Power grid joint scheduling method and system based on improved whale algorithm

Abstract

The invention relates to the technical field of new energy power generation dispatching, and provides a power grid joint dispatching method and system based on an improved whale algorithm, and the method comprises the steps: building a joint optimization dispatching model based on the thermal power waste discharge amount, the photovoltaic unconsumed amount, hydrogen energy storage and a conventional unit; solving the joint optimization scheduling model based on an improved whale algorithm, and randomly generating a group of whale positions as a plurality of initial solutions; the fitness of each whale is calculated within the maximum number of iterations, the optimal whale position is reserved, and a current optimal scheduling scheme is obtained; and judging whether the current optimal scheduling scheme is optimal or not, and if so, converting the optimal whale position vector into a scheduling solution to obtain a scheduling result. According to the invention, the improved whale algorithm is used for solving, and the algorithm is improved, so that the global search capability and convergence speed of the algorithm in solving a multi-objective function are improved, the multi-objective joint optimization scheduling of the power grid is effectively realized, and the photovoltaic absorption capability of the power grid is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation dispatching, and in particular to a power grid joint dispatching method and system based on an improved whale algorithm. Background Art

[0002] At present, domestic and foreign scholars have already conducted a lot of research on improving the grid's ability to absorb photovoltaic power. A combined solar-thermal optimization control method with the goal of minimizing the wind and solar power obstruction in the system has been proposed, using the peak-shaving characteristics of thermal power to solve the problem of insufficient peak-shaving capacity of new energy access to the grid, which leads to obstruction of photovoltaic power generation. A strategy for the joint dispatch of solar-thermal and thermal power with heat storage has been proposed with the goal of cost optimization. A method for optimizing the capacity configuration between new energy and traditional energy has been proposed based on carbon emissions. A multi-objective optimization dispatch model has been established by introducing power-to-gas technology with the goal of optimizing operating costs and minimizing the amount of abandoned light.

[0003] However, most of the above-mentioned traditional schemes only consider the amount of abandoned light or carbon emissions and operating costs to study the photovoltaic absorption. Therefore, under the current "dual carbon" background, with the surge in new energy sources connected to the power grid and the increasing amount of data, traditional schemes have certain limitations in data mining, and cannot achieve multi-objective joint optimization and scheduling of the power grid, and the ability to improve the power grid's absorption of photovoltaics is limited. Summary of the invention

[0004] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a power grid joint scheduling method and system based on an improved whale algorithm.

[0005] To achieve the above object, the present invention provides a power grid joint dispatching method based on an improved whale algorithm, comprising:

[0006] Establish a joint optimization dispatch model based on thermal power waste emissions, photovoltaic unabsorbed capacity, hydrogen energy storage and conventional units;

[0007] The joint optimization scheduling model is solved based on the improved whale algorithm, and a set of whale positions are randomly generated as multiple initial solutions;

[0008] Calculate the fitness of each whale within the maximum number of iterations, retain the optimal whale position, and obtain the current optimal scheduling plan;

[0009] Determine whether the current optimal scheduling solution is optimal. If it is, convert the optimal whale position vector into a scheduling solution to obtain the scheduling result.

[0010] According to one aspect of the present invention, a joint optimization scheduling model is established, including:

[0011] Establish an objective function, the objective function comprising:

[0012] (1) Waste emissions from thermal power plants:

[0013]

[0014] Among them, T is the number of time periods in an optimization cycle; N1 is the number of thermal power units; P Gi,t is the active power output of the i-th unit in period t; α i , β i , γ i , i , i is the emission coefficient of the i-th thermal power unit;

[0015] (2) Unabsorbed photovoltaic power generation:

[0016]

[0017] Where N2 is the number of photovoltaic units; p′ pvj,t is the predicted power generation value of the j-th photovoltaic unit in period t; p pvj,t is the actual power generation value of the j-th photovoltaic unit in period t;

[0018] (3) Operation and start-up and shutdown costs of thermal power and hydropower units:

[0019]

[0020] Among them, S Gi is the startup cost of the i-th thermal power unit; i,t is the start and stop status of the i-th thermal power unit in period t; a i 、b i 、c i is the power generation cost coefficient of the i-th thermal power unit; N3 is the number of hydropower units; d k is the power generation cost coefficient of the kth hydropower unit; P Wk,t is the active power output of the kth hydropower unit in period t;

[0021] (4) Hydrogen energy storage operating costs:

[0022]

[0023] in, The capacity of hydrogen energy storage devices to store and generate electricity; is the energy storage cost coefficient of the hydrogen energy storage device; is the power generation cost coefficient of the hydrogen energy storage device; N4 is the number of hydrogen energy storage devices;

[0024] (5) Overall objective function

[0025] Add weight coefficients to each objective function, generate the total objective function, and find the minimum value of the function:

[0026] min F=eta1f1+eta2f2+eta3f3+eta4f4;

[0027] Among them, η1, η2, η3, and η4 are weight coefficients.

