CN118748436A - A two-layer multi-objective optimization scheduling method for active distribution networks - Google Patents

Abstract

The invention discloses a double-layer multi-objective optimization scheduling method for an active distribution network, comprising the following steps: modeling the output characteristics of distributed resources of the active distribution network; establishing a real-time electricity price response model based on the output of distributed power sources; considering the demand response and the constraints of safe and stable operation of the distribution network, establishing a double-layer multi-objective optimization scheduling model for the active distribution network; proposing an adaptive snake-heron optimization algorithm, and designing a multi-objective snake-heron optimization algorithm based on the Pareto theory, respectively solving the lower layer and upper layer models; obtaining the optimal optimization scheduling strategy for the active distribution network; the invention considers the demand side response, strengthens the interaction between the power supply side and the power consumption side, thereby improving the economy of the distribution network operation; by searching for ASBOA and MOSBOA with good optimization performance, respectively solving the lower layer model and the upper layer model, and finally obtaining the best active distribution network optimization scheduling strategy to meet the operation requirements of the distribution network.

Description

A two-layer multi-objective optimization scheduling method for active distribution networks

Technical Field

The present invention relates to an active distribution network optimization dispatching technology, and in particular to a double-layer multi-objective optimization dispatching method for an active distribution network.

Background Art

With the advancement of the construction of new power systems, the distribution network is gradually transforming from a power network that simply receives and distributes electric energy to users to a power network that integrates source, grid, load and storage and is flexibly coupled with the upper-level grid. Its functions in promoting the local consumption of distributed power sources and carrying new loads are becoming increasingly significant. The active distribution network can play the regulatory function of various controllable resources through the "source, load, grid and storage" collaborative optimization technology, realize the guidance and utilization of distributed power sources, and improve the economy and stability of the distribution network operation. Therefore, it is necessary to coordinate various distributed energy sources, energy storage equipment and demand-side loads in the active distribution network, and effectively participate in the current power market, so as to improve the voltage quality of the distribution network and improve the economy of the distribution network operation. It has become an urgent problem to be solved; therefore, it is particularly important to invent a double-layer multi-objective optimization scheduling method for active distribution networks.

Most of the existing distribution network optimization scheduling methods are aimed at optimizing the traditional distribution network, which does not consider the impact of demand response on scheduling. At the same time, the scheduling model is mostly solved for a single objective, and the solution accuracy still needs to be further improved. For this reason, the present invention proposes a two-layer multi-objective optimization scheduling method for active distribution networks.

Summary of the invention

The purpose of the present invention is to solve the defects in the prior art and to provide a double-layer multi-objective optimization scheduling method for active distribution networks.

In order to solve the above technical problems, the present invention provides the following technical solutions:

The present invention discloses a double-layer multi-objective optimization scheduling method for an active distribution network, comprising the following steps:

S1: Modeling the output characteristics of distributed resources in active distribution networks;

S2: Establish a real-time electricity price response model based on the output of distributed power sources;

S3: Considering the demand response and the constraints of safe and stable operation of distribution network, a two-layer multi-objective optimization scheduling model of active distribution network is established;

S4: An Adaptive Secretary Bird Optimization Algorithm (ASBOA) is proposed, and a Multi-Objective Secretary Bird Optimization Algorithm (MOSBOA) is designed based on the Pareto theory to solve the lower and upper layer models respectively, and obtain the optimal optimization scheduling strategy for the active distribution network.

Specifically, the distributed resource output characteristic modeling of the active distribution network in S1 includes a photovoltaic power generation system, a wind power generation system and an energy storage system.

Specifically, establishing the real-time electricity price response model in S2 includes the following steps:

When the penetration rate of distributed generation in the active distribution network is lower than the benchmark value, the electricity price is proportional to the load level in the current period, and the real-time electricity price model is:

Where m _E (t) is the electricity price in time period t; P(t) is the load demand in time period t; P _sun is the sum of load demands in all time periods; and T is the total number of time periods for demand response.

When the penetration rate of distributed power generation in the active distribution network is higher than the benchmark value, the electricity price is inversely proportional to the output of distributed power generation in the current period, and the real-time electricity price model is:

Where P _PV (t) is the output of distributed power generation in time period t; δ is the photovoltaic compensation coefficient, which is equal to the ratio of the unit distributed power generation compensation amount _ma and the fixed electricity price _ms .

