CN112234658B - Micro-grid time domain rolling optimization scheduling method based on DDR-MPC - Google Patents
Micro-grid time domain rolling optimization scheduling method based on DDR-MPC Download PDFInfo
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Abstract
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, which comprises the following steps: in a day-ahead scheduling stage, establishing a day-ahead optimized scheduling model with the lowest daily comprehensive operation cost as a target based on differentiated price type demand response; in the day scheduling stage, combining the differentiated demand response with a model prediction control method, and establishing a day time domain rolling optimization scheduling model with the aim of minimizing the rolling time domain comprehensive operation cost based on the DDR-MPC; and solving the micro-grid time domain rolling optimization scheduling model by adopting a time domain rolling composite differential evolution algorithm. According to the method, the DDR and MPC methods are combined, so that the comprehensive operation cost of the microgrid can be obviously reduced, and the reliability of optimal scheduling of the microgrid is improved; the time domain rolling composite differential evolution algorithm provided by the invention can efficiently solve the model, can obviously improve the optimizing convergence speed, and has good dynamic environment adaptability.
Description
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 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:
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;
is composed oftIn the first periodvThe dispatching cost of the diesel generating set is reduced,vtaking a positive integer;
is composed oftTime interval battery scheduling cost;
is composed oftIn the first periodmDPDR scheduling cost of class load;
is composed oftWind/light abandon penalty cost per period;
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:
in the formula:α v 、β v 、λ v is as followsvA diesel set scheduling cost coefficient;
is as followsvA diesel generator set is arranged intActive power output in time intervals;
and
are respectively the firstvThe unit capacity installation cost, capital recovery factor and maximum output power of the diesel generating set;
and
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:
in the formula:
the unit capacity installation cost, capital recovery factor and capacity factor of the storage battery respectively;
and
the annual running hours and running management cost coefficients of the storage battery are respectively;
the charging and discharging power of the storage battery in the time period t;
wherein, the DPDR scheduling cost is:
in the formula:
respectively the initial electricity price and the initial electricity consumption of the mth type load in the t period;
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:
in the formula:
punishing cost for unit air volume abandon;
respectively predicting the output power and the consumption of the wind generating set in the time period t in the day ahead;
punishment cost for unit light quantity abandon;
and
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:
in the formula:
exchanging power for the micro-grid and the external grid at a time t, wherein electricity purchasing is positive and electricity selling is negative;
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:
in the formula:Gthe number of diesel generator sets;
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;
is a wind generating set at the day beforetPredicted output power for the time period;
the predicted output power of the photovoltaic generator set in the period t before the day;
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
is as followsmClass load is intThe electric quantity after the time interval participates in DPDR;
and
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:
in the formula:
are respectively the firstvMinimum and maximum output power of the diesel generating set;
and (3) climbing restraint of the diesel generator set:
in the formula:
are respectively the firstvThe ascending and descending climbing rates of the diesel generating set;
is a scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
in the formula:
is a storage batterytA state of charge of the session;
and
are respectively a storage batterytCharging power and discharging power of a time period;
and
the maximum charging power and the maximum discharging power of the storage battery are respectively;
and
respectively charge effect of accumulatorRate and discharge efficiency;
the energy storage capacity of the storage battery;
and
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
in the formula:
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:
in the formula:
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;
is composed oftTime period prediction future
Time-interval controllable unituIncremental output of (d);
is composed oftTime period prediction future
Time interval controllable unituThe output value of (d);Nto optimize the number of time-domain time segments;
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:
in the formula:
is the current time period;Nto optimize the number of time-domain time segments;Gthe number of diesel generator sets;
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;
is composed oftTime interval battery scheduling cost;
is composed oftInteraction cost of the micro-grid and the external network in a time period;
and
are respectively the first daymClass load is intA DPDR scheduling cost and an IDR scheduling cost of a period;
is composed oftThe air volume is abandoned in a time interval,
，
and
are respectively a wind generating set in the daytThe output power and actual consumption of the time period;
in order to discard the amount of light in the day,
