CN110932257A - Micro-grid energy scheduling method - Google Patents

Micro-grid energy scheduling method Download PDF

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CN110932257A
CN110932257A CN201910807120.5A CN201910807120A CN110932257A CN 110932257 A CN110932257 A CN 110932257A CN 201910807120 A CN201910807120 A CN 201910807120A CN 110932257 A CN110932257 A CN 110932257A
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邱革非
余欣蓉
金乐婷
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Kunming University of Science and Technology
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Abstract

The invention provides a microgrid energy scheduling method, which comprises the steps of firstly, constructing a prediction control model objective function, taking the actually measured capacity of an energy storage system at the current moment of a microgrid as an initial value, secondly, taking the output of each gas turbine and the charging and discharging electric quantity of the energy storage system as control quantities, establishing a microgrid prediction model, secondly, optimizing and solving a control variable sequence in a future time period by taking the minimum economic performance of microgrid operation as the objective function under the condition of meeting constraint conditions, secondly, only acting the first control variable sequence on the system to obtain the output of each gas turbine and the capacity of a storage battery at the next moment, and finally, taking the actually measured value at the next moment as the initial value, and optimizing again.

Description

Micro-grid energy scheduling method
Technical Field
The invention relates to the field of power distribution network scheduling, in particular to a micro-grid energy scheduling method.
Background
With the development of the distributed power supply, the permeability of the distributed power supply using wind and light as energy sources in the microgrid is gradual, and although the problem of environmental and energy shortage is relieved to a certain extent, the output instability of a fan and a photovoltaic and the randomness of load cause the microgrid to face the problem of bilateral disturbance, and the difficulty of energy management is increased due to the fact that a large power grid is not used as a backup in an island state.
Disclosure of Invention
Therefore, in order to overcome the above problems, the present invention provides a microgrid energy scheduling method, which includes firstly, constructing a predictive control model objective function, taking the actual measured capacity of an energy storage system at the current moment of a microgrid as an initial value, secondly, taking the output of each gas turbine and the charge and discharge electric quantity of the energy storage system as control quantities, establishing a microgrid predictive model, secondly, optimizing and solving a control variable sequence in a future time period by taking the minimum economic performance of the microgrid as the objective function under the condition of meeting constraint conditions, secondly, only acting the first control variable sequence on the system to calculate the output of each gas turbine and the capacity of a storage battery at the next moment, and lastly, taking the actual measured value at the next moment as the initial value, and optimizing again.
According to the micro-grid energy scheduling method, the micro-grid energy scheduling method comprises the following steps:
step 1: constructing a target function of a predictive control model, and taking the actually measured capacity of the energy storage system of the micro-grid at the current moment as an initial value;
step 2: establishing a micro-grid prediction model by taking the output of each gas turbine and the charge and discharge electric quantity of the energy storage system as control quantities;
and step 3: under the condition of meeting the constraint condition, optimizing and solving a control variable sequence in a future time period by taking the minimum economic performance of micro-grid operation as an objective function;
and 4, step 4: only the first control variable sequence acts on the system, and the output of each gas turbine and the capacity of the storage battery at the next moment are obtained;
and 5: and (3) taking the actual measurement value at the next moment as an initial value, and performing the operation of the step (2).
Preferably, in the step 1, the predicted load information and wind-solar output information are used as input values of the system, and the actual measurement capacity x of the energy storage system at the current moment of the microgrid is used as the actual measurement capacity x of the energy storage systemb.real(k) As an initial value;
in the step 2, the output of each gas turbine and the charge and discharge electric quantity of the energy storage system are used as control quantities, and a microgrid prediction model is established:
xb(k+1|k)＝xb(k|k)+(ηc-1/ηd)zb(k|k)+1/ηd(F′(k|k)u(k|k)+f′(k|k)w(k|k))-xsb；
in the step 3, under the condition that constraint conditions are met, the minimum economic performance of the micro-grid operation is taken as an objective function, and the control variable sequence of the last T time periods is optimized and solved:
uT-1(k|k)＝{u(k|k)，u(k+1|k)，...，u(k+T-1|k)}；
in the step 4, only the first control variable sequence uT-1(k | k) is acted on the system, and the output power and the storage battery capacity of each gas turbine at the moment k +1 are obtained;
in the step 5, the actual measurement value x at the time k +1 is usedb.real(k +1) is an initial value, k is made k +1, and the operation of step 2 is continued.
