CN107069807A

CN107069807A - Containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method

Info

Publication number: CN107069807A
Application number: CN201710173581.2A
Authority: CN
Inventors: 吕智林; 汤泽琦; 孙顺吉; 魏卿; 许柳; 孟泽晨
Original assignee: Guangxi University
Current assignee: Guangxi University
Priority date: 2017-03-22
Filing date: 2017-03-22
Publication date: 2017-08-18
Anticipated expiration: 2037-03-22
Also published as: CN107069807B

Abstract

The present invention containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method, minimum target is fluctuated with operating cost, Environmental costs and regenerative resource, microgrid robust Optimal Operation Model, generation optimal solution set are built for constraint using system safety operation.Using grey entropy relation grade as the evaluation index of optimal solution set, the micro-capacitance sensor Robust Scheduling method using grey entropy relation grade as preferentially index is set up.Current and history the state of energy-storage system is predicted to the uncertain budget of current micro-capacitance sensor scheduling by mapping rule, exerted oneself instruction as generator unit finally by the objective optimal solution of selecting of grey entropy relation grade.The inventive method can effectively strengthening system be to the resistance of uncertain factor, with extremely strong operability；One is adjusted in uncertain budget regulation strategy control, and the control characteristic of micro battery is adjusted while micro battery generation schedule is distributed, make micro-capacitance sensor economy, it is environmentally friendly formulate generation schedule, scheduling process has preferable robustness；Energy-storage system super-charge super-discharge can be prevented effectively from.

Description

Balanced node-free microgrid robust scheduling method with uncertain budget adjustment

Technical Field

The invention belongs to the technical field of power system scheduling automation, and particularly relates to a balance-node-free microgrid robust scheduling method with uncertain budget adjustment.

Background

In recent years, the large-scale access of renewable energy effectively expands the benefit of power supply, improves the energy utilization rate, improves the power supply reliability, promotes the development and the reformation of the power market, reduces the carbon emission, reduces the line loss and relieves the pressure of the expansion and growth of loads. However, the uncertainty factor in the microgrid becomes one of the bottlenecks in the microgrid application, and the research on the uncertainty factor becomes a hotspot and difficulty in the related field.

The micro-grid is a power generation and distribution system which organically integrates a distributed power supply, a load, an energy storage system and a control device thereof. The concept stems from the development of distributed power generation technology. The countries of the United states, Europe, Japan, etc. have introduced the classic structure of micro-grid adapted to itself through exemplary engineering and related projects. China insists on the strategic policy of energy development of 'saving, cleaning and safety', and aims to build a clean, efficient, safe and sustainable modern energy system; therefore, four strategies of saving priority, standing domestic, green and low carbon and innovation driving are proposed in the strategy action plan for energy development (2014-2020); a plurality of micro-grid projects such as a national wind-solar energy storage and transportation demonstration project, a micro-grid demonstration project of the island of Fushan of Zhongshan of Zhejiang and a national golden solar water-light complementary micro-grid power generation demonstration project are established.

For a hybrid microgrid with wind and light power generation, the microgrid scheduling usually aims at economy and takes safe operation as a constraint condition. With the deepening of the environmental protection concept, concepts such as 'clean', 'green', 'sustainable' and the like also become important requirements in the operation process of the micro-grid, and a series of environmental technical indexes and target expectations related to the micro-grid are generated. However, in view of the fact that the photovoltaic output and the load have volatility, intermittence and randomness in space-time distribution, when the system power fluctuates greatly, the system voltage and frequency exceed the operation range, so that the stability of the microgrid system is affected and even an electric power system accident is caused. Therefore, how to realize the safe, environment-friendly and economic operation of the micro-grid in an uncertain environment has important significance.

Currently, power systems deal with uncertainty factors in the microgrid, primarily by increasing spare capacity in the scheduling process and relying on the regulatory capabilities of the factual scheduling. But the increase in sparing causes an increase in the annual computational costs of the system. And the fact scheduling mainly comprises operations of quickly adjusting the generator set, cutting load, abandoning wind power photoelectricity and the like, but the quickly adjusting the generator set can generate higher power generation cost, the cutting load can cause power failure of a coverage area, and the abandoning wind power photoelectricity causes waste of renewable energy. Therefore, many experts and scholars propose scheduling models such as a robust optimization scheduling model and a probability constraint scheduling model, but the robust optimization has the problem of conservative selection, and the probability constraint needs a large amount of historical data support and confidence degree selection. Therefore, in the face of uncertain factors in the operation of the micro-grid, how to improve the robustness of the scheduling strategy and reduce the operation risk of the micro-grid becomes an urgent need for the operation of the power grid.

