CN106022533A - Calculation energy and information binary fusion component optimized access based on cloud platform - Google Patents

Abstract

The invention discloses a calculation energy and information binary fusion component optimized access based on a cloud platform. The method includes that the measurement data to be obtained of a controllable device and the prediction data to be predicted of a uncontrollable device are determined; the controllable device automatically induces the current measurement data based on the measurement data; the uncontrollable device acquires the historical measurement data and the prediction data of the next time is obtained by least square data fitting method; the current measurement data acquired by the controllable device and the prediction data of the next time of the uncontrollable device are received, and the active power and the active power probability are calculated based on the prediction data of the next time of the uncontrollable device; based on the current measurement data of the controllable device and a particle swarm algorithm, the output power of the next time of the controllable device is calculated by the target function representing the all-day energy consumption of a distributed power supply; and the controllable device receives the output power of the next time, and the actual output power of the next time is adjusted based on the output power of the next time.

Description

The optimization cut-in method of energy and information binary fusion element is calculated based on cloud platform

Technical field

The present invention relates to the Appropriate application of energy, be specifically related to a kind of based on cloud platform calculating energy and information two The optimization cut-in method of element is merged in unit.

Background technology

The element merged along with big energy and information binary is linked in power system, for guaranteeing power system Each equipment realize the reasonable distribution of energy, traditional way is energy and information binary to be merged element enter The uncertain subregion of row, is divided into essentially equal N number of region, and the calculating of probability by the value of stochastic variable, Application condition is big, causes the charge volume of controllable device in access power system or discharge capacity the most inaccurate, Make the overtension of equipment in power system or too low, and cause the equipment in power system to suffer in various degree Damage.

Summary of the invention

For above-mentioned deficiency of the prior art, what the present invention provided calculates energy and information two based on cloud platform The optimization cut-in method of unit's fusion element can make charge volume or the discharge capacity of controllable device in access power system More accurate.

In order to reach foregoing invention purpose, the technical solution used in the present invention is:

A kind of optimization cut-in method calculating energy and information binary fusion element based on cloud platform, its bag are provided Include following steps:

Energy merges, with information binary, the measurement data that in element layer, controllable device is to be obtained to use cloud platform to determine The prediction data to be predicted with uncontrollable equipment, builds Energy distribution formula power supply whole day energy of dissolving maximum simultaneously Object function；

Energy and information binary merge element layer and receive the measurement data that controllable device that cloud platform issues is to be obtained The prediction data to be predicted with uncontrollable equipment, controllable device is worked as according to measurement data automatic sensing to be obtained The measurement data in front moment；Uncontrollable equipment obtains its historical measurement data and uses method of least square to carry out data Approximating method obtains the prediction data of its subsequent time；

Cloud platform receives the current time of the controllable device acquisition that energy is uploaded with information binary fusion element layer Measurement data and the prediction data of uncontrollable equipment subsequent time, and pre-according to uncontrollable equipment subsequent time Survey data and calculate the probability of its active power and active power；

Cloud platform, according to the measurement data of controllable device current time and particle cluster algorithm, uses distributed power source The whole day maximum object function of energy of dissolving calculates the size of exerting oneself of controllable device subsequent time；

Exerting oneself of the controllable device subsequent time that energy and information binary fusion element layer reception cloud platform issue is big Little, controllable device adjusts the actual size of exerting oneself of its subsequent time according to the size of exerting oneself of subsequent time.

The invention have the benefit that this programme obtains its active power by the prediction data of uncontrollable equipment With the probability of active power, combining measurement data and the particle cluster algorithm of controllable device current time afterwards, To calculate exerting oneself of controllable device subsequent time big for the maximum object function of energy to use distributed power source whole day to dissolve Little, the size of exerting oneself of the subsequent time by calculating goes to adjust the actual size of exerting oneself of controllable device subsequent time.

By this optimization cut-in method can make the charge volume/discharge capacity of controllable device in access power system/ Watt level is more accurate, it is ensured that in power system, the voltage of equipment remains in normal range of operation, Thus extend the service life of equipment in power system；It addition, use the method can make distributed power source Energy of dissolving is maximum and probabilistic statement more accurately rationally, more standby representative.

