Power distribution network high-proportion wind and light system energy storage site selection optimization method and system

Abstract

The invention provides a power distribution network high-proportion wind and light system energy storage site selection optimization method and system.The method comprises the steps that an energy storage site selection optimization configuration model considering energy storage cost, node voltage deviation and load power fluctuation is constructed, and then the energy storage site selection optimization configuration model is solved through a difference-dung beetle optimization algorithm; and an optimal energy storage site selection scheme is obtained. According to the method, the safety and feasibility of an energy storage site selection optimization scheme in practical application are ensured, multi-target equilibrium optimization is realized, and a higher convergence speed is achieved by performing differential mutation operation on a dung beetle population in a dung beetle optimization algorithm.

Description

Technical Field

[0001] The present invention belongs to the field of distribution network planning, and specifically relates to a method and system for optimizing energy storage site selection for a high-proportion wind-solar system in a distribution network. Background Art

[0002] Energy storage systems are crucial in distribution network power systems. They are the key to balancing power supply and demand and improving system stability. In the face of load fluctuations and the intermittent nature of renewable energy, energy storage systems can store energy when there is excess power and release energy when demand peaks or renewable energy is insufficient, effectively alleviating grid pressure and improving power supply reliability and the utilization rate of renewable energy. In addition, energy storage systems can also reduce the phenomenon of wind and solar power abandonment caused by load fluctuations and unstable power generation, and optimize the power business environment. In the process of building a new power system, energy storage systems, as flexible regulation resources, have unique advantages in improving system flexibility and ensuring the safety and stability of the power grid, and are a key support for the construction of new power systems.

[0003] The site selection of energy storage in the distribution network power system is directly related to the reliability and stability of power supply. As a flexible regulation resource, the energy storage system can better play its peak and frequency regulation capabilities by optimizing the site selection, improve the flexibility and resilience of the power system, and respond to emergencies and load fluctuations. In the early stage of load growth, the optimization of energy storage site selection can balance the supply and demand of electricity, reduce the burden on the power grid, delay the upgrade of the power grid, reduce construction costs, and improve service life and economic benefits. Therefore, the optimization of energy storage site selection is an important link to ensure the stable operation of the power system, improve economic benefits, promote the use of renewable energy, and enhance the flexibility of the system, and it must be given high attention during the construction of the energy storage system.

[0004] Traditional energy storage site optimization methods mainly include cost analysis, grid access condition assessment, and environmental factor consideration. Although these energy storage site optimization methods have their own application scenarios, they all have obvious limitations. Cost data in the cost analysis method may be difficult to obtain accurately, especially in the early stages of the project, which may lead to inaccurate site selection decisions. The grid access condition assessment method limits the flexibility of site selection. The environmental assessment in the environmental factor consideration method may be affected by data availability and the subjectivity of the assessment method. Therefore, these methods cannot achieve good site optimization results. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for optimizing energy storage site selection for a high-proportion wind-solar system in a distribution network in view of the above-mentioned problems existing in the prior art.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention proposes a method for optimizing energy storage site selection for a high-proportion wind-solar system in a distribution network, comprising:

[0008] S1. Construct an energy storage site selection optimization configuration model that considers energy storage cost, node voltage deviation and load power fluctuation;

[0009] S2. Use the differential-dung beetle optimization algorithm to solve the energy storage site optimization configuration model and obtain the optimal energy storage site selection plan.

[0010] The S2 includes:

[0011] S21, determining the scope of the solution space, including the possible locations of energy storage and the upper and lower limits of the fixed capacity, and initializing the dung beetle population, wherein each dung beetle represents an energy storage site selection plan;

[0012] S22, calculating the fitness value of each dung beetle position, that is, the objective function value;

[0013] S23, each dung beetle updates its position according to the population to which it belongs;

[0014] S24, sorting the dung beetles according to their fitness values ​​from large to small, selecting the dung beetles with fitness values ​​in the top 20% and the bottom 20% to perform differential mutation operations, so as to update the positions of the dung beetles with fitness values ​​in the bottom 20%;

[0015] S25, calculating the fitness value of each dung beetle in the new position, and determining the individual optimum and the global optimum according to the fitness value;

[0016] S26, judging whether the iteration termination condition is met, if so, outputting the global optimum as the optimal energy storage site selection scheme; if not, returning to S23 for the next iteration.

