CN109474022A - The power distribution network optimization regulating method of the interaction of consideration source lotus and distributed generation resource power output randomness - Google Patents
The power distribution network optimization regulating method of the interaction of consideration source lotus and distributed generation resource power output randomness Download PDFInfo
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Abstract
The present invention for current distributed generation resource largely access after power distribution network due to its randomness and fluctuation to the operation of distribution regulate and control brought by problem, the power distribution network optimization regulating method for proposing a kind of interaction of consideration source lotus and distributed generation resource power output randomness, optimization distribution operation on the basis of guaranteeing that system is safe and reliable to operation；This method cuts down set by extreme scenes method first according to the randomness of the form characterization renewable energy power output of uncertain collection, and stochastic model is converted to determining model；Response technology according to demand is interacted with price incentive means with electric realization source lotus；The regulating power of renewable energy is matched with the double-deck regulation-control model with power distribution network tradition control measures finally, plans as a whole regulation, reduces the network loss and voltage fluctuation of power distribution network, reduces the adjusting number of traditional equipment, optimization distribution operation.
Description
Technical Field
The invention relates to the technical field related to operation regulation and control of a power distribution network and a distributed power supply, in particular to a power distribution network source load interaction optimization regulation and control technology considering randomness of the distributed power supply.
Background
With the increasing exhaustion of primary fossil energy and the increasing demand of human for environmental protection, more and more renewable energy sources are connected to the power distribution network and are locally consumed to form an active power distribution network. However, the output of distributed power supplies such as wind power and photovoltaic power has the characteristics of volatility, randomness and the like, and under the current technical conditions, the output probability distribution of intermittent distributed power supplies is difficult to accurately obtain by the power distribution network, so that the access of renewable energy sources brings severe challenges to the operation mode of the traditional power distribution network.
Aiming at the problems caused by the output fluctuation of the renewable energy of the active power distribution network, a plurality of people carry out corresponding research. For example, consider that when conventional voltage control methods are all ineffective, the out-of-limit node voltage is restored to normal by reducing DG active output. The aims of reducing the network loss, reducing the equipment adjusting times and optimizing the system operation are achieved by reasonably scheduling DGs and the traditional adjusting equipment of the power distribution network through a two-stage planning method. It has also been proposed to consider the active and reactive outputs of the DG and the charging and discharging of the energy storage device simultaneously to achieve maximum DG power utilization over a period of time.
In order to solve the problem of uncertainty of output of renewable energy, a random optimization model is applied to the optimization regulation and control of the system by many people. When large-scale photovoltaic access systems, scheduling decisions have been proposed that exploit robustness and uncertainty budget optimization systems. The method proposes to realize the matching planning of the standby unit and the wind power output by applying a two-stage opportunity constraint model. It is also proposed to combine pumped storage with thermal power generating units and implement real-time optimal scheduling of a wind energy access system by using robust optimization and uncertain sets. However, with the continuous advance of the power market, the method does not consider that users participate in the regulation and control of the power distribution network, meets the future development trend of the power distribution network, considers the investment, operation and maintenance cost of the actual power distribution network, brings great economic cost due to the fact that a large number of controllable devices are additionally arranged in the power distribution network, and is difficult to popularize and apply in practice.