Disclosure of Invention

Aiming at the technical problems in the prior art, the invention provides a balance-node-free microgrid robust scheduling method with uncertain budget adjustment, which can enhance the robustness of a system scheduling strategy and prevent an energy storage system from being overcharged and overdischarged in an uncertain environment.

In order to achieve the purpose, the invention adopts the following technical scheme:

the method for the microgrid robust scheduling without the balance nodes and with the uncertain budget adjustment comprises the following steps:

firstly, determining parameters according to the microgrid scheduling information, and constructing a microgrid robust scheduling model:

determining random factors according to the overall requirements of uncertain environment power generation; describing a fluctuation interval of uncertain parameters through a box-type uncertain set, and constructing a robust scheduling model of the microgrid by taking the minimum running cost, environmental cost and renewable energy fluctuation as targets and taking the requirements of microgrid scheduling safety and droop control running as constraints;

and secondly, predicting uncertainty prediction through an uncertainty budget adjusting strategy, and transmitting the predicted value to a microgrid robust scheduling model:

predicting uncertainty prediction through a mapping rule according to the current and historical operating states of the microgrid, and applying the predicted value to a current robust scheduling model;

thirdly, respectively solving rated values and droop coefficients of the amplitude, the frequency, the active power and the reactive power of the power generation plan and the droop control voltage by adopting an improved differential evolution algorithm;

the improved differential evolution algorithm is based on a differential evolution algorithm, the gray entropy relevance is taken as a standard when the current generation particles are updated, the excellent particles are subjected to local search by a cloud model, the excellent particles are updated locally and preferentially, and the updated excellent particles are stored in an external elite archive; meanwhile, global search is carried out on non-excellent particles through a chaotic algorithm to select excellent particles, and the selected excellent particles are stored in an external elite archive; then entering the next generation optimizing process until the termination condition is met; and when the scheduling iterative process reaches the maximum algebra each time, selecting the optimal particles as a power generation plan and a droop control plan by taking the gray entropy relevance as an evaluation index, if the scheduling continues, predicting new uncertainty prediction by using an uncertainty budget adjusting strategy, and repeating the second step and the third step until the scheduling is finished.

The uncertainty budget adjustment strategy is:

rule 1 uses a forgetting factor weighting mode to linearly weight the dimensionless amount of state deviation and the historical uncertainty prediction of the energy storage system, and the formula is as follows:

rule 2 weights the historical power fluctuation and historical uncertainty predictions using a linear weighting method, as follows:

rule 3 establishes a mapping relation between uncertainty prediction, load error prediction, photovoltaic output error prediction, fan output error prediction and time, historical uncertainty prediction, historical load error, historical photovoltaic output error, historical fan output error, historical load error prediction, historical photovoltaic output error prediction and historical fan output error prediction by using a neural network, and the formula is as follows:

in the above three formulas, (t) is uncertainty prediction of time period t;_min、_maxupper and lower limits for uncertainty prediction, respectively; omega₁、ω₂、…、ω_n… is a forgetting factor; soc_t-1The state of charge of the energy storage system at the time t-1; Socrespectively an upper limit and a lower limit of the state of charge of the energy storage system; (0) an initial value for the uncertainty budget; a is₁、a₂、…、a_n、…，b₁、b₂、…、b_n… are the mapping rule coefficients, respectively; p_L,t、P_PV,t、P_WT,tThe power of the load, photovoltaic power generation and wind power generation at the moment t respectively; delta P_L,tIs the time interval t load error; delta P_PV,tThe photovoltaic output error is a time period t; delta P_WT,tThe fan output error is a time period t;load error prediction for time period t;predicting photovoltaic output error in a time period t;predicting the output error of the fan in the time period t;

the uncertainty predictions predicted by the above uncertainty budget adjustment strategy are passed to the robust scheduling model.

