A source-grid-load-storage integrated planning method for distribution network considering comprehensive performance

Abstract

The power distribution network "source-network-load-storage" joint planning method considering comprehensive performance comprises the following steps: step one, determining a photovoltaic output probability model and a wind power probability model of a planning area; step two, establishing a mathematical model considering source-network-load-storage joint load scheduling; step three, establishing a source-network-load-storage joint double-layer planning model, wherein an upper layer is a planning layer, and a lower layer is a running layer; and step four, applying a particle swarm algorithm to solve the double-layer planning model established in step three. The photovoltaic power generation output, wind power generation, and power source, power grid, load and energy storage through various interactive forms such as source-source complementation, source-network coordination, network-load interaction, network-storage interaction and source-load interaction are simultaneously considered, so that the fine level of power distribution network planning is improved.

Description

Technical Field

[0001] This invention presents a joint planning method for distribution networks that considers comprehensive performance, encompassing the "source-grid-load-storage" system. This method belongs to the field of distribution network planning that includes distributed generation (DG). Background Technology

[0002] In recent years, with the increasing severity of environmental pollution and energy shortages, distributed generation technology has received growing attention. The penetration rate of distributed power sources in distribution networks is rapidly increasing. On the one hand, the large-scale integration of distributed power sources introduces more uncertainties into the distribution network; on the other hand, the application of energy storage technology effectively smooths fluctuations in distributed generation power and improves power quality. How to scientifically combine and integrate complementary energy internet technologies and construct an energy supply system of "source-grid-load-storage" to maximize energy efficiency is currently a research hotspot in the planning field.

[0003] With the continuous development of the global economy, large-scale factory production has replaced individual workshops and manual production, leading to a surge in energy demand. In the past, power generation relied primarily on fossil fuels. While this was economically viable, excessive extraction has resulted in energy depletion, land subsidence, and the pollution from large-scale combustion, including smog, acid rain, and global warming, causing significant harm to our environment. Therefore, traditional power generation methods are constrained by various factors, and clean, renewable distributed power sources have entered a golden age of development, accelerating the transformation of my country's energy structure from traditional distribution networks to active distribution networks. With the large-scale integration of clean distributed power sources into the power system, new problems have arisen. The greater uncertainty and volatility of energy information has caused voltage fluctuations and increased network losses, significantly impacting the reliability and stability of the power system. Active distribution networks need to regulate various controllable resources to fully utilize their regulatory role and ensure economical and stable system operation. How to achieve coordinated optimization of controllable resources is of significant research importance. Summary of the Invention

[0004] This invention provides a comprehensive planning method for distribution networks that integrates power generation, grid, load, and energy storage. Compared to traditional integrated planning methods, this invention simultaneously considers photovoltaic power generation output, wind power generation, and various interactive forms among power sources, grids, loads, and energy storage, such as source-source complementarity, source-grid coordination, grid-load interaction, grid-storage interaction, and source-load interaction, thereby improving the precision of distribution network planning.

[0005] The technical solution adopted in this invention is as follows:

[0006] A comprehensive planning method for distribution networks that considers overall performance, encompassing the "source-grid-load-storage" model, includes the following steps:

[0007] Step 1: Determine the probability models for photovoltaic power output and wind power generation in the planning area:

[0008] The probability density function describing the randomness of solar irradiance and wind speed intensity over a period of time is:

[0009]

[0010]

[0011] In the above formula, r represents the actual irradiance within the statistical time period; max Γ represents the maximum irradiance; α and β represent the coefficients of the Beta distribution; Γ represents a function of Gamma; v represents the actual wind speed; k and c are the shape factor and scale factor of the Weibull distribution, respectively. f(r) and f(v) represent the photovoltaic and wind power generation models, respectively, and Γ(α), Γ(β), and Γ(α+β) are the simulated probability density functions.

[0012] Step 2: Establish a mathematical model considering joint load scheduling of source, grid, load, and storage:

[0013] Equations (3) and (4) describe the relationship between upper-level and lower-level decisions, respectively:

[0014]

[0015] In equation (3), F represents the upper-level objective function; γ inv δ represents the decision variables; G represents the inequality constraints; and H represents the equality constraints.

[0016]

[0017] In equation (4): f is the lower-level objective function; γ inv γ ope Let γ be the decision variable of the lower-level model. inv 'g' represents the decision variable influenced by the upper level; 'h' represents the lower level inequality constraint; 'h' represents the lower level equality constraint.

[0018] Step 3: Establish a two-layer planning model for source-grid-load-storage integration, with the upper layer being the planning layer and the lower layer being the operation layer;

[0019] (1) Upper-level objective function:

[0020] minf1 = C ess -R ess +C b +C loss (5)

[0021] C ess =e ess ×|P l |(6)

[0022] R ess =C1+C2(7)

[0023]

[0024] C loss =P loss ×C e (9)

[0025]

[0026]

[0027] In the above formula, C ess Indicates the cost of power dispatch from the energy storage system; R ess The revenue generated from charging and discharging the energy storage system and the government subsidy revenue; C g Indicates the cost of upgrading and constructing the power distribution network; d r C represents the discount rate; loss P represents the cost of power loss in power distribution at different times; loss Indicates the power loss of the distribution network at different time periods; C e Indicates electricity price for different time periods; e ess P represents the cost per kilowatt-hour of lithium batteries; l C1 represents the energy storage system's power consumption; C1 represents the energy storage system's revenue; λ dis (i), λ chr (i) represent variables indicating the charging and discharging states at time i, with values ​​of 0 or 1; P dis (i), P chr (i) represents the energy storage charging and discharging power at time i; C0(i) represents the electricity price at time i; C2 represents the government subsidy revenue of the energy storage system; T is the total number of years of planned operation; P gov The government subsidy for discharging energy from energy storage devices.