[0028] According to one aspect of the present invention, a joint optimization scheduling model is established, including:

[0029] Establishing constraints, the constraints include:

[0030] (1) Power balance constraints:

[0031]

[0032] Among them, P L,t is the total system load during period t;

[0033] (2) Output upper and lower limits and climbing constraints of thermal power and hydropower units:

[0034]

[0035] P Gdowni ≤P Gi,t -P Gi,t-1 ≤P Gupi ;

[0036]

[0037] P Wdownk ≤P Wk,t -P Wk,t-1 ≤P Wupk ;

[0038] in, is the upper and lower limits of the active output of thermal power unit i; P Gdowni , P Gupi is the up and down ramp rate of thermal power unit i; is the upper and lower limits of the active output of hydropower unit k; P Wdownk , P Wupk is the up and down ramp rate of hydropower unit k;

[0039] (3) Photovoltaic grid-connected power constraints:

[0040]

[0041] in, is the planned grid-connected power value of photovoltaic j in period t;

[0042] (4) Positive and negative spinning reserve constraints:

[0043] Thermal power is needed to bear the impact of load and photovoltaic fluctuations, so sufficient reserve is required, and the constraints are as follows:

[0044]

[0045]

[0046] Among them, L + , L - is the positive and negative spinning reserve coefficient of the system load; P + , P - are the positive and negative spinning reserve coefficients of photovoltaics;

[0047] (5) Hydrogen energy storage system operating status constraints:

[0048]

[0049] in, It is a binary state quantity of the hydrogen energy storage device in the storage and power generation states.

[0050] (6) Hydrogen capacity constraints in hydrogen energy storage devices

[0051]

[0052]

[0053] in, The state of hydrogen energy storage; It is the upper and lower limits of hydrogen energy storage under different working conditions;

[0054] (7) Hydrogen content restriction in the device

[0055]

[0056]

[0057] in, The efficiency of hydrogen energy storage production and storage; S hl,t is the content of hydrogen in the device; M hl,t The amount of hydrogen energy used for other purposes; are the upper and lower limits of hydrogen content.

[0058] According to one aspect of the present invention, the improved whale algorithm comprises:

[0059] Based on the behavior of whales surrounding prey when foraging, the following model is proposed:

[0060] X(t+1)=X * (t)-AD;

[0061] D=|CX * (t)-X(t)|;

[0062] Among them, t is the current iteration number; A and C are the expression coefficients; X * (t) is the best position vector for whale hunting, and X(t) is the current position vector of the whale;

[0063] A = 2ar1-a;

[0064] C = 2r2;

[0065]

[0066] Among them, r1 and r2 are random numbers (0, 1); T max is the maximum number of iterations;

[0067] When humpback whales are swarming around their prey, they swim in a spiral manner, so the mathematical model for this stage is as follows:

[0068] X(t+1)=X * (t)+D′e bn cos(2πn);

[0069] D′=|X * (t)-X(t)|;

[0070] Where b is a constant used to define the shape of the spiral; n is a random number between [-1,1];

[0071] Use the small step random walk performance of the Levy flight algorithm to search for prey. The Levy flight formula is:

[0072]

[0073] in, is the step length scaling factor, which is 0.1 in the present invention; Levy(t) is the step length that conforms to the Levy flight distribution, and the formula is as follows:

[0074]

[0075]

[0076] Among them, τ and ν are random numbers between [0,1] that conform to the standard normal distribution; β is a constant, which is 1.5; Γ(x) is the gamma function: Γ(x) = (x-1)! .

[0077] According to one aspect of the present invention, the method further includes: initializing the initial position of the whale group by using a Sobol sequence:

[0078] x n =x min +μ n (x max -x min );

[0079] Among them, x n is the initial position of the population, x min 、x max are the upper and lower limits of the initial position; μ n A random number in [0,1].

[0080] According to one aspect of the present invention, the process of determining whether the current optimal scheduling solution is optimal or not, if not, performing the following steps:

[0081] Recalculate the parameters of the improved whale algorithm and update the number of iterations;

[0082] Determine the value of parameter A. If A is less than 1, proceed to the next step. Otherwise, according to the formula Update the whale position, calculate the fitness of each whale within the maximum number of iterations, and retain the optimal whale position to obtain the current optimal scheduling plan;

[0083] Determine the value of parameter p. If p is less than 0.5, then according to the formula X(t+1)=X * (t)-AD updates the whale position, calculates the fitness of each whale within the maximum number of iterations, and retains the optimal whale position to obtain the current optimal scheduling solution; otherwise, according to the formula X(t+1)=X * (t)+D′e bn cos(2πn) updates the whale position, calculates the fitness of each whale within the maximum number of iterations, retains the optimal whale position, and obtains the current optimal scheduling plan.

[0084] To achieve the above object, the present invention also provides a power grid joint dispatching system based on an improved whale algorithm, comprising:

[0085] Joint optimization dispatch model construction module, which establishes a joint optimization dispatch model based on thermal power waste emissions, photovoltaic unabsorbed capacity, hydrogen energy storage and conventional units;

[0086] The model solving module solves the joint optimization scheduling model based on the improved whale algorithm and randomly generates a set of whale positions as multiple initial solutions;

[0087] The scheduling scheme acquisition module calculates the fitness of each whale within the maximum number of iterations, retains the optimal whale position, and obtains the current optimal scheduling scheme;

[0088] The scheduling result acquisition module determines whether the current optimal scheduling solution has reached the optimal state. If it has, the optimal whale position vector is converted into a scheduling solution to obtain the scheduling result.

