CN118117572A

CN118117572A - High-reliability improving method for integrated renewable energy comprehensive energy supply system

Info

Publication number: CN118117572A
Application number: CN202311747577.4A
Authority: CN
Inventors: 马瑞; 李剑锋; 曾四鸣; 郝晓光; 陈二松
Original assignee: State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
Priority date: 2023-12-18
Filing date: 2023-12-18
Publication date: 2024-05-31

Abstract

The invention discloses a high-reliability lifting method of a comprehensive energy supply system integrating renewable energy, which relates to the technical field of electric power and comprises the following steps: step 1: modeling a renewable energy intelligent power grid system; step 2: generating a staged optimization decision of an unstable power supply and energy storage: step 2.1: planning and designing wind/light site selection and volume setting; step 2.2: planning and designing cluster division and energy storage site selection and volume setting; step 2.3: constructing a multi-target interactive decision model; step 2.4: constructing a stage type particle swarm algorithm embedded with tide calculation; step 3: formulating a voltage stabilization and power optimization layering coordination control technology; according to the high-reliability lifting method of the integrated renewable energy comprehensive energy supply system, an optimal configuration scheme of unstable power supply wind/light and energy storage is provided, the tide stability of a renewable energy access intelligent power grid can be effectively improved, the permeability of new energy is further improved, the burden of a power distribution network and a gas engine is reduced, and the economical efficiency is improved.

Description

High-reliability improving method for integrated renewable energy comprehensive energy supply system

Technical Field

The invention relates to the technical field of electric power, in particular to a high-reliability improving method of a comprehensive energy supply system integrating renewable energy sources.

Background

The energy low-carbon transformation still faces the difficulty of how to realize the collaborative sustainable development of the multi-energy system. With the rapid development of distributed clean energy technologies such as cogeneration, heat pump, electric energy storage, heat storage/cold storage, natural gas power generation and the like, the energy coupling and information interaction between an electric power system and other energy systems such as heat supply/cold supply and the like are increasingly deepened, and an area comprehensive energy system taking the electric power system as a core is gradually formed. By means of the rapidly developed information technology and distributed power generation and energy storage technology, the comprehensive energy system can break barriers between traditional isolated energy systems, enlarge control boundaries and operation flexibility of the power system, promote multi-energy efficient complementation and source-network-load cooperative operation, accelerate renewable energy permeation of a heat supply/cold supply system, and provide a feasible path for improving comprehensive energy utilization efficiency and promoting low carbonization transformation of the energy system. China is one of the fastest-developing countries of renewable energy power, and the installed capacity of wind power and photovoltaic is the first in the world. However, under the influence of random natural factors such as wind speed, illumination, ambient temperature and the like, new energy output has obvious intermittence, fluctuation and uncertainty, the system operation regulation capability is limited, the problem of wind abandoning and light abandoning is very serious, renewable energy is connected into a power grid, the tide stability is poor, the distribution network loss and voltage fluctuation are higher due to the influence of the uncertainty of wind/light output on the distribution network, and the stability of the power grid is poor, so that the method for improving the high reliability of the comprehensive energy supply system integrating renewable energy is provided.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a high-reliability improving method of a comprehensive energy supply system integrating renewable energy sources, which solves the problems in the background art.

In order to achieve the above purpose, the invention is realized by the following technical scheme: a high reliability improving method of a comprehensive energy supply system integrating renewable energy sources comprises the following steps:

Step 1: modeling a renewable energy intelligent power grid system:

step 2: generating a staged optimization decision of an unstable power supply and energy storage:

step 2.1: planning and designing wind/light site selection and volume setting;

step 2.2: planning and designing cluster division and energy storage site selection and volume setting;

Step 2.3: constructing a multi-target interactive decision model;

step 2.4: constructing a stage type particle swarm algorithm embedded with tide calculation;

Step 3: formulating a voltage stabilization and power optimization layering coordination control technology;

Step 3.1: establishing a multi-agent-based micro-grid layered control strategy;

Step 3.2: making a three-level control strategy;

Step 3.3: making a secondary control strategy;

step 3.4: and (5) making a primary control strategy.

Optionally, the step 1: modeling a renewable energy intelligent power grid system:

Step 1.1: fan model:

The output power of the wind turbine generator is influenced by wind speed and maximum fan power, and can be described by an approximate piecewise linear function;

wherein Pw is the output power of the wind turbine generator, x is the actual wind speed, M is the maximum power of the fan, alpha and beta are linear parameters, and vci, vco and vr respectively represent the cut-in wind speed, the cut-out wind speed and the rated wind speed;

Step 1.2: photovoltaic modeling: the output power of photovoltaic power generation is mainly affected by temperature, illumination intensity and the like, and a photovoltaic output power model can be established as follows:

Wherein Ps, ppv are respectively the illumination intensity of 1000W/m < 2 > under the standard condition, the temperature is 25 ℃, the photovoltaic output power and the actual photovoltaic output power are respectively the illumination intensity and the actual illumination intensity under the standard condition, gs and Ga are respectively the temperature and the actual temperature under the standard condition, tr and Ta are respectively the power temperature coefficient, and k is the power temperature coefficient;

Step 1.3: energy storage modeling:

The existence of the energy storage can adjust the voltage of the distribution network node, reduce loss and improve the stability of the power grid, and the SOC and the charge-discharge power model are as follows

Wherein sigma is the energy storage self-discharge rate; ηc and ηd are respectively the energy storage charging and discharging efficiency; eess is the energy storage capacity; t is time; Δt is a scheduling period; pch and Pdis are respectively energy storage charging and discharging power;

Step 1.4: topology modeling:

SG1 is apparent power of an upper-layer power distribution network, zi-1, i (i E [1, n ]) represents line impedance from node i-1 to node i, ui is voltage of node i, si-1, i is line flowing power from node i-1 to node i, si is injection apparent power of node i, SLi is sum of load power of node i and access power of distributed power generation units such as wind/light/storage and the like, and the distributed power generation units are accessed to a local line through a power electronic converter to supply power for intelligent power grid loads together with the power distribution network;

Deriving the injection power of any node m and the flow power between nodes,

Where ΔSi is the power loss of the line and can be expressed as

Where Ui is the voltage amplitude of node i; pi, qi are the injected active and reactive power of node i, respectively, ri-1, i and Xi-1, i are the resistance and reactance between nodes i-1 and i,

Node 1 is a balanced node and the voltage at node m (m.epsilon.2, n) can be expressed as

Step 1.5: lightGBM network-based predictive model

In order to predict the typical sunrise of a wind-solar system, based on the wind-force weather typical day, the photovoltaic weather typical day, the historical weather data of the existing wind field light field and the corresponding power generation data, training a wind turbine and photovoltaic weather sunrise prediction model based on LightGBM network, and further using the trained prediction model to predict the typical day power according to the weather typical day, wherein the weather data of wind power generation prediction comprises: wind speeds at different heights, wind directions corresponding to the wind speeds, temperature and humidity, and meteorological data predicted by photovoltaic power generation comprise: short wave radiation intensity, long wave radiation intensity, cloud cover, temperature, and humidity;