Preferably, in the step 1, the target function of the translatable load is solved according to the predicted load information and the predicted wind-solar output information, the load curve obtained after load translation is used as wind-solar output information as a system input value, and the actually measured capacity x of the energy storage system at the current moment of the microgrid is used as the system input valueb.real(k) As an initial value;
in the step 2, the output of each gas turbine and the charge and discharge electric quantity of the energy storage system are used as control quantities, and a microgrid prediction model is established:
xb(k+1|k)＝xb(k|k)+(ηc-1/ηd)zb(k|k)+1/ηd(F′(k|k)u(k|k)+f′(k|k)w(k|k))-xsb；
in the step 3, the minimum economic performance of the micro-grid operation is taken as an objective function, and the control variable sequences of the future N time periods are optimized and solved:
uT-1(k|k)＝{u(k|k)，u(k+1|k)，...，u(k+T-1|k)}；
in the step 4, only the first control variable sequence u (k | k) acts on the system, and the output of each gas turbine and the capacity of the storage battery at the moment of k +1 are obtained;
in the step 5, the actual measurement value x at the time k +1 is usedb.real(k +1) is an initial value, k is made k +1, and the operation of step 2 is continued.
Preferably, the method for constructing the objective function of the predictive control model in step 1 includes:
step 1.1: the construction cost function is:
Figure BDA0002183991590000021
where k denotes time, i denotes a gas turbine number, T denotes a prediction period, 2zb(k)-Pb(k) Representing the charge and discharge of the energy storage system, OMi b[2zb(k)-Pb(k)]The charge and discharge cost of the storage battery is expressed, the charge and discharge cost of the storage battery is introduced into the cost function so as to effectively control the charge and discharge times of the storage battery,
Figure BDA0002183991590000031
representing the operating cost, OM, of the gas turbineiPi(k) Represents the maintenance cost of the gas turbine as a function of load;
step 1.2: using auxiliary variable σi(k) Representing the operating cost of the ith gas turbine at time k and integrating all the auxiliary variables into the array z (k), we can obtain:
Si*Pi(k+j)+sj≤σi(k+j)；
Z(k)＝[σ′i(k)SU′(k)SD′(k)zb(k)]′∈R3Ng+2；
where S and S are the slope and intercept of an approximate line, σ (k), SU (k), and SD (k)For column vectors, all σ's are expressed separatelyi(k) And the start-stop cost of each machine, and all decision variables are integrated into an array u (k) by using uT-1(k) Representing the control sequence derived at time k, uT-1(k) If the cost function is expressed as { u (k) }, u (k +1) }.
Figure BDA0002183991590000032
C′u(k)＝[0 0 OMiOMi]；
Cz′＝[1 1 1 1 1 1 2*OMb]；
Wherein, -OMbF′u(k)-OMbf' w (k) is from OMi bPb(k) Obtaining;
step 1.3: using xb(k + j | k) represents the battery capacity at time k + j predicted at time k, where j > 0, and the objective function for the predictive control model is given by:
Figure BDA0002183991590000033
preferably, the method for constructing the objective function of the predictive control model in step 1 includes:
step 1.1: establishing an objective function by taking the minimum difference of the output between the load and the new energy as a target:
Figure BDA0002183991590000034
wherein D ist(k) Representing the load value, P, at point k after load translationres(k) Representing the new energy output at the moment k, and translating the load in preset time mainly aiming at the industrial load in the load translation process;
step 1.2: constructing a cost function:
Figure RE-GDA0002378486080000043
step 1.3: integrating the auxiliary variables into an array Z (k), and integrating the control variables into an array u (k) to obtain an objective function of the predictive control model as follows:
Figure BDA0002183991590000042
wherein j is 0b(k|k)＝xb(k)；
Figure BDA0002183991590000043
u(k)＝[P′(k)δ′(k)]′；
F′＝[1 1 0 0]。
Preferably, the preset time is from 6:00 early to 8:00 late, T-20, j-7, 8,9, … …, 19.