In step S3, specific constraints include the following: distributed generation active output constraint, ESS response constraint, node voltage constraint, branch power constraint, demand response constraint and total power balance constraint.

In step S3, the active distribution network has two layers and multiple objectives, including: taking the overall benefit of the distribution network as the best and the new energy consumption rate as the maximum as the upper optimization objectives, and taking the load change as the best as the lower optimization objective.

Furthermore, among the upper-level planning objectives, one of them is the optimal overall benefit F, which reflects the power supply stability and economy of the distribution network, mainly including the distribution network dispatching operation cost F ₁ and voltage deviation F ₂ , and its objective function is as follows:

minF＝min(F ₁ +F ₂ ) (3)

In the active distribution network double-layer multi-objective, the new energy consumption rate refers to the proportion of new energy (such as wind energy, solar energy and other renewable energy) in the total power generation in the energy system. Its mathematical model is as follows:

Where _PF represents the actual power generation of renewable energy; _PQ represents the actual power abandonment of renewable energy.

Specifically, the goal of the lower-level planning is to optimize the load variation, which is evaluated by the fluctuation amplitude and fluctuation rate of the comprehensive load on the distribution network side.

Where: T is the load fluctuation amplitude; λ is the load fluctuation rate; η ₁ and η ₂ are the weight coefficients of the two evaluation indicators. Since the load fluctuation rate has a greater impact on the reliability of the distribution network, η ₁ = 0.25 and η ₂ = 0.75 are taken; _Phmax and _Phmin represent the maximum and minimum values of the daily load power of the distribution network respectively; is the average daily load value of the distribution network; _Ph (t+1) and _Ph (t) both represent the momentary value of the daily load of the distribution network.

In step S4, the relevant data of the active distribution network is input, including the objective function, demand response parameters and related variable constraints of the double-layer multi-objective optimization model, and the parameters and decision variables of the active distribution network optimization scheduling strategy are further obtained. The lower layer of the double-layer multi-objective optimization model of the active distribution network is solved by the ASBOA algorithm, and the upper layer model is solved by the improved MOSBOA algorithm to obtain the optimal Pareto non-dominated solution, thereby obtaining the best active distribution network optimization scheduling plan.

The steps include: solving the upper model and the lower model.

Solution of the underlying model:

Initialize the snake heron population: set the maximum number of iterations T, randomly generate a group of snake heron individuals, each individual represents a strategy for active distribution network optimization scheduling, and the individual's position vector represents the decision variable. The initialization formula is as follows:

X _i,j ＝lb _j +r×(ub _j -lb _j ),i＝1,2,...,N,j＝1,2,...,D (8)

where Xi _,j is the position of the jth snake heron at the initial moment; _ubj and _lbj are the upper and lower bounds of the decision variables, respectively; r represents a function that generates random numbers between [0,1]; N represents the population size within the snake heron population; and D represents the number of decision variables in the problem.

Evaluate fitness: Calculate the fitness of each snake heron individual, that is, the value of the underlying objective function, and consider the constraints. The fitness function is as follows:

fitness＝η ₁ ·T+η ₂ ·λ (9)

Where T is the load fluctuation amplitude; λ is the load fluctuation rate; η ₁ and η ₂ are the weight coefficients of the two evaluation indicators; since the load fluctuation rate has a greater impact on the reliability of the distribution network, η ₁ = 0.25 and η ₂ = 0.75 are taken.

Update individual optimal solution and global optimal solution: record the load change corresponding to the individuals in the current population, update the individual optimal solution, and select the global optimal solution, that is, the smallest load change.

According to the biological statistics of the predation stages of the snake heron and the duration of each stage, the entire predation process was divided into three equal time intervals, namely h<1/3H, 1/3H<h<2/3H and 2/3H<h<H, which correspond to the three stages of secretary bird predation: finding prey, consuming prey and attacking prey.

