CN116054241B - Robust energy management method for new energy micro-grid group system - Google Patents

Robust energy management method for new energy micro-grid group system Download PDF

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CN116054241B
CN116054241B CN202211596198.5A CN202211596198A CN116054241B CN 116054241 B CN116054241 B CN 116054241B CN 202211596198 A CN202211596198 A CN 202211596198A CN 116054241 B CN116054241 B CN 116054241B
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徐家文
赵卓立
杨庆刚
陈碧云
倪强
孟安波
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Guangdong University of Technology
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Abstract

The invention provides a robust energy management method of a new energy micro-grid group system, which is based on a strategy of dynamic pipe model predictive control, solves the problem of uncertainty of energy scheduling of each micro-grid by multi-time scale cooperation, and provides a distributed power market transaction strategy based on a limited consistency algorithm, so that the problem of energy scheduling among the micro-grids is solved; the invention converts the traditional uncertainty problem into the certainty problem of two different time scales for processing by using the nominal model predictive controller and the auxiliary model predictive controller which are mutually cascaded, thereby greatly reducing the computational complexity; the method for calculating the dynamic safe operation interval of the micro-grid group system is provided in a long-time scale scheduling stage, and a safety margin is provided for the operation of the generator set in a short-time scale stage; in addition, the distributed transaction strategy can also effectively protect privacy of each micro-grid main body, and meanwhile robustness and economy are considered.

Description

Robust energy management method for new energy micro-grid group system
Technical Field
The invention relates to the technical field of micro-grid control, in particular to a robust energy management method for a new energy micro-grid group system.
Background
In recent years, with the large-scale penetration of renewable energy sources such as distributed photovoltaic, wind power and the like in a micro-grid, great challenges are presented to the energy management technology of the micro-grid group. The renewable energy source has inherent intermittent and high uncertainty characteristics, the traditional scheduling strategy optimizes the deterministic scheduling problem according to the idealized energy source prediction data, the obtained scheduling scheme has lower executable performance when the problem of uncertainty is faced, and moreover, in a micro-grid group system with high robustness requirement, the distributed units in each micro-grid cannot have enough capability of responding to uncertain disturbance due to neglecting the scheduling decision made by the uncertainty of the renewable energy source, so that the safe and stable operation of the micro-grid group system is damaged.
In the research of energy management of the micro-grid group at present, a robust optimization method is often used for processing the uncertainty of renewable energy sources, however, because a large amount of auxiliary variables are required to be introduced into constraint or converted into a robust pair equation, great computational complexity is often caused, the robust optimization process is often carried out in a day-ahead scheduling stage, however, because the day-ahead scheduling needs to consider great uncertainty, the obtained scheduling scheme has extremely high conservation, and in the day-ahead scheduling stage, only the day-ahead scheduling program is tracked, so that unnecessary economic loss is caused, and meanwhile, the flexibility of the scheduling program cannot be ensured.
As the power market system reforms and the energy supply structure reforms, the form of the multi-body micro-grid gradually appears. Each micro-grid belongs to different operation subjects, and the energy trading scheme of the adjacent micro-grids cannot be solved by the traditional centralized trading method, so that the distributed trading mechanism becomes a powerful choice of the multi-subject power market scheme of the micro-grid group. Moreover, due to the fact that the communication topology between the micro power grids cannot be guaranteed to be communicated with each other in real time under the influence of regional geographic distance limitation or uncertain natural disasters, robustness of an electric power market mechanism is particularly important when the communication topology of the micro power grids cannot achieve full interconnection or is changed due to irresistible factors.
The prior art discloses a micro-grid energy scheduling method, firstly, a predictive control model objective function is constructed, and the actual measurement capacity of an energy storage system at the current moment of the micro-grid is used as an initial value; secondly, taking the output of each gas turbine and the charge and discharge electric quantity of an energy storage system as control quantity, and establishing a micro-grid prediction model; then, under the condition that constraint conditions are met, optimizing and solving a control variable sequence of a future period by taking the minimum running economy of the micro-grid as an objective function; then, only the first control variable sequence is acted on the system to obtain the output and the battery capacity of each gas turbine at the next moment; finally, taking the actual measured value at the next moment as an initial value, and optimizing again; the method in the prior art is still a traditional model predictive control method, and although the traditional model predictive control strategy has certain robustness, the capability of processing the uncertainty of renewable energy sources on a time section is still lacking, so that the operation safety of the micro-grid group system in the daily stage can not be ensured.
Disclosure of Invention
The invention provides a robust energy management method for a new energy micro-grid group system, which aims to overcome the defect that the robustness and economy cannot be considered when the micro-grid energy is scheduled and the system controller is used for processing the problem of uncertainty in the prior art, so that the computational burden of the problem of processing the uncertainty in the daily scheduling can be effectively reduced, the robustness and the economy can be simultaneously realized, and the method has better economic benefit expression in the micro-grid with high requirement on the robustness.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a robust energy management method for a new energy micro-grid group system comprises the following steps:
s1: establishing a micro-grid group system model, wherein the micro-grid group system model comprises a plurality of micro-grids, and each micro-grid comprises a micro-grid control center and a plurality of distributed generator sets;
s2: each micro-grid control center takes M minutes as a long-time scale execution period, collects long-time scale prediction information of M future long-time scale execution periods, and obtains the latest running state of each distributed generator set;
s3: according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, each micro-grid carries out self transaction identity decision; establishing a transaction strategy based on a finite time consistency algorithm between every two adjacent micro-grid control centers, and performing power transaction according to the transaction identity of each micro-grid control center;
S4: establishing a nominal compact model of the micro-grid group system by using a preset nominal model prediction controller, calculating dynamic safe operation intervals of the micro-grid group system of M long-time scale execution periods in the future, and setting robust compaction constraint;
s5: establishing an optimization objective function of a nominal compact model of a micro-grid group system, obtaining robust scheduling reference plans of the micro-grid group system for M long-time scale execution periods in future by utilizing the optimization objective function of the nominal compact model of the micro-grid group system and robust compaction constraint, and inputting the robust scheduling reference plans of the current long-time scale execution periods into a preset auxiliary model prediction controller;
s6: the auxiliary model prediction controller takes n minutes as a short time scale to execute period to acquire the futureShort time scale correction information for each short time scale execution period;
s7: establishing an actual micro-grid dispatching system model, and setting a tracking optimization objective function of a robust dispatching plan according to the robust dispatching reference plan of the current long-time scale execution period;
s8: by means ofTracking optimization objective function of robust scheduling plan is performed +.>Sub-optimizing, namely obtaining an optimized robust scheduling reference plan in a current long time scale execution period taking n minutes as an interval, and sending the optimized robust scheduling reference plan in the current short time scale execution period to each distributed generator set for execution;
S9: and repeating the steps S2-S8 for M times to finish robust energy scheduling of the micro-grid group system in M long-time scale execution periods in the future.
