CN112330492A - Active power distribution network energy sharing method based on communication reliability constraint - Google Patents

Active power distribution network energy sharing method based on communication reliability constraint Download PDF

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CN112330492A
CN112330492A CN202011327897.0A CN202011327897A CN112330492A CN 112330492 A CN112330492 A CN 112330492A CN 202011327897 A CN202011327897 A CN 202011327897A CN 112330492 A CN112330492 A CN 112330492A
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李鹏
张艺涵
王世谦
王剑晓
刘湘莅
李慧旋
郑永乐
杨萌
谢安邦
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Abstract

The invention provides an active power distribution network energy sharing method based on communication reliability constraint, which comprises the following steps: firstly, establishing a power distribution network system model, and gathering N energy micro-networks by one power distribution system operator in a power distribution grid, wherein the energy micro-networks are matched with communication base stations in a one-to-one correspondence manner; secondly, controlling energy sharing among the energy micro-grids through a communication base station, and constructing a communication reliability constraint model of an energy sharing mechanism and a power loss constraint model of the communication base station; then, constructing an optimal operation model of an energy sharing mechanism according to the power loss constraint model of the communication base station; and finally, carrying out linear solving on the communication reliability constraint model by adopting a least square method and a piecewise linear method, and further obtaining the minimum value of the overall cost of the power distribution network of the energy sharing mechanism. On the premise that each microgrid meets the physical and communication reliability constraints, the energy sharing mechanism provided by the invention enables the overall cost of the power distribution network based on distributed energy sharing to be the minimum.

Description

Active power distribution network energy sharing method based on communication reliability constraint
Technical Field
The invention relates to the technical field of power distribution network energy sharing, in particular to an active power distribution network energy sharing method based on communication reliability constraint.
Background
With the exhaustion of fossil energy and the pollution to the environment, the novel economic, environment-friendly and flexible power generation mode of distributed power generation is gradually widely applied. However, the large-scale access of the distributed power supply to the power grid may cause reliability problems such as voltage and power flow out-of-limits. Therefore, energy sharing is realized by matching user load and distributed power generation, so that the operation pressure of a power distribution network is reduced, and the renewable energy consumption capacity is improved.
Energy sharing requires advanced information and communication technology support. In recent years, 5G communication technology has been rapidly developed. Compared with 4G communication, 5G communication realizes a great leap in terms of bandwidth, delay, and the like. With the development of 5G networks, a large number of high-density communication base stations under all conditions need to be arranged, and the energy consumption of a communication system is taken into full consideration in the planning process of the mobile communication base stations. As a non-negligible communication base station load will have an impact on the power flow distribution and voltage fluctuations of the distribution network. Therefore, the coordination planning of the communication base station and the distributed energy resource still needs to be further researched.
The interplay of communication systems and energy systems will become increasingly prominent. First, the communications base station requires power from the distribution grid to maintain its daily operation, and its operating strategy in turn affects the distribution line power flow. Secondly, the stability of the communication system is a prerequisite for accurate control of the power grid, and errors on the communication side can cause the power system to erroneously issue control commands. Third, mutual interference of transmission power between communication base stations may also cause bit errors, which may cause power system operation failure.
Disclosure of Invention
Aiming at the defects in the background technology, the invention provides an active power distribution network energy sharing method based on communication reliability constraint, and solves the technical problem that the safe operation of a power system is influenced due to incorrect control commands possibly caused by errors of a communication end.
The technical scheme of the invention is realized as follows:
an active power distribution network energy sharing method based on communication reliability constraint comprises the following steps:
the method comprises the following steps: establishing a power distribution network system model, and aggregating N energy micro-grids by one power distribution system operator in a power distribution grid, wherein the energy micro-grids are matched with the communication base stations in a one-to-one correspondence manner, and all the communication base stations share the same channel;
step two: controlling energy sharing among the energy micro-grids through the communication base station, and constructing a communication reliability constraint model of an energy sharing mechanism and a power loss constraint model of the communication base station;
step three: constructing an optimal operation model of an energy sharing mechanism according to a power distribution network, controllable loads and a communication base station power loss constraint model shared by distributed energy in a given area;
step four: and carrying out linear solving on the communication reliability constraint model by adopting a least square method and a piecewise linear method to obtain model parameters of the reliability constraint model, and applying the model parameters to an optimal operation model of an energy sharing mechanism to obtain the minimum value of the overall cost of the power distribution network of the energy sharing mechanism.
The communication reliability constraint model of the energy sharing mechanism is as follows:
δn-i＝(1-En-i)l
wherein, deltan-iIs a communication reliability index between the nth communication base station and the ith energy microgrid, En-iThe bit error rate between the nth communication base station and the ith energy microgrid is shown, l is the bit length of a data packet, i is 1,2, … …, and N is the number of all energy microgrids in the scheduling range;
the bit error rate En-iExpressed as:
Figure BDA0002794822080000021
where Q () is the integral tail function of the standard Gaussian distribution, BNIs the noise bandwidth of the wireless transmission transceiver, R is the data transmission speed, SNRn-iIs the signal-to-noise ratio of the ith energy piconet accessing the nth communication base station.