[0028] The voltage deviation after distributed generation is connected to the distribution network is shown below:

[0029]

[0030] In equation (12), i represents time; m represents a node; n represents the total number of nodes; V m.i V represents the actual voltage value of node m at time i; N This indicates the node's rated voltage.

[0031] The normalized objective function is shown below:

[0032]

[0033] In equation (13), These represent the power dispatch cost of the energy storage system, the "low storage, high discharge" revenue and government subsidy revenue generated by the energy storage system's charging and discharging, the cost of load aggregators participating in distribution network dispatch in each time period, and the maximum value of power loss costs in the distribution network in each time period.

[0034]

[0035] In equation (14), 24 represents the number of moments in a day; n represents the number of system nodes; V max Let be the maximum allowable voltage deviation at the node. The comprehensive objective function is as follows:

[0036] minf=min(ω1f1+ω2f2) (15)

[0037] In equation (15), ω1 and ω2 are the weight values ​​of the corresponding indicators.

[0038] (2) Lower-level objective function:

[0039]

[0040]

[0041] minf3=η1T+η2λ (18)

[0042] In the above formula, T represents the load fluctuation amplitude; λ represents the load fluctuation rate; η1 and η2 are the weighting coefficients of the two evaluation indicators, respectively; P gmax P gmin These represent the maximum and minimum daily load power of the distribution network, respectively. P represents the average daily load of the distribution network. g (t+1), P g (t) represents the daily load value of the distribution network at that time.

[0043] The constraints of the source-grid-load-storage joint two-level planning model are as follows:

[0044] ① Active power output constraints of distributed generation:

[0045] P DG,min ≤P DG (t)≤P DG,max (19);

[0046] In equation (19): P DG,min PD G,max These represent the upper and lower limits of the active power output of the distributed power source; P DG (t) represents the actual active power output by distributed generation in the distribution network.

[0047] P DG (t)=(1+μfore )P DG.fore (t) (20)

[0048] 0≤|μ fore |≤μ fore.max (twenty one)

[0049] Where: μ fore The coefficient of error for predicting photovoltaic and wind power output is P; the closer it is to 0, the higher the prediction accuracy. DG.fore (t) represents the day-ahead forecast of the active power output of the distributed generation. fore.max This represents the maximum error coefficient for predicting photovoltaic and wind power output.

[0050] ② Node voltage constraints:

[0051] U i,min ≤U i ≤U i,max (twenty two);

[0052] In the formula: U i,max U i,min U represents the upper and lower limits of the allowable voltage at node i, respectively; i This represents the voltage amplitude at the i-th node in the distribution network.

[0053] ③ Branch current constraint:

[0054] 0≤I l ≤I l,max (twenty three)

[0055] In the formula: I l Indicates the magnitude of the current flowing through branch 1; I l,max This represents the maximum value at which branch l satisfies the thermal stability condition.

[0056] ④ Branch power constraints:

[0057] P li,min ≤P li ≤P li,max (twenty four)

[0058] In the formula: P li P represents the power transmitted by the first branch in the distribution network. li,max P li,min These represent the upper and lower limits of the power transmitted by the branch circuit, respectively.

[0059] ⑤. Energy storage device response constraints:

[0060] SOC ESS.min &≤SOC ESS (t)≤SOC ESS.max (25)

[0061]

[0062]

[0063] Where: SOC ESS.min SOC ESS.max These represent the upper and lower limits of the state of charge of the energy storage device, respectively; P N Rated power of the energy storage system; ε represents the charging and discharging power of the energy storage at time t; E represents the allowable charging and discharging energy balance error index of the energy storage; and E represents the storage capacity of the energy storage.

[0064] ⑥ Total power conservation constraint:

[0065] P G . t =P ul.t +P LA，t +P Ess.t -P DG.t (28)

[0066] In the formula: P G.t P represents the total electricity purchased by the distribution network from the main grid at time t; ul . t P represents the active power output required by the uncontrollable load at time t; LA，t P represents the active power output of LA at time t, where an increase is positive and a decrease is negative; ESS.t P represents the active power output of stored energy at time t; DG . t Let t be the active power output of the distributed power source.

[0067] ⑦ Reliability constraints:

[0068] R≥R min (29)

[0069] In the formula: R represents the reliability value of the current distribution network system; R min This indicates the minimum reliability requirements for the power distribution network system.

[0070] Step 4: Apply the particle swarm optimization algorithm to solve the bi-level programming model established in Step 3:

[0071] (1) In the two-level programming model, the active power output of the upper-level energy storage system and controllable load and the daily load power of the lower-level distribution network are variables. These variables are regarded as particles, and the position and velocity of the particles in space are initialized.

[0072] (2) Taking the distribution network dispatching operation cost, comprehensive benefits, load amplitude and volatility as the objectives, the fitness of particles in the population is calculated through power flow calculation. The position and fitness value of these particles are stored in pbest, and the position and fitness value of the optimal solution in all pbest are stored in gbest.

[0073] (3) Update the velocity and position of the particles using the velocity and position update formulas, compare the current pbest and gbest, and update gbest;

[0074] (4) When the stopping condition is met, stop the search and output the result; otherwise, continue from (3) to perform another speed and position update.

[0075] Through the above steps, the integrated planning of the power distribution network, including the source, grid, load, and storage, can be achieved.

[0076] This invention provides a comprehensive "source-grid-load-storage" joint planning method for distribution networks, with the following technical advantages:

[0077] 1) Compared with the traditional source-grid-load-storage joint planning method, this invention simultaneously considers photovoltaic power generation output, wind power generation, and various interactive forms among power sources, grid, load and energy storage, such as source-source complementarity, source-grid coordination, grid-load interaction, grid-storage interaction and source-load interaction, thereby improving the level of planning refinement.