LightGBM the predictive model may be expressed as

Wherein fw/pv represents a wind power generation or photovoltaic power generation prediction model based on LightGBM, xi is an ith meteorological sample point for wind power or photovoltaic power generation,For the predicted generated power corresponding to the i-th sample point,

The training objective function is to minimize the mean square value (REMS) of the prediction error to

Wherein Pi is the real power corresponding to the meteorological sample point i, N is the sample number,

Finally, respectively carrying out power prediction on wind power generation and photovoltaic power generation typical days by using a wind power generation prediction model fw and a photovoltaic power generation prediction model fpv which are completed through training;

Meteorological data for wind power generation representative day and photovoltaic power generation representative day are Xw and Xpv respectively, and the corresponding prediction is predicted as

Wherein Xw, i and Xpv, i are respectively the ith sample point of the typical day of the illumination meteorological typical day of the wind meteorological typical day,

By reasonably predicting the local wind/light time sequence power generation, reasonable basis can be provided for the access capacity of a fan and a photovoltaic, meanwhile, the capacity configuration of energy storage can be guided to effectively reduce the power fluctuation and loss of the system, stabilize the voltage, achieve the scheduling balance relation shown in the formula (11), improve the time sequence matching of each power supply power in the system, reduce the influence caused by the randomness of wind and light while improving the permeability of new energy,

In the formula, PG and PP are respectively the dispatching output power of the gas engine and the power grid, pe and i are energy storage dispatching output, PL is load prediction power, and npv/nw/ne are respectively the wind/light/storage configuration number.

Optionally, the step 2.1: planning and designing wind/light site selection and volume setting:

the wind/light access power distribution network capacity planning model aims at the minimum total cost of power flow stability and investment of the power distribution network, and solves the optimal access fan and photovoltaic capacity of each node;

the objective function of the upper layer optimal planning is

F＝min(F₁,F₂) (12)

Tidal current stability F1: reasonable wind/light access improves the voltage distribution of the whole power distribution network, improves the power flow stability margin of the power distribution network, and takes the maximum value of the power flow voltage stability index L in one day as one of targets, namely

F₁＝max{L₁ L₂ … L₂₄} (13)

Considering the one-time cost price of the fan photovoltaic and the construction cost, F2 is defined as the total cost of the renewable energy wind/light investment construction installation, namely

F₂＝C₁P_W+C₂P_PV (14)

Wherein, C1 and C2 are the price per unit capacity and construction cost of the fan and the photovoltaic, PW and PPV are the installation capacity of the fan photovoltaic, and constraint conditions are as follows: power distribution network tide constraint: when the power distribution network stably operates, the voltage and power of the power distribution network must meet the trend equation

Wherein Gij and Bij are respectively the conductance and susceptance between nodes ij; θij is the phase angle difference between nodes i, j;

Node voltage constraint:

where Ui, min, ui, max are the minimum and maximum values allowed by the node voltage deviation respectively,

Wind/light access total capacity constraint: the wind/light access total capacity minus the load total capacity should not exceed the maximum power that can be borne by the upper grid transformer,

In the formula, PDG is the capacity of actually accessing wind/light, PDG and max are the maximum total capacity of the wind and light.

Optionally, the step 2.2: planning and designing cluster division and energy storage site selection and volume setting: the second stage divides the whole intelligent power distribution network into a plurality of sub-clusters according to an electric distance calculation method after the power distribution network is connected with wind/light, and the third stage selects voltage fluctuation, active loss and capacity as objective functions after the energy storage connection position is analyzed according to sensitivity, and the energy storage time sequence output is used as decision variables to plan the energy storage capacity;

The objective function of the lower energy storage planning is

f＝min(f₁,f₂,f₃)

(18)

Voltage ripple f1:

in the formula, N is the number of nodes, U1 and Ui, and t are the voltage amplitude values of the node 1 (balance node) and the node i at the time t respectively;

line loss f2:

Where Pi, t, qi, t are the active and reactive power injections of node i at time t,

Energy storage system capacity f3: the energy storage voltage fluctuation and loss are considered, and the cost and the installation cost of the unit capacity of the energy storage are also considered, so that the total capacity of the energy storage is selected as an important index for measuring the economical efficiency of the energy storage, the maximum charge/discharge capacity of the energy storage in one day and the upper and lower limits of the charge state are inspected, the corresponding objective function can be obtained,

Wherein tj, s is the starting time of continuous charging/discharging of energy storage in the j th section; tj, e is the end time of continuous charging/discharging of the energy storage in the j th section; pch/dis, i is the charge/discharge power of the ith energy storage period; SOCmax and SOCmin are the upper and lower limits of the stored state of charge, respectively; ne is the amount of stored energy;

Constraint conditions: when the energy storage capacity is configured, not only the power flow constraint and the voltage constraint of the node are needed to be considered, but also the state of charge constraint of the energy storage is needed to be considered, and the energy balance constraint and the charge and discharge constraint in one day are needed to be considered; state of charge constraints

Wherein, SOCi, max and SOCi, min are respectively the upper limit and the lower limit of the ith energy storage charge state,

Energy storage charge-discharge constraint:

the stored charge state is the ratio of the remaining capacity and rated capacity of the battery at a certain moment, and the charge and discharge states are shown in the formula (23):

Stored energy balance constraint:

Energy storage in order to meet the scheduling operation requirement of a day, the charge state of the initial period and the charge state of the final period of the day are expected to be the same as much as possible,

SOC(0)＝SOC(T)

(23)

Optionally, the step 2.3: constructing a multi-target interaction decision model:

to optimize each target to optimize the combination, a multi-target interactive decision model is introduced:

max[f₁(x),f₂(x),…f_n(x)]

(24)

wherein f1 (x), f2 (x) and fn (x) are respectively different targets, the optimal solutions f1, min, f2, min and … fn, min of a plurality of targets are normalized, satisfaction functions xi 1, xi 2 and … zeta n can be obtained,

Let xi (x) = [ ζ1ζ … ζn ] T be comprehensive satisfaction function, and each of ζ1, ζ2, and ζn be optimal expectation, and theoretical value be 1, then the optimal expectation value ζ (x) = [ ζ1ζ … ζn ] T of ζ (x), to solve for the vector solution x, define the overall equalization decision function f as

The larger the xi is, the smaller the f is, namely, the closer each target is to the respective optimal target value, so that the overall balance of a plurality of targets can be fully realized through the f, meanwhile, the contradiction between all parties is considered, and a satisfactory scheme which can be accepted by all parties is obtained.