Accompanying drawing explanation

Fig. 1 is to calculate, based on cloud platform, the flow chart that energy merges the optimization cut-in method of element with information binary.

Detailed description of the invention

Below the detailed description of the invention of the present invention is described, in order to those skilled in the art manage Solve the present invention, it should be apparent that the invention is not restricted to the scope of detailed description of the invention, to the art From the point of view of those of ordinary skill, if the essence of the present invention that various change limits in appended claim and determines In god and scope, these changes are apparent from, and all utilize the innovation and creation of present inventive concept all protecting The row protected.

The optimization access calculating energy and information binary fusion element based on cloud platform is shown with reference to Fig. 1, Fig. 1 The flow chart of method；As it is shown in figure 1, the method 100 includes that step 101 is to step 105:

In a step 101, energy and information binary merge controllable device in element layer and treat to use cloud platform to determine The measurement data obtained and uncontrollable equipment prediction data to be predicted, build Energy distribution formula power supply complete simultaneously The object function that it energy of dissolving is maximum.

Wherein, energy and information binary merge element layer and are made up of energy and information binary fusion element, and energy Measure and be made up of controllable device and uncontrollable equipment portion again with information binary fusion element；Controllable device includes electronic Automobile, energy storage device and Capacitor banks；Uncontrollable equipment includes distributed energy and load, distributed energy It is made up of wind-force power supply and photo-voltaic power supply again.

Photo-voltaic power supply prediction data to be obtained be photovoltaic exert oneself expectation and variance, the prediction that wind-force power supply is to be obtained Data be wind-force exert oneself expectation and variance；Electric automobile measurement data to be obtained be battery power status amount and Charge power, energy storage device measurement data to be obtained is energy state amount and charge-discharge electric power, Capacitor banks Prediction data to be obtained be idle quantity of state, can switching capacity electricity and the number of times of switching.

In a step 102, the controllable device that energy and information binary fusion element layer reception cloud platform issue is treated The measurement data obtained and uncontrollable equipment prediction data to be predicted, controllable device is according to measurement to be obtained The measurement data of data automatic sensing current time；Uncontrollable equipment obtains its historical measurement data and uses minimum Square law carries out data fitting method and obtains the prediction data of its subsequent time；

Merge at energy and information binary and be additionally provided with PnP protocol layer and gateway intelligence between element layer and cloud platform Energy node and two transport layers of transport layer, energy and information binary merge and carry out letter between element layer and cloud platform When ceasing mutual, it is necessary to carry out transfer through PnP protocol layer and gateway intelligence node with transport layer.

In step 103, the controllable device that cloud platform reception energy and information binary fusion element layer are uploaded obtains The measurement data of the current time taken and the prediction data of uncontrollable equipment subsequent time, and set according to uncontrollable The prediction data of standby subsequent time calculates the probability of its active power and active power.

In one embodiment of the invention, calculate it according to the prediction data of uncontrollable equipment subsequent time to have The probability of merit power and active power may further include:

A, the active power calculating photo-voltaic power supply and the probability of active power

A1, exerting oneself owing to photo-voltaic power supply is meritorious is mainly affected by Intensity of the sunlight, and solar irradiation is obeyed Beta is distributed.Then this programme is when asking for the probability of the active power of photo-voltaic power supply and active power, first-selected Need the photovoltaic by photo-voltaic power supply to exert oneself to expect to obtain, with variance, parameter alpha and the β that Beta is distributed.

The parameter alpha being distributed due to Beta and β are that employing is existing to carry out solving than more conventional example, this Place is being not just that the carrying out how to solve repeats to it.

