Power grid dispatching method and device and storage medium

Abstract

The invention discloses a power grid dispatching method and device and a storage medium. The method comprises the following steps: constructing an optimal power flow optimization model of a power grid system and a mobile energy storage vehicle optimization dispatching model; setting a first objective function and a first constraint condition of the optimal power flow optimization model, and setting a second objective function and a second constraint condition of the mobile energy storage vehicle optimization scheduling model; modeling the mobile energy storage optimization scheduling problem into a mixed integer linear programming problem, and solving the mixed integer linear programming problem based on the first objective function, the first constraint condition, the second objective function and the second constraint condition according to the node load and the carbon emission factor of the power grid system to obtain an updated path planning and charging and discharging scheme. According to the method, the carbon emission factor is introduced to fully consider the carbon emission characteristics of energy storage, the carbon emission is taken as a main optimization target, and the charging and discharging behaviors of the energy storage system can be dynamically optimized, so that the carbon emission can be effectively minimized, and the carbon emission effect can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a power grid dispatching method, device and storage medium. Background Art

[0002] As the global climate change problem intensifies, the power industry, as one of the main sources of carbon emissions, faces tremendous pressure to reduce carbon emissions. In order to achieve the "dual carbon" goal, low-carbon dispatch methods for power grids have gradually become a research hotspot. Energy storage technology, as a key technology to promote the sustainable development of power systems, can balance the contradiction between supply and demand in power grids through energy storage and release, promote the effective absorption of renewable energy, and reduce dependence on fossil energy. In recent years, with the large-scale access of distributed renewable energy, the volatility and uncertainty of power grids have greatly increased. At the same time, the popularity of electric vehicles has increased their influence in power grids, especially the impact of charging and discharging behaviors on power grid loads. As a mobile energy storage system, its temporal and spatial dynamic characteristics are increasingly important in power grid dispatching. Low-carbon dispatching of power grids considering the dynamic elevator characteristics of energy storage has become a key research field.

[0003] Existing grid dispatch methods usually do not fully consider the carbon emission characteristics of energy storage, resulting in poor carbon emission reduction effects. Summary of the invention

[0004] The present invention provides a power grid dispatching method, device and storage medium to solve the technical problem that the existing power grid dispatching method generally does not fully consider the carbon emission characteristics of energy storage, resulting in poor carbon emission reduction effect.

[0005] The present invention provides a power grid dispatching method, comprising:

[0006] Construct the optimal power flow optimization model of the power grid system and the optimal dispatch model of mobile energy storage vehicles;

[0007] Setting a first objective function and a first constraint condition of the optimal power flow optimization model, and setting a second objective function and a second constraint condition of the mobile energy storage vehicle optimization scheduling model;

[0008] The mobile energy storage optimization scheduling problem is modeled as a mixed integer linear programming problem. Based on the first objective function, the first constraint condition, the second objective function and the second constraint condition, and according to the node load and carbon emission factor of the power grid system, the mixed integer linear programming problem is solved to obtain an updated path planning and charging and discharging plan.

[0009] Furthermore, the power grid dispatching method further includes:

[0010] According to the route selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved;

[0011] According to the generator power output allocation strategy, the power flow and carbon emission flow in the power grid system are calculated, and the carbon emission factor is determined according to the power flow and carbon emission flow.

[0012] Furthermore, the construction of the optimal power flow optimization model of the power grid system and the optimal dispatching model of the mobile energy storage vehicle includes:

[0013] Constructing the optimal power flow optimization model according to the active power of the generator set, the set of generator nodes and the coefficient of the cost function;

[0014] According to the total charging power of the electric vehicle, the total discharging power of the electric vehicle, the carbon emission price and the carbon emission factor, an optimization scheduling model for the mobile energy storage vehicle is constructed.

[0015] Furthermore, the setting of the first objective function and the first constraint condition of the optimal power flow optimization model, and the setting of the second objective function and the second constraint condition of the mobile energy storage vehicle optimization scheduling model also include:

[0016] The first objective function is constructed according to the sum of the costs of each generator, wherein the first constraint condition is a constraint condition that takes into account the power balance, voltage amplitude, and power flow balance of the upper power grid system;

[0017] The second objective function is constructed according to the charging cost, the discharging benefit and the emission cost, and the second constraint condition is a constraint condition that takes into account the charging and discharging state and the charge state during the moving process and the charging and discharging process of the electric vehicle.

