A method and device for evaluating all-year hourly operation of a source-grid-load-storage project

Abstract

The application discloses a kind of source network load storage project planning annual each hour operation evaluation method and device, according to the characteristics of wind power, photovoltaic, energy storage, power grid, load joint operation, consider the charge and discharge plan of energy storage device in each time period in the operation process of source network load storage project.The sum of annual power purchase cost, energy storage device loss cost, equipment operation and maintenance cost is minimum as target, consider power balance constraint, wind power, photovoltaic, power grid, energy storage each equipment operation constraint, energy storage state-of-charge constraint, etc., a kind of source network load storage 8760 hours operation simulation method based on particle swarm optimization is proposed.Finally, the economic indicators of source network load storage in any time period and the whole year, new energy curtailment rate, new energy power proportion and other technical indicators can be output, which provides favorable support for decision-making.

Description

A method and apparatus for evaluating the operation of a power generation, grid, load, and storage project throughout the year, including hourly operations. Technical Field

[0001] This invention belongs to the field of power supply system simulation technology, specifically relating to a method and device for evaluating the operation of a power source-grid-load-storage project throughout the year for each hour. Background Technology

[0002] A power generation, grid, load, and storage (PGS) system is a novel network structure consisting of distributed power sources, energy storage devices, electrical appliances, the main power grid, and control devices. As a crucial means of addressing numerous problems in the power system, PGS systems need to maximize their significant economic benefits. Therefore, the economical operation of PGS systems is key to attracting users and enabling their widespread adoption in the power system. However, with the increasing variety and scale of equipment in PGS systems, the optimization of economic operation of microgrids becomes increasingly complex.

[0003] The integrated power generation, grid, load, and energy storage project encompasses power sources such as wind and solar power, the power grid, loads, and energy storage systems. Its operational objectives include low overall system lifecycle cost, high reliability, and low wind and solar curtailment rates. Constraints include total power balance, real-time power balance, energy storage device power constraints, energy storage device capacity constraints, renewable energy proportion constraints, and load adjustability constraints, among others. The economic operation problem of power generation, grid, load, and energy storage is a complex nonlinear mixed-integer stochastic programming problem, characterized by high variable dimensionality, large computational scale, a mixture of continuous and discrete variables, numerous constraints, and a nonlinear objective function. Due to the difficulty of the optimization problem, traditional solutions easily fall into the curse of dimensionality within a finite scheduling period, theoretically requiring years to solve or failing to find the optimal solution altogether.

[0004] Compared to traditional programming algorithms, intelligent optimization algorithms are gaining increasing acceptance. Intelligent computing, also known as soft computing, refers to problem-solving algorithms designed based on the principles and patterns of nature or the biological world. In recent years, many biomimetic intelligent optimization algorithms, which differ significantly from the principles of classical mathematical programming and aim to solve complex optimization problems by simulating natural ecosystems, have been proposed one after another. These include simulated annealing, artificial immune algorithms, bacterial foraging algorithms, particle swarm optimization, and genetic algorithms. Each of these algorithms has its advantages and disadvantages, and their unique process characteristics and search properties have led to their widespread application.

[0005] Particle Swarm Optimization (PSO) is a discipline that emerged in the 1990s, known for its simple concept, ease of implementation, and fast convergence. The basic idea of ​​PSO is to simulate the foraging behavior of a flock of birds randomly searching for food. The flock adjusts its search path through its own experience and communication with other birds to find the location with the most food. The position / path of each bird is a combination of independent variables, and the food density at each location reached is the function value. Each search adjusts its search direction and speed based on its own experience (its own historical optimal search location) and population communication (the population's historical optimal search location), a process called tracking the extreme value, thus finding the optimal solution.

[0006] Referring to Figure 1, the basic steps of the particle swarm optimization algorithm are as follows:

[0007] Step 1: The motion state of a particle is described by two parameters: position and velocity. Therefore, these two parameters are initialized.

[0008] Step 2: The result (function value) of each search is the particle fitness. Then, record the individual best position and the group's best position.

[0009] Step 3: The individual's historical best position and the group's historical best position are equivalent to generating two forces, which, together with the particle's own inertia, affect the particle's motion state, thereby updating the particle's position and velocity.

