Power dispatching method, device, electronic device and storage medium for electric power system

Abstract

The present invention provides a power dispatching method, device, electronic device and storage medium for an electric power system, the method comprising: modeling the behaviors of wind energy and solar energy respectively; establishing a power dispatching model of the electric power system under wind energy / solar energy conditions according to the modeling results; integrating adaptive weight coefficients, local frequency parameters and chaos algorithms into a bat algorithm, optimizing the power dispatching model using an improved bat algorithm, and obtaining the minimum cost value, average cost value and maximum cost value of the electric power system under wind energy / solar energy conditions respectively, so as to dispatch the electric power system according to the minimum cost value, average cost value and maximum cost value. The present invention can significantly reduce the total operating cost by enabling the penetration of renewable energy including wind energy and solar energy into the electric power system, and the incorporation of renewable energy including wind energy and solar energy greatly reduces the operating cost of thermal units.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization dispatching, and specifically relates to a power dispatching method, device, electronic equipment and storage medium for a power system. Background Art

[0002] Integration of Renewable Energy Sources (RES) in power systems has recently gained high momentum globally to encourage green energy development and reduce emissions. However, hybrid power generation systems that combine wind and solar energy pose challenges to the operation of power systems due to the intermittency of wind speed and solar irradiance. Economical day-ahead power dispatch is achieved through optimal sharing of all generating units to meet system demand at minimum cost while satisfying all system constraints. These system constraints typically include limitations in thermal units, uncertainties in wind and solar energy. Moreover, hybrid solar / wind systems have inherent variability in their characteristics. Therefore, appropriate models are required to adequately represent the intermittency of wind speed and solar irradiance.

[0003] The impact of renewable energy on power dispatch can be mainly divided into two types, namely robust optimization (RO) and stochastic optimization (SO). It is assumed that the wind and solar output power ranges in RO are provided within an uncertain set, and the correlation of wind and solar output variability between adjacent time periods cannot be evaluated. In addition, the main goal of RO is to find the worst feasible solution without considering the constraints of the system. In contrast, SO-based methods provide a range of possible solutions to model the risks involved in the uncertainty of wind and solar energy. Due to the large number of scenarios considered, this will lead to a significant increase in the dimensionality of the problem.

[0004] The variability of wind and solar energy is modeled in the constraints or total operating cost function in the SO method. The prior art proposes a stochastic dynamic economic dispatch (DED) method based on wind speed Monte Carlo simulation. However, in this method, the valve point effect constraints and the emission operating costs of thermal power generating units are not taken into account. The prior art also provides an algorithm based on multifunctional hybrid distribution (VMD). However, this method does not involve emission costs and valve point effects of thermal power units. The prior art also uses a hybrid genetic algorithm-sequential quadratic programming to minimize the total operating cost, however, in addition to the valve point effects of thermal units and SRRs, system power losses are not included in the system constraints.

[0005] The above existing techniques all sacrifice some thermal unit constraints, and not all methods consider the uncertainty of solar energy in their problem formulation. Summary of the invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art and provides a power dispatching method, device, electronic device and storage medium for an electric power system.

[0007] In one aspect of the present invention, a power dispatching method for an electric power system is provided, the method comprising:

[0008] Model the behavior of wind and solar energy separately;

[0009] According to the modeling results, a power dispatch model of the power system under wind / solar conditions is established;

[0010] The adaptive weight coefficient, local frequency parameter and chaos algorithm are integrated into the bat algorithm, and the improved bat algorithm is used to optimize the power scheduling model to obtain the minimum cost value, average cost value and maximum cost value of the power system under wind / solar energy conditions, so as to schedule the power system according to the minimum cost value, the average cost value and the maximum cost value.

[0011] In some embodiments, the separately modeling of the behaviors of wind energy and solar energy includes:

[0012] Establish the Weibull probability density function f of wind speed v,t v t It is the following formula (1):

[0013]

[0014] In the above formula (1), v t is the wind speed at time t, k t-1 is the shape factor at time t-1, k t and c t are the shape factor and scale factor at time t, respectively, expressed as the following formula (2):

[0015]

[0016] In the above formula (2), Γ is the gamma function, σ t and μ t are the standard deviation and mean of wind speed at time t, respectively;

[0017] The relationship between wind speed v and wind power output w is described by the following equation (3):

[0018]

[0019] In the above formula (3), w r is the rated wind power, v in 、v out 、v rThey are cut-in wind speed, cut-out wind speed and rated wind speed respectively;

[0020] According to the above formula (3), the Weibull probability density function of wind speed is converted into the Weibull probability density function of wind power output f w,t (w t ), expressed as the following formula (4):

[0021]

[0022] In the above formula (4), w t is the wind power output at time t, h is the ratio between the wind speed limit and the cut-in wind speed in the linear region, and

[0023] Establish the solar irradiance Beta probability density function f at time t S,t (S t ) is the following formula (5):

[0024]

[0025] In the above formula (5), S t and S max,t are solar irradiance and maximum solar irradiance, α t and β t are Beta parameters and are calculated by the following formula (6):

[0026]

[0027] In the above formula (6), μ s,t represents the mean solar irradiance, represents the solar irradiance variance;

[0028] Solar output power P PV,t Determined by the following formula (7):

[0029]

[0030] In the above formula (7), T C,t and T a are the battery temperature and the ambient temperature, I t is the current, K c is the current temperature coefficient, V t is the voltage, K v is the voltage temperature coefficient, FF is the fill factor, N is the number of solar cells, OT is the nominal operating temperature of the battery, I SC is the short-circuit current, V OC is the open circuit voltage, V MPP is the maximum power point voltage, I MPPis the maximum power point current;

[0031] When solar irradiance is available, the solar output probability density function f PV,t (P PV,t ) follows the Beta distribution, which can be expressed as the following formula (8):

[0032]

[0033] In the above formula (8), is the maximum solar power generation at time t.

[0034] In some embodiments, establishing a power dispatch model of the power system under wind energy / solar energy conditions according to the modeling results includes:

[0035] The objective function shown in equation (9) is established for the power dispatch of the power system under wind / solar conditions:

[0036]

[0037] Wherein, Min C is the minimum operating cost of the power system, T is the time span, i=1,2,…,n, n is the number of generator sets in the thermal power unit, j=1,2,…,m, m is the number of wind turbines in the wind farm, l=1,2,…,k, k is the number of photovoltaic solar panels in the photovoltaic power station;

[0038] C(P i,t ) is the fuel cost function of the i-th generator set in the thermal power unit at time t, expressed as the following formula (10):

[0039]

[0040] In the above formula (10), P i,t is the active output power of the i-th generator set in the thermal power unit at time t, a i ,b i ,c i are the cost coefficients of the i-th generator set in the thermal power unit, e i and f i are the valve point cost coefficients of the i-th generator set in the thermal power unit, P i min is the minimum output power of the i-th generator set in the thermal power unit;

[0041] C(PW j,t ) is the operating cost of the jth wind turbine in the wind farm at time t, expressed as the following formula (11):

[0042] C(PW j,t ) = d j wj,t +k p,j (W j,a,t -w j,t )+k r,j (w j,t -W j,a,t ) (11)

[0043] In the above formula (11), d j is the wind power cost coefficient of the jth wind turbine in the wind farm, k p,j and k r,j are the underestimated wind power cost coefficient and overestimated wind power cost coefficient of the j-th wind turbine in the wind farm, respectively, j,t and W j,a,t are the planned wind power output power and available wind power output power of the jth wind turbine in the wind farm at time t, respectively;

[0044] C(PV l,t ) is the operating cost of solar energy at time t, expressed as the following formula (12):

[0045]

[0046] In the above formula (12), They are direct solar power generation cost coefficient, underestimated solar power generation cost coefficient and overestimated solar power generation cost coefficient. and are the available solar output power and the predetermined solar output power at time t, respectively;

[0047] The underestimation penalty cost function of wind power generation is established as follows (13):

[0048]

[0049] In the above formula (13), w r,j is the rated wind output power of the jth wind turbine in the wind farm, f w,t (w j,t ) is the probability density function, Y ue for The calculation result is expressed as the following formula (14), where v j,t is the wind speed of the jth wind turbine in the wind farm at time t:

[0050]

[0051] The underestimation penalty cost function of solar power generation is established as follows (15):

[0052]

[0053] In the above formula (15), is the underestimation penalty cost function for solar power generation, is the rated power of the solar cell;

[0054] The penalty cost for establishing wind power output is as follows (16):

[0055]

[0056] In the above formula (16), f(w j,t ) is the penalty cost of wind power output, Y oe for The calculation result is expressed as the following formula (17):

[0057]

[0058] The penalty cost for establishing solar power output is given by the following formula (18):

[0059]

[0060] In the above formula (18), is the solar power output, penalty costs for solar power output;

[0061] The problem constraints are established for the above function, expressed as the following formula (19):

[0062]

[0063] In the above formula (19), P i is the output power of the i-th generator set in the thermal power unit, P i,t-1 is the active output power of the i-th generator set in the thermal power unit at time t-1, P i max is the maximum output power of the i-th generator set in the thermal power unit, P i min is the minimum output power of the ith generator set in the thermal power unit, is the required power, is the transmission power loss, B i,j is the loss coefficient between the i-th generator set in the thermal power plant and the j-th wind turbine set in the wind farm, UR i and DR i are the output power rising ramp limit and falling ramp limit of the i-th generator set in the thermal power unit, SR t and The spinning reserves are respectively the first preset duration and the second preset duration.

