Active power distribution network multi-time scale self-balancing scheduling method and system under source network load storage cooperation

Abstract

According to the active power distribution network multi-time scale self-balancing scheduling method and system under source-network load-storage cooperation, the difference value maximization between the maximum value of the day-ahead market transaction operation income of the active power distribution network and the minimum value of the tie line power is used as a day-ahead scheduling objective function; scheduling power balance constraint is used as a day-ahead scheduling constraint condition; the error between the intra-day predicted value of the output power of the new energy unit and the day-ahead predicted value and the adjustment amount of the intra-day planned value of the building load power compared with the day-ahead planned value are obtained, and the difference value of the error and the adjustment amount is minimized as an intra-day scheduling objective function; taking a day-ahead scheduling constraint condition as an intra-day scheduling constraint condition; forming a multi-time scale self-balancing optimization scheduling model by the day-ahead scheduling objective function, the day-ahead scheduling constraint condition, the intra-day scheduling objective function and the intra-day scheduling constraint condition; and iteratively solving the multi-time-scale self-balancing optimization scheduling model to obtain a scheduling scheme of the active power distribution network under source-network-load-storage coordination, and optimizing the power of a tie line to improve the safety of the active power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of source-grid-load-storage coordinated dispatching, and specifically, relates to a multi-time-scale self-balancing dispatching method and system for an active distribution network under source-grid-load-storage coordinated dispatching. Background Art

[0002] With the continuous growth of electricity demand and the depletion of fossil fuels, new energy sources such as solar energy and wind energy have received great attention. The access of large-capacity wind and solar energy to the distribution network helps to improve the energy structure. However, the connection of distributed generation to the grid makes the operation and control of the distribution network relatively complicated. It is difficult to fully maintain the balance of supply and demand in the distribution network by simply relying on the backup capacity of conventional power sources to ensure the safe operation of the power system, which poses a great challenge to the operation of the system. Therefore, tapping the response potential of source, grid, load and storage, and coordinating and participating in the self-balancing optimization and dispatching of active distribution networks under the high proportion of new energy access, is an effective way to improve the reliability and economy of power system operation.

[0003] In the existing technology, the optimization dispatching method for active distribution network does not fully consider the response potential of source-grid-load-storage coordination, lacks overall consideration of the interactive characteristics of source-grid-load-storage, and especially does not fully consider the interactive characteristics of source-grid-load-storage in the modeling process of active distribution network. In addition, the peak output period of photovoltaic and wind power is not in the same period as the maximum load period. The surplus electric energy of new energy sources passes through the interconnection line, causing the load rate of the section to increase, resulting in grid congestion or fluctuation. The existing technology does not fully consider the impact of the power of the section interconnection line on the safe operation of the active distribution network, does not optimize the power of the section interconnection line, increases the system backup cost and operation risk, and easily causes the occurrence of green power dispatch imbalance, thus leading to the instability of the large power grid; the existing technology rarely mentions the impact of the self-balancing dispatching of the active distribution network taking into account the coordination of source-grid-load-storage on the power of the section interconnection line. Therefore, under the new situation, it is necessary to carry out research on the multi-time scale self-balancing optimization dispatching method of the active distribution network taking into account the coordination of source-grid-load-storage. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a multi-time-scale self-balancing scheduling method and system for an active distribution network under the coordination of source, grid, load and storage. In the modeling process of the active distribution network, the interactive characteristics of the source, grid, load and storage are fully considered, and the influence of the power of the section interconnection line on the safe operation of the active distribution network is fully considered, so as to realize multi-time-scale self-balancing optimization scheduling of the active distribution network taking into account the coordination of source, grid, load and storage.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a multi-time scale self-balancing dispatching method for an active distribution network under the coordination of source, grid, load and storage, comprising:

[0007] Obtain the day-ahead market transaction operating income of the active distribution network, the interconnection line power in the active distribution network, the intraday forecast value and day-ahead forecast value of the output power of the new energy units, and the intraday planned value and day-ahead planned value of the building load power;

[0008] The difference between the maximum day-ahead market transaction operating income of the active distribution network and the minimum tie line power is maximized as the day-ahead dispatch objective function; the dispatch power balance constraint is used as the day-ahead dispatch constraint; the error between the intraday forecast value and the day-ahead forecast value of the output power of the new energy unit and the adjustment amount of the intraday planned value of the building load power compared with the day-ahead planned value are obtained, and the difference between the error and the adjustment amount is minimized as the day-ahead dispatch objective function; the day-ahead dispatch constraint is used as the day-ahead dispatch constraint; the day-ahead dispatch objective function, the day-ahead dispatch constraint, the intraday dispatch objective function, and the intraday dispatch constraint constitute a multi-time scale self-balancing optimization dispatch model;

[0009] The multi-time-scale self-balancing optimization scheduling model is solved iteratively to obtain the scheduling scheme of the active distribution network under the coordination of source, grid, load and storage.

[0010] Preferably, the day-ahead scheduling objective function satisfies the following relationship:

[0011]

[0012] Where max Y is the day-ahead scheduling objective function, and are the transaction income with the electricity market and the electricity sales income to users during period t, is the operation and maintenance cost of wind power and photovoltaic units in period t, P G,t and P L,t They are respectively the power generation power and total load power of the new energy units in period t, and T is the total number of time periods.

[0013] represents the maximum revenue of the day-ahead market transaction of the active distribution network, It represents the minimum power of the active distribution network tie line. The tie line power is the arithmetic value of power supply and demand.