Preferably, the constraint conditions of the objective function of the predictive control model are:
Piminδi(k)≤Pi(k)≤Pimaxδi(k)；
wherein i represents a gas turbine number, PiminAnd PimaxRespectively representing the upper limit and the lower limit of the output of the ith gas turbine;
|Pi(k+1)-Pi(k)|≤Rimax；
wherein Pimax represents the maximum value of the climbing speed of the ith gas turbine;
SUi(k)≥cSUi(k)[δi(k)-δi(k-1)]；
SDi(k)≥cSDi(k)[δi(k-1)-δi(k)]；
SUi(k)≥0；
SDi(k)≥0；
wherein i represents the serial number of the unit, k represents time, cSUi(k) And cSDi(k) Respectively representing the start-stop cost of each unit, wherein the start-stop cost is a fixed value; deltai(k)-δiWhen (k-1) ═ 1, the time whenThe unit start-stop cost at the previous moment;
Figure BDA0002183991590000051
wherein the content of the first and second substances,
Figure BDA0002183991590000052
is a matrix of four coefficients;
xb.min≤xb(k)≤xb.max；
wherein x isb.minAnd xb.maxRespectively representing the upper and lower limits of the capacity of the energy storage system;
Figure BDA0002183991590000053
preferably, the constraint conditions of the objective function of the predictive control model are:
Piminδi(k)≤Pi(k)≤Pimaxδi(k)；
wherein i represents a gas turbine number, PiminAnd PiminRespectively representing the upper limit and the lower limit of the output of the ith gas turbine;
|Pi(k+1)-Pi(k)|≤Rimax；
wherein, PimaxRepresenting the maximum value of the climbing speed of the ith gas turbine;
SUi(k)≥cSUi(k)[δi(k)-δi(k-1)]；
SDi(k)≥cSDi(k)[δi(k-1)-δi(k)]；
SUi(k)≥0；
SDi(k)≥0；
wherein i represents the serial number of the unit, k represents time, cSUi(k) And cSDi(k) Respectively representing the start-stop cost of each unit, wherein the start-stop cost is a fixed value; deltai(k)-δiWhen (k-1) is equal to 1, calculating the start-stop cost of the unit at the current moment;
Figure BDA0002183991590000054
wherein the content of the first and second substances,
Figure BDA0002183991590000055
is a matrix of four coefficients;
xb.min≤xb(k)≤xb.max；
wherein x isb.minAnd xb.maxRespectively representing the upper and lower limits of the capacity of the energy storage system;
Figure BDA0002183991590000056
Figure BDA0002183991590000057
Y(k)≤My。
compared with the prior art, the invention has the following beneficial effects:
(1) the method is based on a microgrid mathematical model, a microgrid objective function is established, programming is carried out through python language, a Gurobi optimization tool is called to solve an actual example, and finally the result obtained by a prediction control method is compared with the optimal control based on the section, so that the superiority of the prediction control is highlighted.
(2) The microgrid energy scheduling method provided by the invention reasonably plans when each generator set should be started or stopped (unit combination), how the load of each unit should be distributed to realize the minimum cost (economic scheduling) and when the energy storage system should be charged or discharged.
(3) The microgrid energy scheduling method provided by the invention is used for controlling loads of a microgrid, the loads are divided into three types, namely uncontrollable loads, translatable loads and controllable loads, the uncontrollable loads are controlled by adopting different control strategies, the uncontrollable loads cannot be adjusted, the load requirements are required to be met at each moment, the translatable loads are mainly controlled aiming at factory loads in a form of scheduling in advance, the controllable loads can be reduced when necessary, and in each time period, a microgrid energy management system needs to reasonably plan when each generator set needs to be started or stopped (unit combination), how the loads of each unit need to be distributed to realize the minimum cost (economic scheduling), when an energy storage system needs to be charged or discharged, and the translation amount and translation time of the translatable loads.
Drawings
FIG. 1 is a flow chart of predictive control calculations according to the present invention.
Detailed Description
The microgrid energy scheduling method of the invention is described in detail below with reference to the accompanying drawings and embodiments.
The microgrid energy scheduling method provided by the invention comprises the following steps:
step 1: constructing a target function of a predictive control model, and taking the actually measured capacity of the energy storage system of the micro-grid at the current moment as an initial value;
step 2: establishing a micro-grid prediction model by taking the output of each gas turbine and the charge and discharge electric quantity of the energy storage system as control quantities;
and step 3: under the condition of meeting the constraint condition, optimizing and solving a control variable sequence in a future time period by taking the minimum economic performance of micro-grid operation as an objective function;
and 4, step 4: only the first control variable sequence acts on the system, and the output of each gas turbine and the capacity of the storage battery at the next moment are obtained;
and 5: and (3) taking the actual measurement value at the next moment as an initial value, and performing the operation of the step (2).
