CN112234658B - A time-domain rolling optimization scheduling method for microgrid based on DDR-MPC - Google Patents

A time-domain rolling optimization scheduling method for microgrid based on DDR-MPC Download PDF

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CN112234658B
CN112234658B CN202011434100.7A CN202011434100A CN112234658B CN 112234658 B CN112234658 B CN 112234658B CN 202011434100 A CN202011434100 A CN 202011434100A CN 112234658 B CN112234658 B CN 112234658B
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孙惠娟
彭春华
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Hefei Wisdom Dragon Machinery Design Co ltd
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East China Jiaotong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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Abstract

本发明涉及微网调度技术领域，具体涉及到一种基于DDR‑MPC的微网时域滚动优化调度方法，包括：在日前调度阶段，基于差异化价格型需求响应，建立以日综合运行成本最低为目标的日前优化调度模型；在日内调度阶段，将差异化需求响应与模型预测控制方法相结合，基于DDR‑MPC建立以滚动时域综合运行成本最小为目标的日内时域滚动优化调度模型；采用时域滚动复合微分进化算法对微网时域滚动优化调度模型进行求解。本发明将DDR与MPC方法结合能显著降低微网综合运行成本，提升了微网优化调度的可靠性；本发明提出的时域滚动复合微分进化算法能对上述模型进行高效求解，可显著提升寻优收敛速度，同时具有良好的动态环境适应性。

Figure 202011434100

The invention relates to the technical field of micro-grid scheduling, in particular to a DDR-MPC-based micro-grid time-domain rolling optimization scheduling method. A day-ahead optimal scheduling model as the target; in the intra-day scheduling stage, the differentiated demand response and model predictive control methods are combined, and an intra-day time-domain rolling optimal scheduling model is established based on DDR‑MPC with the goal of minimizing the comprehensive running cost in the rolling time domain; The time-domain rolling compound differential evolution algorithm is used to solve the microgrid time-domain rolling optimization scheduling model. The invention combines the DDR and the MPC method, which can significantly reduce the comprehensive operation cost of the micro-grid, and improve the reliability of the optimal scheduling of the micro-grid; the time-domain rolling compound differential evolution algorithm proposed by the invention can efficiently solve the above model, and can significantly improve the search It has excellent convergence speed and good adaptability to dynamic environment.

Figure 202011434100

Description

Micro-grid time domain rolling optimization scheduling method based on DDR-MPC
Technical Field
The invention relates to the technical field of microgrid scheduling, in particular to a microgrid time domain rolling optimization scheduling method based on DDR-MPC.
Background
Currently, piconets are rapidly developed due to their flexible regulation capabilities. However, as the permeability of renewable energy sources represented by wind power and photovoltaic is improved, the difficulty of source-load coordination in the micro-grid is increased; uncertainty of renewable energy output often causes deviation of optimal scheduling of the microgrid. Model Predictive Control (MPC) is a finite time domain closed loop optimal control algorithm based on a model, and by using a measured value and a prediction model of a current time period and introducing feedback correction, a prediction error can be corrected in time, so that the optimization control precision can be improved. The MPC method is adopted in the microgrid optimization scheduling, so that the scheduling scheme has strong anti-interference capability and robustness. On the other hand, as demand side management in the microgrid is deepened, demand side response (DR) has become an important means for stabilizing distributed energy output and increasing renewable energy consumption, and therefore, the influence of the demand side response should be fully taken into consideration in optimal scheduling of the microgrid.
Currently, some scholars have developed partial research on DR-considered MPC-based piconet optimization scheduling, such as: in order to solve the problem of microgrid distributed power consumption, a microgrid multi-time scale demand response resource optimization scheduling model based on model predictive control is established in related documents; related documents provide a microgrid spot market operation strategy considering demand response for promoting the economic operation of the microgrid spot market. However, the existing related research does not consider the timeliness of demand response and the influence of different types of DR strategies on MPC-based microgrid optimization scheduling, and also does not relate to a specific DR scheduling strategy, which may cause that demand response resources cannot be fully utilized, so that microgrid scheduling still hardly achieves the expected optimization effect; meanwhile, in the researches, the fact that the response elastic coefficients of different users are different is not considered, the loads are not classified, and the electric quantity variation and the electricity price variation in the considered price type demand response are in a linear relation, so that the price type demand response does not accord with the actual situation, and the characteristics of the actual demand response cannot be reflected.
In summary, in order to deal with the adverse effects of uncertainty factors such as wind power and photovoltaic with high permeability on the optimized scheduling reliability of the microgrid and safe and economic operation of the microgrid, and fully utilize demand response resources to stabilize distributed energy output and increase renewable energy consumption, a microgrid optimized scheduling method with better adaptability is necessary to be provided.
Disclosure of Invention
The invention aims to solve at least one of the technical problems in the prior art and provides a micro-grid time domain rolling optimization scheduling method based on DDR-MPC.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a micro-grid time domain rolling optimization scheduling method based on DDR-MPC comprises the following steps:
step 1, in a day-ahead scheduling stage, a unit and an energy storage battery are considered comprehensively, loads are divided into residential power loads, industrial power loads and commercial power loads, and a day-ahead optimization scheduling model with the lowest day comprehensive operation cost as a target is established for the classified loads on the basis of differentiated price type demand response; wherein, the differentiated price type demand response is marked as DPDR;
step 2, in the in-day scheduling stage, on the basis of a day-ahead optimization scheduling model, combining the differentiated demand response with a model prediction control method, establishing a DDR-MPC-based in-day time domain rolling optimization scheduling model with the aim of minimizing the rolling time domain comprehensive operation cost, and combining the day-ahead optimization scheduling model and the in-day time domain rolling optimization scheduling model to obtain a microgrid time domain rolling optimization scheduling model; the differential demand response is recorded as DDR, and the model prediction control is recorded as MPC;
step 3, solving the micro-grid time domain rolling optimization scheduling model by adopting a time domain rolling composite differential evolution algorithm; wherein, the time domain rolling composite differential evolution is recorded as TDRCDE.
Further, in step 1, an objective function of the day-ahead optimization scheduling model is constructed, where the objective function is:
Figure 681741DEST_PATH_IMAGE001
in the formula:Τthe total number of time segments of the scheduling period is optimized for one day ahead,ttaking a positive integer as a time interval;Gthe number of diesel generator sets;
Figure 753602DEST_PATH_IMAGE002
is composed oftIn the first periodvThe dispatching cost of the diesel generating set is reduced,vtaking a positive integer;
Figure 397073DEST_PATH_IMAGE003
is composed oftTime interval battery scheduling cost;
Figure 528977DEST_PATH_IMAGE004
is composed oftIn the first periodmDPDR scheduling cost of class load;
Figure 523478DEST_PATH_IMAGE005
is composed oftWind/light abandon penalty cost per period;
Figure 195768DEST_PATH_IMAGE006
is composed oftAnd the interaction cost of the microgrid and the external network in the time period.
Further, the mathematical model of each item cost in the objective function of the day-ahead optimization scheduling model is as follows:
wherein, the diesel generating set dispatching cost does:
Figure 224904DEST_PATH_IMAGE007
in the formula:α v 、β v 、λ v is as followsvA diesel set scheduling cost coefficient;
Figure 262130DEST_PATH_IMAGE008
is as followsvA diesel generator set is arranged intActive power output in time intervals;
Figure 743927DEST_PATH_IMAGE009
and
Figure 157590DEST_PATH_IMAGE010
are respectively the firstvThe unit capacity installation cost, capital recovery factor and maximum output power of the diesel generating set;
Figure 41233DEST_PATH_IMAGE011
and
Figure 46098DEST_PATH_IMAGE012
are respectively the firstvRunning pipe of diesel generating setManaging cost coefficient, annual running hours and capacity factor of the unit;
wherein, the battery scheduling cost is:
Figure 26910DEST_PATH_IMAGE013
in the formula:
Figure 978685DEST_PATH_IMAGE014
the unit capacity installation cost, capital recovery factor and capacity factor of the storage battery respectively;
Figure 716834DEST_PATH_IMAGE015
and
Figure 361442DEST_PATH_IMAGE016
the annual running hours and running management cost coefficients of the storage battery are respectively;
Figure 817831DEST_PATH_IMAGE017
the charging and discharging power of the storage battery in the time period t;
wherein, the DPDR scheduling cost is:
Figure 370035DEST_PATH_IMAGE018
in the formula:
Figure 962691DEST_PATH_IMAGE019
respectively the initial electricity price and the initial electricity consumption of the mth type load in the t period;
Figure 778200DEST_PATH_IMAGE020
respectively indicating the electricity price and the electricity quantity of the mth load after the mth load participates in the DPDR at the time t;
wherein, abandoning wind/abandoning light penalty cost is:
Figure 456306DEST_PATH_IMAGE021
in the formula:
Figure 812201DEST_PATH_IMAGE022
punishing cost for unit air volume abandon;
Figure 524942DEST_PATH_IMAGE023
respectively predicting the output power and the consumption of the wind generating set in the time period t in the day ahead;
Figure 511352DEST_PATH_IMAGE024
punishment cost for unit light quantity abandon;
Figure 676755DEST_PATH_IMAGE025
and
Figure 774024DEST_PATH_IMAGE026
respectively predicting the output power and the consumption of the photovoltaic generator set in the period t in the day ahead;
the interaction cost of the micro-grid and the external grid is as follows:
Figure 341271DEST_PATH_IMAGE027
in the formula:
Figure 295321DEST_PATH_IMAGE028
exchanging power for the micro-grid and the external grid at a time t, wherein electricity purchasing is positive and electricity selling is negative;
Figure 948019DEST_PATH_IMAGE029
trading prices for the amount of electricity for the period t.
