Active power distribution network integrated scheduling method and system integrating day-ahead, day-intra-day and real-time

Abstract

The invention provides a day-ahead-intra-day-real-time integrated scheduling method and system for an active power distribution network, and relates to the technical field of power distribution network optimization scheduling, and the method comprises the steps: integrating day-ahead scheduling, intra-day scheduling and real-time scheduling with different look-ahead time scales, time granularities and rolling frequencies; constructing an active power distribution network integrated dispatching model integrating day-ahead, day-intra-day and real-time; capacity reserve and climbing reserve are introduced, the containment relation between the capacity reserve and the climbing reserve is obtained, and the requirements of the capacity reserve and the climbing reserve of the unit are obtained from wind power prediction information; a target function is constructed by taking the minimization of the total cost of a power distribution network in an operation cycle as a target, the target function is solved to realize simultaneous rolling optimization of decision variables such as unit start and stop, unit output, superior power grid electricity purchase, energy storage charge and discharge, standby and the like, and latest prediction information of renewable energy sources and loads is included before rolling optimization each time. Only the optimized scheduling decision of the first time period is really executed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of distribution network optimal scheduling, and particularly to an integrated scheduling method and system for an active distribution network integrating day-ahead, intra-day and real-time scheduling. 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, the access capacity of renewable energy represented by wind power and photovoltaic power has increased rapidly, becoming a "new force" in the power generation system. However, compared with traditional power generation technologies, the output of renewable energy such as wind and light is affected by frequent changes in weather factors such as wind speed and light intensity, and the power generation power has great uncertainty, which is not conducive to the formulation of accurate scheduling plans for the power system. At present, the prediction accuracy of new energy power generation is limited and cannot meet the requirements of the power system scheduling plan. In the operation process of the power system, it is found that there is a large deviation between the actual generated electricity and the predicted generated electricity in the scheduling plan. In the traditional power system scheduling model, the load deviation and uncertainty disturbance of the previous-level scheduling plan are borne by the next-level scheduling. With the increase in the penetration rate of renewable energy, the uncertainty of the power grid increases, and the prediction deviation of the load and renewable energy output further increases. The fixed result of the previous-level scheduling may lead to insufficient adjustment resources at the next level, and there are connection problems between different modules, resulting in suboptimal or even unachievable solutions for the terminal scheduling module. This makes the scheduling work need to be adjusted greatly in a short time, bringing great adverse effects to the power system scheduling.

[0004] Integrating multiple scheduling modules together for collaborative consideration as an optimization method can effectively handle the connection problems between modules. This method does not require each scheduling module to be carried out sequentially in order, but considers each decision variable collaboratively and makes decisions simultaneously. However, in the existing integrated scheduling, only capacity reserve is considered in the reserve deployment. The reserve is configured as a part of the total load or the capacity of the largest online unit or a combination thereof. This method is easy to implement, but does not consider the real-time deliverability of the unit to provide reserve. The power generation output of renewable energy such as wind power and photovoltaic power is volatile and uncertain, and the deviation between the expected generated electricity and the actual generated electricity must be absorbed by the reserve provided by the power system. These reserves must be available and can be deployed in real time. Even within a very short time interval, the output of renewable energy may increase or decrease at a certain rate, which requires traditional generating units to adjust their output in time to maintain the balance between supply and demand. Therefore, only deploying capacity reserve cannot meet the requirements of short-term fluctuations in a system with a high proportion of renewable energy. Therefore, in addition to capacity reserve, it is necessary to deploy a certain amount of ramping reserve.

[0005] In summary, the existing technologies currently available at least have the following disadvantages and deficiencies:

[0006] 1. The existing integrated scheduling model can combine and analyze two scheduling modules with similar time dimensions in traditional sequential scheduling, but lacks the research on the integrated scheduling of the day-ahead, intra-day, and real-time scheduling modules, and still has not fully explored the collaborative potential of the three scheduling modules in the time dimension.

[0007] 2. The existing integrated scheduling model lacks the utilization of adjustable resources such as energy storage. Restricted by the ramp rate of the unit, it is impossible to meet the actual requirements only by adjusting the start-stop and output of the unit to cope with the system volatility.

[0008] 3. Only capacity reserve is deployed when deploying reserve, and the ability of the unit to provide reserve is restricted by the ramp rate and output upper limit. In practical applications, there will be problems where the reserve provided by the unit cannot be delivered as expected. Summary of the Invention

