CN116760008A - Multi-time-scale active and reactive coordination scheduling method considering load characteristics - Google Patents
Multi-time-scale active and reactive coordination scheduling method considering load characteristics Download PDFInfo
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Abstract
A multi-time scale active and reactive coordination scheduling method considering load characteristics comprises the following steps: constructing a load cluster aggregation model by considering the air conditioner load and the energy utilization characteristics of the electric automobile, and quantitatively evaluating the adjustable capacity; in the day-ahead stage, according to the objective function and constraint conditions, setting up an adjustment plan of the transformer tap and the capacitor; in the intra-day stage, taking a scheduling plan determined in the past as a reference, and taking the minimum total cost of the power distribution network as a target to perform intra-day rolling scheduling; and in the real-time stage, the minimum active and reactive power adjustment is taken as a target, a feedback correction link is introduced to form closed-loop control, and the scheduling plan at the next moment is corrected according to the scheduling result deviation of the current time period. The method refines the scheduling plan step by step, can fully exert the adjusting capability of the load, and improves the digestion capability of the photovoltaic.
Description
Technical Field
The invention belongs to the field of coordination scheduling of power distribution networks, and particularly relates to a multi-time-scale active and reactive coordination method considering load characteristics.
Background
With the trend of wind power and photovoltaic in the age of low-price internet surfing, low inertia, randomness and fluctuation of new energy power generation such as wind power and photovoltaic bring great challenges to the power distribution network, the safety and economical efficiency of the operation of the power distribution network are ensured, and the maximum consumption of new energy is the primary target of the power distribution network. The corresponding rotary spare capacity is configured on the power supply side for stabilizing the uncertainty of new energy power generation, so that the investment of power grid construction is increased, waste is caused, the adjustable load on the user side is huge in adjustment potential, the adjustable load is fully excavated and utilized, the running economy of the power grid can be greatly improved, and the new energy consumption rate is improved.
The Chinese patent 'a power distribution network active and reactive coordination regulation method based on model predictive control' (application number: 201611158742.2) performs reactive power regulation from the aspect of power distribution network operation economy, reduces network loss, performs active power regulation from the aspect of power distribution network operation safety, and ensures safe operation of a system. The Chinese patent 'a multi-time scale coordinated optimization scheduling method of an active power distribution network based on MPC' (application number: 201510373573.3) adopts a model prediction control method, takes the optimization scheduling result of a long time scale as a reference, carries out rolling correction in a short time scale, and realizes the optimization scheduling of the active power distribution network, thereby reducing the adverse effect of the prediction precision of a distributed power supply and a low-voltage load on the optimization scheduling.
The method adopts multiple time scales to carry out optimal scheduling, but does not fully consider the characteristics of the load, fully excavate the adjustable capacity of the adjustable load and quantitatively evaluate the adjustable capacity of the load.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-time-scale active and reactive coordination method considering load characteristics, which establishes a load cluster aggregation model considering the air conditioner load and the energy characteristics of an electric automobile, carries out quantitative evaluation on adjustable capacity, determines an active and reactive coordination scheduling plan of a power distribution network under the multi-time scale through adjustable quantity, and can refine the scheduling plan step by day scheduling, daily rolling scheduling and real-time feedback correction, fully utilizes the adjustable capacity of the adjustable load, effectively tracks the fluctuation of the load and absorbs photovoltaic power generation. The invention adopts active and reactive power optimization scheduling, more accords with a large number of active and reactive power equipment connected in the power distribution network, and runs economically and safely through the power distribution network.
The technical scheme adopted by the invention is as follows:
a multi-time scale active and reactive coordination method considering load characteristics comprises the following steps:
step 1: taking the air conditioner load and the energy utilization characteristics of the electric automobile into consideration, constructing a load cluster aggregation model, and quantitatively evaluating the adjustable capacity;
step 2: according to the objective function and the constraint condition, a day-ahead phase adjustment plan of the transformer tap and the capacitor is formulated;
step 3: taking the day-ahead stage adjustment plan in the step 2 as a reference, and taking the minimum total cost of the power distribution network as a target, performing day-in stage rolling scheduling;
step 4: in a real-time stage, model predictive control is adopted, the minimum active and reactive adjustment is taken as a target, a feedback correction link is introduced, closed-loop control is formed, and the scheduling plan at the next moment is corrected according to the scheduling result deviation of the current period.
In the step 1, the aggregation model of the constructed air conditioner load cluster is as follows:
wherein: p (P) t ACLC The aggregation power of the air conditioner cluster at the time t; i is the serial number of the air conditioner; nac is the number of air conditioners in the air conditioner cluster;the power of the ith air conditioner; s is(s) i,t The operation state of the ith air conditioner at the time t is that 1 is opened and 0 is closed.
Based on the built aggregation model of the air conditioner load clusters, the air conditioner is circularly controlled by adopting a wheel control strategy, and the air conditioner cluster load controllable capacity C under the wheel control strategy x The method comprises the following steps:
wherein τ c To control the period τ off Is the shutdown time;is the average power of the air conditioner.
The aggregation model of the constructed electric automobile cluster is as follows:
wherein:charging power and discharging power of the electric automobile clusters are respectively carried out; />Respectively obtaining the maximum charging power and the maximum discharging power of the electric automobile cluster; />The total capacity of the electric automobile cluster which can be scheduled at the time t is obtained; />The total capacity of the electric automobile cluster which can be scheduled at the time t-1 is obtained; Δt is the time interval; />Is the dynamic step deviation electric quantity; η (eta) ch ,η dch Respectively charging and discharging efficiency; />The electric quantity of the ith EV when arriving and leaving at the moment t is respectively; u (u) i,t-1 ,u i,t The states of the ith EV at t-1 and t which are connected with the power grid are respectively equal to 1 and 0, and the states are grid-connected and off-grid; nev is the number of clustered electric vehicles; u is the grid connection condition of the ith vehicle in the t period, and the value is 0 or 1./>The minimum schedulable capacity and the maximum schedulable capacity of the electric automobile cluster at the time t are respectively.
Based on the built aggregation model of the electric automobile cluster, the schedulable capacity of the electric automobile cluster is quantitatively evaluated by adopting a travel chain theory, and the probability density functions of the initial charging time and the daily driving mileage of the electric automobile are as follows:
wherein: f (f) s (t) is a probability density function of the initial charging time of the electric automobile; f (f) d (x) The probability density function of the daily driving mileage of the electric automobile is obtained; t is time; x is the driving mileage; mu (mu) s ,σ s Respectively an expected value and a standard deviation of the initial charging time of the electric automobile; mu (mu) d ,σ d The expected value and standard deviation of the daily driving mileage of the electric automobile are respectively.
In the step 2, at a day-ahead stage, the objective function is the total running cost F of the power distribution network DA Minimum:
wherein:purchasing electric power from an upper power grid at the moment t; />The power of the photovoltaic at the moment t; />The power stored at the time t is; />The power of the air conditioner load cluster at the moment t; />The power of the electric automobile cluster at the moment t;the frequency of gear adjustment of the transformer and the frequency of adjustment of the capacitor are respectively; c (C) G,t ,C PV ,C ESS ,C ACLC ,C EVC The method comprises the following steps of: the method comprises the following steps of purchasing electricity from a superior power grid, obtaining a photovoltaic power generation cost coefficient, obtaining an energy storage operation cost coefficient, obtaining an air conditioner load cluster control cost coefficient and obtaining an electric automobile cluster control cost coefficient; gamma ray OLTC ，γ CB The gear adjusting cost coefficient of the transformer and the adjusting cost coefficient of the capacitor are respectively.