Adaptive update of snake heron position: In the distribution network dispatching strategy, the update of position can represent the update of parameters or decision variables in the dispatching scheme. The position is adaptively updated according to different predation stages, and the formula is as follows:

RB＝randn(1,D) (12)

Where A, B, and C represent the adaptive speed factors at different stages; h represents the current number of iterations; H represents the maximum number of iterations; x _random1 and x _random2 are random candidate solutions in the first stage of iteration; R ₁ represents a randomly generated dimensional array; RB represents Brownian motion; Γ represents the gamma function; x _best represents the best historical position of an individual; s is a fixed constant of 0.01; η is a fixed constant of 1.5. u and v are random numbers in the interval [0,1]; represents a nonlinear perturbation factor.

Perform boundary processing on the position of each snake heron to ensure that the solution vector is within the feasible range; check the constraints and exclude solutions that do not meet the equilibrium conditions; if the set termination conditions are reached, such as the maximum number of iterations or the error threshold, output the snake heron individual with the best fitness as the optimal solution to the problem and end the iteration; otherwise, continue to update the position and continue to iterate.

Upper model solution:

According to the position update formula, the position of the upper snake egret population is updated from generation to generation, and the Pareto solution set with the goals of optimizing the overall benefit of the distribution network and maximizing the new energy consumption rate is continuously updated. Then, the improved ideal point decision method is used to determine the optimal solution for the multi-objective planning.

In the algorithm iteration process, an external archive set is used to store the Pareto non-dominated solution set. The new non-dominated solution set obtained by MOSBOA-SBOA in each iteration must be compared with the original non-dominated solution set one by one and the external archive set is updated. In order to maintain the diversity of the population and the number does not exceed the limit, the method of removing similar individuals according to the size of the crowding distance is used to maintain the balance of the Pareto solution set. The crowding distance calculation formula is:

Where q is the number of objective functions; Fi _,max and Fi _,min are the maximum and minimum values of the i-th objective function, respectively; U(j) is the crowding distance of the j-th snake-heron individual in the Pareto solution set; _Fi (j+1) and _Fi (j-1) are the j-th objective function values of the two individuals adjacent to the j-th snake-heron individual.

Among them, the steps of improving the ideal point decision method are as follows;

First, the fitness values of all Pareto non-dominated solutions are normalized:

Where y( _xi ) is the normalized value of the qth objective function under the i-th non-dominated solution; _Fq is the fitness function of the Pareto non-dominated solution; and the normalized target ideal point is (0,0,0).

Secondly, calculate the degree of proximity of each non-dominated solution to the target ideal point, that is, the square of the Euclidean distance:

Where ω is the weight coefficient of the qth target.

Finally, the optimal solution of the multi-objective programming is determined by minimizing the sum of the squares of the Euclidean distances of all Pareto non-dominated solutions on each objective.

Technical effects and advantages of the present invention: Compared with the prior art, the present invention provides a two-layer multi-objective optimization scheduling method for active distribution networks, which has the following advantages:

Firstly, the demand-side response is taken into account in the traditional optimization scheduling method, and the interaction between the power supply side and the power consumption side is strengthened, thereby improving the economy of the distribution network operation; secondly, due to the research and analysis of the active distribution network optimization model, the future load change trend can be better predicted, and more accurate load forecasting data can be provided for the distribution network; finally, ASBOA and MOSBOA with good optimization performance are designed to solve the lower model and the upper model respectively, and through the nested loop iteration of the upper and lower layers, the best active distribution network optimization scheduling strategy is finally obtained to meet the operation requirements of the distribution network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a double-layer multi-objective optimization scheduling method for an active distribution network according to the present invention.

FIG2 is a flowchart of solving the double-layer multi-objective optimization scheduling model of the active distribution network of the present invention.

Specific implementation methods

The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

1 , the present invention proposes a double-layer multi-objective optimization scheduling method for an active distribution network, comprising the following steps:

S1: Modeling the output characteristics of distributed resources in active distribution networks.

In the embodiment of the present invention, the output of the wind and solar system and the output of the energy storage system are modeled.

S2: Establish a real-time electricity price response model based on distributed power output.

In the embodiment of the present invention, on the one hand, the electricity price affects the electricity demand of the user, and on the other hand, the determination of the electricity price should also take into account the output characteristics of the distributed power source. Therefore, the output of the distributed power source and the load level can be linked through the electricity price, and the distributed power source output can be used to guide the user to adjust the electricity consumption, and a real-time electricity price response model can be established.

S3: Considering the demand response and safe and stable operation constraints of the distribution network, a two-layer multi-objective optimization scheduling model for the active distribution network is established.

