Multi-time scale self-balancing dispatching method and system for active distribution network under source-network-load-storage coordination

Abstract

The source network load storage collaborative multi-time scale self-balancing scheduling method and system of the active power distribution network maximizes the difference between the maximum value of the operation income of the day-ahead market transaction of the active power distribution network and the minimum value of the tie line power as a day-ahead scheduling objective function; takes the scheduling power balance constraint as a day-ahead scheduling constraint condition; obtains the error of the intraday prediction value and the day-ahead prediction value of the new energy unit output power and the adjustment amount of the intraday planning value compared with the day-ahead planning value of the building load power, and minimizes the difference between the error and the adjustment amount as an intraday scheduling objective function; takes the day-ahead scheduling constraint condition as an intraday scheduling constraint condition; takes the day-ahead scheduling objective function, the day-ahead scheduling constraint condition, the intraday scheduling objective function and the intraday scheduling constraint condition to form a multi-time scale self-balancing optimization scheduling model; iteratively solves the multi-time scale self-balancing optimization scheduling model to obtain the scheduling scheme of the active power distribution network under the source network load storage collaboration, and optimizes the tie line power to improve the safety of the active power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of source-grid-load-storage coordinated scheduling technology, specifically, it relates to a method and system for multi-time-scale self-balancing scheduling of active distribution networks under source-grid-load-storage coordination. Background Technology

[0002] With the continuous growth of electricity demand and the depletion of fossil fuels, new energy sources such as solar and wind power have received considerable attention. The integration of large-capacity wind and solar power into the distribution network helps improve the energy structure. However, the grid connection of distributed generation makes the operation and control of the distribution network relatively complex. Relying solely on the reserve capacity of conventional power sources to ensure the safe operation of the power system is insufficient to fully maintain the supply-demand balance of the distribution network, posing a significant challenge to system operation. Therefore, exploring the response potential of power generation, grid, load, and storage, and coordinating their participation in the self-balancing optimization scheduling of active distribution networks under high-proportion new energy integration, is an effective way to improve the reliability and economy of power system operation.

[0003] Existing technologies for optimizing the dispatching of active distribution networks do not fully consider the response potential of source-grid-load-storage synergy, and lack a holistic consideration of the interactive characteristics of source-grid-load-storage, especially in the modeling process of active distribution networks. Furthermore, the peak power generation periods of photovoltaic and wind power do not coincide with the peak load periods. Surplus renewable energy, transmitted through tie lines, increases the load factor at different sections, causing grid congestion or fluctuations. Existing technologies do not adequately consider the impact of tie line power on the safe operation of active distribution networks, failing to optimize tie line power, increasing system reserve costs and operational risks, and easily leading to imbalances in green energy dispatch, thus causing instability in the large power grid. Existing technologies rarely mention the impact of self-balancing dispatching of active distribution networks considering source-grid-load-storage synergy on tie line power. Therefore, under the new circumstances, research is needed on multi-timescale self-balancing optimization dispatching methods for active distribution networks that consider source-grid-load-storage synergy. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-timescale self-balancing scheduling method and system for active distribution networks under source-grid-load-storage coordination. The method fully considers the interactive characteristics of source, grid, load, and storage during the modeling process of the active distribution network, and fully considers the impact of cross-sectional tie-line power on the safe operation of the active distribution network, thereby achieving multi-timescale self-balancing optimization scheduling of the active distribution network that takes into account source-grid-load-storage coordination.

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

[0006] This invention proposes a multi-time-scale self-balancing scheduling method for active distribution networks under source-grid-load-storage coordination, comprising:

[0007] Obtain the day-ahead market transaction operation revenue of the active distribution network, the intraday and day-ahead forecast values ​​of tie line power in the active distribution network, the output power of new energy units, and the intraday and day-ahead planned values ​​of building load power;

[0008] The objective function for daytime dispatch is to maximize the difference between the maximum daytime market transaction revenue and the minimum tie-line power of the active distribution network. The dispatch power balance constraint is used as the daytime dispatch constraint. The objective function for daytime dispatch is to minimize the difference between the intraday forecast value and the daytime forecast value of the output power of the new energy units, and the adjustment amount between the intraday planned value and the daytime planned value of the building load power. The objective function for daytime dispatch is to minimize the difference between the error and the adjustment amount. The objective function for daytime dispatch is used as the intraday dispatch constraint. The multi-timescale self-balancing optimization dispatch model is constructed using the daytime dispatch objective function, the daytime dispatch constraint, the intraday dispatch objective function, and the intraday dispatch constraint.

[0009] The scheduling scheme of the active distribution network under the source-grid-load-storage coordination is obtained by iteratively solving the multi-time-scale self-balancing optimization scheduling model.

