Wind power active load reduction optimization scheduling method and system considering multiple standby demands

Abstract

The invention provides a wind power active load reduction optimization scheduling method and system considering multiple standby demands, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: constructing a unit combination model considering two standby supply and demand constraints of capacity standby and climbing standby at the same time; on the basis of the unit combination model, considering scenes in which wind power provides reserve and wind power has reserve demands, describing wind power output uncertainty by intervals, comparing wind power output after wind power active load reduction with an original predicted wind power output interval, performing classification to obtain the influence of wind power active load reduction on related reserve, and obtaining basic constraint conditions; and on the basis of the basic constraint conditions, with the goal of minimizing the operation cost and the standby cost, constructing a wind power active load reduction-based optimal scheduling model considering multiple standby applications, and solving the optimal scheduling model to obtain a wind power active load reduction optimal scheduling scheme.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power system optimal dispatching, and particularly to a wind power active load reduction optimal dispatching method and system considering multiple reserve requirements. Background Art

[0002] The statements in this part merely provide background technical information related to the present disclosure and do not necessarily constitute prior art.

[0003] In recent years, wind power and photovoltaic power generation, as the most mature and promising power generation methods in the field of new energy power generation, have received extensive attention. The large-scale access of renewable energy has become an inevitable development trend in the future.

[0004] However, the volatility and uncertainty of the power generation output of renewable energy such as wind power and photovoltaic power also pose great challenges to the safe and reliable operation of the power system. The deviation between the expected power generation and the actual power generation must be absorbed by the reserves provided by the power system. These reserves must be available and can be deployed in real time. To ensure this availability, the unit commitment problem is often solved in dispatching to deploy system capacity reserves in advance. When getting closer to the actual time, the range of the deviation value between the expected power generation and the actual power generation will become smaller. However, even within a shorter time interval, the output of renewable energy power generation may increase or decrease at a certain rate, which requires traditional generating units to adjust their output in a timely manner to maintain the balance between supply and demand. Therefore, in addition to the capacity reserves deployed in advance, corresponding ramping reserves also need to be deployed to ensure that the reserves of the deployed power system can cope with the changes in the expected renewable energy power generation output at any time.

[0005] In the rich past research in the field of electric power energy, for the analysis of the output characteristics of wind power generation, a fixed paradigm has been followed for a long time. That is, conventional research generally adheres to the view that the wind power output fluctuates within a deterministic interval range. When constructing relevant theoretical models and carrying out engineering practice applications, the upper and lower limit values of the wind power output within this interval are regarded as constant fixed values. This assumed mode greatly simplifies the analysis process of wind power output under the background of the relatively simple architecture system of the early power system and the low wind power access ratio. It provides a relatively simple and easy-to-operate mathematical model for basic calculations in the operation planning of the power system, such as load forecasting, power source allocation, and power grid power flow calculation, enabling power workers to complete the preliminary system design and operation plan formulation more efficiently.

[0006] However, there are still the following problems in the current power system operation planning scheme:

[0007] The unique physical mechanism of current wind power generation fundamentally determines that its output process is inevitably and naturally filled with significant uncertainties and strong fluctuations. As a clean energy source originating from nature, the generation and distribution of wind energy are affected by the combined action of various complex and dynamically changing factors. In the field of meteorology, large-scale movements of the atmospheric circulation, sharp changes in the pressure gradient, complex distributions of the temperature field, and the diversity of topography and landforms all have a profound impact on the magnitude and direction of the wind force. These factors are constantly changing irregularly, making the wind energy captured by wind turbines during actual operation extremely unstable, and thus making it difficult to accurately predict and control the output electric power. Summary of the Invention

[0008] To solve the above problems, the present disclosure proposes an active load reduction optimization scheduling method and system for wind power considering multiple reserve requirements, provides a unit commitment model considering capacity reserve and ramping reserve, and explicitly considers the impacts of the two reserves from two aspects: quantity and speed. Based on this model, the situation of active load reduction of wind power is studied, with particular consideration of the impact of wind turbines operating at reduced output on the unit commitment model, and scenarios where wind power provides reserve and has reserve requirements are simultaneously considered in the model to better cope with the volatility and uncertainty brought by wind power to the power system.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions:

[0010] An active load reduction optimization scheduling method for wind power considering multiple reserve requirements, including:

[0011] Constructing a unit commitment model that simultaneously considers the supply-demand constraints of two reserves: capacity reserve and ramping reserve;

[0012] Based on the unit commitment model, considering the scenarios where wind power provides reserve and has reserve requirements, describing the uncertainty of wind power output in intervals, and by comparing the wind power output after active load reduction of wind power with the original predicted wind power output interval, classifying the impacts of active load reduction of wind power on relevant reserves to obtain the basic constraint conditions;

[0013] Based on the basic constraint conditions, with the goal of minimizing the operating cost and reserve cost, constructing an optimization scheduling model considering multiple reserves based on active load reduction of wind power, and solving it to obtain the active load reduction optimization scheduling scheme.