Further, in step 1, constraints for constructing a day-ahead optimization scheduling model are included, where the constraints include:
and power balance constraint:
Figure 583400DEST_PATH_IMAGE030
in the formula:Gthe number of diesel generator sets;
Figure 270733DEST_PATH_IMAGE031
is as followsvA diesel generator set is arranged intThe active power output of the time period is,ttaking a positive integer as a whole, and taking the integer,vtaking a positive integer;
Figure 395684DEST_PATH_IMAGE032
is a wind generating set at the day beforetPredicted output power for the time period;
Figure 535678DEST_PATH_IMAGE033
the predicted output power of the photovoltaic generator set in the period t before the day;
Figure 974750DEST_PATH_IMAGE034
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
Figure 251010DEST_PATH_IMAGE035
is as followsmClass load is intThe electric quantity after the time interval participates in DPDR;
Figure 750125DEST_PATH_IMAGE036
and
Figure 377415DEST_PATH_IMAGE037
are respectively a storage batterytCharging power and discharging power of a time period;
the upper and lower limits of the power of the diesel generator set are restricted:
Figure 416915DEST_PATH_IMAGE038
in the formula:
Figure 78841DEST_PATH_IMAGE039
are respectively the firstvMinimum and maximum output power of the diesel generating set;
and (3) climbing restraint of the diesel generator set:
Figure 748856DEST_PATH_IMAGE040
in the formula:
Figure 129022DEST_PATH_IMAGE041
are respectively the firstvThe ascending and descending climbing rates of the diesel generating set;
Figure 909896DEST_PATH_IMAGE042
is a scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
Figure 426328DEST_PATH_IMAGE043
in the formula:
Figure 267245DEST_PATH_IMAGE044
is a storage batterytA state of charge of the session;
Figure 400286DEST_PATH_IMAGE045
and
Figure 984852DEST_PATH_IMAGE046
are respectively a storage batterytCharging power and discharging power of a time period;
Figure 355790DEST_PATH_IMAGE047
and
Figure 102029DEST_PATH_IMAGE048
the maximum charging power and the maximum discharging power of the storage battery are respectively;
Figure 191208DEST_PATH_IMAGE049
and
Figure 110622DEST_PATH_IMAGE050
respectively charge effect of accumulatorRate and discharge efficiency;
Figure 336067DEST_PATH_IMAGE051
the energy storage capacity of the storage battery;
Figure 315525DEST_PATH_IMAGE052
and
Figure 360841DEST_PATH_IMAGE053
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
Figure 287209DEST_PATH_IMAGE054
in the formula:
Figure 429477DEST_PATH_IMAGE055
respectively, the minimum and maximum values of the interaction power.
Further, in step 2, the method specifically includes:
step 2.1, establishing a prediction model based on a model prediction control method;
step 2.2, establishing a rolling optimization scheduling model;
and 2.3, introducing a feedback correction link, correcting the output of the micro-grid time domain rolling optimization scheduling model by using an actual measured value, and taking the actual measured value of the micro-grid time domain rolling optimization scheduling model as an initial value of a new round of rolling optimization to form closed-loop optimization control.
Further, the prediction model is:
Figure 783098DEST_PATH_IMAGE056
in the formula:
Figure 315711DEST_PATH_IMAGE057
is a controllable unituIn thattInitial force output value of time period, fromThe result of the open-loop scheduling in the day ahead,utaking a positive integer as a whole, and taking the integer,ttaking a positive integer;
Figure 45769DEST_PATH_IMAGE058
is composed oftTime period prediction future
Figure 245806DEST_PATH_IMAGE059
Time-interval controllable unituIncremental output of (d);
Figure 567066DEST_PATH_IMAGE060
is composed oftTime period prediction future
Figure 852554DEST_PATH_IMAGE061
Time interval controllable unituThe output value of (d);Nto optimize the number of time-domain time segments;
Figure 386304DEST_PATH_IMAGE062
and the climbing power value to be met by the output of each controllable unit in the adjacent prediction time period is obtained.
Further, in step 2.2, an objective function of the rolling optimization scheduling model is constructed, where the objective function is:
Figure 175268DEST_PATH_IMAGE063
in the formula:
Figure 870692DEST_PATH_IMAGE064
is the current time period;Nto optimize the number of time-domain time segments;Gthe number of diesel generator sets;
Figure 643476DEST_PATH_IMAGE065
is composed oftIn the first periodvThe dispatching cost of the diesel generating set is reduced,ttaking a positive integer as a whole, and taking the integer,vtaking a positive integer;
Figure 523793DEST_PATH_IMAGE066
is composed oftTime interval battery scheduling cost;
Figure 432843DEST_PATH_IMAGE067
is composed oftInteraction cost of the micro-grid and the external network in a time period;
Figure 564747DEST_PATH_IMAGE068
and
Figure 559248DEST_PATH_IMAGE069
are respectively the first daymClass load is intA DPDR scheduling cost and an IDR scheduling cost of a period;
Figure 169221DEST_PATH_IMAGE070
is composed oftThe air volume is abandoned in a time interval,
Figure 729515DEST_PATH_IMAGE071
，
Figure 766742DEST_PATH_IMAGE072
and
Figure 248539DEST_PATH_IMAGE073
are respectively a wind generating set in the daytThe output power and actual consumption of the time period;
Figure 458940DEST_PATH_IMAGE074
in order to discard the amount of light in the day,
Figure 342582DEST_PATH_IMAGE075
，
Figure 816289DEST_PATH_IMAGE076
and
Figure 519803DEST_PATH_IMAGE077
are respectively a solar photovoltaic unittThe output power and actual consumption of the time period;
Figure 737158DEST_PATH_IMAGE078
punishment cost is given to unit air volume abandonment,
Figure 475306DEST_PATH_IMAGE079
punishment cost for unit light quantity abandon;
the DPDR scheduling cost is as follows:
Figure 119914DEST_PATH_IMAGE080
in the formula:
Figure 373041DEST_PATH_IMAGE081
are respectively the firstmClass load is intElectricity price and electricity quantity after the time slot participates in DPDR;
Figure 128508DEST_PATH_IMAGE082
are respectively the firstmClass load is intThe electricity price and the load value after the time interval is regulated before the day and then participates in the DPDR;
the IDR scheduling cost is as follows:
Figure 986742DEST_PATH_IMAGE083
in the formula:
Figure 536672DEST_PATH_IMAGE084
is as followsmClass load is intThe actual load reduction of the time interval;
Figure 214778DEST_PATH_IMAGE085
is as followsmClass I of loadkStep quotation corresponding to the reduction of the grade load;
Figure 773936DEST_PATH_IMAGE086
is as followsmClass I of loadkStep-reducing load interval;Kis prepared by reacting with
Figure 486677DEST_PATH_IMAGE087
Corresponding step quote rating.
Further, in step 2.2, constraints for constructing the rolling optimization scheduling model are included, where the constraints are:
power balance constraint
Figure 269825DEST_PATH_IMAGE088
In the formula:Gthe number of diesel generator sets;
Figure 435227DEST_PATH_IMAGE089
is as followsvA diesel generator set is arranged intThe active power output of the time period is,ttaking a positive integer as a whole, and taking the integer,vtaking a positive integer;
Figure 532496DEST_PATH_IMAGE090
is a wind generating set in the suntAn output power of the time period;
Figure 365323DEST_PATH_IMAGE091
is a photovoltaic unit in the suntAn output power of the time period;
Figure 522635DEST_PATH_IMAGE092
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
Figure 909754DEST_PATH_IMAGE093
is as followsmClass load is intThe load value after the time interval is regulated day before and then participates in the DPDR;
Figure 810714DEST_PATH_IMAGE094
and
Figure 498047DEST_PATH_IMAGE095
are respectively a storage batterytCharging power and discharging power of a time period;
Figure 826260DEST_PATH_IMAGE096
for the usermIn thattThe actual load reduction of the time interval;
upper and lower IDR bound
Figure 966254DEST_PATH_IMAGE097
In the formula:
Figure 202064DEST_PATH_IMAGE098
are respectively the firstmThe minimum value and the maximum value of the reduction amount of the class load;
the upper and lower limits of the power of the diesel generator set are restricted:
Figure 743903DEST_PATH_IMAGE099
in the formula:
Figure 977439DEST_PATH_IMAGE100
the minimum output power and the maximum output power of the v-th diesel generator set are respectively;
and (3) climbing restraint of the diesel generator set:
Figure 604729DEST_PATH_IMAGE101
in the formula:
Figure 847492DEST_PATH_IMAGE102
are respectively the firstvThe ascending and descending climbing rates of the diesel generating set;
Figure 243838DEST_PATH_IMAGE103
is a scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
Figure 710591DEST_PATH_IMAGE104
in the formula:
Figure 825178DEST_PATH_IMAGE105
is a storage batterytTime periodThe state of charge of;
Figure 871631DEST_PATH_IMAGE106
and
Figure 122484DEST_PATH_IMAGE107
are respectively a storage batterytCharging power and discharging power of a time period;
Figure 697822DEST_PATH_IMAGE108
and
Figure 299704DEST_PATH_IMAGE109
the maximum charging power and the maximum discharging power of the storage battery are respectively;
Figure 884269DEST_PATH_IMAGE110
and
Figure 255208DEST_PATH_IMAGE111
respectively charge efficiency and discharge efficiency of the storage battery;
Figure 63764DEST_PATH_IMAGE112
the energy storage capacity of the storage battery;
Figure 152943DEST_PATH_IMAGE113
and
Figure 275620DEST_PATH_IMAGE114
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
Figure 501064DEST_PATH_IMAGE115
in the formula:
Figure 683784DEST_PATH_IMAGE116
respectively, the minimum and maximum values of the interaction power.