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

[0011]

[0012] In the formula, max Y is the day-ahead scheduling objective function. and These represent the revenue from electricity market transactions during time period t and the revenue from selling electricity to users, respectively. P represents the operation and maintenance costs of wind power and photovoltaic units during time period t. G,t and P L,t These represent the generating power of new energy units and the total load power during time period t, respectively, where T is the total number of time periods.

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

[0014]

[0015] In the formula, Let t be the retail electricity price for time period t, and Δt be the time interval.

[0016]

[0017] In the formula, π t Let P be the day-ahead market electricity price forecast for time period t. sell,t and P buy,tThese represent the electricity sold and purchased in the electricity market during time period t, respectively.

[0018]

[0019] In the formula, c w and c v P represents the unit power depreciation cost of wind power and solar power units, respectively. wind,t and P pv,t These are the actual output power values ​​of wind power and solar power during time period t, respectively.

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

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

[0022] In the formula, P load,t P represents the normal load power during time period t. ibs,t Let t be the building load power during time period t.

[0023] The actual output power of wind power and solar power satisfies the following relationship:

[0024]

[0025] In the formula, P wind,t P pv,t These are the actual output power values ​​of wind power and solar power during time period t, respectively. E represents the predicted output power of wind power and solar power during time period t. wind,t E pv,t Let E be the prediction error for wind power and solar power respectively, for time period t; where E is the prediction error for wind power and solar power respectively. wind,t It follows a normal distribution and its expected value is μ. k1 Standard deviation is E pv,t It follows a normal distribution and its expected value is μ. k2 Standard deviation is

[0026] Active power distribution networks include conventional loads and building loads; building loads include: temperature-controlled loads and uncontrollable electrical loads; temperature-controlled loads include: air conditioners and water heaters;

[0027] The building load satisfies the following relationship:

[0028]

[0029] In the formula, P ibs,t Let t be the power of the building load during time period t. Let be the air conditioning power of room n during time period t. Let be the power of the water heater in room n during time period t. Let N be the power of the uncontrollable electrical load in room n during time period t. n The number of rooms;

[0030] The air conditioning load model includes: thermal balance constraints of the building envelope, indoor thermal balance constraints, operating power constraints, operating power variation constraints, and indoor temperature constraints.

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

[0032] The thermal balance constraints of the building envelope satisfy the following relationship:

[0033]

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

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

[0036]

[0037] In the formula, C room,1 For the room's heat capacity, N roomR is the set of walls adjacent to the room. wall,1j R is the thermal resistance of wall 1j. win,15 The thermal resistance of window 15. Let w be the temperature of wall 1j in room n during time period t. 15 A is the transmittance of window 15. win ,15 Q is the area of ​​the form (15). rad The light intensity of the window. E is the heat source inside room n during time period t. EER For air conditioner energy efficiency ratio, Let be the air conditioning power of room n during time period t. If the sign before the symbol is positive, the air conditioning system is heating; if it is negative, the air conditioning system is cooling.

[0038] Operating power constraints, operating power variation constraints, and indoor temperature constraints satisfy the following relationship:

[0039]

[0040] In the formula, This is the upper limit of the operating power of the air conditioning system. and These represent the lower and upper limits of the air conditioner's operating power variation, respectively. and These represent the lower and upper limits of indoor temperature, respectively.

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

[0042]

[0043] In the formula, R represents the water temperature and power of the water heater in room n during time period t. EWH and C EWH These are the thermal resistance and heat capacity of the water heater, respectively, where W is the water capacity of the water heater tank. n,t Let be the water consumption of room n during time period t. This represents the on / off state of the water heater in room n during time period t, where 0 indicates the water heater is keeping the water heater warm and 1 indicates the water heater is heating. T EWH,min and T EWH,max These are the lower and upper limits of the water temperature inside the tank, respectively. Let t be the indoor temperature of room n during time period t, and Δt be the time interval.

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

[0045]

[0046] In the formula, Light represents the power of the uncontrollable electrical load in room n during time period t. n and Equipment n These are the power density values ​​for lighting fixtures and other electrical equipment in room n, respectively, in W / m². 2 ,percent n,t S is the uncertainty coefficient for the hourly usage rate of electrical equipment in room n during time period t. n Let n be 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] In the formula, P cha,t Let P be the charging power of the energy storage station during time period t. dis,t Let P be the discharge power of the energy storage power station during time period t. load,t P represents the normal load power during time period t. sell,t and P buy,t P represents the electricity sold and purchased in the electricity market during time period t, respectively. ibs,t Let P be the building load power during time period t. wind,t and P pv,t P represents the actual output power of wind power and solar power during time period t. j,t Let be the day-ahead scheduling power of node j during time period t.