[0064] In some embodiments, the adaptive weight coefficient, the local frequency parameter and the chaos algorithm are integrated into the bat algorithm, and the power dispatch model is optimized using the improved bat algorithm to obtain the minimum cost value, the average cost value and the maximum cost value of the power system under wind energy / solar energy conditions, respectively, so as to dispatch the power system according to the minimum cost value, the average cost value and the maximum cost value, including:

[0065] Initialization parameters: The search space is defined in a three-dimensional space of L×W×H, where L, W, and H represent the length, width, and height of the three-dimensional space, respectively. The position coordinates (x, y, z) of the bats are constrained by the three-dimensional space. The number of individuals in the initial population is N, the individual loudness of the bats is a, the pulse emission rate is r, and the maximum number of iterations is t. max ;

[0066] Initialize the population individuals: evenly set a number of beacon points in the search space, and the setting of the beacon points is shown in the following formula (20):

[0067]

[0068] Setting the fitness function: The fitness function is set to the following equation (21), where Δf(t) is the system frequency deviation:

[0069]

[0070] Parameter update: The position update formula of a single bat is expressed as the following equation (22), where: is the position of the i-th bat in the t-th iteration, is the speed of the ith bat in iteration t+1, is the position of the i-th bat at the t+1th iteration:

[0071]

[0072] The bat's speed update is expressed as follows (23):

[0073]

[0074] In the above formula (23), V i t and are the speeds of the i-th bat at the t-th and t+1-th iterations, respectively, and x pbest and x gbest are the local optimal position and the global optimal position respectively, rand1 and rand2 are both random numbers between [0,1], Q i1 Q is the frequency parameter for controlling the local optimal flight direction. i2Q is the frequency parameter for controlling the global optimal flight direction. i1 and Q i2 It is expressed as the following formula (24), w v is the adaptive weighting factor, expressed as the following formula (25):

[0075]

[0076] w v =w vmin +(w vmax +w vmin )S t-1 (25)

[0077] Among them, Q 1max ,Q 1min are the maximum and minimum values ​​of the frequency parameter that controls the local optimal flight direction, Q 2max ,Q 2min are the maximum and minimum values ​​of the frequency parameter that controls the global optimal flight direction, w vmax and w vmin are the maximum weight value and the minimum weight value respectively, S is the attenuation value, k q is a random number between [0,1];

[0078] Disturbance operation: The chaotic map perturbs the probability of generating a solution. Assuming that rand3 is a random number generated between the set [0,1], if rand3 is greater than the disturbance threshold r n , then the position coordinates of each new individual will be mapped according to the following rules:

[0079] Through the chaotic map {z y |y=1,2,3,...,N} generates a chaotic sequence, y is the individual number, z y is the position coordinate of individual y before chaotic mapping. The chaotic mapping is defined as the following formula (26), where Rand() is a random number in the interval [0,1], η is the control factor for controlling the chaotic intensity, and z y+1 is the position coordinate of individual y after chaotic mapping:

[0080]

[0081] Generate a disturbance value corresponding to each individual, which is expressed as the following equation (27), where Dis is the generated disturbance value and (1:y) represents y individuals from 1 to y:

[0082] Dis(1:y)=2(t max -t)Rand(1:y) / t max (27)

[0083] The position coordinates of the new individual are disturbed, and the other parameters remain unchanged, which can be expressed as the following formula (28), where x new is an individual x n New individuals after being disturbed:

[0084] x new (1:y)=x n (1:y)-Dis(1:y)+2Dis(1:y)z(1:y) (28)

[0085] Individual selection strategy: Assume that rand4 is a random number in the interval [0,1]. If rand4 is greater than the selection threshold a n And the new individual x new The objective function is less than the individual x n The objective function is to accept the new solution generated by the perturbation operation, and the impulse response and loudness Update them simultaneously and get the following formula (29), where: is the impulse loudness value of the i-th bat at the t+1th iteration, r i 0 is the initial impulse response value of the ith bat, γ is the constant coefficient of the impulse rate, is the response value of the i-th bat at the t+1th iteration, and ε is the attenuation coefficient of the loudness:

[0086]

[0087] Iteration termination condition judgment: the preset maximum number of iterations is used as the iteration termination condition. If the number of iterations does not reach the maximum number of iterations, the above steps of setting the fitness function to the individual selection strategy are repeated until the number of iterations reaches the maximum number of iterations, and the minimum cost value, average cost value and maximum cost value of the power system under wind power / solar energy conditions are obtained respectively.

[0088] Another aspect of the present invention provides a power dispatching device for an electric power system, the device comprising:

[0089] Modeling modules to model the behavior of wind and solar energy separately;

[0090] Establish a module for establishing a power dispatch model of the power system under wind / solar energy conditions based on the modeling results;

[0091] The optimization module is used to integrate the adaptive weight coefficient, local frequency parameter and chaos algorithm into the bat algorithm, and use the improved bat algorithm to optimize the power scheduling model to obtain the minimum cost value, average cost value and maximum cost value of the power system under wind energy / solar energy conditions, so as to schedule the power system according to the minimum cost value, the average cost value and the maximum cost value.

[0092] In some embodiments, the modeling module is specifically used to:

[0093] Establish the Weibull probability density function f of wind speed v,t v t It is the following formula (1):

[0094]

[0095] In the above formula (1), v t is the wind speed at time t, k t-1 is the shape factor at time t-1, k t and c t are the shape factor and scale factor at time t, respectively, expressed as the following formula (2):

[0096]

[0097] In the above formula (2), Γ is the gamma function, σ t and μ t are the standard deviation and mean of wind speed at time t, respectively;

[0098] The relationship between wind speed v and wind power output w is described by the following equation (3):

[0099]

[0100] In the above formula (3), w r is the rated wind power, v in 、v out 、v r They are cut-in wind speed, cut-out wind speed and rated wind speed respectively;

[0101] According to the above formula (3), the Weibull probability density function of wind speed is converted into the Weibull probability density function of wind power output f w,t (w t ), expressed as the following formula (4):

[0102]

[0103] In the above formula (4), w t is the wind power output at time t, h is the ratio between the wind speed limit and the cut-in wind speed in the linear region, and

[0104] Establish the solar irradiance Beta probability density function f at time t S,t (S t ) is the following formula (5):

[0105]

[0106] In the above formula (5), S t and S max,t are solar irradiance and maximum solar irradiance, α t and β t are Beta parameters and are calculated by the following formula (6):

[0107]

[0108] In the above formula (6), μ s,t represents the mean solar irradiance, represents the solar irradiance variance;

[0109] Solar output power P PV,t Determined by the following formula (7):

[0110]

[0111] In the above formula (7), T C,t and T a are the battery temperature and the ambient temperature, I t is the current, K c is the current temperature coefficient, V t is the voltage, K v is the voltage temperature coefficient, FF is the fill factor, N is the number of solar cells, OT is the nominal operating temperature of the battery, I SC is the short-circuit current, V OC is the open circuit voltage, V MPP is the maximum power point voltage, I MPP is the maximum power point current;

[0112] When solar irradiance is available, the solar output probability density function f PV,t (P PV,t ) follows the Beta distribution, which can be expressed as the following formula (8):

[0113]

[0114] In the above formula (8), is the maximum solar power generation at time t.

[0115] In some implementations, the establishment module is specifically used to:

[0116] The objective function shown in equation (9) is established for the power dispatch of the power system under wind / solar conditions:

[0117]

[0118] Wherein, Min C is the minimum operating cost of the power system, T is the time span, i=1,2,…,n, n is the number of generator sets in the thermal power unit, j=1,2,…,m, m is the number of wind turbines in the wind farm, l=1,2,…,k, k is the number of photovoltaic solar panels in the photovoltaic power station;

[0119] C(P i,t ) is the fuel cost function of the i-th generator set in the thermal power unit at time t, expressed as the following formula (10):

[0120]

[0121] In the above formula (10), P i,t is the active output power of the i-th generator set in the thermal power unit at time t, a i ,b i ,c i are the cost coefficients of the i-th generator set in the thermal power unit, e i and f i are the valve point cost coefficients of the i-th generator set in the thermal power unit, P i min is the minimum output power of the i-th generator set in the thermal power unit;

[0122] C(PW j,t ) is the operating cost of the jth wind turbine in the wind farm at time t, expressed as the following formula (11):

[0123] C(PW j,t ) = d j w j,t +k p,j (W j,a,t -w j,t )+k r,j (w j,t +W j,a,t ) (11)

[0124] In the above formula (11), d j is the wind power cost coefficient of the jth wind turbine in the wind farm, k p,j and k r,j are the underestimated wind power cost coefficient and overestimated wind power cost coefficient of the j-th wind turbine in the wind farm, respectively, j,t and Wj,a,t are the planned wind power output power and available wind power output power of the jth wind turbine in the wind farm at time t, respectively;

[0125] C(PV l,t ) is the operating cost of solar energy at time t, expressed as the following formula (12):

[0126]