Further, in step 3, a rolling optimization strategy is introduced into a composite differential evolution CDE algorithm to construct a TDRCDE algorithm, wherein the composite differential evolution is recorded as CDE, and specifically, the dynamic optimization process of the TDRCDE algorithm is as follows:
step 3.1, set the population size to
Figure 994680DEST_PATH_IMAGE117
Optimizing the number of time-domain time segments toNThe maximum number of iterations is
Figure 921047DEST_PATH_IMAGE118
The total time period isTInitialization oftTime interval population
Figure 266578DEST_PATH_IMAGE119
Wherein
Figure 416937DEST_PATH_IMAGE120
Representing the th in the population at time t
Figure 226847DEST_PATH_IMAGE121
(ii) individuals;
Figure 956906DEST_PATH_IMAGE122
the total number of the controllable unitsUAnd optimizing the number of time-domain time-segmentsNBy usingU×NThe individual elements comprise the output of a diesel generator set, the charge and discharge amount of a storage battery and the load reduction amount;
step 3.2, in the optimization stagetThe time interval takes the actual output value of each currently known controllable unit as an initial value based on the futuretTot+The latest wind and light output prediction data of 4 time periods are used for optimally designing the output force value of each controllable unit of 4 future time periods obtained by day-ahead optimal scheduling
Figure 891364DEST_PATH_IMAGE123
As a reference value;
step 3.3, solving the output force values of all the controllable units in the dispatching time domain in 4 future time periods by adopting a CDE algorithm with the lowest comprehensive operation cost of the micro-grid rolling time domain as a targetOptimal rolling optimization adjustment amount
Figure 415886DEST_PATH_IMAGE125
(ii) a In the CDE operation, the population individuals are sorted according to the principle that the lower the comprehensive operation cost is, the higher the fitness is, the population is divided into a dominant population and a subordinate population according to the division ratio of 1:1, the dominant and subordinate populations are respectively subjected to variation by using two different strategies of random variation and optimal solution variation to give consideration to the convergence speed and the individual diversity, wherein a cross probability factor and a variation scale factor can be respectively set to be 0.85 and 0.5;
step 3.4, carrying out merging recombination on the good and bad clusters to obtain new clusters, and continuing iteration on the clustersg=g+1, ifg<
Figure 701374DEST_PATH_IMAGE126
When the condition is satisfied, the step returns to the step 3.3g=
Figure 969544DEST_PATH_IMAGE126
Then, optimizing to obtain optimal population
Figure 24088DEST_PATH_IMAGE127
Outputting the optimal value of the current iteration population
Figure 781828DEST_PATH_IMAGE128
(ii) a And will predict the future made by model scheduling4Sequence of optimal force output values of each controllable unit in each time period
Figure 554612DEST_PATH_IMAGE129
For updating the sequence of optimally planned force values for each controllable unit
Figure 360894DEST_PATH_IMAGE130
；
Step 3.5, dispatching time domain to continue advancingt=t+1 if the condition is satisfiedt=TWhen the optimal value is obtained, the sorting of the optimized and combined population is stopped; if the condition is not satisfied, go to step 3.1, whereOptimal population for a rolling optimization process
Figure 535524DEST_PATH_IMAGE131
To construct an initialization population
Figure 136269DEST_PATH_IMAGE132
And then, obtaining a new generation of population through the variation crossover operation of differential evolution, and repeating the steps until the conditions are met to obtain the optimal value of the comprehensive operation cost of the micro-grid rolling time domain.
As can be seen from the above description of the present invention, compared with the prior art, the microgrid time domain rolling optimization scheduling method based on differential demand response model predictive control according to the present invention at least includes one of the following beneficial effects:
1. due to the combination of the DDR method and the MPC method, the comprehensive operation cost of MPC-based microgrid rolling optimization scheduling can be remarkably reduced, and the wind and light absorption capability of a microgrid system is improved;
2. by providing a micro-grid multi-time scale scheduling strategy based on a DPDR-intra-day DDR-MPC considering demand response timeliness and difference, demand response resources can be fully utilized, output fluctuation of distributed power supplies of the micro-grid is reduced, scheduling errors caused by wind and light prediction errors are reduced, and reliability of optimal scheduling of the micro-grid is improved;
3. the proposed time domain rolling composite differential evolution algorithm can efficiently solve the model, can remarkably improve the optimizing convergence speed and has good dynamic environment adaptability;
4. when the microgrid is in an island operation mode, due to the fact that the microgrid lacks power support provided by an external network and is influenced by self capacity limit and uncertainty of output of renewable energy sources, the reliability of optimized scheduling is relatively poor, and the method provided by the invention can effectively improve the reliability of optimized scheduling of the island microgrid.
Drawings
FIG. 1 is a flowchart illustrating steps of a DDR-MPC based microgrid time domain rolling optimization scheduling method in a preferred embodiment of the present invention;
FIG. 2 is a flow chart of the steps of the TDRCDE algorithm in the preferred embodiment of the present invention;
fig. 3 is a block diagram of a micro-grid time domain rolling optimization scheduling model in a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments.
Referring to fig. 1 to 3, in a preferred embodiment of the present invention, a method for scheduling micro-grid time domain rolling optimization based on DDR-MPC includes the following steps:
step 1, in a day-ahead scheduling stage, a unit and an energy storage battery are considered comprehensively, loads are divided into residential power loads, industrial power loads and commercial power loads, and a day-ahead optimization scheduling model with the lowest day comprehensive operation cost as a target is established for the classified loads on the basis of differentiated price type demand response; wherein, Differential Price Demand Response (DPDR) is recorded as DPDR;
step 2, in the in-day scheduling stage, on the basis of a day-ahead optimization scheduling model, combining the differentiated demand response with a model prediction control method, establishing a DDR-MPC-based in-day time domain rolling optimization scheduling model with the aim of minimizing the rolling time domain comprehensive operation cost, and combining the day-ahead optimization scheduling model and the in-day time domain rolling optimization scheduling model to obtain a microgrid time domain rolling optimization scheduling model; wherein, differential demand response (differentiated demand response) is recorded as DDR, and model predictive control (model predictive control) is recorded as MPC;
step 3, solving the micro-grid time domain rolling optimization scheduling model by adopting a time domain rolling composite differential evolution algorithm; wherein, the time domain rolling composite differential evolution is recorded as TDRCDE.
Specifically, a day-ahead optimization scheduling model taking DPDR into consideration is constructed by considering the charge-discharge process of a storage battery based on a day wind/light predicted output and a day load predicted value in the day ahead and taking the lowest comprehensive operation cost of 24h in the future as an optimization target; on the dayAnd the inner stage is based on a day-ahead optimization scheduling scheme, a day-ahead time domain rolling optimization scheduling model based on the DDR-MPC is constructed by taking the lowest rolling time domain comprehensive operation cost as an optimization target, and the day-ahead scheduling scheme is corrected in time to reduce the scheduling deviation as much as possible. That is, the output and the actual output value of the unit are predicted in real time according to the wind and the light in the day, and the future is contained in every 1 time intervalNPerforming optimized scheduling increment control on the output of the diesel engine set and the charging and discharging of the storage battery at each time interval in the time domain of each time interval, and executing the obtained increment optimization result of the 1 st time interval of the rolling time domain as a real-time control instruction; meanwhile, a real-time feedback correction link is added, and the predicted output of the model is corrected through the actual measurement value to form closed-loop control. A block diagram of the micro-grid time domain rolling optimization scheduling model based on the day-ahead DPDR-intra-day DDR-MPC is shown in fig. 3.
The invention discloses a microgrid time domain rolling optimization scheduling method based on differential demand response model predictive control, which comprises the following steps: by combining the DDR method and the MPC method, the comprehensive operation cost of MPC-based microgrid rolling optimization scheduling can be obviously reduced, and the wind and light absorption capability of a microgrid system is improved; by providing a micro-grid multi-time scale scheduling strategy based on a DPDR-intra-day DDR-MPC considering demand response timeliness and difference, demand response resources can be fully utilized, output fluctuation of distributed power supplies of the micro-grid is reduced, scheduling errors caused by wind and light prediction errors are reduced, and reliability of optimal scheduling of the micro-grid is improved; the proposed time domain rolling composite differential evolution algorithm can efficiently solve the model, can remarkably improve the optimizing convergence speed and has good dynamic environment adaptability; when the microgrid is in an island operation mode, due to the fact that the microgrid lacks power support provided by an external network and is influenced by self capacity limit and uncertainty of output of renewable energy sources, the reliability of optimized scheduling is relatively poor, and the method provided by the invention can effectively improve the reliability of optimized scheduling of the island microgrid.
As a preferred embodiment of the present invention, it may also have the following additional technical features:
in this embodiment, in step 1, an objective function of the day-ahead optimized scheduling model is constructed, where the objective function is:
Figure 396349DEST_PATH_IMAGE133
in the formula:Τthe total number of time segments of the scheduling period is optimized for one day ahead,ttaking a positive integer as a time interval;Gthe number of diesel generator sets;
Figure 6322DEST_PATH_IMAGE134
is composed oftIn the first periodvThe dispatching cost of the diesel generating set is reduced,vtaking a positive integer;
Figure 35458DEST_PATH_IMAGE135
is composed oftTime interval battery scheduling cost;
Figure 338263DEST_PATH_IMAGE136
is composed oftIn the first periodmDPDR scheduling cost of class load;
Figure 616798DEST_PATH_IMAGE137
is composed oftWind/light abandon penalty cost per period;
Figure 30462DEST_PATH_IMAGE138
is composed oftAnd the interaction cost of the microgrid and the external network in the time period.