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

[0051]

[0052] In the formula, min F J Let T′ be the intraday scheduling objective function, and T′ be the total number of time slots planned within a 24-hour period; ΔP pv,t ΔP wind,t ΔP represents the error between the intraday forecast and the day-ahead forecast of the output power of wind turbines and photovoltaic units during time period t. ibs,t This represents the adjustment between the planned daily value and the planned previous day value of the building load power during time period t.

[0053] This invention also proposes a multi-time-scale self-balancing dispatching system for active distribution networks under source-grid-load-storage coordination, comprising:

[0054] The data acquisition module is used to obtain the day-ahead market transaction operation revenue of the active distribution network, the power of the tie lines in the active distribution network, the intraday and day-ahead forecast values ​​of the output power of new energy units, and the intraday and day-ahead planned values ​​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 revenue of the active distribution network and the minimum tie-line power as the day-ahead scheduling objective function; and to use the scheduling power balance constraint as the day-ahead scheduling constraint. It obtains the error between the intraday forecast value and the day-ahead forecast value of the output power of new energy units, and the adjustment amount between the intraday planned value and the day-ahead planned value of building load power, and uses the difference between the error and the adjustment amount as the intraday scheduling objective function; it uses the day-ahead scheduling constraint as the intraday scheduling constraint; and constructs a multi-timescale self-balancing optimization scheduling model using the day-ahead scheduling objective function, the day-ahead scheduling constraint, the intraday scheduling objective function, and the intraday scheduling constraint.

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

[0057] The present invention is also a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations 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, when executed by a processor, implements the steps of the method.

[0059] The beneficial effects of this invention are as follows: Compared with the prior art, the method proposed in this invention, by fully considering the interactive characteristics of source-grid-load-storage in the modeling process of active distribution networks, explores the response potential of source-grid-load-storage synergy, effectively improving the economy and reliability of active distribution network operation under high-proportion renewable energy access; it fully considers the impact of cross-sectional tie line power on the safe operation of active distribution networks, and by optimizing the cross-sectional tie line power, it can reduce grid congestion and fluctuations, lower the cross-sectional load rate, avoid the occurrence of green energy dispatch imbalance, and maintain the stability of the large power grid. By comprehensively considering the interactive characteristics of source-grid-load-storage synergy, a multi-timescale self-balancing optimization dispatch model for active distribution networks considering cross-sectional load rate is established to significantly improve the high-proportion renewable energy consumption level, achieve high-level local renewable energy balance, and ensure the economical and safe operation of active distribution networks, thus achieving the goal of full green energy self-balancing supply. Attached Figure Description

[0060] Figure 1 This is a flowchart of the multi-time-scale self-balancing scheduling method for active distribution networks under source-grid-load-storage coordination proposed in this invention;

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

[0062] Figure 3 This is the power suppression effect curve of the tie line in an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0064] This invention proposes a multi-timescale self-balancing scheduling method for active distribution networks under source-grid-load-storage coordination, such as... Figure 1 As shown, it includes:

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

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

[0067] Specifically, step 1 includes:

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

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

[0070] This invention views the actual output power of wind and solar power as the sum of a definite predicted value and an uncertain prediction error, transforming the uncertainty of wind and solar power output power into the uncertainty of prediction error, thus reducing complexity. The prediction errors of wind and solar power output power tend to follow a normal distribution, and this invention uniformly uses a normal distribution for fitting; 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 solar power are expressed as follows:

[0071]

[0072] In the formula, P wind,t P pv,tThese are the actual output power values ​​of wind power and solar power during time period t, respectively. E represents the predicted output power of wind power and solar power during time period t. wind,t E pv,t These represent the prediction errors for wind power and solar power during time period t, respectively.

[0073] The output power of wind power generation is affected by factors such as terrain and weather. The wind prediction error follows a normal distribution and its expected value is μ. k1 Standard deviation is The photovoltaic (PV) power generation section consists of a photovoltaic array. When the array area is fixed, the main factor affecting the output power is the light intensity. The PV prediction error follows a normal distribution and its expected value is μ. k2 Standard deviation is Therefore, the prediction errors for wind power and solar power in time period t satisfy the following relationship:

[0074]

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

[0076] In this embodiment, the actual output power of wind and solar power is considered as the sum of a definite predicted value and an uncertain prediction error, and the uncertainty of wind and solar power output power is transformed into the uncertainty of prediction error, thus reducing complexity. Through analysis, the actual output power of wind and solar power is as follows: Figure 2 As shown.