，
and
are respectively a solar photovoltaic unittThe output power and actual consumption of the time period;
punishment cost is given to unit air volume abandonment,
punishment cost for unit light quantity abandon;
the DPDR scheduling cost is as follows:
in the formula:
are respectively the firstmClass load is intElectricity price and electricity quantity after the time slot participates in DPDR;
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:
in the formula:
is as followsmClass load is intThe actual load reduction of the time interval;
is as followsmClass I of loadkStep quotation corresponding to the reduction of the grade load;
is as followsmClass I of loadkStep-reducing load interval;Kis prepared by reacting with
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
In the formula:Gthe number of diesel generator sets;
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;
is a wind generating set in the suntAn output power of the time period;
is a photovoltaic unit in the suntAn output power of the time period;
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
is as followsmClass load is intThe load value after the time interval is regulated day before and then participates in the DPDR;
and
are respectively a storage batterytCharging power and discharging power of a time period;
for the usermIn thattThe actual load reduction of the time interval;
upper and lower IDR bound
In the formula:
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:
in the formula:
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:
in the formula:
are respectively the firstvThe ascending and descending climbing rates of the diesel generating set;
is a scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
in the formula:
is a storage batterytTime periodThe state of charge of;
and
are respectively a storage batterytCharging power and discharging power of a time period;
and
the maximum charging power and the maximum discharging power of the storage battery are respectively;
and
respectively charge efficiency and discharge efficiency of the storage battery;
the energy storage capacity of the storage battery;
and
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
in the formula:
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
Optimizing the number of time-domain time segments toNThe maximum number of iterations is
The total time period isTInitialization oftTime interval population
Wherein
Representing the th in the population at time t
(ii) individuals;
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
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
(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<
When the condition is satisfied, the step returns to the step 3.3g=
Then, optimizing to obtain optimal population
Outputting the optimal value of the current iteration population
(ii) a And will predict the future made by model scheduling4Sequence of optimal force output values of each controllable unit in each time period
For updating the sequence of optimally planned force values for each controllable unit
；
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
To construct an initialization population
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 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:
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;
is composed oftIn the first periodvThe dispatching cost of the diesel generating set is reduced,vtaking a positive integer;
is composed oftTime interval battery scheduling cost;
is composed oftIn the first periodmDPDR scheduling cost of class load;
is composed oftWind/light abandon penalty cost per period;
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:
in the formula:α v 、β v 、λ v is as followsvA diesel set scheduling cost coefficient;
is as followsvA diesel generator set is arranged intActive power output in time intervals;
and
are respectively the firstvThe unit capacity installation cost, capital recovery factor and maximum output power of the diesel generating set;
and
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:
in the formula:
the unit capacity installation cost, capital recovery factor and capacity factor of the storage battery respectively;
and
the annual running hours and running management cost coefficients of the storage battery are respectively;
for the accumulator at tCharging and discharging power of a time period;
wherein, the DPDR scheduling cost is:
in the formula:
respectively the initial electricity price and the initial electricity consumption of the mth type load in the t period;
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:
in the formula:
punishing cost for unit air volume abandon;
respectively predicting the output power and the consumption of the wind generating set in the time period t in the day ahead;
punishment cost for unit light quantity abandon;
and
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:
in the formula:
exchanging power for the micro-grid and the external grid at a time t, wherein electricity purchasing is positive and electricity selling is negative;
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:
in the formula:Gthe number of diesel generator sets;
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;
is a wind generating set at the day beforetPredicted output power for the time period;
the predicted output power of the photovoltaic generator set in the period t before the day;
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
is as followsmClass load is intThe electric quantity after the time interval participates in DPDR;
and
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:
in the formula:
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:
in the formula:
the ascending and descending ramp rates of the v-th diesel generator set are respectively;
is the scheduled time difference.