[0017] In S23, the position of the stealing dung beetle is updated based on the golden sine search mechanism:

[0018]

[0019] y 1 =-πh+π(1-h)

[0020] y 2 =πh-π(1-h)

[0021] In the above formula, P i z is the position of the i-th dung beetle at the z-th iteration, z is the current iteration number, r 1 is a random number in [0, 2π], r 2 is a random number in [0, π], y 1 ,y 2 , is the global optimal position, h is the golden ratio;

[0022] The rolling ball dung beetle pushes the ball forward, and updates the position without adjusting the direction when the forward movement is not blocked:

[0023]

[0024] In the above formula, λ is a natural coefficient of -1 or 1, k is a deflection coefficient, and b is a constant of (0, 1). is the global worst position of the z-th iteration;

[0025] When encountering obstacles, use the dancing behavior to adjust the direction and update the position:

[0026]

[0027] In the above formula, θ∈[0,π], when θ=0,π,π / 2, the position of the dung beetle is not updated, randperm(0,45) is a randomly generated number in (0,45), and Z is the maximum number of iterations;

[0028] The probability p of a rolling dung beetle encountering an obstacle is calculated according to the following formula:

[0029] p=0.1+(0.9-0.1) / (1+e 0.04(z-250) ).

[0030] In S24, selecting dung beetles with fitness values ​​in the top 20% and the bottom 20% to perform differential mutation operation includes:

[0031] The dung beetles with fitness values ​​in the top 20% and the bottom 20% are formed into a differential mutation population, and the positions of the dung beetles with fitness values ​​in the bottom 20% are updated according to the following mutation formula:

[0032]

[0033] In the above formula, P i z+1 is the updated position of the i-th dung beetle among the dung beetles with the last 20% fitness values, are the positions of three dung beetles randomly selected from the differential mutation population at the current iteration, F is the mutation factor, and 0≤F≤2;

[0034] The S25 further includes:

[0035] Perform Cauchy mutation perturbation on the globally optimal individual, calculate the fitness value after mutation, and update the optimal position:

[0036]

[0037] In the above formula, are the global optimal before and after updating respectively;

[0038] The S21 initializes the dung beetle population through the Tent chaos map:

[0039]

[0040] In the above formula, x w is the chaotic value generated in the wth iteration, δ is the introduced random variable, and N is the maximum number of iterations of the chaotic mapping.

[0041] In S1, the objective function of the energy storage site selection optimization configuration model includes:

[0042] minF={F 1 ,F 2 ,F 3}

[0043] F 1 =C 1 +C 2

[0044]

[0045]

[0046] In the above formula, F 1 、F 2 、F 3 are energy storage cost, node voltage deviation, load power fluctuation, C 1 , C 2 are the investment cost of energy storage and the subsequent operation and maintenance cost of energy storage, C inν is the fixed investment cost of an energy storage device, N ess is the number of energy storage devices, a and b are the unit power cost and unit energy cost respectively, P ess.n 、E ess.n are the rated power and rated energy of the nth energy storage device, ζ is the annual capital recovery ratio, η is the maintenance coefficient, N bus is the number of nodes, U i,t , U N are the actual voltage value and rated voltage value of node i at time t, T is an operation cycle, P load (k), P pν (k), P c / d (k) are the load power at time k, the active power of photovoltaic output, and the charging and discharging power of energy storage, is the average load power value in time period T;

[0047] The constraints include system power balance and node voltage constraints, energy storage system operation constraints, state of charge constraints, and charging and discharging power constraints.

[0048] In a second aspect, the present invention proposes a distribution network high-proportion wind-solar system energy storage site selection optimization system, including an optimization configuration model construction module and a differential-dung beetle optimization algorithm module;

[0049] The optimization configuration model building module is used to build an energy storage site optimization configuration model that takes into account energy storage costs, node voltage deviations, and load power fluctuations;

[0050] The difference-dung beetle optimization algorithm module is used to solve the energy storage site selection optimization configuration model to obtain the optimal energy storage site selection plan.

[0051] The solution steps of the difference-dung beetle optimization algorithm module include:

[0052] A1. Determine the scope of the solution space, including the possible locations of energy storage and the upper and lower limits of the fixed capacity, and initialize the dung beetle population, where each dung beetle represents an energy storage site selection plan;

[0053] A2. Calculate the fitness value of each dung beetle position, i.e., the objective function value;

[0054] A3. Each dung beetle updates its position according to the population to which it belongs;

[0055] A4, sorting the dung beetles according to their fitness values ​​from large to small, selecting the dung beetles with fitness values ​​in the top 20% and the bottom 20% to perform differential mutation operations, so as to update the positions of the dung beetles with fitness values ​​in the bottom 20%;

[0056] A5. Calculate the fitness value of each dung beetle in the new position, and determine the individual optimum and the global optimum based on the fitness value;

[0057] A6: Determine whether the iteration termination condition is met. If so, output the global optimum as the optimal energy storage site selection scheme; if not, return to A3 for the next iteration.