In the embodiment, the microgrid objective function is established based on a microgrid mathematical model, programming is carried out through python language, a Gurobi optimization tool is called to solve the actual examples, and finally the result obtained by the prediction control method is compared with the optimization control based on the section, so that the superiority of the prediction control is highlighted.
The islanding mode means that a PCC (common node control) between a microgrid and a large power grid is disconnected, the microgrid is in an independent operation state, and no energy exchange exists between the microgrid and the large power grid, and the economic optimization of the microgrid considered by the invention in this state means that the sum of the operation costs of each part inside the microgrid is optimal, so when the optimal scheduling of the operation of the microgrid is performed, an optimal scheduling plan needs to be performed on a load besides a generator and an energy storage system on the source side, namely, the operation cost of the generator, the charging and discharging cost of a storage battery and the compensation cost of a user after the load adjustment are minimum while the supply and demand balance is met, and therefore, in each time period, the microgrid energy management system needs to perform reasonable planning on the following problems:
(1) when each generator set should be started or stopped (set combination);
(2) how the load of each unit should be distributed to achieve the minimum cost (economic dispatching);
(3) when the energy storage system should be charged or discharged.
As shown in fig. 1, in step 1, the predicted load information and wind-solar output information are used as input values of the system, and the actual measurement capacity x of the energy storage system at the current moment of the microgrid is used as the actual measurement capacity x of the energy storage systemb，real(k) As an initial value;
in the step 2, the output of each gas turbine and the charge and discharge electric quantity of the energy storage system are used as control quantities, and a microgrid prediction model is established:
xb(k+1|k)＝xb(k|k)+(ηc-1/ηd)zb(k|k)+1/ηd(F′(k|k)u(k|k)+f′(k|k)w(k|k))-xsb；
in the step 3, the minimum economic performance of the micro-grid operation is taken as an objective function, and the control variable sequence of the T time periods in the future is optimally solved:
uT-1(k|k)＝{u(k|k)，u(k+1|k)，...，u(k+T-1|k)}；
in the step 4, only the first control variable sequence uT-1(k | k) acting on the system to determine the output of each gas turbine and the battery capacity at time k + 1;
in the step 5, the actual measurement value x at the time k +1 is usedb.real(k +1) is set as an initial value, k is set to k +1, and the above-described steps are continued2.
In the above embodiment, a rational plan is made as to when each power generating unit should be started or stopped (unit combination), how the load of each unit should be distributed to achieve the minimum cost (economic dispatch), and when the energy storage system should be charged or discharged.
Specifically, in the step 1, the target function of the translatable load is solved according to the predicted load information and the predicted wind-solar output information, the load curve obtained after load translation is used as wind-solar output information and is used as a system input value, and the actually measured capacity x of the energy storage system at the current moment of the microgrid is used as the system input valueb.real(k) As an initial value;
in the step 2, the output of each gas turbine and the charge and discharge electric quantity of the energy storage system are used as control quantities, and a microgrid prediction model is established:
xb(k+1|k)＝xb(k|k)+(ηc-1/ηd)zb(k|k)+1/ηd(F′(k|k)u(k|k)+f′(k|k)w(k|k))-xsb；
in the step 3, the minimum economic performance of the micro-grid operation is taken as an objective function, and the control variable sequences of the future N time periods are optimized and solved:
uT-1(k|k)＝{u(k|k)，u(k+1|k)，...，u(k+T-1|k)}；
in the step 4, only the first control variable sequence u (k | k) acts on the system, and the output of each gas turbine and the capacity of the storage battery at the moment of k +1 are obtained;
in the step 5, the actual measurement value x at the time k +1 is usedb.real(k +1) is an initial value, k is made k +1, and the operation of step 2 is continued.
In the above embodiment, the microgrid load is controlled, the load is divided into three types, namely an uncontrollable load, a translatable load and a controllable load, and the uncontrollable load cannot be adjusted, and the load demand needs to be met at each moment.
Specifically, in order to comprehensively consider the above problems, the operation cost of the generator set, the start-stop cost, the charge-discharge cost of the storage battery, the compensation for the user after the load reduction, and the like need to be introduced into the objective function when the optimization calculation is performed. The comprehensive cost of each part is minimized through reasonable planning, and the economic optimization of the micro-grid is realized. The optimization problem can be described using a method of mixed integer linear programming.