Preferably, in the step S1, each micro-grid further includes a micro-grid load and an energy storage subsystem;
the microgrid load includes an interruptible load and a critical load;
the distributed generator set comprises a wind generator set, a photovoltaic generator set and a gas turbine generator set;
the micro-grid group system model comprises a renewable energy uncertainty sub-model, a gas turbine sub-model, an energy storage sub-system sub-model and a micro-grid load sub-model;
the renewable energy uncertainty submodel specifically comprises:
wherein , and />Short time scale predicted values of the load of the ith micro-grid at the t-th minute photovoltaic generator set, the wind generator set and the micro-grid are respectively represented by +.> and />Long time scale prediction information of the load of the ith micro grid at the t-th minute photovoltaic generator set, the wind generating set and the micro grid respectively,/for the ith micro grid>Andforecast errors of loads of photovoltaic generator sets, wind generator sets and micro-grids of the ith micro-grid in long time scale and short time scale respectively, < -> and />Respectively representing the maximum value of load prediction errors of the photovoltaic generator set, the wind generator set and the micro-grid;
The gas turbine engine model is specifically as follows:
wherein ,for the power output of the gas turbine, +.>The power adjustment amount for the gas turbine;
the energy storage subsystem sub-model specifically comprises:
b i (t)+z i (t)≤1
wherein ,SoCi Is the state of charge, ζ, of the energy storage subsystem i For the self-discharge rate of the energy storage subsystem, is the charge-discharge efficiency of the energy storage subsystem +.>The charging and discharging power of the energy storage subsystem;
the micro-grid load sub-model specifically comprises the following components:
wherein ,for critical load size, +.>To the size of the interruptible load.
Preferably, in the step S3, the transaction identity decision of each micro-grid is performed according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, and the specific method is as follows:
the transaction identity comprises a buyer and a seller, and each micro-grid determines the own transaction identity according to the following formula:
wherein M is the number of long time scale execution cycles;
for the transaction identity decision value of the ith micro-grid, when +.>And if the transaction identity of the ith micro-grid is the buyer, otherwise, the transaction identity of the ith micro-grid is the seller.
Preferably, in the step S3, a transaction policy is formulated between each adjacent micro-grid control center based on a finite time consistency algorithm, and electric power transaction is performed according to own transaction identity, and the specific method is as follows:
The method comprises the steps that partial electric power market information is transmitted between adjacent micro-grid control centers according to the transaction identity of each micro-grid control center, wherein the partial electric power market information comprises the transaction identity and the transaction willingness quota information of each micro-grid;
the ith buyer micro-grid decides the purchase intention limit b according to the self energy demand i This can be expressed as:
wherein ,for maximum transaction capacity-> and />Respectively the ithThe method comprises the steps that (1) a purchaser micro-grid predicts information on a long time scale of a photovoltaic generator set, a wind generator set and a micro-grid load user at a t-th minute;
determining whether to trade at the t-th minute or not according to the self battery safety state of the seller micro power grid, and selling willingness limit s of the seller micro power grid i This can be expressed as:
wherein, κ is a decision weight coefficient, soC (t) is the charge state of the battery of the seller micro-grid at the t-th minute, soC max For the maximum state of charge of the vendor microgrid battery, and />The method comprises the steps that long-time scale prediction information of a photovoltaic generator set, a wind generator set and a micro-grid load user of an ith seller micro-grid at a t-th minute is obtained respectively;
each micro-grid acquires global consistency transaction credit information through a finite time consistency algorithm, and an iteration equation of the finite time consistency algorithm can be expressed as follows:
Where d represents a trading factor vector, d=s for the buyer microgrid, d=b, w for the seller microgrid ij As the weight matrix element in iteration, lambda is the non-zero independent eigenvalue of the Laplacian matrix corresponding to the communication topology of each micro-grid;
and the control center of the seller micro-grid signs a transaction agreement of m minutes in the future with the control center of the buyer micro-grid according to the global consistency transaction amount information, so as to complete the power transaction between the micro-grids.
Preferably, the transaction agreement of the future m minutes comprises seller micro-grid energy transaction capacity, transaction cost and buyer micro-grid purchase energy amount;
the seller micro-grid energy trading capacity is specifically as follows:
wherein ,trading capacity for the ith vendor microgrid for energy at the t-th minute;
the transaction cost is specifically:
wherein ,Fex (t) is the transaction cost at time t, o is the secondary transaction cost factor, and iota is the primary transaction cost factor;
the purchasing energy amount of the micro-grid of the buyer is distributed according to the proportion of the trading willingness value to the total trading willingness of all the buyers, and the method specifically comprises the following steps:
wherein ,Ns For the seller micro-grid number, N b The number of microgrids for the buyer.
Preferably, in the step S4, the nominal compact model of the micro grid group system is specifically:
Firstly, an actual compact model of a micro-grid group system is established, wherein the actual compact model of the micro-grid group system is specifically as follows:
x(t s +1)＝Ax(t s )+Bu(t s )+Dw(t s )
y(t s )＝Cu(t s )+Ew(t s )
wherein ,for the original constraint of the microgrid group system model represented in aggregate form, < >>Constraint for disturbance variables;
constraint of disturbance variable intoThe nominal compact model of the micro-grid group system is obtained, specifically:
wherein ,is a constraint of a nominally compact model of the microgrid cluster system.
Preferably, in the step S4, after the nominal compact model of the micro grid group system is established by using the preset nominal model prediction controller, the method further includes calculating an error between an actual compact model and a nominal compact model of the micro grid group system, specifically:
the nominal model predictive controller constructs a micro-grid group system error state space equation:
wherein ,is a state error variable, satisfy-> To control the error variable, satisfy For disturbance error variable, satisfy->
The relation between the state error and the control error is simplified by:
wherein K is a feedback correction coefficient, A k ＝A+BK。
Preferably, in the step S4, a dynamic safe operation interval of the micro-grid group system of M long time scale execution periods in the future is calculated, and a robust tightening constraint is set, and the specific method is as follows:
State error disturbance invariant set using micro-grid cluster system modelAcquiring dynamic safe operation intervals of the micro-grid group system of the future M long-time scale execution periods;
constructing a linear programming optimization problem, and calculating a state error disturbance invariant set of a micro-grid group system modelThe optimization problem of the linear programming is specifically as follows:
minc T y
wherein y is an optimization variable and satisfiesc is a coefficient matrix of the optimization variables and satisfies c= [1, …,1,0, …,0] T The method comprises the steps of carrying out a first treatment on the surface of the In the constraint of the optimization problem of the linear planning,e i ∈R n ；
state error disturbance invariant set using micro-grid cluster system modelThe original constraint of the micro-grid group system model is compacted, and the robust compaction constraint is obtained, and the specific method comprises the following steps:
wherein K is a feedback correction coefficient.