[0018] Furthermore, the first objective function is:

[0019]

[0020]

[0021] Among them, C g is the sum of the power generation costs of each generator set, a i , b i are the coefficients of the cost function of generator set i; P i G is the active power of generator set i, n g is the set of power generation nodes;

[0022] The second objective function is:

[0023]

[0024] Among them, C charging,n (t) is the charging cost of electric vehicle n at time t; C discharging,n(t) is the discharge benefit of electric vehicle n at time t; C carbon,n (t) is the carbon emission cost of electric vehicle n at time t.

[0025] Furthermore, according to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved, including:

[0026] According to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the Aurora optimization algorithm is used to solve the optimal generator power output allocation strategy.

[0027] Furthermore, the power flow and carbon emission flow in the power grid system are calculated according to the generator power output allocation strategy, including:

[0028] Combining the generator power output allocation strategy, load demand and grid network topology, the power flow and carbon emission flow in the grid system are calculated.

[0029] The present invention also provides a power grid dispatching device, comprising:

[0030] Model building module, used to build the optimal power flow optimization model of the power grid system and the optimal dispatch model of the mobile energy storage vehicle;

[0031] An objective function setting module, used to set a first objective function and a first constraint condition of the optimal power flow optimization model, and to set a second objective function and a second constraint condition of the mobile energy storage vehicle optimization scheduling model;

[0032] The optimization solution module is used to model the mobile energy storage optimization scheduling problem into a mixed integer linear programming problem. Based on the first objective function, the first constraint condition, the second objective function and the second constraint condition, according to the node load and carbon emission factor of the power grid system, the mixed integer linear programming problem is solved to obtain an updated path planning and charging and discharging plan.

[0033] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the above-mentioned power grid scheduling method when executing the computer program.

[0034] The present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power grid scheduling method as described above.

[0035] The present invention sets the first objective function and the first constraint condition of the optimal power flow optimization model, sets the second objective function and the second constraint condition of the mobile energy storage vehicle optimization scheduling model, and considers the node load and the carbon emission factor of the power grid system to solve and obtain the updated path planning and charging and discharging plan. The carbon emission factor is introduced to fully consider the carbon emission characteristics of energy storage. With carbon emission as the main optimization target, the charging and discharging behavior of the energy storage system can be dynamically optimized so that the final path planning and charging and discharging plan can effectively minimize carbon emissions, thereby effectively improving the carbon emission effect.

[0036] Furthermore, the embodiment of the present invention adopts the Aurora optimization algorithm to solve the optimal generator power output allocation strategy, combines the temporal and spatial dynamic characteristics of the mobile energy storage system, and fully considers the power demand of the power grid in different time periods, renewable energy supply, carbon emissions, and the impact of the spatial location of mobile energy storage on the transmission loss and carbon emissions of the power grid, thereby being able to accurately determine the generator power output allocation strategy and further improve the grid scheduling effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of a power grid dispatching method provided by an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of the structure of an extended IEEE 33 node system power grid provided in an embodiment of the present invention;

[0039] Figure 3 It is a comparison diagram of fitness curves obtained by optimizing the optimal power flow model of the power grid system based on the Aurora optimization algorithm and the genetic algorithm respectively provided by the embodiment of the present invention;

[0040] Figure 4 is another schematic diagram of a flow chart of a power grid dispatching method provided by an embodiment of the present invention;

[0041] Figure 5 It is a structural schematic diagram of a power grid dispatching device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0044] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0045] See also Figure 1 The present invention provides a power grid dispatching method, comprising:

[0046] S1. Construct the optimal power flow optimization model of the power grid system and the optimal dispatch model of the mobile energy storage vehicle;

[0047] See also Figure 2 , which is a schematic diagram of the extended IEEE 33-node system power grid structure provided in an embodiment of the present invention. The embodiment of the present invention takes the extended IEEE 33-node system power grid and a regional 29-node distance matrix combined with a control model as an example. In the control model, the power grid includes different output units such as gas turbines, wind turbines and photovoltaic units. In addition, the load includes multiple dispatchable mobile electric vehicle aggregation groups.

[0048] S2. Setting the first objective function and the first constraint condition of the optimal power flow optimization model, and setting the second objective function and the second constraint condition of the mobile energy storage vehicle optimization scheduling model;

[0049] S3. Model the mobile energy storage optimization scheduling problem as a mixed integer linear programming problem. Based on the first objective function, the first constraint, the second objective function and the second constraint, and according to the node load and carbon emission factor of the power grid system, solve the mixed integer linear programming problem to obtain the updated path planning and charging and discharging plan.