In the DPDR scheduling before the day, wind curtailment/light curtailment punishment factors and an energy storage charging and discharging process are considered. The scheduling day integrated operation cost considers the DPDR cost and the wind/light abandon penalty cost.
In this embodiment, the mathematical model of each item cost in the objective function of the day-ahead optimization scheduling model is as follows:
wherein, the diesel generating set dispatching cost does:
Figure 914104DEST_PATH_IMAGE139
in the formula:α v 、β v 、λ v is as followsvA diesel set scheduling cost coefficient;
Figure 387811DEST_PATH_IMAGE140
is as followsvA diesel generator set is arranged intActive power output in time intervals;
Figure 91325DEST_PATH_IMAGE141
and
Figure 43100DEST_PATH_IMAGE142
are respectively the firstvThe unit capacity installation cost, capital recovery factor and maximum output power of the diesel generating set;
Figure 46828DEST_PATH_IMAGE143
and
Figure 691436DEST_PATH_IMAGE144
are respectively the firstvThe running management cost coefficient, annual running hours and the capacity factor of the diesel generator set;
wherein, the battery scheduling cost is:
Figure 944563DEST_PATH_IMAGE145
in the formula:
Figure 700029DEST_PATH_IMAGE146
the unit capacity installation cost, capital recovery factor and capacity factor of the storage battery respectively;
Figure 292685DEST_PATH_IMAGE147
and
Figure 108194DEST_PATH_IMAGE148
the annual running hours and running management cost coefficients of the storage battery are respectively;
Figure 51879DEST_PATH_IMAGE149
for the accumulator at tCharging and discharging power of a time period;
wherein, the DPDR scheduling cost is:
Figure 345457DEST_PATH_IMAGE150
in the formula:
Figure 58198DEST_PATH_IMAGE151
respectively the initial electricity price and the initial electricity consumption of the mth type load in the t period;
Figure 779030DEST_PATH_IMAGE152
respectively indicating the electricity price and the electricity quantity of the mth load after the mth load participates in the DPDR at the time t;
wherein, abandoning wind/abandoning light penalty cost is:
Figure 210011DEST_PATH_IMAGE153
in the formula:
Figure 104018DEST_PATH_IMAGE154
punishing cost for unit air volume abandon;
Figure 936845DEST_PATH_IMAGE155
respectively predicting the output power and the consumption of the wind generating set in the time period t in the day ahead;
Figure 828577DEST_PATH_IMAGE156
punishment cost for unit light quantity abandon;
Figure 746855DEST_PATH_IMAGE157
and
Figure 382235DEST_PATH_IMAGE158
respectively predicting the output power and the consumption of the photovoltaic generator set in the period t in the day ahead;
the interaction cost of the micro-grid and the external grid is as follows:
Figure 803989DEST_PATH_IMAGE159
in the formula:
Figure 132203DEST_PATH_IMAGE160
exchanging power for the micro-grid and the external grid at a time t, wherein electricity purchasing is positive and electricity selling is negative;
Figure 272197DEST_PATH_IMAGE161
trading prices for the amount of electricity for the period t.
In this embodiment, in step 1, constraints for constructing the day-ahead optimized scheduling model are included, where the constraints include:
and power balance constraint:
Figure 976848DEST_PATH_IMAGE162
in the formula:Gthe number of diesel generator sets;
Figure 315425DEST_PATH_IMAGE163
is as followsvA diesel generator set is arranged intThe active power output of the time period is,ttaking a positive integer as a whole, and taking the integer,vtaking a positive integer;
Figure 814540DEST_PATH_IMAGE164
is a wind generating set at the day beforetPredicted output power for the time period;
Figure 441830DEST_PATH_IMAGE165
the predicted output power of the photovoltaic generator set in the period t before the day;
Figure 684592DEST_PATH_IMAGE166
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
Figure 815360DEST_PATH_IMAGE167
is as followsmClass load is intThe electric quantity after the time interval participates in DPDR;
Figure 485375DEST_PATH_IMAGE168
and
Figure 599962DEST_PATH_IMAGE169
are respectively a storage batterytCharging power and discharging power of a time period;
the upper and lower limits of the power of the diesel generator set are restricted:
Figure 380836DEST_PATH_IMAGE170
in the formula:
Figure 897268DEST_PATH_IMAGE171
the minimum output power and the maximum output power of the v-th diesel generator set are respectively.
And (3) climbing restraint of the diesel generator set:
Figure 534923DEST_PATH_IMAGE172
in the formula:
Figure 136805DEST_PATH_IMAGE173
the ascending and descending ramp rates of the v-th diesel generator set are respectively;
Figure 721370DEST_PATH_IMAGE174
is the scheduled time difference.
And (3) charge and discharge restraint of the storage battery:
Figure 92309DEST_PATH_IMAGE175
in the formula:
Figure 104127DEST_PATH_IMAGE176
is a storage batterytLoad of time periodAn electrical state;
Figure 662148DEST_PATH_IMAGE177
and
Figure 50404DEST_PATH_IMAGE178
are respectively a storage batterytCharging power and discharging power of a time period;
Figure 287567DEST_PATH_IMAGE179
and
Figure 470287DEST_PATH_IMAGE180
the maximum charging power and the maximum discharging power of the storage battery are respectively;
Figure 843499DEST_PATH_IMAGE181
and
Figure 769867DEST_PATH_IMAGE182
respectively charge efficiency and discharge efficiency of the storage battery;
Figure 849819DEST_PATH_IMAGE183
the energy storage capacity of the storage battery;
Figure 203440DEST_PATH_IMAGE184
and
Figure 1631DEST_PATH_IMAGE185
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
Figure 731690DEST_PATH_IMAGE186
in the formula:
Figure 666148DEST_PATH_IMAGE187
respectively, the minimum and maximum values of the interaction power.
In this embodiment, in step 2, the method specifically includes:
step 2.1, establishing a prediction model based on a model prediction control method;
step 2.2, establishing a rolling optimization scheduling model;
and 2.3, introducing a feedback correction link, correcting the output of the micro-grid time domain rolling optimization scheduling model by using an actual measured value, and taking the actual measured value of the micro-grid time domain rolling optimization scheduling model as an initial value of a new round of rolling optimization to form closed-loop optimization control.
Specifically, MPC is mainly composed of three parts, namely, a prediction model, rolling optimization and feedback correction. The basic idea of DDR-MPC scheduling is to obtain an output optimal control sequence of a future prediction time domain according to the current state condition of the system, load information and a prediction model after a past DPDR and a day DDR, to apply the output of each distributed power supply, the storage battery and the load reduction optimization result obtained by a first control time domain of a control instruction issued and executed in the current time period to the next prediction time domain under the condition of participation of the DDR, to continuously roll the optimization process, and to correct the control output of the system at the time by using the actual measurement value of the current system. According to the invention, the rolling control time domain of the intraday optimal scheduling is set to be 1h, the rolling optimal scheduling time domain is set to be 4 time periods, and the time scale is 4 h. In practical application, the time domain of the rolling optimization scheduling and the time scale of the rolling control of the model can be flexibly set according to the actual prediction precision and the control requirement.
The explanation for introducing feedback correction is as follows: the output control sequence obtained by the prediction control is deviated from the actual output control sequence, and the output prediction error of the wind power photovoltaic is larger along with the increase of the time scale. Therefore, a feedback correction link needs to be introduced, so that the control process can correct the scheduling result error generated in the prediction process in time, and the scheduling precision of the micro-grid is improved.
In this embodiment, the prediction model is:
Figure 252987DEST_PATH_IMAGE188
in the formula:
Figure 538475DEST_PATH_IMAGE189
is a controllable unituIn thattThe initial output value of the time interval is obtained by the open-loop scheduling in the day ahead,utaking a positive integer as a whole, and taking the integer,ttaking a positive integer;
Figure 806645DEST_PATH_IMAGE190
is composed oftTime period prediction future
Figure 861189DEST_PATH_IMAGE191
Time-interval controllable unituIncremental output of (d);
Figure 822192DEST_PATH_IMAGE192
is composed oftTime period prediction future
Figure 860555DEST_PATH_IMAGE193
Time interval controllable unituThe output value of (d);Nto optimize the number of time-domain time segments;
Figure 932416DEST_PATH_IMAGE194
and the climbing power value to be met by the output of each controllable unit in the adjacent prediction time period is obtained.
Specifically, the control variables are solved through rolling optimization, and then the optimal output values of each controllable distributed power supply, the load reduction amount and the storage battery in a future limited time domain are obtained.