[0014] According to some embodiments, the present disclosure adopts the following technical solutions:

[0015] An active load reduction optimization scheduling system for wind power considering multiple reserve requirements, including:

[0016] A model construction module for constructing a unit commitment model that simultaneously considers the supply-demand constraints of two reserves: capacity reserve and ramping reserve;

[0017] An optimal scheduling module, which is used to consider the scenarios of wind power providing reserve and having reserve demand based on the unit commitment model, describe the uncertainty of wind power output in intervals, classify the impact of active wind power curtailment on relevant reserves by comparing the wind power output after active wind power curtailment with the original predicted wind power output interval, and obtain the basic constraint conditions; based on the basic constraint conditions, with the goal of minimizing the operation cost and reserve cost, construct an optimal scheduling model considering multiple reserves based on active wind power curtailment, and solve it to obtain the optimal scheduling scheme for active wind power curtailment.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] A computer program product, including a computer program, which when executed by a processor implements the optimal scheduling method for active wind power curtailment considering multiple reserve requirements.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] A non-transitory computer-readable storage medium, which is used to store computer instructions, and when the computer instructions are executed by a processor, the optimal scheduling method for active wind power curtailment considering multiple reserve requirements is implemented.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] An electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes the optimal scheduling method for active wind power curtailment considering multiple reserve requirements.

[0024] Compared with the prior art, the beneficial effects of the present disclosure are:

[0025] An optimized scheduling method for active load reduction of wind power considering multiple reserve requirements in the present disclosure first introduces capacity reserve and ramping reserve on the basis of the unit commitment model, clarifies the restraint relationship between the two reserves, and constructs a unit commitment model that simultaneously considers the supply-demand constraints of the two reserves. It can achieve a refined response to the volatility and uncertainty in the system, reduce and even avoid the frequency fluctuations and even passive curtailment of wind power and load shedding caused by insufficient flexibility in the system, and improve the safe and economic operation level of the system. This method only simply considered the case where the wind power output was within a deterministic interval in previous studies and failed to fully address the changes in various reserve requirements under different wind curtailment scenarios caused by active load reduction operation. The method proposed in the present disclosure has important theoretical and practical significance for improving and enhancing the safe and economic operation level of the power system under high-proportion renewable energy access.

[0026] An optimized scheduling method for active load reduction of wind power considering multiple reserve requirements in the present disclosure has important theoretical value and practical application significance in aspects such as deeply discussing the active load reduction of wind power, improving the acceptance capacity of the power system for wind power, coping with the challenges of wind power volatility and uncertainty, and ensuring the safe, stable, and economic operation of the power system. By optimizing the matching degree between wind power output and the operation of the power system, the operation efficiency of the entire power system is improved, the operation cost is reduced, and the economic operation of the power system is realized. In previous studies, it was generally considered that the output during wind power load reduction operation could be any value lower than the lower limit of the prediction interval, and it still had uncertainty. The present disclosure believes that when the wind turbine is operating under the specific condition of reduced output, its output can be regarded as flexible, that is, the wind power output no longer has uncertainty at this time.

[0027] An optimized scheduling method for active load reduction of wind power considering multiple reserve requirements in the present disclosure, through a detailed discussion of the situation of active load reduction of wind power, not only helps to improve the acceptance capacity of the power system for wind power, but also better cope with the challenges of volatility and uncertainty brought by wind power to the power system, thereby ensuring the safe, stable and economic operation of the power system. During the actual operation of wind power, wind turbines often face various complex working conditions, and the operation of reducing output power is a representative operating state among them. When a wind turbine is operating with reduced output power, its output power will be artificially limited to a level lower than the theoretical maximum power. This operating mode has various non-negligible impacts on the unit commitment model. The present disclosure believes that when a wind turbine is operating under the specific condition of reduced output power, its output can be regarded as flexible, that is, the output of wind power at this time no longer has uncertainty. This is because when a wind turbine operates with reduced output power, usually through a series of precise control means, such as adjusting the blade pitch angle, changing the generator excitation, etc., the output power of the unit is stabilized at a certain set value to flexibly respond to the influence brought by the change of the external wind speed. This set value is not determined randomly, but comprehensively considers various factors, including the current load demand of the power system, the acceptance capacity of the power grid, and the operating limitations of the wind turbine itself. After such precise control and adjustment, during the stage of the wind turbine operating with reduced output power, the fluctuation range of its output power is strictly limited within a very small interval. Therefore, from the actual operation effect, its output characteristics approach those of traditional generating units with stable output characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The schematic diagrams in the specification forming a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0029] Figure 1 It is a schematic diagram of the method flow of the embodiment of the present disclosure;