[0050] In an embodiment of the present invention, power grid scheduling can be performed based on the updated path planning and charging and discharging scheme to reduce the carbon emissions of the power grid system.

[0051] The embodiment of the present invention sets the first objective function and the first constraint of the optimal power flow optimization model, sets the second objective function and the second constraint of the mobile energy storage vehicle optimization scheduling model, and considers the node load and the carbon emission factor of the power grid system to solve and obtain the updated path planning and charging and discharging plan. The carbon emission factor is introduced to fully consider the carbon emission characteristics of energy storage. With carbon emission as the main optimization target, the charging and discharging behavior of the energy storage system can be dynamically optimized so that the final path planning and charging and discharging plan can effectively minimize carbon emissions, thereby effectively improving the carbon emission effect.

[0052] In one embodiment, the power grid dispatching method further includes:

[0053] According to the route selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved;

[0054] In an embodiment of the present invention, a polar lights optimization (PLO) algorithm may be used to solve and obtain an optimal generator power output allocation strategy.

[0055] According to the generator power output allocation strategy, the power flow and carbon emission flow in the power grid system are calculated, and the carbon emission factor is determined based on the power flow and carbon emission flow.

[0056] In the embodiment of the present invention, the carbon emission flow can be constructed based on the proportional sharing principle.

[0057] The embodiment of the present invention calculates the power flow and carbon emission flow in the power grid system according to the generator power output allocation strategy, determines the carbon emission factor according to the power flow and the carbon emission flow, and can accurately determine the dynamic emission factor in the power grid system, so that the final path planning and charging and discharging plan fully consider the dynamic carbon emission characteristics of the energy storage system, thereby fully realizing the potential of the energy storage system in reducing carbon emissions and effectively improving the grid scheduling effect.

[0058] In one embodiment, step S1, constructing an optimal power flow optimization model of a power grid system and an optimal dispatching model of a mobile energy storage vehicle, includes:

[0059] S11, constructing an optimal power flow optimization model according to the active power of the generator set, the set of generator nodes and the coefficient of the cost function;

[0060] In the embodiment of the present invention, the optimal power flow optimization model can be represented by the power generation cost model of each generator set in the power grid system, and the formula is as follows:

[0061]

[0062] In the formula, a i , bi are the coefficients of the cost function of generator set i; P i G is the active power of generator set i, and ng is the set of generating nodes.

[0063] S12. Construct an optimal scheduling model for mobile energy storage vehicles based on the total charging power of electric vehicles, the total discharging power of electric vehicles, the carbon emission price and the carbon emission factor.

[0064] In the embodiment of the present invention, the mobile energy storage vehicle optimization scheduling model mainly includes the optimization scheduling of the charging scheme of the electric vehicle, which can be expressed by the mobile charging and discharging cost and carbon emission cost model:

[0065]

[0066]

[0067]

[0068] in, represents the total charging power of the nth group of electric vehicles; represents the total discharge power of the nth group of electric vehicles; E (t), λ D (t) represent charging price and discharging benefit, λ C (t) represents the carbon emission price; C charging,n (t) is the charging cost of electric vehicle n at time t; C discharging,n (t) is the discharge benefit of electric vehicle n at time t; C carbon,n (t) is the carbon emission cost of electric vehicle n at time t; It represents the dynamic carbon emission factor (CEF) of the nth group of electric vehicles.

[0069] In one embodiment, step S2, setting the first objective function and the first constraint condition of the optimal power flow optimization model, setting the second objective function and the second constraint condition of the mobile energy storage vehicle optimization scheduling model, further includes:

[0070] S21, constructing a first objective function according to the sum of the costs of each generator, wherein the first constraint condition is a constraint condition that considers power balance, voltage amplitude, and power flow balance of the upper power grid system;

[0071] In this embodiment of the present invention, the first objective function is:

[0072]

[0073]

[0074] Among them, C g is the sum of the power generation costs of each generator set, a i , b i are the coefficients of the cost function of generator set i; P i G is the active power of generator set i, n g is a collection of power generation nodes. In the embodiment of the present invention, n g is 3, that is, there are 3 generator sets;

[0075] In the embodiment of the present invention, the first constraint condition is:

[0076]

[0077] Among them, P i g is the output active power of the generator at the i-th node; is the output reactive power of the generator at the i-th node; P i load is the active power demand of the i-th node; is the reactive power demand of the ith node, V i represents the voltage amplitude of the i-th node; is the conductance of line ij; is the inductive reactance of line ij; θ ij represents the voltage phase angle difference between the i-th node and the j-th node; express The lower limit of express The upper limit of express The lower limit of express The upper limit of V i min Indicates V i The lower limit of V i max Indicates V i The upper limit of ; ni represents the total node set; ng represents the generator set, and nb represents the PQ node set.