SNR of nth communication base station accessed by ith energy microgridn-iExpressed as:
Figure BDA0002794822080000022
wherein, Wn-iIs the power, sigma, received by the nth communication base station connected with the ith energy microgridm≠nWm-iThe sum of interference power brought by other communication base stations to the ith energy microgrid, N0Is thermal noise;
the thermal noise N0Expressed as:
N0＝kTB
where B is the channel bandwidth of the wireless transmit transceiver and k is the Boltzmann constantAnd k is 1.3803 × 10-23J/K, T is the temperature in Kelvin;
the power W received by the nth communication base station connected with the ith energy microgridn-iExpressed as:
Figure BDA0002794822080000023
wherein the content of the first and second substances,
Figure BDA0002794822080000024
is BS transmit power, PL is the transmission loss multiple, PL is the dB form of the transmission loss;
Figure BDA0002794822080000025
PL＝h+g log10(dn-i)
where h and g are both coefficients of the path loss model, dn-iIs the distance between the nth communication base station and the ith energy microgrid.
The power loss constraint model of the communication base station is as follows:
Figure BDA0002794822080000026
Figure BDA0002794822080000031
δi′-i≥α
wherein the content of the first and second substances,
Figure BDA0002794822080000032
is the transmission power of the ith' communication base station in the interval of time slot t under scene s,
Figure BDA0002794822080000033
is the power consumption of the ith' communication base station in the interval of time slot t under the scene s, and e is the total power consumption and communicationThe linear ratio coefficient between the transmission powers of the communication base stations, f is the fixed load of the communication base stations,
Figure BDA0002794822080000034
is the minimum value of the communication base station transmit power,
Figure BDA0002794822080000035
is the maximum value of the transmission power of the communication base station, deltai′-iThe communication reliability index of the ith energy microgrid accessing the ith communication base station is shown, and alpha is the communication reliability requirement of the active power distribution network.
The optimal operation model of the energy sharing mechanism is as follows:
Figure BDA0002794822080000036
wherein, ciRepresents the distribution system operator operating cost, gamma, for a given areasIs the probability of the scene s,
Figure BDA0002794822080000037
is the hour controllable load of the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA0002794822080000038
is the load consumption of the ith' communication base station in the interval of time slot t under the scene s,
Figure BDA0002794822080000039
is the electricity price of the residents,
Figure BDA00027948220800000310
is the electricity price of the communication base station, Ui(. is) the utility function of the ith energy microgrid, PijstAnd j is 1,2, … …, where N is the number of all energy micro-grids in the scheduling range.
The constraint conditions of the optimal operation model of the energy sharing mechanism are as follows:
Figure BDA00027948220800000311
Figure BDA00027948220800000312
Figure BDA00027948220800000313
Figure BDA00027948220800000314
Figure BDA00027948220800000315
Vmin≤Vist≤Vmax
θmin≤θist≤θmax
Figure BDA00027948220800000316
Figure BDA00027948220800000317
Figure BDA00027948220800000318
Figure BDA00027948220800000319
Figure BDA00027948220800000320
Figure BDA00027948220800000321
Figure BDA0002794822080000041
Figure BDA0002794822080000042
Figure BDA0002794822080000043
wherein the content of the first and second substances,
Figure BDA0002794822080000044
is the charging power of the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA0002794822080000045
is the discharge power of the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA0002794822080000046
is the reactive power stored by the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA0002794822080000047
is the controllable load of the ith energy microgrid in the time slot t interval under the scene s,
Figure BDA0002794822080000048
is the power, phi, of the photovoltaic system in the interval of time slot t under scene siIs a set of units, Q, connected to the ith energy microgridijstIs the reactive power flow from the ith energy microgrid to the jth energy microgrid, gijIs the susceptance value of the line between the ith energy microgrid and the jth energy microgrid, bijIs the ith energy microgrid and the jth energy microgridConductance, v, of the lines between the networksistIs the voltage amplitude, v, of the ith energy microgridjstIs the voltage amplitude, theta, of the jth energy microgridistIs the voltage phase angle theta of the ith energy microgridjstIs the voltage phase angle, V, of the jth energy microgridminIs the lower bound of the voltage amplitude, VmaxIs the upper bound of the voltage amplitude, θminIs the lower bound of the phase angle of the voltage, thetamaxIs the upper bound of the phase angle of the voltage,
Figure BDA0002794822080000049
is a photovoltaic power generation predicted value of the ith energy microgrid in the time slot t interval,
Figure BDA00027948220800000410
is the lower bound of the controllable load,
Figure BDA00027948220800000411
is the upper bound of the controllable load,
Figure BDA00027948220800000412
is the predicted daily load requirement for the load,
Figure BDA00027948220800000413
is the maximum charging power that can be charged,
Figure BDA00027948220800000414
is the maximum discharge power of the discharge lamp,
Figure BDA00027948220800000415
is the maximum reactive power of the energy storage system,
Figure BDA00027948220800000416
it is the efficiency of the energy storage system,
Figure BDA00027948220800000417
is the stored energy of the energy storage system,
Figure BDA00027948220800000418
is an energy storage systemThe upper limit of the capacity of (a),
Figure BDA00027948220800000419
is the lower limit of the capacity of the energy storage system,
Figure BDA00027948220800000420
is the initial energy level stored by the ith energy microgrid under the scene s,
Figure BDA00027948220800000421
is the final energy level, S, stored by the ith energy microgrid under the scene SijIndicating the apparent power from the ith to the jth energy microgrid.