[0058] The position of the stealing dung beetle is updated based on the golden sine search mechanism:

[0059]

[0060] y 1 =-πh+π(1-h)

[0061] y 2 =πh-π(1-h)

[0062] In the above formula, P i z is the position of the i-th dung beetle at the z-th iteration, z is the current iteration number, r 1 is a random number in [0, 2π], r 2 is a random number in [0, π], y 1 ,y2 , is the global optimal position, h is the golden ratio;

[0063] The rolling ball dung beetle pushes the ball forward, and updates the position without adjusting the direction when the forward movement is not blocked:

[0064]

[0065] In the above formula, λ is a natural coefficient of -1 or 1, k is a deflection coefficient, and b is a constant of (0, 1). is the global worst position of the z-th iteration;

[0066] When encountering obstacles, use the dancing behavior to adjust the direction and update the position:

[0067]

[0068] In the above formula, θ∈[0,π], when θ=0,π,π / 2, the position of the dung beetle is not updated, randperm(0,45) is a randomly generated number in (0,45), and Z is the maximum number of iterations;

[0069] The probability p of a rolling dung beetle encountering an obstacle is calculated according to the following formula:

[0070] p=0.1+(0.9-0.1) / (1+e 0.04(z-250) ).

[0071] The step of selecting dung beetles with fitness values ​​in the top 20% and the bottom 20% for differential mutation operation includes:

[0072] The dung beetles with fitness values ​​in the top 20% and the bottom 20% are formed into a differential mutation population, and the positions of the dung beetles with fitness values ​​in the bottom 20% are updated according to the following mutation formula:

[0073]

[0074] In the above formula, P i z+1 is the updated position of the i-th dung beetle among the dung beetles with the last 20% fitness values, are the positions of three dung beetles randomly selected from the differential mutation population at the current iteration, F is the mutation factor, and 0≤F≤2;

[0075] The A5 also includes:

[0076] Perform Cauchy mutation perturbation on the globally optimal individual, calculate the fitness value after mutation, and update the optimal position:

[0077]

[0078] In the above formula, are the global optimal before and after updating respectively;

[0079] The S21 initializes the dung beetle population through the Tent chaos map:

[0080]

[0081] In the above formula, x w is the chaotic value generated in the wth iteration, δ is the introduced random variable, and N is the maximum number of iterations of the chaotic mapping.

[0082] The objective function of the energy storage site selection optimization configuration model includes:

[0083] minF={F 1 ,F 2 ,F 3}

[0084] F 1 =C 1 +C 2

[0085]

[0086] In the above formula, F 1 、F 2 、F 3 are energy storage cost, node voltage deviation, load power fluctuation, C 1 , C 2 are the investment cost of energy storage and the subsequent operation and maintenance cost of energy storage, C inν is the fixed investment cost of an energy storage device, N ess is the number of energy storage devices, a and b are the unit power cost and unit energy cost respectively, P ess.n 、E ess.n are the rated power and rated energy of the nth energy storage device, ζ is the annual capital recovery ratio, η is the maintenance coefficient, N bus is the number of nodes, U i,t , U N are the actual voltage value and rated voltage value of node i at time t, T is an operation cycle, P load (k), P pν (k), P c / d (k) are the load power at time k, the active power of photovoltaic output, and the charging and discharging power of energy storage, is the average load power value in time period T;

[0087] The constraints include system power balance and node voltage constraints, energy storage system operation constraints, state of charge constraints, and charging and discharging power constraints.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] 1. The present invention provides a method for optimizing the energy storage site selection for a high-proportion wind-solar system in a distribution network. The method first constructs an energy storage site selection optimization configuration model that takes into account energy storage costs, node voltage deviations, and load power fluctuations, and then uses a differential-dung beetle optimization algorithm to solve the energy storage site selection optimization configuration model to obtain the optimal energy storage site selection scheme. On the one hand, the method selects energy storage costs, node voltage deviations, and load power fluctuations as key optimization targets, effectively ensuring the safety and feasibility of the energy storage site selection optimization scheme in practical applications, and achieving balanced optimization among multiple targets; on the other hand, the method proposes a differential-dung beetle optimization algorithm to solve the constructed multi-target optimization model. The algorithm accelerates the optimization process by performing differential mutation operations on the dung beetle population in the dung beetle optimization algorithm, and has a faster convergence speed.

[0090] 2. The present invention provides a method for optimizing the energy storage site selection for a high-proportion wind-solar system in a distribution network. In view of the problems that the local development capability of the dung beetle optimization algorithm around the optimal solution is weak in the later iteration stage, and the position update of the thieving dung beetle is interfered by the local optimal solution, resulting in slow algorithm convergence speed and poor accuracy, the golden sine search mechanism is introduced into the position update of the thieving dung beetle, further improving the convergence speed of the algorithm and the accuracy of the optimal solution.

[0091] 3. The present invention provides a method for optimizing the energy storage site selection for a high-proportion wind-solar system in a distribution network. In the process of updating the position of the dung beetle, a method for calculating the probability p and θ of the dung beetle encountering obstacles is proposed. These two parameters maintain a large value in the early stage of iteration, which can increase the global search capability of the algorithm. They gradually decrease in the later stage of iteration, which can enhance the local exploration capability in the later stage and accelerate the convergence speed.