[0077] Step 1.2: Establish the network-side model based on distribution network power flow constraints and distribution network topology constraints;

[0078] Based on circuit theory and AC distribution network models, this invention uses AC power flow equations in the form of branch power to represent the power flow constraints of the distribution network, satisfying the following relationships:

[0079]

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

[0081] The concepts of parent and child nodes are similar to those in a tree structure. Every node (except the root node) has a parent node, and a parent node can have multiple child nodes. In a power distribution network, power flows from the parent node of node j to node j, and power flows from node j to its child nodes.

[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] In the formula, N and R are the number of nodes and the root node in the power grid, respectively, and D j For the virtual load demand of non-root node j, in this example, it is taken as 1, F ij and F jk These are the virtual power flows for lines ij and jk, respectively.

[0087] All other nodes (parent nodes and 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. Conventional loads do not need to be modeled. For the flexible and controllable building units in the load-side model, temperature-controlled load models and uncontrollable electrical load models are established respectively to form the load-side model.

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

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

[0091] The air conditioning model includes: thermal balance constraints of the building envelope, indoor thermal balance constraints, operating power constraints, operating power variation constraints, and indoor temperature constraints.

[0092] Based on a thermal capacity-thermal resistance network, a detailed thermal dynamic model considering the thermal inertia of the building envelope is constructed. The building envelope structure is identical in all indoor areas, and the thermal equilibrium constraints of the building envelope satisfy the following relationship:

[0093]

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

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

[0096]

[0097] In the formula, C room,1 For the room's heat capacity, N room R is the set of walls adjacent to the room. wall,1j R is the thermal resistance of wall 1j.win,15 The thermal resistance of window 15. Let w be the temperature of wall 1j in room n during time period t. 15 A is the transmittance of window 15. win ,15 Q is the area of ​​the form (15). rad The light intensity of the window. E is the heat source inside room n during time period t. EER For air conditioner energy efficiency ratio, Let be the air conditioning power of room n during time period t. If the sign before the symbol is positive, the air conditioning system is heating; if it is negative, the air conditioning system is cooling.

[0098] Air conditioning systems primarily meet users' temperature comfort requirements through cooling / heating. Their operating power should vary within a certain range, and the indoor temperature should also remain within a certain range. Operating power constraints, operating power variation constraints, and indoor temperature constraints must satisfy the following relationships:

[0099]

[0100] In the formula, This is the upper limit of the operating power of the air conditioning system. and These represent the lower and upper limits of the air conditioner's operating power variation, respectively. and These represent the lower and upper limits of indoor temperature, respectively.

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

[0102] This invention considers a storage-type electric water heater with automatic heating capability. Due to user use or natural heat dissipation from the water heater, when the water temperature reaches the lower limit of the comfortable temperature range, the water heater turns on, entering heating mode until the upper temperature limit is reached. Then, the water heater turns off and enters a heat preservation mode. This cycle repeats continuously, maintaining the water temperature within the user's comfort range. It is assumed that the main circuit power supply during the water heater's heating cycle is the water heater's rated power, and the power consumption during the heat preservation mode is negligible. Simultaneously, to ensure user comfort, the water temperature inside the water heater should be maintained within the comfortable temperature range, constrained by the following relationship:

[0103]

[0104] In the formula, R represents the water temperature and power of the water heater in room n during time period t. EWH and C EWH These are the thermal resistance and heat capacity of the water heater, respectively, where W is the water capacity of the water heater tank. n,tLet be the water consumption of room n during time period t. This represents the on / off state of the water heater in room n during time period t, where 0 indicates the water heater is keeping the water heater warm and 1 indicates the water heater is heating. T EWH,min and T EWH,max These are the lower and upper limits of the water temperature inside the tank, respectively.

[0105] Specifically, uncontrollable electrical loads mainly include lighting loads and electrical equipment within the main functional areas. For these types of uncontrollable electrical loads, hourly usage rates considering uncertainty are generally used for prediction. The uncontrollable electrical load model satisfies the following relationship:

[0106]

[0107] In the formula, Light represents the power of the uncontrollable electrical load in room n during time period t. b and Equipment n These are the power density values ​​for lighting fixtures and other electrical equipment in room n, respectively, in W / m². 2 ,percent n,t S is the uncertainty coefficient for the hourly usage rate of electrical equipment in room n during time period t. n Let n be the area of ​​room n.

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

[0109]

[0110] In the formula, P ibs,t Let t be the power of the building load during time period t. Let be the air conditioning power of room n during time period t. Let be the power of the water heater in room n during time period t. Let N be the power of the uncontrollable electrical load in room n during time period t. n This refers to the number of rooms.

[0111] Based on the heat storage characteristics of load-side buildings, this invention constructs an intelligent building energy consumption model that considers different heating zones inside the building, and integrates the building system as a flexible and controllable unit into the active power distribution network.