[0030] Figure 2 It is a schematic diagram of the piecewise linearization of the unit operation cost in the embodiment of the present disclosure;

[0031] Figure 3 It is a schematic diagram of the predicted wind power output interval of the wind turbine in the embodiment of the present disclosure;

[0032] Figure 4 It is a schematic diagram of the actual output power of the wind turbine generator under the condition of considering the active load reduction operation of the wind turbine in the embodiment of the present disclosure;

[0033] Figure 5 It is a schematic diagram of the upward regulation capacity reserve that the wind turbine can provide after the active load reduction operation of the wind turbine when the wind power output is higher than the lower limit of the original predicted output interval in the embodiment of the present disclosure;

[0034] Figure 6 Schematic diagram of the downward regulation capacity reserve that can be provided by a wind turbine after the wind turbine actively reduces its load when the wind power output is lower than the lower limit of the original predicted output range in the embodiments of the present disclosure;

[0035] Figure 7 Schematic diagram of the upward regulation capacity reserve demand of the system after the wind turbine actively reduces its load when the wind power output is higher than the lower limit of the original predicted output range in the embodiments of the present disclosure;

[0036] Figure 8 Schematic diagram of the downward regulation capacity reserve demand of the system after the wind turbine actively reduces its load when the wind power output is lower than the lower limit of the original predicted output range in the embodiments of the present disclosure;

[0037] Figure 9 Schematic diagram of the up and down ramping reserve demand of the system after the wind turbine actively reduces its load in the embodiments of the present disclosure. Detailed implementation manners

[0038] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present disclosure belongs.

[0040] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0041] Embodiment 1

[0042] Problem description:

[0043] In recent years, vigorously developing the power generation of renewable energy such as wind and solar, and realizing the transformation of energy production to renewable energy have become major requirements for sustainable development. As one of the power generation methods with the most mature technology, the most commercialization potential and development potential in the field of new energy power generation, wind power generation has received extensive attention. However, wind power generation itself has uncertainty, which brings new challenges to the safe and economic operation of the power system. When the proportion of wind power connected to the power system is relatively low, the uncertainty in the power system is relatively low, and a sufficient amount of reserve capacity can be configured based on the traditional reserve optimization theory to ensure the safe and economic operation of the power system; while with the large-scale development and high-proportion grid connection of wind power, the uncertainty in the power system is getting higher and higher, and the traditional reserve optimization theory is facing difficulties.

[0044] In previous studies, it was usually considered that the output of wind power fluctuates within a deterministic range, and the upper and lower limits of the output of wind power in this range were considered fixed. However, due to the uncertainty and volatility of wind power output, it is possible and necessary to consider that the actual wind power output is higher than the original upper limit or lower than the original lower limit in actual situations. During the actual operation of wind power, wind turbines often face various complex working conditions, and the operation of reducing output is one of the representative operating states. When a wind turbine is operating in the state of reducing output, its output power will be artificially limited to a level lower than the theoretical maximum power. This operation mode has various non-negligible impacts on the unit commitment model. By carefully discussing the situation of active load reduction of wind power, it not only helps to improve the acceptance capacity of the power system for wind power, but also better cope with the challenges of volatility and uncertainty brought by wind power to the power system, thus ensuring the safe, stable and economic operation of the power system.