[0014]

[0015] In the formula, is the retail electricity price in period t, Δt is the period interval;

[0016]

[0017] In the formula, π t is the predicted market electricity price for the period t, P sell,t and P buy,tare the electricity sold and purchased from the electricity market during period t, respectively;

[0018]

[0019] In the formula, c w and c v are the unit power depreciation costs of wind power and photovoltaic generators, P wind,t and P pv,t are the actual values ​​of wind power and photovoltaic output power in period t respectively;

[0020] P G,t =P wind,t +P pv,t

[0021] P L,t =P ibs,t +P load,t

[0022] Where P load,t is the normal load power during period t, P ibs,t is the building load power during period t.

[0023] The actual values ​​of wind power and photovoltaic output power satisfy the following relationship:

[0024]

[0025] Where P wind,t , P pv,t are the actual values ​​of wind power and photovoltaic output power in period t, are the predicted values ​​of wind power and photovoltaic output power in period t, E wind,t 、E pv,t are the prediction errors of wind power and photovoltaic power in period t respectively; among them, E wind,t It follows a normal distribution with an expected value of μ k1 , the standard deviation is E pv,t It follows a normal distribution with an expected value of μ k2 , the standard deviation is

[0026] The active distribution network includes conventional loads and building loads; building loads include: temperature control loads, uncontrollable electrical loads; temperature control loads include: air conditioners, water heaters;

[0027] The building load satisfies the following relationship:

[0028]

[0029] Where P ibs,t is the power of the building load during period t, is the air conditioning power of room n during period t, is the power of the water heater in room n during period t, is the power of the uncontrollable electrical load in room n during period t, N n is the number of rooms;

[0030] The air conditioning load model includes: thermal balance constraints of the enclosure structure, indoor thermal balance constraints, operating power constraints, operating power change constraints, and indoor temperature constraints;

[0031] The water heater load model includes: water temperature constraints.

[0032] The thermal balance constraint of the enclosure structure satisfies the following relationship:

[0033]

[0034] In the formula, superscripts 12, 13, 14, and 15 represent the four walls of the room, respectively. wall,12 , C wall,13 , C waii,14 , C wall,15 are the heat capacities of the four walls, are the temperatures of the four walls of room n during period t, is the indoor temperature of room n at time t, are the temperatures of room n adjacent to wall 12 and wall 13 during period t; R wall,12 , R wall,13 , R wall,14 , R wall,15 are the thermal resistances of the four walls, q 14 ,q 15 are the state variables of wall 14 and wall 15 receiving external solar radiation, 1 means receiving external solar radiation, otherwise 0, v 14 、v 15 are the heat absorption rates of wall 14 and wall 15, A wall,14 , A wall,15 are the areas of wall 14 and wall 15 respectively, Q rad,14 , Q rad,15 are the light intensity of wall 14 and wall 15 respectively, is the outdoor temperature of room n in period t, and Δt is the period interval.

[0035] The indoor thermal balance constraint satisfies the following relationship:

[0036]

[0037] In the formula, C room,1 is the room heat capacity, N roomis the set of walls adjacent to the room, R wall,1j is the thermal resistance of wall 1j, R win,15 is the thermal resistance of window 15, is the temperature of wall 1j of room n at time t, w 15 is the transmittance of the window 15, A win ,15 is the area of ​​window 15, Q rad is the light intensity of the window, is the internal heat source of room n at time t, E EER is the air conditioning energy efficiency ratio, is the air conditioning power of room n during period t, if If the sign before is positive, the air conditioning system heats the room, and if it is negative, the air conditioning system cools the room.

[0038] The operating power constraint, operating power change constraint, and indoor temperature constraint satisfy the following relationship:

[0039]

[0040] In the formula, The upper limit of the operating power of the air conditioning system. and are the lower and upper limits of the air conditioner operating power change, and are the lower and upper limits of the indoor temperature respectively.

[0041] The water temperature constraint satisfies the following relationship:

[0042]

[0043] In the formula, are the water temperature and power of the water heater in room n at time t, R EWH and C EWH are the thermal resistance and heat capacity of the water heater, W is the water capacity of the water heater tank, and w n,t is the water consumption of room n during period t, is the switch status of the water heater in room n during period t, 0 means the water heater is keeping warm, 1 means the water heater is heating, T EWH,min and T EWH,max are the lower and upper limits of the water temperature in the water tank, respectively. is the indoor temperature of room n in period t, and Δt is the period interval.

[0044] The uncontrollable electric load model satisfies the following relationship:

[0045]

[0046] In the formula, is the power of the uncontrollable electrical load in room n during period t, Light n and Equipment n are the power density values ​​of lighting equipment and other electrical equipment in room n, in W / m 2 , percent n,t is the uncertainty coefficient of the hourly usage rate of electrical equipment in room n during period t, S n is the area of ​​room n.

[0047] Preferably, the scheduling power balance constraint is:

[0048] P cha,t +P load,t +P sell,t +P ibs,t +P j,t =P pv,t +P wind,t +P dis,t +P buy,t

[0049] Where P cha,t is the charging power of the energy storage station during period t, P dis,t is the discharge power of the energy storage power station during period t, P load,t is the normal load power during period t, P sell,t and P buy,t are the power sold and purchased from the power market during period t, respectively, ibs,t is the building load power in period t, P wind,t and P pv,t are the actual values ​​of wind power and photovoltaic output power in period t, P j,t is the day-ahead dispatching power of node j in period t.