Optionally, the step 2.4: constructing a stage type particle swarm algorithm for embedding tide calculation: according to actual requirements, two capacity configuration schemes are provided, and two phase type particle swarm algorithms embedded with tide are utilized for solving; step 2.41: a single target gas engine output duty ratio self-defining mode; step 2.42: a multi-objective economic custom mode;

The step 2.41: the single target gas engine output duty ratio self-defining mode comprises:

Considering the working condition of the traditional gas engine power generation, the original gas engine output is reduced according to percentage to be used as the spare capacity, meanwhile clean energy sources such as wind/light/storage and the like are integrated to supply power for the intelligent power grid load, a single-target stage particle swarm algorithm embedded with tide calculation is provided, and the flow is as follows:

Step 2.411, initializing particle swarm in the first stage, initializing particle speed, position, iteration times and gas engine output ratio according to constraint conditions of an upper layer and power generated per unit capacity wind/light per hour, carrying into fitness function to calculate tide and objective function to obtain initial individual optimum and global optimum,

Step 2.412, updating the particle swarm of the first stage, updating the speed and position of each particle within the constraint range, updating the individual optimum and the global optimum, adding one to the iteration number,

Step 2.413, judging the iteration number, if the set maximum iteration number is reached, turning to step 2.414, otherwise returning to step 2.412 to continue the iterative computation,

Step 2.414, energy storage and site selection, namely, carrying out cluster division on the whole distribution network according to the voltage and loss sensitivity index of the second stage, determining the number and the positions of the energy storage,

Step 2.415, initializing a third-stage (energy storage constant volume) particle swarm, calculating energy storage capacity according to the wind/light configuration result of the first stage,

Step 2.416, updating the third stage particle group, updating the speed and position of each particle in the constraint range, calculating the power flow and the objective function to update the energy storage capacity, adding one to the iteration times,

Step 2.417, judging the iteration times, if the iteration times reach the maximum iteration times, outputting the optimal result of wind/light/storage capacity configuration, otherwise, returning to the step 2.417 to continue iteration;

step 2.42: the multi-objective economic custom schema includes:

Solving the pareto optimal front edge of the wind/light capacity according to the investment construction amount range given by the investor, calculating the energy storage capacity according to the fixed percentage of the total wind/light capacity, providing a multi-target stage particle swarm algorithm, simultaneously incorporating the tide calculation based on the ecological niche multi-target particle swarm algorithm, and the specific capacity configuration flow is as follows,

Step 2.421, initializing a first stage particle population, initializing wind/light capacity and location, setting the size and number of iterations of the external archive,

Step 2.422, initializing non-inferior solution and global optimum, calculating power flow and each target value according to initial value to obtain first round of non-inferior solution, then randomly selecting individual in external file as global optimum according to roulette method proportional to fitness,

Step 2.423, updating the first stage particle swarm, updating the wind/light capacity and position within the constraint range, calculating the power flow and the objective function,

Step 2.424, updating the external archive and the global optimum, updating the external archive with the non-inferior solution in the current particle, if the number of individuals in the archive reaches the maximum, replacing the individual with the smallest fitness according to the roulette method in step 2.422, updating the global optimum at the same time, adding one to the iteration number,

Step 2.425, judging the iteration times, if the maximum iteration times are reached, outputting pareto optimal solution sets, otherwise, returning to step 2.423 to continue iteration,

Step 2.426, energy storage site selection and volume determination, performing second-stage distribution network cluster division according to the result calculation of step 2.425, performing third-stage site selection and volume determination on the energy storage, outputting a capacity optimization configuration result,

After the capacity allocation, it is also necessary to evaluate the wind/light/storage after the allocation by the stability evaluation index and the power, capacity, and energy permeability index.

Optionally, the step 3.1: establishing a multi-agent-based micro-grid layered control strategy: in a micro grid island mode, a layered control strategy is realized through a consistency algorithm based on a MAS framework, power generation cost is minimum, supply and demand power balance is realized, renewable energy utilization is maximized and the like as an objective function, firstly, a mathematical model of economic cost of each micro source is modeled, a micro grid layered control framework is established, a three-level complete distributed algorithm is used for optimizing and solving optimal power of micro sources in a micro grid system, further, a weight coefficient of a corresponding inverter in primary control is deduced, so that a sagging curve parameter is regulated, in order to respond to the load demand, the power of the load is measured in real time, the data iterative calculation of an upper algorithm is updated again for optimization solving, the sagging curve is continuously corrected to realize bottom-layer power distribution as required, finally, the combination of three-level control and primary sagging control is realized while the increment cost of each micro source is consistent, the layered control strategy is realized, aiming at the sagging control, the problem of frequency and voltage offset is caused, and the stable operation of the system is realized by correcting the sagging curve based on a multi-agent distributed secondary control strategy.

Optionally, the step 3.2: and (3) making a three-level control strategy: the proposed hierarchical control strategy structure of the micro-grid is divided into three layers, each layer can realize distributed control, and the main function of each layer of control is as follows: the primary control adopts droop control, and the output frequency and the voltage amplitude of each micro-source inverter are regulated and controlled by controlling the active power and the reactive power provided by the micro-source inverter according to a droop curve; the second-level control is deviation adjustment, so that the stability of the system voltage is ensured to be in a normal range; the three-level control generally ensures the optimal operation of the system for economic dispatch including coordination of matching of micro sources and load power in the micro grid, and the hierarchical control strategy can enable the power distribution in the micro grid to meet the requirements of precision, system stability and the like on one hand, and can achieve the goal of global optimization without completely relying on communication on the other hand:

The comparison result of the two distributed algorithms shows that the leader-free consistency algorithm has more advantages, and not only can the complete distributed control strategy be truly realized and the global stability performance be enhanced, but also the three-level control algorithm in the hierarchical control strategy adopts the leader-free consistency algorithm;

Where M _ij、N_ij is the communication coefficient, ε is the convergence coefficient, P _D,i is the local supply-demand power mismatch estimate,

The optimal power calculated by the three-level control is used as a given value of the first-level control, the given value is tracked by changing the droop coefficient, the weight coefficient is introduced by a specific method, the weight coefficient is determined by calculating the optimal power by a consistency algorithm,

Wherein P _G,i represents the optimal active power of the ith micro-source when the upper algorithm meets the economic optimal target (the increment cost of each micro-source is consistent),

The topological structure is a micro-grid formed by three micro-sources, the micro-source 1 is taken as a reference, and the weight coefficient of each micro-source is

The weight coefficient is equal to the optimal power ratio calculated in three-stage control, if the bottom inverter can realize the aim of the economy of the whole system according to the optimal ratio such as the actual power sent out by a formula (46),

K₁：K₂：K₃＝P_G1：P_G2：P_G3 (33)

Because the demand of the load side cannot be changed, in the layered control, because the time scale of the upper layer is longer and the time scale of the lower layer is short, in order to ensure that the upper layer optimization algorithm responds to the load change in the lower layer in real time, setting the load power calculated by adopting the voltage and current of the load side at intervals, updating the consistency algorithm formula (28) (29) (43) by re-sending the load power to the consistency algorithm formula (28) (43), setting the micro-source 1 to be close to the load side, sensing the unbalanced load supply and demand power by the micro-source 1 at first, and at the moment, subtracting the last load value from the newly-collected load value to serve as a new local supply and demand power mismatch estimated value of the micro-source 1 to modify the formula (43)

P_D,1[t+1]＝P_load(t+1)-P_load(t)

(34)

And the local supply and demand power mismatch estimation of other micro sources is coordinated with the micro source 1 to be finally stabilized to 0, and the bottom layer inverter output is optimally regulated according to the load demand by calculating a new optimal power weight ratio to readjust the bottom layer sagging parameter.