[0045] In view of this problem, in one embodiment of the present disclosure, an active load reduction optimization scheduling method for wind power considering multiple reserve requirements is provided. For a wind turbine, a wind turbine is a device that converts the kinetic energy in wind energy into electrical energy. Briefly, wind energy is used to drive a set of blades to rotate, and the blades drive a shaft. According to the design of the wind turbine, the shaft is either directly connected to the generator or connected to the generator through a gearbox. Subsequently, the generator converts mechanical energy into electrical energy. According to the position of the rotating shaft, wind turbines can be divided into horizontal axis wind turbines (HAWTs) or vertical axis wind turbines (VAWTs). In the horizontal axis configuration, the wind turbine is aligned with the wind direction, and the generator is located at the top of the tower. In contrast, in a vertical axis wind turbine, the generator is located near the ground, and the wind turbine does not need to be aligned with the wind direction. Other common classification methods for wind turbines include variable pitch and fixed pitch rotor blades, as well as fixed speed and variable speed models. A variable pitch wind turbine can rotate the blade along the longitudinal axis of the blade, while a fixed pitch model cannot. Fixed pitch wind turbines are usually smaller models and basically operate at a constant speed; while variable speed wind turbines can operate efficiently within a wider speed range. Modern large-scale utility-scale wind turbines belong to the variable speed type. Pitch angle control is an effective way to regulate power in variable speed wind turbines. A relatively small change in the pitch angle can have a significant impact on the power generation. It has the following characteristics: when the wind speed fluctuates in a higher speed range, it is difficult for the wind turbine unit to cope with the output change at any higher wind speed through pitch angle control. Therefore, at this time, the thermal power units in the system are required to provide a certain amount of capacity reserve and ramping reserve to cope with the volatility and uncertainty brought by wind power fluctuations to the power system; while when the wind speed is in a lower speed range, usually considered to be below the lower limit of the original prediction range, the wind turbine unit is considered to be able to flexibly cope with the output change occurring at any low wind speed through pitch angle control, and at this time, it is no longer necessary for the thermal power unit to provide reserve. Based on the above description, the wind turbine unit discussed in the present disclosure is a variable speed wind turbine that controls the output power by the pitch angle.

[0046] Furthermore, the specific implementation process of the active load reduction optimization scheduling method for wind power considering multiple reserve requirements in the present disclosure is as follows:

[0047] Step 1: Construct a unit commitment model that simultaneously considers two reserve supply and demand constraints, namely capacity reserve and ramping reserve;

[0048] Specifically, consider the reserve supply and demand constraints of the system when the wind power operates with load reduction to provide reserve

[0049] (1) Total reserve supply and demand constraint

[0050]

[0051] Among them, R up_supRepresents the sum of the upward capacity reserve and upward ramping reserve provided by the system, \(R\) dn_sup Represents the sum of the downward capacity reserve and downward ramping reserve provided by the system; \(R\) up_need Represents the sum of the upward capacity reserve and upward ramping reserve required by the system, \(R\) dn_need Represents the sum of the downward capacity reserve and downward ramping reserve required by the system. Equation (1) indicates that the reserve that the system can provide should meet the reserve demand of the system. In the unit commitment model considering multiple reserves based on active load reduction of wind power proposed in this disclosure, the units providing reserves are considered to be thermal power units and wind power units, and the reserve demand comes from wind power units and loads, which can be specifically expressed as:

[0052]

[0053] Where, \(R\) up_th_sup Represents the upward capacity reserve \(R\) provided by thermal power units up_th_sup_cap And the upward ramping reserve \(R\) up _th_sup_ramp , \(R\) up_w_sup Represents the upward capacity reserve provided by wind power units, \(R\) dn_th_sup Represents the downward capacity reserve \(R\) provided by thermal power units dn_th_sup_cap And the downward ramping reserve \(R\) dn_th_sup_ramp , \(R\) dn_w_sup Represents the downward capacity reserve provided by wind power units; \(R\) up_Load Represents the upward capacity reserve demand generated by the load, \(R\) up_w_need Represents the upward capacity reserve demand \(R\) generated by wind power units up_w_need_cap And the upward ramping reserve demand \(R\) up_w_need_ramp , \(R\) dn_Load Represents the downward capacity reserve demand generated by the load, \(R\) dn_w_need Represents the downward capacity reserve demand \(R\) generated by wind power units dn_w_need_cap And the downward ramping reserve demand \(R\) dn_w_need_ramp .

[0054] (2) The capacity and ramping reserve provided by thermal power units can be expressed as:

[0055]

[0056] Where, \(R\) up_th_sup_cap , \(R\) dn_th_sup_cap Respectively represent the upward and downward capacity reserves provided by thermal power units; \(R\) up _th_sup_ramp , \(R\) dn_th_sup_ramp Respectively represent the upward and downward ramping reserves provided by thermal power units; Is the upward capacity reserve provided by thermal power units, Is the downward capacity reserve provided by thermal power units; The upward ramping reserve provided by thermal power units The downward ramping reserve provided by thermal power units

[0057] The reserve provided by thermal power units is subject to the following constraints:

[0058]

[0059] Among them, P i,t is the output power of thermal power unit i at time t, and P i max is the maximum value of the output power of thermal power unit i, and P i min is the minimum value of the output power of thermal power unit i; UR i,t is the nominal upward ramping rate of unit i during operation at time t, and DR i,t is the nominal downward ramping rate of unit i during operation at time t, that is, UR i,t and DR i,t are the adjustment amounts of the output power of the unit per unit time; ΔT is the optimization time interval, usually one hour.