[0010] Particle swarm optimization has the following characteristics:

[0011] ① Compared to traditional algorithms, it has a very fast computation speed and a strong global search capability;

[0012] ②PSO is not very sensitive to population size, so setting the initial population to 500-1000 will not have a significant impact on speed;

[0013] ③ The particle swarm optimization algorithm is suitable for continuous function extremum problems and has strong global search capabilities for nonlinear and multimodal problems.

[0014] Based on the characteristics of particle swarm optimization algorithm, such as fast computation speed and strong global search capability for nonlinear and multimodal problems, it is very suitable for solving source-network-load-storage optimization problems with high variable dimensionality, large computational scale, mixed continuous and discrete variables, numerous constraints, and nonlinear objective function.

[0015] Currently, most methods for evaluating planning schemes use the four typical days method, selecting one day each in spring, summer, autumn, and winter for calculation and verification. Although the typical day method is faster, it includes fewer scenarios, resulting in significant discrepancies between the calculation results and the actual situation. This can easily lead to wasted investment or failure to achieve the expected operational results. Summary of the Invention

[0016] This invention provides a method and device for evaluating the operation of a power generation, grid, load, and energy storage project throughout the year, allowing for the statistical analysis of operating costs, renewable energy generation ratios, and renewable energy curtailment rates for all time periods throughout the year corresponding to the planned wind power, photovoltaic, and energy storage capacities. The project planning scheme is then evaluated based on these operating costs, renewable energy generation ratios, and renewable energy curtailment rates, resulting in more accurate evaluation results.

[0017] To achieve the above objectives, the present invention provides a method for evaluating the operation of a source-grid-load-storage project throughout the year, including the following steps:

[0018] Step 1: Based on the wind power and solar power capacity configuration determined by the project planning scheme, and the hourly wind power and solar power output throughout the year, construct the hourly annual power generation of wind power and solar power; based on the load information, construct the hourly annual load power consumption.

[0019] Step 2: Construct constraints based on the principle of real-time power balance;

[0020] Step 3, construct the objective function;

[0021] Step 4: Set the number of swarms and the number of iterations for the particle swarm optimization algorithm;

[0022] Step 5: Use the particle swarm optimization algorithm to solve the system of equations consisting of constraints and objective function to obtain the optimal charging and discharging plan for the energy storage device.

[0023] Step 6: Calculate the new energy technical indicators based on the annual hourly power generation of wind power and photovoltaic power, the annual hourly load power consumption, and the optimal charging and discharging plan of the energy storage device. The new energy technical indicators include grid power purchase cost, new energy curtailment, new energy curtailment rate, and new energy power ratio.

[0024] Step 7: Evaluate the planning scheme of the source-grid-load-storage project based on the new energy technical indicators.

[0025] Furthermore, in step 2, the constraints include power balance constraints, wind power equipment operation constraints, photovoltaic equipment operation constraints, grid equipment operation constraints, and energy storage charge rate constraints.

[0026] Furthermore, in step 3, the objective function is to minimize the total annual cost.

[0027] Furthermore, in step 4, the number of groups is 200, and the number of iterations is 10 to 15.

[0028] Furthermore, step 5 includes the following steps:

[0029] Step 5.1: Initialize all particles. Each particle is an array consisting of the number of hours in the current month. Each number represents the charging and discharging plan of the energy storage device at this moment.

[0030] Step 5.2: Calculate the objective function for all particles to obtain the objective function values ​​for different particles;

[0031] Step 5.3: The objective function value with the smallest objective function value is taken as the optimal function value of the population in this round, and the particle corresponding to this optimal function value is the optimal particle in this round.

[0032] Step 5.4: Generate the next round of N particles using the following formula:

[0033] v f (t+1)=v f (t)+c1×rand×(pbest f -SOC f )+c2×rand×(gbest-SOC f )

[0034] SOC f (t+1)=SOC f +v f (t+1)

[0035] Among them, v f (t+1) represents the velocity of the f-th particle during the update, v f (t) represents the velocity of the f-th particle at this moment, c1 represents the self-learning factor, rand is a random number between 0 and 1, and pbest f For the f-th particle's own historical best position, SOC f Let f be the array for the current f-th particle, c2 represent the population learning factor, and gbest is the historical best particle position of the population.

[0036] Step 5.5: Determine the position of each particle. If the position or velocity of a particle exceeds the limit, pull it back to the boundary.

[0037] Step 5.6: Repeat steps 5.2 to 5.5 until the maximum number of iterations is reached;

[0038] Step 5.7: Output the final optimal particle position of the population as the result of the particle swarm optimization algorithm, that is, the optimal charging and discharging plan of the energy storage device.