The method for constructing the target function of the predictive control model in the step 1 comprises the following steps:
step 1.1: the construction cost function is:
Figure BDA0002183991590000081
where k denotes time, i denotes a gas turbine number, T denotes a prediction period, 2zb(k)-Pb(k) Representing the charge and discharge of the energy storage system, OMi b[2zb(k)-Pb(k)]The charge and discharge cost of the storage battery is expressed, the charge and discharge cost of the storage battery is introduced into the cost function so as to effectively control the charge and discharge times of the storage battery,
Figure BDA0002183991590000082
representing the operating cost, OM, of the gas turbineiPi(k) Represents the maintenance cost of the gas turbine as a function of load;
step 1.2: using auxiliary variable σi(k) Representing the operating cost of the ith gas turbine at time k and integrating all the auxiliary variables into the array z (k), we can obtain:
Si*Pi(k+j)+sj≤σi(k+j)；
Z(k)＝[σi′(k)SU′(k)SD′(k)zb(k)]′∈R3Ng+2；
wherein S and S are the slope and intercept of the approximate straight line, and σ (k), SU (k) and SD (k) are column vectors respectively representing all σi(k) And the start-stop cost of each machine, and all decision variables are integrated into an array u (k) by using uT-1(k) Representing the control sequence derived at time k, uT-1(k) If the cost function is expressed as { u (k) }, u (k +1) }.
Figure BDA0002183991590000091
Cu′(k)＝[0 0 OMiOMi]；
Cz′＝[1 1 1 1 1 1 2*OMb]；
Wherein, -OMbF′u(k)-OMbf' w (k) is from OMi bPb(k) And (4) obtaining the product.
The control system established based on the single mixed integer linear programming is an open-loop control system, which cannot reasonably adjust some uncertain changes and has great defects, so the mixed integer linear programming is required to be embedded into a Model Predictive Control (MPC) frame, the control can be converted into a closed-loop control system through the rolling optimization characteristic, the control system can make real-time adjustment according to uncertain changes, and the influence of uncertainty on system stability is eliminated.
Step 1.3: the model prediction control is to optimally plan the output of the system for a period of time in the future on the basis of various constraints of the system and actual measurement information of the system through the prediction information of the future weather condition and the load information. According to the actual situation congratulation of the current point of the microgrid and the prediction of information such as new energy output, load demand, electricity price and the like, the optimal planning (generally 24 hours) is made on the operation mode of the microgrid within a period of time in the future, but only a first group of control sequences are acted on the microgrid until the next sampling time point, the problem is solved again through newly acquired data, and through the rolling optimization strategy, the control method is used for solving the disturbance of the systemThe device has strong adaptability. To better represent the model predictive control strategy, x is usedb(k + j | k) represents the battery capacity at time k + j predicted at time k, where j > 0, and the objective function for the predictive control model is given by:
Figure BDA0002183991590000092
at each time k, an initial value x of the battery capacity is specifiedb(k) And a prediction period T, the prediction control (MPC) solving the objective function to obtain a decision variable sequence uT-1(k) And the first item u (k) of the obtained control sequence is acted on the system, and at the moment k +1, the new sampling information x is used againb(k+1|k+1)＝xb(k +1) solving the objective function again, thus forming a closed-loop control system.
Demand Side Management (Demand Side Management) is an energy Management mode provided based on a smart grid, and resources of a Demand Side and a power supply Side are managed and configured at the same time, so that the power grid can obtain the maximum benefit. The management of the power demand side can regard the saved energy as the fifth novel energy besides thermal power, hydropower, nuclear power and renewable energy. The method emphasizes throttling while opening the source, namely, the consumption of energy is reduced by demand side control while developing and utilizing new energy, and the method has important significance for relieving the shortage of power supply, improving the power utilization efficiency and promoting sustainable development.
According to the invention, a control method of demand side management is introduced in the micro-grid energy management, the aim of optimizing the micro-grid operation economy is taken, resources on the demand side and the power supply side are coordinately controlled, and bilateral fluctuation of the micro-grid is eliminated through translation and control of loads, so that the reserve capacity of the micro-grid is effectively reduced, and the economy and stability of the micro-grid operation are further improved. In the invention, a method for demand side management is used for controlling the load of a microgrid, the load is divided into three types of uncontrollable load, translatable load and controllable load, different control strategies are adopted for controlling the load, the uncontrollable load cannot be adjusted, the load demand needs to be met at each moment, the translatable load is mainly controlled by a factory load in a form of scheduling in advance, the controllable load can be reduced if necessary, and a microgrid energy management system needs to reasonably plan the following problems in each time period:
(1) when each genset should start or stop (. genset combination);
(2) how the load of each unit should be distributed to achieve the minimum cost (economic dispatching);
(3) when the energy storage system should be charged or discharged;
(4) the translation amount and the translation time of the translatable load;
(5) when the controllable load is controlled.