[0127] In the above formula (12), They are direct solar power generation cost coefficient, underestimated solar power generation cost coefficient and overestimated solar power generation cost coefficient. and are the available solar output power and the predetermined solar output power at time t, respectively;

[0128] The underestimation penalty cost function of wind power generation is established as follows (13):

[0129]

[0130] In the above formula (13), w r,j is the rated wind output power of the jth wind turbine in the wind farm, f w,t (w j,t ) is the probability density function, Y ue for The calculation result is expressed as the following formula (14), where v j,t is the wind speed of the jth wind turbine in the wind farm at time t:

[0131]

[0132] The underestimation penalty cost function of solar power generation is established as follows (15):

[0133]

[0134] In the above formula (15), is the underestimation penalty cost function for solar power generation, is the rated power of the solar cell;

[0135] The penalty cost for establishing wind power output is as follows (16):

[0136]

[0137] In the above formula (16), f(w j,t ) is the penalty cost of wind power output, Y oe for The calculation result is expressed as the following formula (17):

[0138]

[0139] The penalty cost for establishing solar power output is given by the following formula (18):

[0140]

[0141] In the above formula (18), is the solar power output, penalty costs for solar power output;

[0142] The problem constraints are established for the above function, expressed as the following formula (19):

[0143]

[0144] In the above formula (19), P i is the output power of the i-th generator set in the thermal power unit, P i,t-1 is the active output power of the i-th generator set in the thermal power unit at time t-1, P i max is the maximum output power of the i-th generator set in the thermal power unit, P i min is the minimum output power of the ith generator set in the thermal power plant, P Dt is the required power, is the transmission power loss, B i,j is the loss coefficient between the i-th generator set in the thermal power plant and the j-th wind turbine set in the wind farm, UR i and DR i are the output power rising ramp limit and falling ramp limit of the i-th generator set in the thermal power unit, SR t and The spinning reserves are respectively the first preset duration and the second preset duration.

[0145] In some embodiments, the optimization module is specifically used to:

[0146] Initialization parameters: The search space is defined in a three-dimensional space of L×W×H, where L, W, and H represent the length, width, and height of the three-dimensional space, respectively. The position coordinates (x, y, z) of the bats are constrained by the three-dimensional space. The number of individuals in the initial population is N, the individual loudness of the bats is a, the pulse emission rate is r, and the maximum number of iterations is t. max ;

[0147] Initialize the population individuals: evenly set a number of beacon points in the search space, and the setting of the beacon points is shown in the following formula (20):

[0148]

[0149] Setting the fitness function: The fitness function is set to the following equation (21), where Δf(t) is the system frequency deviation:

[0150]

[0151] Parameter update: The position update formula of a single bat is expressed as the following equation (22), where: is the position of the i-th bat in the t-th iteration, is the speed of the ith bat in iteration t+1, is the position of the i-th bat at the t+1th iteration:

[0152]

[0153] The bat's speed update is expressed as follows (23):

[0154]

[0155] In the above formula (23), V i t and are the speeds of the i-th bat at the t-th and t+1-th iterations, respectively, and x pbest and x gbest are the local optimal position and the global optimal position respectively, rand1 and rand2 are both random numbers between [0,1], Q i1 Q is the frequency parameter for controlling the local optimal flight direction. i2 Q is the frequency parameter for controlling the global optimal flight direction. i1 and Q i2 It is expressed as the following formula (24), w v is the adaptive weighting factor, expressed as the following formula (25):

[0156]

[0157] w v =w vmin +(w vmax +w vmin )S t-1 (25)

[0158] Among them, Q 1max ,Q 1min are the maximum and minimum values ​​of the frequency parameter that controls the local optimal flight direction, Q 2max ,Q 2min are the maximum and minimum values ​​of the frequency parameter that controls the global optimal flight direction, w vmax and wvmin are the maximum weight value and the minimum weight value respectively, S is the attenuation value, k q is a random number between [0,1];

[0159] Disturbance operation: The chaotic map perturbs the probability of generating a solution. Assuming that rand3 is a random number generated between the set [0,1], if rand3 satisfies the perturbation threshold r n , then the position coordinates of each new individual will be mapped according to the following rules:

[0160] Through the chaotic map {z y |y=1,2,3,...,N} generates a chaotic sequence, y is the individual number, z y is the position coordinate of individual y before chaotic mapping. The chaotic mapping is defined as the following formula (26), where Rand() is a random number in the interval [0,1], η is the control factor for controlling the chaotic intensity, and z y+1 is the position coordinate of individual y after chaotic mapping:

[0161]

[0162] Generate a disturbance value corresponding to each individual, which is expressed as the following equation (27), where Dis is the generated disturbance value and (1:y) represents y individuals from 1 to y:

[0163] Dis(1:y)=2(t max -t)Rand(1:y) / t max (27)

[0164] The position coordinates of the new individual are disturbed, and the other parameters remain unchanged, which can be expressed as the following formula (28), where x new is an individual x n New individuals after being disturbed:

[0165] x new (1:y)=x n (1:y)-Dis(1:y)+2Dis(1:y)z(1:y) (28)

[0166] Individual selection strategy: Assume that rand4 is a random number in the interval [0,1]. If rand4 is greater than the selection threshold a n And the new individual x new The objective function is less than the individual x n The objective function is to accept the new solution generated by the perturbation operation, and the impulse response and loudness Update them simultaneously and get the following formula (29), where: is the impulse loudness value of the i-th bat at the t+1th iteration, r i 0 is the initial impulse response value of the ith bat, γ is the constant coefficient of the impulse rate, is the response value of the i-th bat at the t+1th iteration, and ε is the attenuation coefficient of the loudness:

[0167]

[0168] Iteration termination condition judgment: the preset maximum number of iterations is used as the iteration termination condition. If the number of iterations does not reach the maximum number of iterations, the above steps of setting the fitness function to the individual selection strategy are repeated until the number of iterations reaches the maximum number of iterations, and the minimum cost value, average cost value and maximum cost value of the power system under wind power / solar energy conditions are obtained respectively.

[0169] Another aspect of the present invention provides an electronic device, comprising:

[0170] one or more processors;

[0171] A storage unit is used to store one or more programs, which, when executed by the one or more processors, can enable the one or more processors to implement the power scheduling method of the power system described above.

[0172] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, can implement the power dispatching method of the electric power system as described above.

[0173] The power dispatching method, device, electronic device and storage medium of the electric power system of the present invention respectively model the behavior of wind energy and solar energy, and establish a power dispatching model of the electric power system under wind energy / solar energy conditions based on the modeling results, and use an improved bat algorithm that integrates adaptive weight coefficients, local frequency parameters and chaos algorithm into the bat algorithm to optimize the power dispatching model, and obtain the minimum cost value, average cost value and maximum cost value of the electric power system under wind energy / solar energy conditions, respectively, so as to dispatch the electric power system according to the minimum cost value, average cost value and maximum cost value, so that the penetration of renewable energy including wind energy and solar energy in the electric power system brings about a significant reduction in total operating costs, and the incorporation of renewable energy including wind energy and solar energy greatly reduces the operating costs of thermal units. BRIEF DESCRIPTION OF THE DRAWINGS

[0174] Figure 1 A flow chart of the power dispatching method for the electric power system proposed by the present invention;

[0175] Figure 2This is a schematic diagram of the structure of the power dispatching device of the power system proposed by the present invention. DETAILED DESCRIPTION

[0176] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0177] In one aspect of this embodiment, Figure 1 As shown, it relates to a power dispatching method S100 of a power system, and the method S100 includes:

[0178] S110. Model the behaviors of wind energy and solar energy separately.

[0179] Specifically, due to the randomness of wind and solar energy, the integration of renewable energy including wind and solar energy in the power system brings some challenges to the system operation. Therefore, Weibull and Beta probability density functions can be used to model the behavior of wind and solar energy.

[0180] In some implementations, step S110 may include:

[0181] Establish the Weibull probability density function f of wind speed v,t v t It is the following formula (1):

[0182]

[0183] The behavior of wind is proved to follow the two-parameter Weibull distribution, so the Boolean probability density function of wind speed can be expressed by the above formula (1). t is the wind speed at time t in meters per second, k t-1 is the shape factor at time t-1, k t and c t are the shape factor and scale factor at time t, respectively, expressed as the following formula (2):

[0184]

[0185] In the above formula (2), Γ is the gamma function, σ t and μ t are the standard deviation and mean of wind speed at time t. These values ​​are calculated based on historical wind speed data within a certain time interval, which can reduce the error between adjacent time periods.

[0186] The relationship between wind speed v and wind power output w is described by the following equation (3):

[0187]

[0188] In the above formula (3), w r is the rated wind power, v in 、v out 、v r are cut-in wind speed, cut-out wind speed, and rated wind speed, respectively. The relationship described in the above formula (3) will result in the distribution of wind power output being divided into discrete distribution and continuous distribution. As shown in the above formula (3), the discrete distribution is due to the constant value of wind power output within a certain interval, and the continuous distribution occurs in the area between the cut-in wind speed and the rated wind speed. The Weibull wind speed probability density function of the continuous distribution area can be converted into the wind power output Weibull probability density function by the following formula (4).