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

[0113] The allowable charging and discharging power of an energy storage power station should be within a certain range, and simultaneous charging and discharging are not permitted. The charging and discharging constraints of an energy storage power station must satisfy the following relationship:

[0114]

[0115]

[0116] In the formula, P represents the maximum charge and discharge power of the energy storage power station. cha,t Let P be the charging power of the energy storage station during time period t. dis,t Let t be the discharge power of the energy storage power station during time period t. and These are the charging and discharging state variables of the energy storage station during time period t, and are 0-1 variables.

[0117] Energy storage power stations also satisfy state of charge constraints, which are expressed by the following equation:

[0118]

[0119] In the formula, τ represents the self-discharge efficiency of the energy storage power station. Let η be the electrical energy storage capacity of the energy storage power station during time period t. abs and η relea These represent the charging efficiency and discharging efficiency of the energy storage power station, respectively. This is the maximum capacity of the energy storage power station. and These represent the electricity generated during the start and end periods of the energy storage power station, respectively.

[0120] Step 2: Maximize the difference between the maximum day-ahead market transaction revenue and the minimum tie-line power of the active distribution network 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 units, and the adjustment amount between the intraday planned value and the day-ahead planned value of the building load power; 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 construct a multi-timescale self-balancing optimization scheduling model using the day-ahead scheduling objective function, the day-ahead scheduling constraint, the intraday scheduling objective function, and the intraday scheduling constraint.

[0121] Specifically, step 2 includes:

[0122] Step 2.1: Maximize the difference between the maximum day-ahead market transaction operating revenue of the active distribution network and the minimum tie-line power as the day-ahead scheduling objective function;

[0123] Specifically, based on the wind and solar power output forecast curves, the day-ahead dispatch aims to optimize the economic benefits of the active distribution network under a high proportion of renewable energy access and the section load rate. A dual-objective day-ahead transaction self-balancing optimization dispatch model for the active distribution network, considering the section load rate, is constructed. The optimized results are used as the basis for optimization in the intraday dispatch phase. For convenience, this invention uses the absolute value of the net power flow at the section to represent the section load rate. Its objective function satisfies the following relationship:

[0124]

[0125] In the formula, max Y is the day-ahead scheduling objective function. and These represent the revenue from electricity market transactions during time period t and the revenue from selling electricity to users, respectively. P represents the operation and maintenance costs of wind power and photovoltaic units during time period t. G,t and P L,t These represent the generating capacity of the new energy units and the total load power during time period t, respectively, where T is the total number of time periods.

[0126] In equation (31), This represents the maximum day-ahead market transaction operating revenue of an active distribution network. This represents the minimum power of the tie line in an active distribution network. The tie line power is the arithmetic value of power supply and demand.

[0127] in,

[0128]

[0129] In the formula, Let t be the retail electricity price for time period t, and Δt be the time interval.

[0130]

[0131] In the formula, π t Let P be the day-ahead market electricity price forecast for time period t. sell,t and P buy,t These represent the electricity sold and purchased in the electricity market during time period t, respectively.

[0132]

[0133] In the formula, c w and c v P represents the unit power depreciation cost of wind power and solar power units, respectively. wind,t and P pv,t These are the actual output power values ​​of wind power and solar power during time 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] In the formula, P load,t P represents the normal load power during time period t. ibs,t The building load power during time period t;

[0137] This model coordinates and schedules the output of various internal entities, fully exploring the response potential of source-grid-load-storage synergy. In particular, based on the heat storage characteristics of buildings on the load side, it constructs an intelligent building energy consumption model that considers different heating zones within the building, and integrates the building system as a flexible and controllable unit into the active distribution network, effectively improving the economy of the active distribution network.

[0138] Step 2.2: Use the scheduling power balance constraint as the day-ahead scheduling constraint condition;

[0139] Specifically, to ensure power balance in the active distribution network, the dispatch 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] In the formula, P j,t Let be the day-ahead scheduling power of node j during time period t.

[0142] Step 2.3: Obtain the error between the intraday forecast value and the previous day forecast value of the output power of the new energy units, and the adjustment amount between the intraday planned value and the previous day planned value of the building load power. The intraday scheduling objective function is to minimize the difference between the error and the adjustment amount.

[0143] Specifically, due to significant deviations in the day-ahead forecasts for photovoltaic and wind power, while the forecast deviations are relatively smaller in ultra-short-term forecasts, the objective function for intraday scheduling is to minimize the total active power correction value of wind power, photovoltaic, and load-side resources. The intraday scheduling objective function satisfies the following relationship:

[0144]

[0145] In the formula, min F JLet T′ be the daily scheduling objective function, and T′ be the total number of time slots planned within 24 hours. In this example, the daily planned time slot interval is 15 minutes, and it is updated every 15 minutes. A total of 96 scheduling intervals are included in one day; ΔP pv,t ΔP wind,t ΔP represents the error between the intraday forecast and the day-ahead forecast of the output power of wind turbines and photovoltaic units during time period t. ibs,t This represents the adjustment between the planned daily value and the planned previous day value of the building load power during time period t.