[0092] 4. The energy storage site selection optimization method for a high-proportion wind-solar system in a distribution network of the present invention introduces Gaussian variation to disturb the optimal individual, so that the algorithm can better perform local development, thereby further improving the optimization accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 This is a flow chart of the method described in Example 1.

[0094] Figure 2 This is a flow chart of the difference-dung beetle optimization algorithm in Example 1.

[0095] Figure 3 This is a structural diagram of the system described in Example 2. DETAILED DESCRIPTION

[0096] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0097] Embodiment 1:

[0098] This embodiment selects the IEEE-33 node distribution network system as the research object, and implements the energy storage site selection optimization method for a high-proportion wind-solar system in the distribution network described in the present invention, such as Figure 1 As shown, the specific steps are as follows:

[0099] 1. Determine the objective function of the energy storage site selection optimization configuration model

[0100] According to the actual needs of the optimal configuration of energy storage in wind and solar power distribution networks, three core optimization objectives are determined, namely energy storage cost, node voltage deviation, and load power fluctuation. The following multi-objective optimization function is set:

[0101] minF={F 1 ,F 2 ,F 3}

[0102] In the above formula, F 1 、F 2 、F 3 They are energy storage cost, node voltage deviation, and load power fluctuation respectively.

[0103] Energy storage costs can be roughly divided into two parts: initial investment costs and later operation and maintenance costs, so there are:

[0104] F 1 =C 1 +C 2

[0105] In the above formula, C 1 , C 2 They are respectively the investment cost of energy storage and the subsequent operation and maintenance cost of energy storage.

[0106] As for investment costs, there are:

[0107]

[0108] In the above formula, C inν is the fixed investment cost of an energy storage device, N ess is the number of energy storage devices, a and b are the unit power cost and unit energy cost respectively, P ess.n 、E ess.n are the rated power and rated energy of the nth energy storage device respectively, ζ is the annual capital recovery ratio, r is the discount rate, which is calculated using the weighted average method, and x is the service life of the energy storage device, which is 10 in this embodiment.

[0109] The operation and maintenance costs of energy storage are:

[0110]

[0111] In the above formula, η is the maintenance coefficient.

[0112] The node voltage deviation can be used to reflect the voltage control and grid connection point voltage compensation of the energy storage system after the photovoltaic power generation system is introduced. The minimum node voltage deviation is taken as the goal, and we have:

[0113]

[0114] In the above formula, N bus is the number of nodes, U i,t , U N are the actual voltage value and the rated voltage value of the node i at time t respectively. T is an operation cycle. In this embodiment, T=24h.

[0115] Energy storage can use its flexible charging and discharging characteristics to smooth load fluctuations. This paper also takes load power fluctuation as one of the factors considered when constructing the objective function, and its expression is:

[0116]

[0117] In the above formula, P load (k), P pν (k), P c / d (k) are the load power at time k, the active power of photovoltaic output, and the charging and discharging power of energy storage, is the average load power value in time period T.

[0118] 2. Determine the constraints of the energy storage site selection optimization configuration model

[0119] In order to ensure the safety and feasibility of the energy storage optimization configuration scheme in the actual distribution network, the system power balance and node voltage constraints, energy storage system operation constraints, state of charge constraints, and charging and discharging power constraints are set as the constraints of the model, including:

[0120] In order to ensure that the power injection and power outflow of each node in the distribution network are balanced, the specific expression of the system power balance constraint is:

[0121]

[0122] In the above formula, are the active power and reactive power input by the distributed wind turbine at node i at time t, is the charging and discharging power of the energy storage device configured at node i at time t, are the active load and reactive load of node i at time t, G i,j , B i,j are the real and imaginary parts of the mutual admittance between nodes i and j, respectively, δ ij,t is the phase difference between nodes i and j at time t, N iis the number of nodes connected to node i.

[0123] The node voltage is kept within a certain allowable range to ensure the safe and stable operation of the power grid. The specific expression of the node voltage constraint is:

[0124] U min ≤U i,t ≤U max

[0125] In the above formula, U max , U min are the upper and lower limits of the node voltage amplitude respectively.

[0126] The charging and discharging power of the energy storage system must be within certain limits to prevent equipment damage and ensure stable operation of the power grid. The energy storage operation constraints are:

[0127]

[0128] In the above formula, is the charging and discharging power of the cth energy storage device at time t, are the upper and lower limits of the charging and discharging power of the cth energy storage device, respectively. is the remaining capacity of the cth energy storage device at time t, η ch , η dis are the charging and discharging efficiencies of the energy storage devices, and Δt is the time difference between adjacent moments.