In the step 2, at a day-ahead stage, the power flow constraint is a constraint condition after the second-order cone is relaxed:
wherein: j-k represents a set of all line end nodes k pointed by taking the node j as a head end; r is (r) ij ,x ij The resistance value and the reactance value of the branch ij are respectively; p (P) jk,t ,Q jk,t Active power and reactive power flowing from node j to node k at time t respectively; p (P) ij,t ,Q ij,t The active power and the reactive power respectively flow through the branch ij at the moment t; p (P) j,t ,Q j,t The net injection values of the active power and the reactive power of the node j at the moment t are respectively related to the actual load of the node j, an accessed energy storage device, a photovoltaic system, a controllable load, a capacitor and SVC; i ij,t ,U i,t The current through branch ij and the voltage at node i, respectively.
The voltage of the node j at the moment t; />The voltage of the node j at the moment t; />The current flowing through the branch ij at the time t;
the active power of the load of the node j at the moment t; />The node j is connected with the charging power of the energy storage device at the moment t; />The discharging power of the energy storage device is accessed to the node j at the moment t; />The active power of the node j photovoltaic at the moment t;the power of the air conditioner load cluster of the node j at the moment t; />The power of the electric automobile cluster is the node j at the moment t;
the reactive power of the load of the node j at the moment t; />The reactive power of the energy storage device is accessed to the node j at the moment t; />The reactive power of the node j photovoltaic at the moment t; />The reactive power of the capacitor is node j at the moment t; />SVC reactive power at node j at time t.
In the day-ahead stage, the safety operation constraint conditions of the power distribution network are as follows:
wherein: i ij,max Representing the maximum value of the current of branch ij; u (U) j,max ,U j,min Respectively isThe upper and lower limits of the voltage amplitude of the node j;the upper limit and the lower limit of active power exchanged by the power distribution network and the upper power network connecting line are respectively used.
In the day-ahead stage, the constraint conditions of the energy storage system are as follows:
wherein: i is the node number; omega shape ESS The node set is a node set where the energy storage system is located;charging active power, discharging active power and reactive power of the energy storage system respectively; />Is the maximum apparent power of the energy storage system inverter at node i.
The constraint conditions of the energy storage system should also meet the following charge-discharge constraints:
wherein:and->Respectively charging power and discharging power of the energy storage system at the node i at the moment t;is the maximum capacity of the energy storage system; />And->Maximum energy storage systems at node i, respectivelyCharging power and maximum discharging power; />The charge quantity of the battery energy storage system is respectively t time period and t+1 time period; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the period t is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; Δt is the charge-discharge time interval;
The charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively.
In the day-ahead stage, the constraint conditions of the photovoltaic power generation system are as follows:
wherein: omega shape PV The node set is a node set where the photovoltaic power generation device is located;maximum apparent power for the photovoltaic power generation system inverter at node i; />Active power and reactive power at the moment t of the photovoltaic power generation system at the node i;for time t of photovoltaic power generation system at node iPredicting power; />Is a power factor.
In the day-ahead stage, the reactive equipment constraint conditions are as follows:
1) Transformer tap constraints:
wherein: omega shape OLTC The node set is a node set where the voltage regulating transformer is located; v (V) i,max ,V i,min The upper and lower limits of the voltage respectively;the voltage value is a high-voltage side voltage value and is a constant value; r is (r) i,t Is the square of the actual transformation ratio; r is (r) i,min ,r i,max Square of upper and lower limits of the transformation ratio; r is (r) i,s The difference value of the square of the transformation ratio between the gear s of the transformer and the gear s-1 is obtained; />The state of the gear s of the transformer at the time t and the time t-1; />The gear increasing mark and the gear decreasing mark of the transformer from the t-1 period to the t period are respectively provided; />The maximum change range of the gear is set; />The maximum allowable adjustment times are the gear positions.
2) Capacitor operation constraints:
in omega CB The node set is a node set where the capacitor is located; The total reactive power output by the capacitor at the moment t and the reactive power of the single group of capacitors are respectively; />The number of capacitors put into operation at the moment t and the total number of groups of capacitors are respectively; />A value of 0 indicates that the capacitor is not operating, and a value of 1 indicates that the capacitor is operating; />Is the maximum switching times in a dispatching cycle.
3) SVC operation constraint of the static var compensator:
wherein: omega shape SVC The node set is the node set where SVC is located;the upper and lower limits of SVC reactive power, respectively.
In the step 3, at the intra-day stage, the objective function is:
wherein:the power of each item in the time t period in the day is respectively;
C G,t ,C PV ,C ESS ,C ACLC ,C EVC the method comprises the steps of respectively obtaining a cost coefficient of electricity purchase from a superior power grid, a photovoltaic power generation cost coefficient, an energy storage operation cost coefficient, an air conditioner load cluster control cost coefficient and an electric automobile cluster control cost coefficient;
in the day stage, the constraint condition does not consider the OLTC operation constraint and the capacitor operation constraint, and other constraint conditions are the same as those of day-ahead scheduling, namely:
in the step 3, at the intra-day stage, the power flow constraint is a constraint condition after the second-order cone is relaxed:
wherein: j-k represents a set of all line end nodes k pointed by taking the node j as a head end; r is (r) ij ,x ij The resistance value and the reactance value of the branch ij are respectively; p (P) jk,t ,Q jk,t Active power and reactive power flowing from node j to node k at time t respectively; p (P) ij,t ,Q ij,t The active power and the reactive power respectively flow through the branch ij at the moment t; p (P) j,t ,Q j,t The net injection values of the active power and the reactive power of the node j at the moment t are respectively related to the actual load of the node j, an accessed energy storage device, a photovoltaic system, a controllable load, a capacitor and SVC; i ij,t ,U i,t The current through branch ij and the voltage at node i, respectively.The voltage of the node j at the moment t; />The voltage of the node j at the moment t; />The current flowing through the branch ij at the time t; />The active power of the load of the node j at the moment t; />The node j is connected with the charging power of the energy storage device at the moment t; />The discharging power of the energy storage device is accessed to the node j at the moment t; />The active power of the node j photovoltaic at the moment t; />The power of the air conditioner load cluster of the node j at the moment t; />The power of the electric automobile cluster is the node j at the moment t; />The reactive power of the load of the node j at the moment t; />The reactive power of the energy storage device is accessed to the node j at the moment t; />The reactive power of the node j photovoltaic at the moment t;the reactive power of the capacitor is node j at the moment t; />SVC reactive power at node j at time t.
In the day stage, the safety operation constraint conditions of the power distribution network are as follows:
Wherein: i ij,max Representing the maximum value of the current of branch ij; u (U) j,max ,U j,min The upper limit and the lower limit of the voltage amplitude of the node j are respectively set;the upper limit and the lower limit of active power exchanged by the power distribution network and the upper power network connecting line are respectively used.
In the day stage, the constraint conditions of the energy storage system are as follows:
wherein: i is the node number; omega shape ESS The node set is a node set where the energy storage system is located;charging active power, discharging active power and reactive power of the energy storage system respectively; />Is the maximum apparent power of the energy storage system inverter at node i.
The constraint conditions of the energy storage system should also meet the following charge-discharge constraints:
wherein:and->Respectively charging power and discharging power of the energy storage system at the node i at the moment t;is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; />The charge quantity of the battery energy storage system is respectively t time period and t+1 time period; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the period t is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; Δt is the charge-discharge time interval;
the charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively.