[0078] S22. Construct a second objective function based on the charging cost, the discharging benefit and the emission cost. The second constraint condition is a constraint condition that takes into account the charging and discharging state and the charge state during the movement and charging and discharging process of the electric vehicle.

[0079] In this embodiment of the present invention, the second objective function is:

[0080]

[0081] Among them, C charging,n (t) is the charging cost of electric vehicle n at time t; C discharging,n (t) is the discharge benefit of electric vehicle n at time t; C carbon,n (t) is the carbon emission cost of electric vehicle n at time t.

[0082] The second constraint is:

[0083]

[0084] in, and Respectively represent the access time and exit time of the jth group of electric vehicles; M j is the number of electric vehicles in group j; Δt c Indicates the control time period; represents the expected state of charge (SOC) of the mth EV in the jth group at the exit time; represents the initial SOC; e jm and denote the maximum energy and charging power of the mth electric vehicle in the jth group respectively; η jm is the charging efficiency coefficient of the mth electric vehicle in the jth group, represents the discharge power of the mth electric vehicle in the jth group; γ jm is the discharge efficiency coefficient of the mth electric vehicle in the jth group.

[0085] In the embodiment of the present invention, a first objective function is constructed according to the sum of the costs of each generator, and the first constraint condition is a constraint condition that considers the power balance, voltage amplitude and flow balance of the upper power grid system; a second objective function is constructed according to the charging cost, discharge income and emission cost, and the second constraint condition is a constraint condition that considers the charging and discharging state and the charge state during the movement process and the charging and discharging process of the electric vehicle, so that the charging and discharging strategy and path planning of the energy storage system can not only meet the economic benefits, but also minimize carbon emissions, charge during low-carbon emission time, and release energy during high-carbon emission periods, thereby realizing low-carbon scheduling of the power system and effectively improving the scheduling effect of the power system.

[0086] In one embodiment, according to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved, including:

[0087] According to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the Aurora optimization algorithm is used to solve the optimal generator power output allocation strategy.

[0088] In an embodiment of the present invention, the optimal generator power output allocation strategy is obtained by using the Aurora optimization algorithm, including the following steps:

[0089] S101, set the number of iterations of the Aurora optimization algorithm it=1, and generate an initial population based on pseudo-random numbers during the iteration process:

[0090]

[0091]

[0092] Among them, X h (N, D) represents the initial decision of the hth charged ion. In the embodiment of the present invention, it refers to the power generation of each generator set; ΔP1, ΔP2, and ΔP3 represent the power allocation instructions of the gas turbine, wind turbine, and photovoltaic unit, respectively. In the embodiment of the present invention, the power allocation instructions of the three generator sets represent the power generation size at 24 moments, that is, they are all 1x24 numerical matrices; Equation (12) The entire population is represented in the form of a matrix with N rows and D columns, where N represents the size of the candidate solutions contained in the population, that is, the number of populations. In this embodiment, N is 50; and D represents the scalable dimension of the solution space. In the embodiment of the present invention, D is 24, that is, 24 moments; UB and LB represent the boundaries of the solution space, that is, the upper and lower limits of the power generation of the generator set at each moment. In the embodiment of the present invention, UB is 1200, LB is 0, and R represents a random number sequence with values ​​in [0, 1].

[0093] In the embodiment of the present invention, the individual decision is represented by the solution vector as the initial algorithm parameters and the spatial position of the aurora belt electron ions, that is, the individual decision.