Optionally, the step 3.3: and (3) making a secondary control strategy: in order to ensure the requirement of the electric energy quality of the micro-grid, a distributed control strategy of the micro-grid is provided based on a multi-agent system, a distributed control method is adopted for secondary control to regulate the voltage deviation, a distributed controller interacts with adjacent agents under the clock drive of which the period is Ts, the state information is updated, a controller of each micro-source acquires the local node voltage U _Ni, iteratively converges to U _ave according to a consistency algorithm formula (35), the voltage deviation is regulated by a PI controller, the reference voltage of droop control is corrected to ensure that the voltage of each micro-source is in an allowable range and the voltage stability requirement of a PCC end is ensured, the safe and stable operation of the micro-grid is realized by optimizing the reference voltage of droop control according to the provided strategy,

Optionally, the step 3.4: making a primary control strategy: the traditional droop control is to enable the output voltage and frequency of the inverter and the active power and reactive power of an inverter outlet to meet a droop curve relation, the traditional droop control is to distribute loads according to the capacity proportion of the micro-sources, the cost of the micro-sources is not comprehensively considered, the micro-sources with high power generation cost can bear the loads more, the micro-sources with low power generation cost have small capacity of bearing the loads, so that the system is uneconomical to operate, the traditional droop control imitates primary frequency modulation characteristics of the power system, when the load of the system is increased, the active power output by the bottom micro-source inverter is increased according to the droop curve, the load power is reduced according to the frequency characteristic due to the reduction of the system frequency, and finally, a new balance point b is achieved under the combined action of the negative feedback process;

The sagging control formula is

f＝f₀+(P₀-P)m_p

(36)

Wherein m _p is a sagging coefficient; p ₀ rated active power; p is the actual active power; f ₀ the nominal frequency of the frequency band,

E＝E₀+(Q₀-Q)n_q

(37)

Wherein n _q is a sagging coefficient; q ₀ rated reactive power; q actual reactive power; e ₀ rated voltage;

Each micro source in the system should reasonably bear the load of the demand side and ensure the stable operation of the micro grid, and the traditional droop control method is influenced by the line impedance and the coupling action of active and reactive power, so that the power distribution is poor in readiness and even the stability of the system is influenced, and therefore, the operation condition of the system is improved based on a MAS layered control strategy;

the weight coefficient in the hierarchical control strategy is the key for realizing the connection between the three-level distributed algorithm and the first-level control, only the active power is considered for the three-level control of the set objective function, the actual active power output of the inverter is deduced by the formula (36),

By varying P _0i、m_pi to vary the output power P _{Actual practice is that of i},

The main purpose of adopting droop control is to realize reasonable distribution of the output power of each inverter connected in parallel according to the requirement of the load side, the link of power distribution is realized by mainly adjusting the droop curve parameter P _0i、m_pi,

When the power actually emitted by the inverter meets the formula (39), the economic goal of system optimization can be achieved by combining the primary control and the tertiary control, the formula (51) is brought into the formula (39), if the proportional relation of the formula (40) exists, the formula (39) is established,

P_{Actual practice is that of 1}：P_{Actual practice is that of 2}：P_{Actual practice is that of 3}＝K₁：K₂：K₃

(39)

Weight coefficient:

the relation between the parameters and the weight coefficients in the sagging curve is that

The three inverters at the bottom layer are connected in parallel, the actual output of the other two micro sources is adjusted according to the weight coefficient proportion of the formula (32) by taking the first micro source as a reference (firstly determining P ₀₁、m_p1)

Only reactive power is considered in the secondary control, the original sagging formula (37) is improved to be (42), the voltage deviation of the voltage obtained by the distributed algorithm and each micro source is utilized to adjust, so as to realize the stable operation of the system,

The invention provides a high-reliability improving method of a comprehensive energy supply system integrating renewable energy sources, which has the following beneficial effects:

1. According to the high-reliability lifting method of the integrated renewable energy comprehensive energy supply system, the optimal configuration scheme of unstable power supply wind/light and energy storage is provided, so that the tide stability of a renewable energy access intelligent power grid can be effectively improved, the permeability of new energy is improved, the burden of a power distribution network and a gas engine is reduced, and the economical efficiency is improved.

2. According to the high-reliability improving method of the integrated renewable energy comprehensive energy supply system, the cluster division principle and the energy storage access method are provided, so that the influence of uncertainty of wind/light output on a distribution network can be effectively improved, distribution network loss and voltage fluctuation are reduced, and the stability of a smart grid is further guaranteed.

Drawings

Fig. 1 is a schematic diagram of a radiation type distribution network structure of a smart grid cluster with n nodes;

FIG. 2 is a diagram showing the combined power supply structure of each micro source and the conventional power supply according to the present invention;

FIG. 3 is a hierarchical planning flowchart of the present invention;

FIG. 4 is a diagram of a 10kV distribution network structure of the invention;

FIG. 5 is a graph showing the power flow distribution under the original working condition of the present invention;

FIG. 6 is an iteration graph of the present invention;

FIG. 7 is a graph of the photovoltaic capacity of a blower according to the present invention;

FIG. 8 is a graph showing a post wind/light configuration power flow profile in accordance with the present invention;

FIG. 9 is a graph showing the wind/light planning result for 30% reduction in gas engine output according to the present invention;

FIG. 10 is a graph of wind/light planning results for a 70% reduction in gas turbine output according to the present invention;

FIG. 11 is a schematic illustration of a micro-grid of the present invention;

FIG. 12 is a diagram of a MAS-based distributed control architecture of the present invention;

FIG. 13 is a schematic diagram of Matlab simulation of the present invention;

FIG. 14 is a graph of a mismatch estimate of the local supply and demand power of a micro-source output in three-stage control according to the present invention;

FIG. 15 is a graph of the top calculated optimum power for the micro-source output in the three-stage control of the present invention;

FIG. 16 is a graph of incremental cost of micro-source output in three-stage control according to the present invention;

FIG. 17 is a graph of inverter output power according to the present invention;

FIG. 18 is a graph of the active power sum of the load of the present invention;

FIG. 19 is a graph of PCC voltage according to the present invention;

FIG. 20 is a graph of PCC frequencies in accordance with the present invention;

FIG. 21 is a diagram of the FFT waveform load prior to loading in accordance with the present invention;

FIG. 22 is a diagram of FFT waveform loading inputs according to the present invention;

FIG. 23 is a post-load cut-away diagram of the FFT waveform of the present invention;

fig. 24 is a diagram of an inverter of the present invention without work;

FIG. 25 is a reactive power summation diagram of the load of the present invention;

Fig. 26 is an average voltage plot of an inverter of the present invention;

FIG. 27 is a graph of single-phase voltage effective values for each of the micro-sources of the present invention;

FIG. 28 is a diagram of a micro-source 1 voltage FFT analysis load prior to input according to the present invention;

FIG. 29 is a graph of the micro-source 1 voltage FFT analysis load of the present invention after input;

FIG. 30 is a diagram of a micro-source 2 voltage FFT analysis load prior to input according to the present invention;

FIG. 31 is a graph of the micro-source 2 voltage FFT analysis load of the present invention after input;

FIG. 32 is a diagram of a micro-source 3 voltage FFT analysis load prior to input according to the present invention;

FIG. 33 is a graph of the micro-source 3 voltage FFT analysis load of the present invention after input;

fig. 34 is a sagging control schematic of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.