Preferably, in the step S5, the optimization objective function of the nominal compact model of the micro grid group system is specifically:
an optimization objective function of a nominal compact model of the micro-grid group system is constructed according to the power generation cost function of each distributed generator set of the micro-grid group system, wherein the power generation cost function of each distributed generator set of the micro-grid group system is specifically as follows:
wherein ,lambda represents the power generation cost of the gas turbine i and />Generating cost coefficients for the gas turbine; />Representing the power generation cost of the energy storage subsystem +.>Representing the cost coefficient of deep charge and discharge, < > for>Representing the operational maintenance costs of the energy storage subsystem; />Compensation costs representing interruptible loads, +.>Is a compensation coefficient;
the optimization objective function of the nominal compact model of the micro-grid group system is specifically as follows:
wherein ,Fi,total Representing a night state of charge recovery penalty term set to ensure that the next day energy storage subsystem has sufficient scheduling space.
Preferably, in the step S7, the tracking optimization objective function of the robust scheduling plan is specifically:
wherein , and />Representing a robust scheduling reference plan over a current short time scale execution period.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides a robust energy management method of a new energy micro-grid group system, which comprises the steps of establishing a micro-grid group system model, collecting long-time scale prediction information of M long-time scale execution periods in the future in a long-time scale prediction stage, and obtaining the latest running state of each distributed generator set; each micro-grid makes a trade identity decision, and then each adjacent micro-grid control center makes a trade strategy based on a finite time consistency algorithm and performs electric power trade according to the trade identity of each micro-grid; establishing a nominal compact model of the micro-grid group system by using a nominal model prediction controller, calculating dynamic safe operation intervals of the micro-grid group system of M long-time scale execution periods in the future, and setting robust tightening constraint; then establishing an optimized objective function of a nominal compact model of the micro-grid group system, obtaining a robust scheduling reference plan of the micro-grid group system for M long-time scale execution periods in the future by combining with robust compaction constraint, and inputting the robust scheduling reference plan of the current long-time scale execution period into a preset auxiliary model prediction controller;
In a short time scale optimization stage, the auxiliary model predictive controller firstly acquires the futureShort time scale correction information for each short time scale execution period; establishing an actual micro-grid dispatching system model, and executing robust dispatching of a period according to the current long time scaleSetting a tracking optimization objective function of the robust scheduling plan by the degree reference plan, and performing +.>Sub-optimizing, namely optimizing a robust scheduling reference plan of a current long-time scale execution period by taking the short-time scale execution period as an interval, and sending the robust scheduling reference plan after optimization in the current short-time scale execution period to each distributed generator set for execution;
finally, repeating all steps of a long time scale prediction stage and a short time scale optimization stage to finish robust energy scheduling of the micro-grid group system in M future long time scale execution periods;
aiming at the uncertainty problem, the method processes the uncertainty problem by using a strategy of dynamic management model predictive control, converts the uncertainty problem into the uncertainty problem in two different time scales by using a nominal model predictive controller and an auxiliary model predictive controller which are cascaded with each other, and greatly reduces the computational complexity by a double time scale coordinated scheduling method;
In addition, the calculation method for capturing the dynamic safe operation interval of the micro-grid group system, which is provided in the long-time scale scheduling stage, can effectively guide the establishment of a reference scheduling plan in a robust range, and simultaneously provide a safety margin for the operation of the generator set in the short-time scale stage so as to ensure that the generator set has enough response capability in the short-time scale stage;
the electric power market trading mechanism based on the finite time consistency algorithm provided by the invention in the long-time scale scheduling stage can still ensure the validity of electric power market trading after the communication topology changes, and further ensure the robustness of the energy management strategy; therefore, the invention has better economic benefit performance in the micro-grid group system with high robustness requirement, and can simultaneously consider robustness and economy; in addition, the distributed micro-grid group transaction method is adopted, so that the privacy of each micro-grid main body can be effectively protected, and the utilization rate of renewable energy sources and the economy of a micro-grid group system are further improved.
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Fig. 1 is a flowchart of a robust energy management method of a new energy micro grid group system provided in embodiment 1.
Fig. 2 is a schematic diagram of a micro grid group system model provided in embodiment 2.
Fig. 3 is a specific flowchart of a robust energy management method of the new energy micro grid group system provided in embodiment 2.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, the invention provides a robust energy management method for a new energy micro-grid group system, which comprises the following steps:
s1: establishing a micro-grid group system model, wherein the micro-grid group system model comprises a plurality of micro-grids, and each micro-grid comprises a micro-grid control center and a plurality of distributed generator sets;
s2: each micro-grid control center takes M minutes as a long-time scale execution period, collects long-time scale prediction information of M future long-time scale execution periods, and obtains the latest running state of each distributed generator set;
S3: according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, each micro-grid carries out self transaction identity decision; establishing a transaction strategy based on a finite time consistency algorithm between every two adjacent micro-grid control centers, and performing power transaction according to the transaction identity of each micro-grid control center;
s4: establishing a nominal compact model of the micro-grid group system by using a preset nominal model prediction controller, calculating dynamic safe operation intervals of the micro-grid group system of M long-time scale execution periods in the future, and setting robust compaction constraint;
s5: establishing an optimization objective function of a nominal compact model of a micro-grid group system, obtaining robust scheduling reference plans of the micro-grid group system for M long-time scale execution periods in future by utilizing the optimization objective function of the nominal compact model of the micro-grid group system and robust compaction constraint, and inputting the robust scheduling reference plans of the current long-time scale execution periods into a preset auxiliary model prediction controller;
s6: the auxiliary model prediction controller takes n minutes as a short time scale to execute period to acquire the futureShort time scale correction information for each short time scale execution period;
S7: establishing an actual micro-grid dispatching system model, and setting a tracking optimization objective function of a robust dispatching plan according to the robust dispatching reference plan of the current long-time scale execution period;
s8: by means ofTracking optimization objective function of robust scheduling plan is performed +.>Sub-optimizing, namely obtaining an optimized robust scheduling reference plan in a current long time scale execution period taking n minutes as an interval, and sending the optimized robust scheduling reference plan in the current short time scale execution period to each distributed generator set for execution;
s9: and repeating the steps S2-S8 for M times to finish robust energy scheduling of the micro-grid group system in M long-time scale execution periods in the future.