The method for linearly solving the communication reliability constraint model by adopting the least square method and the piecewise linear method comprises the following steps:
s41, the signal-to-noise ratio of the ith energy microgrid accessing the nth communication base station is expressed by using a regression equation:
SNRn-i＝WEC+ε
wherein the SNRn-iIs a k x 1 dimensional random vector, W, determined from observationsEIs a kx (n +1) matrix determined by the predictor variables, C is the (n +1) x 1 vector of unknown parameters, and epsilon is the k x 1-dimensional vector of random errors;
s42, solving the step S41 through a least square normal equation to obtain a regression coefficient:
Figure BDA00027948220800000422
wherein the content of the first and second substances,
Figure BDA00027948220800000423
a vector, i.e., a regression coefficient, for least squares estimation;
s43, dividing the signal-to-noise ratio domain into equal interval intervals, and writing a communication reliability constraint model into the following steps in the q-th interval:
Figure BDA00027948220800000424
wherein, aqAnd bqAre all coefficients in the interval q,
Figure BDA00027948220800000425
representing the signal-to-noise ratio of the equal interval, and the SNR represents the signal-to-noise ratio;
s44, introducing new binary and continuous variables of q-1 and a new inequality of 4 (q-1) to rewrite the constraint conditions of the optimal operation model into linear constraints:
max{π1,π2}≥0，
introducing new variable t ═ max { pi-1,π2The following new constraints are released from the maximum operator:
π1≤t≤π1+vΩ
π2≤t≤π2+(1-v)
s45, applying the method of the step S44 to a minimum operator: t is min { pi ═ n1,π2}：
π1-vΩ≤t≤π1
π2-(1-v)Ω≤t≤π2
Wherein v is a binary variable and Ω is a positive scalar;
s46, converting the nonlinear constraint of the reliability constraint model into a specific inequality according to the steps S41-S45, and obtaining model parameters of the reliability constraint model through solving the specific inequality.
The beneficial effect that this technical scheme can produce: the invention provides a three-layer framework comprising a decision process and an information physical system: in the framework, an active power distribution network is connected with a microgrid and a communication base station which aggregate a large amount of energy of demand parties, interference and noise exist among the microgrids, communication reliability is a nonlinear function formed by transmitting power of different communication base stations, linearization is carried out on the microgrids by adopting a least square method and a piecewise linearity method, and the total cost of the power distribution network based on distributed energy sharing is the minimum by the proposed energy sharing mechanism on the premise that each microgrid meets the physical and communication reliability constraints.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram of the three-tier system architecture for energy sharing of the present invention.
Fig. 2 is a schematic diagram of interference between base stations according to the present invention.
FIG. 3 shows the non-linear function of SNR and its fitting result of linear approximation.
Fig. 4 is a graph of the nonlinear function δ and its piecewise linear approximation of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
The present invention contemplates a utility-level power distribution grid having N microgrid's. Through the connection of the communication network, each piconet operator manages several customers and distributed energy sources, including photovoltaic systems and battery storage. In addition, the microgrid shares distributed energy with other piconets through a communication network between different base stations. In order to evaluate the information transmission quality of a communication network, a communication reliability index is introduced. It should be noted that there are complex mechanisms for the interaction between the communication network and the distribution network: in order to cover all the customers in the microgrid, the local communication base stations should boost their power and therefore have a great impact on the power flow of the distribution grid. However, high transmission power may cause interference between adjacent communication base stations, possibly resulting in communication errors. These interactions complicate the power scheduling of multiple communication base stations in a power distribution network.
The system architecture of the energy sharing operation mechanism is composed of three layers, namely a physical layer, a communication layer and a decision layer, as shown in fig. 1.
The physical layer is composed of a plurality of nodes, and the nodes of the power distribution network are divided into two types: a connection node and a load node. The load nodes are responsible for bidirectional power transmission to the microgrid on the nodes, and the connecting nodes are hubs of the multiple sections of transmission lines and are not connected with the microgrid. Furthermore, the microgrid aggregates distributed energy sources and communication base stations, these components constituting a physical distribution network that enables energy sharing. Different constraints are defined in this layer to guarantee the quality and reliability of the power supply and to control the power flow. Constraints in the physical layer include voltage constraints, frequency constraints, active power constraints, and the like.
The communication layer consists of base station equipment, protocols, applications and information flows. Information flow refers to the sender, recipient and content of each message transmitted between communication devices. The technology of the internet of things enables a power distribution system operator to collect supply and demand information of a microgrid and realize optimal energy management. It should be noted that a communication base station belongs to both the physical layer and the information layer, since it requires power supply of the physical layer on the one hand and is responsible for information transmission of the information layer on the other hand. In the architecture of the present invention, it is the communication base station that reflects the coupling relationship between the physical system and the information system.
The decision layer decides how the power distribution system operator performs the optimal energy operation. Various models can be developed at this level to enable energy trading. The invention provides an energy trading scheme for energy sharing. Assuming that all microgrids are price makers, they purchase power from the distribution system operator and sell the remaining power. Based on the supply and demand information of different micro-grids, the power distribution system operator optimizes the power scheduling of the micro-grids while meeting the communication reliability requirement.
Each microgrid communicates with the power distribution system operator in the area and receives control signals through communication base stations. Reliable and stable communication networks are the prerequisite for energy sharing mechanisms. The invention introduces a communication reliability model and clarifies the relation between the transmission power of a communication base station and the communication reliability. Communication reliability constraints of the optimized operation model are set forth in combination with energy sharing requirements of communication reliability.
Based on the system architecture of the energy sharing operation mechanism, the embodiment of the invention provides an active power distribution network energy sharing method based on communication reliability constraint, which comprises the following steps:
the method comprises the following steps: establishing a power distribution network system model, and aggregating N energy micro-grids by one power distribution system operator in a power distribution grid, wherein the energy micro-grids are matched with the communication base stations in a one-to-one correspondence manner, and all the communication base stations share the same channel;
step two: controlling energy sharing among the energy micro-grids through the communication base station, and constructing a communication reliability constraint model of an energy sharing mechanism and a power loss constraint model of the communication base station;
the transmission of information between the microgrid and the operator of the power distribution system is efficient if the reliability of the communication between the Microgrid (MG) and the communication Base Station (BS) exceeds a certain value (α).