[0009] To solve the above problems, the present disclosure proposes an integrated scheduling method and system for an active distribution network that integrates day-ahead, intra-day, and real-time scheduling. The day-ahead scheduling, intra-day scheduling, and real-time scheduling with different forward-looking time scales, time granularities, and rolling frequencies are integrated. The unit start-stop and slow adjustment resource plan are no longer fixed plans within a 24-hour cycle determined by day-ahead scheduling, and the unit output is no longer a fixed plan within a 4-hour cycle determined by intra-day scheduling. All adjustable resources are continuously optimized and scheduled with a minimum time granularity of 5 minutes and a maximum time scale of 24 hours, and the scheduling plan is continuously updated during continuous rolling decision-making. Due to the small time granularity of the integrated scheduling, there may be a situation where the reserve cannot be delivered as expected only considering capacity reserve. Therefore, on the basis of deploying capacity reserve, ramp reserve is introduced, and the capacity and speed of the reserve are considered collaboratively. It not only provides capacity reserve to cope with the supply-demand balance, but also provides ramp reserve to ensure that the reserve resources have sufficient adjustment speed to respond to the dynamic changes of the system. The capacity reserve and ramp reserve are jointly provided by the unit and the energy storage, precisely coping with the volatility and uncertainty of the net load in the system, reducing or avoiding the situation where the reserve cannot be delivered due to insufficient system regulation ability, and even passive wind curtailment and load shedding, and improving the flexibility, security, and economy of the system.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions:

[0011] An integrated scheduling method for an active distribution network that integrates day-ahead, intra-day, and real-time scheduling, including:

[0012] Initializing the active distribution network, considering the day-ahead, intra-day, and real-time scheduling modes, and integrating the day-ahead scheduling, intra-day scheduling, and real-time scheduling with different forward-looking time scales, time granularities, and rolling frequencies to construct an integrated scheduling model for an active distribution network that integrates day-ahead, intra-day, and real-time scheduling;

[0013] Introduce capacity reserve and ramping reserve into the integrated scheduling model of active distribution network for pre-day, intra-day, and real-time, obtain the constraint relationship between capacity reserve and ramping reserve, and obtain the requirements for unit capacity reserve and ramping reserve from wind power prediction information;

[0014] Based on the integrated scheduling model of active distribution network for pre-day, intra-day, and real-time and multiple reserve requirements, construct an objective function with the goal of minimizing the total cost of the distribution network during the operation cycle, solve the objective function to realize the simultaneous rolling optimization of decision variables such as unit start-stop, unit output, power purchase from the superior power grid, energy storage charging and discharging, and reserve. The latest prediction information of renewable energy and load is incorporated before each rolling optimization, and only the scheduling decision of the first period after optimization is actually executed.

[0015] According to some embodiments, the present disclosure adopts the following technical solutions:

[0016] An integrated scheduling system for active distribution network for pre-day, intra-day, and real-time, comprising:

[0017] A model construction module, configured to initialize the active distribution network, consider three scheduling modes of pre-day, intra-day, and real-time, and integrate the pre-day scheduling, intra-day scheduling, and real-time scheduling with different look-ahead time scales, time granularities, and rolling frequencies to construct an integrated scheduling model of active distribution network for pre-day, intra-day, and real-time;

[0018] A reserve deployment module, configured to introduce capacity reserve and ramping reserve into the integrated scheduling model of active distribution network for pre-day, intra-day, and real-time. The two reserves are jointly provided by units and energy storage, obtain the constraint relationship between different regulation resources and different reserves, obtain the energy and power balance relationship of the reserve provided by energy storage, and obtain the requirements for unit capacity reserve and ramping reserve from wind power prediction information;

[0019] A scheduling solution module, configured to construct an objective function with the goal of minimizing the total cost of the distribution network during the operation cycle based on the integrated scheduling model of active distribution network for pre-day, intra-day, and real-time and multiple reserve requirements, solve the objective function to realize the simultaneous rolling optimization of decision variables such as unit start-stop, unit output, power purchase from the superior power grid, energy storage charging and discharging, and reserve. The latest prediction information of renewable energy and load is incorporated before each rolling optimization, and only the scheduling decision of the first period after optimization is actually executed.

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

[0021] A computer program product, comprising a computer program, which when executed by a processor implements the integrated scheduling method of active distribution network for pre-day, intra-day, and real-time as described above.

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

[0023] A non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the integrated scheduling method for an active distribution network that integrates day-ahead, intra-day, and real-time

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] An electronic device includes: 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 implements the integrated scheduling method for an active distribution network that integrates day-ahead, intra-day, and real-time

[0026] Compared with the prior art, the beneficial effects of the present disclosure are:

[0027] An integrated scheduling method for an active distribution network that integrates day-ahead, intra-day, and real-time of the present disclosure integrates three scheduling modes of day-ahead, intra-day, and real-time together for collaborative optimization scheduling, and solves the scheduling connection problem faced by the open-loop sequential scheduling framework of multiple time scales. By adopting fine time granularity and high-frequency rolling characteristics, the time points for making decisions on adjustable resources are expanded to the greatest extent, and the latest wind power and load prediction information is incorporated in a timely manner, so as to better promote the consumption level of renewable energy, respond to short-term net load fluctuations in a timely manner, and improve the flexibility and economy of the active distribution network scheduling.