Referring to fig. 1 to 34, the present invention provides a technical solution: a high reliability improving method of a comprehensive energy supply system integrating renewable energy sources comprises the following steps:

Step 1: modeling a renewable energy intelligent power grid system:

Step 1.1: fan model:

Step 1.3: energy storage modeling:

Step 1.4: topology modeling:

The intelligent power distribution network with the number of nodes of n is shown in fig. 1;

Deriving the injection power of any node m and the flow power between nodes,

Where ΔSi is the power loss of the line and can be expressed as

Step 1.5: lightGBM network-based predictive model

LightGBM the predictive model may be expressed as

In the formula, the dispatching output power, pe, i of the gas engine and the power grid respectively are energy storage dispatching output, PL is load prediction power, npv/nw/ne is wind/light/storage configuration number respectively, and the ideal configuration operation result is shown in figure 2.

step 2.1: planning and designing wind/light site selection and volume setting:

the objective function of the upper layer optimal planning is

F＝min(F₁,F₂) (12)

F₁＝max{L₁ L₂ … L₂₄}

(13)

F₂＝C₁P_W+C₂P_PV

(14)

Node voltage constraint:

In the formula, PDG is the capacity of actually accessing wind/light, and PDG and max are the maximum total capacity of wind and light;

The second stage divides the whole intelligent power distribution network into a plurality of sub-clusters according to an electric distance calculation method after the power distribution network is connected with wind/light, and the third stage selects voltage fluctuation, active loss and capacity as objective functions after the energy storage connection position is analyzed according to sensitivity, and the energy storage time sequence output is used as decision variables to plan the energy storage capacity;

The objective function of the lower energy storage planning is

f＝min(f₁,f₂,f₃)

(18)

Voltage ripple f1:

line loss f2:

Energy storage charge-discharge constraint:

Stored energy balance constraint:

SOC(0)＝SOC(T)

(23)

Step 2.3: constructing a multi-target interactive decision model;

max[f₁(x),f₂(x),…f_n(x)]

(24)

The greater the xi is, the smaller the f is, namely, the closer each target is to the respective optimal target value, so that the overall balance of a plurality of targets can be fully realized through the f, meanwhile, the contradiction between all parties is considered, and a satisfactory scheme which can be accepted by all parties is obtained;

As shown in fig. 11, the right side is an electrical connection for supplying power to the load by connecting three micro-sources in parallel, the left side is a communication connection between three micro-sources in the micro-grid and three agents in the MAS, the multiple agents mutually exchange information in two-by-two communication mode, and then the coordinated and unified information is sent to each micro-source. In the hierarchical control, the bottom layer control strategy improves the traditional droop control, a corresponding weight coefficient K is obtained according to the optimal output power of each micro source calculated by the upper layer, and the corresponding droop curve parameter is changed to adjust the actual output power, so that the economic and stable system operation is achieved.

According to actual requirements, two capacity configuration schemes are provided, and two phase type particle swarm algorithms embedded with tide are utilized for solving; step 2.41: a single target gas engine output duty ratio self-defining mode; step 2.42: a multi-objective economic custom mode;

step 2.41: the single target gas engine output duty ratio self-defining mode comprises:

step 2.42: the multi-objective economic custom schema includes:

After the capacity is configured, the configured wind/light/storage is also required to be evaluated through a stability evaluation index and power, capacity and energy permeability indexes;

Step 3.1: establishing a multi-agent-based micro-grid layered control strategy;

in a micro grid island mode, a hierarchical control strategy is realized based on a MAS framework through a consistency algorithm, power generation cost is minimum, supply and demand power balance is realized, renewable energy utilization is maximized and the like as an objective function, firstly, a mathematical model of economic cost of each micro source is modeled, a micro grid hierarchical control framework is established, a three-level complete distributed algorithm is used for optimizing and solving optimal power of micro sources in a micro grid system, and then a weight coefficient of a corresponding inverter in primary control is deduced, so that a sagging curve parameter is regulated, in order to respond to the load demand, the power of the load is measured in real time, the data iterative calculation of an upper algorithm is updated again for optimization solving, a sagging curve is continuously corrected to realize the bottom power distribution as required, finally, the combination of three-level control and primary sagging control is realized while the increment cost of each micro source is consistent, the hierarchical control strategy is realized, aiming at the sagging control, the problem of frequency and voltage offset is caused, and the stable operation of the system is realized by correcting a sagging curve based on a multi-agent distributed secondary control strategy;

Step 3.2: making a three-level control strategy;

The proposed hierarchical control strategy structure of the micro-grid is divided into three layers, each layer can realize distributed control, and the main function of each layer of control is as follows: the primary control adopts droop control, and the output frequency and the voltage amplitude of each micro-source inverter are regulated and controlled by controlling the active power and the reactive power provided by the micro-source inverter according to a droop curve; the second-level control is deviation adjustment, so that the stability of the system voltage is ensured to be in a normal range; the three-level control generally ensures the optimal operation of the system for economic dispatch including coordination of matching of micro sources and load power in the micro grid, and the hierarchical control strategy can enable the power distribution in the micro grid to meet the requirements of precision, system stability and the like on one hand, and can achieve the goal of global optimization without completely relying on communication on the other hand:

K₁：K₂：K₃＝P_G1：P_G2：P_G3

(33)

P_D,1[t+1]＝P_load(t+1)-P_load(t)

(34)

The local supply and demand power mismatch estimation of other micro sources is coordinated with the micro source 1 to be finally stabilized to 0, and the bottom layer inverter output is optimally adjusted according to the load demand by calculating a new optimal power weight ratio to readjust the bottom layer sagging parameter;

Step 3.3: making a secondary control strategy;

In order to ensure the requirement of the electric energy quality of the micro-grid, a distributed control strategy of the micro-grid is provided based on a multi-agent system, a distributed control method is adopted for secondary control to regulate the voltage deviation, a distributed controller interacts with adjacent agents under the clock drive of which the period is Ts, the state information is updated, a controller of each micro-source acquires the local node voltage U _Ni, iteratively converges to U _ave according to a consistency algorithm formula (35), the voltage deviation is regulated by a PI controller, the reference voltage of droop control is corrected to ensure that the voltage of each micro-source is in an allowable range and the voltage stability requirement of a PCC end is ensured, the safe and stable operation of the micro-grid is realized by optimizing the reference voltage of droop control according to the provided strategy,