[0189] According to the above formula (3), the Weibull probability density function of wind speed is converted into the Weibull probability density function of wind power output f w,t (w t ), expressed as the following formula (4):

[0190]

[0191] In the above formula (4), w t is the wind power output at time t, h is the ratio between the wind speed limit and the cut-in wind speed in the linear region, and

[0192] The uncertainty of solar irradiance can be modeled using the Beta probability density distribution. Establish the solar irradiance Beta probability density function f at time t S,t (S t ) is the following formula (5):

[0193]

[0194] In the above formula (5), S t and S max,t kW / m 2 The solar irradiance and maximum solar irradiance in units, α t and β t are Beta parameters, calculated for each hour based on the mean and variance of solar irradiance, and are obtained by the following formula (6):

[0195]

[0196] In the above formula (6), μ s,t represents the mean solar irradiance, Represents the solar irradiance variance.

[0197] Solar output power is greatly affected by weather conditions, mainly solar irradiance and temperature. Therefore, solar output power P PV,t Determined by the following formula (7):

[0198]

[0199] In the above formula (7), T C,t and T a are the battery temperature and the ambient temperature, both in °C, I t is the current, K c is the current temperature coefficient, in A / ℃, V t is the voltage, K v is the voltage temperature coefficient, in V / °C, FF is the fill factor, N is the number of solar cells, OT is the nominal operating temperature of the battery, I SC is the short-circuit current, V OC is the open circuit voltage, V MPP is the maximum power point voltage, I MPP is the maximum power point current.

[0200] When solar irradiance is available, the solar output probability density function f PV,t (P PV,t ) follows the Beta distribution, which is expressed as the following formula (8), where: is the maximum solar power generation at time t:

[0201]

[0202] S120. Based on the modeling results, a power dispatch model of the power system under wind / solar energy conditions is established.

[0203] In some implementations, step S120 may include:

[0204] The goal of the dynamic economic dispatch problem is to minimize the total operating cost of the power system while allocating demand among the online generating units. In addition, different system constraints are considered.

[0205] The objective function shown in equation (9) is established for the power dispatch of the power system under wind / solar conditions:

[0206]

[0207] Wherein, Min C is the minimum operating cost of the power system, T is the time span, i=1,2,…,n, n is the number of generator sets in the thermal power unit, j=1,2,…,m, mj is the number of wind turbines in the wind farm, l=1,2,…,k, k is the number of photovoltaic solar panels in the photovoltaic power station.

[0208] The energy cost of thermal power units includes the valve point effect, which is caused by the sudden opening of the steam valve. This operating fuel cost can be expressed as C(Pi,t ) indicates. C(P i,t ) is the fuel cost function of the i-th generator set in the thermal power unit at time t, in units of $, expressed as the following formula (10):

[0209]

[0210] In the above formula (10), P i,t is the active output power of the i-th generator set in the thermal power unit at time t, in MW, i ,b i ,c i are the cost coefficients of the i-th generator set in the thermal power unit, e i and f i The valve point cost coefficient of the i-th generator set in the thermal power unit, P i min It is the minimum output power of the ith generator set in the thermal power plant, in MW.

[0211] The variability of renewable energy output power is manifested in three main parts of operating costs, which include direct costs, underestimation penalty costs, and overestimation penalty costs. The operating costs of wind farms and solar power can be expressed as C(PW j,t ) and C(PV l,t )express.

[0212] C(PW j,t ) is the operating cost of the jth wind turbine in the wind farm at time t, expressed as the following formula (11): C(PW j,t ) = d j w j,t +k p,j (W j,a,t -w j,t )+k r,j (w j,t -W j,a,t ) (11)

[0213] In the above formula (11), d j is the wind power cost coefficient of the jth wind turbine in the wind farm, k p,j and k r,j are the underestimated wind power cost coefficient and overestimated wind power cost coefficient of the j-th wind turbine in the wind farm, both in $ / MW, w j,t and W a,j,t are the planned wind power output power and available wind power output power of the jth wind turbine in the wind farm at time t, both in MW.

[0214] C(PV l,t ) is the operating cost of solar energy at time t, expressed as the following formula (12):

[0215]

[0216] In the above formula (12), They are direct solar power generation cost coefficient, underestimated solar power generation cost coefficient and overestimated solar power generation cost coefficient, all in $ / MW. and are the available solar power output and the scheduled solar power output at time t, both in MW.

[0217] On the one hand, when the available renewable energy output power is greater than the predetermined power, the underestimation penalty function occurs. In other words, the underestimation penalty function is the penalty cost of wasting the excess available power of renewable energy.

[0218] The underestimation penalty cost function of wind power generation is established as follows (13):

[0219]

[0220] In the above formula (13), w r,j is the rated wind output power of the jth wind turbine in the wind farm, f w,t (w j,t ) is the probability density function, Y ue for The calculation result is expressed as the following formula (14), where v j,t is the wind speed of the jth wind turbine in the wind farm at time t:

[0221]

[0222] The underestimation penalty cost function of solar power generation is established as follows (15):

[0223]

[0224] In the above formula (15), is the underestimation penalty cost function for solar power generation, is the rated power of the solar cell.

[0225] On the other hand, when the planned power exceeds the available power of renewable energy, an overestimation penalty cost will be added.

[0226] The penalty cost for establishing wind power output is as follows (16):

[0227]

[0228] In the above formula (16), f(w j,t ) is the penalty cost of wind power output, Yoe for The calculation result is expressed as the following formula (17):

[0229]

[0230] The penalty cost for establishing solar power output is given by the following formula (18):

[0231]

[0232] In the above formula (18), is the solar power output, Penalty cost for solar power output.

[0233] The problem constraints are established for the above function, expressed as the following formula (19):

[0234]

[0235] The output power of all generating units should be limited between the lower limit and the upper limit. The above formula (19) represents the spinning reserve requirement of the thermal power unit. These limits are used to compensate for power shortages caused by sudden changes in load or outages of any unit. The power balance constraint ensures that the online generating units can meet the demand power and transmission power losses at any time. In the above formula (19), P i is the output power of the i-th generator set in the thermal power unit, P i,t-1 is the active output power of the i-th generator set in the thermal power unit at time t-1, P i max is the maximum output power of the ith generator set in the thermal power unit, in MW, P i min is the minimum output power of the ith generator set in the thermal power plant, in MW, is the required power, is the transmission power loss, B i,j is the loss coefficient between the i-th generator set in the thermal power plant and the j-th wind turbine set in the wind farm, UR i and DR i are the output power rising ramp limit and falling ramp limit of the i-th generator set in the thermal power unit, SR t and The first preset duration and the second preset duration are respectively the rotating reserve. For example, the first preset duration may be 1 hour, and the second preset duration may be 10 minutes, that is, SR t Can provide 1 hour of rotation reserve, It can provide a 10-minute rotation reserve.

[0236] The above power dispatch model takes into account detailed system constraints and limitations of generator sets, including power balance constraints, generator set capacity, transmission losses, spinning reserve requirements, ramp rate limitations and valve point effects, making the power dispatch model more accurate.

[0237] S130. Integrate the adaptive weight coefficient, local frequency parameter and chaos algorithm into the bat algorithm, use the improved bat algorithm to optimize the power scheduling model, and obtain the minimum cost value, average cost value and maximum cost value of the power system under wind / solar energy conditions, so as to schedule the power system according to the minimum cost value, the average cost value and the maximum cost value.

[0238] Specifically, the bat algorithm is a heuristic search algorithm proposed by Professor Yang in 2010 based on swarm intelligence. It is an effective method for searching the global optimal solution. It is a random search algorithm that simulates the use of sonar by bats in nature to detect prey and avoid obstacles, that is, it simulates the most basic detection and positioning capabilities of bats using ultrasound to obstacles or prey and links it with the optimization target function. The algorithm is an iterative optimization technique that is initialized as a set of random solutions, then searches for the optimal solution through iteration, and generates local new solutions around the optimal solution through random flight, which strengthens the local search. Compared with other algorithms, the bat algorithm is far superior to other algorithms in terms of accuracy and effectiveness, and there are not many parameters to adjust.

[0239] Considering the poor search capability of the traditional bat algorithm, this embodiment integrates the adaptive weight coefficient, the local frequency parameter and the chaos algorithm into the bat algorithm to obtain an improved bat algorithm.

[0240] In some implementations, step 130 may include:

[0241] 1) Initialization parameters: The search space is defined in a three-dimensional space of L×W×H, where L, W, and H represent the length, width, and height of the three-dimensional space, respectively. The position coordinates (x, y, z) of the bat are constrained by the three-dimensional space. The number of individuals in the initial population is N, the individual loudness of the bat is a, the pulse emission rate is r, and the maximum number of iterations is t. max ;

[0242] 2) Initialize the population individuals: The complexity and stability of the bat algorithm are related to the number of initialized individuals. At present, in most existing technologies, the first generation of populations is often generated by random initialization. However, this initialization method can easily lead to sparse and uneven distribution of individuals. Therefore, in response to this problem, this embodiment evenly sets a number of beacon points in the search space to guide the algorithm to quickly find the correct search direction at the beginning of the iteration. The setting of the beacon points is shown in the following formula (20):

[0243]

[0244] Compared with the random distribution of all points at initialization, in the first iteration of this embodiment, all individual bats are more likely to search in a better direction.