Disclosure of Invention
The invention provides a power distribution network optimization regulation and control method considering source-load interaction and distributed power output randomness, aiming at the problems caused by the randomness and the fluctuation of a large number of distributed power supplies which are connected into a power distribution network at present, so that the network loss and the voltage fluctuation of the power distribution network can be reduced, the regulation times of traditional equipment are reduced, and the operation of the power distribution network is optimized.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention relates to a power distribution network optimization regulation and control method considering source-load interaction and distributed power supply output randomness, which is characterized by comprising the following steps of:
step 1, establishing and obtaining an objective function by using a formula (1):
f＝min(Crespond+Closs+Cdevice+Cquality) (1)
in formula (1): f is the total operation index of the active power distribution network; crespondThe economic cost index is adjusted for demand response and is obtained by the formula (2); clossThe network loss index of the active power distribution network is obtained through a formula (3); cdeviceThe index of the action times of the traditional equipment of the active power distribution network is obtained through the formula (4); cqualityThe voltage fluctuation index is an operating voltage fluctuation index of the active power distribution network and is obtained through a formula (5);
in formula (2): y istActually purchasing electric quantity for a time period t; rt(yt) The electricity purchasing cost of the power distribution network in the period t;actual electricity purchasing quantity of the nth node of the power distribution network in the period t;the actual electricity purchase price of the nth node of the power distribution network in the period t; t is an optimized regulation time interval set; n is a power distribution network node set;
in formula (3):the network loss of the power distribution network is t time period;
in formula (4): xULTC,tThe gear of the transformer is in the t period;the number of groups put into a capacitor on the nth node in the period t; vt nRepresents the voltage of the nth node during the period t;
step 2, according to the actual operation condition of the distributed power supply, respectively enabling the output of the distributed power supply DG to be respectively shown as a first extreme scene S in a formula (6)1And a second extreme scenario S as shown in equation (7)1Wherein:
the first extreme scenario S1The method comprises the following steps of (1) running a power distribution network in a light-load time period and reaching the upper limit of a predicted value range by the output of a distributed power supply DG;
the second extreme scene S2The method comprises the following steps of (1) running a power distribution network in a time period with heavier load and under the condition that the output of a distributed power supply DG reaches the lower limit of a predicted value range;
in formulae (6) and (7): s is an output scene set of the distributed power supply DG; s is any one scene in the output scene set S; d is the power demand level of the power distribution network; t' represents the time interval set that total demand of electric quantity is greater than distribution network power consumption demand level D, and T "represents the time interval set that total demand of electric quantity is less than distribution network power consumption demand level D, and have:
T′+T″＝T(8)
step 3, aiming at the first extreme scene S1The first extreme scene S is obtained by equation (9)1The objective function of (2):
in formula (9): k is the number of steps of the step electricity price; k is a set of stepped electricity prices;the power consumption requirement of the nth node at the kth order of electricity price in the period t is shown, and omega is a decision variableAnd ytSatisfy a first extreme scenario S1All scene robust feasible regions of (1); s is the first extreme scenario S1In the context of any one of the scenarios,the electricity price of the nth node in the kth stage in the t period; t is any one time interval in the time interval set T';
step 4, aiming at the second extreme scene S2Obtaining a second extreme scene S using equation (10)2The objective function of (2):
in formula (10): s is the second extreme scene S2Any one of the scenes; t is any one period in the period set T';
and 5, obtaining a total objective function by using the formula (11):
f＝f1+f2(11)
step 6, establishing a constraint condition during source load interaction optimization regulation and control of the power distribution network:
step 6.1, establishing power balance constraint by using an equation (12):
in formula (12):the active power output of the ith distributed power supply DG in the t period is represented, and G represents a set of distributed power supplies DG;
step 6.2, establishing node voltage constraint by using the formula (13):
in formula (13):respectively representing the upper limit and the lower limit of the voltage of the nth node of the power distribution network;
step 6.3, establishing reactive power output constraint of the ith distributed power supply DG by using the formula (14):
in formula (14):representing the reactive power output of the ith distributed power supply DG of the power distribution network in the period t;represents the power factor limit of the ith distributed power supply DG;
and 6.4, establishing transformer gear constraint by using the formula (15):
in formula (15), XULTCmin、XULTCmaxRespectively representing the upper limit and the lower limit of the tap position of the transformer;
and 6.5, establishing a capacitor input group number constraint by using an equation (16):
in formula (16):representing the number of capacitor banks mounted on the nth node;