[0028] An integrated scheduling method for an active distribution network that integrates day-ahead, intra-day, and real-time of the present disclosure constructs a mathematical model for integrated scheduling considering multiple reserves that integrates three scheduling modules of day-ahead, intra-day, and real-time. With the objective of minimizing the total cost of the distribution network during the integrated scheduling operation cycle, it includes line power flow constraints, node power balance constraints, line transmission capacity constraints, node voltage constraints, conventional thermal power unit constraints, power purchase constraints from the superior power grid, energy storage constraints, wind turbine output constraints, load shedding constraints, and reserve constraints, while optimizing decision variables such as unit start-stop, unit output, power purchase from the superior power grid, and reserve. Energy storage technology with shorter response time and higher flexibility is introduced into the model, and the energy storage technology is applied to the integrated scheduling of the distribution network to balance power supply and demand and improve the stability and reliability of the power system. The proportion of distributed energy in the distribution network is higher, the types of adjustable resources are more, and the net load fluctuation is stronger. Since the response time of the energy storage technology is short, it can improve the system flexibility and is beneficial to better cope with the intermittency and volatility of the distribution network; by utilizing the characteristics of the energy storage technology to "cut peaks and fill valleys", it helps to balance power supply and demand, smooth the load curve, and reduce the power cost.

[0029] An integrated scheduling method for active distribution network that combines day-ahead, intra-day, and real-time. On the basis of deploying capacity reserve, ramping reserve is introduced. Capacity reserve and ramping reserve are provided by both generators and energy storage simultaneously, clarifying the restraint relationship between the two types of reserve provided by the two adjustable resources. In the model, the uncertainty of wind power output is described by intervals, and the requirements for capacity reserve and ramping reserve of generators and energy storage are obtained from wind power prediction information. The integrated distribution network model considering multiple reserves can finely handle the volatility and uncertainty in the system, reduce or even avoid the situation that reserve cannot be delivered due to insufficient system regulation capacity, and even avoid forced curtailment of wind power and load shedding, improving the safe and economic operation level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings forming a part of this disclosure are used to provide a further understanding of this disclosure. The schematic embodiments and descriptions thereof of this disclosure are used to explain this disclosure and do not constitute an improper limitation to this disclosure.

[0031] Figure 1 It is a schematic flowchart of the method according to the embodiment of this disclosure;

[0032] Figure 2 It is the scheduling framework of the integrated active distribution network model according to the embodiment of this disclosure;

[0033] Figure 3 It is a schematic diagram of upward and downward capacity reserve according to the embodiment of this disclosure;

[0034] Figure 4 It is a schematic diagram of up-ramp and down-ramp reserve according to the embodiment of this disclosure;

[0035] Figure 5 It is a schematic diagram of the power circle linearization method according to the embodiment of this disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed description is illustrative and is intended to provide further explanation 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 this disclosure belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments 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 form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0039] Example 1

[0040] In an embodiment of the present disclosure, an integrated scheduling method for an active distribution network that combines day-ahead, intra-day, and real-time is provided, which integrates day-ahead scheduling, intra-day scheduling, and real-time scheduling with different forward-looking time scales, time granularities, and rolling frequencies. The integrated scheduling model coordinates the scheduling of all adjustable resources, and simultaneously decides variables such as unit start-stop, unit output, power purchase from the superior power grid, energy storage charge and discharge, and reserve during each optimal scheduling. It has a long forward-looking time scale of 24 hours, the finest time granularity of 5 minutes, and formulates a scheduling plan every 5 minutes. The latest prediction information of renewable energy and load is incorporated before each rolling optimization, and only the scheduling decision of the first period after optimization is actually executed, while the scheduling plans of the remaining periods are only for reference. The method steps are as follows:

[0041] Step 1: Initialize the active distribution network. Instead of artificially dividing the day-ahead, intra-day, and real-time scheduling modules, integrate day-ahead scheduling, intra-day scheduling, and real-time scheduling with different forward-looking time scales, time granularities, and rolling frequencies to construct an integrated scheduling model for the active distribution network that combines day-ahead, intra-day, and real-time;

[0042] Step 2: Introduce capacity reserve and ramping reserve into the integrated scheduling model for the active distribution network that combines day-ahead, intra-day, and real-time, obtain the constraint relationship between capacity reserve and ramping reserve, and obtain the requirements for unit capacity reserve and ramping reserve from the wind power prediction information;

[0043] Step 3: Based on the integrated scheduling model for the active distribution network that combines day-ahead, intra-day, and real-time and multiple reserve requirements, construct an objective function with the goal of minimizing the total cost of the distribution network during the operation cycle, solve the objective function to realize the simultaneous rolling optimization of decision variables such as unit start-stop, unit output, power purchase from the superior power grid, energy storage charge and discharge, and reserve. The latest prediction information of renewable energy and load is incorporated before each rolling optimization, and only the scheduling decision of the first period after optimization is actually executed.