In the specific implementation process, firstly, a micro-grid group system model is established, long-time scale prediction information of M future long-time scale execution periods is collected in a long-time scale prediction stage, and the latest running state of each distributed generator set is obtained; each micro-grid makes a trade identity decision, and then each adjacent micro-grid control center makes a trade strategy based on a finite time consistency algorithm and performs electric power trade according to the trade identity of each micro-grid; establishing a nominal compact model of the micro-grid group system by using a nominal model prediction controller, calculating dynamic safe operation intervals of the micro-grid group system of M long-time scale execution periods in the future, and setting robust tightening constraint; then establishing an optimized objective function of a nominal compact model of the micro-grid group system, obtaining a robust scheduling reference plan of the micro-grid group system for M long-time scale execution periods in the future by combining with robust compaction constraint, and inputting the robust scheduling reference plan of the current long-time scale execution period into a preset auxiliary model prediction controller;
In a short time scale optimization stage, the auxiliary model predictive controller firstly acquires the futureShort time scale correction information for each short time scale execution period; an actual micro-grid dispatching system model is established, a tracking optimization objective function of a robust dispatching plan is set according to a robust dispatching reference plan of a current long-time scale execution period, and the tracking optimization objective function of the robust dispatching plan is carried out by using short-time scale correction information>Sub-optimizing, namely optimizing a robust scheduling reference plan of a current long-time scale execution period by taking the short-time scale execution period as an interval, and sending the robust scheduling reference plan after optimization in the current short-time scale execution period to each distributed generator set for execution;
finally, repeating all steps of a long time scale prediction stage and a short time scale optimization stage to finish robust energy scheduling of the micro-grid group system in M future long time scale execution periods;
the method establishes a micro-grid group distributed energy trading scheme based on a finite time consistency theory, and the scheme can solve the problem of energy scheduling among all micro-grids and ensure the robustness of an energy management strategy;
The method has better economic benefit performance in the micro-grid group system with high robustness requirement, and can simultaneously consider robustness and economy; in addition, the distributed micro-grid group transaction method is adopted, and is not a traditional centralized transaction strategy, so that the privacy of each micro-grid main body can be effectively protected, the utilization rate of renewable energy sources is further improved, and the economy of a micro-grid group system is further improved;
the method utilizes multi-time scale coordination to process the problem of uncertainty of energy scheduling of each micro-grid, and proposes a robust energy scheduling strategy based on dynamic pipe model prediction control, wherein the dynamic pipe model prediction control comprises a nominal model prediction controller and an auxiliary model prediction controller which are mutually cascaded, the nominal model prediction controller is executed in a long-time scale prediction stage, and the auxiliary model prediction controller is executed in a short-time scale optimization stage;
the uncertainty problem is converted into the certainty problem in two different time scales through a dynamic pipe model predictive control strategy, so that the calculation complexity is greatly reduced;
in addition, the calculation method for capturing the dynamic safe operation interval of the micro-grid group system, which is provided in the long-time scale scheduling stage, can effectively guide the establishment of a reference scheduling plan in a robust range, and simultaneously provide a safety margin for the operation of the generator set in the short-time scale stage so as to ensure that the generator set has enough response capability in the short-time scale stage.
Example 2
The embodiment provides a robust energy management method for a new energy micro-grid group system, which comprises the following steps:
s1: establishing a micro-grid group system model, wherein the micro-grid group system model comprises a plurality of micro-grids, and each micro-grid comprises a micro-grid control center and a plurality of distributed generator sets;
s2: each micro-grid control center takes M minutes as a long-time scale execution period, collects long-time scale prediction information of M future long-time scale execution periods, and obtains the latest running state of each distributed generator set;
s3: according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, each micro-grid carries out self transaction identity decision; establishing a transaction strategy based on a finite time consistency algorithm between every two adjacent micro-grid control centers, and performing power transaction according to the transaction identity of each micro-grid control center;
s4: establishing a nominal compact model of the micro-grid group system by using a preset nominal model prediction controller, calculating dynamic safe operation intervals of the micro-grid group system of M long-time scale execution periods in the future, and setting robust compaction constraint;
s5: establishing an optimization objective function of a nominal compact model of a micro-grid group system, obtaining robust scheduling reference plans of the micro-grid group system for M long-time scale execution periods in future by utilizing the optimization objective function of the nominal compact model of the micro-grid group system and robust compaction constraint, and inputting the robust scheduling reference plans of the current long-time scale execution periods into a preset auxiliary model prediction controller;
S6: the auxiliary model prediction controller takes n minutes as a short time scale to execute period to acquire the futureShort time scale correction information for each short time scale execution period;
s7: establishing an actual micro-grid dispatching system model, and setting a tracking optimization objective function of a robust dispatching plan according to the robust dispatching reference plan of the current long-time scale execution period;
s8: by means ofRobust adjustment of short time scale correction informationTracking optimization objective function of degree plan>Sub-optimizing, namely obtaining an optimized robust scheduling reference plan in a current long time scale execution period taking n minutes as an interval, and sending the optimized robust scheduling reference plan in the current short time scale execution period to each distributed generator set for execution;
s9: repeating the steps S2-S8 for M times to finish robust energy scheduling of the micro-grid group system in M long-time scale execution periods in the future;
in the step S1, each micro-grid further includes a micro-grid load and an energy storage subsystem;
the microgrid load includes an interruptible load and a critical load;
the distributed generator set comprises a wind generator set, a photovoltaic generator set and a gas turbine generator set;
The micro-grid group system model comprises a renewable energy uncertainty sub-model, a gas turbine sub-model, an energy storage sub-system sub-model and a micro-grid load sub-model;
the renewable energy uncertainty submodel specifically comprises:
wherein , and />Short time scale predicted values of the load of the ith micro-grid at the t-th minute photovoltaic generator set, the wind generator set and the micro-grid are respectively represented by +.> and />Long time scale prediction information of the load of the ith micro grid at the t-th minute photovoltaic generator set, the wind generating set and the micro grid respectively,/for the ith micro grid>Andforecast errors of loads of photovoltaic generator sets, wind generator sets and micro-grids of the ith micro-grid in long time scale and short time scale respectively, < -> and />Respectively representing the maximum value of load prediction errors of the photovoltaic generator set, the wind generator set and the micro-grid;
the gas turbine engine model is specifically as follows:
/>
wherein ,for the power output of the gas turbine, +.>The power adjustment amount for the gas turbine; constraintRepresenting upper and lower limits of the gas turbine output; constraint->An upper limit and a lower limit indicating the power adjustment amount of the gas turbine;
the energy storage subsystem sub-model specifically comprises:
b i (t)+z i (t)≤1
wherein ,SoCi Is the state of charge, ζ, of the energy storage subsystem i For the self-discharge rate of the energy storage subsystem, is the charge-discharge efficiency of the energy storage subsystem +.>The charging and discharging power of the energy storage subsystem; constraint->Representing upper and lower limits of state of charge of the energy storage subsystem; the two constraints in the curly brace represent the upper and lower limits of the charge and discharge of the energy storage subsystem, respectively; constraint b i (t)+z i (t.ltoreq.1) indicates that the energy storage subsystem cannot be simultaneously operatedCharging and discharging;
the micro-grid load sub-model specifically comprises the following components:
wherein ,for critical load size, +.>Is the size of the interruptible load; constraint->Representing a maximum amount of interruption of the interruptible load; constraint->Representing power balance constraints within the microgrid group system;
in the step S3, each micro-grid makes its own transaction identity decision according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, and the specific method is as follows:
the transaction identity comprises a buyer and a seller, and each micro-grid determines the own transaction identity according to the following formula:
wherein M is the number of long time scale execution cycles;
for the transaction identity decision value of the ith micro-grid, when +.>And if the transaction identity of the ith micro-grid is the buyer, otherwise, the transaction identity of the ith micro-grid is the seller.