and 6.7, establishing actual load constraint by using an equation (17):
in formula (17):respectively representing the rigidity requirement and the maximum requirement of the load on the nth node in the t period;
step 6.8, establishing a step electricity price constraint by using the formula (18):
in formula (18):the range of the kth step of the stepped electricity price in the t period;
step 7, generating an output scene set S of the distributed power supply DG through robust optimization, and limiting uncertain parameters by using an extreme scene methodThe output force of the distributed power supply DG in each time period under the extreme scene is determined, so that robust optimization solution in the first stage is completed;
step 8, according to the rigidity demand and the elasticity demand of the user in each period, a power utilization plan of the user is formulated by using a demand response method, so that the load control optimization solution of the second stage is completed;
and 9, optimizing the total objective function by using a double-layer planning method:
step 9.1, the upper-layer plan carries out various combinations on variables participating in the multi-stage decision process and distributes the variables to the lower-layer plan, wherein the variables of the multi-stage decision process comprise: the tap position of the transformer and the input group number of the switchable capacitor group;
9.2, under the condition that the tap position of the transformer and the input group number of the capacitors are determined, the lower-layer planning optimizes and solves the reactive output quantity of the distributed power supply DG by using a differential evolution algorithm, and if the reactive output quantity of the distributed power supply DG in each time period and the corresponding optimal total objective function value of the reactive output quantity can be determined and are jointly used as a local optimal solution; then it indicates that the combination of variables participating in the multi-stage decision process is a benign combination; if the reactive output quantity of the distributed power supply DG at each time interval can not meet all the constraint conditions at the same time, the variable combination participating in the multi-stage decision process is represented as a bad combination;
9.3, the lower-layer plan feeds back the bad combination, the good combination and the corresponding local optimal solution to the upper-layer plan;
and 9.4, eliminating bad combinations according to the feedback results by upper-layer planning, and making decisions on the benign combinations in each time period by using a dynamic planning algorithm, so as to obtain a global optimal scheme for realizing optimal regulation and control of the power distribution network.
Compared with the prior art, the invention has the beneficial effects that:
the invention considers the random output characteristic of the distributed power supply DG, is more close to the output characteristic of the on-site distributed power supply DG, fully utilizes the time shifting property of the load, responds to price or an excitation signal by a user, changes the power consumption mode consciously, ensures that the user can enjoy low-price electric energy in a period with abundant power supply of renewable energy sources, can effectively solve the problem of safe and stable system operation caused by overlarge load fluctuation, carries out peak clipping and valley filling on the load, realizes friendly interaction of the space-time characteristic of the source load, finally carries out double-layer decomposition on the complex random multi-stage decision problem by using a double-layer regulation and control model, improves the calculation speed, and matches the regulation capacity of the renewable energy sources and the time shifting load with the traditional regulation and control means of the distribution network, ensures the safe and stable operation of the distribution network, and reduces the network loss and the voltage mobility of the, the adjusting times of the traditional equipment are reduced, the service life of the traditional equipment of the power grid is prolonged, and the operation of a distribution network is optimized.
Drawings
FIG. 1 is a schematic diagram of a demand response of the present invention;
FIG. 2 is a diagram illustrating the prediction of output force of the wind turbine generator during each period of time;
FIG. 3 is a graph illustrating the prediction of photovoltaic output at various time intervals according to the present invention;
FIG. 4 is a frame diagram of source-load interaction optimization regulation according to the present invention.
Detailed Description
The method is characterized in that aiming at the problems caused by the randomness and the fluctuation of the distribution network after the current distributed power supply is connected into the distribution network in a large quantity, the optimal regulation and control method of the distribution network considering the source-load interaction and the output randomness of the distributed power supply is provided, the network loss and the voltage fluctuation of the distribution network are reduced, the regulation times of traditional equipment are reduced, and the operation of the distribution network is optimized.
The power demand is divided into two types according to different types: the first type is a demand that does not change by fluctuations in electricity prices, called a rigidity demand; the other is a demand in which the demand for electricity changes with the fluctuation of electricity prices, which is called a demand for elasticity. Characterizing the relationship between elastic demand and price by an elastic demand function:
p＝αdσ,α>0,σ<0(1)
in the formula (1), d represents the electricity demand, p represents the unit electricity selling price, and α and sigma represent the elasticity demand coefficient.