[0078] 2) Based on the core concept of active distribution networks, this invention categorizes controllable resources into: distributed generation, distribution network structure, controllable loads, and energy storage systems. Addressing the operational issues arising from the integration of distributed generation into the power system, adjusting the network structure can improve stability and economy. Controllable loads and energy storage systems can further enhance economy and increase the penetration rate of distributed generation. Analysis shows that simultaneously considering multiple controllable resources significantly improves economy, stability, and reliability compared to optimizing a single controllable resource. Attached Figure Description

[0079] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0080] Figure 1 Flowchart for active distribution network optimization strategy.

[0081] Figure 2 This is a diagram of a two-tiered planning model for the source-grid-load-storage system.

[0082] Figure 3 This is a flowchart of the particle swarm optimization algorithm.

[0083] Figure 4 This is a flowchart of the model solution based on the particle swarm optimization algorithm.

[0084] Figure 5This is a structural diagram of the IEEE-33 node power distribution system.

[0085] Figure 6 This is a comparison chart of the predicted values ​​of photovoltaic power output and base load power output.

[0086] Figure 7 This is a comparison chart of the predicted output of the wind turbine and the output of the base load.

[0087] Figure 8 The diagram shows the results of the coordinated optimization scheduling of "source, grid, and load";

[0088] Figure 9 A comparison chart of node voltage deviations for coordinated optimization scheduling of "source, grid, and load".

[0089] Figure 10 A diagram showing the results of coordinated optimization scheduling of "source, network, and storage";

[0090] Figure 11 A comparison chart of node voltage deviations for coordinated optimization scheduling of "source, grid, and storage".

[0091] Figure 12 A diagram showing the results of coordinated optimization scheduling of "source, grid, load, and storage";

[0092] Figure 13 A comparison chart of node voltage deviations for coordinated optimization scheduling of "source, grid, load and storage".

[0093] Figure 14 A comparison chart of optimized scheduling load curves for three scenarios.

[0094] Figure 15 This is a comparison chart of node voltage deviations in three scenarios. Detailed Implementation

[0095] This paper proposes a comprehensive planning method for distribution network sources, grid, load, and storage. First, it briefly analyzes the impact of coordinated planning among various controllable resources on the power grid, providing reliable support for model establishment. Then, it mainly proposes a joint planning model for sources, grid, load, and storage. Based on dispatch control strategies, it considers the volatility and randomness brought about by distributed generation (DG) access to the grid, user satisfaction with controllable load participation in optimization, and the charging and discharging status and characteristics of energy storage devices. Under constraints such as power balance, energy storage charging and discharging power, and flexible load response, a two-layer objective function is established: the upper objective function optimizes overall benefits, and the lower objective function optimizes load changes. The characteristics of DG, controllable loads, ESS (Energy Saving Power Supply), and grid structure under different scenarios and their impact on the power grid are analyzed. A two-layer model that considers reliability, stability, and economy is established to achieve optimal operation. The feasibility and effectiveness of collaborative optimization of source-grid-load-storage joint planning under different scenarios are verified. The results demonstrate that collaborative optimization improves the stability and economy of power grid operation from the perspectives of comprehensive load and voltage deviation. Details are as follows:

[0096] Step 1: Control strategies that consider the participation of distributed wind power and distributed photovoltaic power in optimal dispatch.

[0097] Step 1.1: Active distribution network sources, grids, loads, and storage units participate in the distribution network dispatching process by signing contracts with various load managers.

[0098] The main purpose of the integrated planning of power generation, grid, load, and energy storage in an active distribution network is to fully utilize controllable resources and renewable energy sources within the distribution network, achieving positive multi-cooperative interaction. This reduces distribution network dispatching costs, improves voltage stability, ensures power supply reliability, and better promotes large-scale intermittent power supply and wide-ranging energy consumption. The active distribution network described in this invention includes photovoltaic power generation systems, wind power generation systems, flexible loads, and energy storage devices. Flexible loads include basic loads and controllable loads. Basic loads refer to fixed loads that meet daily human needs, while controllable loads mainly participate in distribution network dispatching through contracts with load managers. The dispatching process is as follows: Figure 1 As shown.

[0099] Step 1.2: Determine the photovoltaic power output probability model and wind power generation probability model for the planning area based on the actual situation.

[0100] Wind power and photovoltaic (PV) power generation are mainly affected by the natural environment. PV power output is low at night and high during the day. The output power of PV cells is closely related to the amount of solar radiation. Studies have shown that the probability of solar irradiance approximately follows a Beta distribution model over a certain period. Wind power, on the other hand, has the opposite characteristic of high output at night and low output during the day, and is closely related to wind speed. Wind speed over a certain period approximately follows a Weibull distribution. The probability density functions describing the randomness of solar irradiance and wind speed intensity over a period of time are:

[0101]

[0102]

[0103] In the formula: r represents the actual irradiance within the statistical time period; r max Γ represents the maximum irradiance; α and β represent the coefficients of the Beta distribution; Γ represents a function of Gamma; v represents the actual wind speed; k and c are the shape factor and scale factor of the Weibull distribution, respectively.

[0104] Step 1.3: Mathematical model considering joint load scheduling of source, grid, load and storage

[0105] Bilevel programming is a mathematical model of a bilevel decision problem. It is a system optimization problem with a hierarchical bilevel structure, where each level has its own objective function and constraints. The objective function and constraints of the upper-level problem are not only related to the upper-level decision variables but also depend on the optimal solution of the lower-level problem. Conversely, the optimal solution of the lower-level problem is influenced by the upper-level decision variables. The decision-maker passes the decision variables to the lower level, which then uses the upper-level decision variables as parameters and its own objective function to find the optimal solution. The found optimal solution is then fed back to the upper level. This decision-making mechanism requires both the upper and lower levels to consider the adverse effects of feedback from the other.

[0106] Equations (1) and (2) are used to illustrate the relationship between upper-level and lower-level decisions:

[0107]

[0108]

[0109] Equation (3): The objective function and constraints of the upper-level model, where F is the upper-level objective function; γ inv δ represents decision variables, where F is a decision variable influenced by the lower levels; G represents inequality constraints, and H represents equality constraints.