[0050] Preferably, the intraday scheduling objective function satisfies the following relationship:

[0051]

[0052] Where, min F J is the intraday scheduling objective function, T′ is the total number of time periods in the intraday plan within 24 hours; ΔP pv,t , ΔP wind,t are the errors between the intraday forecast value and the day-ahead forecast value of the output power of the wind turbine and photovoltaic unit in period t; ΔP ibs,t It is the adjustment amount between the intraday planned value of the building load power and the day-ahead planned value in period t.

[0053] The present invention also proposes a multi-time scale self-balancing dispatching system for active distribution network under the coordination of source, grid, load and storage, comprising:

[0054] The acquisition module is used to obtain the day-ahead market transaction operating income of the active distribution network, the tie line power in the active distribution network, the intraday forecast value and the day-ahead forecast value of the output power of the new energy units, and the intraday planned value and the day-ahead planned value of the building load power;

[0055] The scheduling model establishment module is used to maximize the difference between the maximum day-ahead market transaction operating income of the active distribution network and the minimum tie line power as the day-ahead scheduling objective function; use the scheduling power balance constraint as the day-ahead scheduling constraint; obtain the error between the intraday forecast value and the day-ahead forecast value of the output power of the new energy unit, and the adjustment amount of the intraday planned value of the building load power compared with the day-ahead planned value, and minimize the difference between the error and the adjustment amount as the intraday scheduling objective function; use the day-ahead scheduling constraint as the intraday scheduling constraint; and use the day-ahead scheduling objective function, the day-ahead scheduling constraint, the intraday scheduling objective function, and the intraday scheduling constraint to form a multi-time scale self-balancing optimization scheduling model;

[0056] The dispatching scheme generation module is used to iteratively solve the multi-time scale self-balancing optimization dispatching model to obtain the dispatching scheme of the active distribution network under the coordination of source, grid, load and storage.

[0057] The present invention is also a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0058] The present invention is also a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0059] The beneficial effects of the present invention are that, compared with the prior art, at least the following are included: the method proposed by the present invention fully considers the interactive characteristics of source, grid, load and storage in the modeling process of the active distribution network to tap the response potential of the source, grid, load and storage synergy, effectively improving the economy and reliability of the operation of the active distribution network under the high proportion of new energy access; fully considers the impact of the cross-section interconnection line power on the safe operation of the active distribution network, and by optimizing the cross-section interconnection line power, the grid congestion and fluctuation can be reduced, the cross-section load rate can be reduced, the occurrence of green power dispatch imbalance can be avoided, and the stability of the large power grid can be maintained. Comprehensively considering the interactive characteristics of the source, grid, load and storage synergy, a multi-time scale self-balancing optimization dispatching model of the active distribution network considering the cross-section load rate is established, so as to significantly improve the high proportion of new energy consumption, achieve high-level local balance of new energy, and ensure the economic and safe operation of the active distribution network, and achieve the goal of self-balancing supply of all green electricity. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of the multi-time scale self-balancing dispatching method of active distribution network under the coordination of source, grid, load and storage proposed by the present invention;

[0061] Figure 2 is the actual output power curve of wind and solar power in the embodiment of the present invention;

[0062] Figure 3 It is the tie line power smoothing effect curve in the embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0064] The present invention proposes a multi-time scale self-balancing dispatching method for active distribution network under the coordination of source, grid, load and storage. Figure 1 As shown, including:

[0065] One of the objectives of the objective function of the time-scale self-balancing optimization scheduling model is to minimize the minimum power of the interconnection line (i.e., the minimum arithmetic difference between the power supply and demand of the active distribution network during period t). Its main function is to comprehensively consider the interactive characteristics of the source-grid-load-storage coordination, and use the flexibility of energy storage, load-side buildings and market means to reduce the section load rate as much as possible.

[0066] Step 1: Establish source side model, grid side model, load side model and storage side model in the active distribution network.

[0067] Specifically, step 1 includes:

[0068] Step 1.1, obtain the predicted value and prediction error of the power generation of the new energy unit, and establish a source side model; wherein the prediction error obeys the normal distribution;

[0069] In the embodiment, the new energy units on the source side of the active power distribution network include but are not limited to wind turbine units and photovoltaic units;

[0070] The present invention regards the actual output power of wind and light as the sum of the certain predicted value and the uncertain prediction error, and converts the uncertainty of wind and light output power into the uncertainty of prediction error, reducing complexity. The prediction errors of wind and light output power tend to be normally distributed, and the present invention uniformly uses normal distribution to fit; the expected value and standard deviation of the normal distribution are obtained based on historical data and predicted values. Therefore, the actual power generation of wind and light is expressed as:

[0071]

[0072] Where P wind,t , P pv,tare the actual values ​​of wind power and photovoltaic output power in period t, are the predicted values ​​of wind power and photovoltaic output power in period t, E wind,t 、E pv,t are the prediction errors of wind power and photovoltaic power in period t respectively.

[0073] The output power of wind power generation is affected by factors such as terrain and weather. The wind power prediction error follows a normal distribution and its expected value is μ k1 , the standard deviation is The photovoltaic power generation part is composed of photovoltaic arrays. When the array area is constant, the main factor affecting the output power is the light intensity. The photovoltaic prediction error follows a normal distribution and the expected value is μ k2 , the standard deviation is Therefore, the prediction errors of wind power and photovoltaic power in period t satisfy the following relationship:

[0074]

[0075] In the embodiment, μ k1 is 0.0222, 0.1055, μ k2 is -0.294, It is 0.102.

