Multi-energy coordinated planning method for flexible power distribution network

Abstract

The invention relates to the technical field of power grid coordinated planning, in particular to a flexible power distribution network multi-energy coordinated planning method. Firstly, multi-energy information and demand information including various energy supply node and load node data are collected. Then, a real-time planning model and a prediction planning model are established, and decision variables, objective functions and constraint conditions of all the models are determined; and then obtaining current information, and performing energy matching based on the real-time planning model, including real-time energy scheduling, result evaluation and preparatory energy scheduling. And finally, judging a real-time energy scheduling result, if the real-time energy scheduling result is not satisfied, determining an energy shortage amount, preparing to input and then executing a prediction planning model, and distributing energy based on a greedy algorithm to realize efficient energy management and supply and demand balance. The method comprehensively considers the multi-energy characteristics and the supply-demand relationship, dynamically plans and dispatches, improves the energy utilization efficiency, and enhances the stability and adaptability of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid coordination planning, and in particular to a method for coordinated planning of multiple energy sources in a flexible distribution network. Background Art

[0002] With the intensification of the global energy crisis and the increasing attention paid to environmental protection, the transformation of energy structure has become an inevitable trend. The traditional centralized energy supply model is gradually unable to meet the diversified needs of modern society for energy supply, especially at the distribution network level, which faces many challenges and changes.

[0003] On the one hand, the demand for power load is showing a trend of rapid growth and highly complex changes. The accelerated industrialization process in modern society, the continuous improvement of the electrification level of residents' lives, and the emergence of emerging industries have made the distribution of power load in time and space extremely unbalanced. Peak loads continue to rise and load fluctuations intensify, which puts higher requirements on the power supply capacity, reliability and power quality of the distribution network.

[0004] On the other hand, the penetration rate of distributed energy resources in the distribution network is increasing. These distributed energy resources are characterized by intermittency, volatility and uncertainty, and their large-scale access has brought huge challenges to the planning, operation and control of the distribution network. For example, the output fluctuation of distributed generation may cause the voltage of the distribution network to exceed the limit, the flow direction to change, and even affect the normal operation of relay protection, reducing the safety and stability of the distribution network operation.

[0005] At the same time, the traditional distribution network planning method is mainly based on deterministic load forecasting and a single energy supply model, which is difficult to adapt to the coordinated operation and dynamic changes of multiple energy sources. The lack of effective coordination and planning means leads to low energy utilization efficiency, inability to give full play to the advantages of distributed energy, and difficulty in achieving optimal economic benefits.

[0006] In addition, with the gradual opening up and intelligent development of the energy market, the distribution network needs to have greater flexibility and adaptability to cope with energy price fluctuations, changes in market trading mechanisms, and users' demand for diversified energy services. Therefore, an innovative flexible distribution network multi-energy coordination planning method is urgently needed to solve the above problems and achieve sustainable development of the distribution network and efficient use of energy. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention proposes a flexible distribution network multi-energy coordinated planning method. The present invention comprehensively considers the characteristics of multiple energy sources and the relationship between supply and demand, dynamically plans and dispatches, improves energy utilization efficiency, and enhances the stability and adaptability of the distribution network.

[0008] The technical solution of the present invention is as follows:

[0009] A multi-energy coordinated planning method for a flexible distribution network, comprising:

[0010] Based on the multi-energy information and power demand information of the flexible distribution network, build real-time planning model and predictive planning model;

[0011] Obtain the current flexible distribution network multi-energy information and current power demand information;

[0012] The real-time planning model uses a greedy algorithm to match energy according to the multi-energy information and power demand information at the current moment;

[0013] If the real-time energy scheduling meets the current load, the prediction planning model will not be executed; if the real-time energy scheduling does not meet the current load, the prediction planning model will be executed; the prediction planning model is based on the energy gap of the energy storage equipment caused by the reserve energy scheduling and the real-time energy shortage caused by the real-time energy scheduling. Based on the greedy algorithm, the non-operating equipment is scheduled to generate energy to make up for the deficiencies of the reserve energy scheduling and the real-time energy scheduling.

[0014] Furthermore, the real-time energy scheduling model is established as follows:

[0015] Define the decision variables of the real-time energy scheduling model: Let xij(t) represent the amount of energy distributed from energy supply node i to load node j at time t, where i = 1, 2, ..., m, m is the total number of energy supply nodes, and j = 1, 2, ..., n, n is the total number of load nodes;

[0016] With the goal of minimizing the real-time energy supply cost, a real-time energy scheduling model is constructed by considering the factors of energy production cost and transmission cost. The objective function of the real-time energy scheduling model is expressed as:

[0017]

[0018] Where ci(t) is the unit energy production cost of energy supply node i at time t, and lij(t) is the unit energy transmission cost from energy supply node i to load node j at time t;

[0019] Establish the constraints of the real-time energy dispatch model, including energy supply capacity constraints, load demand balance constraints and variable non-negativity constraints;

[0020] The energy supply capacity constraint is that for each energy supply node i, the total amount of energy allocated to all load nodes at time t cannot exceed its energy supply capacity Pi(t) at the current moment, that is:

[0021]

[0022] The load demand balance constraint is that the total amount of energy received by each load node at a certain time should be equal to its actual demand Dj(t), that is:

[0023]

[0024] The non-negative constraints on the variables are:

[0025]

[0026] Furthermore, the preparation energy scheduling model is established as follows:

[0027] Define the decision variables of the reserve energy scheduling model: Let yk(t) represent the amount of energy provided by energy storage device k to the load node at time t, k = 1, 2, ..., p, p is the total number of energy storage devices;

[0028] With the goal of maximizing the utilization efficiency of energy storage equipment and maintaining system stability, a reserve energy scheduling model is constructed. The objective function of the reserve energy scheduling model is expressed as:

[0029]

[0030] Among them, ηk(t) is the energy storage release efficiency of energy storage device k at time t;

[0031] Establish the constraints of the reserve energy dispatch model, including energy storage equipment capacity constraints, load demand balance constraints and variable non-negative constraints;

[0032] The capacity constraint of the energy storage device is that the amount of energy released by the energy storage device k at time t cannot exceed its current remaining releasable capacity Ek(t), that is:

[0033]

[0034] When the real-time energy dispatch is insufficient, the load demand balance constraint is that the total amount of energy provided by all energy storage devices to the load node plus the energy provided by the real-time energy dispatch should meet the demand of the load node, that is:

[0035]

[0036] The non-negative constraints on the variables are:

[0037]

[0038] Furthermore, the prediction planning model is established as follows:

[0039] Define the decision variables of the forecast planning model: Let zij(t+Δt) represent the amount of energy that the energy supply node i plans to allocate to the load node j at the future time t+Δt, where Δt is the forecast time step;

[0040] With the goal of minimizing future energy supply costs and meeting forecasted demand, a forecasting planning model is constructed by considering factors such as equipment startup costs and operating costs. The objective function is expressed as:

[0041]

[0042] Among them, si(t+Δt) is the startup cost of energy supply node i at time t+Δt, q is the total number of devices that can be started, ci(t+Δt) is the unit energy production cost of energy supply node i at time t+Δt, and lij(t+Δt) is the unit energy transmission cost from energy supply node i to load node j at time t+Δt;

[0043] Establish the constraints of the forecast planning model, including energy supply capacity constraints, load demand balance constraints and variable non-negativity constraints;

[0044] Considering the supply capacity after the equipment is started, the energy supply capacity constraint is that for the energy supply node i that is planned to start, its supply capacity should meet certain restrictions at time t+Δt. At the same time, the dynamic characteristics of the equipment startup process should be considered. Assuming that the startup time of energy supply node i is The climbing rate is ri, then:

[0045]

[0046] in, is the maximum supply capacity of energy supply node i;

[0047] Based on the predicted demand, the load demand balance constraint is that at the future time t+Δt, the total energy supply after all energy supplies and equipment startup should be equal to the predicted load demand, that is:

[0048]

[0049] The non-negative constraints on the variables are:

[0050]

[0051] Furthermore, the operation of the real-time energy scheduling model allocating real-time available energy to current load demand includes:

[0052] Input the current energy supply information and load demand information into the real-time energy scheduling model; suppose the amount of energy distributed from energy supply node i to load node j at the current moment is xij(t), and calculate the unit energy comprehensive cost Cij(t) from each energy supply node to the load node. The calculation formula is:

[0053] Cij(t)=ci(t)+lij(t);

[0054] Where ci(t) is the unit energy production cost of energy supply node i at time t, and lij(t) is the unit energy transmission cost from energy supply node i to load node j at time t;

[0055] The energy supply nodes are sorted in order from the smallest to the largest unit energy comprehensive cost, and then energy is allocated to the load nodes from the energy supply node with the lowest cost until one of the following conditions is met:

[0056] Condition 1: The demand of the load node is fully met, that is:

[0057]

[0058] Where m is the total number of energy supply nodes, Dj(t) is the actual demand of load node j at time t;

[0059] Condition 2: The remaining allocatable energy of the current energy supply node is 0, that is:

[0060]

[0061] Among them, Pi(t) is the energy supply capacity of energy supply node i at time t, and n is the total number of load nodes.

[0062] Furthermore, the result evaluation method of the real-time energy scheduling is as follows;

[0063] Calculate the total supply cost Ctotal after real-time energy scheduling using the following formula:

[0064]

[0065] Check whether the requirements of all load nodes are met, that is, judge: Is it true for all j=1,2,...,n?

[0066] Furthermore, the conditions for executing the scheduling of the reserve energy scheduling model are: if there is a load node demand that is not met, then enter the scheduling of the reserve energy scheduling model;

[0067] The scheduling mode of the reserve energy scheduling model is:

[0068] Determine the set of load nodes with unmet demand Input the energy storage equipment information and the load node information with unmet demand into the reserve energy dispatch model;

[0069] For each load node j in the load node set Junsatisfied whose demand is not met, according to the greedy algorithm, the energy storage device with the highest energy storage release efficiency is selected for energy distribution; the effective unit energy supply cost of each energy storage device to load node j is calculated The calculation formula is:

[0070]

[0071] The energy storage devices are sorted in order from the smallest to the largest effective unit energy supply cost, and then energy is allocated to the unmet load node j from the energy storage device with the lowest cost until one of the following conditions is met:

[0072] Condition 1: The demand of load node j is fully met, that is:

[0073]

[0074] Condition 2: The remaining releasable capacity of the current energy storage device is 0, that is, Ej(t)-yk(t)=0.