In the step S3, a transaction policy is formulated between each adjacent micro-grid control center based on a finite time consistency algorithm, and electric power transaction is performed according to own transaction identity, and the specific method is as follows:
the method comprises the steps that partial electric power market information is transmitted between adjacent micro-grid control centers according to the transaction identity of each micro-grid control center, wherein the partial electric power market information comprises the transaction identity and the transaction willingness quota information of each micro-grid;
the ith buyer micro-grid decides the purchase intention limit b according to the self energy demand i This can be expressed as:
wherein ,for maximum transaction capacity-> and />The method comprises the steps that long-time scale prediction information of a photovoltaic generator set, a wind generating set and a micro-grid load user of an ith buyer micro-grid at a t-th minute is obtained;
determining whether to trade at the t-th minute or not according to the self battery safety state of the seller micro power grid, and selling willingness limit s of the seller micro power grid i This can be expressed as:
wherein, κ is a decision weight coefficient, soC (t) is the charge state of the battery of the seller micro-grid at the t-th minute, soC max For the maximum state of charge of the vendor microgrid battery, and />The method comprises the steps that long-time scale prediction information of a photovoltaic generator set, a wind generator set and a micro-grid load user of an ith seller micro-grid at a t-th minute is obtained respectively;
Each micro-grid acquires global consistency transaction credit information through a finite time consistency algorithm, and an iteration equation of the finite time consistency algorithm can be expressed as follows:
where d represents a trading factor vector, d=s for the buyer microgrid, d=b, w for the seller microgrid ij As the weight matrix element in iteration, lambda is the non-zero independent eigenvalue of the Laplacian matrix corresponding to the communication topology of each micro-grid;
the control center of the seller micro-grid signs a transaction agreement of m minutes in the future with the control center of the buyer micro-grid according to the global consistency transaction limit information, and the electric power transaction among the micro-grids is completed;
the transaction agreement of the future m minutes comprises seller micro-grid energy transaction capacity, transaction cost and buyer micro-grid purchased energy quota;
the seller micro-grid energy trading capacity is specifically as follows:
wherein ,trading capacity for the ith vendor microgrid for energy at the t-th minute;
the transaction cost is specifically:
wherein ,Fex (t) is the transaction cost at time t, o is the secondary transaction cost factor, and iota is the primary transaction cost factor;
the purchasing energy amount of the micro-grid of the buyer is distributed according to the proportion of the trading willingness value to the total trading willingness of all the buyers, and the method specifically comprises the following steps:
wherein ,Ns For the seller micro-grid number, N b The number of microgrids for the buyer;
in the step S4, the nominal compact model of the micro grid group system is specifically:
firstly, an actual compact model of a micro-grid group system is established, wherein the actual compact model of the micro-grid group system is specifically as follows:
x(t s +1)＝Ax(t s )+Bu(t s )+Dw(t s )
y(t s )＝Cu(t s )+Ew(t s )
wherein ,for the original constraint of the microgrid group system model represented in aggregate form, < >>Constraint for disturbance variables;
constraint of disturbance variable intoThe nominal compact model of the micro-grid group system is obtained, specifically:
wherein ,constraints that are nominally compact models of the microgrid cluster system;
in the step S4, after establishing the nominal compact model of the micro-grid group system by using the preset nominal model prediction controller, calculating an actual compact model of the micro-grid group system and an error of the nominal compact model is further included, specifically:
the nominal model predictive controller constructs a micro-grid group system error state space equation:
wherein ,is a state error variable, satisfy-> To control the error variable, satisfy For disturbance error variable, satisfy->
The relation between the state error and the control error is simplified by:
wherein K is a feedback correction coefficient, A k ＝A+BK；
The errors of the actual compact model and the nominal compact model of the micro-grid group system are used for calculating dynamic safe operation intervals of the micro-grid group system of M long-time scale execution periods in the future;
In the step S4, a dynamic safe operation interval of the micro-grid group system of M future long-time scale execution periods is calculated, and a robust compaction constraint is set, and the specific method is as follows:
state error disturbance invariant set using micro-grid cluster system modelAcquiring dynamic safe operation intervals of the micro-grid group system of the future M long-time scale execution periods;
constructing a linear programming optimization problem, and calculating a state error disturbance invariant set of a micro-grid group system modelThe optimization problem of the linear programming is specifically as follows:
minc T y
wherein y is an optimization variable and satisfiesc is a coefficient matrix of the optimization variables and satisfies c= [1, …,1,0, …,0] T The method comprises the steps of carrying out a first treatment on the surface of the In the constraint of the optimization problem of the linear planning,e i ∈R n the method comprises the steps of carrying out a first treatment on the surface of the The first in-curly brace constraint represents the constraint of the control variable, and the second in-curly brace constraint represents the disturbance-invariant constraint;
state error disturbance invariant set using micro-grid cluster system modelThe original constraint of the micro-grid group system model is compacted, and the robust compaction constraint is obtained, and the specific method comprises the following steps:
wherein K is a feedback correction coefficient;
in the step S5, the optimization objective function of the nominal compact model of the micro grid group system is specifically:
An optimization objective function of a nominal compact model of the micro-grid group system is constructed according to the power generation cost function of each distributed generator set of the micro-grid group system, wherein the power generation cost function of each distributed generator set of the micro-grid group system is specifically as follows:
wherein ,lambda represents the power generation cost of the gas turbine i and />Generating cost coefficients for the gas turbine; />Representing the power generation cost of the energy storage subsystem +.>Representing the cost coefficient of deep charge and discharge, < > for>Representing the operational maintenance costs of the energy storage subsystem; />Compensation costs representing interruptible loads, +.>Is a compensation coefficient;
the optimization objective function of the nominal compact model of the micro-grid group system is specifically as follows:
wherein ,Fi,total Representing a night state of charge recovery penalty term set to ensure that the next day energy storage subsystem has sufficient scheduling space.