And (3) charge and discharge restraint of the storage battery:
in the formula:
is a storage batterytLoad of time periodAn electrical state;
and
are respectively a storage batterytCharging power and discharging power of a time period;
and
the maximum charging power and the maximum discharging power of the storage battery are respectively;
and
respectively charge efficiency and discharge efficiency of the storage battery;
the energy storage capacity of the storage battery;
and
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
in the formula:
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:
in the formula:
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;
is composed oftTime period prediction future
Time-interval controllable unituIncremental output of (d);
is composed oftTime period prediction future
Time interval controllable unituThe output value of (d);Nto optimize the number of time-domain time segments;
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:
in the formula:
is the current time period;Nto optimize the number of time-domain time segments;Gis the number of diesel generator sets；
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;
is composed oftTime interval battery scheduling cost;
is composed oftInteraction cost of the micro-grid and the external network in a time period;
and
are respectively the first daymClass load is intA DPDR scheduling cost and an IDR scheduling cost of a period;
is composed oftThe air volume is abandoned in a time interval,
，
and
are respectively a wind generating set in the daytThe output power and actual consumption of the time period;
in order to discard the amount of light in the day,
，
and
are respectively a solar photovoltaic unittThe output power and actual consumption of the time period;
punishment cost is given to unit air volume abandonment,
punishment cost for unit light quantity abandon;
the DPDR scheduling cost is as follows:
in the formula:
are respectively the firstmClass load is intElectricity price and electricity quantity after the time slot participates in DPDR;
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:
in the formula:
is as followsmClass load is intThe actual load reduction of the time interval;
is as followsmClass I of loadkStep quotation corresponding to the reduction of the grade load;
is as followsmClass I of loadkStep-reducing load interval;Kis prepared by reacting with
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
In the formula:Gthe number of diesel generator sets;
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;
is a wind generating set in the suntAn output power of the time period;
is a photovoltaic unit in the suntTime periodThe output power of (d);
for microgrid and external networktThe power is interacted in time intervals, wherein the electricity purchasing is positive and the electricity selling is negative;
is as followsmClass load is intThe load value after the time interval is regulated day before and then participates in the DPDR;
and
are respectively a storage batterytCharging power and discharging power of a time period;
for the usermIn thattThe actual load reduction of the time interval;
upper and lower IDR bound
In the formula:
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:
in the formula:
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:
in the formula:
are respectively the firstvThe ascending and descending climbing rates of the diesel generating set;
is a scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
in the formula:
is a storage batterytA state of charge of the session;
and
are respectively a storage batterytCharging power and discharging power of a time period;
and
the maximum charging power and the maximum discharging power of the storage battery are respectively;
and
respectively charge efficiency and discharge efficiency of the accumulatorElectrical efficiency;
the energy storage capacity of the storage battery;
and
maximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
in the formula:
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
Optimizing the number of time-domain time segments toNThe maximum number of iterations is
The total time period isTInitialization oftTime interval population
Wherein
Representing the th in the population at time t
(ii) individuals;
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
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
(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<
When the bar is satisfied, return to step 3.3Pieceg=
Then, optimizing to obtain optimal population
Outputting the optimal value of the current iteration population
(ii) a And will predict the future made by model scheduling4Sequence of optimal force output values of each controllable unit in each time period
For updating the sequence of optimally planned force values for each controllable unit
；
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
To construct an initialization population
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. A micro-grid time domain rolling optimization scheduling method based on DDR-MPC is characterized by comprising 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; the time domain rolling composite differential evolution is recorded as TDRCDE, specifically, a rolling optimization strategy is introduced into a composite differential evolution CDE algorithm to construct the 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, setting the population scale to be epsilon, optimizing the time domain time period number to be N, and setting the maximum iteration number to be GmaxInitializing population at T period with total period of T
Wherein
Representing the epsilon-th individual in the population at time t;
the middle individual adopts a matrix structure of U multiplied by N based on the total number U of the controllable units and the number N of the optimized time domain time segments, and the 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 time period t of the optimization stage, taking the currently known actual output value of each controllable unit as an initial value based on the future t to tThe latest wind and light output prediction data in the +4 time period is used for optimally designing the power value of each controllable unit in the future 4 time periods obtained by the day-ahead optimal scheduling
As a reference value;
step 3.3, the optimal rolling optimization adjustment quantity { delta x of the force output values of all the controllable units in the scheduling time domain in 4 future time periods is obtained by using the CDE algorithm with the goal of lowest comprehensive running cost of the micro-grid rolling time domainu(t+1|t),Δxu(t+2|t),…,Δxu(t +4| t) }; 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;
and 3.4, merging and recombining the good and bad clusters to obtain a new cluster, and continuously iterating the cluster to obtain g +1, and if g is obtained<GmaxWhen the condition G ═ G is satisfied, the process returns to step 3.3maxThen, optimizing to obtain optimal population
Outputting the optimal value f of the current iteration populationmin(ii) a And the optimal force output value sequence { P ] of each controllable unit in 4 future periods obtained by the scheduling of the prediction modelu(t+1|t)，Pu(t+2|t)，Pu(t+3|t)，Pu(t +4| t) } for updating the optimal planned force value sequence for each controllable unit
Step 3.5, the scheduling time domain continues to advance T to T +1, and if the condition T to T is met, the optimized and combined population is stopped from being sequenced circularly to obtain an optimal value; if the condition is not met, go to step 3.1, where the rolling optimization process is followedOf (2) optimal population
To construct an initialization population
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.