In the day stage, the constraint conditions of the photovoltaic power generation system are as follows:
wherein: omega shape PV The node set is a node set where the photovoltaic power generation device is located;maximum apparent power for the photovoltaic power generation system inverter at node i; />Active power and reactive power at the moment t of the photovoltaic power generation system at the node i;the predicted power at the moment t of the photovoltaic power generation system at the node i is calculated; />Is a power factor.
In the day stage, the SVC operation constraint of the static var compensator is as follows:
wherein: omega shape SVC The node set is the node set where SVC is located;the upper and lower limits of SVC reactive power, respectively.
In the step 4, the model predictive control is adopted, and the model predictive expression is as follows:
wherein: n represents a prediction step length; w (W) RT (t+k|t) is the adjustable resource output of the future t+k moment predicted at the moment of the real-time stage t; w (W) 0 (t) is an adjustable resource output initial value at time t; Δw RT (t+k|t) is the adjustable resource output increment of the future t+k time at the t time; w (W) RT To output adjustable resources including energy storage outputAir conditioner load cluster power +.>Electric automobile cluster power +.>
In the real-time stage, the minimum adjustment amount of the adjustable resource output is taken as a target, and the objective function is as follows:
wherein: ΔF (delta F) RT The adjustment quantity of the resource output can be adjusted at the current moment; w (W) DR And (t+k) is the planned output of the adjustable resource in the daily rolling scheduling stage.
The feedback correction link is to build a new optimization process on the basis of an actual measurement value, so that the influence caused by the deviation of photovoltaic output prediction and the error of adjustable resource output can be solved.
The first step, taking the actual value of the power grid voltage after the previous round of optimization control as the initial value of the new round of rolling optimization control to form rolling optimization:
W 0 (t+1)＝W true (t+1)
wherein: w (W) 0 (t+1) is an initial value of the adjustable resource output at time t+1; w (W) true And (t+1) predicting a force value for the adjustable resource in the t+1 period, and acquiring an actual force value in the t+1 period through actual measurement.
Secondly, correcting a new round of model prediction result according to the previous round of model prediction deviation:
wherein: w (t+1+k|t+1) is the future tunable resource prediction result at t+1+k based on the tunable resource state information at t+1; w' (t+1+k|t+1) is the correction of the voltage prediction result at the future time t+1+k. W (W) err (t) model predictive bias for time t; alpha is a correction compensation coefficient, and the value range is [0,1]The method comprises the steps of carrying out a first treatment on the surface of the W (t+1|t) is the adjustable resource output of the future t+1 moment predicted at the time t of the real-time stage of model prediction.
And thirdly, respectively bringing the results of the first step and the second step into a model prediction expression and an objective function, constructing a real-time stage model at the time k+1 in the day, and repeating the steps, thereby completing real-time rolling optimization and correction control.
The constraint conditions of the real-time stage are as follows:
1) The constraint conditions of the energy storage system are as follows:
wherein:and->Respectively predicting the t moment of the energy storage system at the node i to obtain the charging power and the discharging power at the future t+k moment; />Is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; />The charge quantity of the battery energy storage system is t time periods; />Predicting the t moment to obtain the charge quantity of the battery energy storage system at the future t+k moment; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the t+k period is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; Δt is the charge-discharge time interval;the charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively.
2) Power constraint of air conditioning load clusters:
wherein:the minimum power of the air conditioner load cluster; />The maximum power of the air conditioner load cluster;and predicting the power of the air conditioner load cluster at the future t+k moment for the t moment.
3) Power constraint of electric vehicle clusters:
wherein:the minimum power of the electric automobile cluster; />The maximum power of the electric automobile cluster; And predicting the power of the electric automobile cluster at the future t+k moment for the t moment.
The invention relates to a multi-time scale active and reactive coordination method considering load characteristics, which has the following technical effects:
1): the air conditioner load cluster aggregation model provided by the step 1 has the technical effects that: (1) realizing the efficient dispatching of the air conditioner load: the air conditioner loads are aggregated to be managed and scheduled, so that the air conditioner loads can be efficiently scheduled, and the air conditioner loads can be better balanced to be used in a cluster mode, so that the waste and emission of energy sources are reduced, and the energy source utilization efficiency is improved; (2) facilitating the application of clean energy: the air conditioner load cluster can combine the utilization of clean energy and the management of air conditioner load, thereby promoting the application of clean energy, better utilizing clean energy by adjusting the use period of air conditioner load, the temperature setting of air conditioner and other modes, and reducing the dependence on traditional energy; (3) improving user experience: the air conditioner load cluster can optimize and improve the use of the air conditioner, so that the use experience of a user is improved, the use requirement of the user can be better adapted by adjusting the use period and the temperature setting of the air conditioner, and the comfort of the user is improved. The electric automobile cluster aggregation model provided by the step 1 has the technical effects that: (1) the service efficiency of the electric automobile is improved: through integrating a plurality of electric automobile into a cluster, can realize high-efficient management and dispatch to electric automobile to improve electric automobile's availability factor, the cluster can be according to electric automobile's electric quantity and the service behavior dispatch, lets electric automobile appear in the place that needs when needs, reduces empty load or heavy load's condition, thereby reduces the waste of energy. (2) The use requirement of the electric automobile: according to the coupling of the travel chain and the power grid, the power distribution network is partitioned, and the electric automobile cluster can be helped to determine the use demands of users in different areas. Through analysis and combination of different travel chains, travel demands and behavior characteristics of the user can be determined, so that the demands of the user are better met, and the satisfaction degree of the user is improved.
2): the day-ahead dispatching stage proposed by the step 2 is used for making a regulation plan of the transformer tap and the capacitor, and mainly because the transformer tap and the capacitor are limited by manufacturing technology and service life of equipment, the switching speed and the day switching times are limited to a certain extent, and the day-ahead stage regulation plan of the transformer tap and the capacitor is made in the day-ahead stage, so that the method is an innovative method. Active power and reactive power coordinated optimization scheduling of the active power distribution network is carried out through information such as predicted load and renewable energy sources, and the action state and action quantity of slow dynamic equipment are determined, so that the influence of short-term rolling optimization and real-time feedback correction on the equipment is avoided. The scheduling plan can enable the power system to be more stable, prolong the service life of equipment and reduce the maintenance cost, and is an innovative and advantageous scheduling mode.
3): according to the invention, the step 3 adopts daily rolling optimization scheduling, can optimize according to load and renewable energy prediction data of the same day, has higher prediction precision, and can better cope with sudden load fluctuation and renewable energy output change. The daily rolling optimization scheduling can timely respond to load and renewable energy fluctuation, balance of the power grid is achieved by adjusting the state of the adjustable equipment, and compared with the daily scheduling, the daily rolling optimization scheduling can more rapidly respond to the change of the power grid, and the conditions of insufficient power supply or excessive power supply are reduced.
4): in the step 4 of the invention, real-time feedback correction optimization based on model predictive control is adopted, closed-loop optimization control is constructed based on the latest power predictive information, scheduling result deviation generated by predictive errors is corrected in time, the influence of renewable energy and load power uncertainty on a scheduling scheme is eliminated to the maximum extent, and the accuracy of optimization control is improved.