[0044] As an embodiment, in the integrated scheduling method for active distribution networks that combines day-ahead, intra-day, and real-time scheduling of the present disclosure, since the power outputs of renewable energy power generations such as wind power and photovoltaic power are volatile and uncertain, even within a very short time interval, the power output of renewable energy may increase or decrease at a certain rate. Therefore, to ensure the actual deliverability of the reserve provided by thermal power units and energy storage, on the basis of deploying capacity reserve, the present disclosure introduces ramping reserve, coordinately considers the capacity and speed of the reserve, and finely responds to the volatility and uncertainty of the net load in the system. The model clarifies the restraint relationship between capacity reserve and ramping reserve, describes the uncertainty of wind power output in intervals, and obtains the requirements for unit and energy storage capacity reserve and ramping reserve from wind power prediction information, reducing the inability to deliver reserve and even passive curtailment of wind power and load shedding caused by insufficient system regulation capacity, and improving the safe and economic operation level of the system. The specific implementation process of the method of the present disclosure is as follows:

[0045] Step 1: Initialize the active distribution network, consider three scheduling modes of day-ahead, intra-day, and real-time, and integrate day-ahead scheduling, intra-day scheduling, and real-time scheduling with different look-ahead time scales, time granularities, and rolling frequencies to construct an integrated scheduling model for active distribution networks that combines day-ahead, intra-day, and real-time scheduling;

[0046] Specifically, the integrated scheduling model for active distribution networks that combines day-ahead, intra-day, and real-time scheduling integrates day-ahead scheduling, intra-day scheduling, and real-time scheduling with different look-ahead time scales, time granularities, and rolling frequencies. Adjustable resources with different flexibilities are optimized and decision-making simultaneously, thus eliminating the module connection problems existing in sequential scheduling. The model has a long look-ahead time scale of 24 hours, the finest time granularity of 5 minutes, and a scheduling plan is formulated every 5 minutes. The latest prediction information of renewable energy and load is incorporated before each rolling optimization, and only the scheduling decision of the first period after optimization is actually executed, and the scheduling plans of the remaining periods are only for reference. The scheduling framework of the integrated scheduling model for active distribution networks that combines day-ahead, intra-day, and real-time scheduling is as Figure 2 shown.

[0047] The integrated scheduling model for active distribution networks that combines day-ahead, intra-day, and real-time scheduling aims to minimize the total cost generated during the system operation cycle, as shown in Equation (1); taking into account the unit startup cost as in Equation (2), the unit operation cost as in Equation (3), the unit reserve cost as in Equation (4), the power purchase cost from the superior power grid as in Equation (5), the energy storage cost as in Equations (6)(7), the wind power curtailment cost as in Equation (8), and the load shedding cost as in Equation (9).

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] In the formula, and respectively represent the unit startup cost, unit operation cost, and reserve cost under integrated scheduling; is the cost of purchasing electricity from the superior power grid under integrated scheduling; and respectively represent the energy storage operation cost and the cost of the energy storage providing reserve under integrated scheduling; and are the load shedding and wind curtailment penalty costs respectively. t / N T is the time interval index and set of the 24-hour look-ahead time scale of integrated scheduling with a 5-minute time granularity. g / N G is the index and set of all generating units; represents the startup cost of generating unit g; y g,t is a binary variable indicating whether unit g is in the startup state at time t, y g,t =1 means startup, otherwise it means not startup; H t is the ratio of the duration of the integrated scheduling time interval (5 minutes or 15 minutes) to the 1-hour time granularity; P g,t is the active power output of generating unit g in time period t; a g , b g and c g are the quadratic term coefficient, linear term coefficient, and constant term of the operation cost of unit g respectively; u g,t is a binary variable indicating the startup and shutdown state of unit g in time period t. If the unit is in the operating state during this period, this variable is 1. If the unit is in the non-operating state during this period, this variable is 0; is the reserve unit price of unit g; and are the upward regulation capacity reserve and downward regulation capacity reserve provided by unit g in time period t respectively; and are the upward ramping reserve and downward ramping reserve provided by unit g in time period t respectively; f / N fIt is the index and set of the superior power grid; π f,t is the electricity selling price of the superior power grid f in the t period; P f,t is the active power output of the superior power grid f in the period t; b / N B It is the index and set of energy storage; and are the charging and discharging cost coefficients of energy storage b respectively; and represent the charging power and discharging power of energy storage b in the t period respectively; is the spare unit price of energy storage b; and are the upward regulation capacity reserve and downward regulation capacity reserve provided by energy storage b in the period t respectively; and are the upward ramping reserve and downward ramping reserve provided by energy storage b in the period t respectively; w / N w It is the index and set of wind power; ΔP w,t is the wind power curtailment of wind power w in the period t; m / N D It is the index and set of distribution network load nodes; ΔP m,t is the active load curtailment of load node m in the period t.

[0058] Furthermore, the constraint conditions are as follows:

[0059] (1) Line power flow constraint

[0060] Considering the coupling relationship between active power flow and reactive power flow in the distribution network, this disclosure constructs the line power flow constraint of the system by using the generation load transfer factor based on decoupled linearized power flow that is more suitable for the operation of the distribution network, and makes the following linearization processing for the active power flow and reactive power flow constraint equations:

[0061]

[0062] In the formula, i and j are node indices; P ij,t and Q ij,t are the active power flow and reactive power flow on line ij in the t period respectively; g ij and b ij are the conductance value and susceptance value of line ij respectively; V i,t , V j,t are the voltage values of node i and node j in the t period respectively; θ i,t , θ j,t are the phase angles of topological nodes i and j in the t period respectively.