[0245] 3) Setting the fitness function: The fitness function is set as the following equation (21), where Δf(t) is the system frequency deviation:

[0246] 4) Parameter update: The performance of the improved bat algorithm is related to three parameters, namely the bat's position information, speed and frequency. The position update formula of a single bat is expressed as the following formula (22), where: is the position of the i-th bat in the t-th iteration, is the speed of the ith bat in iteration t+1, is the position of the i-th bat at the t+1th iteration:

[0247] In order to better control the flight direction of a single bat, this embodiment introduces local position information to update the speed. The bat's speed update is expressed as the following formula (23):

[0248]

[0249] In the above formula (23), V i t and are the speeds of the i-th bat at the t-th and t+1-th iterations, respectively, and x pbest and x gbest are the local optimal position and the global optimal position respectively. Both rand1 and rand2 are random numbers between [0,1]. In order to make the algorithm search quickly in the early stage and accurately in the later stage, an adaptive weighting factor w is introduced v , Q i1 and Q i2 To establish the speed frequency update formula, where Q i1 Q is the frequency parameter for controlling the local optimal flight direction. i2 Q is the frequency parameter for controlling the global optimal flight direction. i1 and Q i2 It is expressed as the following formula (24), w v is the adaptive weighting factor, expressed as the following formula (25):

[0250]

[0251] w v =w vmin +(wvmax +w vmin )S t-1 (25)

[0252] Among them, Q 1max ,Q 1min are the maximum and minimum values ​​of the frequency parameter that controls the local optimal flight direction, Q 2max ,Q 2min are the maximum and minimum values ​​of the frequency parameter that controls the global optimal flight direction, w vmax and w vmin are the maximum weight value and the minimum weight value respectively, S is the attenuation value, k q is a random number between [0,1];

[0253] 5) Interference operation: The chaotic map perturbs the probability of generating a solution. Assuming that rand3 is a random number generated between the set [0,1], if rand3 is greater than the perturbation threshold r n That is, rand3>r n If satisfied, the position coordinates of each new individual will be mapped according to the following rules, where the disturbance threshold r n The value range of is (0, 1), and it is generally 0.5:

[0254] Through the chaotic map {z y |y=1,2,3,...,N} generates a chaotic sequence, y is the individual number, z y is the position coordinate of individual y before chaotic mapping. The chaotic mapping is defined as the following formula (26), where Rand() is a random number in the interval [0,1], η is the control factor for controlling the chaotic intensity, and z y+1 is the position coordinate of individual y after chaotic mapping:

[0255]

[0256] Generate a disturbance value corresponding to each individual, which is expressed as the following formula (27), where Dis is the generated disturbance value, and (1:y) represents y individuals from 1 to y: Dis(1:y)=2(t max -t)Rand(1:y) / t max (27).

[0257] The position coordinates of the new individual are disturbed, and the other parameters remain unchanged, which can be expressed as the following formula (28), where x new is an individual x n New individuals after being disturbed:

[0258] x new (1:y)=x n(1:y)-Dis(1:y)+2Dis(1:y)z(1:y) (28)

[0259] 6) Individual selection strategy: Assume that rand4 is a random number in the interval [0,1]. If rand4 is greater than the selection threshold a n And the new individual x new The objective function is less than the individual x n The objective function is to accept the new solution generated by the perturbation operation, and the impulse response and loudness Will be updated simultaneously to obtain the following formula (29), where the threshold a is selected n The value range is (0, 1), and is generally 0.5. new The objective function and individual x n The objective functions of the new individuals x are new and individual x n Based on the objective function established in formula (9), is the impulse loudness value of the i-th bat at the t+1th iteration, r i 0 is the initial impulse response value of the ith bat, γ is the constant coefficient of the impulse rate, is the response value of the i-th bat at the t+1th iteration, and ε is the attenuation coefficient of the loudness:

[0260]

[0261] 7) Determination of iteration termination conditions: The preset maximum number of iterations is used as the iteration termination condition. If the number of iterations does not reach the maximum number of iterations, the steps of setting the fitness function to the individual selection strategy, i.e., the above steps 3) to 6) are repeated until the number of iterations reaches the maximum number of iterations, and the minimum cost value, average cost value and maximum cost value of the power system under wind power / solar energy conditions are obtained respectively.

[0262] The power dispatching method of the electric power system of this embodiment models the behaviors of wind energy and solar energy respectively, and establishes a power dispatching model of the electric power system under wind energy / solar energy conditions based on the modeling results, and optimizes the power dispatching model using an improved bat algorithm that integrates adaptive weight coefficients, local frequency parameters and chaos algorithm into the bat algorithm, so as to obtain the minimum cost value, average cost value and maximum cost value of the electric power system under wind energy / solar energy conditions respectively, and then dispatches the electric power system according to the minimum cost value, average cost value and maximum cost value, so that the penetration of renewable energy including wind energy and solar energy in the electric power system brings about a significant reduction in the total operating cost, and the integration of renewable energy including wind energy and solar energy greatly reduces the operating cost of thermal units.

[0263] Another aspect of the present invention, as Figure 2 As shown, a power dispatching device 100 of a power system is provided, and the device 100 can be applied to the method described above. The device 100 includes:

[0264] A modeling module 110 for modeling the behavior of wind energy and solar energy respectively;

[0265] Establishing module 120, for establishing a power dispatch model of the power system under wind power / solar power conditions according to the modeling results;

[0266] The optimization module 130 is used to integrate the adaptive weight coefficient, the local frequency parameter and the chaos algorithm into the bat algorithm, and use the improved bat algorithm to optimize the power scheduling model, so as to obtain the minimum cost value, the average cost value and the maximum cost value of the power system under wind energy / solar energy conditions, so as to schedule the power system according to the minimum cost value, the average cost value and the maximum cost value.

[0267] The power dispatching device of the electric power system of this embodiment models the behaviors of wind energy and solar energy respectively through the modeling module, establishes a power dispatching model of the electric power system under wind energy / solar energy conditions according to the modeling results by establishing a model, integrates the adaptive weight coefficient, local frequency parameter and chaos algorithm into the bat algorithm through the optimization module, and uses the improved bat algorithm to optimize the power dispatching model, and obtains the minimum cost value, average cost value and maximum cost value of the electric power system under wind energy / solar energy conditions, so as to dispatch the electric power system according to the minimum cost value, average cost value and maximum cost value, so that the penetration of renewable energy including wind energy and solar energy in the electric power system brings about a significant reduction in the total operating cost, and the integration of renewable energy including wind energy and solar energy greatly reduces the operating cost of thermal units.

[0268] In some implementations, the modeling module 110 is specifically configured to:

[0269] Establish the Weibull probability density function f of wind speed v,t v t It is the following formula (1):

[0270]

[0271] In the above formula (1), v t is the wind speed at time t in meters per second, k t-1 is the shape factor at time t-1, k t and c t are the shape factor and scale factor at time t, respectively, expressed as the following formula (2):

[0272]

[0273] In the above formula (2), Γ is the gamma function, σ t and μ t are the standard deviation and mean of wind speed at time t, respectively;

[0274] The relationship between wind speed v and wind power output w is described by the following equation (3):

[0275]

[0276] In the above formula (3), w r is the rated wind power, v in 、v out 、v r They are cut-in wind speed, cut-out wind speed and rated wind speed respectively;

[0277] According to the above formula (3), the Weibull probability density function of wind speed is converted into the Weibull probability density function of wind power output f w,t (w t ), expressed as the following formula (4):

[0278]

[0279] In the above formula (4), w t is the wind power output at time t, h is the ratio between the wind speed limit and the cut-in wind speed in the linear region, and

[0280] Establish the solar irradiance Beta probability density function f at time t S,t (S t ) is the following formula (5):

[0281]

[0282] In the above formula (5), S t and S max,t kW / m 2 The solar irradiance and maximum solar irradiance in units, α t and β t are Beta parameters and are calculated by the following formula (6):

[0283]

[0284] In the above formula (6), μ s,t represents the mean solar irradiance, represents the solar irradiance variance;

[0285] Solar output power P PV,t Determined by the following formula (7):

[0286]

[0287] In the above formula (7), T C,t and T a are the battery temperature and the ambient temperature, both in °C, I t is the current, K c is the current temperature coefficient, in A / ℃, V t is the voltage, K v is the voltage temperature coefficient, in V / °C, FF is the fill factor, N is the number of solar cells, OT is the nominal operating temperature of the battery, I SC is the short-circuit current, V OC is the open circuit voltage, V MPP is the maximum power point voltage, I MPP is the maximum power point current;

[0288] When solar irradiance is available, the solar output probability density function f PV,t (P PV,t ) follows the Beta distribution, which is expressed as the following formula (8), where: is the maximum solar power generation at time t:

[0289]

[0290] In some implementations, the establishing module 120 is specifically configured to:

[0291] The objective function shown in equation (9) is established for the power dispatch of the power system under wind / solar conditions:

[0292] Wherein, Min C is the minimum operating cost of the power system, T is the time span, i=1,2,…,n, n is the number of generator sets in the thermal power unit, j=1,2,…,m, m is the number of wind turbines in the wind farm, l=1,2,…,k, k is the number of photovoltaic solar panels in the photovoltaic power station;

