response capacity constraint condition of the candidate public building participating in the demand response of the power system is introduced, the dual problem model is obtained, and the optimal solution obtained is taken as the shadow price of the candidate public building participating in the demand response.

[0091] Step 210, dispatching and optimizing the power system according to the shadow price.

[0092] The shadow price of each candidate public building can measure the decrease in the operation cost of the power system caused by the demand response of the candidate public building with a unit capacity, and therefore, the candidate public building with a higher decrease value can be selected as the target public building actually participating in the demand response of the power system according to the shadow price. Alternatively, considering that the target public building needs to be paid based on the participation in the demand response, the target public building participating in the demand response can also be evaluated and priced based on the shadow value, thereby helping the system operator to better formulate a price and scheduling scheme during the demand response process. Alternatively, considering that the total cost of the public building participating in the demand response is related to the shadow price and the capacity participating in the response, the capacity of the target public building participating in the demand response of the power system can also be determined based on the shadow price and a preset total cost constraint of the public building participating in the demand response.

[0093] In the power system scheduling optimization method, by introducing the response cost optimization function of the candidate public building participating in the demand response of the power system and the response capacity constraint condition of the candidate public building participating in the demand response of the power system in the process of constructing the operation cost optimization model of the power system, the shadow price of the candidate public building participating in the demand response can be obtained by solving the operation cost optimization model, the shadow price can be used as a scientific and effective basis for adjusting the participation of the public building in the response, and therefore, the efficiency and effectiveness of the power system scheduling optimization can be improved.

[0094] In one embodiment, the determination process of the response cost optimization function includes:

[0095] constructing a total demand response cost function of the plurality of candidate public buildings according to the demand response cost coefficients of the candidate public buildings and the response capacities;

[0096] constructing the response cost optimization function with the function value of the total demand response cost function being minimized as an optimization target.

[0097] The total demand response cost function of the plurality of candidate public buildings can be:

[0098] ;

[0099] wherein, the demand response cost of the public building, the number of candidate public buildings participating in the demand response, the cost coefficient of the candidate public building i participating in the demand response of the power system, that is, the cost of the candidate public building i participating in the demand response with a unit capacity; the capacity of the candidate public building i participating in the demand response at time t.

[0100] In one embodiment, the device parameter information further comprises a capacity threshold of each of the candidate public buildings participating in the demand response of the power system; and the determining process of the response capacity constraint comprises:

[0101] constructing the response capacity constraint with the value of the response capacity of each of the candidate public buildings not exceeding the capacity threshold.

[0102] In one embodiment, the device parameter information further comprises a capacity threshold of each of the candidate public buildings participating in the demand response of the power system; and the determining process of the response capacity constraint comprises:

[0103] In one embodiment, the device parameter information further comprises a capacity threshold of each of the candidate public buildings participating in the demand response of the power system; and the determining process of the response capacity constraint comprises:

[0104]

[0105] wherein, is the maximum capacity of the candidate public building i participating in the demand response of the power system.

[0106] In one embodiment, the solving of the operation cost optimization model to obtain the shadow price of the public building participating in the demand response comprises:

[0107] relaxing the operation cost optimization model to obtain a relaxed model;

[0108] introducing a dual variable corresponding to the response capacity constraint in the relaxed model to obtain a dual problem model corresponding to the operation cost optimization model;

[0109] solving the dual problem model to obtain an optimal solution as the shadow price.

[0110] In one embodiment, the solving of the operation cost optimization model to obtain the shadow price of the public building participating in the demand response comprises:

[0111] Preferably, considering the on-off state variable of the generator unit at time t ​is a binary variable, so the operation cost optimization model constructed in the foregoing steps does not satisfy the strong duality theorem. Therefore, in the embodiment of the application, the model is simplified based on the relaxation theorem of the binary variable, the operation cost optimization model is converted into a convex optimization problem, and a relaxed model is obtained. The binary variable is converted into a continuous variable, which is limited in the interval [0, 1]. Specifically, the operation cost model of the foregoing embodiment can be simply written as:

[0112] ;

[0113] The dual variables are introduced into the above model, and the dual variables of the above model are constructed as follows:

[0114] ;

[0115] wherein, is the dual multiplier of the maximum capacity constraint at time t. and are the Lagrange dual multipliers corresponding to other constraints at time t.

[0116] On this basis, the dual problem of the simplified scheduling model can be expressed as:

[0117] ;

[0118] After the model is solved, is the optimal solution of the dual problem at time t. Therefore represents the total scheduling cost reduction caused by the increase of the unit capacity of the public building at time t, that is, the marginal cost of the public building, also known as the shadow price of the public building.