The density that a2, calculating photo-voltaic power supply are exerted oneself:

$f\left(P\right)=\frac{\mathrm{\Γ}(\mathrm{\α}+\mathrm{\β})}{\mathrm{\Γ}\left(\mathrm{\α}\right)\mathrm{\Γ}\left(\mathrm{\β}\right)}{\left(\frac{P}{P_{max}}\right)}^{\mathrm{\α}-1}{(1-\frac{P}{P_{max}})}^{\mathrm{\β}-1}$

In formula: α Yu β refers to the parameter that Beta is distributed；Γ represents Gamma function；P refers to photo-voltaic power supply Actual exert oneself；P_maxRefer to photo-voltaic power supply peak power output；P, P of photo-voltaic power supply_maxAnd P_min? Obtain with historical data analysis to be set by the active perception of photo-voltaic power supply.

The active power that a3, calculating photo-voltaic power supply are dissolved:

$p_i=p_{x,i-1}+\frac{p_{max}-p_{\min }}{2N_s}$

In formula, P_minRefer to photo-voltaic power supply minimum output power, P_maxRefer to that photo-voltaic power supply maximum possible exports Power, p_x,i-1Refer to that the meritorious of i-1 state is exerted oneself；Ns refers to total status number；

The probability of the active power that calculating photo-voltaic power supply is dissolved:

$g_i=\underset{p_{i,l}}{\overset{p_{i,r}}{\∫}}f\left(x\right)dx$

In formula, P_{I, l}Refer to that the minimum of i-th state is meritorious exert oneself；P_{I, r}Refer to that the maximum of i-th state has Merit is exerted oneself.

B, the active power calculating fan power and the probability of active power

The density that b1, employing Weibull distribution calculating wind-force power supply are exerted oneself:

$f\left(pj\right)=\frac{k}{c}{\left(\frac{pj}{c}\right)}^{k-1}\exp \[-{\left(\frac{pj}{c}\right)}^k\]$

In formula, pj is wind speed；K and c is two parameters of weibull distribution, i.e. k is form parameter, c For scale parameter；

The active power that b2, calculating wind-force power supply are dissolved:

$p_j=p_{x,j-1}+\frac{p_{max}-p_{\min }}{2N_c}$

In formula, P_minRefer to wind-force power supply minimum capability, P_maxRefer to that wind-force power supply maximum possible is defeated Go out power, p_x,j-1Refer to the meritorious representative value of exerting oneself of j-1 state；Ns refers to total status number；

$g_j=\underset{p_{x,l}}{\overset{p_{x,r}}{\∫}}f\left(x\right)dx$

In formula, P_{X, l}Refer to that the minimum of x-th state is meritorious exert oneself；P_{X, r}Refer to that the maximum of i-th state has Merit is exerted oneself；F (x) is the density that wind-force power supply is exerted oneself.

At step 104, cloud platform according to the measurement data of controllable device current time and particle cluster algorithm, To calculate exerting oneself of controllable device subsequent time big for the maximum object function of energy to use distributed power source whole day to dissolve Little.

In one embodiment of the invention, the distributed power source whole day maximum object function of energy of dissolving is:

$\max f=\underset{t=1}{\overset{n}{\Σ}}\underset{c=1}{\overset{N}{\Σ}}{g}_{c,t}(P_{t,pv}+P_{t,wt})\mathrm{\Δ}t$

In formula, n refer to by one day≤be divided into n period；P_t,pvAnd P_t,wtRepresent i-th period photovoltaic respectively Active power that power supply is dissolved and the merit power that wind-force power supply is dissolved, P_t,pv≤P_i, P_t,wt≤P_j；g_c,tFor the t period The c shape probability of state, g_c,t=g_i*g_j, c=1,2 ... N；Δ t is any time period.

In order to make object function reach optimum, distributed power source whole day is dissolved the maximum object function needs of energy The constraints met is equality constraint and inequality constraints condition and probabilistic constraints；

Wherein, equality constraint is:

$\left\{\begin{array}{c}{P}_i-P_{Li}=U_i\underset{k\∈A\left(i\right)}{\Σ}{U}_k(G_{ik}{\mathrm{cos\δ}}_{ik}+B_{ik}{\mathrm{sin\δ}}_{ik})\\ Q_i-Q_{Li}=U_i\underset{k\∈A\left(i\right)}{\Σ}{U}_k(G_{ik}{\mathrm{sin\δ}}_{ik}-B_{ik}{\mathrm{cos\δ}}_{ik})\end{array}\right.$