[0076] In the embodiment, the actual wind and solar output power is regarded as the sum of the certain prediction value and the uncertain prediction error, and the uncertainty of wind and solar output power is converted into the uncertainty of the prediction error to reduce the complexity. Figure 2 shown.

[0077] Step 1.2, establishing a grid-side model based on distribution network power flow constraints and distribution network topology constraints;

[0078] Based on circuit theory and AC distribution network model, the present invention adopts AC power flow equation in branch power form to express the power flow constraint of distribution network, which satisfies the following relationship:

[0079]

[0080] Where P ij,t and Q ij,t are the active power and reactive power transmitted by line ij during period t, respectively, ij and b ij are the conductance and susceptance of line ij, respectively, and v i,t and v j,t are the voltages of nodes i and j at time period t, θ ij,t is the voltage phase difference between node i and node j during period t, ψ N is the set of all nodes in the distribution network, ψb is the set of all lines in the distribution network, α ij The value 1 indicates that line ij is in operation, and the value 0 indicates that line ij is out of operation. j,t and Q j,t are the active power and reactive power injected into node j during period t, P jk,t and Q jk,t are the active power and reactive power transmitted by line jk in period t, respectively. f(j) and s(j) are the sets of parent nodes and child nodes of node j, respectively.

[0081] The concept of parent node and child node is similar to that of parent node and child node in a tree structure. Each node (except the root node) has a parent node, and a parent node can have multiple child nodes. In the distribution network, power flows from the parent node of node j to node j, and power flows from node j to the child nodes of node j.

[0082] Based on radial topology graph theory, the established distribution network topology constraints satisfy the following relationship:

[0083]

[0084] ∑ i∈f(j) F ij =∑ k∈s(j) F jk +D j (10)

[0085] -α ij N≤F ij ≤α ij N (11)

[0086] Where N and R are the number of nodes and root nodes in the power grid, respectively. j is the virtual load requirement of non-root node j, which is 1 in the embodiment, and F ij and F jk are the virtual power flows of line ij and line jk respectively.

[0087] All other nodes (parent nodes, child nodes) extend from the root node in a radial or tree-like structure.

[0088] Step 1.3, the load-side model includes conventional loads and building loads, wherein the conventional loads do not need to be modeled. For the flexible and controllable building units in the load-side model, a temperature control load model and an uncontrollable electric load model are established respectively to form the load-side model;

[0089] In the embodiment, the building loads of the active power distribution network include but are not limited to: temperature-controlled loads and uncontrollable electrical loads; wherein the temperature-controlled loads include but are not limited to: air conditioners and water heaters;

[0090] Specifically, the temperature control load model includes: air conditioning model, water heater model;

[0091] The air conditioning model includes: thermal balance constraints of the enclosure structure, indoor thermal balance constraints, operating power constraints, operating power change constraints, and indoor temperature constraints;

[0092] Based on the thermal capacitance-resistance network, a detailed thermal dynamic model considering the thermal inertia of the envelope structure is constructed. The envelope structure of all indoor areas is the same, and the thermal balance constraint of the envelope structure satisfies the following relationship:

[0093]

[0094] In the formula, superscripts 12, 13, 14, and 15 represent the four walls of the room, respectively. wall,12 , C wall,13 , C wall,14 , C wall,15 are the heat capacities of the four walls, are the temperatures of the four walls of room n during period t, is the indoor temperature of room n at time t, are the temperatures of room n adjacent to wall 12 and wall 13 during period t; R wall,12 , R wall,13 , R wall,14 , R wall,15 are the thermal resistances of the four walls, q 14 ,q 15 are the state variables of wall 14 and wall 15 receiving external solar radiation, 1 means receiving external solar radiation, otherwise 0, v 14 、v 15 are the heat absorption rates of wall 14 and wall 15, A wall,14 , A wall,15 are the areas of wall 14 and wall 15 respectively, Q rad,14 , Q rad,15 are the light intensity of wall 14 and wall 15 respectively, is the outdoor temperature of room n in period t, and Δt is the period interval;

[0095] Indoor temperature is affected by many factors, including external environment temperature, light intensity, wall temperature, and air conditioning system cooling / heating. The indoor thermal balance constraint satisfies the following relationship:

[0096]

[0097] In the formula, C room,1 is the room heat capacity, N room is the set of walls adjacent to the room, R wall,1j is the thermal resistance of wall 1j, Rwin,15 is the thermal resistance of window 15, is the temperature of wall 1j of room n at time t, w 15 is the transmittance of the window 15, A win ,15 is the area of ​​window 15, Q rad is the light intensity of the window, is the internal heat source of room n at time t, E EER is the air conditioning energy efficiency ratio, is the air conditioning power of room n during period t, if If the sign before is positive, the air conditioning system heats the room, and if it is negative, the air conditioning system cools the room.

[0098] The air conditioning system mainly meets the user's temperature comfort through cooling / heating. Its operating power change should be within a certain range, and the indoor temperature should be within a certain range. The operating power constraint, operating power change constraint, and indoor temperature constraint satisfy the following relationship:

[0099]

[0100] In the formula, The upper limit of the operating power of the air conditioning system. and are the lower and upper limits of the air conditioner operating power change, and are the lower and upper limits of the indoor temperature respectively.