[0063] (2) Node power balance constraint

[0064]

[0065]

[0066] In the formula, st(i) represents the set of end nodes with node i as the head node; en(i) is the set of head nodes with node i as the end node; N L is the set of grid lines; and are the sets of generator sets, superior power grids, wind power, and energy storage connected to node i respectively; Q g,t 、Q f,t are the reactive power of generator set g and superior power grid f during time period t within the integrated dispatching period respectively; P w,t 、Q w,t are the active and reactive power of wind turbine w during time period t within the integrated dispatching period respectively; P m,t 、Q m,t and ΔQ m,t are the active load, reactive load, and reactive load reduction of load node m during the integrated dispatching period respectively.

[0067] (3) Line transmission capacity constraint

[0068]

[0069] In the formula, is the line capacity of line ij.

[0070] (4) Node voltage constraint

[0071] V i min ≤V i,t ≤V i max (14)

[0072] In the formula, V i min and V i max are the lower and upper voltage limits of node i respectively.

[0073] (5) Conventional thermal power unit related constraints

[0074] 1) Unit start-stop logic variable constraint

[0075]

[0076]

[0077]

[0078] In the formula, z g,tA binary variable z indicating whether unit g is in the shutdown state at time t g,t = 1 indicates shutdown, otherwise not shutdown; IC g respectively represent the initial operating state and the initial cumulative operating time of unit g in the day-ahead scheduling stage. When IC g > 0, the specific value represents the time (number of time periods) that unit g has been operating. Corresponding to this IC g When < 0, the absolute value of the specific value represents the time (number of time periods) that unit g has been out of service. Corresponding to this

[0079] 2) Minimum start-stop time constraint of the unit

[0080]

[0081]

[0082]

[0083]

[0084] tt is the index of the time period from t to u represents the operating state of unit g within the minimum operating / outage time period after time t. g,tt

[0085] Equation (18) indicates that if unit g starts at time t, then u g,t = 1, u g,t-1 = 0, that is, u g,tt ≥ 1, and unit g remains in the operating state within the minimum operating time period after time t and cannot be shut down. If unit g does not start at time t, then u g,tt ≥ 0 / -1, then unit g can be shut down in subsequent time periods. Equation (19) indicates that unit g also needs to satisfy the initial minimum operating time constraint, that is, the operation of unit g is also restricted by its operating state before the day-ahead scheduling period. When IC g ≥ 1, that is, the initial state of unit g in the day-ahead scheduling period is the operating state, and that is, when the operating time of unit g at the beginning of the scheduling period is less than the minimum operating time of the unit, unit g will remain in the operating state within this time range and cannot be shut down.

[0086] Equation (20) indicates that if unit g shuts down at time t, then u g,t = 0, u g,t-1 = 1, that is, u g,tt ​≤0, the unit g remains in the shutdown state throughout the minimum outage period after time period t and cannot be started. Equation (21) indicates that the unit g needs to satisfy the initial minimum outage time constraint, that is, the operation of the unit g is also restricted by its operating state before the day-ahead scheduling period. When IC g ≤ -1, that is, the initial state of the unit g in the day-ahead scheduling period is the shutdown state, and when the outage time of the unit g at the beginning of the scheduling period is less than the minimum outage time of the unit, the unit g will remain in the outage state within this time range and cannot be started.

[0087] 3) Unit ramp rate constraint

[0088]

[0089]

[0090]

[0091]

[0092] Equations (22) and (24) are the upward ramp rate constraint equation and the downward ramp rate constraint equation of the generating unit respectively. In the equations, RU g and SU g represent the upward ramp rate when the unit g is in the operating state and the upward ramp rate when the unit is in the starting state respectively. To ensure that the unit can start smoothly, SU g takes the larger value of the lower limit of the active power output of the unit and the upward ramp ability within the time period; similarly, RD g and SD g represent the downward ramp rate when the unit g is in the operating state and the downward ramp rate when the unit is in the shutdown state respectively. To ensure that the unit can shut down smoothly, SD g takes the larger value of the lower limit of the active power output of the unit and the downward ramp ability within the time period; is the initial power of the unit g within the integrated scheduling period; The absolute value of represents the minimum outage time (number of time periods) of the unit g; represents the minimum running time (number of time periods) of the unit g.

[0093] 4) Unit output constraint

[0094]

[0095] In the equation, are the lower limit and upper limit of the active power of the generating unit g respectively; S g is the unit capacity of the generating unit g.

[0096] (6) Superior power grid power supply constraint

[0097]

[0098] Wherein, are respectively the upper limit values of the active power and reactive power that the superior power grid f can provide.

[0099] (7) Energy storage constraint

[0100]

[0101] Wherein, and are respectively the upper limits of the energy released and absorbed by the energy storage b; E b,t is the energy stored by the energy storage b in the t time period; and are respectively the minimum and maximum energy capacities stored by the energy storage b; and are respectively the charging and discharging efficiencies of the energy storage b; z b,t is a binary variable representing the state of the energy storage b in the t time period. If the energy storage is in the discharging state, this variable is 1. If the energy storage is in the charging state, this variable is 0.