[0129] The state of charge (SOC) of the energy storage system must be kept within a reasonable range to avoid equipment damage caused by overcharging or over-discharging. The state of charge constraints are:

[0130]

[0131] In the above formula, is the state of charge of the cth energy storage device at time t, are the upper and lower limits of the state of charge of the cth energy storage device respectively.

[0132] To ensure that the energy storage system maintains a balanced charge and discharge energy in a complete operation cycle, the charge and discharge power constraints are specifically set as follows:

[0133]

[0134] 3. The differential-dung beetle optimization algorithm DE-DBO is used to solve the energy storage site selection optimization configuration model and obtain the optimal energy storage site selection plan, which includes:

[0135] 3.1. Determine the scope of the solution space, including the possible locations of energy storage and the upper and lower limits of the fixed capacity. Initialize the dung beetle population through the Tent chaotic map, where each dung beetle represents an energy storage site selection plan. The expression of the Tent chaotic map is:

[0136]

[0137] In the above formula, x w is the chaotic value generated in the wth iteration, δ is the introduced random variable, and N is the maximum number of iterations of the chaotic mapping.

[0138] 3.2. Calculate the fitness value of each dung beetle position, that is, the objective function value.

[0139] 3.3. Each dung beetle updates its position according to the population it belongs to. The position of the stealing dung beetle is updated based on the golden sine search mechanism:

[0140]

[0141] y 1 =-πh+π(1-h)

[0142] y 2 =πh-π(1-h)

[0143] In the above formula, P i z is the position of the i-th dung beetle at the z-th iteration, z is the current iteration number, r 1 is a random number in [0, 2π], r 2 is a random number in [0, π], y 1 ,y 2 , is the global optimal position, h is the golden ratio,

[0144] The rolling ball dung beetle pushes the ball forward, and updates the position without adjusting the direction when the forward movement is not blocked:

[0145]

[0146] In the above formula, λ is a natural coefficient of -1 or 1, k is a deflection coefficient, a constant belonging to (0, 0.2], and b is a constant belonging to (0, 1). is the global worst position of the z-th iteration;

[0147] When encountering obstacles, use the dancing behavior to adjust the direction and update the position:

[0148]

[0149] In the above formula, θ∈[0,π], when θ=0,π,π / 2, the position of the dung beetle is not updated, randperm(0,45) is a randomly generated number in (0,45), and Z is the maximum number of iterations;

[0150] The probability p of a rolling dung beetle encountering an obstacle is calculated according to the following formula:

[0151] p=0.1+(0.9-0.1) / (1+e 0.04(z-250) ).

[0152] 3.4. Sort the dung beetles according to their fitness values ​​from large to small, select the dung beetles with fitness values ​​in the top 20% and the bottom 20% to form a differential mutation population, and update the positions of the dung beetles with fitness values ​​in the bottom 20% through the following differential mutation operation:

[0153]

[0154] In the above formula, P i z+1 is the updated position of the i-th dung beetle among the dung beetles with the last 20% fitness values, are the positions of three dung beetles randomly selected from the differential mutation population at the current iteration, F is the mutation factor, and 0≤F≤2.

[0155] 3.5. Calculate the fitness value of each dung beetle in the new position, and determine the individual optimum and the global optimum based on the fitness value;

[0156] 3.6. Perform Cauchy mutation perturbation on the globally optimal individual, calculate the fitness value after mutation, and update the optimal position:

[0157]

[0158] In the above formula, are the global optimal values ​​before and after updating respectively.

[0159] 3.7. Determine whether the maximum number of iterations has been reached. If so, output the global optimum as the optimal energy storage site selection plan; if not, return to 3.3 for the next iteration.

[0160] Embodiment 2:

[0161] A distribution network high proportion of wind and solar energy storage system site selection optimization system, such as Figure 3 As shown, it includes an optimization configuration model building module and a differential-dung beetle optimization algorithm module.

[0162] The optimization configuration model building module is used to build an energy storage site optimization configuration model that takes into account energy storage costs, node voltage deviations, and load power fluctuations, wherein the objective function of the energy storage site optimization configuration model includes:

[0163] minF={F 1 ,F 2 ,F 3}

[0164] F 1 =C 1 +C 2

[0165]

[0166] In the above formula, F 1 、F 2 、F 3 are energy storage cost, node voltage deviation, load power fluctuation, C 1 , C 2 are the investment cost of energy storage and the subsequent operation and maintenance cost of energy storage, C inν is the fixed investment cost of an energy storage device, N ess is the number of energy storage devices, a and b are the unit power cost and unit energy cost respectively, P ess.n 、E ess.n are the rated power and rated energy of the nth energy storage device, ζ is the annual capital recovery ratio, η is the maintenance coefficient, N bus is the number of nodes, U i,t , U N are the actual voltage value and rated voltage value of node i at time t, T is an operation cycle, P load (k), P pν (k), P c / d (k) are the load power at time k, the active power of photovoltaic output, and the charging and discharging power of energy storage, is the average load power value in time period T;