According to the problems, firstly, the load capable of translating is scheduled and planned, and because the load capable of translating mainly aims at factory loads, the load capable of translating is translated in a day-ahead scheduling mode, so that a load curve is closer to a wind-light output curve, the influence of new energy disturbance is further eliminated, and the running stability of a micro-grid is improved.
Specifically, the method for constructing the objective function of the predictive control model in step 1 includes:
step 1.1: establishing an objective function by taking the minimum difference of the output between the load and the new energy as a target:
Figure BDA0002183991590000101
wherein D ist(k) Representing the load value, P, at point k after load translationres(k) Representing the new energy output at the moment k, and translating the load in preset time mainly aiming at the industrial load in the load translation process;
step 1.2: constructing a cost function:
Figure RE-GDA0002378486080000121
step 1.3: integrating the auxiliary variables into an array Z (k), and integrating the control variables into an array u (k) to obtain an objective function of the predictive control model as follows:
Figure RE-GDA0002378486080000122
wherein j is 0b(k|k)＝xb(k)；
Figure BDA0002183991590000113
u(k)＝[P′(k)δ′(k)]′；
F′＝[1 1 0 0]。
Specifically, the preset time is from 6:00 early to 8:00 late, T is 20, j is 7,8,9, … …, 19.
Specifically, the constraint conditions of the objective function of the predictive control model are as follows:
Piminδi(k)≤Pi(k)≤Pimaxδi(k)；
wherein i represents a gas turbine number, PiminAnd PimaxRespectively representing the upper limit and the lower limit of the output of the ith gas turbine;
|Pi(k+1)-Pi(k)|≤Rimax；
wherein, PimaxRepresenting the maximum value of the climbing speed of the ith gas turbine;
SUi(k)≥cSUi(k)[δi(k)-δi(k-1)]；
SDi(k)≥cSDi(k)[δi(k-1)-δi(k)]；
SUi(k)≥0；
SDi(k)≥0；
wherein i represents the serial number of the unit, k represents time, sCUi(k) And cSDi(k) Respectively representing the start-stop cost of each unit, wherein the start-stop cost is a fixed value; deltai(k)-δiWhen (k-1) ═ 1, calculation is carried outThe unit start-stop cost at the current moment;
Figure BDA0002183991590000114
wherein the content of the first and second substances,
Figure BDA0002183991590000121
is a matrix of four coefficients;
xb.min≤xb(k)≤xb.max；
wherein x isb.minAnd xb.maxRespectively representing the upper and lower limits of the capacity of the energy storage system;
Figure BDA0002183991590000122
specifically, the constraint conditions of the objective function of the predictive control model are as follows:
Piminδi(k)≤Pi(k)≤Pimaxδi(k)；
wherein i represents a gas turbine number, PiminAnd PiminRespectively representing the upper limit and the lower limit of the output of the ith gas turbine;
|Pi(k+1)-Pi(k)|≤Rimax；
wherein R isimaxRepresenting the maximum value of the climbing speed of the ith gas turbine;
SUi(k)≥cSUi(k)[δi(k)-δi(k-1)]；
SDi(k)≥cSDi(k)[δi(k-1)-δi(k)]；
SUi(k)≥0；
SUi(k)≥0；
wherein i represents the serial number of the unit, k represents time, cSUi(k) And cSDi(k) Respectively representing the start-stop cost of each unit, wherein the start-stop cost is a fixed value; deltai(k)-δiWhen (k-1) is equal to 1, calculating the start-stop cost of the unit at the current moment;
Figure BDA0002183991590000123
wherein the content of the first and second substances,
Figure BDA0002183991590000124
is a matrix of four coefficients;
xb.min≤xb(k)≤xb.max；
wherein x isb.minAnd xb.maxRespectively representing the upper and lower limits of the capacity of the energy storage system;
Figure BDA0002183991590000125
Figure BDA0002183991590000126
Y(k)≤My。
finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (8)

1. A microgrid energy scheduling method is characterized by comprising the following steps:
step 1: constructing a target function of a predictive control model, and taking the actually measured capacity of the energy storage system of the micro-grid at the current moment as an initial value;
step 2: establishing a micro-grid prediction model by taking the output of each gas turbine and the charge and discharge electric quantity of the energy storage system as control quantities;
and step 3: under the condition of meeting the constraint condition, optimizing and solving a control variable sequence in a future time period by taking the minimum economic performance of micro-grid operation as an objective function;
and 4, step 4: only the first control variable sequence acts on the system to calculate the output of each gas turbine and the capacity of the storage battery at the next moment;
and 5: and (3) taking the actual measurement value at the next moment as an initial value, and performing the operation of the step (2).