5): according to the day-before-day-in-real-time multi-time-scale optimal scheduling method, the scheduling plan is refined step by step, the load adjusting capability can be fully exerted, and the photovoltaic digestion capability is improved.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
fig. 1 is a schematic view of a wheel control strategy for an air conditioning load cluster.
Fig. 2 is a view of an electric vehicle travel chain structure.
Fig. 3 is a graph of quantitative evaluation of the adjustability of an electric vehicle under travel chain theory.
Fig. 4 is a multi-time scale scheduling flow diagram.
Fig. 5 is a modified IEEE33 node topology.
Fig. 6 is an OLTC adjustment plan view.
Fig. 7 is a capacitor adjustment plan view.
Fig. 8 is a graph of each adjustable load profile during the day.
Fig. 9 is an SVC diagram.
Fig. 10 is a real-time phase schedule.
Detailed Description
A multi-time scale active reactive coordination method taking load characteristics into account, comprising: model construction: constructing a load cluster aggregation model by considering the air conditioner load and the energy utilization characteristics of the electric automobile, and quantitatively evaluating the adjustable capacity; the day-ahead stage: according to the objective function and the constraint condition, an adjustment plan of the transformer tap and the capacitor is formulated; day period: taking a scheduling plan determined in the past as a reference, and taking the minimum total cost of the power distribution network as a target, performing rolling scheduling in the past; real-time stage: and introducing a feedback correction link to form closed loop control by taking the minimum active and reactive power adjustment amount as a target, and correcting a scheduling plan at the next moment according to the scheduling result deviation of the current time period. The method comprises the following steps:
The model construction, the aggregation model of the air conditioner load cluster is as follows:
wherein: p (P) t ACLC The aggregate power of the air conditioner cluster at the time t is represented; nac is the number of air conditioners in the air conditioner cluster;the power of the ith air conditioner; s is(s) i,t The air conditioner is operated at the time t, wherein 1 is opened, and 0 is closed.
The wheel control strategy of the air conditioner load cluster is shown in figure 1, and the temperature control interval of the air conditioner is [ T ] min ,T max ]When the indoor temperature reaches the minimum value T min When the air conditioner enters a shut-down state, the indoor temperature reaches the maximum temperature T max When the air conditioner is in an on state. The control period of the wheel control strategy is tau c Minute, time interval of each state is tau g Minute, totally divide into τ c /τ g In each state, n air conditioners are also divided into tau c /τ g A group.
The wheel control strategy of the air conditioner load cluster has the advantages that the temperature intervals among adjacent groups in the same state are the same, the time interval of the air conditioner opening group is larger than the time interval of the closing group, and each time the next time interval is entered, a group of air conditioners are closed, a group of air conditioners are opened, and the same number of air conditioner opening groups and the same number of air conditioner closing groups are ensured.
Controllable capacity C of air conditioner load cluster aggregation under wheel control strategy x The method comprises the following steps:
wherein τ c To control the period τ off Is the shutdown time; Is the average power of the air conditioner.
Table 1 quantitative assessment of air conditioner load adjustability under a wheel control strategy
The model construction is carried out, and an aggregation model of the electric automobile cluster is as follows:
wherein:charging power and discharging power of the electric automobile clusters are respectively carried out; />Respectively obtaining the maximum charging power and the maximum discharging power of the electric automobile cluster; />The total capacity of the electric automobile cluster which can be scheduled at the time t is obtained; />The total capacity of the electric automobile cluster which can be scheduled at the time t-1 is obtained; Δt is the time interval; />Is the dynamic step deviation electric quantity; η (eta) ch ,η dch Respectively charging and discharging efficiency; />The electric quantity of the ith EV when arriving and leaving at the moment t is respectively; u (u) i,t-1 ,u i,t The states of the ith EV at t-1 and t which are connected with the power grid are respectively equal to 1 and 0, and the states are grid-connected and off-grid; nev is the number of clustered electric vehicles; u is the grid connection condition of the ith vehicle in the t period, and the value is 0 or 1.The minimum schedulable capacity and the maximum schedulable capacity of the electric automobile cluster at the time t are respectively.
The result of the electric automobile cluster travel chain is shown in fig. 2, the travel area is divided into a living area, a working area and a business area, and the electric automobile travel chain is defined as the process that electric automobile users start from the living area and finally return to the living area after a series of activities.
Travel chain theory quantitative evaluation electric vehicle cluster schedulable capacity and electric vehicle initial charging time f s And daily mileage f d Probability distribution function:
wherein: t is time; mu (mu) s ,σ s Respectively an expected value and a standard deviation of the initial charging time of the electric automobile; mu (mu) d ,σ d The expected value and standard deviation of the daily driving mileage of the electric automobile are respectively.
Fig. 3 is a graph of quantitative evaluation of the adjustability of an electric vehicle under travel chain theory.
In the day-ahead stage, the objective function is the total running cost F of the power distribution network DA Minimum:
/>
wherein:the power of each item in the t period; />The frequency of gear adjustment of the transformer and the frequency of adjustment of the capacitor are respectively; c (C) G,t ,C PV ,C ESS ,C ACLC ,C EVC The method comprises the steps of respectively obtaining a cost coefficient of electricity purchase from a superior power grid, a photovoltaic power generation cost coefficient, an energy storage operation cost coefficient, an air conditioner load cluster control cost coefficient and an electric automobile cluster control cost coefficient; gamma ray OLTC ，γ CB The gear adjusting cost coefficient of the transformer and the adjusting cost coefficient of the capacitor are respectively.
In the day-ahead stage, the tide constraint is a constraint condition after the second-order cone is relaxed:
wherein: j-k represents a set of all line end nodes k pointed by taking the node j as a head end; r is (r) ij ,x ij The resistance value and the reactance value of the branch ij are respectively; p (P) jk,t ,Q jk,t Active power and reactive power flowing from node j to node k at time t respectively; p (P) ij,t ,Q ij,t The active power and the reactive power respectively flow through the branch ij at the moment t; p (P) j,t ,Q j,t The net injection values of the active power and the reactive power of the node j at the moment t are respectively related to the actual load of the node j, an accessed energy storage device, a photovoltaic system, a controllable load, a capacitor and SVC; i ij,t ,U i,t Respectively, the current flowing through the branches ijAnd the voltage at node i.The voltage of the node j at the moment t; />The voltage of the node j at the moment t; />The current flowing through the branch ij at the time t; />The active power of the load of the node j at the moment t; />The node j is connected with the charging power of the energy storage device at the moment t; />The discharging power of the energy storage device is accessed to the node j at the moment t; />The active power of the node j photovoltaic at the moment t; />The power of the air conditioner load cluster of the node j at the moment t; />The power of the electric automobile cluster is the node j at the moment t; />The reactive power of the load of the node j at the moment t; />The reactive power of the energy storage device is accessed to the node j at the moment t; />The reactive power of the node j photovoltaic at the moment t;the reactive power of the capacitor is node j at the moment t; />SVC reactive power at node j at time t.
Second order cone relaxation is to make the second order in the trend constraint And (3) performing secondary equal relaxation transformation to obtain:
the standard second order cone is formed by the following steps:
in the day-ahead stage, the safety operation constraint conditions of the power distribution network are as follows:
wherein: i ij,max Representing the maximum value of the current of branch ij; u (U) j,max ,U j,min Respectively the upper and lower limits of the voltage amplitude of the node j;and the upper limit and the lower limit of active power are respectively exchanged between the power distribution network and the upper power grid tie line.