[0110] Equation (4): The objective function and constraints of the lower-level model, where f is the lower-level objective function; γ inv γ ope Let γ be the decision variable of the lower-level model. inv 'g' represents the decision variable influenced by the upper level; 'h' represents the lower level inequality constraint; 'h' represents the lower level equality constraint.

[0111] In this study of the integrated planning method for power generation, grid, load, and energy storage, the main considerations are the overall economic benefits and operational safety and stability of the power grid. Since distributed generation (DG) access to the grid introduces significant randomness and volatility, it also impacts the load. The load optimization characteristic curve is influenced by both the location of DG access and the capacity of energy storage devices, thus altering dispatch costs. Based on the interplay among these four factors, a two-layer integrated planning model for power generation, grid, load, and energy storage is established. The upper layer is the planning layer, and the lower layer is the operation layer, ensuring that the power system maximizes economic benefits and reliability during operation. The block diagram of the two-layer integrated planning model is shown below. Figure 2 As shown.

[0112] Step 2: Propose a two-tiered joint planning model that considers comprehensive benefits.

[0113] Step 2.1: Propose an upper-level objective function that achieves optimal benefits while considering the power supply stability and economy of the distribution network, mainly including the distribution network dispatching and operation costs, comprehensive benefits, and voltage deviation.

[0114] Objective 1: The operating costs of distribution network dispatching include the power consumption costs of energy storage devices, the revenue from charging and discharging, government subsidies, and network losses. The overall revenue includes the costs of grid expansion or upgrades, as well as system reserve costs. The revenue generated from charging and discharging energy storage devices refers to the difference between storing energy when electricity prices are low and releasing it when prices are high; the government subsidies mentioned in this invention are in the form of electricity price subsidies.

[0115] minf1 = C ess -R ess +C b +C 1oss (5)

[0116] C ess =e ess ×|P l | (6)

[0117] R ess =C1+C2 (7)

[0118]

[0119] C loss =P loss ×C e (9)

[0120]

[0121]

[0122] In the formula: C ess Indicates the cost of power dispatch from the energy storage system; R ess The revenue generated from charging and discharging the energy storage system and the government subsidy revenue; C g Indicates the cost of upgrading and constructing the power distribution network; d r The discount rate is 7.7%; C loss P represents the cost of power loss in power distribution at different times; loss Indicates the power loss of the distribution network at different time periods; C e Indicates electricity price for different time periods; e ess This indicates the cost per kilowatt-hour (kWh) of lithium batteries. For reference, the kWh cost of lithium batteries in 2020 was 0.25 yuan / kWh; P l C1 represents the energy storage system's power consumption; C1 represents the energy storage system's revenue; λ dix (i), λ ch r(i) represents the variable representing the charging and discharging state at time i, with a value of 0 or 1; P dis (i), P chr(i) represents the energy storage charging and discharging power at time i; C0(i) represents the electricity price at time i; C2 represents the government subsidy revenue of the energy storage system. This invention considers electricity price subsidies.

[0123] Objective 2: Another indicator that needs optimization in the distribution network is the voltage at each node. Under normal operating conditions and with certain safety constraints, we ideally want the node voltage to be near its rated voltage, with the smallest possible voltage deviation. The voltage deviation after distributed generation is connected to the distribution network is shown below:

[0124]

[0125] In the formula: i represents time; m represents a node; n represents the total number of nodes; V m.f V represents the actual voltage value of node m at time i; N This indicates the node's rated voltage.

[0126] Since the aforementioned objectives lack a consistent dimension, they are normalized for ease of subsequent research to balance their individual impacts on the objective function. The normalized objective function is shown below:

[0127]

[0128]

[0129] The overall objective function is as follows:

[0130] minf=min(ω1f1+ω2f2) (15)

[0131] In equation (13), f1 in the denominator represents the cost of peak electricity price when the grid dispatching operation cost and comprehensive benefits reach their maximum. 24 represents the number of moments in a day; n represents the number of system nodes; V max The maximum allowable voltage deviation of the node is 0.05 pu; the maximum allowable voltage deviation of the node in this invention is taken as 0.05 pu.

[0132] Step 2.2: Propose a lower-level objective function that considers the optimal load variation and reflects the power supply reliability of the distribution network.

[0133] The goal of lower-level planning is to optimize load variation, reflecting the power supply reliability of the distribution network. It is evaluated jointly by the amplitude and rate of fluctuation of the overall load on the distribution network side.

[0134]

[0135]

[0136] minf3=η1T+η2λ(18)

[0137] In the formula: T represents the load fluctuation amplitude; λ represents the load fluctuation rate; η1 and η2 represent the weighting coefficients of the two evaluation indicators. Since the load fluctuation rate has a greater impact on the reliability of the distribution network, this invention takes η1 = 0.25 and η2 = 0.75; P gmax P gmin : These represent the maximum and minimum daily load power of the distribution network, respectively; Average daily load of the distribution network; P g (t+1), P g (t): Represents the daily load value of the distribution network at any given time.

[0138] Constraints:

[0139] The two-layer model for integrated planning of energy sources, grids, loads, and energy storage also needs to meet certain constraints. The planning layer (upper layer) mainly includes constraints on the active power output of distributed generation sources, node voltage constraints, branch current constraints, branch power constraints, and energy storage system response constraints. The operation layer (lower layer) mainly includes reliability constraints and load response constraints. Additionally, both layers need to satisfy the total power conservation constraint.

[0140] ① Active power output constraints of distributed generation:

[0141] P DG,min ≤P DG (t)≤P DG,max (19)

[0142] In the formula: P DG,min P DG,max These represent the upper and lower limits of the active power output of the distributed power source; P DG (t) represents the actual active power output by distributed generation in the distribution network.

[0143] P DG (t)=(1+μ for e)P DG.fore (t)(20)

[0144] 0≤|μ fore |≤μ fore.max (twenty one)

[0145] Where: μ fore This represents the prediction error coefficient for photovoltaic and wind power output; the closer it is to 0, the higher the prediction accuracy.