In formula: P_i、P_LiRefer respectively at node i meritorious exerts oneself and burden with power size；Q_i、Q_LiRespectively Refer at node i idle exerts oneself and load or burden without work size；U_i、U_kRefer respectively to the electricity of node i and node k Pressure amplitude value；δ_ikRefer to the phase difference of voltage between node i and node k；G_ik、B_ikRefer respectively to system admittance The real part of matrix and imaginary part；

Inequality constraints and probabilistic constraints:

$\left\{\begin{array}{c}0\≤Q_{t,c}^i\≤Q_c^{\max }\\ 0\≤P_{t,k}^i\≤P_k^{\max }\\ P_{t,ev}^{\min }\≤P_{t,ev}^i\≤P_{t,ev}^{\max }\end{array}\right.,$

$\left\{\begin{array}{cc}P\{U_{\min }\≤U_i\≤U_{\max }\}\≥{\mathrm{\α}}_U& i\∈{\mathrm{\Ω}}_{node}\\ P\{S_{ij}\≤S_{\max }\}\≥{\mathrm{\α}}_S& i,j\∈{\mathrm{\Ω}}_{node}\end{array}\right.,$

$\underset{i=1}{\overset{N_c}{\Σ}}{T}_{i,c}\≤4$

In formula: P{A} represents the probability that event A occurs；α_UWith α_SRefer respectively to the confidence level of voltage and capacity； U_iIt it is node voltage；S_ijFor tributary capacity；Ω_nodeRefer to the node set of system；Refer to the t period i-th The idle size of reality of individual state micro-gas-turbine；For referring to the reality of t period i-th state energy storage device The meritorious size of exerting oneself in border；For referring to the actual size of exerting oneself of t period i-th state photovoltaic.

In step 105, under energy and information binary merge the controllable device that element layer reception cloud platform issues The size of exerting oneself in one moment, controllable device adjusts the reality of its subsequent time according to the size of exerting oneself of subsequent time Exert oneself size.

The size of exerting oneself of electric automobile subsequent time refers to the charge power size of its subsequent time, energy storage device Subsequent time exert oneself size time refer to the energy storage charge-discharge electric power size of its subsequent time, under Capacitor banks for the moment The size of exerting oneself carved refers to that the capacitor of its subsequent time can switching capacity.

After the actual size of exerting oneself of its subsequent time is adjusted by controllable device, and by adjustment information through net Close intelligence node to transmit to cloud platform with transport layer and PnP protocol layer, after being finished, perform the next one and follow Ring.

In order to realize magnanimity in power system, diversified energy and information binary merge the information of element Shared resources and the optimized distribution of energy, this programme by cloud platform calculate needed for various energy and letter Cease binary fusion element carries out real-time perception and monitoring for information about, and then realizes massive energy and information two The real time bidirectional intercommunication of element and cloud platform that unit merges and sharing, it is achieved energy and information binary fusion element For information about being actively discovered of cycle cloud computing platform, distribute rationally and intelligent scheduling.Manager can also root According to application demand, increase and decrease energy and information binary merge the merit of controllable device in element the most dynamically The size of rate, it is achieved each equipment of power system realizes the reasonable distribution of energy.

Claims (6)

1. the optimization cut-in method of energy and information binary fusion element is calculated based on cloud platform, it is characterised in that Comprise the following steps:

Energy merges, with information binary, the measurement data that in element layer, controllable device is to be obtained to use cloud platform to determine The prediction data to be predicted with uncontrollable equipment, builds Energy distribution formula power supply whole day energy of dissolving maximum simultaneously Object function；

Energy and information binary merge element layer and receive the measurement data that controllable device that cloud platform issues is to be obtained The prediction data to be predicted with uncontrollable equipment, controllable device is worked as according to measurement data automatic sensing to be obtained The measurement data in front moment；Uncontrollable equipment obtains its historical measurement data and uses method of least square to carry out data Approximating method obtains the prediction data of its subsequent time；

Cloud platform receives the current time of the controllable device acquisition that energy is uploaded with information binary fusion element layer Measurement data and the prediction data of uncontrollable equipment subsequent time, and pre-according to uncontrollable equipment subsequent time Survey data and calculate the probability of its active power and active power；

Cloud platform, according to the measurement data of controllable device current time and particle cluster algorithm, uses distributed power source The whole day maximum object function of energy of dissolving calculates the size of exerting oneself of controllable device subsequent time；

Exerting oneself of the controllable device subsequent time that energy and information binary fusion element layer reception cloud platform issue is big Little, controllable device adjusts the actual size of exerting oneself of its subsequent time according to the size of exerting oneself of subsequent time.