In the embodiment of the present invention, the constructed active distribution network double-layer multi-objective optimization scheduling model has the upper layer with the goal of optimizing overall benefits and maximizing the new energy consumption rate, and the lower layer with the goal of minimizing load changes. The constraints include distributed power generation active output constraints, ESS response constraints, node voltage constraints, branch power constraints, demand response constraints, and total power balance constraints.

S4: An Adaptive Secretary Bird Optimization Algorithm (ASBOA) is proposed, and a Multi-Objective Secretary Bird Optimization Algorithm (MOSBOA) is designed based on the Pareto theory to solve the lower and upper layer models respectively, and obtain the optimal optimization scheduling strategy for the active distribution network.

In an embodiment of the present invention, the ASBOA algorithm is used to solve the lower layer of the two-layer multi-objective optimization model of the active distribution network, and the improved MOSBOA algorithm is used to solve the upper model to obtain the optimal Pareto non-dominated solution, thereby obtaining the best active distribution network optimization scheduling plan.

Specifically, step S1 includes the following steps:

S11. Mathematical model of photovoltaic power generation system output:

In the formula: P _P (t) represents the output power of the photovoltaic device at time t (W); k _T represents the temperature coefficient (%/℃); P _set represents the rated power of the photovoltaic device (W); S _set represents the standard light intensity of the photovoltaic unit (kW/m ² ); S(t) represents the light intensity of the photovoltaic unit at time t (kW/m ² ); S(t) represents the ambient temperature at time t (℃); T _set is the standard ambient temperature (℃).

S12. The wind speed variation of the wind power generation system satisfies the Weibull distribution, that is:

Where: v represents the actual wind speed; k and c are the shape factor and scale factor of the Weibull distribution, and k is generally 1.8 to 2.8. Combined with the wind speed model, the mathematical model of the wind power generation system output can be obtained:

Wherein, P _WT and _Pra are the active output and rated output of the wind turbine respectively; _Vin , _Vout and _Vn are the cut-in, cut-out and rated wind speed of the wind turbine respectively.

S13. Mathematical model of energy storage system:

Where SOC(t) represents the state of charge of the energy storage device at time t; _ηc and _ηd represent the charging and discharging efficiencies of the energy storage system, respectively; _pc (t) and _pd (t) represent the charging and discharging rates of the energy storage system at time t; _En is the rated capacity of the energy storage system.

Specifically, step S2 includes the following steps:

S21. When the penetration rate of distributed power generation in the active distribution network is lower than the benchmark value, the electricity price is proportional to the load level in the current period, and the real-time electricity price model is:

Where m _E (t) is the electricity price in time period t; P(t) is the load demand in time period t; P _sun is the sum of load demands in all time periods; and T is the total number of time periods for demand response.

S22. When the penetration rate of distributed power generation in the active distribution network is higher than the benchmark value, the electricity price is inversely proportional to the output of distributed power generation in the current period, and the real-time electricity price model is:

Where P _PV (t) is the output of distributed power generation in time period t; δ is the photovoltaic compensation coefficient, which is equal to the ratio of the unit distributed power generation compensation amount _ma and the fixed electricity price _ms .

Specifically, step S3 includes the following steps:

S31. Among the upper-level planning objectives, one is the optimal overall benefit F, which mainly includes the distribution network dispatching operation cost F ₁ and the voltage deviation F ₂ . Its objective function is as follows:

minF＝min(F ₁ +F ₂ ) (8)

S311. The dispatching and operating costs of the distribution network include the electricity purchase cost C ₁ , the power generation cost C ₂ , the energy storage cost C ₃ and the demand response cost C ₄ . The objective function is as follows:

minF ₁ =C ₁ +C ₂ +C ₃ +C ₄ (9)

C ₂ =C _DC (t)·P _DC (t) (11)

C ₃ =C _ESS (t)·P _ESS (t) (12)

C ₄ =C _X (t)·P _X (t)-C(t)·P(t) (13)

Wherein, T is the dispatching period, T = 24 hours; C _buy (t) is the electricity purchase price of the system from the upper power grid during period t; P _grid (t) is the amount of electricity purchased by the system from the upper power grid during period t; C _DC (t) and P _DC (t) represent the output cost and active output of distributed energy during period t; C _ESS (t) and P _ESS (t) represent the output cost and active output of the energy storage system during period t; _CX (t) is the original electricity price at time t, _PX (t) is the load demand at the original electricity price, C(t) is the electricity price at time t after the demand side responds, and P(t) is the load demand after the optimal demand side response when the electricity price C(t) is.