In this embodiment, in step 2.2, an objective function of the rolling optimization scheduling model is constructed, where the objective function is:
Figure 107045DEST_PATH_IMAGE195
in the formula:
Figure 973370DEST_PATH_IMAGE196
is the current time period;Nto optimize the number of time-domain time segments;Gis the number of diesel generator sets；
Figure 233450DEST_PATH_IMAGE197
Is composed oftIn the first periodvThe dispatching cost of the diesel generating set is reduced,ttaking a positive integer as a whole, and taking the integer,vtaking a positive integer;
Figure 577844DEST_PATH_IMAGE198
is composed oftTime interval battery scheduling cost;
Figure 606980DEST_PATH_IMAGE199
is composed oftInteraction cost of the micro-grid and the external network in a time period;
Figure 644206DEST_PATH_IMAGE200
and
Figure 188320DEST_PATH_IMAGE201
are respectively the first daymClass load is intA DPDR scheduling cost and an IDR scheduling cost of a period;
Figure 601984DEST_PATH_IMAGE202
is composed oftThe air volume is abandoned in a time interval,
Figure 220047DEST_PATH_IMAGE203
，
Figure 224912DEST_PATH_IMAGE204
and
Figure 725163DEST_PATH_IMAGE205
are respectively a wind generating set in the daytThe output power and actual consumption of the time period;
Figure 473676DEST_PATH_IMAGE206
in order to discard the amount of light in the day,
Figure 274142DEST_PATH_IMAGE207
，
Figure 715488DEST_PATH_IMAGE208
and
Figure 703035DEST_PATH_IMAGE209
are respectively a solar photovoltaic unittThe output power and actual consumption of the time period;
Figure 520819DEST_PATH_IMAGE210
punishment cost is given to unit air volume abandonment,
Figure 379053DEST_PATH_IMAGE211
punishment cost for unit light quantity abandon;
the DPDR scheduling cost is as follows:
Figure 522459DEST_PATH_IMAGE212
in the formula:
Figure 997302DEST_PATH_IMAGE213
are respectively the firstmClass load is intElectricity price and electricity quantity after the time slot participates in DPDR;
Figure 556460DEST_PATH_IMAGE214
are respectively the firstmClass load is intThe electricity price and the load value after the time interval is regulated before the day and then participates in the DPDR;
an Incentive Demand Response (IDR) is that a scheduling mechanism cuts off the load of a user and gives the user certain compensation according to the cut-off load amount, so that the user can respond to system scheduling in time. The IDR scheduling cost is as follows:
Figure 659414DEST_PATH_IMAGE215
in the formula:
Figure 973720DEST_PATH_IMAGE216
is as followsmClass load is intThe actual load reduction of the time interval;
Figure 478737DEST_PATH_IMAGE217
is as followsmClass I of loadkStep quotation corresponding to the reduction of the grade load;
Figure 638323DEST_PATH_IMAGE218
is as followsmClass I of loadkStep-reducing load interval;Kis prepared by reacting with
Figure 533467DEST_PATH_IMAGE219
Corresponding step quote rating.
Specifically, in the scheduling stage in the day, the minimum rolling time domain comprehensive operation cost is taken as an objective function, and the scheduling cost of the DDR is considered in the cost, wherein the scheduling cost of the DDR includes the scheduling cost of the DPDR and the IDR. The solution time length is changed from 24 hours to the time length required by a future section of the rolling time domain. In order to embody the significance of the day-ahead scheduling, the unit output plan determined by the day-ahead scheduling and the charge-discharge state of the storage battery are kept.
In this embodiment, in step 2.2, constraints for constructing the rolling optimization scheduling model are included, and the constraints are:
power balance constraint
Figure 956358DEST_PATH_IMAGE220
In the formula:Gthe number of diesel generator sets;
Figure 405794DEST_PATH_IMAGE221
is as followsvA diesel generator set is arranged intThe active power output of the time period is,ttaking a positive integer as a whole, and taking the integer,vtaking a positive integer;
Figure 369071DEST_PATH_IMAGE222
is a wind generating set in the suntAn output power of the time period;
Figure 790825DEST_PATH_IMAGE223
is a photovoltaic unit in the suntTime periodThe output power of (d);
Figure 650196DEST_PATH_IMAGE224
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
Figure 321349DEST_PATH_IMAGE225
is as followsmClass load is intThe load value after the time interval is regulated day before and then participates in the DPDR;
Figure 26000DEST_PATH_IMAGE226
and
Figure 630157DEST_PATH_IMAGE227
are respectively a storage batterytCharging power and discharging power of a time period;
Figure 519484DEST_PATH_IMAGE228
for the usermIn thattThe actual load reduction of the time interval;
upper and lower IDR bound
Figure 271408DEST_PATH_IMAGE229
In the formula:
Figure 983012DEST_PATH_IMAGE230
are respectively the firstmThe minimum value and the maximum value of the reduction amount of the class load;
the upper and lower limits of the power of the diesel generator set are restricted:
Figure 441676DEST_PATH_IMAGE231
in the formula:
Figure 908429DEST_PATH_IMAGE232
the minimum output power and the maximum output power of the v-th diesel generator set are respectively;
and (3) climbing restraint of the diesel generator set:
Figure 819753DEST_PATH_IMAGE233
in the formula:
Figure 600627DEST_PATH_IMAGE234
are respectively the firstvThe ascending and descending climbing rates of the diesel generating set;
Figure 851480DEST_PATH_IMAGE235
is a scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
Figure 489135DEST_PATH_IMAGE236
in the formula:
Figure 91018DEST_PATH_IMAGE237
is a storage batterytA state of charge of the session;
Figure 410003DEST_PATH_IMAGE226
and
Figure 577680DEST_PATH_IMAGE227
are respectively a storage batterytCharging power and discharging power of a time period;
Figure 589498DEST_PATH_IMAGE238
and
Figure 147518DEST_PATH_IMAGE239
the maximum charging power and the maximum discharging power of the storage battery are respectively;
Figure 535774DEST_PATH_IMAGE240
and
Figure 557957DEST_PATH_IMAGE241
respectively charge efficiency and discharge efficiency of the accumulatorElectrical efficiency;
Figure 740677DEST_PATH_IMAGE242
the energy storage capacity of the storage battery;
Figure 51572DEST_PATH_IMAGE243
and
Figure 977940DEST_PATH_IMAGE244
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
Figure 854629DEST_PATH_IMAGE245
in the formula:
Figure 473829DEST_PATH_IMAGE246
respectively, the minimum and maximum values of the interaction power.
In this embodiment, in step 3, a rolling optimization strategy is introduced into a composite differential evolution CDE algorithm to construct a TDRCDE algorithm, where the composite differential evolution is recorded as CDE, and specifically, the dynamic optimization process of the TDRCDE algorithm is as follows:
step 3.1, set the population size to
Figure 272021DEST_PATH_IMAGE247
Optimizing the number of time-domain time segments toNThe maximum number of iterations is
Figure 470921DEST_PATH_IMAGE248
The total time period isTInitialization oftTime interval population
Figure 670958DEST_PATH_IMAGE249
Wherein
Figure 269516DEST_PATH_IMAGE250
Representing the th in the population at time t
Figure 23845DEST_PATH_IMAGE251
(ii) individuals;
Figure 557595DEST_PATH_IMAGE252
the total number of the controllable unitsUAnd optimizing the number of time-domain time-segmentsNBy usingU×NThe individual elements comprise the output of a diesel generator set, the charge and discharge amount of a storage battery and the load reduction amount;
step 3.2, in the optimization stagetThe time interval takes the actual output value of each currently known controllable unit as an initial value based on the futuretTot+The latest wind and light output prediction data of 4 time periods are used for optimally designing the output force value of each controllable unit of 4 future time periods obtained by day-ahead optimal scheduling
Figure 408876DEST_PATH_IMAGE253
As a reference value;
step 3.3, the optimal rolling optimization adjustment quantity of the force output values of all the controllable units in the scheduling time domain in 4 future time periods is obtained by using a CDE algorithm with the lowest comprehensive running cost of the micro-grid rolling time domain as a target
Figure 838721DEST_PATH_IMAGE255
(ii) a In the CDE operation, the population individuals are sorted according to the principle that the lower the comprehensive operation cost is, the higher the fitness is, the population is divided into a dominant population and a subordinate population according to the division ratio of 1:1, the dominant and subordinate populations are respectively subjected to variation by using two different strategies of random variation and optimal solution variation to give consideration to the convergence speed and the individual diversity, wherein a cross probability factor and a variation scale factor can be respectively set to be 0.85 and 0.5; wherein, the random variation is represented as DE/rand/1 variation, and the variation is represented as DE/best/1 variation based on the optimal solution.
Step 3.4, carrying out merging recombination on the good and bad clusters to obtain new clusters, and continuing iteration on the clustersg=g+1, ifg<
Figure 611505DEST_PATH_IMAGE256
When the bar is satisfied, return to step 3.3Pieceg=
Figure 480103DEST_PATH_IMAGE256
Then, optimizing to obtain optimal population
Figure 389154DEST_PATH_IMAGE257
Outputting the optimal value of the current iteration population
Figure 255479DEST_PATH_IMAGE258
(ii) a And will predict the future made by model scheduling4Sequence of optimal force output values of each controllable unit in each time period
Figure 249979DEST_PATH_IMAGE259
For updating the sequence of optimally planned force values for each controllable unit
Figure 453428DEST_PATH_IMAGE260
；
Step 3.5, dispatching time domain to continue advancingt=t+1 if the condition is satisfiedt=TWhen the optimal value is obtained, the sorting of the optimized and combined population is stopped; if the condition is not met, the method goes to step 3.1, and the optimal population according to the last rolling optimization process is obtained
Figure 482563DEST_PATH_IMAGE261
To construct an initialization population
Figure 254210DEST_PATH_IMAGE262
And then, obtaining a new generation of population through the variation crossover operation of differential evolution, and repeating the steps until the conditions are met to obtain the optimal value of the comprehensive operation cost of the micro-grid rolling time domain.
Specifically, a differential evolution algorithm (DE) has been widely used in an optimal scheduling problem in a power system because of its characteristics of good convergence rate, stability, high efficiency, and the like. However, in the later period of evolution, the traditional DE algorithm still has the characteristics of individual diversity reduction and the like. The CDE algorithm mainly comprises operations of sequencing population individuals, dividing populations into good and bad, performing composite differential evolution, recombining populations and the like. By mutually complementing the advantages of different variation strategies, the convergence rate and diversity of individuals can be simultaneously considered. The micro-grid time domain rolling optimization scheduling model has high dimensionality, real-time performance and multi-constraint performance. The feasible solution of the method also has a dynamic change process when the scheduling time domain changes. The conventional CDE algorithm cannot fully utilize the historical population and the historical optimal solution information when the scheduling time domain changes, cannot quickly construct the initial population in a new environment, and cannot reduce the difference between the initial population and the optimal population in the new environment. Therefore, the invention introduces a rolling optimization strategy into a CDE algorithm, constructs the CDE algorithm based on dynamic optimization, and provides a new time domain rolling differential evolution (TDRCDE) algorithm. The flow chart of the TDRCDE algorithm constructed by the invention is shown in FIG. 2.