[0146] P DG.fore (t) represents the day-ahead forecast of the active power output of the distributed generation.

[0147] ② Node voltage constraints:

[0148] U i,min ≤U i ≤Ui,max (twenty two)

[0149] In the formula: U i,max U i,min These are the upper and lower limits of the allowable voltage at node i, respectively; U i : Voltage amplitude of the i-th node in the distribution network.

[0150] ③ Branch current constraint:

[0151] 0≤I l ≤I l,max (twenty three)

[0152] In the formula: I l I represents the amplitude of the current flowing through branch 1. l,max This represents the maximum value for branch l to satisfy the thermal stability condition.

[0153] ④ Branch power constraints:

[0154] P li,min ≤P li ≤P li，max (twenty four)

[0155] In the formula: P li P represents the power transmitted by the first branch in the distribution network. li,max P li,min These represent the upper and lower limits of the power transmitted by the branch circuit.

[0156] ⑤ Energy storage device response constraints:

[0157] To ensure that energy storage devices operate under normal conditions and meet state of charge (SOC) and charge / discharge power constraints,

[0158] It is also necessary to satisfy the ESS charging and discharging power constraints and power conservation constraints.

[0159] SOC ESS . min &≤SOC ESS (t)≤SOC ESS . max (25)

[0160]

[0161]

[0162] Where: SOC ESS.min SOC ESS . max These represent the upper and lower limits of the state of charge of the energy storage device; P N Rated power of the energy storage system; ε: the charge and discharge power of energy storage at time t; E: the allowable charge and discharge energy balance error index of energy storage; E: the storage capacity of energy storage.

[0163] ⑥. Total power conservation constraint:

[0164] At any given time, the distribution network maintains a balance between the total electricity purchased from the main grid (generally the output of conventional generating units) and the combined active power output from uncontrollable loads, distributed generation output, power LA, and energy storage dispatch in the distribution network.

[0165] P G . t =P ul.t +F LA，t +P ESS.t -P DG.t (28)

[0166] In the formula: P G.t P represents the total electricity purchased by the distribution network from the main grid at time t; ul.t P represents the active power output required by the uncontrollable load at time t; LA，t P represents the active power output of LA at time t, where an increase is positive and a decrease is negative; ESS.t P represents the active power output of stored energy at time t; DG.t This is represented as the active power output of the distributed power source at time t.

[0167] ⑦. Reliability constraints:

[0168] R≥R min (29)

[0169] In the formula: R represents the reliability value of the current distribution network system; R min This indicates the minimum reliability requirements for the power distribution network system.

[0170] Step 3: Immune Genetic Algorithm

[0171] Particle Swarm Optimization (PSO), also known as particle swarm optimization, was invented by Dr. Eberhart and Dr. Kennedy. This algorithm, developed by simulating the foraging behavior of flocks of birds, is a collaborative stochastic search algorithm based on group cooperation. Individuals within the group can share information and cooperate to search for the optimal solution, which is the fundamental idea behind particle swarm optimization. It is generally considered a type of swarm intelligence (SI) and is also included in Multiagent Optimization Systems (MAOS). Essentially, PSO is an iterative optimization tool; the iterative search continues as fitness increases, searching for the optimal solution from random solutions.

[0172] All solutions to the optimization objective problem in PSO can be regarded as a bird, and the particle swarm is equivalent to a flock of birds. In nature, there is food in a certain fixed area. The specific location of the food has not yet been discovered, but the distance between the bird and the food can be perceived. In the optimization problem, these solutions, which are equivalent to birds, are abstracted into particles with zero volume and mass. These particles are placed in an infinitely extending N-dimensional space. In this N-dimensional space, the position and flight speed of the particle can be represented as a vector, and the direction and speed of the particle's next flight are determined by these vectors. The optimal solution we are looking for is the position of the "food". The fitness value of the particle's current position needs to be calculated according to the objective function, which is the distance between the "food" and the particle. In each search iteration, in addition to the optimal solution found by the individual particle based on its own experience, called the individual extreme value (Pbest), the individual particle can also know the optimal solution found by its companions in the whole swarm, called the global extreme value (Gbest). The particle can continuously correct its flight direction and speed through its own and others' experience, and finally find the global optimal solution

[57] .

[0173] PSO generates a swarm of random particles through initialization. Assuming there is a swarm of n particles in an N-dimensional target search space, then:

[0174] The position of the i-th particle:

[0175] X i =(x i1 x i2 , ..., x id i = 1, 2, ..., n (30)

[0176] The velocity of the i-th particle:

[0177] V i =(v i1 v i2 , ..., v id i = 1, 2, ..., n (31)

[0178] The current individual extreme value of the i-th particle:

[0179] P best =(p i1 p i2 , ..., p id i = 1, 2, ..., n (32)

[0180] The current global extremum of the entire population:

[0181] g best = (g1, g2, ..., g d (33)

[0182] Speed ​​update formula:

[0183]

[0184] Position update formula:

[0185]

[0186] In the formula: ω is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers with values ​​ranging from [0, 1]. and Let N represent the N-dimensional components of the velocity and position vector of particle i after the k-th iteration.

[0187] Particle Swarm Optimization Algorithm Flow Figure 3 As shown.

[0188] The particle velocity update formula consists of three parts: the first part is inertial behavior, which mainly plays a role in balancing global and local search capabilities; the second part is "cognitive" behavior, indicating that the particle has a tendency to seek its own optimal value; and the third part is the "social" part, indicating that the particle has a tendency to seek the global optimal value. These three parts work together to determine the particle's trajectory.

[0189] Solving the model using the particle swarm optimization algorithm

[0190] (1) In the two-level programming model, the active power output of the upper-level energy storage system and controllable load and the daily load power of the lower-level distribution network are variables. These variables are regarded as particles, and the position and velocity of the particles in space are initialized.