S312. The voltage of each node in the distribution network is also a very important optimization indicator. During normal operation, under certain safety constraints, the voltage deviation _F2 is as small as possible, and its objective function is as follows:

In the formula, t represents the time; m represents the node; n represents the number of nodes; _Vmi represents the actual voltage value of the node at time t; _VN represents the rated voltage of the node.

S313. New energy consumption rate refers to the proportion of new energy in the total power generation in the energy system. It is an important indicator to measure the proportion and influence of renewable energy in the entire energy system. Its mathematical model is as follows:

Where _PF represents the actual power generation of renewable energy; _PQ represents the actual power abandonment of renewable energy.

S32. The specific goal of the lower-level planning is to optimize the load variation, which is evaluated by the fluctuation amplitude and fluctuation rate of the comprehensive load on the distribution network side.

minF ₄ =η ₁ T +η ₂ λ (18)

Where: T is the load fluctuation amplitude; λ is the load fluctuation rate; η ₁ and η ₂ are the weight coefficients of the two evaluation indicators. Since the load fluctuation rate has a greater impact on the reliability of the distribution network, η ₁ = 0.25 and η ₂ = 0.75 are taken; _Phmax and _Phmin represent the maximum and minimum values of the daily load power of the distribution network respectively; is the average daily load value of the distribution network; _Ph (t+1) and _Ph (t) both represent the momentary value of the daily load of the distribution network.

S33. The specific constraints are as follows:

S331, Distributed power generation active output constraints:

P _min ≤P(t)≤P _max (19)

Where P _min and P _max are the upper and lower limits of the active power output of renewable energy, and P(t) is the actual active power output of renewable energy in the distribution network.

S332, ESS response constraints:

SOC ^ESS,min ≤SOC ^ESS (t)≤SOC ^ESS,max (20)

Where SOC ^ESS,min and SOC ^ESS,max are the upper and lower limits of the state of charge of the energy storage device respectively; _PN is the rated power of the energy storage device.

S333, node voltage constraint:

U _i.min ≤U _i ≤U _i.max (22)

Where U _i.max and U _i.min are the upper and lower limits allowed for node i respectively; U _i is the voltage amplitude of the i-th node in the distribution network.

S334, branch power constraint:

P _l.min ≤P _l ≤P _l.max (23)

Where P _l.max and P _l.min are the upper and lower limits of the branch transmission power respectively; U _i is the power transmitted by the lth branch in the distribution network.

S335. Demand response constraints:

m _min (t)≤m(t)≤m _max (t) (24)

In the formula: m _min (t)m _max (t) and are the lower and upper limits of the electricity price in time period t respectively.

S336, total power balance constraint:

P _grid (t)＝P _ul (t)+P _ESS (t)-P _DG (t) (25)

Where: P _grid (t) is the total power purchased by the distribution network from the main grid at time t; P _ul (t) is the active power output required by the uncontrollable load at time t; P _ESS (t) is the active power output of energy storage at time t; P _DG (t) is the active power output of distributed energy at time t.

Specifically, step S4 includes the following steps:

S41. Input relevant data of the active distribution network, including the objective function, demand response parameters and related variable constraints of the double-layer multi-objective optimization model, and further derive the parameters and decision variables of the active distribution network optimization scheduling strategy.

S42, Solution of the lower model:

S421. Initialize the snake heron population: set the maximum number of iterations T, randomly generate a group of snake heron individuals, each of which represents a strategy for active distribution network optimization scheduling, and the position vector of the individual represents the decision variable. The initialization formula is as follows:

X _i,j ＝lb _j +r×(ub _j -lb _j ),i＝1,2,...,N,j＝1,2,...,D (26)

where Xi _,j is the position of the jth snake heron at the initial moment; _ubj and _lbj are the upper and lower bounds of the decision variables, respectively; r represents a function that generates random numbers between [0,1]; N represents the population size within the snake heron population; and D represents the number of decision variables in the problem.