[0094] Step S102: Calculate individual fitness and select the best individual, and calculate the current fitness value (i.e., objective function value) f(X) of the charged ion by the second objective function formula. h ), and the particle with the smallest fitness value is determined as the best particle, that is, the current optimal solution. Its decision is

[0095] Step S103, rotational motion, to find the optimal solution in the optimization process of the aurora optimization algorithm. The algorithm can simulate the particles emitted by the sun approaching the earth, encountering resistance from the earth's magnetic field, and radiating in all directions under its influence. In this process, the charged particles approaching the earth interact with the earth's magnetic field and rotate along the magnetic field lines. Mathematically, assuming the charge (q) and velocity (v) of the charged particles in the earth's magnetic field (B), the Lorentz force FL can be expressed as the following equation:

[0096] F L =qνB

[0097] FL exerts a centripetal force on charged particles, causing them to rotate along the magnetic field lines in the magnetic field. In addition, the equations of charged particles can be described by the Lorentz force and Newton's second law:

[0098]

[0099] Where m is the mass of the charged particle. This equation describes the change in the velocity of the charged particle over time, so that its trajectory in the magnetic field can be determined. By combining the above two equations, a first-order ordinary differential equation is obtained, as shown below:

[0100]

[0101] The first-order ordinary differential equation describes the change of the velocity of a charged particle with time. By solving this differential equation, we can get the law of the change of the particle velocity with time, so as to gain a deeper understanding of the movement of particles in the Earth's magnetic field, as shown below. Rearrange both sides and integrate them at the same time, with the integration interval from the initial velocity v0 to v and the time from 0 to t:

[0102]

[0103]

[0104] High-energy particles encounter resistance from air molecules in the atmosphere, resulting in non-smooth spiral motion. This non-smooth spiral motion is due to the damping effect of the atmosphere. After considering the damping effect of the atmosphere on charged particles, a damping factor α can be introduced to represent the decay rate of the particle velocity. Therefore, the first-order ordinary differential equation describing the change of the velocity of charged particles over time can be modified accordingly to obtain the following equation:

[0105]

[0106] After modification, a non-homogeneous first-order linear differential equation can be introduced to solve the above equation. Specifically: using the constant variation method, set the solution v = Ceλt, where C and λ are unknown coefficients. Substituting it into the equation, we get:

[0107] mλCe λt =qBCe λt -aCe λt

[0108] Solving for λ=qB-αm, we get the particle velocity:

[0109]

[0110] In this step, the rotational motion within the PLO is calculated by introducing an equation describing the velocity of a charged particle as a function of time to obtain the velocity of the particle, i.e., the solution.

[0111] Step S104, execute the aurora ellipse walk in the aurora optimization algorithm; the aurora tends to form along an elliptical band called the aurora oval, and the complex fluctuations of the aurora ellipse band will have a significant impact on the global search. This unpredictable chaos meets the needs of the aurora optimization algorithm for fast global search in the solution space. Among them, Levy Flight (LF) is often used in metaheuristic algorithms (MAs) to enhance global exploration, which is essentially a random non-Gaussian walk. Its step value is dispersed based on the Levy stable distribution. Levy flight can be expressed by the following formula:

[0112] Levy(d)~|d| -1-β ,0<β≤2

[0113] where β is an important LF indicator for regulating stability and d is the step size.

[0114] During the auroral oval movement, the high-energy particles simulated by LF are affected by geomagnetic activity and the atmosphere, showing that the auroral oval boundary shrinks in the pole direction and expands in the equatorial direction. The specific change process is as follows:

[0115] Ao=Levy(d)×(X avg (j)-X(i,j))+LB+r1×(UB-LB) / 2

[0116]

[0117] Among them, X avg is the center of mass position of the high-energy particle detonation, calculated by the above equation. X(i, j) is the current position of the energetic particle, and X avg (j)-X(i,j) represents the trend of particle movement. Ao is the complex variation of the auroral ellipse, simulated by the dispersed LFs that drive energetic particles between the poles and the equator.

[0118] The gyration motion is manifested as particles moving in a spiral along the Earth's magnetic field lines, presenting a slow motion along a fixed orbit. This mode of motion emphasizes local development and fine-tuning to explore the local solution space more deeply to find a local optimal solution or optimize the local structure of the current solution. This is consistent with the local search phase in MA, in which subtle adjustments and small steps are taken to improve the quality of the solution and bring it closer to the optimal solution.

[0119] Auroral ellipse walks involve rapid movement around candidate points to obtain the best solution or local optimum. This mode emphasizes the global exploration property, where particles explore the solution space with larger step sizes to discover more valuable areas. This corresponds to the global exploration phase in MA, where the solution space is searched with larger step sizes to find the global optimal solution or a better solution.