2. The DDR-MPC based microgrid time domain rolling optimization scheduling method of claim 1, wherein: in step 1, an objective function of a day-ahead optimization scheduling model is constructed, wherein the objective function is as follows:
in the formula: t is the total time period number of a day-ahead optimized scheduling cycle, t is a time period, and a positive integer is taken; g is the number of the diesel generator sets;
for the scheduling cost of the vth diesel generating set in the t time period, v is a positive integer; ft ESSScheduling cost for the storage battery at the time t;
scheduling cost for DPDR of the mth type load in the t period; ft WPWind abandon/light abandon punishment cost for t time period; ft GridAnd the interaction cost of the microgrid and the external network in the period of t is shown.
3. The DDR-MPC based microgrid time domain rolling optimization scheduling method of claim 2, wherein: 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:
in the formula: alpha is alphav、βv、λvScheduling a cost coefficient for the vth diesel engine set;
the active power output of a v-th diesel generator set in a t time period;
and
respectively the unit capacity installation cost, the capital recovery coefficient and the maximum output power of the v-th diesel generator set;
and
the operation management cost coefficient, the annual operation hours and the capacity factor of the Vth diesel generator set are respectively;
wherein, the battery scheduling cost is:
in the formula: f. ofESS、SESSAnd bESSThe unit capacity installation cost, capital recovery factor and capacity factor of the storage battery respectively; t isESSAnd OMESSThe annual running hours and running management cost coefficients of the storage battery are respectively; pt ESSThe charging and discharging power of the storage battery in the time period t;
wherein, the DPDR scheduling cost is:
in the formula:
and
respectively the initial electricity price and the initial electricity consumption of the mth type load in the t period;
and
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:
in the formula: zeta is unit air volume abandon punishment cost; pt windAnd
respectively predicting the output power and the consumption of the wind generating set in the time period t in the day ahead;
punishment cost for unit light quantity abandon; pt PVAnd
predicted output power sum of photovoltaic generator set in time t period before dayConsumption;
the interaction cost of the micro-grid and the external grid is as follows:
Ft Grid＝qt×Pt Grid
in the formula: pt GridExchanging power for the micro-grid and the external grid at a time t, wherein electricity purchasing is positive and electricity selling is negative; q. q.stTrading prices for the amount of electricity for the period t.
4. The DDR-MPC based microgrid time domain rolling optimization scheduling method of claim 1, wherein: in step 1, the method comprises the steps of constructing constraints of a day-ahead optimization scheduling model, wherein the constraints comprise:
and power balance constraint:
in the formula: g is the number of the diesel generator sets;
taking t as the active power output of a vth diesel generator set in a t period, wherein t is a positive integer, and v is a positive integer; pt windThe predicted output power of the wind generating set in the time period t before the day; pt PVThe predicted output power of the photovoltaic generator set in the period t before the day; pt GridExchanging power for the micro-grid and the external grid at a time t, wherein electricity purchasing is positive and electricity selling is negative;
the electric quantity of the mth load after the mth load participates in the DPDR in the time period t;
and
respectively charging work of the storage battery in the t periodRate and discharge power;
the upper and lower limits of the power of the diesel generator set are restricted:
in the formula:
and
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:
in the formula:
the ascending and descending ramp rates of the v-th diesel generator set are respectively; Δ T is the scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
in the formula: SOCtThe state of charge of the storage battery in a time period t;
and
respectively charging power and discharging power of the storage battery in a time period t;
and
the maximum charging power and the maximum discharging power of the storage battery are respectively; deltacAnd deltadRespectively charge efficiency and discharge efficiency of the storage battery; cESSThe energy storage capacity of the storage battery; SOCmaxAnd SOCminMaximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
in the formula:
and
respectively, the minimum and maximum values of the interaction power.