[0039] Furthermore, in step 6, the power grid purchase cost is the sum of the product of the main grid power per hour and its electricity price throughout the year.

[0040] Furthermore, in step 6, the amount of abandoned renewable energy P is calculated using either hourly calculation or summation method. abd,t ,

[0041] The calculation formula for the hourly calculation method is as follows:

[0042] P abd,t =P WT,t +P PV,t +P BESS,t -P Load,t

[0043] Among them, P WT,t Let P be the wind power output at hour t. PV,t Let P be the photovoltaic power at hour t. BESS,t Let P be the power of the energy storage device in hour t. Load,t Let be the power consumption of the load in hour t;

[0044] The summation calculation method is as follows:

[0045] P abd,sum =P WT,sum +P PV,sum +P Grid,sum -P Load,sum

[0046]

[0047]

[0048]

[0049]

[0050] Among them, P abd,sum P represents the amount of renewable energy power that has been wasted throughout the year. WT,sum P represents the total annual wind power generation. PV,sum P represents the total photovoltaic power generation for the entire year. Grid,sum P represents the total amount of electricity purchased from the grid throughout the year. Load,sum This represents the total annual load power consumption.

[0051] Furthermore, in step 6, the renewable energy curtailment rate γ abd The calculation method is as follows:

[0052]

[0053] Among them, P abd,sum P represents the amount of renewable energy power that has been wasted throughout the year. WT,sum P represents the total annual wind power generation. PV,sum This represents the total photovoltaic power generation for the entire year.

[0054] Furthermore, in step 6, the proportion of renewable energy power γ green The calculation method is as follows:

[0055]

[0056] In the above formula, P abd,sum P represents the amount of renewable energy power that has been wasted throughout the year. WT, P represents the total annual wind power generation. PV, P represents the total photovoltaic power generation for the entire year. Load, This represents the total annual load power consumption.

[0057] An evaluation device for the operation of a power generation, grid, load, and storage project throughout the year, comprising:

[0058] The input module is used to collect basic data, including the capacity configuration of wind power and photovoltaic power, and hourly wind power and photovoltaic power output and load information throughout the year;

[0059] The processing module stores the objective function and constraints, and is used to solve the objective function and constraints according to the particle swarm algorithm to obtain the optimal charging and discharging plan for the energy storage device.

[0060] The output module is used to calculate new energy technical indicators based on basic data and the optimal charging and discharging plan of the energy storage device. The new energy technical indicators include grid power purchase cost, new energy curtailment amount, new energy curtailment rate, and new energy power ratio.

[0061] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0062] This invention, based on the characteristics of the joint operation of wind power, photovoltaics, energy storage, power grid, and load, and according to the capacity configuration of wind power and photovoltaics determined by the source-grid-load-storage project planning scheme, considers the charging and discharging plan of the energy storage device in every period of the year during the simulated operation of the source-grid-load-storage project. It calculates the annual new energy technical indicators of the source-grid-load-storage project under the charging and discharging plan. The new energy technical indicators include the scenario of 365 days a year, and its accuracy and reliability are significantly higher than the method that only calculates 4 typical days of data. It can effectively evaluate the source-grid-load-storage project planning scheme, thereby guiding investment and avoiding waste or failure to meet expectations in operation.

[0063] With the goal of minimizing annual operating costs, and constrained by real-time power balance, upper and lower power limits for each device, upper and lower charge rate limits for energy storage, and the equality of charge rate of energy storage at the initial and final moments, a full feasible region optimization solution based on particle swarm optimization was completed. This resulted in economic indicators such as the annual grid purchase cost, and technical indicators such as the amount of renewable energy curtailment, the renewable energy curtailment rate, and the proportion of renewable energy power generation. It has the following advantages:

[0064] First, it can perform calculations on ultra-large-scale (8760 hours) data. By adopting a segmented calculation method, it significantly reduces the computational difficulty and improves the computational speed without affecting the accuracy of the results. For the first time, it has realized the 365-day source-grid-load-storage operation evaluation based on the particle swarm algorithm.

[0065] Second, the calculation process and results considered the charging and discharging costs of the energy storage device and the grid purchase cost. The grid purchase cost calculation fully considered peak, valley, and flat electricity prices. The calculated costs are relatively accurate, providing strong support for decision-making.