In the step S7, the tracking optimization objective function of the robust scheduling plan specifically includes:
wherein , and />Representing a robust scheduling reference plan over a current short time scale execution period, and />Representing the output power after the controllable unit is regulated in the execution period of the short time scale;
in order to ensure the stability of the electric energy supply of the micro-grid group system in a short time scale execution period, emergency forced load shedding measures are needed when the electric energy is insufficient;
ConstraintIndicating that a power balance constraint of an emergency cut load item is added; constraint->Representing the total load of a short time scale execution cycle cut-out, including the interruptible load amount +.>And emergency load shedding amount->Constraint->Representing the economic loss function caused by the emergency load.
In the specific implementation process, firstly, a micro-grid group system model is established;
as shown in fig. 2, the micro-grid group system model includes a plurality of micro-grids, each including a micro-grid control center, a plurality of distributed generator sets, a micro-grid load and an energy storage subsystem;
the micro-grid load comprises an interruptible load and a critical load, and the micro-grid can give corresponding compensation according to the response degree of the interruptible load;
the distributed generator set comprises a wind generator set, a photovoltaic generator set and a gas turbine generator set;
the photovoltaic generator set and the energy storage subsystem are integrated into a power grid through a DC/AC electric energy conversion device, and the wind generator set is connected into the power grid through the AC/AC electric energy conversion device;
the energy decision-making instruction in the micro-grid is sent to each distributed generator set by the micro-grid control center through a preset communication facility, and each distributed generator set feeds back the latest running state to the control center through an established communication channel;
As shown in fig. 3, the specific flow of the robust energy management method for the new energy micro-grid group system provided in this embodiment is as follows:
first of all for the energy scheduling problem between micro-grids,
in the long-time scale prediction stage, each micro-grid control center takes 30 minutes as a long-time scale execution period, collects long-time scale prediction information of 8 future long-time scale execution periods (4 hours in total), and acquires the latest running state of each distributed generator set; then, entering a preset micro-grid group system scheduling plan pre-optimization module, and acquiring charge and discharge prediction states of the micro-grid energy storage subsystem at 8 moments in the future through pre-optimization operation;
then entering a trading stage of the electric power market of each micro-grid, carrying out trading identity decision of each micro-grid according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, making a trading strategy between each two adjacent micro-grid control centers based on a finite time consistency algorithm, and carrying out electric power trading according to the trading identity of each adjacent micro-grid control center;
determining whether to start an electric power market according to transaction identities of all micro-grids, if the micro-grids of a buyer and a micro-grid of a seller exist, starting the electric power market, determining an energy purchase intention limit by the micro-grid of the buyer according to self energy conditions, determining an energy sales intention limit by the micro-grid of the seller according to self charge states and energy surplus conditions, transmitting private transaction factor condition information with adjacent micro-grids through a finite time consistency algorithm, obtaining global consistency transaction limit information through finite iterations, and signing a future 30-minute transaction protocol by a control center of the micro-grid of the seller according to the global consistency transaction limit information and a control center of the micro-grid of the buyer, and finishing electric power transaction;
The energy scheduling problem between the buyer micro-grid and the seller micro-grid is solved, and the following is a specific process for solving the energy management problem of the micro-grid by utilizing a strategy based on dynamic management model predictive control:
the dynamic pipe model prediction control comprises a nominal model prediction controller and an auxiliary model prediction controller which are mutually cascaded, wherein the nominal model prediction controller is executed in a long-time scale prediction stage, and the auxiliary model prediction controller is executed in a short-time scale optimization stage;
still in a long-time scale prediction stage, a nominal compact model of the micro-grid group system is established by using a nominal model prediction controller, dynamic safe operation intervals of 8 long-time scale execution periods of the micro-grid group system in the future are calculated, and robust tightening constraint is set;
establishing an optimization objective function of a nominal compact model of a micro-grid group system, obtaining a robust scheduling reference plan of 8 long-time scale execution periods of the micro-grid group system in the future by utilizing the optimization objective function of the nominal compact model of the micro-grid group system and robust compaction constraint, and inputting the robust scheduling reference plan of the long-time scale execution period of 30 minutes at present into a preset auxiliary model prediction controller;
Then entering a short time scale optimization stage, and in the short time scale optimization stage, the auxiliary model prediction controller takes 10 minutes as a short time scale execution period to acquire short time scale correction information of 3 short time scale execution periods (30 minutes in total) in the future;
establishing an actual micro-grid dispatching system, setting a tracking optimization objective function of a robust dispatching plan according to the robust dispatching reference plan of the current long-time scale execution period, optimizing the tracking optimization objective function of the robust dispatching plan for 3 times by using 3 short-time scale correction information, acquiring the robust dispatching reference plan optimized within the current 30 minutes taking 10 minutes as an interval, and transmitting the robust dispatching reference plan corrected and optimized within the current 10 minutes to each distributed generator set for execution;
finally, repeating all steps of the long time scale prediction stage and the short time scale correction stage for 8 times to finish energy robust scheduling of the micro-grid group system within 4 hours in the future;
as shown in table 1, it can be seen from table 1 that the method of the present embodiment shows better economical efficiency compared with the conventional model predictive control strategy;
Aiming at the micro-grid 1, the phenomenon of emergency load shedding occurs in the night peak due to the insufficient robustness degree of the traditional model predictive control strategy, so that the economic loss is seriously increased, and the overall dispatching economic cost is larger; for the micro-grid 1 and the micro-grid 2, the method in the embodiment can combine with the prospective idea of model predictive control to better distribute the interruptible load in advance in a long-time scale scheduling stage so as to ensure that other short-time scale adjustable units have enough adjustment allowance for coping with wind-light load uncertainty, while the traditional model predictive control strategy is used for pursuing economic benefit, and the energy is supplied by using a gas turbine system and a battery energy storage system to the maximum at each long-time scale scheduling moment, so that the phenomenon that more users need to respond to load interruption measures can occur in the highest load period, and the economic efficiency can be reduced;
TABLE 1 comparison Table of economic costs under different model predictive control strategies
Aiming at the uncertainty problem, the method in the embodiment applies a strategy of dynamic management model predictive control to process the uncertainty problem, converts the uncertainty problem into a certainty problem in two different time scales to process, and greatly reduces the computational complexity through a double time scale coordinated scheduling method;
In addition, the calculation method for capturing the dynamic safe operation interval of the micro-grid group system, which is provided in the long-time scale scheduling stage, can effectively guide the establishment of a reference scheduling plan in a robust range, and simultaneously provide a safety margin for the operation of the generator set in the short-time scale stage so as to ensure that the generator set has enough response capability in the short-time scale stage;
the electric power market trading mechanism based on the finite time consistency algorithm provided by the method in the long-time scale scheduling stage can still ensure the validity of electric power market trading after the communication topology changes, and further ensure the robustness of the energy management strategy; therefore, the invention has better economic benefit performance in the micro-grid group system with high robustness requirement, and can simultaneously consider robustness and economy; in addition, the distributed micro-grid group transaction method is adopted, instead of the traditional centralized transaction strategy, the privacy of each micro-grid main body can be effectively protected, and the utilization rate of renewable energy sources and the economy of a micro-grid group system are further improved.