The calculation equation of the communication reliability is as follows:
δn-i＝(1-En-i)l (1)
wherein, deltan-iIs a communication reliability index between the nth communication base station and the ith energy microgrid, En-iThe Bit Error Rate (BER) between the nth communication base station and the ith energy microgrid, l is the bit length of the data packet (usually 125bits), i is 1,2, … …, and N is the number of all energy microgrids within the scheduling range.
The bit error rate En-iExpressed as:
Figure BDA0002794822080000071
where Q () is the integral tail function of the standard Gaussian distribution, BNIs the noise bandwidth of the wireless transmission transceiver, R is a numberData transmission rate, SNRn-iIs the signal-to-noise ratio (SNR) of the ith energy piconet accessing the nth communications base station. According to the property of the integral tail function, the high signal-to-noise ratio represents the low bit error rate.
The signal-to-noise ratio (SNR) is the ratio of the received effective signal strength to the noise signal strength. The effective signal strength refers to the signal strength obtained by the user from the access communication base station. The noise signal of the invention comprises interference power and thermal noise interference of other communication base stations. The calculation equation of the signal-to-noise ratio is as follows:
Figure BDA0002794822080000072
wherein, Wn-iIs the power, sigma, received by the nth communication base station connected with the ith energy microgridm≠nWm-iThe sum of interference power brought by other communication base stations to the ith energy microgrid, N0Is thermal noise.
The thermal noise N0Expressed as:
N0＝kTB (4)
where B is the channel bandwidth of the wireless transmit transceiver, k is Boltzmann (Boltzmann) constant, and k is 1.3803 × 10-23J/K, T is the kelvin temperature, and the temperature is typically T290K (17 ℃).
The path loss models under various propagation scenarios are different. Based on an analysis method and an empirical method, through the combination of the analysis method and the empirical method, a lognormal shadow path loss model is summarized in the LTE heterogeneous network physical layer specification (TR 36.814). The path loss model is generally expressed as:
PL＝h+g log10(dn-i) (5)
where PL is the dB form of the transmission loss, h and g are the coefficients of the path loss model, dn-iIs the distance between the nth communication base station and the ith energy microgrid.
The power W received by the nth communication base station connected with the ith energy microgridn-iExpressed as:
Figure BDA0002794822080000073
wherein the content of the first and second substances,
Figure BDA0002794822080000074
is the BS transmit power, PL is the transmission loss multiple converted from dB form (PL);
PL＝h+g log10(dn-i) (7)
based on equations (3) to (7), the final calculation equation for the signal-to-noise ratio is shown in equation (8):
Figure BDA0002794822080000081
as is clear from equation (8), increasing the signal-to-noise ratio can reduce the bit error rate. Furthermore, based on the equation, reducing the bit error rate may improve the reliability of wireless communication. According to equation (8), increasing the transmission power of the communication base stations is a key approach to increase the signal-to-noise ratio, but increasing the transmission power of one communication base station increases the interference noise of other communication base stations, thereby decreasing the signal-to-noise ratio.
Although the communication base station can communicate through different channels by adopting the channel resource allocation method, network equipment using quasi-orthogonal channel resources is always in the same or adjacent areas, so that co-channel interference is difficult to avoid. Moreover, the power distribution network communication private network cannot necessarily distribute excessive channel resources, so that the problem of common channel interference is more prominent. The model of the invention therefore assumes that all communication stations are connected to the same channel. The analysis of the interaction between the communicating base stations is shown in figure 2. In fig. 2, all communication base stations share the same channel. When the transmit power of BS1 is low, its coverage area is also small (as shown by the inner circle in fig. 2), which does not affect the communication between other BSs. And all BSs communicate simultaneously, so that the overall network efficiency is high. When BS1 is operating improperly at high transmit power, although its SNR and CR are high, the wireless communication range is increased (as shown by the outer circles in fig. 2). The communications of BS2-9 are affected, reducing overall network efficiency.
The higher the transmit power, the more communication base stations are affected. Therefore, the BS transmit power needs to be properly planned to ensure its communication reliability without affecting neighboring base stations.
From the above conclusions, it is necessary to optimize the BS transmit power so that the communication of all MGs meets the reliability requirements.
In the optimized operation model, the BS transmission power must satisfy the power loss constraint model of the communication base station as follows:
Figure BDA0002794822080000082
Figure BDA0002794822080000083
δi′-i≥α (11)
wherein the content of the first and second substances,
Figure BDA0002794822080000084
is the transmission power of the ith' communication base station in the interval of time slot t under scene s,
Figure BDA0002794822080000085
the power consumption of the ith' communication base station in the time slot t interval under the scene s, e is a linear ratio coefficient between the total power consumption and the transmission power of the communication base station, and the value of e is related to the loss of a radio frequency amplifier, the loss of a feeder line and the load consumption of related air conditioning equipment. The total power consumption of the BS and the transmitting power are in a linear increasing relationship, f is the fixed load of the communication base station, and the value of f is independent of the transmitting power of the BS. Mainly comprises a signal processing device, a storage battery and other load consumption of related parts of air conditioning equipment.
Figure BDA0002794822080000086
Is the minimum value of the communication base station transmit power,
Figure BDA0002794822080000087
is a communication baseMaximum value of station transmission power, deltai′-iThe communication reliability index of the ith energy microgrid accessing the ith communication base station is alpha, which is a fixed value and is the communication reliability requirement of the active power distribution network. The constraint (10) represents a limit of the BS transmit power. The constraint (11) represents the CR requirement for the optimal operating model in the smart grid.