[0120] That is, the new calculation model proposed by the Aurora optimization algorithm combining these two strategies is expressed as the following equation:

[0121] X new (i,j)=X(i,j)+r2×(W1×v(t)+W2×Ao)

[0122] Among them, X new (i, j) is the position of the high-energy particle after the update is completed, and r2 represents the interference caused by factors such as the uncontrollable environment in which the particle is located, and its value is [0, 1]. In order to maximize the local and global development efficiency of the algorithm, two adaptive weights W1 and W2 are introduced. These two weights change with each iteration of the algorithm. The weights of the rotational motion and the auroral elliptical walk are calculated by the following two formulas:

[0123]

[0124]

[0125] Where W1 and W2 are the weights of the rotational motion and the auroral elliptical walk in the new computational model. The size of v(t) will increase with W1; the size of Ao will gradually decrease with W2. As the algorithm iterates, global search and local development rely on constantly changing weights to achieve a balance and explore the optimal solution.

[0126] Step S105: Update the alternative solution. As these particles enter the atmosphere and converge in the auroral ellipse, collisions occur more frequently, causing the shape of the aurora to change continuously. The calculation model is:

[0127] X new (i,j)=X(i,j)+sin(r3×π)×(X(i,j)-X(a,j)),r4<K and r5<0.05

[0128]

[0129] Where X(a, j) represents any particle in the particle cluster. As the algorithm proceeds, collisions between particles become more frequent, controlled by K as the collision probability, and r3 and r4 are random values, taking values ​​in [0, 1]. Step S105 will update the solution, and when the conditions are met, update the current optimal solution.

[0130] Step S106: Update the fitness value of the aurora particle group and select the best aurora particle.

[0131] Step S107, determine whether it is necessary to continue iteration, if it≥It (in this embodiment, the maximum number of iterations It=50), output the decision and fitness value of the current best aurora particle; otherwise return to step 103 to continue optimizing.

[0132] See also Figure 3 , the fitness curve comparison diagram obtained by optimizing the optimal power flow model of the power grid system based on the Aurora optimization algorithm and the genetic algorithm provided in the embodiment of the present invention, by referring to Figure 3 It can be seen that compared with the genetic algorithm, the distributed motor power allocation strategy obtained by the Aurora optimization algorithm is a better solution, and its solution speed is faster.

[0133] The Aurora optimization algorithm of the embodiment of the present invention can effectively solve the balance problem between global optimum and local optimum in the optimization problem by combining global exploration and local development, and can find the optimal solution in a complex power grid system; and the generator power output allocation strategy obtained by the Aurora optimization algorithm can accurately calculate the power flow and carbon emission flow in the power system, and then determine the carbon emission factor to carry out power grid scheduling and improve the power grid scheduling effect.

[0134] Furthermore, the embodiment of the present invention adopts the Aurora optimization algorithm to solve the optimal generator power output allocation strategy, combines the temporal and spatial dynamic characteristics of the mobile energy storage system, and fully considers the power demand of the power grid in different time periods, renewable energy supply, carbon emissions, and the impact of the spatial location of mobile energy storage on the transmission loss and carbon emissions of the power grid, thereby being able to accurately determine the generator power output allocation strategy and further improve the grid scheduling effect.

[0135] In one embodiment, according to the generator power output allocation strategy, the power flow and carbon emission flow in the power grid system are calculated, including:

[0136] Combining the generator power output allocation strategy, load demand and grid network topology, the power flow and carbon emission flow in the grid system are calculated.

[0137] In the embodiment of the present invention, the carbon emission factor is determined according to the calculated current flow and carbon emission flow, including:

[0138] The dynamic carbon emission factor of a node is calculated according to the following formula:

[0139]

[0140] Among them, δ i is the dynamic CEF of the ith node; P g,i represents the power output received by the i-th node from the distributed generator g; α g represents the carbon emission rate of generator g; Pg is the total power output of generator g; P load,i is the load demand of load node i; P total is the total load demand of the entire network.

[0141] In the embodiment of the present invention, the carbon emission factor of the power grid system is determined according to the carbon emission flow calculation principle of the above dynamic carbon emission factor formula:

[0142]

[0143] In one embodiment, after solving the mixed integer linear programming problem and obtaining the updated path planning and charging and discharging scheme, the method further includes:

[0144] In an embodiment of the present invention, the updated path planning and charging and discharging scheme are used as the current path planning and charging and discharging scheme, and the model construction, objective function setting and power grid scheduling are repeated based on the current path planning and charging and discharging, and the path planning and charging and discharging scheme are iteratively updated until the maximum number of iterations is reached to obtain the final path planning and charging and discharging scheme.