[0293] C(P i,t ) is the fuel cost function of the i-th generator set in the thermal power unit at time t, in units of $, expressed as the following formula (10):

[0294] In the above formula (10), P i,t is the active output power of the i-th generator set in the thermal power unit at time t, in MW, i ,b i ,c iare the cost coefficients of the i-th generator set in the thermal power unit, e i and f i are the valve point cost coefficients of the i-th generator set in the thermal power unit, P i min is the minimum output power of the ith generator set in the thermal power unit, in MW;

[0295] C(PW j,t ) is the operating cost of the jth wind turbine in the wind farm at time t, expressed as the following formula (11): C(PW j,t ) = d j w j,t +k p,j (W j,a,t +w j,t )+k r,j (w j,t -W j,a,t ) (11)

[0296] In the above formula (11), d j is the wind power cost coefficient of the jth wind turbine in the wind farm, k p,j and k r,j are the underestimated wind power cost coefficient and overestimated wind power cost coefficient of the j-th wind turbine in the wind farm, both in $ / MW, w j,t and W j,a,t are the planned wind power output power and available wind power output power of the j-th wind turbine in the wind farm at time t, both in MW;

[0297] C(PV l,t ) is the operating cost of solar energy at time t, expressed as the following formula (12):

[0298]

[0299] In the above formula (12), They are direct solar power generation cost coefficient, underestimated solar power generation cost coefficient and overestimated solar power generation cost coefficient, all in $ / MW. and are the available solar power output and the scheduled solar power output at time t, both in MW;

[0300] The underestimation penalty cost function of wind power generation is established as follows (13):

[0301]

[0302] In the above formula (13), w r,j is the rated wind output power of the jth wind turbine in the wind farm, f w,t (wj,t ) is the probability density function, Y ue for The calculation result is expressed as the following formula (14), where v j,t is the wind speed of the jth wind turbine in the wind farm at time t:

[0303]

[0304] The underestimation penalty cost function of solar power generation is established as follows (15):

[0305]

[0306] In the above formula (15), is the underestimation penalty cost function for solar power generation, is the rated power of the solar cell;

[0307] The penalty cost for establishing wind power output is as follows (16):

[0308]

[0309] In the above formula (16), f(w j,t ) is the penalty cost of wind power output, Y oe for The calculation result is expressed as the following formula (17):

[0310]

[0311] The penalty cost for establishing solar power output is given by the following formula (18):

[0312]

[0313] In the above formula (18), is the solar power output, penalty costs for solar power output;

[0314] The problem constraints are established for the above function, expressed as the following formula (19):

[0315]

[0316] In the above formula (19), P i is the output power of the i-th generator set in the thermal power unit, P i,t-1 is the active output power of the i-th generator set in the thermal power unit at time t-1, P i max is the maximum output power of the ith generator set in the thermal power unit, in MW, P i minis the minimum output power of the ith generator set in the thermal power plant, in MW, is the required power, is the transmission power loss, B i,j is the loss coefficient between the i-th generator set in the thermal power plant and the j-th wind turbine set in the wind farm, UR i and DR i are the output power rising ramp limit and falling ramp limit of the i-th generator set in the thermal power unit, SR t and The spinning reserves are respectively the first preset duration and the second preset duration.

[0317] The above power dispatch model takes into account detailed system constraints and limitations of generator sets, including power balance constraints, generator set capacity, transmission losses, spinning reserve requirements, ramp rate limitations and valve point effects, making the power dispatch model more accurate.

[0318] In some implementations, the optimization module 130 is specifically configured to:

[0319] Initialization parameters: The search space is defined in a three-dimensional space of L×W×H, where L, W, and H represent the length, width, and height of the three-dimensional space, respectively. The position coordinates (x, y, z) of the bats are constrained by the three-dimensional space. The number of individuals in the initial population is N, the individual loudness of the bats is a, the pulse emission rate is r, and the maximum number of iterations is t. max ;

[0320] Initialize the population individuals: evenly set a number of beacon points in the search space, and the setting of the beacon points is shown in the following formula (20):

[0321]

[0322] Setting the fitness function: The fitness function is set to the following equation (21), where Δf(t) is the system frequency deviation:

[0323] Parameter update: The position update formula of a single bat is expressed as the following equation (22), where: is the position of the i-th bat in the t-th iteration, is the speed of the ith bat in iteration t+1, is the position of the i-th bat at the t+1th iteration:

[0324] The bat's speed update is expressed as follows (23):

[0325]

[0326] In the above formula (23), V i t and are the speeds of the i-th bat at the t-th and t+1-th iterations, respectively, and x pbest and x gbest are the local optimal position and the global optimal position respectively, rand1 and rand2 are both random numbers between [0,1], Q i1 Q is the frequency parameter for controlling the local optimal flight direction. i2 Q is the frequency parameter for controlling the global optimal flight direction. i1 and Q i2 It is expressed as the following formula (24), w v is the adaptive weighting factor, expressed as the following formula (25):

[0327]

[0328] w v =w vmin +(w vmax +w vmin )S t-1 (25)

[0329] Among them, Q 1max ,Q 1min are the maximum and minimum values ​​of the frequency parameter that controls the local optimal flight direction, Q 2max ,Q 2min are the maximum and minimum values ​​of the frequency parameter that controls the global optimal flight direction, w vmax and w vmin are the maximum weight value and the minimum weight value respectively, S is the attenuation value, k q is a random number between [0,1];

[0330] Disturbance operation: The chaotic map perturbs the probability of generating a solution. Assuming that rand3 is a random number generated between the set [0,1], if rand3 is greater than the disturbance threshold r n That is, rand3>r n If satisfied, the position coordinates of each new individual will be mapped according to the following rules, where the disturbance threshold r n The value range of is (0, 1), and it is generally 0.5:

[0331] Through the chaotic map {z y |y=1,2,3,...,N} generates a chaotic sequence, y is the individual number, z y is the position coordinate of individual y before chaotic mapping. The chaotic mapping is defined as the following formula (26), where Rand() is a random number in the interval [0,1], η is the control factor for controlling the chaotic intensity, and zy+1 is the position coordinate of individual y after chaotic mapping:

[0332]

[0333] Generate a disturbance value corresponding to each individual, which is expressed as the following formula (27), where Dis is the generated disturbance value, and (1:y) represents y individuals from 1 to y: Dis(1:y)=2(t max -t)Rand(1:y) / t max (27).

[0334] The position coordinates of the new individual are disturbed, and the other parameters remain unchanged, which can be expressed as the following formula (28), where x new is an individual x n New individuals after being disturbed:

[0335] x new (1:y)=x n (1:y)-Dis(1:y)+2Dis(1:y)z(1:y) (28)

[0336] Individual selection strategy: Assume that rand4 is a random number in the interval [0,1]. If rand4 is greater than the selection threshold a n And the new individual x new The objective function is less than the individual x n The objective function is to accept the new solution generated by the perturbation operation, and the impulse response and loudness Will be updated simultaneously to obtain the following formula (29), where the threshold a is selected n The value range is (0, 1), and is generally 0.5. new The objective function and individual x n The objective functions of the new individuals x are new and individual x n Based on the objective function established in formula (9), is the impulse loudness value of the i-th bat at the t+1th iteration, r i 0 is the initial impulse response value of the ith bat, γ is the constant coefficient of the impulse rate, is the response value of the i-th bat at the t+1th iteration, and ε is the attenuation coefficient of the loudness:

[0337]

[0338] Iteration termination condition judgment: the preset maximum number of iterations is used as the iteration termination condition. If the number of iterations does not reach the maximum number of iterations, the above steps of setting the fitness function to the individual selection strategy are repeated until the number of iterations reaches the maximum number of iterations, and the minimum cost value, average cost value and maximum cost value of the power system under wind power / solar energy conditions are obtained respectively.

[0339] Another aspect of the present invention provides an electronic device, comprising:

[0340] One or more processors; a storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the power scheduling method of the power system described above.

[0341] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, can implement the power dispatching method of the electric power system as described above.

[0342] The computer-readable medium may be included in the apparatus, device, or system of the present invention, or may exist independently.