Step three: constructing an optimal operation model of an energy sharing mechanism according to a power distribution network, controllable loads and a communication base station power loss constraint model shared by distributed energy in a given area;
in the present invention, the Distribution System Operator (DSO) in a given area is responsible for optimal control of distributed energy, controllable loads and communication base station power consumption. And the DSO uniformly schedules all the MGs through an energy sharing scheme to optimize energy operation. Energy sharing requires that the MGs deviate from their respective optimal schedules to accommodate surplus or demand from neighboring piconets, but the overall cost may be minimized. Thus, the optimal operation model of the energy sharing mechanism is as follows:
Figure BDA0002794822080000091
wherein, ciRepresents the distribution system operator operating cost, gamma, for a given areasIs the probability of the scene s,
Figure BDA0002794822080000092
is the hour controllable load of the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA0002794822080000093
is the load consumption of the ith' communication base station in the interval of time slot t under the scene s,
Figure BDA0002794822080000094
is the electricity price of the residents,
Figure BDA0002794822080000095
is the electricity price of the communication base station, Ui(. is) the utility function of the ith energy microgrid, PijstAnd j is 1,2, … …, where N is the number of all energy micro-grids in the scheduling range. In consideration of different pricing systems, the model takes the electricity prices of residents and BS (base station) as two different variables, can be reasonably set according to different regulations, does not lose generality, and applies a concave quadratic utility function.
The constraint conditions of the optimal operation model of the energy sharing mechanism are as follows:
Figure BDA0002794822080000096
Figure BDA0002794822080000097
Figure BDA0002794822080000098
Figure BDA0002794822080000099
Figure BDA00027948220800000910
Vmin≤Vist≤Vmax (18)
θmin≤θist≤θmax (19)
Figure BDA00027948220800000911
Figure BDA00027948220800000912
Figure BDA00027948220800000913
Figure BDA00027948220800000914
Figure BDA00027948220800000915
Figure BDA00027948220800000916
Figure BDA00027948220800000917
Figure BDA00027948220800000918
Figure BDA00027948220800000919
wherein the content of the first and second substances,
Figure BDA00027948220800000920
is the charging power of the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA00027948220800000921
is the discharge power of the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA00027948220800000922
is the reactive power stored by the ith energy microgrid within the time slot t interval under the scene s,
Figure BDA0002794822080000101
is the ith energy micro-meter in the interval of time slot t under the scene sThe controllable load of the network is controlled by the load,
Figure BDA0002794822080000102
is the power, phi, of the photovoltaic system in the interval of time slot t under scene siIs a set of units, Q, connected to the ith energy microgridijstIs the reactive power flow from the ith energy microgrid to the jth energy microgrid, gijIs the susceptance value of the line between the ith energy microgrid and the jth energy microgrid, bijIs the conductance value, v, of the line between the ith energy microgrid and the jth energy microgridistIs the voltage amplitude, v, of the ith energy microgridjstIs the voltage amplitude, theta, of the jth energy microgridistIs the voltage phase angle theta of the ith energy microgridjstIs the voltage phase angle, V, of the jth energy microgridminIs the lower bound of the voltage amplitude, VmaxIs the upper bound of the voltage amplitude, θminIs the lower bound of the phase angle of the voltage, thetamaxIs the upper bound of the phase angle of the voltage,
Figure BDA0002794822080000103
is a photovoltaic power generation predicted value of the ith energy microgrid in the time slot t interval,
Figure BDA0002794822080000104
is the lower bound of the controllable load,
Figure BDA0002794822080000105
is the upper bound of the controllable load,
Figure BDA0002794822080000106
is the predicted daily load requirement for the load,
Figure BDA0002794822080000107
is the maximum charging power that can be charged,
Figure BDA0002794822080000108
is the maximum discharge power of the discharge lamp,
Figure BDA0002794822080000109
is the maximum reactive power of the energy storage systemThe ratio of the total weight of the particles,
Figure BDA00027948220800001010
it is the efficiency of the energy storage system,
Figure BDA00027948220800001011
is the stored energy of the energy storage system,
Figure BDA00027948220800001012
is the upper limit of the capacity of the energy storage system,
Figure BDA00027948220800001013
is the lower limit of the capacity of the energy storage system,
Figure BDA00027948220800001014
is the initial energy level stored by the ith energy microgrid under the scene s,
Figure BDA00027948220800001015
is the final energy level, S, stored by the ith energy microgrid under the scene SijIndicating the apparent power from the ith to the jth energy microgrid.
Constraints (13) - (14) represent the power balance of the entire system. Constraints (15) - (17) and (18) - (19) account for line flow constraints and node phase angle and voltage constraints, respectively. The constraint (20) indicates that the photovoltaic power generation power of MGi is limited by the predicted value. At (21), the hour load of MGi is limited by a lower and an upper bound. Constraint (22) reveals the daily minimum load requirement of MGi. (26) And (27) limit the charge and discharge power, respectively. (26) And (27) using the constraints to give the energy balance equation and capacity limit of the ESS, respectively. The constraint (28) ensures that the energy stored in the end state is the same as in the initial state.
Step four: and carrying out linear solving on the communication reliability constraint model by adopting a least square method and a piecewise linear method to obtain model parameters of the reliability constraint model, and applying the model parameters to an optimal operation model of an energy sharing mechanism to obtain the minimum value of the overall cost of the power distribution network of the energy sharing mechanism.