[0060] (3) The capacity reserve provided by wind power units can be expressed as:

[0061]

[0062] Among them, is the upward capacity reserve provided by wind power units; is the downward capacity reserve provided by wind power units.

[0063] (4) The reserve demand of the load is described as follows:

[0064]

[0065] Among them, K up and K dn respectively represent the upward reserve coefficient and the downward reserve coefficient required by the load, and P t L is the total load at time t.

[0066] Step 2: Based on the unit commitment model, considering the scenarios where wind power provides reserve and wind power has reserve demand, describe the uncertainty of wind power output in intervals. By comparing the wind power output after active load reduction of wind power with the original predicted wind power output interval, classify the impact of active load reduction of wind power on relevant reserves to obtain the basic constraint conditions; based on the basic constraint conditions, with the goal of minimizing the operating cost and reserve cost, construct an optimal scheduling model considering multiple reserves based on active load reduction of wind power, and solve it to obtain the optimal scheduling plan for active load reduction of wind power.

[0067] Specifically, the uncertainty of the output of the wind turbine is described in the form of an interval:

[0068] The predicted wind power output interval is as Figure 3 shown. The relationship between each predicted value satisfies the following constraints:

[0069]

[0070] where is the minimum value of the predicted output power of the i-th wind turbine at time t; is the predicted value of the output power of the i-th wind turbine at time t; is the maximum value of the predicted output power of the i-th wind turbine at time t.

[0071] The wind power output interval after the wind turbine actively reduces its load is as Figure 4 shown. The relationship between each wind power load reduction output value satisfies the following constraints:

[0072]

[0073] where is the minimum value of the output power of the i-th wind turbine at time t considering active load reduction operation; is the output power of the i-th wind turbine at time t considering active load reduction operation; is the maximum value of the output power of the i-th wind turbine at time t considering active load reduction operation.

[0074] In addition, the wind power output before and after load reduction operation needs to satisfy the following constraints:

[0075]

[0076] Furthermore, the wind turbine provides up and down regulation reserve supply and the up and down regulation capacity reserve and up and down ramping reserve requirements of the wind turbine. The improvement points of the present disclosure focus on the description of the up and down regulation reserve supply provided by the wind turbine and the up and down regulation reserve requirements of the wind turbine. The present disclosure considers that the wind turbine provides reserve supply and has reserve requirements, specifically as follows:

[0077] (1) The up and down regulation reserve supply that the wind turbine can provide

[0078]

[0079] When the wind power output interval after the wind turbine actively reduces its load is as Figure 5As shown. It can be seen from the figure that when the wind turbine actively reduces its load and operates at point A at time T, the upward regulation capacity reserve that the wind turbine can provide is 0, because the output power of the wind turbine above point A is uncertain at this time. The downward regulation capacity reserve that the wind turbine can provide at this time is As shown by line segment ①, since the wind power output below the lower limit of the predicted output range is considered to be certain, the wind power output in this area can be used to provide reserve. When occurs, the wind power output range after the wind turbine actively reduces its load is as Figure 6 shown. Similarly, it can be seen from the figure that when the wind turbine actively reduces its load and operates at point A at time T, the upward regulation capacity reserve that the wind turbine can provide is as shown by line segment ①. The downward regulation capacity reserve that the wind turbine can provide at this time is as shown by line segment ②.

[0080] (2) Upward and downward regulation capacity reserve requirements of wind turbines

[0081]

[0082] When occurs, the wind power output range after the wind turbine actively reduces its load is as Figure 7 shown. Since the upward reserve requirement of the wind turbine is used to cope with the scenario when the wind turbine output power fluctuates downward, when the wind turbine actively reduces its load and operates at point A at time T, the upward regulation capacity reserve requirement of the wind turbine is as shown by line segment ①. Similarly, the downward reserve requirement of the wind turbine is used to cope with the scenario when the wind turbine output power fluctuates upward, so the downward regulation capacity reserve requirement of the wind turbine at this time is as shown by line segment ②. When occurs, the wind power output range after the wind turbine actively reduces its load is as Figure 8 shown. When the wind turbine actively reduces its load and operates at point A at time T, since the wind power output below the lower limit of the predicted output range is considered to be certain, the upward reserve requirement of the wind turbine used to cope with the downward fluctuation of the wind power output is 0. The downward regulation capacity reserve requirement of the wind turbine at this time is as shown by line segment ①.