[0089] To achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the power grid joint dispatching method based on the improved whale algorithm as described above is implemented.

[0090] To achieve the above objectives, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the power grid joint scheduling method based on the improved whale algorithm as described above is implemented.

[0091] According to the solution of the present invention, the present invention uses hydrogen energy storage in conjunction with other conventional units to absorb photovoltaics. Compared with pumped storage, hydrogen energy storage has both the ability of fast electrochemical energy storage response speed and the characteristics of supporting cross-seasonal energy storage. The present invention uses the improved whale algorithm to solve the multi-objective scheduling scheme proposed above. By improving the algorithm, the global search ability and convergence speed of the algorithm in solving multi-objective functions are improved, effectively realizing the multi-objective joint optimization scheduling of the power grid, and effectively improving the power grid's ability to absorb photovoltaics.

[0092] The model considered in this invention introduces the waste emissions from thermal power plants, the amount of photovoltaic power not absorbed, hydrogen energy storage and the joint scheduling of other conventional units under the premise of ensuring the safe and stable operation of the power grid. It can reduce carbon emissions while improving the power grid's ability to absorb photovoltaic power. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 A flowchart schematically showing a method for joint dispatching of power grids based on an improved whale algorithm according to an embodiment of the present invention;

[0094] Figure 2 This is a curve diagram of photovoltaic consumption in different modes in summer;

[0095] Figure 3 This is a curve diagram of photovoltaic consumption in different modes in winter;

[0096] Figure 4 This is a curve diagram of thermal power output in different modes in summer;

[0097] Figure 5 This is a curve chart of thermal power output in different modes in winter. DETAILED DESCRIPTION

[0098] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0099] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including, but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on.” The terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0100] Figure 1 The flowchart of the power grid joint dispatching method based on the improved whale algorithm according to one embodiment of the present invention is schematically shown. Figure 1 As shown, in this embodiment, the power grid joint dispatching method based on the improved whale algorithm includes:

[0101] Establish a joint optimization dispatch model based on thermal power waste emissions, photovoltaic unabsorbed capacity, hydrogen energy storage and conventional units;

[0102] The joint optimization scheduling model is solved based on the improved whale algorithm, and a set of whale positions are randomly generated as multiple initial solutions;

[0103] Calculate the fitness of each whale within the maximum number of iterations, retain the optimal whale position, and obtain the current optimal scheduling plan;

[0104] Determine whether the current optimal scheduling solution is optimal. If it is, convert the optimal whale position vector into a scheduling solution to obtain the scheduling result.

[0105] Furthermore, according to an embodiment of the present invention, a joint optimization scheduling model is established, including:

[0106] Establish the objective function, which includes:

[0107] (1) Waste emissions from thermal power plants:

[0108]

[0109] Among them, T is the number of time periods in an optimization cycle; N1 is the number of thermal power units; P Gi,t is the active power output of the i-th unit in period t; α i , β i , γ i , i , i is the emission coefficient of the i-th thermal power unit;

[0110] (2) Unabsorbed photovoltaic power generation:

[0111]

[0112] Where N2 is the number of photovoltaic units; p p ' vj,t is the predicted power generation value of the j-th photovoltaic unit in period t; p pvj,t is the actual power generation value of the j-th photovoltaic unit in period t;

[0113] (3) Operation and start-up and shutdown costs of thermal power and hydropower units:

[0114]

[0115] Among them, S Gi is the startup cost of the i-th thermal power unit; i,t is the start and stop status of the i-th thermal power unit in period t; a i 、b i 、c i is the power generation cost coefficient of the i-th thermal power unit; N3 is the number of hydropower units; d k is the power generation cost coefficient of the kth hydropower unit; P Wk,t is the active power output of the kth hydropower unit in period t;

[0116] (4) Hydrogen energy storage operating costs:

[0117]

[0118] in, The capacity of hydrogen energy storage devices to store and generate electricity; is the energy storage cost coefficient of the hydrogen energy storage device; is the power generation cost coefficient of the hydrogen energy storage device; N4 is the number of hydrogen energy storage devices;

[0119] (5) Overall objective function

[0120] Add weight coefficients to each objective function, generate the total objective function, and find the minimum value of the function:

[0121] min F=eta1f1+eta2f2+eta3f3+eta4f4;

[0122] Among them, η1, η2, η3, and η4 are weight coefficients.