In the day-ahead stage, the constraint conditions of the energy storage system are as follows:
wherein: omega shape ESS The node set is a node set where the energy storage system is located;respectively is a storage deviceCharging active power, discharging active power and reactive power of the energy system; />Is the maximum apparent power of the energy storage system inverter at node i.
The constraint conditions of the energy storage system should also meet the following charge-discharge constraints:
wherein:and->Respectively charging power and discharging power of the energy storage system at the node i at the moment t;is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; />The charge quantity of the battery energy storage system is respectively t time period and t+1 time period; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the period t is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; Δt is the charge-discharge time interval; / >The charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively.
In the day-ahead stage, the reactive equipment constraint conditions are as follows:
1) Transformer tap constraints:
wherein: omega shape OLTC The node set is a node set where the voltage regulating transformer is located; v (V) i,max ,V i,min The upper and lower limits of the voltage respectively;the voltage value is a high-voltage side voltage value and is a constant value; r is (r) i,t Is the square of the actual transformation ratio; r is (r) i,min ,r i,max Square of upper and lower limits of the transformation ratio; r is (r) i,s The difference value of the square of the transformation ratio between the gear s of the transformer and the gear s-1 is obtained; />The state of the gear s of the transformer at the time t and the time t-1; />The gear increasing mark and the gear decreasing mark of the transformer from the t-1 period to the t period are respectively provided; />The maximum change range of the gear is set; />The maximum allowable adjustment times are the gear positions.
2) Capacitor operation constraints:
in omega CB The node set is a node set where the capacitor is located;the total reactive power output by the capacitor at the moment t and the reactive power of the single group of capacitors are respectively; />The number of capacitors put into operation at the moment t and the total number of groups of capacitors are respectively; />A value of 0 indicates that the capacitor is not operating, and a value of 1 indicates that the capacitor is operating; />Is the maximum switching times in a dispatching cycle.
3) SVC operation constraint of the static var compensator:
wherein: omega shape SVC The node set is the node set where SVC is located; Is the upper and lower limit of SVC reactive power. />
In the intra-day stage, the objective function is:
wherein:the power of each item in the time t period in the day is respectively; c (C) G,t ,C PV ,C ESS ,C ACLC ,C EVC Respectively the cost coefficient of purchasing electricity from the upper power grid, the photovoltaic power generation cost coefficient, the energy storage operation cost coefficient,Air conditioner load cluster control cost coefficient and electric automobile cluster control cost coefficient.
In the daytime, the tide constraint is a constraint condition after the second-order cone is relaxed:
wherein: j-k represents a set of all line end nodes k pointed by taking the node j as a head end; r is (r) ij ,x ij The resistance value and the reactance value of the branch ij are respectively; p (P) jk,t ,Q jk,t Active power and reactive power flowing from node j to node k at time t respectively; p (P) ij,t ,Q ij,t The active power and the reactive power respectively flow through the branch ij at the moment t; p (P) j,t ,Q j,t The net injection values of the active power and the reactive power of the node j at the moment t are respectively related to the actual load of the node j, an accessed energy storage device, a photovoltaic system, a controllable load, a capacitor and SVC; i ij,t ,U i,t The current through branch ij and the voltage at node i, respectively.The voltage of the node j at the moment t; />The voltage of the node j at the moment t; />The current flowing through the branch ij at the time t; />The active power of the load of the node j at the moment t; />The node j is connected with the charging power of the energy storage device at the moment t; / >The discharging power of the energy storage device is accessed to the node j at the moment t; />The active power of the node j photovoltaic at the moment t; />The power of the air conditioner load cluster of the node j at the moment t; />The power of the electric automobile cluster is the node j at the moment t; />The reactive power of the load of the node j at the moment t; />The reactive power of the energy storage device is accessed to the node j at the moment t; />The reactive power of the node j photovoltaic at the moment t;the reactive power of the capacitor is node j at the moment t; />SVC reactive power at node j at time t.
In the day stage, the safety operation constraint conditions of the power distribution network are as follows:
wherein: i ij,max Representing the maximum value of the current of branch ij; u (U) j,max ,U j,min The upper limit and the lower limit of the voltage amplitude of the node j are respectively set;the upper limit and the lower limit of active power exchanged by the power distribution network and the upper power network connecting line are respectively used.
In the day stage, the constraint conditions of the energy storage system are as follows:
wherein: i is the node number; omega shape ESS The node set is a node set where the energy storage system is located;charging active power, discharging active power and reactive power of the energy storage system respectively; />Is the maximum apparent power of the energy storage system inverter at node i.
The constraint conditions of the energy storage system should also meet the following charge-discharge constraints:
wherein:and->Respectively charging power and discharging power of the energy storage system at the node i at the moment t; Is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; />The charge quantity of the battery energy storage system is respectively t time period and t+1 time period; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the period t is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; Δt is the charge-discharge time interval;
the charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively.
In the day stage, the constraint conditions of the photovoltaic power generation system are as follows:
wherein: omega shape PV The node set is a node set where the photovoltaic power generation device is located;maximum apparent power for the photovoltaic power generation system inverter at node i; />Active power and reactive power at the moment t of the photovoltaic power generation system at the node i;the predicted power at the moment t of the photovoltaic power generation system at the node i is calculated; />Is a power factor.
In the day stage, the SVC operation constraint of the static var compensator is as follows:
wherein: omega shape SVC The node set is the node set where SVC is located;the upper and lower limits of SVC reactive power, respectively.
In a real-time stage, the model prediction control is adopted, and the model prediction expression is as follows:
Wherein: n represents a prediction step length; w (W) RT (t+k|t) is the adjustable resource output of the future t+k moment predicted at the moment of the real-time stage t; w (W) 0 (t) is an adjustable resource output initial value at time t; Δw RT (t+k|t) is the adjustable resource output increment of the future t+k time at the t time; w (W) RT To output adjustable resources including energy storage outputAir conditioner load cluster power +.>Electric automobile cluster power +.>
In the real-time stage, the minimum adjustment amount of the adjustable resource output is taken as a target, and the objective function is as follows:
wherein: ΔF (delta F) RT The adjustment quantity of the resource output can be adjusted at the current moment; w (W) DR And (t+k) is the planned output of the adjustable resource in the daily rolling scheduling stage.
The feedback correction link is to build a new optimization process on the basis of an actual measurement value, so that the influence caused by the deviation of photovoltaic output prediction and the error of adjustable resource output can be solved.
The first step, taking the actual value of the power grid voltage after the previous round of optimization control as the initial value of the new round of rolling optimization control to form rolling optimization:
W 0 (t+1)＝W true (t+1)
wherein: w (W) 0 (t+1) is an initial value of the adjustable resource output at time t+1; w (W) true And (t+1) predicting a force value for the adjustable resource in the t+1 period, and acquiring an actual force value in the t+1 period through actual measurement.
Secondly, correcting a new round of model prediction result according to the previous round of model prediction deviation:
wherein: w (t+1+k|t+1) is the future tunable resource prediction result at t+1+k based on the tunable resource state information at t+1; w' (t+1+k|t+1) is the correction of the voltage prediction result at the future time t+1+k. W (W) err (t) model predictive bias for time t; alpha is a correction compensation coefficient, and the value range is [0,1]The method comprises the steps of carrying out a first treatment on the surface of the W (t+1|t) is the adjustable resource output of the future t+1 moment predicted at the time t of the real-time stage of model prediction.
And thirdly, respectively bringing the results of the first step and the second step into a model prediction expression and an objective function, constructing a real-time stage model at the time k+1 in the day, and repeating the steps, thereby completing real-time rolling optimization and correction control.