[0101] The water heater model includes: water temperature constraints;

[0102] The present invention considers a water storage electric water heater with the ability to automatically heat. Due to user use or natural heat dissipation of the water heater, when the water temperature reaches the lower limit of the comfortable temperature, the water heater turns on and enters the heating state until the upper limit of the temperature, and then the water heater turns off and enters the insulation state. This cycle is repeated to keep the water temperature within the user's comfortable range. Assuming that the main circuit power supply power of the water heater is the rated power of the water heater when it is cyclically heated, and it enters the insulation state when it is not heated, the power consumption at this time can be ignored. At the same time, in order to ensure the comfort of the user using the water heater, the hot water temperature in the water heater should be kept within the comfortable water temperature range, and its constraints satisfy the following relationship:

[0103]

[0104] In the formula, are the water temperature and power of the water heater in room n at time t, R EWH and C EWH are the thermal resistance and heat capacity of the water heater, W is the water capacity of the water heater tank, and w n,tis the water consumption of room n during period t, is the switch status of the water heater in room n during period t, 0 means the water heater is keeping warm, 1 means the water heater is heating, T EWH,min and T EWH,max are the lower and upper limits of the water temperature in the water tank respectively;

[0105] Specifically, the uncontrollable electrical load mainly includes lighting load and electrical equipment in the main functional area. For this type of uncontrollable electrical load, the hourly usage rate considering uncertainty is generally used for prediction. The uncontrollable electrical load model satisfies the following relationship:

[0106]

[0107] In the formula, is the power of the uncontrollable electrical load in room n during period t, Light b and Equipment n are the power density values ​​of lighting equipment and other electrical equipment in room n, in W / m 2 , percent n,t is the uncertainty coefficient of the hourly usage rate of electrical equipment in room n during period t, S n is the area of ​​room n.

[0108] The load model of the load-side building satisfies the following relationship:

[0109]

[0110] Where P ibs,t is the power of the building load during period t, is the air conditioning power of room n during period t, is the power of the water heater in room n during period t, is the power of the uncontrollable electrical load in room n during period t, N n is the number of rooms.

[0111] According to the heat storage characteristics of load-side buildings, the present invention constructs an intelligent building energy consumption model that takes into account different heating areas inside the building, and integrates the building system into the active distribution network as a flexible and controllable unit.

[0112] Step 1.4, constructing a storage side model based on the charging and discharging constraints and the state of charge constraints of the energy storage power station;

[0113] The allowed charging power and discharging power of the energy storage station should be within a certain range, and charging and discharging cannot be performed at the same time. The charging and discharging constraints of the energy storage station satisfy the following relationship:

[0114]

[0115]

[0116] In the formula, is the maximum charging and discharging power of the energy storage station, P cha,t is the charging power of the energy storage station during period t, P dis,t is the discharge power of the energy storage power station in period t, and They are the charging and discharging state variables of the energy storage power station in period t, which are 0-1 variables.

[0117] The energy storage power station also meets the state of charge constraint and satisfies the following relationship:

[0118]

[0119] In the formula, τ is the self-discharge efficiency of the energy storage power station, is the energy storage capacity of the energy storage power station in period t, η abs and η relea are the charging efficiency and discharging efficiency of the energy storage power station, is the maximum capacity of the energy storage power station, and They are respectively the electricity consumption at the beginning and end of the energy storage power station.

[0120] Step 2, maximize the difference between the maximum day-ahead market transaction operating revenue of the active distribution network and the minimum interconnection line power as the day-ahead dispatch objective function; use the dispatch power balance constraint as the day-ahead dispatch constraint; obtain the error between the intraday forecast value of the output power of the new energy unit and the day-ahead forecast value, and the adjustment amount of the intraday planned value of the building load power compared with the day-ahead planned value, and minimize the difference between the error and the adjustment amount as the intraday dispatch objective function; use the day-ahead dispatch constraint as the intraday dispatch constraint; and form a multi-time scale self-balancing optimization dispatch model with the day-ahead dispatch objective function, the day-ahead dispatch constraint, the intraday dispatch objective function, and the intraday dispatch constraint.

[0121] Specifically, step 2 includes:

[0122] Step 2.1, maximizing the difference between the maximum day-ahead market transaction operating revenue of the active distribution network and the minimum tie line power is used as the day-ahead dispatch objective function;

[0123] Specifically, according to the wind and solar output forecast curve, the day-ahead dispatch takes the economic benefits of the active distribution network under the high proportion of new energy access and the optimal section load rate as the goal, constructs a dual-objective day-ahead transaction self-balancing optimization dispatching model of the active distribution network considering the section load rate, and uses the optimized results as the basis for the optimization of the intraday dispatching stage. For convenience, the present invention uses the absolute value of the section net flow to characterize the section load rate, and its objective function satisfies the following relationship:

[0124]

[0125] Where max Y is the day-ahead scheduling objective function, and are the transaction income with the electricity market and the electricity sales income to users during period t, is the operation and maintenance cost of wind power and photovoltaic units in period t, P G,t and P L,t are the power generation power and total load power of the new energy units in period t, respectively, and T is the total number of periods;

[0126] In formula (31), represents the maximum revenue of the day-ahead market transaction of the active distribution network, It indicates the minimum power of the tie line of the active distribution network. The tie line power is the arithmetic value of power supply and demand;

[0127] in,

[0128]

[0129] In the formula, is the retail electricity price in period t, Δt is the period interval;

[0130]

[0131] In the formula, π t is the predicted market electricity price for the period t, P sell,t and P buy,t are the electricity sold and purchased from the electricity market during period t, respectively;

[0132]

[0133] In the formula, c w and c v are the unit power depreciation costs of wind power and photovoltaic generators, P wind,t and P pv,t are the actual values ​​of wind power and photovoltaic output power in period t respectively;

[0134] P G,t =P wind,t +P pv,t(35)

[0135] P L,t =P ibs,t +P load,t (36)

[0136] Where P load,t is the normal load power during period t, P ibs,t is the building load power in period t;

[0137] The model coordinates the output of each internal entity and fully taps the response potential of source-grid-load-storage synergy. In particular, based on the heat storage characteristics of the load-side building, an intelligent building energy consumption model that considers different heating areas inside the building is constructed, and the building system is integrated into the active distribution network as a flexible and controllable unit, effectively improving the economy of the active distribution network.