[0123] Furthermore, according to an embodiment of the present invention, establishing a joint optimization scheduling model further includes:

[0124] Establish constraints, including:

[0125] (1) Power balance constraints:

[0126]

[0127] Among them, P L,t is the total system load during period t;

[0128] (2) Output upper and lower limits and climbing constraints of thermal power and hydropower units:

[0129]

[0130] P Gdowni ≤P Gi,t -P Gi,t-1 ≤P Gupi ;

[0131]

[0132] P Wdownk ≤P Wk,t -P Wk,t-1 ≤P Wupk ;

[0133] in, is the upper and lower limits of the active output of thermal power unit i; P Gdowni , P Gupi is the up and down ramp rate of thermal power unit i; is the upper and lower limits of the active output of hydropower unit k; P Wdownk , P Wupk is the up and down ramp rate of hydropower unit k;

[0134] (3) Photovoltaic grid-connected power constraints:

[0135]

[0136] in, is the planned grid-connected power value of photovoltaic j in period t;

[0137] (4) Positive and negative spinning reserve constraints:

[0138] Thermal power is needed to bear the impact of load and photovoltaic fluctuations, so sufficient reserve is required, and the constraints are as follows:

[0139]

[0140]

[0141] Among them, L + , L - is the positive and negative rotating reserve coefficient of the system load; P + , P - are the positive and negative spinning reserve coefficients of photovoltaics;

[0142] (5) Hydrogen energy storage system operating status constraints:

[0143]

[0144] in, It is a binary state quantity of the hydrogen energy storage device in the storage and power generation states.

[0145] (6) Hydrogen capacity constraints in hydrogen energy storage devices

[0146]

[0147]

[0148] in, The state of hydrogen energy storage; It is the upper and lower limits of hydrogen energy storage under different working conditions;

[0149] (7) Hydrogen content restriction in the device

[0150]

[0151]

[0152] in, The efficiency of hydrogen energy storage production and storage; S hl,t is the content of hydrogen in the device; M hl,t The amount of hydrogen energy used for other purposes; are the upper and lower limits of hydrogen content.

[0153] Further, according to an embodiment of the present invention, the improved whale algorithm of the present invention includes:

[0154] Based on the behavior of whales surrounding prey when foraging, the following model is proposed:

[0155] X(t+1)=X * (t)-AD;

[0156] D=|CX * (t)-X(t)|;

[0157] Among them, t is the current iteration number; A and C are the expression coefficients; X * (t) is the best position vector for whale hunting, and X(t) is the current position vector of the whale;

[0158] A = 2ar1-a;

[0159] C = 2r2;

[0160]

[0161] Among them, r1 and r2 are random numbers (0, 1); T max is the maximum number of iterations;

[0162] When humpback whales are swarming around their prey, they swim in a spiral manner, so the mathematical model for this stage is as follows:

[0163] X(t+1)=X * (t)+D′e bn cos(2πn);

[0164] D′=|X * (t)-X(t)|;

[0165] Where b is a constant used to define the shape of the spiral; n is a random number between [-1,1];

[0166] Use the small step random walk performance of the Levy flight algorithm to search for prey. The Levy flight formula is:

[0167]

[0168] in, is the step length scaling factor, which is 0.1 in the present invention; Levy(t) is the step length that conforms to the Levy flight distribution, and the formula is as follows:

[0169]

[0170]

[0171] Among them, τ and ν are random numbers between [0,1] that conform to the standard normal distribution; β is a constant, which is 1.5; Γ(x) is the gamma function: Γ(x) = (x-1)! .

[0172] In this embodiment, under the current "dual carbon" background, the number of new energy sources connected to the power grid has increased dramatically, and the amount of data has continued to increase. Because the traditional whale algorithm has certain limitations in data mining, the convergence factor a of the traditional algorithm is linear and cannot well reflect the actual optimization process. Therefore, the above nonlinear convergence factor a proposed in the present invention In the early stage of optimization, the nonlinear change rate of this convergence factor is fast, which can continuously expand the search range and enhance the global search capability; as the number of iterations increases, the nonlinear change rate slows down, enhancing the local search capability, and can better find the optimal result, thereby improving the convergence speed of the algorithm.

[0173] In this embodiment, the present invention uses the small-step random walk performance of the Levy flight algorithm to replace the search model of the traditional whale algorithm, which can improve the controllability of the algorithm.

[0174] Further, according to an embodiment of the present invention, the present invention further comprises: initializing the initial position of the whale group by using a Sobol sequence:

[0175] x n =x min +μ n (x max -x min );

[0176] Among them, x n is the initial position of the population, x min 、x max are the upper and lower limits of the initial position; μ n A random number in [0,1].

[0177] In this embodiment, because the traditional whale algorithm cannot evenly distribute the population in the region when initializing the population, it is very random and may cause the existence of closely adjacent points. When iterating to an unstable point, the algorithm will not be able to continue. The present invention uses the Sobol sequence method to make the population model more uniform when initializing, and the method has a short calculation cycle, which can improve the efficiency of the algorithm initialization population and increase the diversity of the population.

[0178] Further, according to an embodiment of the present invention, when judging whether the current optimal scheduling scheme is optimal, if it is not optimal, the following steps are performed:

[0179] Recalculate the parameters of the improved whale algorithm and update the number of iterations;

[0180] Determine the value of the above parameter A. If A is less than 1, proceed to the next step; otherwise, according to the formula Update the whale position, calculate the fitness of each whale within the maximum number of iterations, and retain the optimal whale position to obtain the current optimal scheduling plan;

[0181] Determine the value of the above parameter p. If p is less than 0.5, then according to the formula X(t+1)=X * (t)-AD updates the whale position, calculates the fitness of each whale within the maximum number of iterations, and retains the optimal whale position to obtain the current optimal scheduling solution; otherwise, according to the formula X(t+1)=X * (t)+D′e bn cos(2πn) updates the whale position, calculates the fitness of each whale within the maximum number of iterations, retains the optimal whale position, and obtains the current optimal scheduling plan.