[0119] In one embodiment, the device parameter information further includes the power generation capacity, the start-up cost coefficient, the standby cost coefficient, the power generation power, the switch state and the standby capacity of the generator set; and the objective function further includes a power generation cost optimization function of the generator set and a standby cost optimization function of the generator set.

[0120] The determination process of the objective function further includes:

[0121] According to the power generation capacity, the start-up cost coefficient, the power generation power and the switch state of the generator set, the power generation cost optimization function is constructed;

[0122] According to the standby cost coefficient and the standby capacity, the standby cost optimization function is constructed.

[0123] wherein the power generation cost optimization function of the generator set can take the minimization of the power generation cost of the generator set as the optimization objective, and the power generation cost of the generator set The determination process of the standby cost optimization function can be as follows:

[0124] ;

[0125] wherein, is the start-up cost coefficient of the generator g, is the number of dispatching periods, is the number of generators, is the switch state variable of the generator g at time t, which is a binary variable, is the output of the generator g at time t. a, b, and c are the quadratic coefficient, the linear coefficient, and the constant coefficient of the generator g, respectively.

[0126] The reserve cost optimization function can take the minimization of the reserve cost of the generator as the optimization objective, and specifically, the reserve cost of the generator is The calculation process of the reserve cost of the generator g can be as follows: ;

[0127] wherein, is the number of dispatching periods, is the number of generators g, is the reserve cost coefficient of the generator g, is the reserve capacity of the generator g at time t.

[0128] The reserve cost coefficient of the generator is used to represent the cost required by the generator per unit of reserve capacity.

[0129] In an embodiment, the device parameter information further includes power distribution influence of the power transmission line, transmission capacity condition; the constraint condition further includes line transmission capacity constraint; the determination process of the line transmission capacity constraint includes: calculating the power distribution of the power transmission line according to the power distribution influence of the power transmission line; and constructing the line transmission capacity constraint with the power distribution satisfying the transmission capacity condition as the target.

[0130] The power distribution influence is used to represent the parameter that has influence on the power distribution of the power transmission line, which can include phase angle of the first section and the end of the power transmission line, reactance of the power transmission line, etc., and the transmission capacity condition is used to limit the transmission capacity of the power transmission line, and the power distribution can include power flow of the power transmission line.

[0131] Specifically, the power flow of the line can be calculated according to the phase angle of the first section and the end of the line and the reactance of the line, and the line transmission capacity constraint is obtained with the power flow being limited between the maximum transmission capacity and the minimum transmission capacity of the power transmission line as the target.

[0132] For example, the line transmission capacity constraint can be expressed as follows:

[0133]

[0134]

[0135] wherein, is the power flow of the transmission line l at time t. and are the phase angles of the head and tail of the transmission line at time t, respectively. and are the minimum and maximum transmission capacity of the transmission line, respectively. is the susceptance of the transmission line at time t.

[0136] In one embodiment, the dispatching optimization of the power system according to the shadow price comprises:

[0137] determining a reference cost of each of the candidate public buildings according to the shadow price;

[0138] determining a target public building participating in the demand response of the power system and a target demand response capacity of the target public building from the plurality of candidate public buildings according to the reference cost.

[0139] wherein, for each candidate public building, the reference cost can represent the return that the candidate public building should get for participating in the demand response, and it can be understood that, for the sustainability of the demand response of the public building, the reference cost of the candidate public building should be roughly similar to the shadow price, which can be adaptively adjusted based on the number and the responsive capacity of the candidate public buildings that can participate in the demand response at present.

[0140] In order to reduce the total operating cost of the power system and improve the resource utilization efficiency of the power system, the candidate public building with a higher shadow price or located in a preset ideal price region can be selected as the target public building.

[0141] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0142] ​​Based on the same inventive concept, the embodiments of the present application further provide a power system dispatch optimization apparatus for implementing the power system dispatch optimization method described above. The apparatus provides a solution implementation similar to the implementation described in the above method, and therefore the specific limitations in one or more power system dispatch optimization apparatus embodiments provided below can refer to the limitations of the power system dispatch optimization method described above, which will not be repeated here.