[0167] The constraints include:

[0168] System power balance constraints

[0169]

[0170] In the above formula, are the active power and reactive power input by the distributed wind turbine at node i at time t, is the charging and discharging power of the energy storage device configured at node i at time t, are the active load and reactive load of node i at time t, G i,j , B i,j are the real and imaginary parts of the mutual admittance between nodes i and j, respectively, δ ij,t is the phase difference between nodes i and j at time t, N iis the number of nodes connected to node i;

[0171] Node Voltage Constraints

[0172] U min ≤U i,t ≤U max

[0173] In the above formula, U max , U min are the upper and lower limits of the node voltage amplitude respectively;

[0174] Energy storage operation constraints

[0175]

[0176] In the above formula, is the charging and discharging power of the cth energy storage device at time t, are the upper and lower limits of the charging and discharging power of the cth energy storage device, respectively. is the remaining capacity of the cth energy storage device at time t, η ch , η dis are the charging and discharging efficiencies of the energy storage device, respectively, and Δt is the time difference between adjacent moments;

[0177] State of Charge Constraints

[0178]

[0179] In the above formula, is the state of charge of the cth energy storage device at time t, are the upper and lower limits of the state of charge of the cth energy storage device respectively;

[0180] Charge and discharge power constraints

[0181]

[0182] The difference-dung beetle optimization algorithm module is used to solve the energy storage site selection optimization configuration model to obtain the optimal energy storage site selection plan. The specific solution steps include:

[0183] A1. Determine the scope of the solution space, including the possible locations of energy storage and the upper and lower limits of the fixed capacity. Initialize the dung beetle population through the Tent chaotic map, where each dung beetle represents an energy storage site selection plan. The expression of the Tent chaotic map is:

[0184]

[0185] In the above formula, x w is the chaotic value generated in the wth iteration, δ is the introduced random variable, and N is the maximum number of iterations of the chaotic mapping.

[0186] A2. Calculate the fitness value of each dung beetle position, that is, the objective function value.

[0187] A3. Each dung beetle updates its position according to the population to which it belongs. The position of the stealing dung beetle is updated based on the golden sine search mechanism:

[0188]

[0189] y 1 =-πh+π(1-h)

[0190] y 2 =πh-π(1-h)

[0191] In the above formula, P i z is the position of the i-th dung beetle at the z-th iteration, z is the current iteration number, r 1 is a random number in [0, 2π], r 2 is a random number in [0, π], y 1 ,y 2 , is the global optimal position, h is the golden ratio,

[0192] The rolling ball dung beetle pushes the ball forward, and updates the position without adjusting the direction when the forward movement is not blocked:

[0193]

[0194] In the above formula, λ is a natural coefficient of -1 or 1, k is a deflection coefficient, a constant belonging to (0, 0.2], and b is a constant belonging to (0, 1). is the global worst position of the z-th iteration;

[0195] When encountering obstacles, use the dancing behavior to adjust the direction and update the position:

[0196]

[0197] In the above formula, θ∈[0,π], when θ=0,π,π / 2, the position of the dung beetle is not updated, randperm(0,45) is a randomly generated number in (0,45), and Z is the maximum number of iterations;

[0198] The probability p of a rolling dung beetle encountering an obstacle is calculated according to the following formula:

[0199] p=0.1+(0.9-0.1) / (1+e 0.04(z-250) ).

[0200] A4. Sort the dung beetles according to their fitness values ​​from large to small, select the dung beetles with fitness values ​​in the top 20% and the bottom 20% to form a differential mutation population, and update the positions of the dung beetles with fitness values ​​in the bottom 20% through the following differential mutation operation:

[0201]

[0202] In the above formula, P i z+1 is the updated position of the i-th dung beetle among the dung beetles with the last 20% fitness values, are the positions of three dung beetles randomly selected from the differential mutation population at the current iteration, F is the mutation factor, and 0≤F≤2.

[0203] A5. Calculate the fitness value of each dung beetle in the new position, and determine the individual optimum and the global optimum based on the fitness value;

[0204] A6. Perform Cauchy mutation perturbation on the globally optimal individual, calculate the fitness value after mutation, and update the optimal position:

[0205] Perform Cauchy mutation perturbation on the globally optimal individual, calculate the fitness value after mutation, and update the optimal position:

[0206]

[0207] In the above formula, are the global optimal values ​​before and after updating respectively.

[0208] A7. Determine whether the maximum number of iterations has been reached. If so, output the global optimum as the optimal energy storage site selection plan. If not, return to A3 for the next iteration.