The constraint conditions of the real-time stage are as follows:
1) The constraint conditions of the energy storage system are as follows:
wherein:and->Respectively predicting the t moment of the energy storage system at the node i to obtain the charging power and the discharging power at the future t+k moment; />Is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; />The charge quantity of the battery energy storage system is t time periods; />Predicting the t moment to obtain the charge quantity of the battery energy storage system at the future t+k moment; / >The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the t+k period is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; Δt is the charge-discharge time interval;the charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively.
2) Power constraint of air conditioning load clusters:
wherein:the minimum power of the air conditioner load cluster; />The maximum power of the air conditioner load cluster;and predicting the power of the air conditioner load cluster at the future t+k moment for the t moment.
3) Power constraint of electric vehicle clusters:
wherein:the minimum power of the electric automobile cluster; />The maximum power of the electric automobile cluster;and predicting the power of the electric automobile cluster at the future t+k moment for the t moment.
The real-time multi-time scale scheduling flow before, in the day is shown in fig. 4, which comprises the following steps:
step 1: carrying out period 24h on photovoltaic, load and adjustable resources before the day, and predicting the photovoltaic, load and adjustable resources for a long term with resolution of 1 h;
step 2: carrying out day-ahead scheduling at the minimum total running cost of the power distribution network in advance for 24 hours;
step 3: determining a transformer tap and a capacitor regulation plan;
Step 4: carrying out short-term prediction on photovoltaic, load and adjustable resources in the day for 1h with resolution of 15 min;
step 5: scheduling in a day with the minimum total running cost of the power distribution network 1h in advance;
step 6: determining an adjustment plan for the adjustable resource within the day;
step 7: carrying out short-term prediction on real-time photovoltaic, load and adjustable resources for 15min with resolution of 5 min;
step 8: 15min in advance, and taking the minimum adjustable resource adjustment amount as a target;
step 9: determining an adjustment plan for the real-time adjustable resource;
the IEEE33 node system is modified, the reference voltage of the system is 12.66kV, the daily maximum load is 24.85+j15.43MVA, and the OLTC represents a transformer connected with an upper power grid at a node 1; photovoltaic was installed at nodes 18, 22 and 33, all with installed capacity of 8.5MW; the energy storage is arranged at the nodes 16, 20 and 31, the capacities are respectively 8MWh, 7MWh and 7.5MWh, the upper limit of the charge and discharge power is 2800kW, 2450kW and 2625kW, and the charge and discharge efficiency is set to be 0.98; SVC is arranged at nodes 17, 21 and 32, and the dynamic reactive power provided by the SVC is-4 Mvar-4 MVar; capacitor banks are mounted at nodes 6 and 25, each with compensation capacities of 500kVar, 400kVar, respectively, 10 and 15 banks, respectively. The modified system topology is shown in fig. 5.
Under the calculation condition, the multi-time-scale active and reactive coordination degree result considering the load characteristic is as follows by applying the method of the invention:
as shown in fig. 6 and 7, the scheduling result in the day-ahead stage shows that, under the premise of considering the reactive power generated by the photovoltaic power generation system, the reactive power output in the period of 11:00-15:00 with higher photovoltaic output is also higher, so that the OLTC is not out of limit for the storage voltage, and the capacitor banks at the 1 st gear, node 6 and node 25 are not put into operation.
The scheduling result in the daytime is shown in fig. 8, in the valley period, the system load is small, the energy storage system starts to charge, the charge quantity is improved, the electric vehicle cluster charge-discharge power in the living area is larger than that in the working area and the business area, and the electric vehicle cluster charge-discharge power in the working area is minimum; at the usual morning, the system load is increased, the energy storage system starts to discharge, and the charge quantity is reduced; in the peak period of noon, the system load is higher, the photovoltaic output is larger, the permeability reaches 98%, the energy storage system is charged to the maximum charge quantity, the electric automobile clusters in the working area and the commercial area start to be charged in a large scale, the air conditioner load clusters cut down part of the load under a wheel control strategy, the peak clipping effect is achieved, the reactive power of the system is larger, the reactive power balance is ensured, the SVC reversely adjusts, and reactive power is absorbed, as shown in fig. 9; in the afternoon normal period, the charge quantity of the energy storage system is kept constant, electric automobile clusters in the living area, the working area and the business area all have charge and discharge phenomena, and the charge and discharge load of the living area is gradually increased; in the late peak period, the system load reaches a peak value, the electric vehicle cluster charging power in the living area reaches a peak value, but the electric vehicle cluster charging power in the working area is smaller, the energy storage system is discharged in a large quantity, and the load reduced by the late peak air conditioner load cluster under the wheel control strategy is smaller than the noon peak.
The real-time stage scheduling result is shown in fig. 10, and it can be seen that the real-time stage adopts ultra-short-term prediction and closed-loop optimization scheduling with feedback correction, so that the effect of tracking the load is more accurate, the uncertainty of the photovoltaic output and the load can be effectively coped with, and the photovoltaic power generation can be absorbed to the maximum extent.
Claims (10)
1. A multi-time-scale active and reactive coordination scheduling method considering load characteristics is characterized by comprising the following steps:
step 1: taking the air conditioner load and the energy utilization characteristics of the electric automobile into consideration, constructing a load cluster aggregation model, and quantitatively evaluating the adjustable capacity;
step 2: according to the objective function and the constraint condition, a day-ahead phase adjustment plan of the transformer tap and the capacitor is formulated;
step 3: taking the day-ahead stage adjustment plan in the step 2 as a reference, and taking the minimum total cost of the power distribution network as a target, performing day-in stage rolling scheduling;
step 4: in a real-time stage, model predictive control is adopted, the minimum active and reactive adjustment is taken as a target, a feedback correction link is introduced, closed-loop control is formed, and the scheduling plan at the next moment is corrected according to the scheduling result deviation of the current period.
2. The multi-time-scale active and reactive coordination scheduling method considering load characteristics according to claim 1, wherein the method comprises the following steps: in the step 1, the aggregation model of the constructed air conditioner load cluster is as follows:
Wherein: p (P) t ACLC The aggregation power of the air conditioner cluster at the time t; i is the serial number of the air conditioner; nac is the number of air conditioners in the air conditioner cluster; p (P) i AC The power of the ith air conditioner; s is(s) i,t For the operation state of the ith air conditioner at the moment t, 1 represents opening and 0 represents closing;
based on the built aggregation model of the air conditioner load clusters, the air conditioner is circularly controlled by adopting a wheel control strategy, and the air conditioner cluster load controllable capacity C under the wheel control strategy x The method comprises the following steps:
wherein τ c To control the period τ off Is the shutdown time;the average power of the air conditioner;
the aggregation model of the constructed electric automobile cluster is as follows:
wherein:charging power and discharging power of the electric automobile clusters are respectively carried out; />Respectively obtaining the maximum charging power and the maximum discharging power of the electric automobile cluster; />The total capacity of the electric automobile cluster which can be scheduled at the time t is obtained; />The total capacity of the electric automobile cluster which can be scheduled at the time t-1 is obtained; Δt is the time interval; />Is the dynamic step deviation electric quantity; η (eta) ch ,η dch Respectively charging and discharging efficiency; />The electric quantity of the ith EV when arriving and leaving at the moment t is respectively; u (u) i,t-1 ,u i,t The states of the ith EV at t-1 and t which are connected with the power grid are respectively equal to 1 and 0, and the states are grid-connected and off-grid; nev is the number of clustered electric vehicles; u is the grid connection condition of the ith vehicle in the t period, and the value is 0 or 1; / >The minimum schedulable capacity and the maximum schedulable capacity of the electric automobile cluster at the time t are respectively;
based on the built aggregation model of the electric automobile cluster, the schedulable capacity of the electric automobile cluster is quantitatively evaluated by adopting a travel chain theory, and the probability density functions of the initial charging time and the daily driving mileage of the electric automobile are as follows:
wherein: f (f) s (t) is a probability density function of the initial charging time of the electric automobile; f (f) d (x) The probability density function of the daily driving mileage of the electric automobile is obtained; t is time; x is the driving mileage; mu (mu) s ,σ s Respectively an expected value and a standard deviation of the initial charging time of the electric automobile; mu (mu) d ,σ d The expected value and standard deviation of the daily driving mileage of the electric automobile are respectively.