[0066] Third: It can calculate the amount of power curtailment, curtailment rate, and green electricity ratio at any given moment and time period, as well as the total amount of power curtailment, curtailment rate, and green electricity ratio for the entire year. This provides a quantitative basis for determining whether a project's planning scheme meets the mandatory requirements of the policy. Attached Figure Description

[0067] Figure 1 is a flowchart of the particle swarm optimization algorithm;

[0068] Figure 2 shows the hourly output curve of photovoltaic power in January;

[0069] Figure 3 shows the hourly power output curve of wind power in January;

[0070] Figure 4 is a flowchart of a method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour.

[0071] Figure 5 shows an example of the velocity generation process in the particle swarm optimization algorithm;

[0072] Figure 6 shows the monthly electricity purchase curves for the entire year;

[0073] Figure 7 shows the percentage of electricity generated from new energy sources each month throughout the year;

[0074] Figure 8 shows the monthly renewable energy curtailment rate throughout the year;

[0075] Figure 9 is a schematic diagram of an annual 8760-hour operation evaluation device for a source-grid-load-storage project. Detailed Implementation

[0076] To make the objectives and technical solutions of this invention clearer and easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0077] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0078] This invention provides a rapid, efficient, and comprehensive method for simulating the operation of power generation, grid, load, and energy storage over a year-round (8760 hours) period, capable of handling various complex constraints. This method allows for the statistical analysis of annual operating costs, renewable energy generation ratios, and renewable energy curtailment rates corresponding to planned wind power, solar power, and energy storage capacities. This provides quantitative data support for decision-makers.

[0079] Example 1

[0080] A method for evaluating the operation of a power-source-grid-load-storage project throughout the year, including the following steps:

[0081] Step 1: Based on the wind power and solar power capacity configuration determined in the project planning scheme, and the hourly wind power and solar power generation throughout the year, construct the hourly annual power generation curves for wind power and solar power. Based on the load information, construct the hourly annual load power consumption curve. Figure 2 shows the hourly solar power generation curve for January, and Figure 3 shows the hourly wind power generation curve for January.

[0082] Step 2, Construct constraints

[0083] Based on the principle of real-time power balance, power balance constraints are constructed:

[0084] P WT,t +P PV,t +P Grid,t +P BESS,t =P Load,t

[0085] In the above formula, P WT,t Let P be the wind power output at hour t. PV,t Let P be the photovoltaic power at hour t. Grid,t Let P be the main grid power at hour t. BESS,t Let P be the power of the energy storage device in hour t, where the power of the energy storage device can be either positive, representing power supply to the load, or negative, representing charging. Load,t Let be the power consumption of the load in hour t.

[0086] Based on the characteristics of wind power, photovoltaics, power grid, and energy storage, operational constraints are constructed for each device:

[0087] P WT,min ≤P WT,t ≤P WT,max

[0088] P PV,min ≤P PV,t ≤P PV,max

[0089] P Grid,min ≤P Grid,t ≤P Grid,max

[0090] P BESS,min ≤P BESS,t ≤P BESS,max

[0091] In the above formula, P WT,max P represents the upper limit of wind power output. WT,min P represents the lower limit of wind power output. PV,max P represents the upper limit of photovoltaic power output. PV,min P represents the lower limit of photovoltaic power output. Grid,max P represents the upper limit of the power output of the large power grid. Grid,min This represents the lower limit of wind power output. According to policy requirements, power generation, grid, load, and storage are not allowed to send electricity to the grid. Therefore, P Grid,min ≥0, P BESS,max P represents the upper limit of the power output of energy storage. BESS,min This represents the lower limit of the power output of energy storage.

[0092] Based on the operating characteristics of energy storage, a state of charge (SOC) constraint for energy storage is constructed:

[0093] SOC BESS,min ≤SOC BESS,t ≤SOC BESS,max

[0094] In the above formula, SOC BESS,t SOC represents the charge rate (percentage of remaining energy) of the energy storage device in hour t. BESS,max State of Charge (SOC) represents the upper limit of the energy storage device's charge rate. BESS,min This represents the lower limit of the charge rate of an energy storage device.

[0095] SOC BESS,start =SOC BESS,End

[0096] In the above formula, SOC BESS,start State of Charge (SOC) represents the charge rate of the energy storage device at the start of the dispatch cycle. BESS,End This represents the charge rate of the energy storage device at the end of the scheduling cycle.