The same or similar reference numerals correspond to the same or similar components;
the terms describing the positional relationship in the drawings are merely illustrative, and are not to be construed as limiting the present patent;
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.

Claims (9)

1. The robust energy management method for the new energy micro-grid group system is characterized by comprising the following steps of:
s1: establishing a micro-grid group system model, wherein the micro-grid group system model comprises a plurality of micro-grids, and each micro-grid comprises a micro-grid control center and a plurality of distributed generator sets;
s2: each micro-grid control center takes M minutes as a long-time scale execution period, collects long-time scale prediction information of M future long-time scale execution periods, and obtains the latest running state of each distributed generator set;
s3: according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, each micro-grid carries out self transaction identity decision; establishing a transaction strategy based on a finite time consistency algorithm between every two adjacent micro-grid control centers, and performing power transaction according to the transaction identity of each micro-grid control center;
S4: establishing a nominal compact model of the micro-grid group system by using a preset nominal model prediction controller, calculating dynamic safe operation intervals of the micro-grid group system of M long-time scale execution periods in the future, and setting robust compaction constraint;
the nominal compact model of the micro-grid group system is specifically as follows:
firstly, an actual compact model of a micro-grid group system is established, wherein the actual compact model of the micro-grid group system is specifically as follows:
x(t s +1)＝Ax(t s )+Bu(t s )+Dw(t s )
y(t s )＝Cu(t s )+Ew(t s )
wherein ,for the original constraint of the micro grid group system model expressed in aggregate form,constraint for disturbance variables;
constraint of disturbance variable intoThe nominal compact model of the micro-grid group system is obtained, specifically:
wherein ,constraints that are nominally compact models of the microgrid cluster system;
s5: establishing an optimization objective function of a nominal compact model of a micro-grid group system, obtaining robust scheduling reference plans of the micro-grid group system for M long-time scale execution periods in future by utilizing the optimization objective function of the nominal compact model of the micro-grid group system and robust compaction constraint, and inputting the robust scheduling reference plans of the current long-time scale execution periods into a preset auxiliary model prediction controller;
S6: the auxiliary model prediction controller takes n minutes as a short time scale to execute period to acquire the futureShort time scale correction information for each short time scale execution period;
s7: establishing an actual micro-grid dispatching system model, and setting a tracking optimization objective function of a robust dispatching plan according to the robust dispatching reference plan of the current long-time scale execution period;
s8: by means ofTracking optimization objective function of robust scheduling plan is performed +.>Sub-optimizing, namely obtaining an optimized robust scheduling reference plan in a current long time scale execution period taking n minutes as an interval, and sending the optimized robust scheduling reference plan in the current short time scale execution period to each distributed generator set for execution;
s9: and repeating the steps S2-S8 for M times to finish robust energy scheduling of the micro-grid group system in M long-time scale execution periods in the future.
2. The method for robust energy management of a new energy microgrid cluster system according to claim 1, wherein in said step S1, each microgrid further comprises a microgrid load and an energy storage subsystem;
the microgrid load includes an interruptible load and a critical load;
The distributed generator set comprises a wind generator set, a photovoltaic generator set and a gas turbine generator set;
the micro-grid group system model comprises a renewable energy uncertainty sub-model, a gas turbine sub-model, an energy storage sub-system sub-model and a micro-grid load sub-model;
the renewable energy uncertainty submodel specifically comprises:
wherein , and />Short time scale predicted values of the load of the ith micro-grid at the t-th minute photovoltaic generator set, the wind generator set and the micro-grid are respectively represented by +.> and />Long time scale prediction information of the load of the ith micro grid at the t-th minute photovoltaic generator set, the wind generating set and the micro grid respectively,/for the ith micro grid> and />Forecast errors of loads of photovoltaic generator sets, wind generator sets and micro-grids of the ith micro-grid in long time scale and short time scale respectively, < -> and />Respectively representing the maximum value of load prediction errors of the photovoltaic generator set, the wind generator set and the micro-grid;
the gas turbine engine model is specifically as follows:
wherein ,for the size of the gas turbine output of the ith microgrid at time t, +.>For the size of the gas turbine output of the ith microgrid at time t+1, +.>The power adjustment quantity of the gas turbine at the time t is the ith micro-grid;
The energy storage subsystem sub-model specifically comprises:
b i (t)+z i (t)≤1
wherein ,SoCi Is the state of charge, ζ, of the energy storage subsystem i For the self-discharge rate of the energy storage subsystem, is the charge-discharge efficiency of the energy storage subsystem +.>The charging and discharging power of the energy storage subsystem;
the micro-grid load sub-model specifically comprises the following components:
wherein ,for critical load size, +.>To the size of the interruptible load.
3. The method for managing the robust energy of the new energy micro-grid group system according to claim 2, wherein in the step S3, the transaction identity decision of each micro-grid is performed according to the obtained long-time scale prediction information and the latest running state of each distributed generator set, and the specific method comprises the following steps:
the transaction identity comprises a buyer and a seller, and each micro-grid determines the own transaction identity according to the following formula:
wherein M is the number of long time scale execution cycles;
for the transaction identity decision value of the ith micro-grid, when +.>And if the transaction identity of the ith micro-grid is the buyer, otherwise, the transaction identity of the ith micro-grid is the seller.