With the obvious improvement of the electricity consumption under the electricity price, when a distribution network operator participates in a trading market and the actual load exceeds the electricity quantity purchased by a protocol, a corresponding price punishment coefficient mu exists, namely, the electricity purchasing cost of the distribution network is obviously increased, and the stable operation of the distribution network is influenced when the load is overweight. Relation between purchase price and purchase quantity of the distribution network:
in the formula (2), β is the basic electricity purchase price, ytActually purchasing electric quantity from the main network for the distribution network in the t period; y ist 0And purchasing electric quantity for the distribution network protocol in the t period.
Fig. 1 shows the relationship between the amount of electricity used and the price per unit electricity and the purchase cost. In the drawingsRespectively representing the rigid demand and the maximum demand of the user's electric energy during the period t, obviouslyThe intersection point of the 2 curves can be seenThe user can obtain preferential electricity price and the power distribution network is operated in an economic environment. When it exceedsAnd in time, the electricity purchasing price of the distribution network is larger than the electricity selling price of the user side. Defining an elastic load low price electric energy profit function Pt(dt)：
Defining an electricity purchasing cost function R similar to the formula (3)t(yt)：
By synthesizing the elastic load price gain by the formula (3) and the distribution network electricity purchasing cost by the formula (4), the economic cost of response adjustment required in any time period can be obtained:
due to the complex calculation and inconvenient operation, the price curve is not changed smoothly, but is simplified into a step-shaped change along with the change of the power consumption. Based on the thought, the invention adopts the step-by-step processing to equivalent demand response function:
due to the fact thatIs monotonically decreasing as the order k increases, we can get:
wherein k is0Is the stage of the actual elasticity demand.
Thus, for any period of time, the economic cost of demand response adjustment can be rewritten as
The traditional optimization regulation and control model is established on the premise that the distributed power supply output prediction is accurate, however, when the actual situation and the prediction deviation are large, some constraint conditions of the model can not be met any more, so that the optimal solution under the deterministic model can not be optimal or even feasible any more. Robust optimization is therefore introduced into the model of the invention.
Considering the uncertainty of the wind power and photovoltaic output prediction, the output of the ith DG in the t period can be expressed as:
wherein,the actual output, the predicted output and the deviation value of the actual and predicted DGs of the ith time period t are respectively;is composed ofIs measured.
Suppose that the ith DG output is within the t periodInternally satisfy withFor normal distribution of expected values, an interval control coefficient z is defined for measuring robustnesst,i∈[0,1]Such that the predicted DG output is In the meantime. From this, z can be seent,iThe larger the output interval range of the DG is, the wider the output interval range of the DG is, and the better the robust performance of the control scheme is.
Considering that the output predictions of multiple DGs in the same time period have no correlation, the sum of the coefficients of the output intervals of the DGs in the time period t is defined as an uncertainty coefficient of a model, and is used for representing the uncertainty of the output of the distribution network DG as a whole:
it is obvious thatCan be made according to the actual situation on siteThereby regulating and controlling the robustness of the model. It can be seen that whenThe model is a deterministic model.
According to the theory, a DG output prediction scene S can be obtained, and the set S is as follows:
according to the theory, the invention constructs an uncertain multi-stage decision process hybrid optimization model, provides a power distribution network optimization regulation and control method considering source-load interaction and distributed power supply output randomness, and optimizes the operation of a distribution network on the basis of ensuring the safe and reliable operation of a system. The method comprises the steps of firstly representing the randomness of the output of the renewable energy sources according to the form of an uncertain set, reducing the set through an extreme scene method, and converting a random model into a determined model; according to the demand response technology, a price incentive means is applied to realize source-charge interaction with electricity; and finally, the double-layer regulation and control model is used for matching the regulation capacity of renewable energy sources with the traditional regulation and control means of the power distribution network, so that the regulation and control are integrated, the network loss and the voltage fluctuation of the power distribution network are reduced, the regulation times of traditional equipment are reduced, and the operation of the power distribution network is optimized.