[0138] Step 2.2, using the scheduling power balance constraint as the day-ahead scheduling constraint;

[0139] Specifically, to ensure the power balance of the active distribution network, the dispatching power balance constraint must be met:

[0140] P cha,t +P load,t +P sell,t +P ibs,t +P j,t =P pv,t +P wind,t +P dis,t +P buy,t (37)

[0141] Where P j,t is the day-ahead dispatching power of node j in period t.

[0142] Step 2.3, obtaining the error between the intraday forecast value and the day-ahead forecast value of the output power of the new energy unit, and the adjustment amount of the intraday planned value of the building load power compared with the day-ahead planned value, and minimizing the difference between the error and the adjustment amount is used as the intraday scheduling objective function;

[0143] Specifically, since there is a large deviation in the day-ahead forecast values ​​of photovoltaic and wind power, and in the ultra-short-term forecast, the forecast deviation is relatively small. Therefore, in the intraday stage, the minimum total active power correction value of wind power, photovoltaic, and load-side resources is used as the objective function. The intraday scheduling objective function satisfies the following relationship:

[0144]

[0145] Where, min F Jis the intraday scheduling objective function, T′ is the total number of time periods in the intraday plan in 24 hours. In the embodiment, the intraday plan time interval is 15 minutes, and it is updated every 15 minutes. There are 96 scheduling intervals in a day; ΔP pv,t , ΔP wind,t are the errors between the intraday forecast value and the day-ahead forecast value of the output power of the wind turbine and photovoltaic unit in period t; ΔP ibs,t It is the adjustment amount between the intraday planned value of the building load power and the day-ahead planned value in period t.

[0146] In the embodiment, the intraday forecast value of the output power of the wind turbine and photovoltaic unit is the relevant data in the intraday scheduling plan, and the day-ahead forecast value of the output power of the wind turbine and photovoltaic unit is the relevant data in the day-ahead scheduling plan; the intraday planned value of the building load power is the relevant data in the intraday scheduling plan, and the day-ahead planned value of the building load power is the relevant data in the day-ahead scheduling plan.

[0147] Step 2.4, using the day-ahead scheduling constraints as intraday scheduling constraints;

[0148] Intraday optimal scheduling also needs to meet scheduling power balance constraints, energy storage and other related constraints. These constraints are hard constraints and must be strictly met during the optimization process.

[0149] The present invention expresses the multi-time scale self-balancing optimization scheduling problem of active distribution network taking into account the coordination of source, grid, load and storage as a day-ahead and intraday optimization scheduling model. Firstly, the source, grid, load and storage in the active distribution network are modeled, and a multi-time scale self-balancing optimization scheduling model is established based on the section load rate and self-balancing scheduling requirements. Finally, the DE algorithm is used to solve the optimization model.

[0150] Step 3, iteratively solve the multi-time scale self-balancing optimization scheduling model to obtain the scheduling plan of the active distribution network under the coordination of source, grid, load and storage.

[0151] The dispatching scheme of the active distribution network under the coordination of source, grid, load and storage obtained includes the source side dispatching scheme, the grid side dispatching scheme, the load side dispatching scheme and the storage side dispatching scheme.

[0152] Specifically, since the optimization accuracy and convergence speed must be taken into account in the multi-time scale scheduling model of active distribution network, the differential evolution (DE) algorithm is adopted. The algorithm first randomly generates an initial population S = {X 1 ,X 2 ,…,X i ,…,X NP},X i ∈R n , where NP is the total number of the initial population. i =(xi,1 ,x i,2 ,…,x i,p ), where p is the spatial dimension of the optimization problem; then, three individuals that are different from the current individual are randomly selected from the population of the kth generation, and the vectors of the two individuals are weighted, and the vectors are summed with the selected third individual according to a certain strategy to complete the variation evolution, as shown in formula (39); the evolved individuals realize the automatic update of the population through the crossover mechanism, as shown in formula (40); the dominant individuals are selected by comparing the size of the fitness function, as shown in formula (41).

[0153] V i,G+1 =X r1,G +F(X r2,G -X r3,G ) (39)

[0154] Where V i,G+1 is the individual after mutation and evolution, X r1,G , X r2,G , X r3,G are three different individuals randomly selected from the population, and F is the weighting coefficient.

[0155]

[0156] Where randb(i) is a variable randomly generated for the i-th dimension component; C R ∈(0,1) is the crossover factor of the algorithm, which controls the probability of the mutated individual component replacing the current component as an algorithm control parameter; q i is an integer randomly selected from (1, p) to ensure that Z i,G At least from V i,G Get a portion.

[0157]

[0158] In the formula, f(Z i )、f(X i ) Individual Z i and individual X i The objective function is that when individual Z i The objective function is not greater than the individual X i When the objective function is i Replace individual X i Enter the next generation of population; otherwise, it will continue to be retained in the next generation of population.