[0191] (2) Taking the distribution network dispatching operation cost, comprehensive benefits, load amplitude and volatility as the objectives, the fitness of particles in the population is calculated by power flow. The position and fitness value of these particles are stored in pbest, and the position and fitness value of the optimal solution in all pbest are stored in gbest.

[0192] (3) Update the velocity and position of the particle using the velocity and position update formula, compare the current pbest and gbest, and update gbest.

[0193] (4) When the stopping condition is met, stop the search and output the result; otherwise, continue from (3) to perform another velocity and position update. The process is as follows: Figure 4 As shown.

[0194] Verification Example:

[0195] This invention selects the IEEE-33 node distribution system and constructs an active distribution network model on it, including distributed generation, energy storage systems, and loads, to complete the simulation analysis. Figure 5 As shown in the table. This power distribution system has 32 branches. The power base value is selected as 100MVA, the base voltage as 12.7KV, and the total active load of the network is 4950KVA. The system active load and branch resistance values ​​are shown in the table. A 1MW distributed photovoltaic power source is connected to node 6, and 1MW and 1.5MW distributed wind power sources are connected to nodes 10 and 26 respectively. Node 23 is connected to an energy storage system. Relevant parameters are shown in Table 1. The simulation step size is set to 1 hour, and the scheduling cycle is 24 hours. Energy storage data is shown in Table 2, and load response data are shown in Tables 3 to 6.

[0196] Table 1 Active load and branch resistance values ​​of IEEE-33 node

[0197]

[0198]

[0199] Table 2 Energy Storage Data

[0200] category data Charging power (MW) 0.5 Discharge power (MW) 0.4 Active power (MW) 0.375 State of charge (SOC) 0.3-0.98 Call cost (RMB / MWh) 250 Discharge subsidy (RMB / kWh) 0.1 Charge and discharge efficiency 0.95

[0201] Table 3. Parameters related to distributed power sources

[0202] type Load transferred out (MW) Load transferred in (MW) Investment and installation costs 45,300 yuan / kW 10,500 yuan / kW Operation and maintenance costs 0.102 (yuan / kWh) 0.030 (yuan / kWh)

[0203] Table 4 Time-of-use electricity prices

[0204] Time period Electricity price (RMB / kWh) 7：00-11：0019：00-23：00 0.5583 23：00-7：00 0.3583 11：00-19：00 0.5283

[0205] Table 5 Interruptible Load Data

[0206] Number of nodes Disconnectable power Number of interruptions 17 0.15 4 25 0.12 5

[0207] Table 6 Transferable Load Data

[0208]

[0209]

[0210] from Figure 6 It is known that from 6:00 PM to 6:00 AM the following day, sunlight is weak and almost no longer contributes to power generation. Photovoltaic output reaches its maximum at 12:00 PM. Photovoltaic power generation increases and decreases at fixed intervals, with relatively low volatility. In contrast, wind power generation is highly random, with its curve almost opposite to that of photovoltaic power generation, exhibiting greater volatility. Based on the predicted active power output of distributed generation sources, it is evident that when a large number of distributed generation sources are connected, the stable operation of the power system is disrupted, and its reliability decreases accordingly.

[0211] Joint planning results in different scenarios

[0212] When analyzing the joint planning method of "source-grid-load-storage" in active distribution networks, the optimization results are compared by setting three scenarios: "source-grid-load", "source-grid-storage", and "source-grid-load-storage". The optimization effects are analyzed in the scenarios of "source-grid-load" (no controllable loads participating), "source-grid-storage" (no energy storage system participating), and "source-grid-load-storage" (considering all four controllable resources simultaneously).

[0213] 1) Coordinated optimization of "source, grid, and load"

[0214] In this scenario, the energy storage system does not participate in coordination; only the source-grid-load interaction is considered. The transferable and interruptible load data in Tables 3 and 4 serve as the standard for load dispatching. The joint planning and dispatching results are as follows: Figure 8 As shown, the node voltage deviation is as follows Figure 9 As shown.

[0215] Controllable loads, without the participation of energy storage systems, can only be managed through day-ahead dispatch. Using controllable load regulation is costly and economically inefficient. Figure 8 , Figure 9 It can be seen that the peak load periods (3-5 and 9-12) with the highest dispatch volume are 3-5 and 9-12, while the off-peak periods are 6-8 and 19-21, during which no load participates in dispatch. This reduces the amount of load participating in dispatch and has a reducing effect on the active power of the load in the distribution network, but it does not have the effect of filling the valleys. Compared with the unoptimized curve, the optimized curve is more stable. It can be seen that the extreme values ​​of active power are all within the range of the extreme values ​​before optimization, and the load fluctuation amplitude has decreased. The node voltage deviation has also decreased, proving that when controllable loads are connected to the grid, the randomness and volatility of distributed power source connection are effectively controlled, and the reliability and stability are improved.

[0216] 2) Coordination and optimization of "source, network, and storage"

[0217] In this scenario, controllable loads do not participate in coordination; only the source-grid-storage functions are considered. The charging and discharging output of the energy storage system is set based on the basic conditions of the comprehensive load in real-time scheduling. The joint planning and scheduling results are as follows: Figure 10 As shown, the node voltage deviation is as follows Figure 11 As shown.

[0218] Depend on Figure 10 , Figure 11It can be seen that the discharge time of the energy storage system is during the peak load periods of 2-6, 8-11, and 19-21, while the charging time is during the off-peak load periods of 0-2, 6-8, 11-19, and 21-24. At 10-12, the state of charge constraint avoids overcharging and affects the lifespan while ensuring economy. The energy storage system constrains the timely reduction of load. However, due to the influence of the reliability requirements of the lower-level target, charging is carried out in time during the 12-14 interval. Compared with the first case, the curve is more stable, and the effect of reducing load fluctuation is more obvious. Compared with before optimization, the node voltage deviation is also reduced, and the optimization effect is better.