In this embodiment, as shown in fig. 4, a power distribution network optimization regulation and control method considering source-load interaction and distributed power supply output randomness is performed according to the following steps:
step 1, establishing and obtaining an objective function by using a formula (14):
f＝min(Crespond+Closs+Cdevice+Cquality)(14)
in formula (1): f is the total operation index of the active power distribution network; crespondThe economic cost index is adjusted for demand response and is obtained by the formula (2); clossThe network loss index of the active power distribution network is obtained through a formula (3); cdeviceThe index of the action times of the traditional equipment of the active power distribution network is obtained through the formula (4); cqualityThe voltage fluctuation index is an operating voltage fluctuation index of the active power distribution network and is obtained through a formula (5);
in formula (15): y istActually purchasing electric quantity for a time period t; rt(yt) The electricity purchasing cost of the power distribution network in the period t;actual electricity purchasing quantity of the nth node of the power distribution network in the period t;the actual electricity purchase price of the nth node of the power distribution network in the period t; t is an optimized regulation time interval set; n is a power distribution network node set;
in formula (16):the network loss of the power distribution network is t time period;
in formula (17): xULTC,tThe gear of the transformer is in the t period;the number of groups put into a capacitor on the nth node in the period t; vt nRepresents the voltage of the nth node during the period t;
step 2, according to the actual operation condition of the distributed power supply, respectively enabling the output of the distributed power supply DG to be respectively shown as a first extreme scene S in a formula (19)1And a second extreme scenario S as shown in equation (20)2Wherein:
first extreme scenario S1The method comprises the following steps of (1) running a power distribution network in a light-load time period and reaching the upper limit of a predicted value range by the output of a distributed power supply DG;
second extreme scene S2The method comprises the following steps of (1) running a power distribution network in a time period with heavier load and under the condition that the output of a distributed power supply DG reaches the lower limit of a predicted value range;
in formulae (19) and (20): s is an output scene set of the distributed power supply DG; s is any one scene in the output scene set S; d is the power demand level of the power distribution network; t 'represents a time period set that the total demand of electric quantity is greater than the power demand level D of the power distribution network, and T' represents that the total demand of electric quantity is less than the power demand level D of the power distribution network*And (ii) a time period set of (d) and having:
T′+T″＝T(21)
step 3, aiming at the first extreme scene S1The first extreme scene S is obtained by equation (22)1The objective function of (2):
in formula (22): k is the number of steps of the step electricity price; k is a set of stepped electricity prices;the power consumption requirement of the nth node at the kth order of electricity price in the period t is shown, and omega is a decision variableAnd ytSatisfy a first extreme scenario S1All scene robust feasible regions of (1); s is the first extreme scenario S1In the context of any one of the scenarios,the electricity price of the nth node in the kth stage in the t period; t is any one time interval in the time interval set T';
step 4, aiming at the second extreme scene S2Obtaining a second extreme scene S using equation (23)2The objective function of (2):
in formula (23): s is the second extreme scene S2Any one of the scenes; t is any one period in the period set T';
and 5, obtaining a total objective function by using the formula (24):
f＝f1+f2(24)
step 6, establishing a constraint condition during source load interaction optimization regulation and control of the power distribution network:
step 6.1, establishing power balance constraint by using an equation (25):
in formula (25):the active power output of the ith distributed power supply DG in the t period is represented, and G represents a set of distributed power supplies DG;
and 6.2, establishing node voltage constraint by using the formula (26):
in formula (26):respectively representing the upper limit and the lower limit of the voltage of the nth node of the power distribution network;
step 6.3, establishing reactive power output constraint of the ith distributed power supply DG by using the formula (27):
in formula (27):representing the reactive power output of the ith distributed power supply DG of the power distribution network in the period t;represents the power factor limit of the ith distributed power supply DG;
and 6.4, establishing a transformer gear constraint by using the formula (28):