[0182] According to the above solution of the present invention, Figure 2 and Figure 3As shown, the present invention introduces a hydrogen energy storage system and uses the above-mentioned improved whale optimization algorithm. The photovoltaic absorption curve (mode 4 curve in each figure) is closer to the actual photovoltaic output curve, which can improve the grid's ability to absorb photovoltaics.

[0183] like Figure 4 and Figure 5 As shown, the present invention introduces a hydrogen energy storage system and uses the above-mentioned improved whale optimization algorithm, such as the mode 4 curve in each figure, to compare the proportion of thermal power processing under the non-above method. The proportion of thermal power output under different modes in summer and winter is shown in Table 1. It can be seen that the use of the above method (mode 4) can more effectively reduce the output of thermal power generation, thereby reducing carbon emissions, reducing costs, and promoting the development of the "dual carbon" goals.

[0184] model summer winter 1 82.41％ 52.32％ 2 80.29％ 50.72％ 3 78.99％ 49.42％ 4 77.13％ 47.71％

[0185] Table 1

[0186] According to the above scheme of the present invention, the present invention uses hydrogen energy storage in conjunction with other conventional units to absorb photovoltaics. Compared with pumped storage, hydrogen energy storage has both the ability of fast electrochemical energy storage response speed and the characteristics of supporting cross-seasonal energy storage. The present invention uses the improved whale algorithm to solve the multi-objective scheduling scheme proposed above. By improving the algorithm, the global search ability and convergence speed of the algorithm in solving multi-objective functions are improved, effectively realizing the multi-objective joint optimization scheduling of the power grid, and effectively improving the power grid's ability to absorb photovoltaics.

[0187] The model considered in this invention introduces the waste emissions from thermal power plants, the amount of photovoltaic power not absorbed, hydrogen energy storage and the joint scheduling of other conventional units under the premise of ensuring the safe and stable operation of the power grid. It can reduce carbon emissions while improving the power grid's ability to absorb photovoltaic power.

[0188] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a power grid joint dispatching system based on an improved whale algorithm, comprising:

[0189] Joint optimization dispatch model construction module, which establishes a joint optimization dispatch model based on thermal power waste emissions, photovoltaic unabsorbed capacity, hydrogen energy storage and conventional units;

[0190] The model solving module solves the joint optimization scheduling model based on the improved whale algorithm and randomly generates a set of whale positions as multiple initial solutions;

[0191] The scheduling scheme acquisition module calculates the fitness of each whale within the maximum number of iterations, retains the optimal whale position, and obtains the current optimal scheduling scheme;

[0192] The scheduling result acquisition module determines whether the current optimal scheduling solution has reached the optimal state. If it has, the optimal whale position vector is converted into a scheduling solution to obtain the scheduling result.

[0193] Furthermore, according to an embodiment of the present invention, a joint optimization scheduling model is established, including:

[0194] Establish the objective function, which includes:

[0195] (1) Waste emissions from thermal power plants:

[0196]

[0197] Among them, T is the number of time periods in an optimization cycle; N1 is the number of thermal power units; P Gi,t is the active power output of the i-th unit in period t; α i , β i , γ i , i , i is the emission coefficient of the i-th thermal power unit;

[0198] (2) Unabsorbed photovoltaic power generation:

[0199]

[0200] Where N2 is the number of photovoltaic units; p′ pvj,t is the predicted power generation value of the j-th photovoltaic unit in period t; p pvj,t is the actual power generation value of the j-th photovoltaic unit in period t;

[0201] (3) Operation and start-up and shutdown costs of thermal power and hydropower units:

[0202]

[0203] Among them, S Gi is the startup cost of the i-th thermal power unit; i,t is the start and stop status of the i-th thermal power unit in period t; a i 、b i 、c i is the power generation cost coefficient of the i-th thermal power unit; N3 is the number of hydropower units; d k is the power generation cost coefficient of the kth hydropower unit; P Wk,t is the active power output of the kth hydropower unit in period t;

[0204] (4) Hydrogen energy storage operating costs:

[0205]

[0206] in, The capacity of hydrogen energy storage devices to store and generate electricity; is the energy storage cost coefficient of the hydrogen energy storage device; is the power generation cost coefficient of the hydrogen energy storage device; N4 is the number of hydrogen energy storage devices;

[0207] (5) Overall objective function

[0208] Add weight coefficients to each objective function, generate the total objective function, and find the minimum value of the function:

[0209] min F=eta1f1+eta2f2+eta3f3+eta4f4;

[0210] Among them, η1, η2, η3, and η4 are weight coefficients.