[0146] In this embodiment, the intraday forecast values ​​of the output power of wind turbines and photovoltaic units are relevant data in the intraday scheduling plan, and the day-ahead forecast values ​​of the output power of wind turbines and photovoltaic units are relevant data in the day-ahead scheduling plan; the intraday planned values ​​of the building load power are relevant data in the intraday scheduling plan, and the day-ahead planned values ​​of the building load power are relevant data in the day-ahead scheduling plan.

[0147] Step 2.4: Use the day-ahead scheduling constraints as the intraday scheduling constraints;

[0148] Intraday optimization scheduling also needs to meet relevant constraints such as scheduling power balance constraints and energy storage. These constraints are hard constraints and must be strictly met during the optimization process.

[0149] This invention represents the multi-timescale self-balancing optimization scheduling problem of active distribution networks, considering the coordination of power generation, grid, load, and storage, as a day-ahead to intraday optimization scheduling model. First, a model is constructed for the power generation, grid, load, and storage systems in the active distribution network. Based on the cross-sectional load rate and self-balancing scheduling requirements, a multi-timescale self-balancing optimization scheduling model is established. Finally, the DE algorithm is used to solve this optimization model.

[0150] Step 3: Iteratively solve the multi-timescale self-balancing optimization scheduling model to obtain the scheduling scheme of the active distribution network under the source-grid-load-storage coordination.

[0151] The obtained dispatch schemes for active distribution networks under source-grid-load-storage coordination include source-side dispatch schemes, grid-side dispatch schemes, load-side dispatch schemes, and storage-side dispatch schemes.

[0152] Specifically, since the multi-time-scale scheduling model of active distribution networks needs to simultaneously consider the accuracy of optimization and the speed of convergence, the differential evolution (DE) algorithm is adopted. This algorithm first randomly generates an initial population S = {X1, X2, ..., X...} within the range of control variables. i ,…,X NP},X i ∈R n Where NP is the total number of individuals in the initial population. i =(x i,1 ,x i,2,…,x i,p ), where p is the spatial dimension of the optimization problem; then, in the population of the kth generation, three individuals that are different from the current individual are randomly selected, and the vector weights of two individuals are selected and summed with the selected third individual according to a certain strategy to complete the mutation evolution, as shown in equation (39); the evolved individuals realize the automatic update of the population through the crossover mechanism, as shown in equation (40); the dominant individuals are selected by comparing the size of the fitness function, as shown in equation (41).

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

[0154] In the formula, V i,G+1 For individuals that have undergone mutation and evolution, X r1,G X r2,G X r3,G These are three different individuals randomly selected from the population, and F is the weighting coefficient.

[0155]

[0156] In the formula, 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, as a control parameter, controls the probability that the mutated individual component replaces the current component; q i It is an integer randomly selected from (1, p) that guarantees Z i,G At least from V i,G Obtain a component.

[0157]

[0158] In the formula, f(Z) i f(X) i Individual Z i and individual X i The objective function, when individual Z i The objective function is no greater than individual X i When the objective function is set, then the individual Z... i Replace individual X i They will enter the next generation of the species; otherwise, they will remain in the next generation of the species.

[0159] Dispatch scheme for active distribution networks under the coordinated operation of power generation, grid, load and storage

[0160] The method proposed in this invention provides the following effects on the smoothing of the tie lines before and after control: Figure 3As shown, the peak-valley characteristics of the tie line are effectively improved, and the peak-valley difference is reduced by 72.22%. This indicates that the proposed method can reduce grid congestion and fluctuations, reduce the load factor of the section by optimizing the cross-sectional tie line power, avoid the occurrence of green power dispatch imbalance, and maintain the stability of the large power grid.

[0161] This invention also proposes a multi-time-scale self-balancing dispatching system for active distribution networks under source-grid-load-storage coordination, comprising:

[0162] The data acquisition module is used to obtain the day-ahead market transaction operation revenue of the active distribution network, the power of the tie lines in the active distribution network, the intraday and day-ahead forecast values ​​of the output power of new energy units, and the intraday and day-ahead planned values ​​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 revenue of the active distribution network and the minimum tie-line power as the day-ahead scheduling objective function; and to use the scheduling power balance constraint as the day-ahead scheduling constraint. It obtains the error between the intraday forecast value and the day-ahead forecast value of the output power of new energy units, and the adjustment amount between the intraday planned value and the day-ahead planned value of building load power, and uses the difference between the error and the adjustment amount as the intraday scheduling objective function; it uses the day-ahead scheduling constraint as the intraday scheduling constraint; and constructs a multi-timescale self-balancing optimization scheduling model using the day-ahead scheduling objective function, the day-ahead scheduling constraint, the intraday scheduling objective function, and the intraday scheduling constraint.