[0075] Furthermore, the method for determining whether to execute the forecast planning model is:

[0076] Calculate the total actual distributed energy of all load nodes after real-time energy scheduling The calculation formula is:

[0077]

[0078] Calculate the total energy demand of all load nodes at the current moment The calculation formula is:

[0079]

[0080] Compare and

[0081] like If the real-time energy scheduling meets the current load, the prediction planning model is not executed, and this step ends;

[0082] like Then the real-time energy scheduling does not meet the current load, and the next step is to execute the forecast planning model.

[0083] Furthermore, the forecast planning model execution step includes:

[0084] Determine the energy deficit:

[0085] Calculate the real-time energy shortage ΔEreal caused by real-time energy scheduling. The calculation formula is:

[0086]

[0087] Calculate the total remaining releasable capacity of energy storage equipment after reserve energy dispatch The calculation formula is:

[0088]

[0089] Where Ek(t) is the remaining releasable capacity of energy storage device k at time t, and p is the total number of energy storage devices;

[0090] Calculate the energy shortage of energy storage equipment ΔEstorage caused by reserve energy scheduling, and the calculation formula is:

[0091]

[0092] Among them, yk(t) is the amount of energy provided by energy storage device k to the load node in the reserve energy scheduling;

[0093] Forecast planning model input preparation:

[0094] Collect information about non-operating equipment, including equipment type, startup cost si(t+Δt), equipment operating cost, and equipment maximum supply capacity And the device startup time and climbing rate ri;

[0095] According to the historical multi-energy information and demand information data, the energy demand change trend of each load node in the future period is predicted to obtain the predicted demand curve Dj(t+Δt), j=1,2,...,n;

[0096] Based on the operating status and trend of the current energy supply nodes, the energy supply capacity change trend of each energy supply node in the future is estimated to obtain the predicted energy supply curve Pi(t+Δt), i=1,2,...,m, m is the total number of energy supply nodes;

[0097] Based on the greedy algorithm, the predictive planning model is executed:

[0098] For each prediction time t+Δt, according to the greedy algorithm principle, the device with the lowest unit energy comprehensive cost is selected for energy distribution, and the unit energy comprehensive cost Cij(t+Δt) of each device to the load node is calculated. The calculation formula is:

[0099] Cij(t+Δt)=si(t+Δt)+ci(t+Δt)+lij(t+Δt);

[0100] Sort the devices in order of unit energy comprehensive cost from small to large, and then allocate energy to the load nodes from the device with the lowest cost until one of the following conditions is met:

[0101] Condition 1: The predicted demand of the load node is fully met, that is:

[0102]

[0103] Condition 2: All power-enabled devices have been allocated energy or have reached their maximum supply capacity limit

[0104] The beneficial effects of the present invention are: by constructing a real-time and predictive planning model, it is possible to effectively respond to energy demand and supply conditions at different times and improve energy utilization efficiency.

[0105] Using greedy algorithms for energy scheduling and matching can maximize energy efficiency and economic benefits while meeting load demand.

[0106] Reasonable model structure and execution logic make the entire planning method adaptable and flexible, and suitable for complex flexible distribution network multi-energy coordinated planning scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 This is a schematic diagram of the overall process of the multi-energy coordination planning method for flexible distribution networks;

[0108] Figure 2 A detailed flow chart of the steps S100 of the multi-energy coordination planning method for a flexible distribution network;

[0109] Figure 3 A detailed flow chart of the steps of the flexible distribution network multi-energy coordination planning method S300;

[0110] Figure 4 The following is a detailed flow chart of step S400 of the method for multi-energy coordinated planning of flexible distribution networks. DETAILED DESCRIPTION

[0111] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0112] See also Figure 1 , which shows a method for coordinated planning of multiple energy sources in a flexible distribution network provided by an embodiment of the present invention, including:

[0113] S100: Based on the multi-energy information and power demand information of the flexible distribution network, a real-time planning model and a predictive planning model are constructed, wherein the real-time planning model includes a real-time energy scheduling model and a preparatory energy scheduling model.

[0114] S200: Acquire the flexible distribution network multi-energy information and current power demand information at the current moment.

[0115] S300: The real-time planning model uses a greedy algorithm to match energy according to the multi-energy information and power demand information at the current moment. Among them: Based on the greedy algorithm, the real-time energy scheduling model is used to allocate the real-time available energy to the current load demand. If the real-time energy is not enough to meet the demand, the reserve energy scheduling is entered; Based on the greedy algorithm, the reserve energy scheduling model is used to schedule the energy of the energy storage device to the new current load demand.