The above additional technical features can be freely combined and used in superposition by those skilled in the art without conflict.
It is to be understood that the present invention has been described with reference to certain embodiments, and that various changes in the features and embodiments, or equivalent substitutions may be made therein by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (8)

1.一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于，包括以下步骤：1. a micro-grid time-domain rolling optimization scheduling method based on DDR-MPC, is characterized in that, comprises the following steps: 步骤1，在日前调度阶段，综合考虑机组和储能蓄电池，并将负荷分为居民用电负荷、工业用电负荷、商业用电负荷，在差异化价格型需求响应的基础上，对分类后的负荷建立以日综合运行成本最低为目标的日前优化调度模型；其中，差异化价格型需求响应记为DPDR；Step 1: In the day-ahead scheduling stage, the unit and the energy storage battery are considered comprehensively, and the loads are divided into residential electricity loads, industrial electricity loads, and commercial electricity loads. Establish a day-ahead optimal scheduling model aiming at the lowest daily comprehensive operating cost; among them, the differentiated price demand response is recorded as DPDR; 步骤2，在日内调度阶段，在日前优化调度模型基础上，将差异化需求响应与模型预测控制方法相结合，建立以滚动时域综合运行成本最小为目标的基于DDR-MPC的日内时域滚动优化调度模型，结合日前优化调度模型和日内时域滚动优化调度模型，得到微网时域滚动优化调度模型；其中，差异化需求响应记为DDR，模型预测控制记为MPC；Step 2: In the intraday scheduling stage, on the basis of the day-ahead optimal scheduling model, the differentiated demand response and the model predictive control method are combined to establish a DDR-MPC-based intraday rolling time domain with the goal of minimizing the comprehensive running cost in the rolling time domain. The optimal scheduling model is combined with the day-ahead optimal scheduling model and the intra-day time-domain rolling optimal scheduling model to obtain the microgrid time-domain rolling optimal scheduling model; among them, the differentiated demand response is recorded as DDR, and the model predictive control is recorded as MPC; 步骤3，采用时域滚动复合微分进化算法对微网时域滚动优化调度模型进行求解；其中，时域滚动复合微分进化记为TDRCDE，具体的，将滚动优化策略引入到复合微分进化CDE算法中，构建TDRCDE算法，其中，复合微分进化记为CDE，具体的，TDRCDE算法进行动态寻优的过程如下：Step 3: Use the time-domain rolling compound differential evolution algorithm to solve the time-domain rolling optimization scheduling model of the microgrid; wherein, the time-domain rolling compound differential evolution is recorded as TDRCDE. Specifically, the rolling optimization strategy is introduced into the compound differential evolution CDE algorithm. , and construct the TDRCDE algorithm, in which the compound differential evolution is recorded as CDE. Specifically, the dynamic optimization process of the TDRCDE algorithm is as follows: 步骤3.1、设置种群规模为ε，优化时域时段数为N，最大迭代次数为Gmax，总时段为T，初始化t时段种群
Figure FDA0002935632090000011
其中
Figure FDA0002935632090000012
表示t时段种群中的第ε个个体；
Figure FDA0002935632090000013
中个体基于可控单元总数U和优化时域时段数N采用U×N的矩阵构造，个体元素包括柴油发电机组出力、蓄电池的充放电量、负荷削减量；Step 3.1. Set the population size as ε, the number of optimization time-domain time periods as N, the maximum number of iterations as G max , the total time period as T, and initialize the population at time period t
Figure FDA0002935632090000011
in
Figure FDA0002935632090000012
represents the εth individual in the population in the t period;
Figure FDA0002935632090000013
The medium individual is constructed by a U×N matrix based on the total number of controllable units U and the number of optimized time-domain periods N, and the individual elements include diesel generator set output, battery charge and discharge capacity, and load reduction; 步骤3.2、在优化阶段的t时段，将当前已知的各可控单元实际出力值作为初始值，基于未来t到t+4时段的最新风光出力预测数据，以日前优化调度所得到的未来4个时段的各可控单元最优计划出力值
Figure FDA0002935632090000014
作为参考值；Step 3.2. In the t period of the optimization stage, the current known actual output value of each controllable unit is used as the initial value, based on the latest wind and solar output forecast data in the future t to t+4 period, and the future 4 obtained by the optimization scheduling. The optimal planned output value of each controllable unit for each time period
Figure FDA0002935632090000014
as a reference value; 步骤3.3、以微网滚动时域综合运行成本最低为目标采用CDE算法求取调度时域内各可控单元在未来4个时段出力值的最优滚动优化调整量{Δxu(t+1|t),Δxu(t+2|t),…,Δxu(t+4|t)}；在CDE操作中根据综合运行成本越低适应度越高的原则对种群个体进行排序，并按分割比例1:1将种群划分为优势种群和劣势种群，将优劣种群集分别用随机变异和基于最优解变异两种不同策略进行变异以兼顾收敛速度和个体多样性，其中，交叉概率因子和变异尺度因子可分别设为0.85和0.5；Step 3.3, aiming at the lowest comprehensive running cost in the rolling time domain of the microgrid, use the CDE algorithm to obtain the optimal rolling optimization adjustment of the output value of each controllable unit in the scheduling time domain in the next four periods {Δx u (t+1|t ),Δx u (t+2|t),…,Δx u (t+4|t)}; in the CDE operation, the population individuals are sorted according to the principle that the lower the comprehensive operating cost, the higher the fitness, and divide them according to the The ratio of 1:1 divides the population into dominant populations and disadvantaged populations, and mutates the superior and inferior species clusters by two different strategies: random mutation and mutation based on the optimal solution to take into account the convergence speed and individual diversity. Among them, the crossover probability factor and The variation scale factor can be set to 0.85 and 0.5, respectively; 步骤3.4、将优劣种群集进行合并重组得到新种群，种群继续进行迭代g＝g+1，若g<Gmax时，返回步骤3.3，当满足条件g＝Gmax时，寻优得到最优种群
Figure FDA0002935632090000015
输出当前迭代种群最优值fmin；并将通过预测模型调度所得到的未来4个时段各可控单元最优出力值的序列{Pu(t+1|t)，Pu(t+2|t)，Pu(t+3|t)，Pu(t+4|t)}用以更新各可控单元最优计划出力值序列中的
Figure FDA0002935632090000016
Step 3.4. Merge and reorganize the clusters of superior and inferior species to obtain a new population. The population continues to iterate g=g+1. If g<G max , go back to step 3.3. When the condition g=G max is satisfied, the optimization is obtained. population
Figure FDA0002935632090000015
Output the optimal value f min of the current iterative population; and assign the sequence of optimal output values of each controllable unit in the next 4 periods obtained by scheduling the prediction model {P u (t+1|t), P u (t+2 |t), P u (t+3|t), P u (t+4|t)} are used to update the optimal planned output value sequence of each controllable unit.