[0083] (3) Upward and downward ramping reserve requirements of wind turbines

[0084]

[0085] Equation (12) gives the range of ramping reserve that the system needs to provide according to the deviation between the output power of the wind turbine and the nominal situation. These equations can be obtained from Figure 9It is obtained from... It should be noted that the schedulable wind range within time t is defined by the lower bound of wind dispatch and the upper bound . The maximum possible upward ramp rate within this range is , and its deviation from the maximum possible upward ramp of the nominal wind dispatch ramp is Similarly, the maximum possible downward ramp rate within this range is , and its deviation from the maximum possible downward ramp of the nominal wind dispatch ramp is It should be noted that when the wind power output is lower than the lower limit of the original predicted wind power output interval, since the wind power output at this time is regarded as arbitrarily adjustable, the part of the wind power output lower than the original predicted wind power output interval does not need to provide ramp reserve.

[0086] Linearize the above-mentioned equations (10), (11), and (12).

[0087] Introduce the 0 / 1 variable b i,t to satisfy:

[0088]

[0089] Then equation (10) can be expressed as:

[0090]

[0091] Equation (11) can be expressed as:

[0092]

[0093] Equation (12) can be expressed as:

[0094]

[0095] Furthermore, construct an objective function with the minimization of the total cost as the goal, and construct an optimal scheduling model considering multiple reserves based on active load reduction of wind power. The total cost mainly includes operation cost and reserve cost. Among them, the operation cost consists of three parts: running cost, startup cost, and shutdown cost; the reserve cost consists of the reserve cost provided by thermal power units and the reserve cost provided by wind turbines. The minimization of the total cost can be expressed by the following equation (17):

[0096]

[0097] Among them, N T is the total number of optimized time periods; N G is the total number of schedulable thermal power units; N W is the total number of schedulable wind turbines; a i , bi , c i is the parameter of the cost function of the generator set; P i,t is the output power of thermal power unit i at time t; U i,t is a binary variable indicating the start-stop state of thermal power unit i in period t; is a binary variable indicating whether thermal power unit i is turned on in period t; is the start-up cost of thermal power unit i; is a binary variable indicating whether thermal power unit i is turned off in period t; is the shutdown cost of thermal power unit i; is the reserve price of thermal power unit i; is the reserve price of wind power unit w; R i,t is the reserve provided by thermal power unit i during period t; R w,t is the reserve provided by wind power unit w during period t.

[0098] The operating cost part of the objective function of this model can be piecewise linearized, as Figure 2 shown.

[0099] To show in detail the impact of capacity reserve and ramping reserve on the total cost, the reserve cost part of the thermal power unit in (17) is rewritten as:

[0100]

[0101] where is the upward capacity reserve provided by the thermal power unit; is the downward capacity reserve provided by the thermal power unit; is the upward ramping reserve provided by the thermal power unit; is the downward ramping reserve provided by the thermal power unit.

[0102] Similarly, the reserve cost part of the wind power unit is rewritten as:

[0103]

[0104] where is the upward capacity reserve provided by the wind power unit; is the downward capacity reserve provided by the wind power unit. For the sake of simplicity in analysis, the case where the wind power unit provides ramping reserve is not considered here.

[0105] Furthermore, based on the consideration of active load reduction of wind power, the general constraint conditions in the optimal scheduling model with multiple reserves are as follows.

[0106] (1) Unit start-stop constraint

[0107] Once a unit is synchronized and put into operation, a fixed start-up cost will be generated. Once it is desynchronized and taken out of operation, a fixed shutdown cost will be generated. Among these two costs and are parameters. When unit i starts up, the cost is When unit i shuts down, the cost is Binary variable is used to indicate whether unit i starts up in period t. If the unit starts up, takes 1, otherwise takes 0. Similarly, binary variable is used to indicate whether unit i shuts down in period t. and satisfy the following logical constraint conditions:

[0108]

[0109]

[0110] where is a parameter representing the initial start-stop state of unit i, IC i is the initial state of unit i. A positive value indicates that the unit is on, and a negative value indicates that the unit is off. The absolute value represents the number of periods during which the unit has been on or off.