When the micro-grid is in an island operation mode, the micro-grid adopts an equivalent control strategy, namely droop control; the dispatching method taking a robust dispatching model containing an uncertainty budget adjustment strategy as a core coordinates and dispatches control parameters of droop control of each inverter while a power generation plan is formulated, so that the micro-grid operation has robustness.

The invention has the advantages that:

the robust optimization theory is adopted to improve the dispatching, so that the resistance of the system to uncertain factors can be effectively enhanced, the method does not need accurate probability distribution, only needs the fluctuation range of uncertain parameters, and has strong operability.

The method integrates the uncertainty budget adjustment strategy control and adjustment, adjusts the control characteristics of the micro power supply while distributing the micro power supply power generation plan, enables the micro power grid to economically and environmentally make the power generation plan, and has better robustness in the scheduling process.

And a feedback regulation strategy of the uncertainty budget is introduced to realize the dynamic regulation of the uncertainty budget, so that the overcharge and the overdischarge of the energy storage system are effectively avoided.

Drawings

Fig. 1 is a scheduling circuit diagram of a hybrid independently operated microgrid system according to embodiment 1 of the present invention;

in the figure:

1 accumulator

2 accumulator Boost/Buck charging and discharging circuit

3 storage battery Boost/Buck charging and discharging circuit filter capacitor

4 storage battery Boost/Buck charging and discharging circuit filter inductor

5 DC bus capacitor

6 one-way inverter circuit

7 one-way inverter circuit filter inductance

8 one-way inverter circuit filter capacitor

9 three-phase load

LC filter of transformation circuit of 10 micro gas turbine

11 miniature gas turbine transformer circuit filter capacitor

12 miniature gas turbine transformer circuit filter inductance

13 micro gas turbine inverter

14 miniature gas turbine Boost circuit filter capacitor

15 miniature gas turbine Boost circuit

16 miniature gas turbine Boost circuit inductance

17 miniature gas turbine Boost circuit capacitor

Three-stage power electronic transformer of 18 miniature gas turbine

19 micro gas turbine

20 bidirectional DC/AC converter

LC filter inductor of 21 bidirectional DC/AC converter

22 bidirectional DC/AC converter LC filter capacitor

23 bidirectional DC/AC converter LC filter

LC filter of 24 diesel engine transformation circuit

25 diesel engine voltage transformation circuit filter capacitor

Filter inductor of 26 diesel engine voltage transformation circuit

27 diesel engine inverter

Filter capacitor of Boost circuit of 28 diesel engine

Boost circuit of 29 diesel engine

30 diesel engine Boost circuit inductance

31 diesel engine Boost circuit capacitor

Three-stage power electronic transformer for 32 diesel engine

33 diesel engine

34 photovoltaic cell

35 photovoltaic cell Boost circuit

36 photovoltaic cell Boost circuit capacitor

37 photovoltaic cell Boost circuit inductance

38 wind driven generator Boost voltage-boosting circuit

39 wind driven generator Boost circuit inductor

Boost circuit capacitor of 40 wind driven generator

Uncontrolled rectification of 41 wind driven generator

42 wind power generator

43 LC filter for fuel cell transformer circuit

44 fuel cell voltage transformation circuit filter capacitor

45 fuel cell voltage transformation circuit filter inductance

46 fuel cell inverter

Filter capacitor of Boost circuit of 47 fuel cell

48 fuel cell Boost booster circuit

49 fuel cell Boost circuit inductor

50 fuel cell Boost circuit capacitor

51 fuel cell secondary power electronic transformer

52 fuel cell

Fig. 2 is a flowchart of the microgrid robust scheduling method with the uncertainty budget adjustment strategy of the present invention.

FIG. 3 is a mapping rule of the uncertainty budget adjustment strategy of the present invention.

Detailed Description

The following detailed description of the embodiments of the present invention, taken in conjunction with the accompanying drawings, is not intended to limit the scope of the invention, as claimed.

1. For convenience of description, a photovoltaic power generation unit is abbreviated as PV, a wind power generation unit is abbreviated as WT, a micro gas turbine power generation unit is abbreviated as MT, a diesel engine power generation unit is abbreviated as DG, a fuel cell power generation unit is abbreviated as FC, and an alternating current Bus is abbreviated as Bus.