[0143] In one exemplary embodiment, as shown in Figure 2 a power system dispatch optimization apparatus is provided, and the apparatus comprises:

[0144] an acquisition module configured to acquire device parameter information of a power system, wherein the device parameter information comprises demand response cost coefficients and response capacities of a plurality of candidate public buildings respectively;

[0145] a determination module configured to determine a target function and a constraint condition for operation cost optimization of the power system according to the device parameter information, wherein the target function comprises a response cost optimization function of the candidate public buildings participating in demand response of the power system, and the constraint condition comprises a response capacity constraint condition of the candidate public buildings participating in demand response of the power system;

[0146] a construction module configured to construct an operation cost optimization model of the power system according to the target function and the constraint condition;

[0147] a solution module configured to solve the operation cost optimization model to obtain shadow prices of the candidate public buildings participating in demand response of the power system;

[0148] an optimization module configured to perform dispatch optimization on the power system according to the shadow prices.

[0149] Each of the modules in the power system dispatch optimization apparatus described above can be realized in whole or in part by software, hardware, and a combination thereof. Each of the modules described above can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to each of the modules.

[0150] In one exemplary embodiment, a computer device is provided, which can be a terminal, and an internal structure diagram of the computer device can be as shown in Figure 3As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the external terminal in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. The computer program is executed by the processor to realize a power system scheduling optimization method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. It can also be an external keyboard, touchpad or mouse, etc.

[0151] Those skilled in the art can understand that, Figure 3 The skilled in the art can understand that,

[0152] In one exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the following steps:

[0153] Obtaining device parameter information of a power system; wherein the device parameter information includes demand response cost coefficients and response capacities of a plurality of candidate public buildings respectively;

[0154] According to the device parameter information, a target function and a constraint condition for optimizing the operation cost of the power system are determined; wherein the target function includes a response cost optimization function of the candidate public buildings participating in the demand response of the power system; and the constraint condition includes a response capacity constraint condition of the candidate public buildings participating in the demand response of the power system;

[0155] constructing an operation cost optimization model of the power system according to the objective function and the constraint condition;

[0156] solving the operation cost optimization model to obtain a shadow price of the candidate public buildings participating in demand response of the power system;

[0157] scheduling and optimizing the power system according to the shadow price.

[0158] In one embodiment, the processor, when executing the computer program, further implements the following steps:

[0159] constructing a total demand response cost function of the plurality of candidate public buildings according to the demand response cost coefficients of the respective candidate public buildings and the response capacities;

[0160] constructing the response cost optimization function with the minimum function value of the total demand response cost function as an optimization objective.

[0161] In one embodiment, the device parameter information further comprises a capacity threshold of each of the candidate public buildings participating in demand response of the power system; and the processor, when executing the computer program, further implements the following steps:

[0162] constructing the response capacity constraint condition with the value of the response capacity of each of the candidate public buildings not exceeding the capacity threshold.

[0163] In one embodiment, the processor, when executing the computer program, further implements the following steps:

[0164] relaxing the operation cost optimization model to obtain a relaxed model;

[0165] introducing a dual variable corresponding to the response capacity constraint condition in the relaxed model to obtain a dual problem model corresponding to the operation cost optimization model;

[0166] solving the dual problem model to obtain an optimal solution as the shadow price.

[0167] In one embodiment, the device parameter information further comprises generating capacity, starting cost coefficient, standby cost coefficient, generating power, switch state and standby capacity of a generator set; the objective function further comprises a generating cost optimization function of the generator set and a standby cost optimization function of the generator set; and the processor, when executing the computer program, further implements the following steps:

[0168] constructing the generating cost optimization function according to the generating capacity, starting cost coefficient, generating power and switch state of the generator set;

[0169] According to the backup cost coefficient and the backup capacity, the backup cost optimization function is constructed.

[0170] In one embodiment, the device parameter information further comprises power distribution influence of the power transmission line, transmission capacity condition; the constraint condition further comprises power balance constraint; the processor further implements the following steps when executing the computer program:

[0171] According to the power distribution influence of the power transmission line, the power distribution of the power transmission line is calculated;

[0172] The line transmission capacity constraint is constructed with the power distribution satisfying the transmission capacity condition as the target.

[0173] In one embodiment, the processor further implements the following steps when executing the computer program:

[0174] According to the shadow price, the reference cost of each candidate public building is determined;

[0175] According to the reference cost, the target public building participating in the demand response of the power system and the target demand response capacity of the target public building are determined from the plurality of candidate public buildings.

[0176] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the following steps:

[0177] Obtain device parameter information of a power system; wherein the device parameter information comprises demand response cost coefficient and response capacity of a plurality of candidate public buildings respectively;

[0178] According to the device parameter information, a target function and a constraint condition for optimizing the operation cost of the power system are determined; wherein the target function comprises a response cost optimization function of the candidate public buildings participating in the demand response of the power system; the constraint condition comprises a response capacity constraint condition of the candidate public buildings participating in the demand response of the power system;

[0179] According to the target function and the constraint condition, an operation cost optimization model of the power system is constructed;

[0180] The operation cost optimization model is solved to obtain the shadow price of the candidate public buildings participating in the demand response of the power system;

[0181] According to the shadow price, the power system is dispatched and optimized.