Claims

1. A method for optimizing energy storage site selection for a high-proportion wind-solar system in a distribution network, characterized in that: The method comprises: S1. Construct an energy storage site selection optimization configuration model that considers energy storage cost, node voltage deviation and load power fluctuation; S2. Use the differential-dung beetle optimization algorithm to solve the energy storage site optimization configuration model and obtain the optimal energy storage site selection plan.

2. The method for optimizing energy storage site selection for a high-ratio wind-solar system in a distribution network according to claim 1, characterized in that: The S2 includes: S21, determining the scope of the solution space, including the possible locations of energy storage and the upper and lower limits of the fixed capacity, and initializing the dung beetle population, wherein each dung beetle represents an energy storage site selection plan; S22, calculating the fitness value of each dung beetle position, that is, the objective function value; S23, each dung beetle updates its position according to the population to which it belongs; S24, sorting the dung beetles according to their fitness values ​​from large to small, selecting the dung beetles with fitness values ​​in the top 20% and the bottom 20% to perform differential mutation operations, so as to update the positions of the dung beetles with fitness values ​​in the bottom 20%; S25, calculating the fitness value of each dung beetle in the new position, and determining the individual optimum and the global optimum according to the fitness value; S26, judging whether the iteration termination condition is met, if so, outputting the global optimum as the optimal energy storage site selection scheme; if not, returning to S23 for the next iteration.

3. The method for optimizing energy storage site selection for a high-ratio wind-solar system in a distribution network according to claim 2, characterized in that: In S23, the position of the stealing dung beetle is updated based on the golden sine search mechanism: y1=-πh+π(1-h) y2=πh-π(1-h) In the above formula, P i z is the position of the ith dung beetle at the zth iteration, z is the current iteration number, r1 is a random number in [0, 2π], r2 is a random number in [0, π], y1, y2, is the global optimal position, h is the golden ratio; The rolling ball dung beetle pushes the ball forward, and updates the position without adjusting the direction when the forward movement is not blocked: In the above formula, λ is a natural coefficient of -1 or 1, k is a deflection coefficient, and b is a constant of (0, 1). is the global worst position of the z-th iteration; When encountering obstacles, use the dancing behavior to adjust the direction and update the position: P i z+1 =P i z +tanθ·|P i z -P i z-1 | In the above formula, θ∈[0,π], when θ=0,π,π / 2, the position of the dung beetle is not updated, randperm(0,45) is a randomly generated number in (0,45), and Z is the maximum number of iterations; The probability p of a rolling dung beetle encountering an obstacle is calculated according to the following formula: p=0.1+(0.9-0.1) / (1+e 0.04(z-250) )。 4. The method for optimizing energy storage site selection for a high-ratio wind-solar system in a distribution network according to claim 2, characterized in that: In S24, selecting dung beetles with fitness values ​​in the top 20% and the bottom 20% to perform differential mutation operation includes: The dung beetles with fitness values ​​in the top 20% and the bottom 20% are formed into a differential mutation population, and the positions of the dung beetles with fitness values ​​in the bottom 20% are updated according to the following mutation formula: In the above formula, P i z+1 is the updated position of the i-th dung beetle among the dung beetles with the last 20% fitness values, are the positions of three dung beetles randomly selected from the differential mutation population at the current iteration, F is the mutation factor, and 0≤F≤2; The S25 further includes: Perform Cauchy mutation perturbation on the globally optimal individual, calculate the fitness value after mutation, and update the optimal position: In the above formula, are the global optimal before and after the update respectively; The S21 initializes the dung beetle population through the Tent chaos map: In the above formula, x w is the chaotic value generated in the wth iteration, δ is the introduced random variable, and N is the maximum number of iterations of the chaotic mapping.

5. A method for optimizing energy storage site selection for a high-ratio wind-solar system in a distribution network according to any one of claims 1 to 4, characterized in that: In S1, the objective function of the energy storage site selection optimization configuration model includes: minF={F1,F2,F3} F1=C1+C2 In the above formula, F1, F2, and F3 are energy storage cost, node voltage deviation, and load power fluctuation, respectively; C1 and C2 are investment cost and later operation and maintenance cost of energy storage, respectively; C inν is the fixed investment cost of an energy storage device, N ess is the number of energy storage devices, a and b are the unit power cost and unit energy cost respectively, P ess.n 、E ess.n are the rated power and rated energy of the nth energy storage device, ζ is the annual capital recovery ratio, η is the maintenance coefficient, N bus is the number of nodes, U i,t , U N are the actual voltage value and rated voltage value of node i at time t, T is an operation cycle, P load (k), P pν (k), P c / d (k) are the load power at time k, the active power of photovoltaic output, and the charging and discharging power of energy storage, is the average load power value in time period T; The constraints include system power balance and node voltage constraints, energy storage system operation constraints, state of charge constraints, and charging and discharging power constraints.