[0102] Equation (28) indicates that the charging and discharging power of the energy storage b in the t time period is within the maximum charging and discharging capabilities of the energy storage b. Equation (29) indicates that the energy stored by the energy storage b at time t should be within the allowable range.

[0103] (8) Wind turbine output constraint

[0104]

[0105] (P w,t ) 2 +(Q w,t ) 2 ≤(S w ) 2 (31)

[0106] Wherein, and respectively represent the predicted values of the upper and lower bounds of the output of the wind turbine w in the t time period. Equation (30) indicates that the upper and lower bounds of the output of the wind turbine w cannot exceed the upper and lower bounds of its output prediction value.

[0107] (10) Load shedding constraint

[0108]

[0109] Step 2: Introduce capacity reserve and ramping reserve into the integrated scheduling model of the active distribution network on the day-ahead, intra-day, and real-time basis, obtain the constraint relationship between the capacity reserve and the ramping reserve, and obtain the requirements for the unit capacity reserve and ramping reserve from the wind power prediction information;

[0110] Since the output of renewable energy may increase or decrease at a certain rate even within a very short time interval, it is required that thermal power units and energy storage adjust their outputs in a timely manner to maintain the balance between supply and demand. However, when the output of renewable energy fluctuates in the short term, deploying only capacity reserve cannot guarantee the timely deliverability of the reserve. Therefore, in addition to capacity reserve, a certain amount of ramping reserve also needs to be deployed.

[0111] Specifically, the model proposed by the integrated scheduling model considering multiple reserves requires the wind power output profile as input data because the capacity reserve and ramping reserve requirements of this disclosure are determined based on the power capacity range and ramping ability of the wind turbine output. Wind turbine power capacity range: The wind power generation at a node in the t time period is expected to be within the power capacity range defined by the lower and upper bounds Ramping ability range: The ramping ability of the wind power generation at a node at time t is expected to be within the range defined by the maximum downward and upward ramping speeds The power capacity and ramping ability ranges defined here are deterministic and must be set by the Independent System Operator (ISO). These ranges are determined based on wind power prediction and / or historical information.

[0112] (1) Capacity reserve constraint

[0113] Once the capacity range of the wind power output is obtained from the wind power prediction information The capacity reserve provided by thermal power units and energy storage needs to meet the following requirements:

[0114]

[0115] In the formula, and respectively represent the upper and lower limits of the output of wind turbine w in the t time period. Equation (33) means that the upward and downward capacity reserves provided by the unit and energy storage should make up the difference between the expected power generation of the wind turbine and the upper and lower limits of the wind turbine power generation.

[0116] (2) Ramping reserve constraint

[0117] Once the ramping ability of the wind power output is obtained from the wind power prediction information The ramping reserve provided by thermal power units and energy storage needs to meet the following requirements:

[0118]

[0119]

[0120] In the formula, and respectively represent the upper and lower limits of the ramping ability of the wind turbine w within the time period t. Formulas (34)-(35) indicate that the upper and lower ramping reserves provided by the unit and the energy storage can make up for the deviation between the change in the wind turbine output and its ramping ability. Within the time period t, the dispatchable range of the wind turbine power generation is determined by the upper and lower limits of the wind turbine output. Within the dispatchable range, the maximum possible increase in the wind turbine output is The maximum deviation between it and the change in the nominal wind power output (P w,t -P w,t-1 ) is Similarly, the maximum deviation between the maximum possible decrease in the wind turbine output and the change in the nominal wind power output is That is, formula (35).

[0121] (3) Reserve constraint provided by the unit

[0122]

[0123]

[0124]

[0125] Formula (36) indicates that the ability of the unit g to provide up / down regulation capacity reserve is restricted by the upper / lower limits of the unit output. Formula (37) indicates that the ability of the unit g to provide up / down ramping reserve is restricted by the up / down ramping rates of the unit.

[0126] (4) Reserve constraint provided by the energy storage

[0127]

[0128]

[0129]

[0130]

[0131] In the formula, UR b,t , DR b,t respectively represent the up-ramping rate and the down-ramping rate of the energy storage b; and They are the shortest times for energy storage b to maintain capacity reserve and ramping reserve respectively, with the unit of hour. Equation (39) indicates that the ability of energy storage b to provide up / down regulation capacity reserve is restricted by the charging and discharging power of the energy storage. Equation (40) indicates that the ability of energy storage b to provide up / down ramping reserve is restricted by the charging and discharging ramping rates of the energy storage. Equation (41) indicates that the reserve provided by the energy storage is restricted by energy. Equation (42) is the energy balance constraint, and it is assumed that 20% of the capacity reserve will always be called.

[0132] Step 3: Solve the objective function to realize the simultaneous rolling optimization of decision variables such as unit start-stop, unit output, power purchase from the superior power grid, energy storage charging and discharging, and reserve. The latest prediction information of renewable energy and load is incorporated before each rolling optimization, and only the scheduling decision of the first period after optimization is actually executed.