[0116] S400: If the real-time energy scheduling meets the current load, the prediction planning model will not be executed. If the real-time energy scheduling does not meet the current load, the prediction planning model will be executed. The prediction planning model is based on the energy shortage of energy storage equipment caused by the reserve energy scheduling and the real-time energy shortage caused by the real-time energy scheduling. Based on the greedy algorithm, the non-operating equipment is scheduled to generate energy to make up for the shortage of reserve energy scheduling and real-time energy scheduling.

[0117] S100 builds a real-time planning model and a predictive planning model based on the multi-energy information and demand information of the flexible distribution network, in which the real-time planning model includes real-time energy scheduling and reserve energy scheduling. First, the energy supply capacity data is obtained by traversing various energy supply nodes, distributed energy resources and collecting energy conversion equipment parameters, and the power demand data and other energy demand information are obtained from various distribution networks. Then a real-time energy scheduling model is established to minimize the real-time energy supply cost, determine the decision variables, objective functions, and constraints such as energy supply capacity, load demand balance, and non-negative variables; a reserve energy scheduling model is established to maximize the utilization efficiency of energy storage equipment and maintain system stability. The relevant decision variables, objective functions and constraints are also determined. Finally, a predictive planning model is established to determine the decision variables, objective functions, and energy supply capacity constraints, load demand balance constraints, and non-negative variables that take into account the startup characteristics of the equipment.

[0118] Please refer to Figure 2 , which shows a flowchart of an exemplary flexible distribution network multi-energy coordinated planning method S100 of the present application, and its contents include:

[0119] S110: Collect multi-energy information and demand information.

[0120] Traverse various energy supply nodes in the flexible distribution network, including but not limited to solar power stations, wind farms, traditional thermal power plants, biomass power generation facilities, and connected external power grids, and obtain the energy supply capacity data of each energy supply node at the current moment.

[0121] For distributed energy resources, their power generation information is collected through sensors or smart meters installed at the device end.

[0122] Collect parameters such as the working status and conversion efficiency of each energy conversion device, which will affect the conversion and transmission of energy between different forms.

[0123] Obtain real-time electricity demand data from smart meters, power monitoring systems and various energy consumption terminal devices in the distribution network, including information on power consumption, electricity consumption, etc. for residential, commercial and industrial users.

[0124] Collect other energy demand information, such as heat demand, gas demand, etc.

[0125] S120: Establish a real-time planning model, where the real-time planning model includes a real-time energy scheduling model and a reserve energy scheduling model.

[0126] The real-time energy scheduling model is established as follows:

[0127] Define the decision variables of the real-time energy scheduling model: Let xij(t) represent the amount of energy allocated from energy supply node i to load node j at time t, where i = 1, 2, ..., m, m is the total number of energy supply nodes, and j = 1, 2, ..., n, n is the total number of load nodes.

[0128] With the goal of minimizing the real-time energy supply cost, a real-time energy scheduling model is constructed by considering factors such as energy production cost and transmission cost. The objective function is expressed as:

[0129]

[0130] Among them, ci(t) is the unit energy production cost of energy supply node i at time t, and lij(t) is the unit energy transmission cost from energy supply node i to load node j at time t.

[0131] Establish the constraints of the real-time energy dispatch model, including energy supply capacity constraints, load demand balance constraints and variable non-negativity constraints;

[0132] The energy supply capacity constraint is that for each energy supply node i, the total amount of energy allocated to all load nodes at time t cannot exceed its energy supply capacity Pi(t) at the current moment, that is:

[0133]

[0134] The load demand balance constraint is that the total amount of energy received by each load node at a certain time should be equal to its actual demand Dj(t), that is:

[0135]

[0136] The non-negative constraints on the variables are:

[0137]

[0138] The preparation energy dispatch model is established as follows:

[0139] Define the decision variables of the reserve energy scheduling model: Let yk(t) represent the amount of energy provided by energy storage device k to the load node at time t, k = 1, 2, ..., p, p is the total number of energy storage devices.

[0140] With the goal of maximizing the utilization efficiency of energy storage equipment and maintaining system stability, a reserve energy scheduling model is constructed, and the objective function is expressed as:

[0141]

[0142] Among them, ηk(t) is the energy storage release efficiency of energy storage device k at time t.

[0143] The constraints of the reserve energy scheduling model are established, including energy storage equipment capacity constraints, load demand balance constraints and variable non-negative constraints.

[0144] The capacity constraint of the energy storage device is that the amount of energy released by the energy storage device k at time t cannot exceed its current remaining releasable capacity Ek(t), that is:

[0145]

[0146] When the real-time energy dispatch is insufficient, the load demand balance constraint is that the total amount of energy provided by all energy storage devices to the load node plus the energy provided by the real-time energy dispatch should meet the demand of the load node, that is:

[0147]

[0148] The non-negative constraints on the variables are:

[0149]

[0150] S130: Establish a forecasting planning model.

[0151] The forecast planning model is established as follows:

[0152] Define the decision variables of the forecast planning model: Let zij(t+Δt) represent the amount of energy allocated by the energy supply node i to the load node j at the future time t+Δt, where Δt is the forecast time step.