[0211] Furthermore, according to an embodiment of the present invention, establishing a joint optimization scheduling model further includes:

[0212] Establish constraints, including:

[0213] (1) Power balance constraints:

[0214]

[0215] Among them, P L,t is the total system load during period t;

[0216] (2) Output upper and lower limits and climbing constraints of thermal power and hydropower units:

[0217]

[0218] P Gdowni ≤P Gi,t -P Gi,t-1 ≤P Gupi ;

[0219]

[0220] P Wdownk ≤P Wk,t -P Wk,t-1 ≤P Wupk ;

[0221] in, is the upper and lower limits of the active output of thermal power unit i; P Gdowni , P Gupi is the up and down ramp rate of thermal power unit i; is the upper and lower limits of the active output of hydropower unit k; P Wdownk , P Wupk is the up and down ramp rate of hydropower unit k;

[0222] (3) Photovoltaic grid-connected power constraints:

[0223]

[0224] in, is the planned grid-connected power value of photovoltaic j in period t;

[0225] (4) Positive and negative spinning reserve constraints:

[0226] Thermal power is needed to bear the impact of load and photovoltaic fluctuations, so sufficient reserve is required, and the constraints are as follows:

[0227]

[0228]

[0229] Among them, L + , L - is the positive and negative spinning reserve coefficient of the system load; P + , P - are the positive and negative spinning reserve coefficients of photovoltaics;

[0230] (5) Hydrogen energy storage system operating status constraints:

[0231]

[0232] in, It is a binary state quantity of the hydrogen energy storage device in the storage and power generation states.

[0233] (6) Hydrogen capacity constraints in hydrogen energy storage devices

[0234]

[0235]

[0236] in, The state of hydrogen energy storage; It is the upper and lower limits of hydrogen energy storage under different working conditions;

[0237] (7) Hydrogen content restriction in the device

[0238]

[0239]

[0240] in, The efficiency of hydrogen energy storage production and storage; S hl,t is the content of hydrogen in the device; M hl,t The amount of hydrogen energy used for other purposes; are the upper and lower limits of hydrogen content.

[0241] Further, according to an embodiment of the present invention, the improved whale algorithm of the present invention includes:

[0242] Based on the behavior of whales surrounding prey when foraging, the following model is proposed:

[0243] X(t+1)=X * (t)-AD;

[0244] D=|CX * (t)-X(t)|;

[0245] Among them, t is the current iteration number; A and C are the expression coefficients; X * (t) is the best position vector for whale hunting, and X(t) is the current position vector of the whale;

[0246] A = 2ar1-a;

[0247] C = 2r2;

[0248]

[0249] Among them, r1 and r2 are random numbers (0, 1); T max is the maximum number of iterations;

[0250] When humpback whales are swarming around their prey, they swim in a spiral manner, so the mathematical model for this stage is as follows:

[0251] X(t+1)=X * (t)+D′e bn cos(2πn);

[0252] D′=|X * (t)-X(t)|;

[0253] Where b is a constant used to define the shape of the spiral; n is a random number between [-1,1];

[0254] Use the small step random walk performance of the Levy flight algorithm to search for prey. The Levy flight formula is:

[0255]

[0256] in, is the step length scaling factor, which is 0.1 in the present invention; Levy(t) is the step length that conforms to the Levy flight distribution, and the formula is as follows:

[0257]

[0258]

[0259] Among them, τ and ν are random numbers between [0,1] that conform to the standard normal distribution; β is a constant, which is 1.5; Γ(x) is the gamma function: Γ(x) = (x-1)! .

[0260] In this embodiment, under the current "dual carbon" background, the number of new energy sources connected to the power grid has increased dramatically, and the amount of data has continued to increase. Because the traditional whale algorithm has certain limitations in data mining, the convergence factor a of the traditional algorithm is linear and cannot well reflect the actual optimization process. Therefore, the above nonlinear convergence factor a proposed in the present invention In the early stage of optimization, the nonlinear change rate of this convergence factor is fast, which can continuously expand the search range and enhance the global search capability; as the number of iterations increases, the nonlinear change rate slows down, enhancing the local search capability, and can better find the optimal result, thereby improving the convergence speed of the algorithm.

[0261] In this embodiment, the present invention uses the small-step random walk performance of the Levy flight algorithm to replace the search model of the traditional whale algorithm, which can improve the controllability of the algorithm.

[0262] Further, according to an embodiment of the present invention, the present invention further comprises: initializing the initial position of the whale group by using a Sobol sequence:

[0263] x n =x min +μ n (x max -x min );

[0264] Among them, x n is the initial position of the population, x min 、x max are the upper and lower limits of the initial position; μ n A random number in [0,1].

[0265] In this embodiment, because the traditional whale algorithm cannot evenly distribute the population in the region when initializing the population, it is very random and may cause the existence of closely adjacent points. When iterating to an unstable point, the algorithm will not be able to continue. The present invention uses the Sobol sequence method to make the population model more uniform when initializing, and the method has a short calculation cycle, which can improve the efficiency of the algorithm initialization population and increase the diversity of the population.