Adopting a micro-grid layered control method according to different time scales: zero-level control, first-level control, second-level control and third-level control. Wherein the zero order control (millisecond) is: the control unit is positioned in each power generation unit and each energy storage unit, so that the normal operation of each unit is maintained, the control performance and the economical efficiency of each unit are improved, and preparation is provided for primary control; the first control (second) is: the droop control of the controllable micro power supply is utilized to execute a power base point and a droop coefficient distributed by an energy management system, so that the instantaneous small-amplitude fluctuation of the load in the micro power grid is restrained; the secondary control (minutes) was: the droop control of the controllable micro power supply is utilized to execute the no-load frequency and the no-load voltage in the droop characteristic distributed by the energy management system, so that the serious deviation of a micro power supply operation power base point caused by long-time large-amplitude fluctuation of a load is responded, and the safe operation of the system frequency and the system voltage is ensured; the three-level control (day level/hour level/real-time level) is as follows: and in combination with power prediction and load prediction, the base value, droop coefficient, droop no-load frequency, droop no-load voltage and running state of each unit power are specified by means of day-ahead scheduling, day-internal scheduling and rescheduling of the microgrid respectively.

As shown in fig. 1, the microgrid system is composed of an energy storage system Boost/Buck charging and discharging circuit 2, a unidirectional inverter circuit 6, a micro gas turbine three-level power electronic transformer 18, a bidirectional AD/DC converter 20, a diesel engine three-level power electronic transformer 32, a photovoltaic Boost circuit 35, a fan Boost circuit 38 and a fuel cell inverter 51.

The hybrid microgrid connects the energy storage system 1, the wind driven generator 42, the photovoltaic cell 34 and the load 6 through a direct current bus, an MTTP (maximum power point tracking) controller controls the wind power unit and the photoelectric unit to output at the maximum power, and the voltage of the direct current bus is stabilized at 750V through a photovoltaic cell Boost circuit 35 and a wind driven generator Boost circuit 38; the energy storage system adopts a Boost/Buck charging and discharging circuit 2 to control and regulate the charging and discharging process of the energy storage system; the micro gas turbine 13, the diesel generator 33, the fuel cell 52 and the alternating current load 9 are connected by an alternating current bus, the three micro power sources respectively regulate the bus voltage through a multi-stage power electronic transformer comprising a micro gas turbine three-stage power electronic transformer 18, a diesel engine three-stage power electronic transformer 32 and a fuel cell two-stage power electronic transformer 51, and an inversion unit in the multi-stage power electronic transformer comprises a micro gas turbine inverter 13, a diesel engine inverter 27 and a fuel cell inverter 46 which respectively adopt droop control to automatically regulate the amplitude and the frequency of alternating current; the bi-directional AC/DC converter 20 performs bi-directional conversion between DC and AC, wherein the inverter process also uses droop control to adjust the amplitude and frequency of the AC power. Because the system adopts a large number of power electronic equipment, the direct current generating units need to stabilize direct current bus voltage through the bus capacitor 5, and the output ends of the direct current units also need to stabilize the voltage through capacitors, such as the storage battery Boost/Buck charging and discharging circuit filter capacitor 3, the unidirectional inverter circuit filter capacitor 8, the photovoltaic battery Boost circuit capacitor 36 and the wind driven generator Boost circuit capacitor 40. And the alternating current side power generation unit adopts an LC filter to filter harmonic waves, so that the steady state analysis of the hybrid micro-grid is facilitated, such as a micro gas turbine transformation circuit LC filter 10, a bidirectional DC/AC converter LC filter 23, a diesel transformation circuit LC filter 24 and a fuel cell transformation circuit LC filter 43. Therefore, the key to the transformer and converter is how to properly adjust the voltage amplitude, frequency, active and reactive power ratings and droop coefficients in droop control.

As shown in fig. 2, firstly, the system has no balance node because the system adopts an island operation mode, i.e. droop control, of a peer-to-peer control strategy and a comprehensive control strategy. The droop control node generally adopts a P-f/Q-V control mode, and the droop characteristic equation is as follows:

in the formula (1), the reaction mixture is,ω_k,N、V_k,Nk ∈ { MT, FC, DG, Bus } which are the inverter output voltage frequency reference value, amplitude reference value, frequency nominal value and amplitude nominal value, m_i、n_iStatic droop coefficients for active and reactive power, respectively; p_k,N、P_k、Q_k,N、Q_kRated active power, actual active power, rated reactive power and actual reactive power of the droop characteristic inverter are respectively.