in the formula (28), XULTCmin、XULTCmaxRespectively representing the upper limit and the lower limit of the tap position of the transformer;
and 6.5, establishing a capacitor input group number constraint by using an equation (29):
in formula (29):representing the number of capacitor banks mounted on the nth node;
and 6.7, establishing actual load constraint by using an equation (30):
in formula (30):respectively representing the rigidity requirement and the maximum requirement of the load on the nth node in the t period;
step 6.8, establishing a step electricity price constraint by using the formula (31):
in formula (31):the range of the kth step of the stepped electricity price in the t period;
step 7, generating an output scene set S of the distributed power supply DG through robust optimization, and limiting uncertain parameters by using an extreme scene methodThe output force of the distributed power supply DG in each time period under the extreme scene is determined, so that robust optimization solution in the first stage is completed;
step 8, according to the rigidity demand and the elasticity demand of the user in each period, a power utilization plan of the user is formulated by using a demand response method, so that the load control optimization solution of the second stage is completed;
and 9, optimizing the total objective function by using a double-layer planning method:
step 9.1, the upper-layer plan carries out various combinations on variables participating in the multi-stage decision process and distributes the variables to the lower-layer plan, wherein the variables of the multi-stage decision process comprise: the tap position of the transformer and the input group number of the switchable capacitor group;
9.2, under the condition that the tap position of the transformer and the input group number of the capacitors are determined, the lower-layer planning optimizes and solves the reactive output quantity of the distributed power supply DG by using a differential evolution algorithm, and if the reactive output quantity of the distributed power supply DG in each time period and the corresponding optimal total objective function value of the reactive output quantity can be determined and are jointly used as a local optimal solution; then it indicates that the combination of variables participating in the multi-stage decision process is a benign combination; if the reactive output quantity of the distributed power supply DG at each time interval can not meet all the constraint conditions at the same time, the variable combination participating in the multi-stage decision process is represented as a bad combination;
9.3, the lower-layer plan feeds back the bad combination, the good combination and the corresponding local optimal solution to the upper-layer plan;
and 9.4, eliminating bad combinations according to the feedback results by upper-layer planning, and making decisions on the benign combinations in each time period by using a dynamic planning algorithm, so as to obtain a global optimal scheme for realizing optimal regulation and control of the power distribution network.
To verify the effectiveness of the methods presented herein and their feasibility for use in power systems, simulation verification was performed using the U.S. PG & E69 node power distribution system. The reference voltage of the test system is 12.66kV, and relevant parameters of the distribution network regulating and controlling equipment are shown in table 1.
TABLE 1 detailed parameters of pressure regulating devices
The test system comprises 69 load nodes, wherein 1 wind driven generator with the rated capacity of 2.5MW is connected to the node 27, and 2 distributed photovoltaic power stations with the capacity of 2MW are respectively connected to the node 54 and the node 69. By combining the actual output situation of the renewable power source, the testing time period is 12 hours in total from 6:00 to 17:00, and the maximum deviation of the output of the renewable energy source in the calculation time period is considered to be +/-20% of the expected output, so that the predicted output interval can be obtained as shown in fig. 2 to 3.
The force intervals are measured as shown in the figure, and the allowable deviation range of the power supply voltage of the 10kV power distribution network is +/-7% of the rated voltage. The power factor limits Pf of 3 DG are all 0.97, the initial tap position of the transformer is 9, and the initial number of groups of capacitors to be charged is 1.
When the method provided by the invention is not introduced, the traditional adjusting equipment is only used for regulation and control, and the obtained indexes of the scheduling control plan are respectively as follows: the adjustment times of the tap joint of the transformer are 7 times, the switching times of the capacitor bank are 1 time, the total grid loss in the system adjustment period is 4.0963MW & h, and the voltage fluctuation index USSVF1.008kV, because the load fluctuation wave of each period is large, the power purchase cost of the power distribution network is 14975.63 yuan.