3. The multi-time-scale active and reactive coordination scheduling method considering load characteristics according to claim 1, wherein the method comprises the following steps: in the step 2, at a day-ahead stage, the objective function is the total running cost F of the power distribution network DA Minimum:
wherein:purchasing electric power from an upper power grid at the moment t; />The power of the photovoltaic at the moment t; />The power stored at the time t is; />The power of the air conditioner load cluster at the moment t; />The power of the electric automobile cluster at the moment t;the frequency of gear adjustment of the transformer and the frequency of adjustment of the capacitor are respectively; c (C) G,t ,C PV ,C ESS ,C ACLC ,C EVC The method comprises the following steps of: the method comprises the following steps of purchasing electricity from a superior power grid, obtaining a photovoltaic power generation cost coefficient, obtaining an energy storage operation cost coefficient, obtaining an air conditioner load cluster control cost coefficient and obtaining an electric automobile cluster control cost coefficient; gamma ray OLTC ，γ CB The gear adjusting cost coefficient of the transformer and the adjusting cost coefficient of the capacitor are respectively.
4. The multi-time-scale active and reactive coordination scheduling method considering load characteristics according to claim 2, wherein the method comprises the following steps: in the step 2, at a day-ahead stage, the power flow constraint is a constraint condition after the second-order cone is relaxed:
wherein: j-k represents a set of all line end nodes k pointed by taking the node j as a head end; r is (r) ij ,x ij The resistance value and the reactance value of the branch ij are respectively; p (P) jk,t ,Q jk,t Active power and reactive power flowing from node j to node k at time t respectively; p (P) ij,t ,Q ij,t The active power and the reactive power respectively flow through the branch ij at the moment t; p (P) j,t ,Q j,t The net injection values of the active power and the reactive power of the node j at the moment t are respectively; i ij,t ,U i,t The current through branch ij and the voltage at node i, respectively;
the voltage of the node j at the moment t; />The voltage of the node j at the moment t; />The current flowing through the branch ij at the time t;
the active power of the load of the node j at the moment t; />The node j is connected with the charging power of the energy storage device at the moment t; The discharging power of the energy storage device is accessed to the node j at the moment t; />The active power of the node j photovoltaic at the moment t; />The power of the air conditioner load cluster of the node j at the moment t; />The power of the electric automobile cluster is the node j at the moment t;
the reactive power of the load of the node j at the moment t; />The reactive power of the energy storage device is accessed to the node j at the moment t;the reactive power of the node j photovoltaic at the moment t; />The reactive power of the capacitor is node j at the moment t; />SVC as node j at time tReactive power;
in the day-ahead stage, the safety operation constraint conditions of the power distribution network are as follows:
wherein: i ij,max Representing the maximum value of the current of branch ij; u (U) j,max ,U j,min The upper limit and the lower limit of the voltage amplitude of the node j are respectively set;the upper limit and the lower limit of active power are exchanged between the power distribution network and the upper power grid tie line respectively;
in the day-ahead stage, the constraint conditions of the energy storage system are as follows:
wherein: i is the node number; omega shape ESS The node set is a node set where the energy storage system is located;charging active power, discharging active power and reactive power of the energy storage system respectively; />Maximum apparent power for the energy storage system inverter at node i;
the constraint conditions of the energy storage system should also meet the following charge-discharge constraints:
wherein:and->Respectively charging power and discharging power of the energy storage system at the node i at the moment t; / >Is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; />The charge quantity of the battery energy storage system is respectively t time period and t+1 time period; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the period t is equal to 1 in a charging state and equal to 0 in a discharging state;and->Charging efficiency and discharging efficiency respectively; delta t is the charge-discharge time interval; />The charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively;
in the day-ahead stage, the constraint conditions of the photovoltaic power generation system are as follows:
wherein: omega shape PV The node set is a node set where the photovoltaic power generation device is located;maximum apparent power for the photovoltaic power generation system inverter at node i; />Active power and reactive power at the moment t of the photovoltaic power generation system at the node i; />The predicted power at the moment t of the photovoltaic power generation system at the node i is calculated; />Is a power factor;
in the day-ahead stage, the reactive equipment constraint conditions are as follows:
1) Transformer tap constraints:
wherein: omega shape OLTC The node set is a node set where the voltage regulating transformer is located; v (V) i,max ,V i,min The upper and lower limits of the voltage respectively;the voltage value is a high-voltage side voltage value and is a constant value; r is (r) i,t Is the square of the actual transformation ratio; r is (r) i,min ,r i,max Square of upper and lower limits of the transformation ratio; r is (r) i,s The difference value of the square of the transformation ratio between the gear s of the transformer and the gear s-1 is obtained; />The state of the gear s of the transformer at the time t and the time t-1; />The gear increasing mark and the gear decreasing mark of the transformer from the t-1 period to the t period are respectively provided; />The maximum change range of the gear is set; />The maximum allowable adjustment times of the gear are set;
2) Capacitor operation constraints:
in omega CB The node set is a node set where the capacitor is located;the total reactive power output by the capacitor at the moment t and the reactive power of the single group of capacitors are respectively; />The number of capacitors put into operation at the moment t and the total number of groups of capacitors are respectively;a value of 0 indicates that the capacitor is not operating, and a value of 1 indicates that the capacitor is operating; />The maximum switching times in a scheduling period are set;
3) SVC operation constraint of the static var compensator:
wherein: omega shape SVC The node set is the node set where SVC is located;the upper and lower limits of SVC reactive power, respectively.
5. The multi-time-scale active and reactive coordination scheduling method considering load characteristics according to claim 1, wherein the method comprises the following steps: in the step 3, at the intra-day stage, the objective function is:
wherein:the power of each item in the time t period in the day is respectively; c (C) G,t ,C PV ,C ESS ,C ACLC ,C EVC The cost coefficient of purchasing electricity from a superior power grid, the photovoltaic power generation cost coefficient, the energy storage operation cost coefficient, the air conditioner load cluster control cost coefficient and the electric automobile cluster control cost coefficient are respectively.