Step 3.4: making a primary control strategy;

the conventional droop control is to make the output voltage and frequency of the inverter and the active power and reactive power of the inverter outlet meet the droop curve relation, the conventional droop control is to distribute the load according to the capacity proportion of the micro-source, the cost of the micro-source is not comprehensively considered, the micro-source with high power generation cost can bear the load more, the micro-source with low power generation cost has small capability of bearing the load, thus the system is uneconomical to operate, the conventional droop control imitates the primary frequency modulation characteristic of the power system, when the load of the system is increased, the active power output by the bottom micro-source inverter is increased according to the droop curve, the load power is reduced according to the frequency characteristic due to the decrease of the system frequency, finally, a new balance point b is reached under the combined action of the negative feedback process, as shown in figure 34,

The sagging control formula is

f＝f₀+(P₀-P)m_p

(36)

E＝E₀+(Q₀-Q)n_q

(37)

P_{Actual practice is that of 1}：P_{Actual practice is that of 2}:P_{Actual practice is that of 3}＝K₁：K₂：K₃

(39)

Weight coefficient:

Examples:

And (5) analyzing by using a 10kV system of a certain oil field of Bohai sea. The system grid comprises 16 nodes, the nodes 16 are upper-level 35kV power distribution networks, the whole system is connected into the upper-level power distribution networks through cables, the total load of the network is 103.892MW, and the load of the whole oil field hardly changes with time. Under the existing working condition, the gas engine 6 in the whole area uses 2 equipment, and can meet the load electricity consumption of the whole oil field by matching with the output of the upper-level distribution grid. The overall system architecture is shown in fig. 4.

Based on the topology, rationality of wind/light/storage capacity planning design is studied. Considering actual demands, the two schemes are adopted to plan wind/light/storage, and the wind/light/storage is compared with the original working condition for analysis.

Original working condition:

The 8 generators (6 are 2) in the power distribution network region can meet the load demand by matching with the superior power distribution network, the load hardly changes with time, and the power flow distribution is stable, as shown in fig. 5.

Because the load hardly changes with time, the power flow hardly changes in one day under the condition of only the output of the gas engine, the calculated stability index F1 is 0.094, the permeability index is 0, the voltage fluctuation index F1 is 11.96, and the total loss in one day is 103.2MWh.

Scheme one: gas engine output self-defining first stage

The original gas engine output is reduced according to proportion and simultaneously is combined with clean energy sources such as wind/light/storage, and the like, and considering that the economic and stability indexes are important as well, both xi 1 (x) and xi 2 (x) are 1. When the gas engine output is reduced by 50%, the overall wind-solar balance optimization curve is shown as 6, and the upper wind/light configuration result is shown as fig. 7.

The total amount of the configuration fans is 70MW, the total amount of the photovoltaic is 24MW, and the distribution network power flow after wind/light configuration is shown in figure 8; the results of the wind/light planning are shown in fig. 9 and 10 when the gas engine output is reduced by 30% and 70%.

Embodiment two:

hierarchical control simulation verification based on multi-agent micro-grid:

a three-level control simulation model is built as shown in fig. 13;

It can be seen from fig. 14 that there is a large fluctuation at 0.15s, since the estimated value of the local supply-demand power mismatch of each micro source at this time deviates from the initial given initial value. The initial load is 40kW, a load of 5kW is put into 3s-5.5s, and the load side demand is restored to 40kW after 5.6 s. Because of the sampling time interval, larger peaks appear at 3.15s and 5.55s, and each micro-source locally estimates that the supply and demand power deviation is 0 when stabilizing. Fig. 15 shows that when three stages of control calculate at 0-3s, the output power of the micro source 1 is stabilized at 18kW, the output power of the micro source 2 is stabilized at 6.5kW, the output power of the micro source 3 is stabilized at 14.2kW, the output power of the micro source 1 is 19.7kW after the adjustment and stabilization due to the load increase at 3.1-5.6s, the micro source 2 is stabilized at 15.6kW, the micro source 3 is stabilized at 7.3kW, and the state before the load change is restored at 5.6 s. At 0.5s shown in FIG. 16, the incremental cost of the three micro-sources is consistent, the value is 0.46$/kWh, the incremental cost is 0.49$/kWh for 3.1-5.6s, and the state is restored to the state before load step by 0.46$/kWh after 5.6 s.

Cost function coefficients for each distributed unit

As shown in fig. 17, the inverter actually outputs active power in the primary control: PG1,1s is stabilized at 18.5kW, the load step of 3.1s-5.6s is regulated briefly to be finally stabilized at 19.7kW, and 5.7s is stabilized to a state before the load step; PG2,1s is stabilized at 6.0kW,3.1s-5.6s is stabilized at 7.3kW, and 5.7s is stabilized at 6.0kW; PG3,1s is stabilized at 14.2kW,3.1s-5.6s is stabilized at 15.6kW,5.7s is stabilized at 14.2kW;

In fig. 19, the PCC voltage reaches a plateau of 374V at 0.4s, and since a large fluctuation in the voltage waveform occurs due to the load power step at 3s, the reactive power requirement on the load side becomes large, the supply side cannot instantaneously compensate the required reactive power, so that the voltage drop eventually stabilizes at 370V, and the voltage returns to 374V after load removal; according to the FFT analysis of the PCC terminal phase voltage distortion rate, it can be seen from fig. 21 that thd=0.08 before loading, fig. 22 that thd=0.08 after increasing load, fig. 33 that thd=0.1 after cutting off load, the PCC terminal voltage satisfies the power quality requirement; in fig. 22, the PCC frequency reaches a stable 50.02Hz at 0.8s, after 3s, the supply side relative output active power decreases due to the load increase, resulting in the frequency fluctuation to eventually stabilize at 50.01Hz, after 5.5s, the increased load is cut off, at which time the frequency rise is regulated briefly, and then stabilized at 50.02Hz, so that the PCC end frequency meets the power quality requirement.

FIG. 24 shows reactive power of each micro-source after secondary control, the initial load reactive power is 10kvar,3s-5.5 drops one reactive load to 5kvar,5.6s and then cuts off 5kvar load. Because the second level is adjusted to be second level, the micro source 1 is stabilized at 5556var, the micro source 2 is stabilized 5532var, the micro source 3 is stabilized 1919var, the redundant reactive power is consumed by line loss, the 3s increased load power is briefly adjusted to be stabilized at 8631var, the micro source 2 is stabilized 7487var, the micro source 3 is stabilized at 2372var, the 5.5s load is removed, and the reactive power of each micro source is restored to the initial stable state. Fig. 25 is a sum of actual reactive demands on the load side.

The secondary control is based on the average voltage of the distributed control computing system, as shown in fig. 26, the average voltage of the system meets the power quality requirement, the average voltage is stabilized at 216.7V before load step, is stabilized at 215.3V after load step and is restored to the initial stable state after load is removed. The effective value of the single voltage of each micro source in the system is shown in fig. 27, the voltage of the micro source 1 is stabilized at 217V through rapid transient adjustment during 0-3 s, the micro source 2 is stabilized at 216.3V, the micro source 3 is stabilized at 216.6V, the micro source 1 is stabilized at 215.7V after load step adjustment, the micro source 2 is 214.5V, and the micro source 3 is 215.2V. FFT analysis was performed on each micro-source phase voltage distortion rate, and as shown in fig. 28 and 29, the micro-source 1 phase voltage had=0.09% before load step, and had=0.08% after load step. Fig. 30 and 31 are FFT analyses of micro-source 2 phase voltage, pre-load-step thd=0.13%, post-load-step thd=0.11%. Fig. 32 and 33 are FFT analyses of the micro-source 3-phase voltage, pre-load-step thd=0.21%, post-load-step thd=0.11%. The phase voltages of the individual micro-sources in the established micro-grid model thus meet the power quality requirements.