2. The microgrid energy scheduling method of claim 1, characterized in that in the step 1, the predicted load information and wind-solar output information are used as input values of the system, and the actually measured capacity x of the energy storage system at the current moment of the microgrid is used as the actually measured capacity x of the energy storage systemb.real(k) As an initial value;
in the step 2, the output of each gas turbine and the charge and discharge electric quantity of the energy storage system are used as control quantities, and a microgrid prediction model is established:
xb(k+1|k)＝xb(k|k)+(ηc-1/ηd)zb(k|k)+1/ηd(F′(k|k)u(k|k)+f′(k|k)w(k|k))-x；
in the step 3, the minimum economic performance of the micro-grid operation is taken as an objective function, and the control variable sequence of the future T time intervals is optimized and solved:
uT-1(k|k)＝{u(k|k)，u(k+1|k)，...，u(k+T-1|k)}；
in the step 4, only the first control variable sequence uT-1(k | k) acting on the system, and calculating the output of each gas turbine and the capacity of the storage battery at the moment of k + 1;
in the step 5, the actual measurement value x at the time k +1 is usedb.real(k +1) is an initial value, k is made k +1, and the operation of step 2 is continued.
3. The microgrid energy scheduling method of claim 1, wherein in the step 1, the translatable load objective function is solved according to the predicted load information and wind-solar output information, a load curve obtained after load translation is used as wind-solar output information as a system input value, and the actually measured capacity x of the energy storage system at the current moment of the microgrid is used as the system input valueb.real(k) As an initial value;
in the step 2, the output of each gas turbine and the charge and discharge electric quantity of the energy storage system are used as control quantities, and a microgrid prediction model is established:
xb(k+1|k)＝xb(k|k)+(ηc-1/ηd)zb(k|k)+1/ηd(F′(k|k)u(k|k)+f′(k|k)w(k|k))-xsb；
in the step 3, the minimum economic performance of the micro-grid operation is taken as an objective function, and the control variable sequences of the future N time periods are optimized and solved:
uT-1(k|k)＝{u(k|k)，u(k+1|k)，...，u(k+T-1|k)}；
in the step 4, only the first control variable sequence u (k | k) acts on the system, and the output of each gas turbine and the capacity of the storage battery at the moment of k +1 are obtained;
in the step 5, the actual measurement value x at the time k +1 is usedb.real(k +1) is an initial value, k is made k +1, and the operation of step 2 is continued.
4. The microgrid energy scheduling method according to claim 1 or 2, characterized in that the method for constructing the target function of the predictive control model in step 1 is as follows:
step 1.1: the construction cost function is:
Figure FDA0002183991580000021
where k denotes time, i denotes a gas turbine number, T denotes a prediction period, 2zb(k)-Pb(k) Representing the charge and discharge of the energy storage system, OMi b[2zb(k)-Pb(k)]The charge and discharge cost of the storage battery is expressed, the charge and discharge cost of the storage battery is introduced into the cost function so as to effectively control the charge and discharge times of the storage battery,
Figure FDA0002183991580000022
representing the operating cost, OM, of the gas turbineiPi(k) Represents the maintenance cost of the gas turbine as a function of load;
step 1.2: using auxiliary variable σi(k) Representing the operating cost at time k for the ith gas turbine and integrating all auxiliary variables into the array Z (k), we can obtain:
Si*Pi(k+j)+sj≤σi(k+j)；
Z(k)＝[σ′i(k) SU′(k) SD′(k) zb(k)]′∈R3Ng+2；
wherein S and S are the slope and intercept of the approximate straight line, and σ (k), SU (k) and SD (k) are column vectors respectively representing all σi(k) And the start-stop cost of each machine, and all decision variables are integrated into an array u (k) by using uT-1(k) Indicating the control sequence derived at time k, uT-1(k) If the cost function is expressed as { u (k) }, u (k +1) }.