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

[0165] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0166] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

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

[0168] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status 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 the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving 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., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination, characterized in that, include: Obtain the day-ahead market transaction operation revenue of the active distribution network, the intraday and day-ahead forecast values ​​of tie line power in the active distribution network, the output power of new energy units, and the intraday and day-ahead planned values ​​of building load power; The objective function for daytime dispatch is to maximize the difference between the maximum daytime market transaction revenue of the active distribution network and the minimum tie-line power. The dispatch power balance constraint is used as the daytime dispatch constraint. The objective function for daytime dispatch is to minimize the difference between the intraday forecast value and the daytime forecast value of the output power of the new energy units, and the adjustment amount between the intraday planned value and the daytime planned value of the building load power. The objective function for daytime dispatch is to minimize the difference between the error and the adjustment amount. The daytime dispatch constraint is used as the daytime dispatch constraint. A multi-timescale self-balancing optimization scheduling model is constructed using the day-ahead scheduling objective function, day-ahead scheduling constraints, intraday scheduling objective function, and intraday scheduling constraints. The scheduling scheme of the active distribution network under the source-grid-load-storage coordination is obtained by iteratively solving the multi-time-scale self-balancing optimization scheduling model. The current scheduling objective function satisfies the following relationship: In the formula, The objective function for the current day's scheduling is... and They are respectively in Revenue from electricity market transactions and revenue from selling electricity to users during specific time periods. In order to be in Operating and maintenance costs of wind and solar power units during certain periods. and They are respectively in The power generation capacity and total load capacity of new energy generating units during the period. Total number of time periods; This represents the maximum day-ahead market transaction operating revenue of an active distribution network. This represents the minimum power of the tie line in an active distribution network. The tie line power is the arithmetic value of power supply and demand.

2. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 1, characterized in that, In the formula, for Retail electricity price during the time period For time intervals; In the formula, for Forecast of market electricity prices for the current period. and They are respectively in Time period and electricity sales and purchase volume in the electricity market; In the formula, and These are the unit power depreciation costs for wind power and solar power units, respectively. and They are respectively Actual values ​​of wind and solar power output during the time period; In the formula, for Typical load power during the period for Building load power during a given time period.

3. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 2, characterized in that, The actual output power of wind power and solar power satisfies the following relationship: In the formula, , They are respectively The actual values ​​of wind and solar power output during the time period. , They are respectively Forecast values ​​of wind and solar power output during the time period. , They are respectively The prediction errors for wind and solar power over time periods; among which, It follows a normal distribution and its expected value is Standard deviation is , It follows a normal distribution and its expected value is Standard deviation is .

4. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 2, characterized in that, Active power distribution networks include conventional loads and building loads; building loads include: temperature-controlled loads and uncontrollable electrical loads; temperature-controlled loads include: air conditioners and water heaters; The building load satisfies the following relationship: （23） In the formula, for The power of building load during a given time period For the room exist Air conditioner power during the time period For the room exist The power of the water heater during different time periods For the room exist The power of uncontrollable electrical loads during a given time period. The number of rooms; The air conditioning load model includes: thermal balance constraints of the building envelope, indoor thermal balance constraints, operating power constraints, operating power variation constraints, and indoor temperature constraints. The water heater load model includes: water temperature constraints.

5. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 4, characterized in that, The thermal balance constraints of the building envelope satisfy the following relationship: In the formula, the superscripts 12, 13, 14, and 15 represent the four walls of the room, respectively. , , , These are the heat capacities of the four walls, respectively. , , , Rooms The four walls are Temperature during the period For the room exist Indoor temperature during the period , The rooms adjacent to wall 12 and wall 13 are respectively exist Temperature during the period; , , , These represent the thermal resistances of the four walls. , These are the state variables for walls 14 and 15, respectively, regarding their exposure to external solar radiation. A value of 1 indicates that the wall is receiving external solar radiation, while a value of 0 indicates otherwise. , The heat absorption rates of wall 14 and wall 15 are respectively. , These are the areas of wall 14 and wall 15, respectively. , The light intensity for walls 14 and 15 are respectively. For the room exist Outdoor temperature during the time period This refers to the time interval.

6. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 5, characterized in that, The indoor thermal balance constraint satisfies the following relationship: (16) In the formula, For room heat capacity, For the collection of walls adjacent to the room, For walls thermal resistance, The thermal resistance of window 15. For the room The wall exist Temperature during the period The transmittance of window 15. The area of ​​the form is 15. The light intensity of the window. For the room exist The room's internal heat source during the period of time, For air conditioner energy efficiency ratio, For the room exist Air conditioner power during the time period, if If the sign before the symbol is positive, the air conditioning system is heating; if it is negative, the air conditioning system is cooling.

7. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 6, characterized in that, Operating power constraints, operating power variation constraints, and indoor temperature constraints satisfy the following relationship: （17） （18） （19） In the formula, This is the upper limit of the operating power of the air conditioning system. and These represent the lower and upper limits of the air conditioner's operating power variation, respectively. and These represent the lower and upper limits of indoor temperature, respectively.

8. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 4, characterized in that, The water temperature constraint satisfies the following relationship: In the formula, , Rooms exist The water temperature and power of the water heater in the tank during different time periods. and These are the thermal resistance and heat capacity of the water heater, respectively. W This refers to the water capacity of the water heater tank. For the room exist Water consumption during a given time period For the room exist The on / off status of the water heater during a given time period, with 0 indicating the water heater is keeping warm and 1 indicating the water heater is heating. and These are the lower and upper limits of the water temperature inside the tank, respectively. For the room exist Indoor temperature during the period This refers to the time interval.

9. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 4, characterized in that, The uncontrollable electrical load model satisfies the following relationship: In the formula, For the room exist The power of uncontrollable electrical loads during a given time period. and Rooms The power density value of lighting equipment and other electrical equipment, in W / m². 2 , For the room exist The uncertainty coefficient of hourly usage rate of electrical equipment during a given period. For the room The area.

10. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 1, characterized in that, Power balance constraints for scheduling: In the formula, In order to be in The charging power of the time-of-use energy storage power station In order to be in The discharge power of the time-limited energy storage power station for Typical load power during the period and They are respectively in Time period and electricity sales and purchase volume in the electricity market for Building load power during the time period and They are respectively The actual values ​​of wind and solar power output during the time period. for Time period nodes The daytime scheduled power.

11. The method for multi-time-scale self-balancing dispatching of active distribution networks under source-grid-load-storage coordination according to claim 1, characterized in that, The intraday scheduling objective function satisfies the following relationship: （38） In the formula, The objective function for intraday scheduling is... This represents the total number of time slots planned within a 24-hour period during the day. , The intraday and day-ahead forecasts for the output power of wind turbines and photovoltaic units, respectively. Error in time period; The intraday planned value and the previous day's planned value of building load power are within the range of Adjustments for different time periods.

12. A multi-time-scale self-balancing dispatching system for an active distribution network under source-grid-load-storage coordination, used to implement the multi-time-scale self-balancing dispatching method for an active distribution network under source-grid-load-storage coordination as described in any one of claims 1 to 11, characterized in that, include: The data acquisition module is used to obtain the day-ahead market transaction operation revenue of the active distribution network, the power of the tie lines in the active distribution network, the intraday and day-ahead forecast values ​​of the output power of new energy units, and the intraday and day-ahead planned values ​​of the building load power. The scheduling model establishment module is used to maximize the difference between the maximum day-ahead market transaction operating revenue of the active distribution network and the minimum tie-line power as the day-ahead scheduling objective function; and to use the scheduling power balance constraint as the day-ahead scheduling constraint. It obtains the error between the intraday forecast value and the day-ahead forecast value of the output power of new energy units, and the adjustment amount between the intraday planned value and the day-ahead planned value of building load power. The module minimizes the difference between the error and the adjustment amount as the intraday scheduling objective function; and uses the day-ahead scheduling constraint as the intraday scheduling constraint. A multi-timescale self-balancing optimization scheduling model is constructed using the day-ahead scheduling objective function, day-ahead scheduling constraints, intraday scheduling objective function, and intraday scheduling constraints. The day-ahead scheduling objective function satisfies the following relationship: In the formula, The objective function for the current day's scheduling is... and They are respectively in Revenue from electricity market transactions and revenue from selling electricity to users during specific time periods. In order to be in Operating and maintenance costs of wind and solar power units during certain periods. and They are respectively in The power generation capacity and total load capacity of new energy generating units during the period. Total number of time periods; This represents the maximum day-ahead market transaction operating revenue of an active distribution network. This represents the minimum power of the tie line in an active distribution network. The tie line power is the arithmetic value of power supply and demand. The scheduling scheme generation module is used to iteratively solve the multi-time-scale self-balancing optimization scheduling model to obtain the scheduling scheme of the active distribution network under the coordination of source, grid, load and storage.

13. 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 perform the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-11.

Images