[0182] In one embodiment, the computer program is executed by the processor to further implement the following steps:

[0183] constructing a total demand response cost function of the plurality of candidate public buildings according to the demand response cost coefficients of the plurality of candidate public buildings and the response capacities of the plurality of candidate public buildings;

[0184] constructing a response cost optimization function with a function value of the total demand response cost function as an optimization objective.

[0185] In one embodiment, the device parameter information further comprises a capacity threshold of participation of each of the candidate public buildings in the demand response of the power system; and the computer program, when executed by the processor, further implements the following steps:

[0186] constructing a response capacity constraint condition with a value of the response capacity of each of the candidate public buildings not exceeding the capacity threshold.

[0187] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0188] relaxing the operation cost optimization model to obtain a relaxed model;

[0189] introducing a dual variable corresponding to the response capacity constraint condition in the relaxed model to obtain a dual problem model corresponding to the operation cost optimization model;

[0190] solving the dual problem model to obtain an optimal solution as the shadow price.

[0191] In one embodiment, the device parameter information further comprises a power generation capacity, a start-up cost coefficient, a standby cost coefficient, a power generation power, a switch state and a standby capacity of a power generator set; the objective function further comprises a power generation cost optimization function of the power generator set and a standby cost optimization function of the power generator set; and the computer program, when executed by the processor, further implements the following steps:

[0192] constructing the power generation cost optimization function according to the power generation capacity, the start-up cost coefficient, the power generation power and the switch state of the power generator set;

[0193] constructing the standby cost optimization function according to the standby cost coefficient and the standby capacity.

[0194] In one embodiment, the device parameter information further comprises a power distribution influence condition and a transmission capacity condition of a power transmission line; and the constraint condition further comprises a power balance constraint; and the computer program, when executed by the processor, further implements the following steps:

[0195] calculating a power distribution condition of the power transmission line according to the power distribution influence condition of the power transmission line;

[0196] A line transmission capacity constraint is constructed with the power distribution satisfying the transmission capacity condition as a target.

[0197] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0198] A reference cost of each of the candidate public buildings is determined according to the shadow price;

[0199] A target public building participating in demand response of the power system and a target demand response capacity of the target public building are determined from the multiple candidate public buildings according to the reference cost.

[0200] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by the processor, implements the following steps:

[0201] Obtaining device parameter information of a power system; wherein the device parameter information comprises demand response cost coefficients and response capacities of multiple candidate public buildings respectively;

[0202] A target function and a constraint condition for operation cost optimization of the power system are determined according to the device parameter information; wherein the target function comprises a response cost optimization function of the candidate public buildings participating in demand response of the power system; and the constraint condition comprises a response capacity constraint condition of the candidate public buildings participating in demand response of the power system;

[0203] An operation cost optimization model of the power system is constructed according to the target function and the constraint condition;

[0204] The operation cost optimization model is solved to obtain a shadow price of the candidate public buildings participating in demand response of the power system;

[0205] The power system is dispatched and optimized according to the shadow price.

[0206] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0207] A demand response total cost function of the multiple candidate public buildings is constructed according to the demand response cost coefficients and the response capacities of each of the candidate public buildings;

[0208] The response cost optimization function is constructed with minimization of a function value of the demand response total cost function as an optimization target.

[0209] In one embodiment, the device parameter information further comprises capacity thresholds of each of the candidate public buildings participating in demand response of the power system; and the computer program, when executed by the processor, further implements the following steps:

[0210] constructing the response capacity constraint condition aiming at the value of the response capacity of each candidate public building not exceeding the capacity threshold.

[0211] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0212] relaxing the operation cost optimization model to obtain a relaxed model;

[0213] introducing a dual variable corresponding to the response capacity constraint condition in the relaxed model to obtain a dual problem model corresponding to the operation cost optimization model;

[0214] solving the dual problem model to obtain an optimal solution as the shadow price.

[0215] In one embodiment, the device parameter information further includes power generation capacity, start-up cost coefficient, standby cost coefficient, power generation power, switch state and standby capacity of a power generator set; the objective function further includes a power generation cost optimization function of the power generator set and a standby cost optimization function of the power generator set; and the computer program, when executed by the processor, further implements the following steps:

[0216] constructing the power generation cost optimization function according to the power generation capacity, start-up cost coefficient, power generation power and switch state of the power generator set;

[0217] constructing the standby cost optimization function according to the standby cost coefficient and standby capacity.