Figure FDA0002183991580000023
C′u(k)＝[0 0 OMiOMi]；
Cz′＝[1 1 1 1 1 1 2*OMb]；
Wherein, -OMbF′u(k)-OMbf' w (k) is from OMi bPb(k) Obtaining;
step 1.3: using xb(k + j | k) represents the battery capacity at time k + j predicted at time k, where j > 0, and the objective function for the predictive control model is given by:
Figure FDA0002183991580000031
5. the microgrid energy scheduling method according to claim 1 or 3, characterized in that the method for constructing the target function of the predictive control model in the step 1 is as follows:
step 1.1: establishing an objective function by taking the minimum difference of the output between the load and the new energy as a target:
Figure FDA0002183991580000032
wherein D ist(k) Representing the load value, P, at point k after load translationres(k) Representing the new energy output at the moment k, and translating the load in preset time mainly aiming at the industrial load in the load translation process;
step 1.2: constructing a cost function:
Figure FDA0002183991580000033
step 1.3: integrating the auxiliary variables into an array Z (k), and integrating the control variables into an array u (k) to obtain an objective function of the predictive control model as follows:
Figure FDA0002183991580000034
wherein j is 0b(k|k)＝xb(k)；
Figure FDA0002183991580000035
u(k)＝[P′(k) δ′(k)]′；
F′＝[1 1 0 0]。
6. The microgrid energy scheduling method of claim 5, wherein the preset time is 6:00 early to 8:00 late, T-20, j-7, 8,9, … …, 19.
7. The microgrid energy scheduling method of claim 4, characterized in that the constraint conditions of the objective function of the predictive control model are:
Piminδi(k)≤Pi(k)≤Pimaxδi(k)；
wherein i represents a gas turbine number,Piminand PimaxRespectively representing the upper limit and the lower limit of the output of the ith gas turbine;
|Pi(k+1)-Pi(k)|≤Rimax；
wherein R isimaxRepresenting the maximum value of the climbing speed of the ith gas turbine;
SUi(k)≥cSUi(k)[δi(k)-δi(k-1)]；
SDi(k)≥cSDi(k)[δi(k-1)-δi(k)]；
SDi(k)≥0；
SDi(k)≥0；
wherein i represents the serial number of the unit, k represents time, cSUi(k) And cSUi(k) Respectively representing the start-stop cost of each unit, wherein the start-stop cost is a fixed value; deltai(k)-δiWhen (k-1) is equal to 1, calculating the start-stop cost of the unit at the current moment;
Figure FDA0002183991580000041
wherein the content of the first and second substances,
Figure FDA0002183991580000042
is a matrix of four coefficients;
xb.min≤xb(k)≤xb.max；
wherein x isb.minAnd xb.maxRespectively representing the upper and lower limits of the capacity of the energy storage system;
Figure FDA0002183991580000043
8. the microgrid energy scheduling method of claim 5, characterized in that the constraint conditions of the objective function of the predictive control model are:
Piminδi(k)≤pi(k)≤Pimaxδi(k)；
wherein i represents a gas turbine number, PiminAnd PiminRespectively representing the upper limit and the lower limit of the output of the ith gas turbine;
|Pi(k+1)-Pi(k)|≤Rimax；
wherein R isimaxRepresenting the maximum value of the climbing speed of the ith gas turbine;
SUi(k)≥cSUi(k)[δi(k)-δi(k-1)]；
SDi(k)≥cSDi(k)[δi(k-1)-δi(k)]；
SUi(k)≥0；
SDi(k)≥0；
wherein i represents the serial number of the unit, k represents time, cSUi(k) And cSDi(k) Respectively representing the start-stop cost of each unit, wherein the start-stop cost is a fixed value; deltai(k)-δiWhen (k-1) is equal to 1, calculating the start-stop cost of the unit at the current moment;
Figure FDA0002183991580000051
wherein the content of the first and second substances,
Figure FDA0002183991580000052
is a matrix of four coefficients;
xb.min≤xb(k)≤xb.max；
wherein x isb.minAnd xb.maxRespectively representing the upper and lower limits of the capacity of the energy storage system;
Figure FDA0002183991580000053
Figure FDA0002183991580000054
Y(k)≤My。
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