The optimization calculating energy and information binary fusion element based on cloud platform the most according to claim 1 Cut-in method, it is characterised in that

Described uncontrollable equipment includes that distributed power source, described distributed energy are wind-force power supply and photo-voltaic power supply； Described photo-voltaic power supply prediction data to be obtained be photovoltaic exert oneself expectation and variance；Described wind-force power supply is to be obtained Prediction data be wind-force exert oneself expectation and variance；

Described controllable device includes electric automobile, energy storage device and Capacitor banks；Described electric automobile is to be obtained Measurement data be battery power status amount and charge power, energy storage device measurement data to be obtained is energy Quantity of state and charge-discharge electric power, Capacitor banks prediction data to be obtained be idle quantity of state, can switching capacity The number of times of electricity and switching.

The optimization calculating energy and information binary fusion element based on cloud platform the most according to claim 2 Cut-in method, it is characterised in that the described prediction data according to uncontrollable equipment subsequent time calculates it and gains merit The probability of power and active power farther includes:

A, the active power calculating photo-voltaic power supply and the probability of active power

A1, by the photovoltaic of photo-voltaic power supply exert oneself expectation and variance obtain Beta distribution parameter alpha and β；

The density that a2, calculating photo-voltaic power supply are exerted oneself:

$f\left(P\right)=\frac{\mathrm{\Γ}(\mathrm{\α}+\mathrm{\β})}{\mathrm{\Γ}\left(\mathrm{\α}\right)\mathrm{\Γ}\left(\mathrm{\β}\right)}{\left(\frac{P}{P_{max}}\right)}^{\mathrm{\α}-1}{(1-\frac{P}{P_{max}})}^{\mathrm{\β}-1}$

In formula: α Yu β refers to the parameter that Beta is distributed；Γ represents Gamma function；P refers to photo-voltaic power supply Actual exert oneself；P_maxRefer to photo-voltaic power supply peak power output；

The active power that a3, calculating photo-voltaic power supply are dissolved:

$p_i=p_{x,i-1}+\frac{p_{max}-p_{\min }}{2N_s}$

In formula, P_minRefer to photo-voltaic power supply minimum capability, P_maxRefer to that photo-voltaic power supply maximum possible is defeated Go out power, p_x,i-¹Refer to the meritorious representative value of exerting oneself of i-1 state；Ns refers to total status number；

The probability of the active power that calculating photo-voltaic power supply is dissolved:

$g_i=\underset{p_{i,l}}{\overset{p_{i,r}}{\∫}}f\left(x\right)dx$

In formula, P_{I, l}Refer to that the minimum of i-th state is meritorious exert oneself；P_{I, r}Refer to that the maximum of i-th state has Merit is exerted oneself；F (x) is the density that photo-voltaic power supply is exerted oneself.

B, the active power calculating fan power and the probability of active power

The density that b1, employing Weibull distribution calculating wind-force power supply are exerted oneself:

$f\left(pj\right)=\frac{k}{c}{\left(\frac{pj}{c}\right)}^{k-1}\exp \[-{\left(\frac{pj}{c}\right)}^k\]$

In formula, pj is wind speed；K and c is two parameters of weibull distribution, i.e. k is form parameter, c For scale parameter；

The active power that b2, calculating wind-force power supply are dissolved:

$p_j=p_{x,j-1}+\frac{p_{max}-p_{\min }}{2N_c}$

In formula, P_minRefer to wind-force power supply minimum capability, P_maxRefer to that wind-force power supply maximum possible is defeated Go out power, p_x,j-1Refer to the meritorious representative value of exerting oneself of j-1 state；Ns refers to total status number；

$g_j=\underset{p_{x,l}}{\overset{p_{x,r}}{\∫}}f\left(x\right)dx$

In formula, P_{X, l}Refer to that the minimum of x-th state is meritorious exert oneself；P_{X, r}Refer to that the maximum of i-th state has Merit is exerted oneself；F (x) is the density that wind-force power supply is exerted oneself.