For the optimal operating model, the variables are
Figure BDA00027948220800001016
And
Figure BDA00027948220800001017
the objective function is intended to determine the minimum of the sum of the load consumption costs of all BSs and all MGs. Under the condition of ensuring CR, the energy and the operation strategy of the BS can be optimized in the model. However, the bit error rate function in CR is an integral tail function of the standard gaussian distribution, which is a non-linear constraint. Since the solution of non-linear problems usually involves a high computational burden, the present invention seeks to reduce them to linear constraints that are easy to represent, i.e. to replace the non-linear constraints with its linearized equivalent constraint set.
To represent the constraints (11), a linear form needs to be found
Figure BDA00027948220800001018
Figure BDA00027948220800001019
The value ranges from 50 to 150, deltan-iLess than or equal to 1. However, as an intermediate variable, SNRn-iIs very high, from 0 to 107Are not equal. To obtain a better linear fit, it is split into two equations, δn-i(SNRn-i) And
Figure BDA00027948220800001020
δn-i(SNRn-i) Is a non-linear function that increases monotonically with respect to SNR. The equation is:
Figure BDA0002794822080000111
according to the equation (2),
Figure BDA0002794822080000112
is a non-linear function of n variables. Equations (2) and (29) are expressed as linear functions using the least squares method and the piecewise linear method (PWL), respectively.
For equation (2), there are n nonlinear equations to fit. Taking one as an example, the others can be obtained in a similar way.
S41, the signal-to-noise ratio of the ith energy microgrid accessing the nth communication base station is expressed by using a regression equation:
SNRn-i＝WEC+ε (30)
wherein the SNRn-iIs a k x 1 dimensional random vector, W, determined from observationsEIs a kx (n +1) matrix determined by the predictor variables, C is the (n +1) x 1 vector of unknown parameters, and epsilon is the k x 1-dimensional vector of random errors;
in a matrix representation can be written:
Figure BDA0002794822080000113
in a first step, a vector of least squares estimates is determined
Figure BDA0002794822080000114
Giving linear combinations
Figure BDA0002794822080000115
The minimum error vector length is minimized. Basically, a vector is estimated
Figure BDA0002794822080000116
Characterization of
Figure BDA0002794822080000117
And SNRn-iThe smallest possible value of the sum of variances. Variables of
Figure BDA0002794822080000118
Are linearly independent. Now, since the objective of multivariate regression is to minimize the sum of variances, the regression coefficients that satisfy this condition are determined by solving the least squares normal equation:
Figure BDA0002794822080000119
if variable
Figure BDA00027948220800001110
Is linearly independent, then (W)E)TWEI.e., [ (W)E)TWE]-1Will be present.
S42, solving the step S41 by the least square normal equation, and multiplying the two sides of the equation (32) [ (W)E)TWE]-1Obtaining a regression coefficient:
Figure BDA00027948220800001111
wherein the content of the first and second substances,
Figure BDA00027948220800001112
a vector, i.e., a regression coefficient, for least squares estimation;
to examine the linear fitting effect of the least square method, assuming that 27 base stations are uniformly distributed at random in an area with a diameter of 4 km, linear fitting of the signal-to-noise ratio is performed on the basis of B ═ 10MHz, and fig. 3 shows that
Figure BDA00027948220800001113
And approximation thereof
Figure BDA00027948220800001114
And
Figure BDA00027948220800001115
the gray curved surface is linear approximation, and the color curved surface is an original function.
S43, equation (29) is a one-dimensional nonlinear function. In order to apply the piecewise linearity method, a signal-to-noise ratio domain is divided into equal interval intervals, and in a q-th interval, a communication reliability constraint model is written as follows:
Figure BDA00027948220800001116
wherein, aqAnd bqAre all coefficients in the interval q,
Figure BDA00027948220800001117
representing the signal-to-noise ratio of the equal interval, and the SNR represents the signal-to-noise ratio;
applying piecewise linear method to equation (29), where BNand/R is 0.002898, and l is 125 bits. From the above results, the signal-to-noise ratio was in the range of 0 to 107In the meantime. The original function is shown in FIG. 4, with a constant length set to 106，aqAnd bqDetermined by break points at two ends of the interval. In fig. 4, the present invention obtains a set of linear functions as follows:
a.1 linearized Power flow model
In order to solve the mixed integer programming model, a linearized power distribution network power flow method is adopted.
Regardless of network losses, the power flow equation can be approximated in the following linear form:
Figure BDA0002794822080000121
Figure BDA0002794822080000122
wherein, bij、gijRespectively the susceptance value and the conductance value of the line between MGi and j. Pij、QijRepresenting the real and reactive power of the line between MGi and MGj, V being the square of the node voltage magnitude and θ being the voltage phase angle.
The line power constraint may be approximated as a piecewise linear form,
Figure BDA0002794822080000123
where N is the number of stages.
a.2 piecewise linear equation (32)
The set of linear functions is:
Figure BDA0002794822080000124
from all intervals and slopes of the equation, the constraint can be expressed as:
Figure BDA0002794822080000125
πjas a linear function of the interval j. The function δ is again piecewise linearly represented using a method in section 5.2, where equations (35a) - (35d) avoid the use of the max-min operator. In addition, define t1＝max(π2,π3)，t2＝max(π1,t1)，t3＝min(π6,t2)，t4＝min(π5,t3)，
Figure BDA0002794822080000126
By this splitting method, t5The values are the same in equation (33). Constraint conditions are as follows:
π2≤t1≤π2+v1Ω (A6)
π3≤t1≤π3+(1-v1)Ω (A7)
π1≤t2≤π1+v2Ω (A8)
t1≤t2≤t1+(1-v2)Ω (A9)
π6-v3Ω≤t3≤π6 (A10)
t2-(1-v3)Ω≤t3≤t2 (A11)
π5-v4Ω≤t4≤π5 (A12)
t3-(1-v4)Ω≤t4≤t3 (A13)
π4-v5Ω≤t5≤π4 (A14)
t4-(1-v5)Ω≤t5≤t4 (A15)
nonlinear constraint transformation t5≥α，t5δ. These 5 binary variables v1,v2,v3,v4v5There are 32 possible combinations. However, 16 of these 32 combinations result in infeasible constraints that will be captured by the MILP solver.