In order to apply the model presented herein, the electricity demand level D ═ 5.703MW is defined in combination with the actual situation of the test system, and the elastic benefit and the electricity purchase cost related parameters are set, as shown in tables 2 and 3.
TABLE 2 stepped tariff related parameters
TABLE 3 cost of electricity purchase-related parameters
After the power consumption of the user load at each time interval is determined, a two-layer planning technology is applied to obtain a system optimized scheduling control scheme at each time interval on the next day as shown in table 4.
Table 4 scheduling control scheme
As can be derived from table 4, the number of times of gear adjustment of the tap of the transformer is 2, the number of times of switching of the switchable capacitor bank is 1, and the reactive output of the DG actively participates in the voltage adjustment: in the time period with light load, the reactive output of the DG is capacitive, so that the node voltage is ensured not to exceed the upper safety limit; on the contrary, in a time period with a heavier load, the reactive output of the DG is inductive, and plays a role of lifting the node voltage, so that the node voltage is ensured not to exceed the safety lower limit.
In order to more fully reflect the actual control effect of the demand response technique, the control effect obtained when only the specific gravity of the elastic load is changed while keeping the above system parameters unchanged is shown in table 5.
TABLE 5 elastic demand control Effect
As can be seen from Table 5, when the proportion of the elastic load is increased, various indexes for evaluating the operation of the power distribution network are obviously improved. The larger the proportion of the elastic demand is, the better the regulation capacity of the total load is, the load can be effectively regulated and controlled, the regulation and control pressure of the power distribution network equipment can be effectively relieved, and the influence of the output fluctuation of the renewable energy sources is smaller.
Claims (1)
1. A power distribution network optimization regulation and control method considering source-load interaction and distributed power supply output randomness is characterized by comprising the following steps:
step 1, establishing and obtaining an objective function by using a formula (1):
f＝min(Crespond+Closs+Cdevice+Cquality) (1)
in formula (1): f is the total operation index of the active power distribution network; crespondThe economic cost index is adjusted for demand response and is obtained by the formula (2); clossFor active distribution of electricityThe network loss index is obtained by the formula (3); cdeviceThe index of the action times of the traditional equipment of the active power distribution network is obtained through the formula (4); cqualityThe voltage fluctuation index is an operating voltage fluctuation index of the active power distribution network and is obtained through a formula (5);
in formula (2): y istActually purchasing electric quantity for a time period t; rt(yt) The electricity purchasing cost of the power distribution network in the period t;actual electricity purchasing quantity of the nth node of the power distribution network in the period t;the actual electricity purchase price of the nth node of the power distribution network in the period t; t is an optimized regulation time interval set; n is a power distribution network node set;
in formula (3):the network loss of the power distribution network is t time period;
in formula (4): xULTC,tThe gear of the transformer is in the t period;the number of groups put into a capacitor on the nth node in the period t; vt nRepresents the voltage of the nth node during the period t;
step 2, according to the actual operation condition of the distributed power supply, respectively enabling the output of the distributed power supply DG to be respectively shown as a first extreme scene S in a formula (6)1And a second extreme scenario S as shown in equation (7)1Wherein:
the first extreme scenario S1The method comprises the following steps of (1) running a power distribution network in a light-load time period and reaching the upper limit of a predicted value range by the output of a distributed power supply DG;
the second extreme scene S2The method comprises the following steps of (1) running a power distribution network in a time period with heavier load and under the condition that the output of a distributed power supply DG reaches the lower limit of a predicted value range;
in formulae (6) and (7): s is an output scene set of the distributed power supply DG; s is any one scene in the output scene set S; d is the power demand level of the power distribution network; t 'represents a time period set that the total demand of electric quantity is greater than the power demand level D of the power distribution network, and T' represents that the total demand of electric quantity is less than the power demand level D of the power distribution network*And (ii) a time period set of (d) and having:
T′+T″＝T (8)