6. A distribution network high-proportion wind and solar system energy storage site selection optimization system, characterized in that: The system includes an optimization configuration model building module and a difference-dung beetle optimization algorithm module; The optimization configuration model building module is used to build an energy storage site optimization configuration model that takes into account energy storage costs, node voltage deviations, and load power fluctuations; The difference-dung beetle optimization algorithm module is used to solve the energy storage site selection optimization configuration model to obtain the optimal energy storage site selection plan.

7. The energy storage site selection optimization system for a high-ratio wind-solar system in a distribution network according to claim 6, characterized in that: The solution steps of the difference-dung beetle optimization algorithm module include: A1. Determine the scope of the solution space, including the possible locations of energy storage and the upper and lower limits of the fixed capacity, and initialize the dung beetle population, where each dung beetle represents an energy storage site selection plan; A2. Calculate the fitness value of each dung beetle position, i.e., the objective function value; A3. Each dung beetle updates its position according to the population to which it belongs; A4, sorting the dung beetles according to their fitness values ​​from large to small, selecting the dung beetles with fitness values ​​in the top 20% and the bottom 20% to perform differential mutation operations, so as to update the positions of the dung beetles with fitness values ​​in the bottom 20%; A5. Calculate the fitness value of each dung beetle in the new position, and determine the individual optimum and the global optimum based on the fitness value; A6: Determine whether the iteration termination condition is met. If so, output the global optimum as the optimal energy storage site selection scheme; if not, return to A3 for the next iteration.

8. The energy storage site selection optimization system for a high-ratio wind-solar system in a distribution network according to claim 7, characterized in that: The position of the stealing dung beetle is updated based on the golden sine search mechanism: y1=-πh+π(1-h) y2=πh-π(1-h) In the above formula, P i z is the position of the ith dung beetle at the zth iteration, z is the current iteration number, r1 is a random number in [0, 2π], r2 is a random number in [0, π], y1, y2, is the global optimal position, h is the golden ratio; The rolling ball dung beetle pushes the ball forward, and updates the position without adjusting the direction when the forward movement is not blocked: In the above formula, λ is a natural coefficient of -1 or 1, k is a deflection coefficient, and b is a constant of (0, 1). is the global worst position of the z-th iteration; When encountering obstacles, use the dancing behavior to adjust the direction and update the position: P i z+1 =P i z +tanθ·|P i z -P i z-1 | In the above formula, θ∈[0,π], when θ=0,π,π / 2, the position of the dung beetle is not updated, randperm(0,45) is a randomly generated number in (0,45), and Z is the maximum number of iterations; The probability p of a rolling dung beetle encountering an obstacle is calculated according to the following formula: p=0.1+(0.9-0.1) / (1+e 0.04(z-250) )。 9. The energy storage site selection optimization system for a high-ratio wind-solar system in a distribution network according to claim 7, characterized in that: The step of selecting dung beetles with fitness values ​​in the top 20% and the bottom 20% for differential mutation operation includes: The dung beetles with fitness values ​​in the top 20% and the bottom 20% are formed into a differential mutation population, and the positions of the dung beetles with fitness values ​​in the bottom 20% are updated according to the following mutation formula: In the above formula, P i z+1 is the updated position of the i-th dung beetle among the dung beetles with the last 20% fitness values, are the positions of three dung beetles randomly selected from the differential mutation population at the current iteration, F is the mutation factor, and 0≤F≤2; The A5 also includes: Perform Cauchy mutation perturbation on the globally optimal individual, calculate the fitness value after mutation, and update the optimal position: In the above formula, are the global optimal before and after the update respectively; The S21 initializes the dung beetle population through the Tent chaos map: In the above formula, x w is the chaotic value generated in the wth iteration, δ is the introduced random variable, and N is the maximum number of iterations of the chaotic mapping.

10. A distribution network high-ratio wind-solar system energy storage site selection optimization system according to any one of claims 6-9, characterized in that: The objective function of the energy storage site selection optimization configuration model includes: minF={F1,F2,F3} F1=C1+C2 In the above formula, F1, F2, and F3 are energy storage cost, node voltage deviation, and load power fluctuation, respectively; C1 and C2 are investment cost and later operation and maintenance cost of energy storage, respectively; C inν is the fixed investment cost of an energy storage device, N ess is the number of energy storage devices, a and b are the unit power cost and unit energy cost respectively, P ess.n 、E ess.n are the rated power and rated energy of the nth energy storage device, ζ is the annual capital recovery ratio, η is the maintenance coefficient, N bus is the number of nodes, U i,t , U N are the actual voltage value and rated voltage value of node i at time t, T is an operation cycle, P load (k), P pν (k), P c / d (k) are the load power at time k, the active power of photovoltaic output, and the charging and discharging power of energy storage, is the average load power value in time period T; The constraints include system power balance and node voltage constraints, energy storage system operation constraints, state of charge constraints, and charging and discharging power constraints.

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