[0111] (2) Minimum running time and outage time constraints

[0112] Since each start-up and shutdown of a unit requires a certain cost, to reduce the operating cost, it is necessary to control the start-stop frequency of the unit. Therefore, the minimum running time and outage time constraints of the unit need to be considered to ensure the economy of the unit operation. In addition, for some units that did not operate or ended their outage the previous day, the minimum running time and outage time are also required for constraints.

[0113] Minimum running time constraint:

[0114]

[0115] where T i on is the minimum running time of unit i.

[0116] Initial minimum running time constraint:

[0117]

[0118] Minimum outage time constraint:

[0119]

[0120] Among them, T i off is the minimum outage time of unit i.

[0121] Initial minimum outage time constraint:

[0122]

[0123] (3) Output power constraint

[0124]

[0125] Equation (26) ensures that the power output P of each generator i,t should be within the maximum or minimum allowable output power range specified for the unit.

[0126] (4) Power balance constraint

[0127]

[0128] Among them, P i,t is the output power of thermal power unit i at time t, P t L is the total load at time t, is the output power of the i-th wind turbine at time t considering wind curtailment.

[0129] (5) Ramping up and ramping down constraints

[0130] Since it takes time for the output of the generating unit to change, ramping constraints need to be considered. The ramping up and ramping down constraints of the unit are respectively:

[0131]

[0132]

[0133] Among them, UR i,t is the nominal ramping up rate of unit i at time t during operation, DR i,t is the nominal ramping down rate of unit i at time t during operation, that is, UR i,t and DR i,t are the output power adjustment amounts of the unit per unit time; is the nominal ramping up rate of unit i during startup; is the nominal ramping down rate of unit i during shutdown.

[0134] If the ramping rates during startup and shutdown are not explicitly given, the following values can be adopted:

[0135]

[0136] If a unit starts up, if UR i,t <P i min , then it can reach its lower output limit P during the startup process i min . Similarly, if DR i,t <P i min , then the unit can only shut down when its output reaches the lower limit P i min . If UR i,t >P i min , then the rising rate during the startup process is the same as that during normal operation. If DR i,t >P i min , the descending rate during the shutdown process is the same as the climbing rate during normal operation.

[0137] Initial climbing and descending constraints:

[0138]

[0139] where P i 0 is the initial power of unit i.

[0140] (6) Unit capacity reserve constraint and ramping reserve constraint

[0141]

[0142]

[0143] Equation (34) indicates that the upward and downward capacity reserves provided by the unit must be within the limit of the unit capacity. That is to say, the capacity reserve that the unit can provide is determined by the upper and lower limits of the operation output of the generator set. Equation (35) indicates that the actually available capacity reserve and ramping reserve of the unit are affected by the ramping constraint of the unit. ΔT is the optimization time interval, usually one hour.

[0144] Finally, a mature commercial solver can be used for solving.

[0145] Embodiment 2

[0146] In an embodiment of the present disclosure, a wind power active load reduction optimization scheduling system considering multiple reserve requirements is provided, including:

[0147] A model construction module, configured to construct a unit commitment model that simultaneously considers two types of reserve supply-demand constraints, namely capacity reserve and ramping reserve;

[0148] An optimization scheduling module, configured to, based on the unit commitment model, consider scenarios where wind power provides reserve and has reserve demand, describe the uncertainty of wind power output in intervals, classify the impact of active wind power curtailment on relevant reserves by comparing the wind power output after active wind power curtailment with the original predicted wind power output interval, and obtain basic constraint conditions; based on the basic constraint conditions, with the objective of minimizing the operation cost and reserve cost, construct an optimization scheduling model considering multiple reserves based on active wind power curtailment, and solve it to obtain an active wind power curtailment optimization scheduling scheme.

[0149] Embodiment 3

[0150] In an embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, it implements the active wind power curtailment optimization scheduling method considering multiple reserve demands.

[0151] Embodiment 4

[0152] In an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, and the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, it implements the active wind power curtailment optimization scheduling method considering multiple reserve demands.

[0153] Embodiment 5

[0154] In an embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes to implement the active wind power curtailment optimization scheduling method considering multiple reserve demands.

[0155] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to generate a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process or multiple processes and / or one block or multiple blocks in the flow Figure 1 one process or multiple processes and / or blocks Figure 1 or steps for realizing the functions specified in multiple blocks.