step 3, aiming at the first extreme scene S1The first extreme scene S is obtained by equation (9)1The objective function of (2):
in formula (9): k is the number of steps of the step electricity price; k is a set of stepped electricity prices;at kth order for nth node during t periodThe electricity demand under the electricity price is omega as a decision variableAnd ytSatisfy a first extreme scenario S1All scene robust feasible regions of (1); s is the first extreme scenario S1In the context of any one of the scenarios,the electricity price of the nth node in the kth stage in the t period; t is any one time interval in the time interval set T';
step 4, aiming at the second extreme scene S2Obtaining a second extreme scene S using equation (10)2The objective function of (2):
in formula (10): s is the second extreme scene S2Any one of the scenes; t is any one period in the period set T';
and 5, obtaining a total objective function by using the formula (11):
f＝f1+f2(11)
step 6, establishing a constraint condition during source load interaction optimization regulation and control of the power distribution network:
step 6.1, establishing power balance constraint by using an equation (12):
in formula (12):the active power output of the ith distributed power supply DG in the t period is represented, and G represents a set of distributed power supplies DG;
step 6.2, establishing node voltage constraint by using the formula (13):
in formula (13):respectively representing the upper limit and the lower limit of the voltage of the nth node of the power distribution network;
step 6.3, establishing reactive power output constraint of the ith distributed power supply DG by using the formula (14):
in formula (14):representing the reactive power output of the ith distributed power supply DG of the power distribution network in the period t;represents the power factor limit of the ith distributed power supply DG;
and 6.4, establishing transformer gear constraint by using the formula (15):
in formula (15), XULTCmin、XULTCmaxRespectively representing the upper limit and the lower limit of the tap position of the transformer;
and 6.5, establishing a capacitor input group number constraint by using an equation (16):
in formula (16):representing the number of capacitor banks mounted on the nth node;
and 6.7, establishing actual load constraint by using an equation (17):
in formula (17):respectively representing the rigidity requirement and the maximum requirement of the load on the nth node in the t period;
step 6.8, establishing a step electricity price constraint by using the formula (18):
in formula (18):the range of the kth step of the stepped electricity price in the t period;
step 7, generating an output scene set S of the distributed power supply DG through robust optimization, and limiting uncertain parameters by using an extreme scene methodThe output force of the distributed power supply DG in each time period under the extreme scene is determined, so that robust optimization solution in the first stage is completed;
step 8, according to the rigidity demand and the elasticity demand of the user in each period, a power utilization plan of the user is formulated by using a demand response method, so that the load control optimization solution of the second stage is completed;
and 9, optimizing the total objective function by using a double-layer planning method:
step 9.1, the upper-layer plan carries out various combinations on variables participating in the multi-stage decision process and distributes the variables to the lower-layer plan, wherein the variables of the multi-stage decision process comprise: the tap position of the transformer and the input group number of the switchable capacitor group;
9.2, under the condition that the tap position of the transformer and the input group number of the capacitors are determined, the lower-layer planning optimizes and solves the reactive output quantity of the distributed power supply DG by using a differential evolution algorithm, and if the reactive output quantity of the distributed power supply DG in each time period and the corresponding optimal total objective function value of the reactive output quantity can be determined and are jointly used as a local optimal solution; then it indicates that the combination of variables participating in the multi-stage decision process is a benign combination; if the reactive output quantity of the distributed power supply DG at each time interval can not meet all the constraint conditions at the same time, the variable combination participating in the multi-stage decision process is represented as a bad combination;
9.3, the lower-layer plan feeds back the bad combination, the good combination and the corresponding local optimal solution to the upper-layer plan;
and 9.4, eliminating bad combinations according to the feedback results by upper-layer planning, and making decisions on the benign combinations in each time period by using a dynamic planning algorithm, so as to obtain a global optimal scheme for realizing optimal regulation and control of the power distribution network.
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