4. The robust energy management method of a new energy micro grid group system according to claim 3, wherein in the step S3, a transaction policy is formulated between each adjacent micro grid control center based on a finite time consistency algorithm, and electric power transaction is performed according to own transaction identity, and the specific method comprises the following steps:
The method comprises the steps that partial electric power market information is transmitted between adjacent micro-grid control centers according to the transaction identity of each micro-grid control center, wherein the partial electric power market information comprises the transaction identity and the transaction willingness quota information of each micro-grid;
the ith buyer micro-grid decides the purchase intention limit b according to the self energy demand i This can be expressed as:
wherein ,for maximum transaction capacity-> and />The method comprises the steps that long-time scale prediction information of a photovoltaic generator set, a wind generating set and a micro-grid load user of an ith buyer micro-grid at a t-th minute is obtained;
determining whether to trade at the t-th minute or not according to the self battery safety state of the seller micro power grid, and selling willingness limit s of the seller micro power grid i This can be expressed as:
wherein, κ is a decision weight coefficient, soC (t) is the charge state of the battery of the seller micro-grid at the t-th minute, soC max For the maximum state of charge of the vendor microgrid battery, and />The method comprises the steps that long-time scale prediction information of a photovoltaic generator set, a wind generator set and a micro-grid load user of an ith seller micro-grid at a t-th minute is obtained respectively;
each micro-grid acquires global consistency transaction credit information through a finite time consistency algorithm, and an iteration equation of the finite time consistency algorithm can be expressed as follows:
Where d represents a trading factor vector, d=s for the buyer microgrid, d=b, w for the seller microgrid ij As the weight matrix element in iteration, lambda is the non-zero independent eigenvalue of the Laplacian matrix corresponding to the communication topology of each micro-grid;
and the control center of the seller micro-grid signs a transaction agreement of m minutes in the future with the control center of the buyer micro-grid according to the global consistency transaction amount information, so as to complete the power transaction between the micro-grids.
5. The method for robust energy management of a new energy micro-grid cluster system according to claim 4, wherein the future m minutes trading agreement includes a seller micro-grid energy trading capacity, a trading fee, and a buyer micro-grid purchased energy credit;
the seller micro-grid energy trading capacity is specifically as follows:
wherein ,trading capacity for the ith vendor microgrid for energy at the t-th minute;
the transaction cost is specifically:
wherein ,Fex (t) is the transaction cost at time t, omicron is the secondary transaction cost factor, iota is the primary transaction cost factor;
the purchasing energy amount of the micro-grid of the buyer is distributed according to the proportion of the trading willingness value to the total trading willingness of all the buyers, and the method specifically comprises the following steps:
wherein ,Ns For the seller micro-grid number, N b The number of microgrids for the buyer.
6. The method for robust energy management of a new energy micro grid system according to claim 5, wherein in step S4, after establishing a nominal compact model of the micro grid system by using a preset nominal model prediction controller, further comprises calculating errors of an actual compact model and a nominal compact model of the micro grid system, specifically:
the nominal model predictive controller constructs a micro-grid group system error state space equation:
wherein ,is a state error variable, satisfy-> To control the error variable, satisfy For disturbance error variable, satisfy->
The relation between the state error and the control error is simplified by:
wherein K is a feedback correction coefficient, A k ＝A+BK。
7. The method for robust energy management of a new energy micro grid system according to claim 6, wherein in the step S4, a dynamic safe operation interval of the micro grid system for M long time scale execution periods in the future is calculated, and a robust compaction constraint is set, and the specific method is as follows:
state error disturbance invariant set using micro-grid cluster system modelAcquiring dynamic safe operation intervals of the micro-grid group system of the future M long-time scale execution periods;
Constructing a linear programming optimization problem and calculating a micro-grid group systemModel state error disturbance invariant setThe optimization problem of the linear programming is specifically as follows:
min c T y
wherein y is an optimization variable and satisfiesc is a coefficient matrix of the optimization variables and satisfies c= [1, …,1,0, …,0] T The method comprises the steps of carrying out a first treatment on the surface of the In the constraint of the optimization problem of the linear planning,e i ∈R n ；
state error disturbance invariant set using micro-grid cluster system modelThe original constraint of the micro-grid group system model is compacted, and the robust compaction constraint is obtained, and the specific method comprises the following steps:
wherein K is a feedback correction coefficient.
8. The method for robust energy management of a new energy micro-grid system according to claim 7, wherein in the step S5, the optimization objective function of the nominal compact model of the micro-grid system is specifically:
an optimization objective function of a nominal compact model of the micro-grid group system is constructed according to the power generation cost function of each distributed generator set of the micro-grid group system, wherein the power generation cost function of each distributed generator set of the micro-grid group system is specifically as follows:
wherein ,lambda represents the power generation cost of the gas turbine i and />Generating cost coefficients for the gas turbine; />Representing the power generation cost of the energy storage subsystem +. >Representing the cost coefficient of deep charge and discharge, < > for>Representing the operational maintenance costs of the energy storage subsystem; />Compensation costs representing interruptible loads, +.>Is a compensation coefficient;
the optimization objective function of the nominal compact model of the micro-grid group system is specifically as follows:
wherein ,representing a night state of charge recovery penalty term set to ensure that the next day energy storage subsystem has sufficient scheduling space.
9. The method for managing the robust energy of the new energy micro grid group system according to claim 8, wherein in the step S7, the tracking optimization objective function of the robust scheduling plan is specifically:
wherein , and />Representing a robust scheduling reference plan over a current short time scale execution period.
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CN107565607A (en) * 2017-10-24 2018-01-09 华北电力大学（保定） A kind of micro-capacitance sensor Multiple Time Scales energy dispatching method based on Spot Price mechanism
CN112865174A (en) * 2021-01-07 2021-05-28 福州大学 Micro-energy network multi-time scale optimization control method based on double-layer model prediction control
CN114172180A (en) * 2021-11-25 2022-03-11 国网福建省电力有限公司电力科学研究院 Battery energy storage participating peak regulation double-layer rolling optimization control method considering degradation cost

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Publication number Priority date Publication date Assignee Title
CN107565607A (en) * 2017-10-24 2018-01-09 华北电力大学（保定） A kind of micro-capacitance sensor Multiple Time Scales energy dispatching method based on Spot Price mechanism
CN112865174A (en) * 2021-01-07 2021-05-28 福州大学 Micro-energy network multi-time scale optimization control method based on double-layer model prediction control
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