[0266] Further, according to an embodiment of the present invention, when judging whether the current optimal scheduling scheme is optimal, if it is not optimal, the following steps are performed:

[0267] Recalculate the parameters of the improved whale algorithm and update the number of iterations;

[0268] Determine the value of the above parameter A. If A is less than 1, proceed to the next step; otherwise, according to the formula Update the whale position, calculate the fitness of each whale within the maximum number of iterations, and retain the optimal whale position to obtain the current optimal scheduling plan;

[0269] Determine the value of the above parameter p. If p is less than 0.5, then according to the formula X(t+1)=X * (t)-AD updates the whale position, calculates the fitness of each whale within the maximum number of iterations, and retains the optimal whale position to obtain the current optimal scheduling solution; otherwise, according to the formula X(t+1)=X * (t)+D′e bn cos(2πn) updates the whale position, calculates the fitness of each whale within the maximum number of iterations, retains the optimal whale position, and obtains the current optimal scheduling plan.

[0270] According to the above solution of the present invention, Figure 2 and Figure 3 As shown, the present invention introduces a hydrogen energy storage system and uses the above-mentioned improved whale optimization algorithm. The photovoltaic absorption curve (mode 4 curve in each figure) is closer to the actual photovoltaic output curve, which can improve the grid's ability to absorb photovoltaics.

[0271] like Figure 4 and Figure 5 As shown, the present invention introduces a hydrogen energy storage system and uses the above-mentioned improved whale optimization algorithm, such as the mode 4 curve in each figure, and compares the proportion of thermal power processing under the non-above method, as shown in Table 1. It can be seen that the use of the above method (mode 4) can more effectively reduce the output of thermal power generation, thereby reducing carbon emissions, reducing costs, and promoting the development of the "dual carbon" goals.

[0272] According to the above scheme of the present invention, the present invention uses hydrogen energy storage in conjunction with other conventional units to absorb photovoltaics. Compared with pumped storage, hydrogen energy storage has both the ability of fast electrochemical energy storage response speed and the characteristics of supporting cross-seasonal energy storage. The present invention uses the improved whale algorithm to solve the multi-objective scheduling scheme proposed above. By improving the algorithm, the global search ability and convergence speed of the algorithm in solving multi-objective functions are improved, effectively realizing the multi-objective joint optimization scheduling of the power grid, and effectively improving the power grid's ability to absorb photovoltaics.

[0273] The model considered in this invention introduces the waste emissions from thermal power plants, the amount of photovoltaic power not absorbed, hydrogen energy storage and the joint scheduling of other conventional units under the premise of ensuring the safe and stable operation of the power grid. It can reduce carbon emissions while improving the power grid's ability to absorb photovoltaic power.

[0274] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for joint dispatching of power grids based on the improved whale algorithm as described above is implemented.

[0275] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the power grid joint dispatching method based on the improved whale algorithm as described above is implemented.

[0276] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0277] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0278] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0279] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0280] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0281] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0282] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0283] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A power grid joint dispatching method based on an improved whale algorithm, characterized in that: include: Establish a joint optimization dispatch model based on thermal power waste emissions, photovoltaic unabsorbed capacity, hydrogen energy storage and conventional units; The joint optimization scheduling model is solved based on the improved whale algorithm, and a set of whale positions are randomly generated as multiple initial solutions; Calculate the fitness of each whale within the maximum number of iterations, retain the optimal whale position, and obtain the current optimal scheduling plan; Determine whether the current optimal scheduling solution is optimal. If it is, convert the optimal whale position vector into a scheduling solution to obtain the scheduling result.

2. The power grid joint dispatching method based on the improved whale algorithm according to claim 1 is characterized in that: Establish a joint optimization scheduling model, including: Establish an objective function, the objective function comprising: (1) Waste emissions from thermal power plants: Among them, T is the number of time periods in an optimization cycle; N1 is the number of thermal power units; P Gi,t is the active power output of the i-th unit in period t; α i , β i , γ i , i , i is the emission coefficient of the i-th thermal power unit; (2) Unabsorbed photovoltaic power generation: Where N2 is the number of photovoltaic units; p p ' vj,t is the predicted power generation value of the j-th photovoltaic unit in period t; p pvj,t is the actual power generation value of the j-th photovoltaic unit in period t; (3) Operation and start-up and shutdown costs of thermal power and hydropower units: Among them, S Gi is the startup cost of the i-th thermal power unit; i,t is the start and stop status of the i-th thermal power unit in period t; a i , b i 、c i is the power generation cost coefficient of the i-th thermal power unit; N3 is the number of hydropower units; d k is the power generation cost coefficient of the kth hydropower unit; P Wk,t is the active power output of the kth hydropower unit in period t; (4) Hydrogen energy storage operating costs: in, The capacity of hydrogen energy storage devices to store and generate electricity; is the energy storage cost coefficient of the hydrogen energy storage device; is the power generation cost coefficient of the hydrogen energy storage device; N4 is the number of hydrogen energy storage devices; (5) Overall objective function Add weight coefficients to each objective function, generate the total objective function, and find the minimum value of the function: min F=eta1f1+eta2f2+eta3f3+eta4f4; Among them, η1, η2, η3, and η4 are weight coefficients.