Figure FDA0002935632090000016
 步骤3.5、调度时域继续向前推进t＝t+1，若满足条件t＝T时，则停止循坏对优化合并后的种群排序得到最优值；若不满足条件则转到步骤3.1，此时依照上一个滚动优化过程的最优种群
Figure FDA0002935632090000017
来构造初始化种群
Figure FDA0002935632090000018
然后通过微分进化的变异交叉操作得到新的一代种群，如此循坏直至满足条件为止，得到微网滚动时域综合运行成本的最优值。Step 3.5, the scheduling time domain continues to advance t=t+1. If the condition t=T is satisfied, stop the loop and sort the optimized and merged population to obtain the optimal value; if the condition is not satisfied, go to step 3.1, At this time, according to the optimal population of the previous rolling optimization process
Figure FDA0002935632090000017
to construct the initial population
Figure FDA0002935632090000018
Then a new generation of population is obtained through the mutation crossover operation of differential evolution, and this cycle is repeated until the conditions are met, and the optimal value of the comprehensive running cost of the microgrid rolling time domain is obtained. 2.根据权利要求1所述的一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于：在步骤1中，包括构建日前优化调度模型的目标函数，目标函数如下：2. a kind of micro-grid time-domain rolling optimization scheduling method based on DDR-MPC according to claim 1, is characterized in that: in step 1, comprise the objective function of building the optimization scheduling model a few days ago, objective function is as follows:
Figure FDA0002935632090000021
Figure FDA0002935632090000021
 式中：Τ为一个日前优化调度周期的总时段数，t为时段，取正整数；G为柴油发电机组的数量；
Figure FDA0002935632090000022
为t时段第v台柴油发电机组调度成本，v取正整数；Ft ESS为t时段蓄电池调度成本；
Figure FDA0002935632090000023
为t时段第m类负荷的DPDR调度成本；Ft WP为t时段弃风/弃光惩罚成本；Ft Grid为t时段微网与外网的交互成本。In the formula: Τ is the total number of time periods of a day-ahead optimal scheduling cycle, t is the time period, a positive integer; G is the number of diesel generator sets;
Figure FDA0002935632090000022
is the dispatching cost of the vth diesel generator set in the t period, v is a positive integer; F t ESS is the battery dispatching cost in the t period;
Figure FDA0002935632090000023
is the DPDR scheduling cost of the mth load in the t period; F t WP is the penalty cost of wind abandonment/light abandonment in the t period; F t Grid is the interaction cost between the microgrid and the external network in the t period. 3.根据权利要求2所述的一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于：所述日前优化调度模型的目标函数中的各项成本的数学模型如下：3. a kind of micro-grid time domain rolling optimization scheduling method based on DDR-MPC according to claim 2, is characterized in that: the mathematical model of each cost in the objective function of described optimization scheduling model a few days ago is as follows: 其中，柴油发电机组调度成本为：Among them, the dispatch cost of diesel generator set is:
Figure FDA0002935632090000024
Figure FDA0002935632090000024
 式中：αv、βv、λv为第v台柴油机组调度成本系数；
Figure FDA0002935632090000025
为第v台柴油发电机组在t时段的有功出力；
Figure FDA0002935632090000026
和
Figure FDA0002935632090000027
分别为第v台柴油发电机组的单位容量安装成本、资本回收系数和最大输出功率；
Figure FDA0002935632090000028
和
Figure FDA0002935632090000029
分别为第v台柴油发电机组的运行管理成本系数、年运行小时数和机组的容量因素；In the formula: α v , β v , λ v are the dispatching cost coefficients of the vth diesel unit;
Figure FDA0002935632090000025
is the active power output of the vth diesel generator set in the period t;
Figure FDA0002935632090000026
and
Figure FDA0002935632090000027
are the installation cost per unit capacity, capital recovery coefficient and maximum output power of the vth diesel generator set;
Figure FDA0002935632090000028
and
Figure FDA0002935632090000029
are the operation and management cost factor of the vth diesel generator set, the annual operating hours and the capacity factor of the unit; 其中，蓄电池调度成本为：Among them, the battery dispatch cost is:
Figure FDA00029356320900000210
Figure FDA00029356320900000210
 式中：fESS、SESS和bESS分别为蓄电池的单位容量安装成本、资本回收系数和容量因素；TESS和OMESS分别为蓄电池的年运行小时数和运行管理成本系数；Pt ESS为蓄电池在t时段的充放电功率；In the formula: f ESS , S ESS and b ESS are the unit capacity installation cost, capital recovery factor and capacity factor of the battery, respectively; T ESS and OM ESS are the annual operating hours and operation management cost factor of the battery, respectively; P t ESS is The charging and discharging power of the battery in the t period; 其中，DPDR调度成本为：Among them, the DPDR scheduling cost is:
Figure FDA00029356320900000211
Figure FDA00029356320900000211
 式中：
Figure FDA00029356320900000212
和
Figure FDA00029356320900000213
分别为第m类负荷在t时段的初始电价和初始用电量；
Figure FDA00029356320900000214
和
Figure FDA00029356320900000215
分别为第m类负荷在t时段参与DPDR后的电价和电量；where:
Figure FDA00029356320900000212
and
Figure FDA00029356320900000213
are the initial electricity price and initial electricity consumption of the m-th load in period t, respectively;
Figure FDA00029356320900000214
and
Figure FDA00029356320900000215
are the electricity price and electricity quantity of the m-th load after participating in the DPDR in the t period; 其中，弃风/弃光惩罚成本为：Among them, the penalty cost of abandoning wind/light abandonment is:
Figure FDA00029356320900000216
Figure FDA00029356320900000216
 式中：ζ为单位弃风量惩罚费用；Pt wind和
Figure FDA00029356320900000217
分别为日前风力发电机组在t时段的预计输出功率和消纳量；
Figure FDA00029356320900000218
为单位弃光量惩罚费用；Pt PV和
Figure FDA00029356320900000219
分别为日前光伏发电机组在t时段的预计输出功率和消纳量；In the formula: ζ is the penalty cost per unit of abandoned air volume; P t wind and
Figure FDA00029356320900000217
are the estimated output power and consumption of the wind turbine in the t period of the day before;
Figure FDA00029356320900000218
is the penalty cost per unit of abandoned light; P t PV and
Figure FDA00029356320900000219
are the estimated output power and consumption of the photovoltaic generator set in the t period of the day before; 其中，微网与外网的交互成本为：Among them, the interaction cost between the microgrid and the external network is: Ft Grid＝qt×Pt Grid F t Grid =q t ×P t Grid 式中：Pt Grid为微网与外网在t时段交互功率，购电为正、售电为负；qt为t时段电量交易价格。In the formula: P t Grid is the interactive power between the microgrid and the external grid in the t period, the purchase of electricity is positive and the electricity sale is negative; q t is the electricity transaction price in the t period. 4.根据权利要求1所述的一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于：在步骤1中，包括构建日前优化调度模型的约束条件，其约束条件包括：4. a kind of DDR-MPC-based microgrid time-domain rolling optimization scheduling method according to claim 1, is characterized in that: in step 1, comprise the constraint condition of constructing the optimization scheduling model a few days ago, and its constraint condition comprises: 功率平衡约束：Power Balance Constraints:
Figure FDA0002935632090000031
Figure FDA0002935632090000031
 式中：G为柴油发电机组的数量；
Figure FDA0002935632090000032
为第v台柴油发电机组在t时段的有功出力，t取正整数，v取正整数；Pt wind为日前风力发电机组在t时段的预计输出功率；Pt PV为日前光伏发电机组在t时段的预计输出功率；Pt Grid为微网与外网在t时段交互功率，购电为正、售电为负；
Figure FDA0002935632090000033
为第m类负荷在t时段参与DPDR后的电量；
Figure FDA0002935632090000034
和
Figure FDA0002935632090000035
分别为蓄电池在t时段的充电功率和放电功率；In the formula: G is the number of diesel generator sets;
Figure FDA0002935632090000032
is the active power output of the v-th diesel generator set in the period t, t is a positive integer, and v is a positive integer; P t wind is the expected output power of the wind turbine in the period t before the day before; P t PV is the day before the photovoltaic generator set at t The estimated output power of the period; P t Grid is the interactive power between the microgrid and the external grid in the t period, and the electricity purchase is positive and the electricity sale is negative;
Figure FDA0002935632090000033
is the power of the m-th load after participating in DPDR in period t;
Figure FDA0002935632090000034
and
Figure FDA0002935632090000035
are the charging power and discharging power of the battery in the period t, respectively; 柴油发电机组功率上下限约束：Diesel generator set power upper and lower limit constraints:
Figure FDA0002935632090000036
Figure FDA0002935632090000036
 式中：
Figure FDA0002935632090000037
和
Figure FDA0002935632090000038
分别为第v台柴油发电机组的最小和最大输出功率；where:
Figure FDA0002935632090000037
and
Figure FDA0002935632090000038
are the minimum and maximum output power of the vth diesel generator set; 柴油发电机组爬坡约束：Diesel generator set climbing constraints:
Figure FDA0002935632090000039
Figure FDA0002935632090000039
 式中：
Figure FDA00029356320900000310
分别为第v台柴油发电机组的上升和下降爬坡速率；ΔT为调度的时间差；where:
Figure FDA00029356320900000310
are the ascending and descending ramp rates of the vth diesel generator set respectively; ΔT is the time difference of scheduling; 蓄电池充放电约束：Battery charge and discharge constraints:
Figure FDA00029356320900000311
Figure FDA00029356320900000311
 式中：SOCt为蓄电池在t时段的荷电状态；
Figure FDA00029356320900000312
和
Figure FDA00029356320900000313
分别为蓄电池在t时段的充电功率和放电功率；
Figure FDA00029356320900000314
和
Figure FDA00029356320900000315
分别为蓄电池的最大充、放电功率；δc和δd分别为蓄电池的充电效率和放电效率；CESS为蓄电池的储能容量；SOCmax和SOCmin分别为蓄电池的最大和最小荷电状态；In the formula: SOC t is the state of charge of the battery in the t period;
Figure FDA00029356320900000312
and
Figure FDA00029356320900000313
are the charging power and discharging power of the battery in the period t, respectively;
Figure FDA00029356320900000314
and
Figure FDA00029356320900000315
are the maximum charging and discharging power of the battery, respectively; δ c and δ d are the charging efficiency and discharging efficiency of the battery, respectively; C ESS is the energy storage capacity of the battery; SOC max and SOC min are the maximum and minimum state of charge of the battery, respectively; 微网与外网交互功率约束：Interaction power constraints between microgrid and external grid:
Figure FDA00029356320900000316
Figure FDA00029356320900000316
 式中：
Figure FDA00029356320900000317
和
Figure FDA00029356320900000318
分别为交互功率的最小值和最大值。where:
Figure FDA00029356320900000317
and
Figure FDA00029356320900000318