[0343] Among them, computer-readable storage media can be any tangible media that contains or stores a program, which can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment. More specific examples include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, an optical fiber, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0344] The computer-readable storage medium may also include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code, specific examples of which include but are not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0345] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A power dispatching method for an electric power system, characterized in that: The method comprises: Model the behavior of wind and solar energy separately; According to the modeling results, a power dispatch model of the power system under wind / solar conditions is established; Integrate the adaptive weight coefficient, local frequency parameter and chaos algorithm into the bat algorithm, use the improved bat algorithm to optimize the power dispatch model, and obtain the minimum cost value, average cost value and maximum cost value of the power system under wind energy / solar energy conditions, so as to dispatch the power system according to the minimum cost value, the average cost value and the maximum cost value; The behavior of wind and solar energy is modeled separately, including: Establish the Weibull probability density function f of wind speed v,t (v t ) is the following formula (1): In the above formula (1), v t is the wind speed at time t, k t-1 is the shape factor at time t-1, k t and c t are the shape factor and scale factor at time t, respectively, expressed as the following formula (2): In the above formula (2), Γ is the gamma function, σ t and μ t are the standard deviation and mean of wind speed at time t, respectively; The relationship between wind speed v and wind power output w is described by the following equation (3): In the above formula (3), w r is the rated wind power, v in 、v out 、v r They are cut-in wind speed, cut-out wind speed and rated wind speed; According to the above formula (3), the Weibull probability density function of wind speed is converted into the Weibull probability density function of wind power output f w,t (w t ), expressed as the following formula (4): In the above formula (4), w t is the wind power output at time t, h is the ratio between the wind speed limit and the cut-in wind speed in the linear region, and Establish the solar irradiance Beta probability density function f at time t S,t (S t ) is the following formula (5): In the above formula (5), S t and S max,t are solar irradiance and maximum solar irradiance, α t and β t are Beta parameters and are calculated by the following formula (6): In the above formula (6), μ s,t represents the mean solar irradiance, represents the solar irradiance variance; Solar output power P PV,t Determined by the following formula (7): In the above formula (7), T C,t and T a are the battery temperature and the ambient temperature, I t is the current, K c is the current temperature coefficient, V t is the voltage, K v is the voltage temperature coefficient, FF is the fill factor, N is the number of solar cells, OT is the nominal operating temperature of the battery, I SC is the short-circuit current, V OC is the open circuit voltage, V MPP is the maximum power point voltage, I MPP is the maximum power point current; When solar irradiance is available, the solar output probability density function f PV,t (P PV,t ) follows the Beta distribution, which can be expressed as the following formula (8): In the above formula (8), is the maximum solar power generation at time t; According to the modeling results, a power dispatch model of the power system under wind energy / solar energy conditions is established, including: The objective function shown in equation (9) is established for the power dispatch of the power system under wind / solar conditions: Wherein, Min C is the minimum operating cost of the power system, T is the time span, i=1,2,…,n, n is the number of generator sets in the thermal power unit, j=1,2,…,m, m is the number of wind turbines in the wind farm, l=1,2,…,k, k is the number of photovoltaic solar panels in the photovoltaic power station; C(P i,t ) is the fuel cost function of the i-th generator set in the thermal power unit at time t, expressed as the following formula (10): In the above formula (10), P i,t is the active output power of the i-th generator set in the thermal power unit at time t, a i ,b i ,c i are the cost coefficients of the i-th generator set in the thermal power unit, e i and f i are the valve point cost coefficients of the i-th generator set in the thermal power unit, is the minimum output power of the i-th generator set in the thermal power unit; C(PW j,t ) is the operating cost of the jth wind turbine in the wind farm at time t, expressed as the following formula (11): C(PW j,t )=d j w j,t +k p,j (W j,a,t -w j,t )+k r,j (w j,t -W j,a,t )(11) In the above formula (11), d j is the wind power cost coefficient of the jth wind turbine in the wind farm, k p,j and k r,j are the underestimated wind power cost coefficient and overestimated wind power cost coefficient of the j-th wind turbine in the wind farm, respectively, j,t and W j,a,t are the planned wind power output power and available wind power output power of the jth wind turbine in the wind farm at time t, respectively; C(PV l,t ) is the operating cost of solar energy at time t, expressed as the following formula (12): In the above formula (12), They are direct solar power generation cost coefficient, underestimated solar power generation cost coefficient and overestimated solar power generation cost coefficient. and are the available solar output power and the predetermined solar output power at time t, respectively; The underestimation penalty cost function of wind power generation is established as follows (13): In the above formula (13), w r,j is the rated wind output power of the jth wind turbine in the wind farm, f w,t (w j,t ) is the probability density function, Y ue for The calculation result is expressed as the following formula (14), where v j,t is the wind speed of the jth wind turbine in the wind farm at time t: The underestimation penalty cost function of solar power generation is established as follows (15): In the above formula (15), is the underestimation penalty cost function for solar power generation, is the rated power of the solar cell; The penalty cost for establishing wind power output is as follows (16): In the above formula (16), f(w j,t ) is the penalty cost of wind power output, Y oe for The calculation result is expressed as the following formula (17): The penalty cost for establishing solar power output is given by the following formula (18): In the above formula (18), is the solar power output, penalty costs for solar power output; The problem constraints are established for the above function, expressed as the following formula (19): In the above formula (19), P i is the output power of the i-th generator set in the thermal power unit, P i,t-1 is the active output power of the i-th generator set in the thermal power unit at time t-1, is the maximum output power of the i-th generator set in the thermal power unit, is the minimum output power of the ith generator set in the thermal power unit, is the required power, is the transmission power loss, B i,j is the loss coefficient between the i-th generator set in the thermal power plant and the j-th wind turbine set in the wind farm, UR i and DR i are the output power rising ramp limit and falling ramp limit of the i-th generator set in the thermal power unit, SR t and The spinning reserves are respectively the first preset duration and the second preset duration.

2. The method according to claim 1, characterized in that The method integrates the adaptive weight coefficient, the local frequency parameter and the chaos algorithm into the bat algorithm, uses the improved bat algorithm to optimize the power dispatch model, obtains the minimum cost value, the average cost value and the maximum cost value of the power system under the wind energy / solar energy conditions, and dispatches the power system according to the minimum cost value, the average cost value and the maximum cost value, including: Initialization parameters: The search space is defined in a three-dimensional space of L×W×H, where L, W, and H represent the length, width, and height of the three-dimensional space, respectively. The position coordinates (x, y, z) of the bats are constrained by the three-dimensional space. The number of individuals in the initial population is N, the individual loudness of the bats is a, the pulse emission rate is r, and the maximum number of iterations is t. max ; Initialize the population individuals: evenly set a number of beacon points in the search space, and the setting of the beacon points is shown in the following formula (20): Setting the fitness function: The fitness function is set to the following equation (21), where Δf(t) is the system frequency deviation: Parameter update: The position update formula of a single bat is expressed as the following equation (22), where: is the position of the i-th bat in the t-th iteration, is the speed of the ith bat in iteration t+1, is the position of the i-th bat at the t+1th iteration: The bat's speed update is expressed as follows (23): In the above formula (23), and are the speeds of the i-th bat at the t-th and t+1-th iterations, respectively, and x pbest and x gbest are the local optimal position and the global optimal position respectively, rand1 and rand2 are both random numbers between [0,1], Q i1 Q is the frequency parameter for controlling the local optimal flight direction. i2 Q is the frequency parameter for controlling the global optimal flight direction. i1 and Q i2 It is expressed as the following formula (24), w v is the adaptive weighting factor, expressed as the following formula (25): w v =w vmin +(w vmax +w vmin )S t-1 (25) Among them, Q 1max ,Q 1min are the maximum and minimum values ​​of the frequency parameter that controls the local optimal flight direction, Q 2max ,Q 2min are the maximum and minimum values ​​of the frequency parameter that controls the global optimal flight direction, w vmax and w vmin are the maximum weight value and the minimum weight value respectively, S is the attenuation value, k q is a random number between [0,1]; Disturbance operation: The chaotic map perturbs the probability of generating a solution. Assuming that rand3 is a random number generated between the set [0,1], if rand3 satisfies the perturbation threshold r n , then the position coordinates of each new individual will be mapped according to the following rules: Through the chaotic map {z y |y=1,2,3,...,N} generates a chaotic sequence, y is the individual number, z y is the position coordinate of individual y before chaotic mapping. The chaotic mapping is defined as the following formula (26), where Rand() is a random number in the interval [0,1], η is the control factor for controlling the chaotic intensity, and z y+1 is the position coordinate of individual y after chaotic mapping: Generate a disturbance value corresponding to each individual, which is expressed as the following equation (27), where Dis is the generated disturbance value and (1:y) represents y individuals from 1 to y: Dis(1:y)=2(t max -t)Rand(1:y) / t max (27) The position coordinates of the new individual are disturbed, and the other parameters remain unchanged, which can be expressed as the following formula (28), where x new is an individual x n New individuals after being disturbed: x new (1:y)=x n (1:y)-Dis(1:y)+2Dis(1:y)z(1:y) (28) Individual selection strategy: Assume that rand4 is a random number in the interval [0,1]. If rand4 is greater than the selection threshold a n And the new individual x new The objective function is less than the individual x n The objective function is to accept the new solution generated by the perturbation operation, and the impulse response and loudness Update them simultaneously and get the following formula (29), where: is the impulse loudness value of the i-th bat at the t+1 iteration, is the initial impulse response value of the ith bat, γ is the constant coefficient of the impulse rate, is the response value of the i-th bat at the t+1th iteration, and ε is the attenuation coefficient of the loudness: Iteration termination condition judgment: the preset maximum number of iterations is used as the iteration termination condition. If the number of iterations does not reach the maximum number of iterations, the above steps of setting the fitness function to the individual selection strategy are repeated until the number of iterations reaches the maximum number of iterations, and the minimum cost value, average cost value and maximum cost value of the power system under wind power / solar energy conditions are obtained respectively.