[0219] 3) Coordinated optimization of "source-grid-load-storage"

[0220] In this scenario, controllable resources participate in joint planning. Based on the load scheduling arrangements described earlier and the charging and discharging output of the energy storage system, and considering the overall optimization effect, the joint planning and scheduling results are as follows: Figure 12 As shown, the node voltage deviation is as follows Figure 13 As shown.

[0221] Overall, the amount of controllable load called up has increased significantly compared to Scenario 1, with loads that can still be reduced being the main component. Peak shaving and valley filling effects are the most pronounced among the three scenarios. Compared to Scenario 1 and Scenario 2, the optimized Scenario 3 shows a particularly significant reduction in node voltage deviation, making it the best-performing of the three scenarios.

[0222] The optimization results of the three scenarios are comprehensively analyzed to optimize the scheduling load curve and voltage deviation, such as... Figure 14 , Figure 15 As shown.

[0223] By placing the data from the three scenarios into the same graph, it can be clearly seen that when the source-grid-load-storage joint planning is implemented, the peak value decreases and the trough value increases, resulting in the most significant peak shaving and valley filling effect. The curve is also smoother, indicating that the load fluctuation amplitude is the smallest compared to the other scenarios, and the voltage deviation decreases significantly. Whether from the perspective of comprehensive load or voltage deviation, the optimization effect of the source-grid-load-storage joint planning is the most ideal, which is in line with the economic reliability of power grid operation.

[0224] 4) Data analysis of the optimization effect of the integrated planning of power generation, grid, load and storage:

[0225] The amplitude and volatility of the lower-level optimization objectives are clearly reflected in the integrated load active power curve, while the benefits of the upper-level optimization objectives require analysis of the distribution network's operational economy and stability using specific data. The voltage deviation of the upper-level objective function is calculated from the power flow after connecting the corresponding controllable resources.

[0226] Table 7 Upper-layer target voltage deviation

[0227] Number of nodes Before optimization Scene 1 Scene 2 Scene 3 6 0.0045 0.0041 0.004 0.003 10 0.0185 0.0175 0.016 0.013 23 0.006 0.0055 0.005 0.0039

[0228] As shown in Table 7, the voltage deviation before optimization is larger than that after optimization. Moreover, the optimization effect is the best after the joint planning of source, grid, load and storage, and the stability of the distribution network operation is greatly improved after optimization.

[0229] Table 8 Upper-level optimization scheduling objectives

[0230]

[0231] Table 9 Upper-level optimization objectives

[0232] upper target Before optimization Scene 1 Scene 2 Scene 3 minf 0.577 0.535 0.512 0.446

[0233] Based on Objective 1 (operational cost of distribution network dispatch) in the upper-level model, which includes economic objectives such as the cost of power dispatching from energy storage devices, the revenue from charging and discharging, government subsidies, investment, installation, operation and maintenance costs of distributed power sources, and network losses, a specific data analysis is conducted. Table 9 shows that the optimized upper-level objective value is better, and the economic efficiency of distribution network operation is stronger when controllable resources participate in the planning. The difference in the effectiveness of the optimized upper-level objective mainly lies in the cost of power dispatching from energy storage devices, the revenue from charging and discharging and government subsidies, and network losses. According to the analysis in Chapter 3 of this paper, after the joint planning of source-grid-load-storage, the stability of system operation is greatly improved, and network losses are reduced accordingly.

[0234] As shown in Table 8, Scenario 3 has the lowest network loss cost. Although Scenario 2 needs to consider the cost of calling the energy storage system, the energy storage system has the function of "low storage and high discharge," and it has discharge revenue and government subsidies when performing peak shaving and valley filling. It also reduces network losses, avoids large-capacity equipment from using electricity during peak hours, and has lower charging costs. Therefore, Scenario 1 has a better optimization effect than Scenario 2. Scenario 3 combines the optimization effects of Scenario 1 and Scenario 2 on power system operation, achieving the best results in both economy and stability, and thus has the best optimization effect.

[0235] This invention considers the operational characteristics of distributed generation, controllable loads, and energy storage systems in distribution networks, while also taking into account the active power output constraints of distributed generation, the response constraints of energy storage systems, and the power supply reliability constraints. A two-layer model for joint planning of source-grid-load-storage is proposed. The upper layer achieves optimal efficiency while considering the power supply stability and economy of the distribution network, mainly including distribution network dispatching and operation costs, comprehensive benefits, and voltage deviation. The lower layer achieves optimal load variation, and the model is solved using a particle swarm optimization algorithm. Finally, simulations are performed on the IEEE 33-node distribution system to verify the optimization effect of the model. Joint planning and dispatching for three scenarios—"source-grid-load," "source-grid-storage," and "source-grid-load-storage"—are studied respectively. The reliability and economy of power system operation are verified mainly through the active power output and voltage deviation of the comprehensive load. The results show that the joint planning of source-grid-load-storage fully utilizes the controllable resource regulation characteristics and achieves the best optimization effect.