[0153] With the goal of minimizing future energy supply costs and meeting forecasted demand, a forecast planning model is constructed by considering factors such as equipment startup costs and operating costs. The objective function is expressed as:

[0154]

[0155] Among them, si(t+Δt) is the startup cost of energy supply node i at time t+Δt, q is the total number of startable devices, ci(t+Δt) is the unit energy production cost of energy supply node i at time t+Δt, and lij(t+Δt) is the unit energy transmission cost from energy supply node i to load node j at time t+Δt.

[0156] The constraints of the forecast planning model are established, including energy supply capacity constraints, load demand balance constraints and variable non-negative constraints.

[0157] Considering the supply capacity after the equipment is started, the energy supply capacity constraint is that for the energy supply node i that is planned to start, its supply capacity should meet certain restrictions at time t+Δt. At the same time, the dynamic characteristics of the equipment startup process, such as equipment startup time, ramp rate, etc., should be considered. Assume that the startup time of energy supply node i is The climbing rate is ri, then:

[0158]

[0159] in, is the maximum supply capacity of energy supply node i.

[0160] Based on the predicted demand, the load demand balance constraint is that at the future time t+Δt, the total energy supply after all energy supplies and equipment startup should be equal to the predicted load demand, that is:

[0161]

[0162] The non-negative constraints on the variables are:

[0163]

[0164] S200 obtains the flexible distribution network multi-energy information and current power demand information at the current moment.

[0165] In order to obtain the multi-energy information and current power demand information of the flexible distribution network at the current moment, intelligent monitoring equipment is fully deployed in the entire distribution network. At various energy supply ends, such as photovoltaic panels of solar power stations, wind turbines of wind farms, units of traditional thermal power plants, and biomass power generation facilities, high-precision sensors are installed to accurately measure parameters such as power generation power and energy flow. The sensors transmit data to the distribution network data center in real time through high-speed and stable communication networks (such as 5G or optical fiber networks). At the same time, on the power demand side, whether it is residential users, commercial users or industrial users, the electricity terminals are equipped with smart meters, which can not only accurately record power and electricity consumption, but also monitor quality parameters such as voltage and current. The data is also uploaded to the data center in real time. After receiving these raw data, the data center will perform a series of preprocessing operations, including data cleaning to remove noise and outliers, data calibration to ensure accuracy, and data integration to keep various energy information and demand information consistent in terms of time scale and data format, so as to provide accurate, reliable and synchronized input data for the subsequent real-time planning model, and ensure the effective implementation of the multi-energy coordinated planning of the entire flexible distribution network.

[0166] S300 uses a real-time planning model to match energy based on the multi-energy information and power demand information at the current moment. It specifically includes two links: real-time energy scheduling and reserve energy scheduling. In real-time energy scheduling, first input the energy supply and load demand information. For each load node, calculate the comprehensive unit energy cost of each energy supply node to it, and allocate energy after sorting it from small to large according to the cost until the load node demand is met or the energy supply node has no remaining energy to allocate, and update the relevant information at the same time. Then evaluate the real-time energy scheduling results, calculate the total supply cost and check whether the needs of all load nodes are met. If not, enter the reserve energy scheduling. During the reserve energy scheduling, determine the set of load nodes that do not meet the demand, input the energy storage device information, calculate the effective unit energy supply cost of each energy storage device to the load node, and allocate energy according to the cost sorting until the load node demand is met or the energy storage device has no remaining capacity. After updating the information, process the next load node that does not meet the demand.

[0167] Please refer to Figure 3 , which shows a flowchart of an exemplary flexible distribution network multi-energy coordinated planning method S300 of the present application, and its contents include:

[0168] S310: Input the current energy supply information and load demand information into the real-time energy scheduling model to perform real-time energy scheduling.

[0169] Input the current energy supply information and load demand information into the real-time energy scheduling model. Energy supply information includes the available energy of each energy supply node, and the data comes from the real-time multi-energy information collected and processed before; load demand information is the actual power demand of each load node at present, which is obtained by real-time monitoring of smart meters and other equipment.

[0170] For each load node, according to the principle of greedy algorithm, the energy supply node with the lowest cost is selected for energy distribution. Assume that the amount of energy distributed from energy supply node i to load node j at the current moment is xij(t), and calculate the unit energy comprehensive cost Cij(t) from each energy supply node to the load node. The calculation formula is:

[0171] Cij(t)=ci(t)+lij(t);

[0172] Among them, ci(t) is the unit energy production cost of energy supply node i at time t, and lij(t) is the unit energy transmission cost from energy supply node i to load node j at time t.

[0173] The energy supply nodes are sorted in order from the smallest to the largest unit energy comprehensive cost, and then energy is allocated to the load nodes from the energy supply node with the lowest cost until one of the following conditions is met:

[0174] Condition 1: The demand of the load node is fully met, that is:

[0175]

[0176] Where m is the total number of energy supply nodes, and Dj(t) is the actual demand of load node j at time t.

[0177] Condition 2: The remaining allocatable energy of the current energy supply node is 0, that is:

[0178]

[0179] Among them, Pi(t) is the energy supply capacity of energy supply node i at time t, and n is the total number of load nodes.