[0133] Specifically, the specific process of solving the objective function is as follows: The unit operation cost item in the objective function is linearized by using the three-segment linearization method; the quadratic terms in the branch capacity constraint, thermal power unit capacity constraint, and wind power unit capacity constraint are linearized by using the power circle linearization method, so as to transform the integrated active distribution network scheduling model integrating day-ahead, intra-day, and real-time into a mixed integer linear programming model, and a commercial solver is used to solve it.

[0134] Furthermore, the quadratic term in the operation cost part of the objective function based on the integrated active distribution network scheduling model integrating day-ahead, intra-day, and real-time is processed by three-segment linearization. This method is a common technique and will not be elaborated here.

[0135] Since equations (13), (26), and (31) in the constraint conditions all contain quadratic terms, the present disclosure uses the power circle linearization method to linearly process the above formulas. The power circle linearization method is as Figure 5 shown.

[0136] The following takes the line transmission capacity constraint equation (13) as an example for illustration:

[0137] Inequality (13) is non-convex and thus is relaxed in the problem. From the perspective of analytic geometry, equation (13) represents a solid circle with a radius of . Divide the circle into m equal parts to obtain an inscribed regular m-sided polygon inside it. When m approaches infinity, the circle is equivalent to an equilateral polygon with m sides, and the point (P ij,t , Q ij,t ) falling inside the circle is equivalent to falling inside the equilateral polygon. Considering the accuracy and the calculation efficiency of the model, the present disclosure uses the area enclosed by the inscribed dodecagon of the circle to approximately replace the area enclosed by the circle.

[0138] Let A and B be two adjacent vertices of the inscribed regular dodecagon of the circle, and their radian angles are α and β respectively. It can be obtained that:

[0139]

[0140] Thus, the coordinates of points A and B can be obtained as and Then, the side AB of the inscribed dodecagon in the circle can be expressed as:

[0141]

[0142] Based on the above analytic geometry theory, the transmission capacity constraint (13) of the distribution network line can be linearized as:

[0143]

[0144] Similarly, the generator output constraint (26) and the wind turbine output constraint (31) can be linearized into equations (46) and (47) respectively:

[0145] (sinβ - sinα)P g,t -(cosβ - cosα)Q g,t ≤sin(β - α)S g ·u g,t (46)

[0146] (sinβ - sinα)P w,t -(cosβ - cosα)Q w,t ≤sin(β - α)S w (47)

[0147] After linearization, the integrated active distribution network scheduling mathematical model considering multiple reserves constructed in this disclosure, which integrates day-ahead, intra-day, and real-time modules, can be transformed into a mixed-integer linear programming model, and a mature CPLEX commercial solver can be directly called for efficient solution.

[0148] Embodiment 2

[0149] In an embodiment of the present disclosure, an integrated active distribution network scheduling system integrating day-ahead, intra-day, and real-time is provided, including:

[0150] A model construction module, which is used to initialize the active distribution network, consider three scheduling modes of day-ahead, intra-day, and real-time, and integrate day-ahead scheduling, intra-day scheduling, and real-time scheduling with different forward-looking time scales, time granularities, and rolling frequencies to construct an integrated active distribution network scheduling model integrating day-ahead, intra-day, and real-time;

[0151] A spare deployment module is used to introduce capacity reserve and ramping reserve into the integrated day-ahead, intra-day, and real-time active distribution network scheduling model, obtain the constraint relationship between capacity reserve and ramping reserve, and obtain the requirements for unit capacity reserve and ramping reserve from wind power prediction information.

[0152] A solution scheduling module is used to construct an objective function with the goal of minimizing the total cost of the distribution network during the operation cycle based on the integrated day-ahead, intra-day, and real-time active distribution network scheduling model and multiple reserve requirements, solve the objective function to realize the simultaneous rolling optimization of decision variables such as unit start-stop, unit output, power purchase from the superior power grid, energy storage charge and discharge, and reserve. The latest prediction information of renewable energy and load is incorporated before each rolling optimization, and only the scheduling decision of the first period after optimization is actually executed.

[0153] Embodiment 3

[0154] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the integrated day-ahead, intra-day, and real-time active distribution network scheduling method described above.

[0155] Embodiment 4

[0156] A non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the integrated day-ahead, intra-day, and real-time active distribution network scheduling method described above is implemented.

[0157] Embodiment 5

[0158] An electronic device includes: 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 integrated day-ahead, intra-day, and real-time active distribution network scheduling method described above.

[0159] This disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, 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 functions specified in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0160] 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 produce a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for implementing 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 Figure 1 one block or multiple blocks.

[0161] Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, they are not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that, based on the technical solutions of the present disclosure, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the scope of protection of the present disclosure.