[0159] Dispatching scheme of active distribution network under the coordination of source, grid, load and storage

[0160] The tie line leveling effect before and after control by the method proposed in the present invention is as follows: Figure 3As shown in the figure, the peak-to-valley characteristics of the interconnection line are effectively improved, and the peak-to-valley difference is reduced by 72.22%. This shows that the proposed method can reduce grid congestion and fluctuations by optimizing the power of the section interconnection line, reduce the section load rate, avoid the occurrence of green power scheduling imbalance, and maintain the stability of the large power grid.

[0161] The present invention also proposes a multi-time scale self-balancing dispatching system for active distribution network under the coordination of source, grid, load and storage, comprising:

[0162] The acquisition module is used to obtain the day-ahead market transaction operating income of the active distribution network, the tie line power in the active distribution network, the intraday forecast value and the day-ahead forecast value of the output power of the new energy units, and the intraday planned value and the day-ahead planned value of the building load power;

[0163] The scheduling model establishment module is used to maximize the difference between the maximum day-ahead market transaction operating income of the active distribution network and the minimum tie line power as the day-ahead scheduling objective function; use the scheduling power balance constraint as the day-ahead scheduling constraint; obtain the error between the intraday forecast value and the day-ahead forecast value of the output power of the new energy unit, and the adjustment amount of the intraday planned value of the building load power compared with the day-ahead planned value, and minimize the difference between the error and the adjustment amount as the intraday scheduling objective function; use the day-ahead scheduling constraint as the intraday scheduling constraint; and use the day-ahead scheduling objective function, the day-ahead scheduling constraint, the intraday scheduling objective function, and the intraday scheduling constraint to form a multi-time scale self-balancing optimization scheduling model;

[0164] The dispatching scheme generation module is used to iteratively solve the multi-time scale self-balancing optimization dispatching model to obtain the dispatching scheme of the active distribution network under the coordination of source, grid, load and storage.

[0165] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0166] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0167] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0168] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-time scale self-balancing dispatching method for active distribution network under source-grid-load-storage coordination, characterized in that: include: Obtain the day-ahead market transaction operating income of the active distribution network, the interconnection line power in the active distribution network, the intraday forecast value and day-ahead forecast value of the output power of the new energy units, and the intraday planned value and day-ahead planned value of the building load power; The maximum difference between the maximum day-ahead market transaction operating income of the active distribution network and the minimum tie line power is maximized as the day-ahead dispatch objective function; the dispatch power balance constraint is used as the day-ahead dispatch constraint; the error between the intraday forecast value and the day-ahead forecast value of the output power of the new energy unit and the adjustment amount of the intraday planned value of the building load power compared with the day-ahead planned value are obtained, and the difference between the error and the adjustment amount is minimized as the intraday dispatch objective function; the day-ahead dispatch constraint is used as the intraday dispatch constraint; A multi-time scale self-balancing optimization scheduling model is constructed with the day-ahead scheduling objective function, day-ahead scheduling constraints, intra-day scheduling objective function, and intra-day scheduling constraints. The multi-time-scale self-balancing optimization scheduling model is solved iteratively to obtain the scheduling scheme of the active distribution network under the coordination of source, grid, load and storage.

2. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 1 is characterized in that: The day-ahead scheduling objective function satisfies the following relationship: Where max Y is the day-ahead scheduling objective function, and are the transaction income with the electricity market and the electricity sales income to users during period t, is the operation and maintenance cost of wind power and photovoltaic units in period t, P G,t and P L,t They are respectively the power generation power and total load power of the new energy units in period t, and T is the total number of time periods. represents the maximum revenue of the day-ahead market transaction of the active distribution network, It represents the minimum power of the active distribution network tie line. The tie line power is the arithmetic value of power supply and demand.

3. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 2 is characterized in that: In the formula, is the retail electricity price in period t, Δt is the period interval; In the formula, π t is the predicted market electricity price for the period t, P sell,t and P buy,t are the electricity sold and purchased from the electricity market during period t, respectively; In the formula, c w and c v are the unit power depreciation costs of wind power and photovoltaic generators, P wind,t and P pv,t are the actual values ​​of wind power and photovoltaic output power in period t respectively; P G,t =P wind,t +P pv,t P L,t =P ibs,t +P load,t Where P load,t is the normal load power during period t, P ibs,t is the building load power during period t.

4. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 3 is characterized in that: The actual values ​​of wind power and photovoltaic output power satisfy the following relationship: Where P wind,t , P pv,t are the actual values ​​of wind power and photovoltaic output power in period t, are the predicted values ​​of wind power and photovoltaic output power in period t, E wind,t 、E pv,t are the prediction errors of wind power and photovoltaic power in period t respectively; among them, E wind,t It follows a normal distribution with an expected value of μ k1 , the standard deviation is E pv,t It follows a normal distribution with an expected value of μ k2 , the standard deviation is 5. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 3 is characterized in that: The active distribution network includes conventional loads and building loads; building loads include: temperature control loads, uncontrollable electrical loads; temperature control loads include: air conditioners, water heaters; The building load satisfies the following relationship: Where P ibs,t is the power of the building load during period t, is the air conditioning power of room n during period t, is the power of the water heater in room n during period t, is the power of the uncontrollable electrical load in room n during period t, N n is the number of rooms; The air conditioning load model includes: thermal balance constraints of the enclosure structure, indoor thermal balance constraints, operating power constraints, operating power change constraints, and indoor temperature constraints; The water heater load model includes: water temperature constraints.

6. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 5 is characterized in that: The thermal balance constraint of the enclosure structure satisfies the following relationship: In the formula, superscripts 12, 13, 14, and 15 represent the four walls of the room, respectively. wall,12 , C wall,13 , C wall,14 , C wall,15 are the heat capacities of the four walls, are the temperatures of the four walls of room n during period t, is the indoor temperature of room n at time t, are the temperatures of room n adjacent to wall 12 and wall 13 during period t; R wall,12 , R wall,13 , R wall,14 , R wall,15 are the thermal resistances of the four walls, q 14 ,q 15 are the state variables of wall 14 and wall 15 receiving external solar radiation, 1 means receiving external solar radiation, otherwise 0, v 14 、v 15 are the heat absorption rates of wall 14 and wall 15, A wall,14 , A wall,15 are the areas of wall 14 and wall 15 respectively, Q rad,14 , Q rad,15 are the light intensity of wall 14 and wall 15 respectively, is the outdoor temperature of room n in period t, and Δt is the period interval.

7. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 6 is characterized in that: The indoor thermal balance constraint satisfies the following relationship: In the formula, C room,1 is the room heat capacity, N room is the set of walls adjacent to the room, R wall,1j is the thermal resistance of wall 1j, R win,15 is the thermal resistance of window 15, is the temperature of wall 1j of room n at time t, w 15 is the transmittance of the window 15, A win,15 is the area of ​​window 15, Q rad is the light intensity of the window, is the internal heat source of room n at time t, E EER is the air conditioning energy efficiency ratio, is the air conditioning power of room n during period t, if If the sign before is positive, the air conditioning system heats the room, and if it is negative, the air conditioning system cools the room.

8. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 7 is characterized in that: The operating power constraint, operating power change constraint, and indoor temperature constraint satisfy the following relationship: In the formula, The upper limit of the operating power of the air conditioning system. and are the lower and upper limits of the air conditioner operating power change, and are the lower and upper limits of the indoor temperature respectively.

9. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 5 is characterized in that: The water temperature constraint satisfies the following relationship: In the formula, are the water temperature and power of the water heater in room n at time t, R EWH and C EWH are the thermal resistance and heat capacity of the water heater, W is the water capacity of the water heater tank, and w n,t is the water consumption of room n during period t, is the switch status of the water heater in room n during period t, 0 means the water heater is keeping warm, 1 means the water heater is heating, T EWH,min and T EWH,max are the lower and upper limits of the water temperature in the water tank, respectively. is the indoor temperature of room n in period t, and Δt is the period interval.

10. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 5 is characterized in that: The uncontrollable electric load model satisfies the following relationship: In the formula, is the power of the uncontrollable electrical load in room n during period t, Light n and Equipment n are the power density values ​​of lighting equipment and other electrical equipment in room n, in W / m 2 , percent n,t is the uncertainty coefficient of the hourly usage rate of electrical equipment in room n during period t, S n is the area of ​​room n.

11. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 1 is characterized in that: Scheduling power balance constraints: P cha,t +P load,t +P sell,t +P ibs,t +P j,t =P pv,t +P wind,t +P dis,t +P buy,t Where P cha,t is the charging power of the energy storage station during period t, P dis,t is the discharge power of the energy storage power station in period t, P load,t is the normal load power during period t, P sell,t and P buy,t are the power sold and purchased from the power market during period t, respectively, ibs,t is the building load power in period t, P wind,t and P pv,t are the actual values ​​of wind power and photovoltaic output power in period t, P j,t is the day-ahead dispatching power of node j in period t.

12. The multi-time scale self-balancing dispatching method of active distribution network under source-grid-load-storage coordination according to claim 1 is characterized in that: The intraday scheduling objective function satisfies the following relationship: Where, min F J is the intraday scheduling objective function, T′ is the total number of time periods in the intraday plan within 24 hours; ΔP pv,t , ΔP wind,t are the errors between the intraday forecast value and the day-ahead forecast value of the output power of the wind turbine and photovoltaic unit in period t; ΔP ibs,t It is the adjustment amount between the intraday planned value of the building load power and the day-ahead planned value in period t.

13. A multi-time scale self-balancing dispatching system for active distribution network under the coordination of source, grid, load and storage, characterized in that: include: The acquisition module is used to obtain the day-ahead market transaction operating income of the active distribution network, the tie line power in the active distribution network, the intraday forecast value and the day-ahead forecast value of the output power of the new energy units, and the intraday planned value and the day-ahead planned value of the building load power; The scheduling model establishment module is used to maximize the difference between the maximum day-ahead market transaction operating income of the active distribution network and the minimum tie line power as the day-ahead scheduling objective function; the scheduling power balance constraint is used as the day-ahead scheduling constraint; obtain the error between the intraday forecast value and the day-ahead forecast value of the output power of the new energy unit, and the adjustment amount of the intraday planned value of the building load power compared with the day-ahead planned value, and minimize the difference between the error and the adjustment amount as the intraday scheduling objective function; and use the day-ahead scheduling constraint as the intraday scheduling constraint; A multi-time scale self-balancing optimization scheduling model is constructed with the day-ahead scheduling objective function, day-ahead scheduling constraints, intra-day scheduling objective function, and intra-day scheduling constraints. The dispatching scheme generation module is used to iteratively solve the multi-time scale self-balancing optimization dispatching model to obtain the dispatching scheme of the active distribution network under the coordination of source, grid, load and storage.

14. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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