[0180] Update the remaining energy of the energy supply node and the allocated energy of the load node, and prepare for the energy allocation of the next load node.

[0181] S320: Evaluate the results of real-time energy scheduling.

[0182] Calculate the total supply cost Ctotal after real-time energy scheduling using the following formula:

[0183]

[0184] Check whether the requirements of all load nodes are met, that is, judge: Is it true for all j=1,2,...,n?

[0185] If all load node demands are met, the real-time energy scheduling is completed and this step ends; if there are load node demands that are not met, the preliminary energy scheduling step is entered.

[0186] S330: Prepare energy dispatch.

[0187] Determine the set of load nodes with unmet demand Input the energy storage equipment information and the load node information with unmet demand into the reserve energy scheduling model.

[0188] For each load node j in the set of load nodes Junsatisfied with unmet demand, according to the greedy algorithm, the energy storage device with the highest energy storage release efficiency is preferentially selected for energy distribution. Suppose the amount of energy provided by the energy storage device k to the load node j at time t is yk(t).

[0189] Calculate the effective unit energy supply cost of each energy storage device to load node j The calculation formula is:

[0190]

[0191] The energy storage devices are sorted in order from the smallest to the largest effective unit energy supply cost, and then energy is allocated to the unmet load node j from the energy storage device with the lowest cost until one of the following conditions is met:

[0192] Condition 1: The demand of load node j is fully met, that is:

[0193]

[0194] Condition 2: The remaining releasable capacity of the current energy storage device is 0, that is: Ej(t)-yk(t)=0

[0195] Update the remaining capacity of the energy storage device and the allocated energy amount information of the load node, and continue to allocate energy to the next load node that does not meet the demand until all load nodes that do not meet the demand have been processed.

[0196] S400 judges the real-time energy dispatch result, and executes the forecast planning model if it is not satisfied. First, the total actual allocated energy and total required energy of all load nodes after real-time energy dispatch are calculated, and the two are compared to determine whether the real-time energy dispatch meets the current load. If not, the energy shortage is determined, including the real-time energy shortage and the energy vacancy of the energy storage device after the reserve energy dispatch. Then, the forecast planning model input is prepared, the information of non-operating equipment is collected, and the future trend of energy demand changes at load nodes and the trend of supply capacity changes at energy supply nodes are predicted. Finally, the forecast planning model is executed based on the greedy algorithm, and the comprehensive unit energy cost of each device is calculated and sorted at each forecast moment, and energy is allocated in order until the load node forecast demand is met or the device reaches the allocation limit, and the next forecast moment is entered after the equipment and load node information is updated.

[0197] Please refer to Figure 4 , which shows a flowchart of an exemplary flexible distribution network multi-energy coordinated planning method S400 of the present application, and its contents include:

[0198] S410: Determine the real-time energy dispatch result.

[0199] Calculate the total actual distributed energy of all load nodes after real-time energy scheduling The calculation formula is:

[0200]

[0201] Calculate the total energy demand of all load nodes at the current moment The calculation formula is:

[0202]

[0203] Compare and

[0204] like Then the real-time energy scheduling meets the current load, the prediction planning model is not executed, and this step ends.

[0205] like Then the real-time energy scheduling does not meet the current load, and the next step is to execute the forecast planning model.

[0206] S420: Determine the energy shortage amount.

[0207] Calculate the real-time energy shortage ΔEreal caused by real-time energy scheduling. The calculation formula is:

[0208]

[0209] Calculate the total remaining releasable capacity of energy storage equipment after reserve energy dispatch The calculation formula is:

[0210]

[0211] Wherein, Ek(t) is the remaining releasable capacity of energy storage device k at time t, and p is the total number of energy storage devices.

[0212] Calculate the energy shortage of energy storage equipment ΔEstorage caused by reserve energy scheduling, and the calculation formula is:

[0213]

[0214] Among them, yk(t) is the amount of energy provided by energy storage device k to the load node in the reserve energy scheduling.

[0215] S430: Preparing forecast planning model input.

[0216] Collect information about non-operating equipment, including equipment type, startup cost si(t+Δt), equipment operating cost, and equipment maximum supply capacity And the device startup time and climbing rate ri.

[0217] According to the historical multi-energy information and demand information data, the energy demand change trend of each load node in the future period is predicted to obtain the predicted demand curve Dj(t+Δt), j=1,2,...,n.

[0218] Based on the operating status and trend of the current energy supply nodes, the energy supply capacity change trend of each energy supply node in the future is estimated to obtain the predicted energy supply curve Pi(t+Δt), i=1,2,...,m, m is the total number of energy supply nodes.

[0219] S440: Execute the prediction planning model based on the greedy algorithm.

[0220] For each prediction time t+Δt, according to the greedy algorithm principle, the device with the lowest unit energy comprehensive cost is selected for energy distribution, and the unit energy comprehensive cost Cij(t+Δt) of each device to the load node is calculated. The calculation formula is:

[0221] Cij(t+Δt)=si(t+Δt)+ci(t+Δt)+lij(t+Δt).