Among the sag characteristics, it is necessary to satisfy:

constructing a robust optimization model:

objective function

min F_t＝min[C(P_t),B(P_t),F(P_t)](3)

In formulae (3) and (4): f_tIs an objective function for the t period; c (P)_t) Operating costs for the t-slot microgrid include operating costs for micro gas turbines, fuel cells and diesel engines, i.e.j∈S_C＝{MT,FC,DG}；B(P_t) Inclusion of pollutant and penalty costs for micro gas turbine, fuel cell and diesel engine for period t micro grid environmental costs, i.e.j∈S_C＝{MT,FC,DG}；F(P_t) Outputting the fluctuation degree of the renewable energy source of the microgrid in a time period t;the active power generation plans of the photovoltaic cell, the wind driven generator and the energy storage system are respectively.

The traditional robust scheduling constraints are respectively as follows:

-R_i·Δt≤P_i,t-P_i,t-1≤R_i·Δt (8)

-D_i·Δt≤Q_i,t-Q_i,t-1≤D_i·Δt (10)

Soc_t＝Soc_t-1(1-η)+P_ESS,tη_c/S_ESS,P_ESS,t＞0 (12)

Soc_t＝Soc_t-1(1-η)+P_ESS,t/η_dS_ESS,P_ESS,t≤0 (13)

p_q,z≥0 (18)

in formula (5) -formula (18), P_i,tFor the active power output, Q, of the micro-power source i during a time period t_i,tFor reactive power output of the micro-power source i during a time period t, i ∈ S_DG＝{PV,WT,MT,FC,DG}，j∈S_C＝{MT,FC,DG}；P_Load,tIs the active load of time period t; p_ESS,tCharging and discharging the energy storage system for a time t; q_Load,tReactive load for time period t; _iP、 _iQthe upper limit and the lower limit of the active power and the reactive power of the micro power source i are respectively set; r_i、D_iActive and reactive climbing limits respectively; delta t is a scheduling step length; soc_tThe state of charge of the energy storage system at time t;Soc、respectively the upper limit and the lower limit of the charge state of the energy storage system; s_ESSη, η for the capacity of the energy storage system_c、η_dThe self consumption efficiency, the charging efficiency and the discharging efficiency of the energy storage system are respectively; _ESSP、respectively the upper limit and the lower limit of the energy storage system; l% is the standby rate; z, p_q，q∈S_UAuxiliary variables are { PV, WT } respectively; predicting uncertainty; rho is the maximum value of the prediction error;the output power is predicted for the wind and light time periods t, respectively.

The sag factor related technical constraints are respectively:

in the formulae (19) to (22),respectively expectation of k voltage frequency and amplitude of the micro power supply; _PfC、 _QVC、respectively the upper and lower coefficient limits. The rest is defined above.

The enhancement constraints are respectively:

wherein,the power planning system comprises a photovoltaic cell, a wind driven generator, a fuel cell, an AC/DC bus bidirectional converter, an active power plan of a micro gas turbine and a diesel generator, and a reactive power plan of the AC/DC bus bidirectional converter, the micro gas turbine and the diesel generator. The rest is defined above.

As described above, the present invention introduces droop characteristics (as in formulas (1) to (2)) and related art constraints (as in formulas (19) to (22)) on the basis of conventional robust optimization (as in formulas (3) to (18)). In the traditional power grid, various power generation equipment and loads are connected through transmission lines, alternating current is transmitted from a power generation node to a power utilization node through the transmission lines, the micro-grid is provided with an alternating current bus and a direct current bus, and the two buses transmit energy through a bidirectional AC/DC converter, so that constraints (formula (23) -formula (30)) are added to improve a robust optimization scheduling model.