However, non-linear functions are constraints in the optimization problem. Therefore, the approximation of the non-linear function makes the problem unsmooth. Therefore, a method is introduced for range division before piecewise linear approximation.
S44, introducing new binary and continuous variables of q-1 and a new inequality of 4 (q-1) to rewrite the constraint conditions of the optimal operation model into linear constraints:
max{π1,π2}≥0， (35)
introducing new variable t ═ max { pi-1,π2The following new constraints are released from the maximum operator:
π1≤t≤π1+vΩ (36)
π2≤t≤π2+(1-v) (37)
s45, applying the method of the step S44 to a minimum operator: t is min { pi ═ n1,π2}：
π1-vΩ≤t≤π1 (38)
π2-(1-v)Ω≤t≤π2 (39)
Wherein v is a binary variable and Ω is a positive scalar;
s46, converting the nonlinear constraint of the reliability constraint model into a specific inequality according to the steps S41-S45, and obtaining model parameters of the reliability constraint model through solving the specific inequality.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. An active power distribution network energy sharing method based on communication reliability constraint is characterized by comprising the following steps:
the method comprises the following steps: establishing a power distribution network system model, and aggregating N energy micro-grids by one power distribution system operator in a power distribution grid, wherein the energy micro-grids are matched with the communication base stations in a one-to-one correspondence manner, and all the communication base stations share the same channel;
step two: controlling energy sharing among the energy micro-grids through the communication base station, and constructing a communication reliability constraint model of an energy sharing mechanism and a power loss constraint model of the communication base station;
step three: constructing an optimal operation model of an energy sharing mechanism according to a power distribution network, controllable loads and a communication base station power loss constraint model shared by distributed energy in a given area;
step four: and carrying out linear solving on the communication reliability constraint model by adopting a least square method and a piecewise linear method to obtain model parameters of the reliability constraint model, and applying the model parameters to an optimal operation model of an energy sharing mechanism to obtain the minimum value of the overall cost of the power distribution network of the energy sharing mechanism.
2. The active power distribution network energy sharing method based on the communication reliability constraint is characterized in that the communication reliability constraint model of the energy sharing mechanism is as follows:
δn-i＝(1-En-i)l
wherein, deltan-iIs a communication reliability index between the nth communication base station and the ith energy microgrid, En-iThe bit error rate between the nth communication base station and the ith energy microgrid is shown, l is the bit length of a data packet, i is 1,2, … …, and N is the number of all energy microgrids in the scheduling range;
the bit error rate En-iExpressed as:
Figure FDA0002794822070000011
where Q () is the integral tail function of the standard Gaussian distribution, BNIs the noise bandwidth of the wireless transmission transceiver, R is the data transmission speed, SNRn-iIs the signal-to-noise ratio of the ith energy piconet accessing the nth communication base station.
3. The active power distribution network energy sharing method based on communication reliability constraint of claim 2, wherein the signal-to-noise ratio SNR of the ith energy microgrid accessing the nth communication base stationn-iExpressed as:
Figure FDA0002794822070000012
wherein, Wn-iIs the power, sigma, received by the nth communication base station connected with the ith energy microgridm≠nWm-iThe sum of interference power brought by other communication base stations to the ith energy microgrid, N0Is thermal noise;
the thermal noise N0Expressed as:
N0＝kTB
where B is the channel bandwidth of the wireless transmit transceiver, k is Boltzmann (Boltzmann) constant, and k is 1.3803 × 10-23J/K, T is the temperature in Kelvin;
the power W received by the nth communication base station connected with the ith energy microgridn-iExpressed as:
Figure FDA0002794822070000021
wherein the content of the first and second substances,
Figure FDA0002794822070000022
is BS transmit power, PL is the transmission loss multiple, PL is the dB form of the transmission loss;
Figure FDA0002794822070000023
PL＝h+g log10(dn-i)
where h and g are both coefficients of the path loss model, dn-iIs the distance between the nth communication base station and the ith energy microgrid.
4. The active power distribution network energy sharing method based on the communication reliability constraint is characterized in that the power loss constraint model of the communication base station is as follows:
Figure FDA0002794822070000024
Figure FDA00027948220700000216
δi′-i≥α
wherein the content of the first and second substances,
Figure FDA0002794822070000025
is the transmission power of the ith' communication base station in the interval of time slot t under scene s,
Figure FDA0002794822070000026
is the power consumption of the ith' communication base station in the time slot t interval under the scene s, e is the linear ratio coefficient between the total power consumption and the transmission power of the communication base station, f is the fixed load of the communication base station,
Figure FDA0002794822070000027
is the minimum value of the communication base station transmit power,
Figure FDA0002794822070000028
is the maximum value of the transmission power of the communication base station, deltai′-iThe communication reliability index of the ith energy microgrid accessing the ith communication base station is shown, and alpha is the communication reliability requirement of the active power distribution network.