3. A power dispatching device for an electric power system, characterized in that: The device comprises: Modeling modules to model the behavior of wind and solar energy separately; Establish a module for establishing a power dispatch model of the power system under wind / solar energy conditions according to the modeling results; An optimization module, for integrating the adaptive weight coefficient, the local frequency parameter and the chaos algorithm into the bat algorithm, using the improved bat algorithm to optimize the power dispatch model, and obtaining the minimum cost value, the average cost value and the maximum cost value of the power system under wind energy / solar energy conditions, so as to dispatch the power system according to the minimum cost value, the average cost value and the maximum cost value; The modeling module is specifically used for: Establish the Weibull probability density function f of wind speed v,t v t It is the following formula (1): In the above formula (1), v t is the wind speed at time t, k t-1 is the shape factor at time t-1, k t and c t are the shape factor and scale factor at time t, respectively, expressed as the following formula (2): In the above formula (2), Γ is the gamma function, σ t and μ t are the standard deviation and mean of wind speed at time t, respectively; The relationship between wind speed v and wind power output w is described by the following equation (3): In the above formula (3), w r is the rated wind power, v in 、v out 、v r They are cut-in wind speed, cut-out wind speed and rated wind speed; According to the above formula (3), the Weibull probability density function of wind speed is converted into the Weibull probability density function of wind power output f w,t (w t ), expressed as the following formula (4): In the above formula (4), w t is the wind power output at time t, h is the ratio between the wind speed limit and the cut-in wind speed in the linear region, and Establish the solar irradiance Beta probability density function f at time t S,t (S t ) is the following formula (5): In the above formula (5), S t and S max,t are solar irradiance and maximum solar irradiance, α t and β t are Beta parameters and are calculated by the following formula (6): In the above formula (6), μ s,t represents the mean solar irradiance, represents the solar irradiance variance; Solar output power P PV,t Determined by the following formula (7): In the above formula (7), T C,t and T a are the battery temperature and the ambient temperature, I t is the current, K c is the current temperature coefficient, V t is the voltage, K v is the voltage temperature coefficient, FF is the fill factor, N is the number of solar cells, OT is the nominal operating temperature of the battery, I SC is the short-circuit current, V OC is the open circuit voltage, V MPP is the maximum power point voltage, I MPP is the maximum power point current; When solar irradiance is available, the solar output probability density function f PV,t (P PV,t ) follows the Beta distribution, which can be expressed as the following formula (8): In the above formula (8), is the maximum solar power generation at time t; The establishment module is specifically used for: The objective function shown in equation (9) is established for the power dispatch of the power system under wind / solar conditions: Wherein, Min C is the minimum operating cost of the power system, T is the time span, i=1,2,…,n, n is the number of generator sets in the thermal power unit, j=1,2,…,m, m is the number of wind turbines in the wind farm, l=1,2,…,k, k is the number of photovoltaic solar panels in the photovoltaic power station; C(P i,t ) is the fuel cost function of the i-th generator set in the thermal power unit at time t, expressed as the following formula (10): In the above formula (10), P i,t is the active output power of the i-th generator set in the thermal power unit at time t, a i ,b i ,c i are the cost coefficients of the i-th generator set in the thermal power unit, e i and f i are the valve point cost coefficients of the i-th generator set in the thermal power unit, is the minimum output power of the i-th generator set in the thermal power unit; C(PW j,t ) is the operating cost of the jth wind turbine in the wind farm at time t, expressed as the following formula (11): C(PW j,t )=d j w j,t +k p,j (W j,a,t -w j,t )+k r,j (w j,t -W j,a,t ) (11) In the above formula (11), d j is the wind power cost coefficient of the jth wind turbine in the wind farm, k p,j and k r,j are the underestimated wind power cost coefficient and overestimated wind power cost coefficient of the j-th wind turbine in the wind farm, respectively, j,t and W j,a,t are the planned wind power output power and available wind power output power of the jth wind turbine in the wind farm at time t, respectively; C(PV l,t ) is the operating cost of solar energy at time t, expressed as the following formula (12): In the above formula (12), They are direct solar power generation cost coefficient, underestimated solar power generation cost coefficient and overestimated solar power generation cost coefficient. and are the available solar output power and the predetermined solar output power at time t, respectively; The underestimation penalty cost function of wind power generation is established as follows (13): In the above formula (13), w r,j is the rated wind output power of the jth wind turbine in the wind farm, f w,t (w j,t ) is the probability density function, Y ue for The calculation result is expressed as the following formula (14), where v j,t is the wind speed of the jth wind turbine in the wind farm at time t: The underestimation penalty cost function of solar power generation is established as follows (15): In the above formula (15), is the underestimation penalty cost function for solar power generation, is the rated power of the solar cell; The penalty cost for establishing wind power output is as follows (16): In the above formula (16), f(w j,t ) is the penalty cost of wind power output, Y oe for The calculation result is expressed as the following formula (17): The penalty cost for establishing solar power output is given by the following formula (18): In the above formula (18), is the solar power output, penalty costs for solar power output; The problem constraints are established for the above function, expressed as the following formula (19): In the above formula (19), P i is the output power of the i-th generator set in the thermal power unit, P i,t-1 is the active output power of the i-th generator set in the thermal power unit at time t-1, is the maximum output power of the i-th generator set in the thermal power unit, is the minimum output power of the ith generator set in the thermal power unit, is the required power, is the transmission power loss, B i,j is the loss coefficient between the i-th generator set in the thermal power plant and the j-th wind turbine set in the wind farm, UR i and DR i are the output power rising ramp limit and falling ramp limit of the i-th generator set in the thermal power unit, SR t and The spinning reserves are respectively the first preset duration and the second preset duration.

4. The device according to claim 3, characterized in that The optimization module is specifically used for: Initialization parameters: The search space is defined in a three-dimensional space of L×W×H, where L, W, and H represent the length, width, and height of the three-dimensional space, respectively. The position coordinates (x, y, z) of the bats are constrained by the three-dimensional space. The number of individuals in the initial population is N, the individual loudness of the bats is a, the pulse emission rate is r, and the maximum number of iterations is t. max ; Initialize the population individuals: evenly set a number of beacon points in the search space, and the setting of the beacon points is shown in the following formula (20): Setting the fitness function: The fitness function is set to the following equation (21), where Δf(t) is the system frequency deviation: Parameter update: The position update formula of a single bat is expressed as the following equation (22), where: is the position of the i-th bat in the t-th iteration, is the speed of the ith bat in iteration t+1, is the position of the i-th bat at the t+1th iteration: The bat's speed update is expressed as follows (23): In the above formula (23), and are the speeds of the i-th bat at the t-th and t+1-th iterations, respectively, and x pbest and x gbest are the local optimal position and the global optimal position respectively, rand1 and rand2 are both random numbers between [0,1], Q i1 Q is the frequency parameter for controlling the local optimal flight direction. i2 Q is the frequency parameter for controlling the global optimal flight direction. i1 and Q i2 It is expressed as the following formula (24), w v is the adaptive weighting factor, expressed as the following formula (25): w v =w vmin +(w vmax +w vmin )S t-1 (25) Among them, Q 1max ,Q 1min are the maximum and minimum values ​​of the frequency parameter that controls the local optimal flight direction, Q 2max ,Q 2min are the maximum and minimum values ​​of the frequency parameter that controls the global optimal flight direction, w vmax and w vmin are the maximum weight value and the minimum weight value respectively, S is the attenuation value, k q is a random number between [0,1]; Disturbance operation: The chaotic map perturbs the probability of generating a solution. Assuming that rand3 is a random number generated between the set [0,1], if rand3 is greater than the disturbance threshold r n , then the position coordinates of each new individual will be mapped according to the following rules: Through the chaotic map {z y |y=1,2,3,...,N} generates a chaotic sequence, y is the individual number, z y is the position coordinate of individual y before chaotic mapping. The chaotic mapping is defined as the following formula (26), where Rand() is a random number in the interval [0,1], η is the control factor for controlling the chaotic intensity, and z y+1 is the position coordinate of individual y after chaotic mapping: Generate a disturbance value corresponding to each individual, which is expressed as the following equation (27), where Dis is the generated disturbance value and (1:y) represents y individuals from 1 to y: Dis(1:y)=2(t max -t)Rand(1:y) / t max (27) The position coordinates of the new individual are disturbed, and the other parameters remain unchanged, which can be expressed as the following formula (28), where x new is an individual x n New individuals after being disturbed: x new (1:y)=x n (1:y)-Dis(1:y)+2Dis(1:y)z(1:y) (28) Individual selection strategy: Assume that rand4 is a random number in the interval [0,1]. If rand4 is greater than the selection threshold a n And the new individual x new The objective function is less than the individual x n The objective function is to accept the new solution generated by the perturbation operation, and the impulse response and loudness Update them simultaneously and get the following formula (29), where: is the impulse loudness value of the i-th bat at the t+1 iteration, is the initial impulse response value of the ith bat, γ is the constant coefficient of the impulse rate, is the response value of the i-th bat at the t+1th iteration, and ε is the attenuation coefficient of the loudness: Iteration termination condition judgment: the preset maximum number of iterations is used as the iteration termination condition. If the number of iterations does not reach the maximum number of iterations, the above steps of setting the fitness function to the individual selection strategy are repeated until the number of iterations reaches the maximum number of iterations, and the minimum cost value, average cost value and maximum cost value of the power system under wind power / solar energy conditions are obtained respectively.

5. An electronic device, characterized in that: include: one or more processors; A storage unit, used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the power dispatching method of the power system according to claim 1 or 2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the power dispatching method of the power system according to claim 1 or 2.

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