2. In the day scheduling process, an improved robust optimization model (formula (1) -formula (30)) and an uncertain budget adjusting strategy are introduced to carry out micro-grid multi-objective dynamic scheduling; the key is how to predict the "uncertainty prediction". The method collects different historical data to construct a mapping rule, wherein the rule 1 is to collect the historical data of the offset and uncertainty prediction of the current state of the energy storage system and predict the dimensionless quantity of the uncertainty prediction by linear weighting; the method 2 is that the current uncertainty prediction is predicted by a linear weighting method according to the collected historical fluctuation conditions of wind power, photoelectricity and load and historical uncertainty prediction data; rule 3 is: as shown in fig. 3, a black box strategy is adopted, a model of a prediction value of a prediction error of a load, wind power and photovoltaic, a current uncertainty prediction and time, a historical uncertainty prediction, and historical data and a prediction value of the prediction error of the load, the wind power and the photovoltaic is constructed through a neural network, and a mapping rule is completed through historical data training; the predicted uncertainty predictions are numerically passed to an improved robust scheduling model to improve the differential evolution algorithm to solve the power generation plan and droop control voltage amplitude, frequency, active power and reactive power ratings and droop coefficients.

The uncertain budget adjustment strategy is shown in the following formulas (31) and (33):

rule 1:

rule 2:

rule 3:

neural network

In equations (31) to (33), (t) is a period t uncertainty prediction;_min、_maxis not sureUpper and lower limits of qualitative predictions; omega₁、ω₂、…、ω_n… is a forgetting factor; soc_t-1The state of charge of the energy storage system at the time t-1; Socthe upper and lower limits of the state of charge of the energy storage system; (0) an initial value for the uncertainty budget; a is₁、a₂、…、a_n、…，b₁、b₂、…、b_n… are the mapping rule coefficients, respectively; p_L,tP_PV,tP_WT,tThe power of the load, photovoltaic power generation and wind power generation at the moment t respectively; delta P_L,tIs the time interval t load error; delta P_PV,tThe photovoltaic output error is a time period t; delta P_WT,tThe fan output error is a time period t;load error prediction for time period t;predicting photovoltaic output error in a time period t;and predicting the output error of the fan in the time period t.

Rule 1: the closer the state of the energy storage system is to the limit value, the larger the system prediction deviation is, and the more conservative the micro-grid scheduling is; conversely, the more robust the microgrid. The forgetting factor is introduced to eliminate the data saturation phenomenon, and the influence of the historical data is reduced while the influence of the current data is strengthened. The algorithm has the characteristics of high convergence rate and strong tracking capability. Rule 2: the method is characterized in that historical data are adopted for data directly aiming at the fluctuation of load, wind power and photoelectric output, the influence of uncertain factors such as power flow and network loss is avoided, meanwhile, the uncertain budget historical data are weighted and summed, and the convergence rate of the mapping process is enhanced while the tracking capability is ensured. Rule 3: and (3) directly analyzing the relation between uncertainty prediction and wind power, photoelectric and load historical errors and prediction errors in time distribution by adopting a black box strategy. The strategy enhances the nonlinear fitting capability of the mapping rule, has simple learning rule, is convenient for computer realization, and has stronger robustness, memory capability, nonlinear mapping capability and self-learning capability.

3. And solving the rated values and droop coefficients of the amplitude, the frequency, the active power and the reactive power of the power generation plan and the droop control voltage by using an improved differential evolution algorithm. Introducing a chaotic algorithm on the basis of a differential evolution algorithm, and enhancing the global search capability of the algorithm by utilizing chaotic ergodicity; introducing a cloud model, and enhancing the local searching capability of the algorithm by using the distribution characteristics of 'cloud droplets' in the multi-dimensional normal cloud; and grey entropy and grey correlation degree are introduced to evaluate each generation of particles, so that the degree of the particles approaching the expected degree is distinguished, and further processing of an algorithm is facilitated.

Claims

1. The method for the microgrid robust scheduling without the balance nodes with the uncertain budget adjustment is characterized by comprising the following steps of:

2. The unbalanced node-free microgrid robust scheduling method with uncertain budget adjustment as recited in claim 1, wherein: the uncertainty budget adjustment strategy is:

<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&Gamma;</mi> <mi>min</mi> </msub> </mrow> <mrow> <msub> <mi>&Gamma;</mi> <mi>max</mi> </msub> <mo>-</mo> <msub> <mi>&Gamma;</mi> <mi>min</mi> </msub> </mrow> </mfrac> <mo>=</mo> <msub> <mi>&omega;</mi> <mn>1</mn> </msub> <mfrac> <mrow> <mo>|</mo> <msub> <mi>Soc</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <mfrac> <mrow> <mover> <mrow> <mi>S</mi> <mi>o</mi> <mi>c</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>+</mo> <munder> <mrow> <mi>S</mi> <mi>o</mi> <mi>c</mi> </mrow> <mo>&OverBar;</mo> </munder> </mrow> <mn>2</mn> </mfrac> <mo>|</mo> </mrow> <mfrac> <mrow> <mover> <mrow> <mi>S</mi> <mi>o</mi> <mi>c</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>-</mo> <munder> <mrow> <mi>S</mi> <mi>o</mi> <mi>c</mi> </mrow> <mo>&OverBar;</mo> </munder> </mrow> <mn>2</mn> </mfrac> </mfrac> <mo>+</mo> <msub> <mi>&omega;</mi> <mn>2</mn> </msub> <mfrac> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&Gamma;</mi> <mi>min</mi> </msub> </mrow> <mrow> <msub> <mi>&Gamma;</mi> <mi>max</mi> </msub> <mo>-</mo> <msub> <mi>&Gamma;</mi> <mi>min</mi> </msub> </mrow> </mfrac> <mo>+</mo> <mn>...</mn> <mo>+</mo> <msub> <mi>&omega;</mi> <mi>n</mi> </msub> <mfrac> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&Gamma;</mi> <mi>min</mi> </msub> </mrow> <mrow> <msub> <mi>&Gamma;</mi> <mi>max</mi> </msub> <mo>-</mo> <msub> <mi>&Gamma;</mi> <mi>min</mi> </msub> </mrow> </mfrac> <mo>+</mo> <mn>...</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munder> <mi>&Sigma;</mi> <mi>i</mi> </munder> <msub> <mi>&omega;</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced>

<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>a</mi> <mn>1</mn> </msub> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>a</mi> <mn>2</mn> </msub> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> <mo>+</mo> <mn>...</mn> <mo>+</mo> <msub> <mi>a</mi> <mi>n</mi> </msub> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mi>n</mi> </mrow> <mo>)</mo> </mrow> <mo>+</mo> <mn>...</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <msub> <mi>b</mi> <mn>1</mn> </msub> <mrow> <mo>&lsqb;</mo> <mrow> <msub> <mi>P</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>W</mi> <mi>T</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> <mo>&rsqb;</mo> </mrow> <mo>+</mo> <msub> <mi>b</mi> <mn>2</mn> </msub> <mrow> <mo>&lsqb;</mo> <mrow> <msub> <mi>P</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mn>2</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mn>2</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>W</mi> <mi>T</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mn>2</mn> </mrow> </msub> </mrow> <mo>&rsqb;</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <mn>...</mn> <mo>+</mo> <msub> <mi>b</mi> <mi>n</mi> </msub> <mrow> <mo>&lsqb;</mo> <mrow> <msub> <mi>P</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>W</mi> <mi>T</mi> <mo>,</mo> <mi>i</mi> <mo>-</mo> <mi>n</mi> </mrow> </msub> </mrow> <mo>&rsqb;</mo> </mrow> <mo>+</mo> <mn>...</mn> </mrow> </mtd> </mtr> </mtable> </mfenced>

<mrow> <mrow> <mo>&lsqb;</mo> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>&Delta;</mi> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mrow> <mi>L</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>,</mo> <mi>&Delta;</mi> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>,</mo> <mi>&Delta;</mi> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mrow> <mi>W</mi> <mi>T</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> <mo>&rsqb;</mo> </mrow> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>,</mo> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>&Delta;P</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>&Delta;P</mi> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>&Delta;P</mi> <mrow> <mi>W</mi> <mi>T</mi> <mo>,</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mi>&Delta;</mi> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mrow> <mi>L</mi> <mo>,</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mi>&Delta;</mi> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mi>&Delta;</mi> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mrow> <mi>W</mi> <mi>T</mi> <mo>,</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow>1

3. The method for the robust scheduling of the unbalanced node microgrid with the uncertain budget adjustment as recited in claim 1, wherein when the microgrid is in an island operation mode, the microgrid adopts a peer-to-peer control strategy, namely droop control; the dispatching method taking a robust dispatching model containing an uncertainty budget adjustment strategy as a core coordinates and dispatches control parameters of droop control of each inverter while a power generation plan is formulated, so that the micro-grid operation has robustness.