5. The active power distribution network energy sharing method based on the communication reliability constraint is characterized in that the optimal operation model of the energy sharing mechanism is as follows:
Figure FDA0002794822070000029
wherein, ciRepresents the distribution system operator operating cost, gamma, for a given areasIs the probability of the scene s,
Figure FDA00027948220700000210
is the hour controllable load of the ith energy microgrid within the time slot t interval under the scene s,
Figure FDA00027948220700000211
is the load consumption of the ith' communication base station in the interval of time slot t under the scene s,
Figure FDA00027948220700000212
is the electricity price of the residents,
Figure FDA00027948220700000213
is the electricity price of the communication base station, Ui(. is) the utility function of the ith energy microgrid, PijstAnd j is 1,2, … …, where N is the number of all energy micro-grids in the scheduling range.
6. The active power distribution network energy sharing method based on the communication reliability constraint is characterized in that the constraint conditions of the optimal operation model of the energy sharing mechanism are as follows:
Figure FDA00027948220700000214
Figure FDA00027948220700000215
Figure FDA0002794822070000031
Figure FDA0002794822070000032
Figure FDA0002794822070000033
Vmin≤Vist≤Vmax
θmin≤θist≤θmax
Figure FDA0002794822070000034
Figure FDA0002794822070000035
Figure FDA0002794822070000036
Figure FDA0002794822070000037
Figure FDA0002794822070000038
Figure FDA0002794822070000039
Figure FDA00027948220700000310
Figure FDA00027948220700000311
Figure FDA00027948220700000312
wherein the content of the first and second substances,
Figure FDA00027948220700000313
is the charging power of the ith energy microgrid within the time slot t interval under the scene s,
Figure FDA00027948220700000314
is the discharge power of the ith energy microgrid within the time slot t interval under the scene s,
Figure FDA00027948220700000315
is the reactive power stored by the ith energy microgrid within the time slot t interval under the scene s,
Figure FDA00027948220700000316
is the controllable load of the ith energy microgrid in the time slot t interval under the scene s,
Figure FDA00027948220700000317
is the power, phi, of the photovoltaic system in the interval of time slot t under scene siIs a set of units, Q, connected to the ith energy microgridijstIs the reactive power flow from the ith energy microgrid to the jth energy microgrid, gijIs the susceptance value of the line between the ith energy microgrid and the jth energy microgrid, bijIs the conductance value, v, of the line between the ith energy microgrid and the jth energy microgridistIs the voltage amplitude, v, of the ith energy microgridjstIs the voltage amplitude, theta, of the jth energy microgridistIs the voltage phase angle theta of the ith energy microgridjstIs the voltage phase angle, V, of the jth energy microgridminIs the lower bound of the voltage amplitude, VmaxIs the upper bound of the voltage amplitude, θminIs the lower bound of the phase angle of the voltage, thetamaxIs the upper bound of the phase angle of the voltage,
Figure FDA00027948220700000318
is a photovoltaic power generation predicted value of the ith energy microgrid in the time slot t interval,
Figure FDA00027948220700000319
is the lower bound of the controllable load,
Figure FDA00027948220700000320
is the upper bound of the controllable load,
Figure FDA00027948220700000321
is the predicted daily load requirement for the load,
Figure FDA00027948220700000322
is the maximum charging power that can be charged,
Figure FDA00027948220700000323
is the maximum discharge power of the discharge lamp,
Figure FDA00027948220700000324
is the maximum reactive power of the energy storage system,
Figure FDA00027948220700000325
it is the efficiency of the energy storage system,
Figure FDA00027948220700000326
is the stored energy of the energy storage system,
Figure FDA00027948220700000327
is the upper limit of the capacity of the energy storage system,
Figure FDA00027948220700000328
is the lower limit of the capacity of the energy storage system,
Figure FDA00027948220700000329
is the initial energy level stored by the ith energy microgrid under the scene s,
Figure FDA00027948220700000330
is the final energy level, S, stored by the ith energy microgrid under the scene SijIndicating the apparent power from the ith to the jth energy microgrid.
7. The active power distribution network energy sharing method based on the communication reliability constraint is characterized in that the method for solving the communication reliability constraint model in a linearization manner by adopting a least square method and a piecewise linear method is as follows:
s41, the signal-to-noise ratio of the ith energy microgrid accessing the nth communication base station is expressed by using a regression equation:
SNRn-i＝WEC+ε
wherein the SNRn-iIs a k x 1 dimensional random vector, W, determined from observationsEIs a kx (n +1) matrix determined by the predictor variables, C is the (n +1) x 1 vector of unknown parameters, and epsilon is the k x 1-dimensional vector of random errors;
s42, solving the step S41 through a least square normal equation to obtain a regression coefficient:
Figure FDA0002794822070000041
wherein the content of the first and second substances,
Figure FDA0002794822070000042
a vector, i.e., a regression coefficient, for least squares estimation;
s43, dividing the signal-to-noise ratio domain into equal interval intervals, and writing a communication reliability constraint model into the following steps in the q-th interval:
Figure FDA0002794822070000043
wherein, aqAnd bqAre all coefficients in the interval q,
Figure FDA0002794822070000044
representing the signal-to-noise ratio of the equal interval, and the SNR represents the signal-to-noise ratio;
s44, introducing new binary and continuous variables of q-1 and a new inequality of 4 (q-1) to rewrite the constraint conditions of the optimal operation model into linear constraints:
max{π1,π2}≥0，
introducing new variable t ═ max { pi-1,π2The following new constraints are released from the maximum operator:
π1≤t≤π1+vΩ
π2≤t≤π2+(1-v)
s45, applying the method of the step S44 to a minimum operator: t is min { pi ═ n1,π2}：
π1-vΩ≤t≤π1
π2-(1-v)Ω≤t≤π2
Wherein v is a binary variable and Ω is a positive scalar;
s46, converting the nonlinear constraint of the reliability constraint model into a specific inequality according to the steps S41-S45, and obtaining model parameters of the reliability constraint model through solving the specific inequality.
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