Claims

1. An integrated dispatching method for active distribution network integrating day-ahead, intra-day and real-time is characterized in that: include: Initialize the active distribution network, consider the three dispatching modes of day-ahead, intraday and real-time, and integrate the day-ahead dispatching, intraday dispatching and real-time dispatching with different forward-looking time scales, time granularity and rolling frequency to build an integrated dispatching model of active distribution network integrating day-ahead, intraday and real-time. Introduce capacity reserve and ramp reserve into the integrated dispatch model of active distribution network integrating day-ahead, intra-day and real-time, obtain the restraining relationship between capacity reserve and ramp reserve, and obtain the requirements of unit capacity reserve and ramp reserve from wind power forecast information; Based on the integrated dispatching model of active distribution network integrating day-ahead, intra-day and real-time as well as multiple reserve requirements, the objective function is constructed with the goal of minimizing the total cost of the distribution network during its operation cycle. The objective function is solved to achieve simultaneous rolling optimization of decision variables such as unit start and stop, unit output, power purchase by the upper power grid, energy storage charging and discharging, and reserve. Before each rolling optimization, the latest forecast information of renewable energy and load is included, and only the dispatching decision of the first period after optimization is actually executed.

2. The integrated dispatching method of active distribution network integrating day-ahead, day-intraday and real-time according to claim 1 is characterized in that: The day-ahead dispatch, intraday dispatch and real-time dispatch with different forward-looking time scales, time granularities and rolling frequencies are integrated into one, using a set twenty-four-hour long forward-looking time scale and five-minute fine time granularity, and implementing high-frequency rolling to build an integrated dispatch model of active distribution network integrating day-ahead, intraday and real-time.

3. The integrated dispatching method of active distribution network integrating day-ahead, day-intraday and real-time according to claim 1 is characterized in that: The constraints of the integrated dispatching model of active distribution network integrating day-ahead, intra-day and real-time include line flow constraints, node power balance constraints, line transmission capacity constraints, node voltage constraints, conventional thermal power unit constraints, upper power grid power purchase constraints, energy storage constraints, wind turbine output constraints, load shedding constraints and standby constraints.

4. The integrated dispatching method of active distribution network integrating day-ahead, day-intraday and real-time according to claim 1 is characterized in that: Capacity reserve and ramp reserve are introduced into the integrated dispatching model of active distribution network integrating day-ahead, intra-day and real-time. The wind power output profile is used as input data. The capacity reserve and ramp reserve requirements are determined based on the power capacity range and ramp capacity range of wind turbine output. The wind turbine power capacity range is the wind power generation of the node in the time period t, which is expected to be within the lower and upper bounds. The power capacity range is defined as the climbing capacity of the wind power generation at the node at time t, which is expected to be within the maximum down-climbing and up-climbing speeds. within the defined range.

5. The integrated dispatching method of active distribution network integrating day-ahead, day-intraday and real-time according to claim 1 is characterized in that: The total cost of the distribution network during its operation cycle includes unit startup cost, unit operation cost, unit standby cost, upstream power grid power purchase cost, energy storage cost, wind curtailment cost and load shedding cost.

6. The integrated dispatching method of active distribution network integrating day-ahead, day-intraday and real-time according to claim 1 is characterized in that: The specific process of solving the objective function is as follows: the unit operation cost item in the objective function is linearized by a three-segment linearization method; the square terms in the branch capacity constraints, thermal power unit capacity constraints, and wind turbine unit capacity constraints are linearized by a power circle linearization method, thereby converting the active distribution network integrated dispatching model that integrates day-ahead, intra-day, and real-time into a mixed integer linear programming model, and solving it using a commercial solver.

7. The integrated dispatching system of active distribution network integrating day-ahead, day-intraday and real-time is characterized by: include: The model building module is used to initialize the active distribution network, taking into account the three dispatching modes of day-ahead, intraday and real-time, and integrating the day-ahead dispatching, intraday dispatching and real-time dispatching with different forward-looking time scales, time granularity and rolling frequency to build an integrated dispatching model of active distribution network integrating day-ahead, intraday and real-time. The reserve deployment module is used to introduce capacity reserve and ramp reserve into the integrated dispatching model of active distribution network integrating day-ahead, intra-day and real-time, obtain the restraining relationship between capacity reserve and ramp reserve, and obtain the requirements of unit capacity reserve and ramp reserve from wind power forecast information; The solution scheduling module is used to construct an objective function based on the integrated scheduling model of the active distribution network that integrates day-ahead, intra-day and real-time, and multi-reserve requirements, with the goal of minimizing the total cost of the distribution network during the operation cycle. The objective function is solved to achieve simultaneous rolling optimization of decision variables such as unit start and stop, unit output, power purchase by the upper power grid, energy storage charging and discharging, and backup. Before each rolling optimization, the latest forecast information on renewable energy and load is included, and only the scheduling decision of the first period after optimization is actually executed.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by the processor, the active distribution network integrated dispatching method integrating day-ahead, day-intraday and real-time 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 active distribution network integrated scheduling method integrating day-ahead, day-intraday and real-time as described in any one of claims 1 to 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 active distribution network integrated scheduling method that integrates day-ahead, day-intraday and real-time as described in any one of claims 1 to 6.

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