The optimization calculating energy and information binary fusion element based on cloud platform the most according to claim 3 Cut-in method, it is characterised in that the described distributed power source whole day maximum object function of energy of dissolving is:

$\max f=\underset{t=1}{\overset{n}{\Σ}}\underset{c=1}{\overset{N}{\Σ}}{g}_{c,t}(P_{t,pv}+P_{t,wt})\mathrm{\Δ}t$

In formula, n refer to by one day≤be divided into n period；P_t,pvAnd P_t,wtRepresent i-th period photovoltaic respectively Active power that power supply is dissolved and the merit power that wind-force power supply is dissolved, P_t,pv≤P_i, P_t,wt≤P_j；g_c,tFor the t period The c shape probability of state, g_c,t=g_i*g_j；Δ t is any time period.

The optimization calculating energy and information binary fusion element based on cloud platform the most according to claim 4 Cut-in method, it is characterised in that the constraints that described need meet is equality constraint and inequality constraints Condition and probabilistic constraints；

Wherein, equality constraint is:

$\begin{array}{c}{P}_i-P_{Li}=U_i\underset{k\∈A\left(i\right)}{\Σ}{U}_k(G_{ik}{\mathrm{cos\δ}}_{ik}+B_{ik}{\mathrm{sin\δ}}_{ik})\\ Q_i-Q_{Li}=U_i\underset{k\∈A\left(i\right)}{\Σ}{U}_k(G_{ik}{\mathrm{sin\δ}}_{ik}-B_{ik}{\mathrm{cos\δ}}_{ik})\end{array}$

In formula: P_i、P_LiRefer respectively at node i meritorious exerts oneself and burden with power size；Q_i、Q_LiRespectively Refer at node i idle exerts oneself and load or burden without work size；U_i、U_kRefer respectively to the electricity of node i and node k Pressure amplitude value；δ_ikRefer to the phase difference of voltage between node i and node k；G_ik、B_ikRefer respectively to system admittance The real part of matrix and imaginary part；

Inequality constraints and probabilistic constraints:

$\left\{\begin{array}{c}0\≤Q_{t,c}^i\≤Q_c^{\max }\\ 0\≤P_{t,k}^i\≤P_k^{\max }\\ P_{t,ev}^{\min }\≤P_{t,ev}^i\≤P_{t,ev}^{\max }\end{array}\right.,$

$\underset{i=1}{\overset{N_c}{\Σ}}{T}_{i,c}\≤4$

$\left\{\begin{array}{cc}P\{U_{\min }\≤U_i\≤U_{\max }\}\≥{\mathrm{\α}}_U& i\∈{\mathrm{\Ω}}_{node}\\ P\{S_{ij}\≤S_{\max }\}\≥{\mathrm{\α}}_S& i,j\∈{\mathrm{\Ω}}_{node}\end{array}\right.,$

In formula: P{A} represents the probability that event A occurs；α_UWith α_SRefer respectively to the confidence water of voltage and capacity Flat；U_iIt it is node voltage；S_ijFor tributary capacity；Ω_nodeRefer to the node set of system；Refer to the t period The idle size of reality of i-th state micro-gas-turbine；For referring to t period i-th state energy storage device The meritorious size of exerting oneself of reality；For referring to the actual size of exerting oneself of t period i-th state photovoltaic.

6. described calculate energy based on cloud platform according to claim 1-5 is arbitrary and merge element with information binary Optimization cut-in method, it is characterised in that described cloud platform and energy and information binary merge element layer and carry out The PnP protocol layer that sets gradually between both and gateway intelligence node and transport layer is needed during data transmission.