S422, evaluate fitness: calculate the fitness of each snake heron individual, that is, the value of the lower layer objective function, and consider the constraints. The fitness function is as follows:

fitness＝η ₁ ·T+η ₂ ·λ (27)

Where T is the load fluctuation amplitude; λ is the load fluctuation rate; η ₁ and η ₂ are the weight coefficients of the two evaluation indicators; since the load fluctuation rate has a greater impact on the reliability of the distribution network, η ₁ = 0.25 and η ₂ = 0.75 are taken.

S423, update individual optimal solution and global optimal solution: record the load change corresponding to the individuals in the current population, update the individual optimal solution, and select the global optimal solution, that is, the minimum load change.

S424. According to the biological statistics of the snake-heron predation stage and the duration of each stage, the entire predation process is divided into three equal time intervals, namely h<1/3H, 1/3H<h<2/3H and 2/3H<h<H, which correspond to the three stages of secretary bird predation: finding prey, consuming prey and attacking prey. In the prey search stage, diversity can help avoid falling into the local optimal state by introducing differential mutation operations. In the prey consumption stage, the introduction of the randomness of Brownian motion enables individuals to explore the solution space more effectively and provides opportunities to avoid falling into the local optimal state, thereby obtaining better results when solving complex problems. In the prey attack stage, the Levy flight strategy is used to enhance the global search capability of the optimizer, reduce the risk of ASBOA getting stuck in a local solution, and improve the convergence accuracy of the algorithm.

S425, adaptively update the position of the snake heron: In the distribution network dispatching strategy, the update of the position can represent the update of the parameters or decision variables in the dispatching scheme. The position is adaptively updated according to different predation stages, and the formula is as follows:

RB＝randn(1,D) (30)

Where A, B, and C represent the adaptive speed factors at different stages; h represents the current number of iterations; H represents the maximum number of iterations; x _random1 and x _random2 are random candidate solutions in the first stage of iteration; R ₁ represents a randomly generated dimensional array; RB represents Brownian motion; Γ represents the gamma function; x _best represents the best historical position of an individual; s is a fixed constant of 0.01; η is a fixed constant of 1.5. u and v are random numbers in the interval [0,1]; represents a nonlinear perturbation factor.

S426, perform boundary processing on the position of each snake heron to ensure that the solution vector is within the feasible range; check the constraint conditions and exclude solutions that do not meet the equilibrium conditions; if the set termination conditions are reached, such as the maximum number of iterations or the error threshold, output the snake heron individual with the best fitness as the optimal solution to the problem and end the iteration; otherwise, return to step S423 and continue iteration.

S43, upper model solution:

S431. Update the position of the upper snake heron population generation by generation according to the position update formula, continuously update the Pareto solution set with the goal of optimizing the overall benefit of the distribution network and maximizing the new energy consumption rate, and then use the improved ideal point decision method to determine the optimal solution for the multi-objective planning.

S432. During the algorithm iteration process, an external archive set is used to store the Pareto non-dominated solution set. The new non-dominated solution set obtained by MOSBOA-ASBOA in each iteration must be compared with the original non-dominated solution set one by one and the external archive set is updated. In order to maintain the diversity of the population and the number does not exceed the limit, the method of removing similar individuals according to the size of the crowding distance is used to maintain the balance of the Pareto solution set. The crowding distance calculation formula is:

Where q is the number of objective functions; Fi _,max and _Fi,min are the maximum and minimum values of the i-th objective function, respectively; U(j) is the crowding distance of the j-th snake-heron individual in the Pareto solution set; _Fi (j+1) and _Fi (j-1) are the j-th objective function values of the two individuals adjacent to the j-th snake-heron individual.

S433, the steps of improving the ideal point decision method are as follows;

S4331. First, the fitness values of all Pareto non-dominated solutions are normalized:

Where y( _xi ) is the normalized value of the qth objective function under the i-th non-dominated solution; _Fq is the fitness function of the Pareto non-dominated solution; and the normalized target ideal point is (0,0,0).

S4332. Secondly, calculate the degree of proximity of each non-dominated solution to the target ideal point, that is, the square of the Euclidean distance:

Where ω is the weight coefficient of the qth target.