The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.

Claims

1. A high reliability lifting method of a comprehensive energy supply system integrating renewable energy sources is characterized by comprising the following steps: the method comprises the following steps:

Step 1: modeling a renewable energy intelligent power grid system;

Step 2: generating a staged optimization decision of an unstable power supply and energy storage: step 2.1: planning and designing wind/light site selection and volume setting; step 2.2: planning and designing cluster division and energy storage site selection and volume setting;

Step 2.3: constructing a multi-target interactive decision model; step 2.4: constructing a stage type particle swarm algorithm embedded with tide calculation;

Step 3.1: establishing a multi-agent-based micro-grid layered control strategy;

Step 3.2: making a three-level control strategy;

Step 3.3: making a secondary control strategy;

step 3.4: and (5) making a primary control strategy.

2. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 1: modeling a renewable energy intelligent power grid system;

Step 1.1: fan model:

Step 1.2: photovoltaic modeling: the output power of photovoltaic power generation is mainly influenced by temperature and illumination intensity, and a photovoltaic output power model can be established as follows:

Step 1.3: energy storage modeling:

Step 1.4: topology modeling:

SG1 is apparent power of an upper-layer power distribution network, zi-1, i (i E [1, n ]) represents line impedance from node i-1 to node i, ui is voltage of node i, si-1, i is line flowing power from node i-1 to node i, si is injection apparent power of node i, SLi is sum of load power of node i and access power of a distributed power generation unit, and wind/light/storage distributed power generation unit is accessed to a local line through a power electronic converter to supply power for intelligent power grid load together with the power distribution network;

Deriving injection power of any node m and flow power among nodes;

Where ΔSi is the power loss of the line and can be expressed as

Step 1.5: a LightGBM network-based predictive model;

the meteorological data predicted by wind power generation comprises: wind speeds at different heights, wind directions corresponding to the wind speeds, temperature and humidity, and meteorological data predicted by photovoltaic power generation comprise: short wave radiation intensity, long wave radiation intensity, cloud cover, temperature, and humidity;

LightGBM the predictive model may be expressed as

meteorological data for wind power generation representative day and photovoltaic power generation representative day are Xw and Xpv respectively, which are correspondingly predicted as

Wherein Xw, i and Xpv, i are respectively the ith sample point of the typical day of the illumination meteorological typical day of the wind meteorological typical day;

The reasonable prediction of local wind/light time sequence power generation can provide reasonable basis for the access capacity of a fan and a photovoltaic, and meanwhile, the capacity configuration of energy storage can be guided to effectively reduce the power fluctuation and loss of a system, stabilize voltage, achieve the scheduling balance relation shown in a formula (11), improve the time sequence matching of each power supply power in the system, and reduce the influence caused by wind and light randomness while improving the permeability of new energy;

3. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 2.1 is as follows: planning and designing wind/light site selection and volume setting:

the objective function of the upper layer optimal planning is

F＝min(F₁,F₂) (12)

F₁＝max{L₁ L₂ … L₂₄} (13)

F₂＝C₁P_W+C₂P_PV (14)

Node voltage constraint:

Wind/light access total capacity constraint: the wind/light access total capacity minus the load total capacity should not exceed the maximum power that the upper power grid transformer can bear;

4. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 2.2: planning and designing cluster division and energy storage site selection and volume setting: the second stage divides the whole intelligent power distribution network into a plurality of sub-clusters according to an electric distance calculation method after the power distribution network is connected with wind/light, and the third stage selects voltage fluctuation, active loss and capacity as objective functions after the energy storage connection position is analyzed according to sensitivity, and the energy storage time sequence output is used as decision variables to plan the energy storage capacity;

The objective function of the lower energy storage planning is

f＝min(f1,f2,f3)(18)

Voltage ripple f1:

line loss f2:

Energy storage system capacity f3: the energy storage voltage fluctuation and loss are considered, and the cost and the installation cost of the energy storage unit capacity are also considered, so that the total energy storage capacity is selected as an important index for measuring the economical efficiency of the energy storage unit capacity, and the maximum charge/discharge capacity and the upper and lower limits of the state of charge of the energy storage in one day are inspected to obtain corresponding objective functions;

Energy storage charge-discharge constraint:

Stored energy balance constraint:

In order to meet the scheduling operation requirement of a day, the energy storage hopes that the charge state of the initial period and the charge state of the final period of the day are the same as much as possible;

SOC(0)＝SOC(T)。 (23)。

5. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 2.3 is as follows: constructing a multi-target interaction decision model:

maX[f1(x),f20,…n0(24)

Wherein f1 (x), f2 (x) and fn (x) are respectively different targets, and optimal solutions f1, min, f2, min and … fn of a plurality of targets are subjected to normalization processing to obtain satisfaction functions zeta 1, zeta 2 and … zeta n;

6. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 2.4 is as follows: constructing a stage type particle swarm algorithm for embedding tide calculation: according to actual requirements, two capacity configuration schemes are provided, and two phase type particle swarm algorithms embedded with tide are utilized for solving; step 2.41: a single target gas engine output duty ratio self-defining mode; step 2.42: a multi-objective economic custom mode;

considering the working condition of the traditional gas engine power generation, the original gas engine output is reduced according to percentage to be used as the spare capacity, meanwhile, wind/light/clean energy is integrated to supply power for the intelligent power grid load, and a single-target stage particle swarm algorithm embedded with tide calculation is provided, and the flow is as follows:

Step 2.411, initializing a particle swarm in the first stage, initializing the particle speed, the position, the iteration times and the gas engine output ratio according to the constraint condition of an upper layer and the power generated per hour of unit capacity wind/light, and carrying into an fitness function to calculate a power flow and an objective function to obtain initial individual optimal and global optimal;

Step 2.412, updating the particle swarm in the first stage, updating the speed and the position of each particle in a constraint range, updating the individual optimum and the global optimum, and adding one to the iteration times;

step 2.413, judging the iteration times, if the iteration times reach the set maximum iteration times, turning to step 2.414, otherwise, returning to step 2.412 to continue the iteration calculation;

step 2.414, energy storage site selection, namely performing cluster division on the whole distribution network according to the voltage and loss sensitivity indexes of the second stage, and determining the number and the positions of the energy storage;

Step 2.415, initializing a third-stage (energy storage constant volume) particle swarm, and calculating energy storage capacity according to the wind/light configuration result of the first stage;

step 2.416, updating the particle swarm in the third stage, updating the speed and the position of each particle in the constraint range, calculating the power flow and the objective function to update the energy storage capacity, and adding one to the iteration times;

step 2.42: the multi-objective economic custom schema includes:

solving the pareto optimal front edge of the wind/light capacity according to the investment construction amount range given by the investor, calculating the energy storage capacity according to the fixed percentage of the total wind/light capacity, providing a multi-target stage particle swarm algorithm, simultaneously incorporating the tide calculation based on the ecological niche multi-target particle swarm algorithm, and specifically carrying out the capacity configuration flow as follows;