6. The multi-time scale active and reactive coordination scheduling method considering load characteristics according to claim 5, wherein the method comprises the following steps: in the day stage, the constraint condition does not consider the OLTC operation constraint and the capacitor operation constraint, and other constraint conditions are the same as those of day-ahead scheduling, namely:
in the daytime, the tide constraint is a constraint condition after the second-order cone is relaxed:
wherein: j-k represents a set of all line end nodes k pointed by taking the node j as a head end; r is (r) ij ,x ij The resistance value and the reactance value of the branch ij are respectively; p (P) jk,t ,Q jk,t Active power and reactive power flowing from node j to node k at time t respectively; p (P) ij,t ,Q ij,t The active power and the reactive power respectively flow through the branch ij at the moment t; p (P) j,t ,Q j,t Respectively are provided withThe net injection values of the active power and the reactive power of the node j at the moment t are respectively related to the actual load of the node j, an accessed energy storage device, a photovoltaic system, a controllable load, a capacitor and SVC; i ij,t ,U i,t The current through branch ij and the voltage at node i, respectively;the voltage of the node j at the moment t; />The voltage of the node j at the moment t; />The current flowing through the branch ij at the time t; />The active power of the load of the node j at the moment t; />The node j is connected with the charging power of the energy storage device at the moment t; / >The discharging power of the energy storage device is accessed to the node j at the moment t; />The active power of the node j photovoltaic at the moment t; />The power of the air conditioner load cluster of the node j at the moment t; />The power of the electric automobile cluster is the node j at the moment t; />The reactive power of the load of the node j at the moment t; />The reactive power of the energy storage device is accessed to the node j at the moment t; />The reactive power of the node j photovoltaic at the moment t; />The reactive power of the capacitor is node j at the moment t; />SVC reactive power of node j at time t;
in the day stage, the safety operation constraint conditions of the power distribution network are as follows:
wherein: i ij,max Representing the maximum value of the current of branch ij; u (U) j,max ,U j,min The upper limit and the lower limit of the voltage amplitude of the node j are respectively set;the upper limit and the lower limit of active power are exchanged between the power distribution network and the upper power grid tie line respectively;
in the day stage, the constraint conditions of the energy storage system are as follows:
wherein: i is the node number; omega shape ESS The node set is a node set where the energy storage system is located;charging active power, discharging active power and reactive power of the energy storage system respectively; />Maximum apparent power for the energy storage system inverter at node i;
the constraint conditions of the energy storage system should also meet the following charge-discharge constraints:
wherein:and->Respectively charging power and discharging power of the energy storage system at the node i at the moment t; / >Is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; />The charge quantity of the battery energy storage system is respectively t time period and t+1 time period; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the period t is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; delta t is the charge-discharge time interval; />The charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively;
in the day stage, the constraint conditions of the photovoltaic power generation system are as follows:
wherein: omega shape PV The node set is a node set where the photovoltaic power generation device is located;maximum apparent power for the photovoltaic power generation system inverter at node i; />Active power and reactive power at the moment t of the photovoltaic power generation system at the node i; />The predicted power at the moment t of the photovoltaic power generation system at the node i is calculated; />Is a power factor;
in the day stage, the SVC operation constraint of the static var compensator is as follows:
wherein: omega shape SVC The node set is the node set where SVC is located;the upper and lower limits of SVC reactive power, respectively.
7. The multi-time-scale active and reactive coordination scheduling method considering load characteristics according to claim 1, wherein the method comprises the following steps: in the step 4, the model predictive control is adopted, and the model predictive expression is as follows:
Wherein: n represents a prediction step length; w (W) RT (t+k|t) is the adjustable resource output of the future t+k moment predicted at the moment of the real-time stage t; w (W) 0 (t) is an adjustable resource output initial value at time t; deltaw RT (t+k|t) is the adjustable resource output increment of the future t+k time at the t time; w (W) RT To output adjustable resources including energy storage outputAir conditioner load cluster power +.>Electric automobile cluster power +.>
8. The multi-time scale active and reactive coordination scheduling method considering load characteristics according to claim 7, wherein the method comprises the following steps: in the real-time stage, the minimum adjustment amount of the adjustable resource output is taken as a target, and the objective function is as follows:
wherein: deltaF RT The adjustment quantity of the resource output can be adjusted at the current moment; w (W) DR And (t+k) is the planned output of the adjustable resource in the daily rolling scheduling stage.
9. The multi-time scale active and reactive coordination scheduling method considering load characteristics according to claim 8, wherein the method comprises the following steps: the constraint conditions of the real-time stage are as follows:
1) The constraint conditions of the energy storage system are as follows:
wherein:and->Respectively predicting the t moment of the energy storage system at the node i to obtain the charging power and the discharging power at the future t+k moment; />Is the maximum capacity of the energy storage system; />And->The maximum charging power and the maximum discharging power of the energy storage system at the node i are respectively; / >The charge quantity of the battery energy storage system is t time periods; />Predicting the t moment to obtain the charge quantity of the battery energy storage system at the future t+k moment; />The maximum electric quantity of the battery energy storage system is; />The running state of the battery in the t+k period is equal to 1 in a charging state and equal to 0 in a discharging state; />And->Charging efficiency and discharging efficiency respectively; delta t is the charge-discharge time interval;
the charge quantity at the initial moment and the charge quantity at the end moment of the battery energy storage system are respectively;
2) Power constraint of air conditioning load clusters:
wherein:the minimum power of the air conditioner load cluster; />The maximum power of the air conditioner load cluster;predicting and obtaining air conditioner load cluster power at a future t+k moment for the t moment;
3) Power constraint of electric vehicle clusters:
wherein:the minimum power of the electric automobile cluster; />The maximum power of the electric automobile cluster;and predicting the power of the electric automobile cluster at the future t+k moment for the t moment.
10. The multi-time-scale active and reactive coordination scheduling method considering load characteristics according to claim 1, wherein the method comprises the following steps: the feedback correction link includes:
the first step, taking the actual value of the power grid voltage after the previous round of optimization control as the initial value of the new round of rolling optimization control to form rolling optimization:
W 0 (t+1)＝W true (t+1)
Wherein: w (W) 0 (t+1) is an initial value of the adjustable resource output at time t+1; w (W) true After the adjustable resource forecast output force value in the t+1 period is issued, the actual output force value in the t+1 period is acquired through actual measurement;
secondly, correcting a new round of model prediction result according to the previous round of model prediction deviation:
wherein: w (t+1+k|t+1) is the future tunable resource prediction result at t+1+k based on the tunable resource state information at t+1; w' (t+1+k|t+1) is the correction of the voltage prediction result at the future time t+1+k; w (W) err (t) model predictive bias for time t; alpha is a correction compensation coefficient, and the value range is [0,1]The method comprises the steps of carrying out a first treatment on the surface of the W (t+1|t) is the adjustable resource output of the future t+1 moment predicted at the moment t in the real-time stage of model prediction;
and thirdly, respectively bringing the results of the first step and the second step into a model prediction expression and an objective function, constructing a real-time stage model at the time k+1 in the day, and repeating the steps, thereby completing real-time rolling optimization and correction control.
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| CN117578498A (en) * | 2024-01-15 | 2024-02-20 | 江苏米特物联网科技有限公司 | Distributed optical storage system cluster control method oriented to electricity utilization side |
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| CN117477673B (en) * | 2023-12-25 | 2024-05-07 | 武汉市豪迈电力自动化技术有限责任公司 | Dynamic adaptation type industrial park load modeling and intelligent regulation and control system |
| CN117578498A (en) * | 2024-01-15 | 2024-02-20 | 江苏米特物联网科技有限公司 | Distributed optical storage system cluster control method oriented to electricity utilization side |
| CN117578498B (en) * | 2024-01-15 | 2024-04-09 | 江苏米特物联网科技有限公司 | Distributed optical storage system cluster control method oriented to electricity utilization side |
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