Claims

1. A comprehensive planning method for distribution networks integrating "source-grid-load-storage" considering overall performance, characterized in that... Includes the following steps: Step 1: Determine the probability models for photovoltaic power output and wind power generation in the planning area: Step 2: Establish a mathematical model considering joint load scheduling of source, grid, load, and storage: Step 3: Establish a two-layer planning model for source-grid-load-storage integration, with the upper layer being the planning layer and the lower layer being the operation layer; Step 4: Apply the particle swarm optimization algorithm to solve the bi-level programming model established in Step 3; Through the above steps, the integrated planning of the power distribution network, encompassing "source-grid-load-storage", can be achieved. Step 3: Establish a joint two-tier planning model for source-grid-load-storage systems: (1) Upper-level objective function: （5）； （6）； （7）； （8）； （9）； （10）； （11）； In the above formula, This indicates the cost of power dispatching from the energy storage system; Revenue generated from charging and discharging energy storage systems and government subsidies; This indicates the cost of upgrading and constructing the power distribution network; Indicates the discount rate; This indicates the cost of power loss during power distribution at different times. This indicates the power loss of the distribution network at different time periods; Indicates electricity prices for different time periods; This indicates the cost per kilowatt-hour of lithium batteries; Indicates the power consumed by the energy storage system; Indicates the revenue of the energy storage system; , These variables represent the charging and discharging states at time i, with values ​​of 0 or 1. , These represent the energy storage charging and discharging power at time i, respectively. Indicates the electricity price at time i; These represent government subsidies received by the energy storage system; T represents the total number of years of the planned operation. The government subsidy for the discharge of energy storage devices; The voltage deviation after distributed generation is connected to the distribution network is shown below: （12）； In equation (12), Indicates time; Represents a node; Indicates the total number of nodes; express Node at The actual voltage value at that moment; Indicates the node's rated voltage; The normalized objective function is shown below: （13）； In equation (13), These represent the power dispatch cost of the energy storage system, the "low storage, high discharge" revenue and government subsidy revenue generated by the charging and discharging of the energy storage system, the cost of load aggregators participating in distribution network dispatch in each time period, and the maximum value of power loss costs of the distribution network in each time period, respectively. （14）； In equation (14), 24 represents the number of moments in a day; This represents the number of system nodes. This represents the maximum permissible voltage deviation at the node. The overall objective function is as follows: （15）； In equation (15), These are the weight values ​​for the corresponding indicators; (2) Lower-level objective function: （16） （17）； （18）； In the above formula, Indicates the magnitude of load fluctuation; Indicates load volatility; , These are the weighting coefficients for the two evaluation indicators; , These represent the maximum and minimum daily load power of the distribution network, respectively. This represents the average daily load of the distribution network; , This indicates the daily load value of the distribution network at any given time.

2. The "source-grid-load-storage" joint planning method for distribution networks considering comprehensive performance as described in claim 1, characterized in that: In step one, the probability density function describing the randomness of solar irradiance and wind speed intensity over a period of time is: （1）； （2）； In the above formula, This represents the actual irradiance intensity within the statistical period; Indicates the maximum radiation intensity; , The coefficient representing the Beta distribution; A function representing Gamma; ; , These are the shape factor and scale factor of the Weibull distribution, respectively; , These represent photovoltaic and wind power generation models, respectively. , , This is the simulated probability density function.

3. The "source-grid-load-storage" joint planning method for distribution networks considering comprehensive performance as described in claim 1, characterized in that: Step two involves establishing a mathematical model that considers the joint load scheduling of source, grid, load, and storage, as follows: Equations (3) and (4) describe the relationship between upper-level and lower-level decisions, respectively: （3）； In equation (3), This represents the upper-level objective function; , For decision variables; For inequality constraints, Equality constraint; （4）； In equation (4): The lower-level objective function; , Let be the decision variables of the lower-level model, where These are decision variables influenced by higher levels. For lower-level inequality constraints; This serves as a constraint for the lower-level equality.

4. The "source-grid-load-storage" joint planning method for distribution networks considering comprehensive performance as described in claim 1, characterized in that: The constraints of the source-grid-load-storage joint two-level planning model are as follows: ①. Active power output constraints of distributed generation: （19）； In equation (19): , These are the upper and lower limits of the active power output of the distributed power source, respectively. This represents the actual active power output by distributed generation sources in the distribution network. （20）； （21）； In the formula: This represents the prediction error coefficient for photovoltaic and wind power output; the closer it is to 0, the higher the prediction accuracy. This represents the day-ahead forecast of the active power output of distributed generation. This represents the maximum error coefficient in the prediction of photovoltaic and wind power output; ②. Node voltage constraints: （22）； In the formula: , Representing nodes respectively The upper and lower limits of the permissible voltage; Indicates the first in the distribution network Voltage amplitude at each node; ③. Branch current constraint: （23）； In the formula: This indicates the magnitude of the current flowing through branch l; Indicates a branch The maximum value that satisfies the thermal stability condition; ④. Branch power constraints: （24）； In the formula: This represents the power transmitted by the l-th branch in the distribution network; , These represent the upper and lower limits of the branch transmission power, respectively. ⑤. Energy storage device response constraints: （25）； （26）； （27）； In the formula: , These represent the upper and lower limits of the state of charge of the energy storage device, respectively. Rated power of the energy storage system; , Let represent the charging and discharging power of the energy storage at time t, respectively; This indicates the permissible charge and discharge energy balance error index for energy storage. Indicates the storage capacity of energy storage; ⑥. Total power conservation constraint: （28）； In the formula: This represents the total electricity purchased by the distribution network from the main grid at time t; This represents the active power required by the uncontrollable load at time t; This represents the active power output of LA at time t, where an increase is positive and a decrease is negative. This indicates the active power output of the stored energy at time t; Let t be the active power output of the distributed power source. ⑦. Reliability constraints: （29）； In the formula: This indicates the current reliability value of the power distribution network system; This indicates the minimum reliability requirements for the power distribution network system.

5. The "source-grid-load-storage" joint planning method for distribution networks considering comprehensive performance as described in claim 1, characterized in that: Step four includes the following steps: (1): In the two-level programming model, the active power output of the upper-level energy storage system and controllable load and the daily load power of the lower-level distribution network are variables. These variables are regarded as particles, and the position and velocity of the particles in space are initialized. (2): Taking the distribution network dispatching operation cost, comprehensive benefits, load amplitude and volatility as the objectives, the fitness of particles in the population is calculated through power flow. The position and fitness value of these particles are stored in pbest, and the position and fitness value of the optimal solution in all pbest are stored in gbest. (3): Update the velocity and position of the particle using the velocity and position update formula, compare the current pbest and gbest, and update gbest; (4): When the stopping condition is met, stop the search and output the result; otherwise, continue from (3) to perform another speed and position update.

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