Step 2.421, initializing a first stage particle swarm, initializing wind/light capacity and position, and setting the size and iteration times of external archiving;

step 2.422, initializing a non-inferior solution and global optimum, calculating a power flow and each target value according to an initial value to obtain a first round of non-inferior solution, and randomly selecting individuals in an external file as the global optimum according to a roulette method proportional to fitness;

step 2.423, updating the first stage particle swarm, updating the wind/light capacity and the position in a constraint range, and calculating the power flow and the objective function;

Step 2.424, updating the external archive and the global optimum, updating the external archive by using a non-inferior solution in the current particle, and if the number of individuals in the archive reaches the maximum, replacing the individual with the minimum fitness according to the roulette method in step 2.422, and simultaneously updating the global optimum, wherein the iteration number is increased by one;

Step 2.425, judging the iteration times, if the iteration times reach the maximum iteration times, outputting pareto optimal solution sets, otherwise, returning to step 2.423 to continue iteration;

7. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 3.1: establishing a multi-agent-based micro-grid layered control strategy: in a micro grid island mode, a layered control strategy is realized through a consistency algorithm based on a MAS framework, so that the power generation cost is minimum, the supply and demand power is balanced, renewable energy utilization is maximized to be an objective function, firstly, a mathematical model of economic cost of each micro source is modeled, a micro grid layered control framework is established, a three-level complete distributed algorithm is used for optimizing the optimal power solved by the micro source in a micro grid system, further, the weight coefficient of a corresponding inverter in primary control is deduced, so that the sagging curve parameter is regulated, in order to respond to the load demand, the power of the load is measured in real time, the data iterative calculation of an upper algorithm is updated again, the sagging curve is continuously corrected to realize the required distribution of bottom layer power, finally, the combination of three-level control and primary sagging control is realized while the increment cost is consistent when all the micro sources are satisfied, the layered control strategy is realized, the frequency and voltage offset problem can be caused by sagging control, and the system stable operation is realized by correcting the sagging curve based on a multi-agent distributed secondary control strategy.

8. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 3.2 is as follows: and (3) making a three-level control strategy: the proposed hierarchical control strategy structure of the micro-grid is divided into three layers, each layer can realize distributed control, and the main function of each layer of control is as follows: the primary control adopts droop control, and the output frequency and the voltage amplitude of each micro-source inverter are regulated and controlled by controlling the active power and the reactive power provided by the micro-source inverter according to a droop curve; the second-level control is deviation adjustment, so that the stability of the system voltage is ensured to be in a normal range; the three-level control generally ensures the optimal operation of the system for economic dispatch including coordination of matching of micro sources and load power in the micro grid, and the hierarchical control strategy can enable the power distribution in the micro grid to meet the requirements of precision and system stability on one hand and achieve the goal of global optimization without completely relying on communication on the other hand:

the three-level control algorithm in the hierarchical control strategy adopts a leader-free consistency algorithm;

Wherein M _ij、N_ij is a communication coefficient, epsilon is a convergence coefficient, and P _D,i is a local supply and demand power mismatch estimation;

the optimal power calculated by the three-level control is used as a given value of the first-level control, the given value is tracked by changing the sagging coefficient, the weight coefficient is introduced by a specific method, and the weight coefficient is determined by calculating the optimal power by a consistency algorithm;

wherein P _G,i represents the optimal active power of the ith micro-source when the upper algorithm meets the economic optimal target (the increment cost of each micro-source is consistent);

At this time, the weight coefficient is equal to the optimal power ratio calculated in three-stage control, and if the bottom inverter can realize the aim of the economy of the whole system according to the optimal ratio, such as the actual power sent out by a formula (46);

K₁:K₂:K₃＝P_G1:P_G2:P_G3 (33)

Setting that load power is calculated at intervals by taking voltage and current of a load side, re-feeding the load power into a consistency algorithm formula (28) (29) (43) to update, setting that a micro source 1 is close to the load side, and firstly sensing that load supply and demand power is unbalanced by the micro source 1, wherein a new local supply and demand power mismatch estimated value of the micro source 1 is obtained by subtracting a last load value from a newly-acquired load value at the moment, and modifying the formula (43) into a new local supply and demand power mismatch estimated value of the micro source 1

P_D,1[t+1]＝P_load(t+1)-P_load(t) (34)

9. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 3.3: and (3) making a secondary control strategy: in order to ensure the requirement of the electric energy quality of the micro-grid, a micro-grid distributed control strategy is provided based on a multi-agent system, a distributed control method is adopted for secondary control to adjust voltage deviation, a distributed controller interacts with adjacent agents under the clock drive of which the period is Ts, state information is updated, and a controller of each micro-source acquires local node voltage U _Ni and iteratively converges to U _ave according to a consistency algorithm formula (35);

10. The method for improving the high reliability of the integrated energy supply system integrating renewable energy sources according to claim 1, wherein the method comprises the following steps: the step 3.4 is as follows: making a primary control strategy: the traditional droop control is to enable the output voltage and frequency of the inverter and the active power and reactive power of an inverter outlet to meet a droop curve relation, the traditional droop control is to distribute loads according to the capacity proportion of the micro-sources, the cost of the micro-sources is not comprehensively considered, the micro-sources with high power generation cost can bear the loads more, the micro-sources with low power generation cost have small capacity of bearing the loads, so that the system is uneconomical to operate, the traditional droop control imitates primary frequency modulation characteristics of the power system, when the load of the system is increased, the active power output by the bottom micro-source inverter is increased according to the droop curve, the load power is reduced according to the frequency characteristic due to the reduction of the system frequency, and finally, a new balance point b is achieved under the combined action of the negative feedback process;

The sagging control formula is

f＝f₀+(P₀-P)m_p (36)

E＝E₀+(Q₀-Q)n_q (37)

Introducing a MAS-based hierarchical control strategy to improve the running condition of the system, and deriving the actual active power output of the inverter by a formula (36);

The main purpose of adopting droop control is to realize reasonable distribution of the output power of each inverter connected in parallel according to the requirement of a load side, and the link of power distribution is mainly realized by adjusting a droop curve parameter P _0i、m_pi;

When the power actually sent by the inverter meets the formula (39), the economic goal of system optimization can be achieved by combining the primary control and the tertiary control, and the formula (51) is brought into the formula (39), and if the formula (40) proportion relation exists, the formula (39) is established;

p _{Actual practice is that of 1}∶P_{Actual practice is that of 2}∶P_{Actual practice is that of 3}＝K₁∶K₂∶K₃ (39) weight coefficient:

Only the reactive power aspect is considered in the secondary control, the original sagging formula (37) is improved to be (42), and the voltage deviation of the voltage obtained by the distributed algorithm and each micro source is utilized to adjust so as to realize the stable operation of the system;