5. The DDR-MPC based microgrid time domain rolling optimization scheduling method of claim 1, wherein: in step 2, the method specifically comprises the following steps:
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.
6. The DDR-MPC based microgrid time domain rolling optimization scheduling method of claim 5, wherein the prediction model is as follows:
in the formula: pu0(t) is an initial output value of the controllable unit u in a time period t, and is obtained by open-loop scheduling in the day ahead, wherein u is a positive integer, and t is a positive integer; Δ xu(t + i | t) predicts the future [ t + (i-1), t + i | t) for the t period]The output increment of the controllable unit u in the time period; pu(t + i | t) predicting the output value of the controllable unit u for a future t + i period for the t period; n is the number of the optimized time domain time segments; rup、RdownAnd the climbing power value to be met by the output of each controllable unit in the adjacent prediction time period is obtained.
7. The DDR-MPC based microgrid time domain rolling optimization scheduling method of claim 5, wherein: in step 2.2, an objective function of the rolling optimization scheduling model is constructed, wherein the objective function is as follows:
in the formula: t is t0Is the current time period; n is the number of the optimized time domain time segments; g is the number of the diesel generator sets;
scheduling cost of a vth diesel generator set in a time period t, wherein t is a positive integer, and v is a positive integer; ft ESSScheduling cost for the storage battery at the time t; ft GridThe interaction cost of the microgrid and the external network in the time period t is shown;
and
DPDR scheduling cost and IDR scheduling for mth load in day in t periodThe cost is reduced; delta Pt windThe air volume is abandoned for the period t,
and
respectively representing the output power and the actual consumption of the wind generating set in the time period t in the day; delta Pt PVIn order to discard the amount of light in the day,
and
respectively representing the output power and the actual consumption of the photovoltaic unit in the time period t in the day; zeta is the punishment cost of unit air volume abandon,
punishment cost for unit light quantity abandon;
the DPDR scheduling cost is as follows:
in the formula:
and
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;
and
respectively the electricity price and the load value of the mth load after the mth load is regulated before the day and then participates in DPDR in the t period;
the IDR scheduling cost is as follows:
in the formula: delta Pm,tReducing the actual load of the mth type load in the t period;
step quotation corresponding to the kth level load reduction of the mth type load;
load interval is reduced for the kth level of the mth type load; k is equal to Δ Pm,tCorresponding step quote rating.
8. The DDR-MPC based microgrid time domain rolling optimization scheduling method of claim 5, wherein: in step 2.2, the method includes the constraint conditions for constructing the rolling optimization scheduling model, wherein the constraint conditions are as follows:
power balance constraint
In the formula: g is the number of the diesel generator sets;
for the active power output of the v-th diesel generator set in the t time period, t is a positive integer, and v is a positive integerA positive integer;
the output power of the wind generating set in the time period t in the day;
the output power of the photovoltaic unit in the time period t in the day is obtained; pt GridExchanging power for the micro-grid and the external grid at a time t, wherein electricity purchasing is positive and electricity selling is negative;
the load value of the mth load after the mth load participates in DPDR after being adjusted day-ahead in the t period;
and
respectively charging power and discharging power of the storage battery in a time period t; delta Pm,tReducing the actual load of the mth type load in the t period;
upper and lower IDR bound
In the formula:
and
respectively the minimum value and the maximum value of the reduction amount of the mth type load;
the upper and lower limits of the power of the diesel generator set are restricted:
in the formula:
and
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:
in the formula:
the ascending and descending ramp rates of the v-th diesel generator set are respectively; Δ T is the scheduled time difference;
and (3) charge and discharge restraint of the storage battery:
in the formula: SOCtThe state of charge of the storage battery in a time period t;
and
respectively charging power and discharging power of the storage battery in a time period t;
and
respectively the maximum charge of the storage battery,Discharge power; deltacAnd deltadRespectively charge efficiency and discharge efficiency of the storage battery; cESSThe energy storage capacity of the storage battery; SOCmaxAnd SOCminMaximum and minimum states of charge of the battery, respectively;
and (3) interactive power constraint of the microgrid and the external network:
in the formula:
and
respectively, the minimum and maximum values of the interaction power.
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