[0145] See also Figure 4 In one embodiment, another flow chart of a power grid scheduling method is provided.

[0146] The implementation of the embodiments of the present invention has the following beneficial effects:

[0147] The embodiment of the present invention sets the first objective function and the first constraint of the optimal power flow optimization model, sets the second objective function and the second constraint of the mobile energy storage vehicle optimization scheduling model, and considers the node load and the carbon emission factor of the power grid system to solve and obtain the updated path planning and charging and discharging plan. The carbon emission factor is introduced to fully consider the carbon emission characteristics of energy storage. With carbon emission as the main optimization target, the charging and discharging behavior of the energy storage system can be dynamically optimized so that the final path planning and charging and discharging plan can effectively minimize carbon emissions, thereby effectively improving the carbon emission effect.

[0148] Furthermore, the embodiment of the present invention adopts the Aurora optimization algorithm to solve the optimal generator power output allocation strategy, combines the temporal and spatial dynamic characteristics of the mobile energy storage system, and fully considers the power demand of the power grid in different time periods, renewable energy supply, carbon emissions, and the impact of the spatial location of mobile energy storage on the transmission loss and carbon emissions of the power grid, thereby being able to accurately determine the generator power output allocation strategy and further improve the grid scheduling effect.

[0149] See also Figure 5 Based on the same inventive concept as the above embodiment, the present invention also provides a power grid dispatching device, comprising

[0150] A model building module 10 is used to build an optimal power flow optimization model of the power grid system and an optimal dispatch model of the mobile energy storage vehicle;

[0151] An objective function setting module 20, used to set a first objective function and a first constraint condition of an optimal power flow optimization model, and to set a second objective function and a second constraint condition of a mobile energy storage vehicle optimization scheduling model;

[0152] The optimization solution module 30 is used to model the mobile energy storage optimization scheduling problem as a mixed integer linear programming problem. Based on the first objective function, the first constraint condition, the second objective function and the second constraint condition, according to the node load and carbon emission factor of the power grid system, the mixed integer linear programming problem is solved to obtain an updated path planning and charging and discharging plan.

[0153] In one embodiment, the power grid dispatching device further includes an emission factor determination module, which is used to:

[0154] According to the route selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved;

[0155] According to the generator power output allocation strategy, the power flow and carbon emission flow in the power grid system are calculated, and the carbon emission factor is determined based on the power flow and carbon emission flow.

[0156] In one embodiment, the model building module 10 is further configured to:

[0157] According to the active power of the generator units, the set of generator nodes and the coefficient of the cost function, an optimal power flow optimization model is constructed;

[0158] According to the total charging power of electric vehicles, the total discharging power of electric vehicles, the carbon emission price and the carbon emission factor, an optimal scheduling model for mobile energy storage vehicles is constructed.

[0159] In one embodiment, the first objective function and the first constraint condition of the optimal power flow optimization model are set, and the objective function setting module 20 is further used to:

[0160] The first objective function is constructed according to the sum of the costs of each generator, and the first constraint condition is the constraint condition that considers the power balance, voltage amplitude and power flow balance of the upper power grid system;

[0161] The second objective function is constructed according to the charging cost, discharging benefit and emission cost. The second constraint condition is a constraint condition that takes into account the charging and discharging state and the charge state during the movement and charging and discharging process of the electric vehicle.

[0162] In one embodiment, the first objective function is:

[0163]

[0164]

[0165] Among them, C g is the sum of the power generation costs of each generator set, a i , b i are the coefficients of the cost function of generator set i; P i G is the active power of generator set i, n g is the set of power generation nodes;

[0166] The second objective function is:

[0167]

[0168] Among them, C charging,n (t) is the charging cost of electric vehicle n at time t; C discharging,n (t) is the discharge benefit of electric vehicle n at time t; C carbon,n (t) is the carbon emission cost of electric vehicle n at time t.

[0169] In one embodiment, according to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved, including:

[0170] According to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the Aurora optimization algorithm is used to solve the optimal generator power output allocation strategy.

[0171] In one embodiment, according to the generator power output allocation strategy, the power flow and carbon emission flow in the power grid system are calculated, including:

[0172] Combining the generator power output allocation strategy, load demand and grid network topology, the power flow and carbon emission flow in the grid system are calculated.