are the minimum and maximum values of the interaction power, respectively. 5.根据权利要求1所述的一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于：在步骤2中，具体包括：5. a kind of DDR-MPC-based micro-grid time domain rolling optimization scheduling method according to claim 1, is characterized in that: in step 2, specifically comprises: 步骤2.1，基于模型预测控制方法建立预测模型；Step 2.1, establishing a prediction model based on the model predictive control method; 步骤2.2，建立滚动优化调度模型；Step 2.2, establish a rolling optimization scheduling model; 步骤2.3，引入反馈校正环节，以实际测量值来对微网时域滚动优化调度模型的输出进行修正，把微网时域滚动优化调度模型实际的测量值作为新一轮滚动优化的初始值，构成闭环优化控制。In step 2.3, a feedback correction link is introduced, and the output of the microgrid time-domain rolling optimization scheduling model is corrected with the actual measured value, and the actual measured value of the microgrid time-domain rolling optimization scheduling model is used as the initial value of a new round of rolling optimization, It constitutes a closed-loop optimization control. 6.根据权利要求5所述的一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于，所述预测模型为：6. a kind of DDR-MPC-based microgrid time-domain rolling optimization scheduling method according to claim 5, is characterized in that, described prediction model is:
Figure FDA0002935632090000041
Figure FDA0002935632090000041
 式中：Pu0(t)为可控单元u在t时段的初始出力值，由日前开环调度所得到，u取正整数，t取正整数；Δxu(t+i|t)为t时段预测未来[t+(i-1),t+i]时段内可控单元u的出力增量；Pu(t+i|t)为t时段预测未来t+i时段可控单元u的出力值；N为优化时域时段数；Rup、Rdown为相邻预测时段内各可控单元出力需满足的爬坡功率值。In the formula: P u0 (t) is the initial output value of the controllable unit u in the t period, obtained from the open-loop scheduling a few days ago, u is a positive integer, and t is a positive integer; Δx u (t+i|t) is t The time period predicts the output increment of the controllable unit u in the future [t+(i-1), t+i] period; P u (t+i|t) is the predicted output of the controllable unit u in the future t+i period in the t period value; N is the number of optimal time-domain time periods; R up and R down are the ramping power values that the output of each controllable unit needs to meet in the adjacent prediction time period. 7.根据权利要求5所述的一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于：在步骤2.2中，包括构建滚动优化调度模型的目标函数，其目标函数为：7. a kind of microgrid time-domain rolling optimization scheduling method based on DDR-MPC according to claim 5, is characterized in that: in step 2.2, comprise the objective function of building rolling optimization scheduling model, and its objective function is:
Figure FDA0002935632090000042
Figure FDA0002935632090000042
 式中：t0为当前时段；N为优化时域时段数；G为柴油发电机组的数量；
Figure FDA0002935632090000043
为t时段第v台柴油发电机组调度成本，t取正整数，v取正整数；Ft ESS为t时段蓄电池调度成本；Ft Grid为t时段微网与外网的交互成本；
Figure FDA0002935632090000044
和
Figure FDA0002935632090000045
分别为日内第m类负荷在t时段的DPDR调度成本和IDR调度成本；ΔPt wind为t时段弃风量，
Figure FDA0002935632090000046
Figure FDA0002935632090000047
和
Figure FDA0002935632090000048
分别为日内风力发电机组在t时段的输出功率和实际消纳量；ΔPt PV为日内弃光量，
Figure FDA0002935632090000049
Figure FDA00029356320900000410
和
Figure FDA00029356320900000411
分别为日内光伏机组在t时段的输出功率和实际消纳量；ζ为单位弃风量惩罚费用，
Figure FDA00029356320900000412
为单位弃光量惩罚费用；In the formula: t 0 is the current time period; N is the number of optimal time-domain time periods; G is the number of diesel generator sets;
Figure FDA0002935632090000043
is the dispatching cost of the vth diesel generator set in the t period, t is a positive integer, and v is a positive integer; F t ESS is the battery dispatching cost in the t period; F t Grid is the interaction cost between the microgrid and the external grid in the t period;
Figure FDA0002935632090000044
and
Figure FDA0002935632090000045
are the DPDR dispatch cost and the IDR dispatch cost of the m-th load in the t period, respectively; ΔP t wind is the abandoned wind volume in the t period,
Figure FDA0002935632090000046
Figure FDA0002935632090000047
and
Figure FDA0002935632090000048
are the daily output power and actual consumption of wind turbines in the t period; ΔP t PV is the daily abandoned light amount,
Figure FDA0002935632090000049
Figure FDA00029356320900000410
and
Figure FDA00029356320900000411
are the output power and the actual consumption of photovoltaic units in the t period of the day, respectively; ζ is the penalty fee per unit of abandoned air volume,
Figure FDA00029356320900000412
The penalty fee for the amount of abandoned light per unit; DPDR调度成本如下：DPDR scheduling costs are as follows:
Figure FDA00029356320900000413
Figure FDA00029356320900000413
 式中：
Figure FDA00029356320900000414
和
Figure FDA00029356320900000415
分别为第m类负荷在t时段参与DPDR后的电价和电量；
Figure FDA00029356320900000416
和
Figure FDA00029356320900000417
分别为第m类负荷在t时段经日前调整后再参与DPDR后的电价和负荷值；where:
Figure FDA00029356320900000414
and
Figure FDA00029356320900000415
are the electricity price and electricity quantity of the m-th load after participating in DPDR in the t period;
Figure FDA00029356320900000416
and
Figure FDA00029356320900000417
are the electricity price and load value of the m-th type of load after being adjusted before the day before participating in the DPDR in the t period; IDR调度成本如下：The IDR scheduling costs are as follows:
Figure FDA00029356320900000418
Figure FDA00029356320900000418
 式中：ΔPm,t为第m类负荷在t时段的实际负荷削减量；
Figure FDA00029356320900000419
为第m类负荷的第k级负荷削减量所对应的阶梯报价；
Figure FDA00029356320900000420
为第m类负荷的第k级削减负荷区间间隔；K为与ΔPm,t对应的阶梯报价等级。In the formula: ΔP m,t is the actual load reduction amount of the m-th load in the period t;
Figure FDA00029356320900000419
It is the step quotation corresponding to the k-th load reduction amount of the m-th load;
Figure FDA00029356320900000420
is the interval interval of the k-th cut load of the m-th type of load; K is the stepped quotation level corresponding to ΔP m,t . 8.根据权利要求5所述的一种基于DDR-MPC的微网时域滚动优化调度方法，其特征在于：在步骤2.2中，包括构建滚动优化调度模型的约束条件，其约束条件为：8. a kind of DDR-MPC-based microgrid time-domain rolling optimization scheduling method according to claim 5, is characterized in that: in step 2.2, comprise the constraint condition of constructing rolling optimization scheduling model, and its constraint condition is: 功率平衡约束Power Balance Constraints
Figure FDA0002935632090000051
Figure FDA0002935632090000051
 式中：G为柴油发电机组的数量；
Figure FDA0002935632090000052
为第v台柴油发电机组在t时段的有功出力，t取正整数，v取正整数；
Figure FDA0002935632090000053
为日内风力发电机组在t时段的输出功率；
Figure FDA0002935632090000054
为日内光伏机组在t时段的输出功率；Pt Grid为微网与外网在t时段交互功率，购电为正、售电为负；
Figure FDA0002935632090000055
为第m类负荷在t时段经日前调整后再参与DPDR后的负荷值；
Figure FDA0002935632090000056
和
Figure FDA0002935632090000057
分别为蓄电池在t时段的充电功率和放电功率；ΔPm,t为第m类负载在t时段的实际负荷削减量；In the formula: G is the number of diesel generator sets;
Figure FDA0002935632090000052
is the active power output of the vth diesel generator set in the period t, t is a positive integer, and v is a positive integer;
Figure FDA0002935632090000053
is the output power of the wind turbine in the t period of the day;
Figure FDA0002935632090000054
is the daily output power of photovoltaic units in period t; P t Grid is the interactive power between the microgrid and the external grid in period t, and the electricity purchase is positive and the electricity sale is negative;
Figure FDA0002935632090000055
is the load value of the m-th type of load after it participates in DPDR after being adjusted a few days ago in period t;
Figure FDA0002935632090000056
and
Figure FDA0002935632090000057
are the charging power and discharging power of the battery in the t period, respectively; ΔP m,t is the actual load reduction amount of the mth load in the t period; IDR上下限约束IDR upper and lower bounds
Figure FDA0002935632090000058
Figure FDA0002935632090000058
 式中：
Figure FDA0002935632090000059
和
Figure FDA00029356320900000510
分别为第m类负荷的削减量的最小值和最大值；where:
Figure FDA0002935632090000059
and
Figure FDA00029356320900000510
are the minimum and maximum reductions of the m-th load, respectively; 柴油发电机组功率上下限约束：Diesel generator set power upper and lower limit constraints:
Figure FDA00029356320900000511
Figure FDA00029356320900000511
 式中：
Figure FDA00029356320900000512
和
Figure FDA00029356320900000513
分别为第v台柴油发电机组的最小和最大输出功率；where:
Figure FDA00029356320900000512
and
Figure FDA00029356320900000513
are the minimum and maximum output power of the vth diesel generator set; 柴油发电机组爬坡约束：Diesel generator set climbing constraints:
Figure FDA00029356320900000514
Figure FDA00029356320900000514
 式中：
Figure FDA00029356320900000515
分别为第v台柴油发电机组的上升和下降爬坡速率；ΔT为调度的时间差；where:
Figure FDA00029356320900000515
are the ascending and descending ramp rates of the vth diesel generator set respectively; ΔT is the time difference of scheduling; 蓄电池充放电约束：Battery charge and discharge constraints:
Figure FDA00029356320900000516
Figure FDA00029356320900000516
 式中：SOCt为蓄电池在t时段的荷电状态；
Figure FDA00029356320900000517
和
Figure FDA00029356320900000518
分别为蓄电池在t时段的充电功率和放电功率；
Figure FDA00029356320900000519
和
Figure FDA00029356320900000520
分别为蓄电池的最大充、放电功率；δc和δd分别为蓄电池的充电效率和放电效率；CESS为蓄电池的储能容量；SOCmax和SOCmin分别为蓄电池的最大和最小荷电状态；In the formula: SOC t is the state of charge of the battery in the t period;
Figure FDA00029356320900000517
and
Figure FDA00029356320900000518
are the charging power and discharging power of the battery in the period t, respectively;
Figure FDA00029356320900000519
and
Figure FDA00029356320900000520
are the maximum charging and discharging power of the battery, respectively; δ c and δ d are the charging efficiency and discharging efficiency of the battery, respectively; C ESS is the energy storage capacity of the battery; SOC max and SOC min are the maximum and minimum state of charge of the battery, respectively; 微网与外网交互功率约束：Interaction power constraints between microgrid and external grid:
Figure FDA00029356320900000521
Figure FDA00029356320900000521
 式中：
Figure FDA00029356320900000522
和
Figure FDA00029356320900000523
分别为交互功率的最小值和最大值。where:
Figure FDA00029356320900000522
and
Figure FDA00029356320900000523
are the minimum and maximum values of the interaction power, respectively.
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