3. The power grid joint dispatching method based on the improved whale algorithm according to claim 2 is characterized in that: Establish a joint optimization scheduling model, including: Establishing constraints, the constraints include: (1) Power balance constraints: Among them, P L,t is the total system load during period t; (2) Output upper and lower limits and climbing constraints of thermal power and hydropower units: P Gdowni ≤P Gi,t -P Gi,t-1 ≤P Gupi ; P Wdownk ≤P Wk,t -P Wk,t-1 ≤P Wupk ; in, is the upper and lower limits of the active output of thermal power unit i; P Gdowni , P Gupi is the up and down ramp rate of thermal power unit i; is the upper and lower limits of the active output of hydropower unit k; P Wdownk , P Wupk is the up and down ramp rate of hydropower unit k; (3) Photovoltaic grid-connected power constraints: in, is the planned grid-connected power value of photovoltaic j in period t; (4) Positive and negative spinning reserve constraints: Thermal power is needed to bear the impact of load and photovoltaic fluctuations, so sufficient reserve is required, and the constraints are as follows: Among them, L + , L - is the positive and negative spinning reserve coefficient of the system load; P + , P - are the positive and negative spinning reserve coefficients of photovoltaics; (5) Hydrogen energy storage system operating status constraints: in, It is a binary state quantity of the hydrogen energy storage device in the storage and power generation states. (6) Hydrogen capacity constraints in hydrogen energy storage devices in, The state of hydrogen energy storage; It is the upper and lower limits of hydrogen energy storage under different working conditions; (7) Hydrogen content restriction in the device in, The efficiency of hydrogen energy storage production and storage; S hl,t is the content of hydrogen in the device; M hl,t The amount of hydrogen energy used for other purposes; are the upper and lower limits of hydrogen content.

4. The power grid joint dispatching method based on the improved whale algorithm according to claim 3 is characterized in that: The improved whale algorithm comprises: Based on the behavior of whales surrounding prey when foraging, the following model is proposed: X(t+1)=X * (t)-AD; D=|CX * (t)-X(t)|; Among them, t is the current iteration number; A and C are the expression coefficients; X * (t) is the best position vector for whale hunting, and X(t) is the current position vector of the whale; A = 2ar1-a; C＝2r2； Among them, r1 and r2 are random numbers (0, 1); T max is the maximum number of iterations; When humpback whales are swarming around their prey, they swim in a spiral manner, so the mathematical model for this stage is as follows: X(t+1)=X * (t)+D′e bn cos(2πn); D′=|X * (t)-X(t)|; Where b is a constant used to define the shape of the spiral; n is a random number between [-1,1]; Use the small step random walk performance of the Levy flight algorithm to search for prey. The Levy flight formula is: in, is the step length scaling factor, which is 0.1 in the present invention; Levy(t) is the step length that conforms to the Levy flight distribution, and the formula is as follows: Among them, τ and ν are random numbers between [0,1] that conform to the standard normal distribution; β is a constant, which is 1.5; Γ(x) is the gamma function: Γ(x) = (x-1)! .

5. The power grid joint dispatching method based on the improved whale algorithm according to claim 4 is characterized in that: Also includes: Initialize the initial position of the whale group through the Sobol sequence: x n =x min +μ n (x max -x min ); Among them, x n is the initial position of the population, x min 、x max are the upper and lower limits of the initial position; μ n A random number in [0,1].

6. The power grid joint dispatching method based on the improved whale algorithm according to claim 5 is characterized in that: The determination of whether the current optimal scheduling scheme is optimal or not, if not, executing the following steps: Recalculate the parameters of the improved whale algorithm and update the number of iterations; Determine the value of parameter A. If A is less than 1, proceed to the next step. On the contrary, according to the formula Update the whale position, calculate the fitness of each whale within the maximum number of iterations, and retain the optimal whale position to obtain the current optimal scheduling plan; Determine the value of parameter p. If p is less than 0.5, then according to the formula X(t+1)=X * (t)-AD updates the whale position, calculates the fitness of each whale within the maximum number of iterations, and retains the optimal whale position to obtain the current optimal scheduling solution; otherwise, according to the formula X(t+1)=X * (t)+D′e bn cos(2πn) updates the whale position, calculates the fitness of each whale within the maximum number of iterations, retains the optimal whale position, and obtains the current optimal scheduling plan.

7. The power grid joint dispatching system based on the improved whale algorithm is characterized by: include: Joint optimization dispatch model construction module, which establishes a joint optimization dispatch model based on thermal power waste emissions, photovoltaic unabsorbed capacity, hydrogen energy storage and conventional units; The model solving module solves the joint optimization scheduling model based on the improved whale algorithm and randomly generates a set of whale positions as multiple initial solutions; The scheduling scheme acquisition module calculates the fitness of each whale within the maximum number of iterations, retains the optimal whale position, and obtains the current optimal scheduling scheme; The scheduling result acquisition module determines whether the current optimal scheduling solution has reached the optimal state. If it has, the optimal whale position vector is converted into a scheduling solution to obtain the scheduling result.

8. An electronic device, characterized in that It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the power grid joint dispatching method based on the improved whale algorithm as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the power grid joint dispatching method based on the improved whale algorithm as described in any one of claims 1 to 6 is implemented.

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