[0218] In one embodiment, the device parameter information further includes power distribution influence of a power transmission line and transmission capacity condition; and the constraint condition further includes a power balance constraint; and the computer program, when executed by the processor, further implements the following steps: calculating a power distribution condition of the power transmission line according to the power distribution influence of the power transmission line; and constructing a line transmission capacity constraint aiming at the power distribution condition satisfying the transmission capacity condition.

[0219] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0220] determining a reference cost of each candidate public building according to the shadow price;

[0221] determining a target public building participating in demand response of the power system and a target demand response capacity of the target public building from the multiple candidate public buildings according to the reference cost.

[0222] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0223] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. In the embodiments provided in the present application, any reference to memory, database or other medium can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0224] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, any combination of these technical features is deemed to be within the scope of the present application.

[0225] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A power system dispatch optimization method, characterized in that, The method includes: Obtain equipment parameter information of the power system; wherein, the equipment parameter information includes the demand response cost coefficient and response capacity of multiple candidate public buildings; Based on the equipment parameter information, an objective function and constraints for optimizing the operating cost of the power system are determined; wherein, the objective function includes a response cost optimization function for the candidate public buildings participating in the demand response of the power system; and the constraints include response capacity constraints for the candidate public buildings participating in the demand response of the power system. Construct an operation cost optimization model for the power system based on the objective function and constraints. Solving the operating cost optimization model yields the shadow price of the candidate public building participating in the demand response of the power system. The power system is optimized for scheduling based on the shadow price.

2. The method according to claim 1, characterized in that, The process of determining the response cost optimization function includes: Based on the demand response cost coefficients of each candidate public building and the response capacity, a total demand response cost function for the multiple candidate public buildings is constructed. The response cost optimization function is constructed with the goal of minimizing the function value of the total demand response cost function.

3. The method according to claim 1, characterized in that, The equipment parameter information also includes the capacity threshold for each of the candidate public buildings to participate in the demand response of the power system; The process of determining the response capacity constraint includes: The response capacity constraint is constructed with the objective that the response capacity value of each of the candidate public buildings does not exceed the capacity threshold.

4. The method according to claim 1, characterized in that, Solving the operating cost optimization model to obtain the shadow price of the candidate public building participating in the demand response of the power system includes: The operating cost optimization model is relaxed to obtain the relaxed model; By introducing the dual variable corresponding to the response capacity constraint into the relaxed model, the dual problem model corresponding to the operating cost optimization model is obtained. The dual problem model is solved to obtain the optimal solution as the shadow price.

5. The method according to claim 1, characterized in that, The equipment parameter information also includes the generator set's power generation capacity, start-up cost coefficient, standby cost coefficient, power generation output, switch status, and standby capacity; the objective function also includes the generator set's power generation cost optimization function and standby cost optimization function. The process of determining the objective function also includes: Based on the generator set's power generation capacity, start-up cost coefficient, power generation output, and switching status, the power generation cost optimization function is constructed. Based on the reserve cost coefficient and reserve capacity, the reserve cost optimization function is constructed.

6. The method according to claim 1, characterized in that, The equipment parameter information also includes the impact of power distribution on the transmission line and transmission capacity conditions; the constraints also include line transmission capacity constraints. The process of determining the line transmission capacity constraint includes: Based on the influence of the power distribution of the transmission line, calculate the power distribution of the transmission line; The line transmission capacity constraint is constructed with the goal of satisfying the transmission capacity condition based on the power distribution.

7. The method according to claim 1, characterized in that, The method of optimizing the power system dispatch based on the shadow price includes: The reference cost for each of the candidate public buildings is determined based on the shadow price; Based on the reference cost, target public buildings participating in the demand response of the power system and the target demand response capacity of the target public buildings are determined from the plurality of candidate public buildings.

8. A power system dispatch optimization device, characterized in that, The device includes: An acquisition module is used to acquire equipment parameter information of the power system; wherein, the equipment parameter information includes the demand response cost coefficient and response capacity of multiple candidate public buildings respectively; The determination module is used to determine, based on the equipment parameter information, the objective function and constraints for optimizing the operating cost of the power system; wherein, the objective function includes the response cost optimization function for the candidate public buildings participating in the demand response of the power system; and the constraints include the response capacity constraints for the candidate public buildings participating in the demand response of the power system. The construction module is used to construct the operating cost optimization model of the power system based on the objective function and constraints. The solution module is used to solve the operating cost optimization model to obtain the shadow price of the candidate public building participating in the demand response of the power system; An optimization module is used to optimize the scheduling of the power system based on the shadow price.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images