[0173] Correspondingly, an embodiment of the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the function recovery execution method of any one of the above embodiments.

[0174] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above embodiment 1 is implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment are implemented, such as the overall function execution module 20.

[0175] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units may be a series of computer program instruction segments capable of completing a specific function, and the instruction segments are used to describe the execution process of the computer program in a terminal device. For example, the total function execution module 20 is used to determine the association relationship between function groups and execute the total function based on the association relationship.

[0176] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0177] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0178] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0179] Among them, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0180] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the function recovery execution method of any one of the above embodiments.

[0181] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A power grid dispatching method, characterized in that: include: Construct the optimal power flow optimization model of the power grid system and the optimal dispatch model of mobile energy storage vehicles; Setting a first objective function and a first constraint condition of the optimal power flow optimization model, and setting a second objective function and a second constraint condition of the mobile energy storage vehicle optimization scheduling model; The mobile energy storage optimization scheduling problem is modeled as a mixed integer linear programming problem. Based on the first objective function, the first constraint condition, the second objective function and the second constraint condition, and according to the node load and carbon emission factor of the power grid system, the mixed integer linear programming problem is solved to obtain an updated path planning and charging and discharging plan.

2. The power grid dispatching method according to claim 1, characterized in that: Also includes: According to the route selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved; According to the generator power output allocation strategy, the power flow and carbon emission flow in the power grid system are calculated, and the carbon emission factor is determined according to the power flow and carbon emission flow.

3. The power grid dispatching method according to claim 2, characterized in that: The optimal power flow optimization model of the power grid system and the optimal dispatching model of the mobile energy storage vehicle are constructed, including: Constructing the optimal power flow optimization model according to the active power of the generator set, the set of generator nodes and the coefficient of the cost function; According to the total charging power of the electric vehicle, the total discharging power of the electric vehicle, the carbon emission price and the carbon emission factor, an optimization scheduling model for the mobile energy storage vehicle is constructed.

4. The power grid dispatching method according to claim 3, characterized in that: The setting of the first objective function and the first constraint condition of the optimal power flow optimization model, and the setting of the second objective function and the second constraint condition of the mobile energy storage vehicle optimization scheduling model also include: The first objective function is constructed according to the sum of the costs of each generator, wherein the first constraint condition is a constraint condition that takes into account the power balance, voltage amplitude, and power flow balance of the upper power grid system; The second objective function is constructed according to the charging cost, the discharging benefit and the emission cost, and the second constraint condition is a constraint condition that takes into account the charging and discharging state and the charge state during the moving process and the charging and discharging process of the electric vehicle.

5. The power grid dispatching method according to claim 4, characterized in that: The first objective function is: Among them, C g is the sum of the power generation costs of each generator set, a i , b i are the coefficients of the cost function of generator set i; P i G is the active power of generator set i, n g is the set of power generation nodes; The second objective function is: Among them, C charging,n (t) is the charging cost of electric vehicle n at time t; C discharging,n (t) is the discharge benefit of electric vehicle n at time t; C carbon,n (t) is the carbon emission cost of electric vehicle n at time t.

6. The power grid dispatching method according to claim 2, characterized in that: According to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the optimal generator power output allocation strategy is solved, including: According to the path selection and charging and discharging scheme of the mobile energy storage vehicle, the Aurora optimization algorithm is used to solve the optimal generator power output allocation strategy.

7. The power grid dispatching method according to claim 2, characterized in that: The step of calculating the power flow and carbon emission flow in the power grid system according to the generator power output allocation strategy includes: Combining the generator power output allocation strategy, load demand and grid network topology, the power flow and carbon emission flow in the grid system are calculated.

8. A power grid dispatching device, characterized in that: include: Model building module, used to build the optimal power flow optimization model of the power grid system and the optimal dispatch model of the mobile energy storage vehicle; An objective function setting module, used to set a first objective function and a first constraint condition of the optimal power flow optimization model, and to set a second objective function and a second constraint condition of the mobile energy storage vehicle optimization scheduling model; The optimization solution module is used to model the mobile energy storage optimization scheduling problem into a mixed integer linear programming problem. Based on the first objective function, the first constraint condition, the second objective function and the second constraint condition, according to the node load and carbon emission factor of the power grid system, the mixed integer linear programming problem is solved to obtain an updated path planning and charging and discharging plan.

9. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the power grid dispatching method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the power grid dispatching method according to any one of claims 1 to 7.

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