S4333. Finally, the optimal solution of the multi-objective programming is determined by minimizing the sum of the squares of the Euclidean distances of all Pareto non-dominated solutions on each objective.

Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims (9)

1. The double-layer multi-target optimized scheduling method for the active power distribution network is characterized by comprising the following steps of:

s1: modeling aiming at the output characteristics of distributed resources of the active power distribution network;

s2: establishing a real-time electricity price response model based on the distributed power supply output;

S3: the method comprises the steps that a double-layer multi-target optimal scheduling model of an active power distribution network is established in consideration of demand response and power distribution network safe and stable operation constraint conditions;

S4: an adaptive snake aigrette optimization algorithm (ADAPTIVE SECRETARY Bird Optimization Algorithm, ASBOA) is provided, a Multi-objective snake aigrette optimization algorithm (Multi-Objective Secretary Bird Optimization Algorithm, MOSBOA) is designed based on the Pareto theory, and a lower layer model and an upper layer model are solved respectively to obtain an optimal optimization scheduling strategy of the active power distribution network.

2. The active power distribution network-oriented double-layer multi-objective optimal scheduling method according to claim 1, wherein the method comprises the following steps: the active power distribution network distributed resource output characteristic modeling comprises a photovoltaic power generation system, a wind power generation system and an energy storage system.

3. The active power distribution network-oriented double-layer multi-objective optimal scheduling method according to claim 1, wherein the method comprises the following steps: the electricity price influences the electricity demand of a user, and meanwhile, the output characteristics of the distributed power supply are considered in the determination of the electricity price; and the output of the distributed power supply is used for guiding a user to adjust power consumption by the output of the distributed power supply to construct a real-time power price model.

4. The active power distribution network-oriented double-layer multi-objective optimal scheduling method according to claim 2, wherein the method comprises the following steps: the construction of the real-time electricity price model specifically comprises the following steps:

when the permeability of the distributed power supply in the active power distribution network is lower than a reference value, the electricity price is in direct proportion to the load level of the current period, and the real-time electricity price model is as follows:

Where m _E (t) is the electricity price over time period t; p (t) is the load demand during period t; p _sun is the sum of the load demands in all time periods; t is the total number of time periods of demand response;

When the permeability of the distributed power supply in the active power distribution network is higher than a reference value, the electricity price is inversely proportional to the output of the distributed power supply in the current period, and the real-time electricity price model is as follows:

Wherein P _PV (t) is the distributed power supply output in the period t; delta is a photovoltaic compensation coefficient, and the value of delta is equal to the ratio of unit distributed power compensation amount m _a to fixed electricity price m _s.

5. The active power distribution network-oriented double-layer multi-objective optimal scheduling method according to claim 1, wherein the method comprises the following steps: the constraint conditions comprise a distributed power source active output constraint, an ESS response constraint, a node voltage constraint, a branch power constraint, a demand response constraint and a total power balance constraint; the upper layer of the constructed active power distribution network double-layer multi-target optimization scheduling model aims at optimizing the overall benefit and maximizing the new energy consumption rate, and the lower layer aims at minimizing the load variation.

6. The active power distribution network-oriented double-layer multi-objective optimal scheduling method according to claim 1, wherein the method comprises the following steps: inputting relevant data of the active power distribution network, including objective functions, demand response parameters and relevant variable constraint conditions of a double-layer multi-objective optimization model, and further obtaining parameters and decision variables of an active power distribution network optimization scheduling strategy; and solving the lower layer of the double-layer multi-target optimization model of the active power distribution network by utilizing a ASBOA algorithm, and solving the upper layer model by utilizing an improved MOSBOA algorithm to obtain an optimal Pareto non-dominant solution.