[0222] Sort the devices in order of unit energy comprehensive cost from small to large, and then allocate energy to the load nodes from the device with the lowest cost until one of the following conditions is met:

[0223] Condition 1: The predicted demand of the load node is fully met, that is:

[0224]

[0225] Condition 2: All startable devices have been allocated energy or have reached their maximum supply capacity limit.

[0226] Update the remaining energy supply of the equipment and the allocated energy information of the load nodes to prepare for energy allocation at the next forecast time.

[0227] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0228] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0229] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0230] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.

[0231] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for coordinated planning of multiple energy sources in a flexible distribution network, characterized in that: include: Based on the multi-energy information and power demand information of the flexible distribution network, a real-time planning model and a predictive planning model are constructed, wherein the real-time planning model includes a real-time energy scheduling model and a reserve energy scheduling model; Obtain the current flexible distribution network multi-energy information and current power demand information; The real-time planning model uses a greedy algorithm to match energy according to the multi-energy information and power demand information at the current moment; among them: based on the greedy algorithm, the real-time energy scheduling model is used to allocate the real-time available energy to the current load demand. If the real-time energy is not enough to meet the demand, the reserve energy scheduling is entered; based on the greedy algorithm, the reserve energy scheduling model is used to schedule the energy of the energy storage device to the new current load demand; If the real-time energy scheduling meets the current load, the prediction planning model will not be executed; if the real-time energy scheduling does not meet the current load, the prediction planning model will be executed; the prediction planning model is based on the energy gap of the energy storage equipment caused by the reserve energy scheduling and the real-time energy shortage caused by the real-time energy scheduling. Based on the greedy algorithm, the non-operating equipment is scheduled to generate energy to make up for the deficiencies of the reserve energy scheduling and the real-time energy scheduling.

2. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 1, characterized in that: The real-time energy scheduling model is established as follows: Define the decision variables of the real-time energy scheduling model: Let x ij (t) represents the amount of energy distributed from energy supply node i to load node j at time t, where i = 1, 2, ..., m, m is the total number of energy supply nodes, and j = 1, 2, ..., n, n is the total number of load nodes; With the goal of minimizing the real-time energy supply cost, a real-time energy scheduling model is constructed by considering the factors of energy production cost and transmission cost. The objective function of the real-time energy scheduling model is expressed as: Among them, c i (t) is the unit energy production cost of energy supply node i at time t, l ij (t) is the unit energy transmission cost from energy supply node i to load node j at time t; Establish the constraints of the real-time energy dispatch model, including energy supply capacity constraints, load demand balance constraints and variable non-negativity constraints; The energy supply capacity constraint is that for each energy supply node i, the total amount of energy allocated to all load nodes at time t cannot exceed its energy supply capacity P at the current time i (t), that is: The load demand balance constraint is that the total amount of energy received by each load node at time should be equal to its actual demand D j (t), that is: The non-negative constraints on the variables are:

3. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 1, characterized in that: The preparation energy scheduling model is established as follows: Define the decision variables of the reserve energy scheduling model: Let yk(t) represent the amount of energy provided by energy storage device k to the load node at time t, k = 1, 2, ..., p, p is the total number of energy storage devices; With the goal of maximizing the utilization efficiency of energy storage equipment and maintaining system stability, a reserve energy scheduling model is constructed. The objective function of the reserve energy scheduling model is expressed as: Among them, η k (t) is the energy storage release efficiency of energy storage device k at time t; Establish the constraints of the reserve energy dispatch model, including energy storage equipment capacity constraints, load demand balance constraints and variable non-negative constraints; The capacity constraint of the energy storage device is that the amount of energy released by the energy storage device k at time t cannot exceed its current remaining releasable capacity E k (t), that is: When the real-time energy dispatch is insufficient, the load demand balance constraint is that the total amount of energy provided by all energy storage devices to the load node plus the energy provided by the real-time energy dispatch should meet the demand of the load node, that is: The non-negative constraints on the variables are:

4. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 1, characterized in that: The forecast planning model is established as follows: Define the decision variables of the forecast planning model: Let z ij (t+Δt) represents the amount of energy that the energy supply node i plans to allocate to the load node j at the future time t+Δt, where Δt is the prediction time step; With the goal of minimizing future energy supply costs and meeting forecasted demand, a forecasting planning model is constructed by considering factors such as equipment startup costs and operating costs. The objective function is expressed as: Among them, s i (t+Δt) is the startup cost of energy supply node i at time t+Δt, q is the total number of devices that can be started, c i (t+Δt) is the unit energy production cost of energy supply node i at time t+Δt, l ij (t+Δt) is the unit energy transmission cost from energy supply node i to load node j at time t+Δt; Establish the constraints of the forecast planning model, including energy supply capacity constraints, load demand balance constraints and variable non-negativity constraints; Considering the supply capacity after the equipment is started, the energy supply capacity constraint is that for the energy supply node i that is planned to start, its supply capacity should meet certain restrictions at time t+Δt. At the same time, the dynamic characteristics of the equipment startup process should be considered. Assuming that the startup time of energy supply node i is T i start , the climbing rate is r i , then: in, is the maximum supply capacity of energy supply node i; Based on the predicted demand, the load demand balance constraint is that at the future time t+Δt, the total energy supply after all energy supplies and equipment startup should be equal to the predicted load demand, that is: The non-negative constraints on the variables are:

5. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 2, characterized in that: The operation of the real-time energy dispatching model to allocate real-time available energy to current load demand includes: Input the current energy supply information and load demand information into the real-time energy scheduling model; suppose the amount of energy distributed from energy supply node i to load node j at the current moment is x ij (t), calculate the unit energy comprehensive cost C from each energy supply node to the load node ij (t), the calculation formula is: C ij (t)=c i (t)+l ij (t); Among them, c i (t) is the unit energy production cost of energy supply node i at time t, l ij (t) is the unit energy transmission cost from energy supply node i to load node j at time t; The energy supply nodes are sorted in order from the smallest to the largest unit energy comprehensive cost, and then energy is allocated to the load nodes from the energy supply node with the lowest cost until one of the following conditions is met: Condition 1: The demand of the load node is fully met, that is: Where m is the total number of energy supply nodes, D j (t) is the actual demand of load node j at time t; Condition 2: The remaining allocatable energy of the current energy supply node is 0, that is: Among them, P i (t) is the energy supply capacity of energy supply node i at time t, and n is the total number of load nodes.

6. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 5, characterized in that: The result evaluation method of the real-time energy scheduling is as follows: Calculate the total supply cost C after real-time energy scheduling total , the calculation formula is: Check whether the requirements of all load nodes are met, that is, judge: Is it true for all j=1,2,...,n? 7. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 3, characterized in that: The conditions for executing the scheduling of the reserve energy scheduling model are: if there is a load node demand that is not met, then enter the scheduling of the reserve energy scheduling model; The scheduling mode of the reserve energy scheduling model is: Determine the set of load nodes with unmet demand Input the energy storage equipment information and the load node information with unmet demand into the reserve energy dispatch model; For the load node set J whose demand is not met unsatisfied For each load node j in the system, according to the greedy algorithm, the energy storage device with the highest energy storage release efficiency is selected for energy distribution; the effective unit energy supply cost of each energy storage device to the load node j is calculated. The calculation formula is: The energy storage devices are sorted in order from the smallest to the largest effective unit energy supply cost, and then energy is allocated to the unmet load node j from the energy storage device with the lowest cost until one of the following conditions is met: Condition 1: The demand of load node j is fully met, that is: Condition 2: The remaining releasable capacity of the current energy storage device is 0, that is: E j (t)-y k (t)=0.

8. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 4, characterized in that: The method for determining whether to execute the forecast planning model is: Calculate the total actual distributed energy of all load nodes after real-time energy scheduling The calculation formula is: Calculate the total energy demand of all load nodes at the current moment The calculation formula is: Compare and like If the real-time energy scheduling meets the current load, the prediction planning model is not executed, and this step ends; like Then the real-time energy scheduling does not meet the current load, and the next step is to execute the forecast planning model.

9. A method for coordinated planning of multiple energy sources in a flexible distribution network according to claim 8, characterized in that: The forecast planning model execution steps include: Determine the energy deficit: Calculate the real-time energy shortage ΔE caused by real-time energy scheduling real , the calculation formula is: Calculate the total remaining releasable capacity of energy storage equipment after reserve energy dispatch The calculation formula is: Among them, E k (t) is the remaining releasable capacity of energy storage device k at time t, and p is the total number of energy storage devices; Calculate the energy gap ΔE of energy storage equipment caused by reserve energy scheduling storage , the calculation formula is: Among them, yk(t) is the amount of energy provided by energy storage device k to the load node in the reserve energy scheduling; Forecast planning model input preparation: Collect information about non-operating equipment, including equipment type, startup cost i (t+Δt), equipment operating cost, equipment maximum supply capacity And the device startup time T i start and the climbing rate r i ; Based on historical multi-energy information and demand information data, the energy demand change trend of each load node in the future is predicted to obtain the predicted demand curve D j (t+Δt), j=1,2,...,n; Based on the current operating status and trend of the energy supply nodes, the energy supply capacity change trend of each energy supply node in the future is estimated to obtain the predicted energy supply curve P i (t+Δt), i=1,2,...,m, m is the total number of energy supply nodes; Based on the greedy algorithm, the predictive planning model is executed: For each prediction time t+Δt, according to the greedy algorithm principle, the device with the lowest unit energy comprehensive cost is selected for energy distribution, and the unit energy comprehensive cost C of each device to the load node is calculated. ij (t+Δt), the calculation formula is: C ij (t+Δt)=s i (t+Δt)+c i (t+Δt)+l ij (t+Δt); Sort the devices in order of unit energy comprehensive cost from small to large, and then allocate energy to the load nodes from the device with the lowest cost until one of the following conditions is met: Condition 1: The predicted demand of the load node is fully met, that is: Condition 2: All startable devices have been allocated energy or have reached their maximum supply capacity limit.

Images