[0157] Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, they are not limitations on the protection scope of the present disclosure. Those skilled in the art should understand that, based on the technical solutions of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present disclosure.

Claims

1. A wind power active load reduction optimization scheduling method considering multiple reserve requirements, characterized in that: include: Construct a unit commitment model that considers both capacity reserve and ramp reserve supply and demand constraints; Based on the unit combination model, considering the scenarios of wind power providing backup and wind power having backup demand, the uncertainty of wind power output is described by intervals. By comparing the wind power output after active wind power load reduction with the original predicted wind power output interval, the impact of active wind power load reduction on related backup is classified and the basic constraint conditions are obtained. Based on the basic constraints, with the goal of minimizing operating costs and reserve costs, an optimization scheduling model based on active wind power load reduction and considering multiple reserve costs is constructed, and the optimization scheduling scheme for active wind power load reduction is obtained by solving it.

2. The wind power active load reduction optimization scheduling method considering multiple backup requirements as claimed in claim 1 is characterized in that: The wind turbine is a variable-speed wind turbine whose output power is controlled by the pitch angle. In the unit combination model considering the two reserve supply and demand constraints of capacity reserve and climbing reserve, the units providing reserve are thermal power units and wind turbine units, and the reserve demand comes from wind turbine units and loads.

3. The wind power active load reduction optimization scheduling method considering multiple backup requirements as claimed in claim 1 is characterized in that: The uncertainty of wind power output is described by intervals, and the relationship between the predicted values ​​of the wind power output interval is predicted to meet the following constraints: in, is the minimum predicted output power of the i-th wind turbine in period t; is the predicted output power of the i-th wind turbine in period t; is the maximum predicted output power of the i-th wind turbine in period t.

4. The wind power active load reduction optimization scheduling method considering multiple backup requirements as claimed in claim 1 is characterized in that: The relationship between the wind power load reduction output values ​​in the wind power output interval after the wind turbine set actively reduces load and operates satisfies the following constraints: in, is the minimum output power of the i-th wind turbine in period t when active load reduction is considered; The output power of the i-th wind turbine in period t when active load reduction is considered; is the maximum output power of the i-th wind turbine in period t when active load reduction is considered.

5. The wind power active load reduction optimization scheduling method considering multiple reserve requirements as claimed in claim 1 is characterized in that: when According to the wind power output range after the wind turbine group actively reduces its load, we can get: when the wind turbine group actively reduces its load and runs at point A at time T, the upward capacity reserve that the wind turbine group can provide is 0. At this time, the output of the wind turbine group above point A is uncertain. At this time, the downward capacity reserve that the wind turbine group can provide is The wind power output below the lower limit of the predicted output range is considered to be certain, and the wind power output in this area is used to provide backup; According to the wind power output range after the wind turbine group actively reduces its load, when the wind turbine group actively reduces its load and runs at point A at time T, the upward capacity reserve that the wind turbine group can provide is At this time, the wind turbine can provide a reserve capacity of 6. The wind power active load reduction optimization scheduling method considering multiple reserve requirements as claimed in claim 1 is characterized in that: when According to the wind power output range after the wind turbine actively reduces its load, we can get: Since the upward reserve demand of the wind turbine is used to deal with the scenario when the wind turbine output fluctuates downward, when the wind turbine actively reduces its load and runs at point A at time T, the upward reserve demand of the wind turbine is The downward reserve demand of the wind turbine is used to deal with the scenario when the wind turbine output fluctuates upward. Therefore, the downward reserve demand of the wind turbine at this time is 7. A wind power active load reduction optimization dispatching system considering multiple reserve requirements, characterized by comprising: Model building module, used to build a unit commitment model that considers both capacity reserve and ramp reserve supply and demand constraints; The optimization scheduling module is used to consider the scenarios of wind power providing backup and wind power having backup demand based on the unit combination model, describe the uncertainty of wind power output with intervals, and classify the impact of wind power active load reduction on related backup by comparing the wind power output after active wind power load reduction with the original predicted wind power output interval, and obtain basic constraints; based on the basic constraints, with the goal of minimizing operating costs and backup costs, construct an optimization scheduling model based on active wind power load reduction and considering multiple backups, and solve it to obtain an optimized scheduling plan for active wind power load reduction.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the wind power active load reduction optimization scheduling method considering multiple backup demands as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the wind power active load reduction optimization scheduling method considering multiple backup requirements as described in any one of claims 1-6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the wind power active load reduction optimization scheduling method considering multiple backup needs as described in any one of claims 1-6.

Images