7. The active power distribution network oriented double-layer multi-objective optimal scheduling method according to claim 6, wherein the method comprises the following steps: the lower model solving specifically comprises the following steps:

Initializing a snake aigrette population: setting the maximum iteration times T, randomly generating a group of aigrette individuals, wherein each individual represents an active power distribution network optimal scheduling strategy, and the position vector of the individual represents a decision variable; the initialization formula is as follows:

X_i,j＝lb_j+r×(ub_j-lb_j),i＝1,2,,N,j＝1,2,...,D (3)

Wherein X _i,j is the position of the jth snake aigrette at the initial time; ub _j and lb _j are the upper and lower bounds, respectively, of the decision variables; r represents a function that generates a random number between 0, 1; n represents the population size in the snake aigrette population; d represents the number of decision variables in the problem;

Evaluating fitness: calculating the fitness of each snake aigrette individual, namely the value of a lower objective function, and taking constraint conditions into consideration; the fitness function is as follows:

fitness＝η₁·T+η₂·λ (4)

Wherein T is the fluctuation amplitude of load; lambda is the load fluctuation rate; η ₁、η₂ is the weight coefficient of two evaluation indexes; as the fluctuation rate of the load has a larger influence on the reliability of the power distribution network, η ₁＝0.25,η₂ =0.75 is taken;

Updating the individual optimal solution and the global optimal solution: recording the corresponding load variation of individuals in the current population, and updating the optimal solution of the individuals; selecting a global optimal solution, namely the minimum load variation;

According to the biological statistics of the snake ait predation stage and the duration of each stage, the whole predation process is divided into three equal time intervals, namely H <1/3H, 1/3H < H <2/3H and 2/3H < H < H, corresponding to three stages of secretary bird predation respectively: searching for hunting, consumption hunting and attack hunting;

adaptively updating the position of the snake aigrette: in a power distribution network scheduling strategy, the update of the position can represent the update of parameters or decision variables in the scheduling scheme; the position is adaptively updated according to different predation stages, and the formula is as follows:

wherein A, B, C denotes the adaptive speed factor at different stages; h represents the current iteration number; h represents the maximum number of iterations; x _random1 and x _random2 are random candidate solutions in the first phase iteration; r ₁ represents a randomly generated array of dimensions; RB represents brownian motion; Γ represents gamma function; x _best represents the individual's historical optimal position; s is a fixed constant of 0.01; η is a fixed constant of 1.5. u and v are random numbers in interval [0,1 ]; Representing a nonlinear perturbation factor;

carrying out boundary processing on the position of each snake aigrette to ensure that the solution vector is within a feasible range; checking constraint conditions, and eliminating solutions which do not meet balance conditions; if the set termination condition is reached, such as the maximum iteration number or the error threshold, outputting the snake ait with the best fitness as the optimal solution of the problem, and ending the iteration; otherwise, the position is continuously updated, and iteration is continuously performed.

8. The active power distribution network oriented double-layer multi-objective optimal scheduling method according to claim 6, wherein the method comprises the following steps: the upper model solving specifically comprises the following steps: updating the position of the upper layer snake aigrette population generation by generation according to a position updating formula, continuously updating a Pareto solution set with the optimal overall benefit of the power distribution network and the maximum new energy consumption rate as targets, and further determining the optimal solution of multi-target planning by utilizing an improved ideal point decision method;

In the algorithm iteration process, an external archive set is adopted to store a Pareto non-dominant solution set, new non-dominant solution sets obtained by MOSBOA-ASBOA in each iteration are compared with an original non-dominant solution set one by one and updated, in order to keep diversity of population and the number of the population not exceeding a limit, the balance of the Pareto solution set is kept according to a method of removing similar individuals by the crowding distance, and a crowding distance calculation formula is as follows:

Wherein q is the number of objective functions; f _i,max、F_i,min is the maximum value and the minimum value of the ith objective function respectively; u (j) is the crowding distance of the j-th snake aigrette individual in the Pareto solution set; f _i (j+1) and F _i (j-1) are the j-th objective function values of two individuals adjacent to the j-th snake aigrette individual.

9. The active power distribution network oriented double-layer multi-objective optimal scheduling method according to claim 8, wherein the method comprises the following steps: the improved ideal point decision method comprises the following steps of;

first, the fitness values of all Pareto non-dominant solutions are normalized:

Where y (x _i) is the normalized value of the q-th objective function under the i-th non-dominant solution; f _q is the fitness function of the Pareto non-dominant solution; the normalized target ideal point is (0, 0);

Secondly, calculating the approach of each non-dominant solution to the target ideal point, namely the square of Euclidean distance:

Wherein ω is a weight coefficient of the q-th target;

And finally, determining an optimal solution of the multi-objective planning by using the minimum sum of Euclidean distance squares of all the Pareto non-dominant solutions on each objective as an optimal scheduling method of the power distribution network.