[0097] Step 3, construct the objective function

[0098] Taking into full account the operation and maintenance costs, loss costs, and electricity purchase costs of individual functional devices, the objective function is economic cost, as shown below:

[0099]

[0100] In the above formula, minF1 is the objective function value, F Op,t F represents the maintenance cost at hour t. Loss,t F represents the energy loss cost of the energy storage device in hour t. Grid,t This represents the cost of purchasing electricity from the main grid in hour t.

[0101] F Op, = WT ×C WT + PV ×C PV + BESS ×C BESS

[0102] In the above formula, γ WT C represents the operation and maintenance factor of wind power (unit: yuan / kW). WT Represents the installed capacity of wind power, γ PV The operation and maintenance factor (in yuan / kW) represents the photovoltaic system's performance coefficient. PV S represents the installed capacity of photovoltaics. BESS The operation and maintenance factor (in yuan / kW) represents the energy storage operation and maintenance factor. BESSThis represents the installed capacity of energy storage.

[0103] F Loss, = ch, ×E ch, ×Cost t + dis,t ×E dis,t ×Cost t

[0104] In the above formula, η ch, E represents the efficiency of energy storage charging in hour t. ch, η represents the energy stored and charged in hour t. dis,t E represents the efficiency of energy storage and discharge in hour t. dis,t Cost represents the energy discharged from the energy storage system in hour t. t This represents the electricity cost for hour t.

[0105] F Grid, =rice Buy,t ×E Grid,

[0106] In the above formula, Price Buy,t E represents the price of electricity purchased from the grid in hour t. Grid, This represents the energy purchased from the grid in hour t.

[0107] Step 4, set the parameters of the particle swarm algorithm.

[0108] We need to set the particle swarm size N, the number of iterations Iter, the self-learning factor c1, the population learning factor c2, and the upper velocity limit v of the particle swarm. max The lower limit of the particle swarm velocity v min A larger population size and more iterations result in a higher accuracy of the final solution, but also a longer computation time. Conversely, a smaller population size and fewer iterations result in a lower accuracy of the final solution, but a shorter computation time. A population size of 200 and a number of iterations of 10–15 are recommended.

[0109] Step 5: Referring to Figures 1 and 5, use the particle swarm optimization algorithm to solve the system of equations consisting of the constraints and the objective function.

[0110] To accelerate the calculation, the entire year was divided into 12 segments within the 8760-hour period. Each segment was calculated using the method described below, and the results were then summed up.

[0111] 1) Initialize all particles. Each particle is an array consisting of the number of hours in the current month. Each number represents the charging and discharging plan of the energy storage device at this moment. During the initialization process, all particles are randomly and uniformly distributed in the solution space, which is a multi-dimensional space that satisfies the constraints.

[0112] 2) Calculate the objective function for all particles and obtain the function values;

[0113] 3) Select the optimal function value as the optimal function value of the population in this round, and the particle corresponding to this optimal function value is the optimal particle in this round.

[0114] 4) Generate the next round of N particles using the following formula:

[0115] v f (t+1)=v f (t)+c1×rand×(pbest f -SOC f )+c2×rand×(gbest-SOC f )

[0116] SOC f (t+1)=SOC f +v f (t+1)

[0117] In the above formula, v f (t+1) represents the velocity of the f-th particle during the update, v f (t) represents the velocity of the f-th particle at this moment, c1 represents the self-learning factor, rand is a random number between 0 and 1, and pbest f For the f-th particle's own historical best position, SOC f Let f be the array for the f-th particle in this iteration, c2 represent the population learning factor, and gbest is the historical best particle position of the population.

[0118] The velocity of each particle can be calculated using the formula above. (SOC) f (t+1) represents the updated array after the f-th particle. Fixed values ​​during initialization are maintained unchanged. The concept is illustrated in Figure 4. In each iteration, the optimal value pbest for each particle and the optimal value gbest for the population are updated.

[0119] 5) Determine the position of each particle.

[0120] When a particle's position or velocity exceeds the limit, it needs to be pulled back to the boundary.

[0121] (1) When the particle velocity v f(t) > upper velocity limit v of the particle swarm max When it is, let v f (t) = v max ;

[0122] When the particle velocity v f (t) < upper velocity limit v of the particle swarm min When it is, let v f (t) = v min ;

[0123] (2) When SOC f > SOC max When it is, let SOC f = SOC max ;

[0124] When SOC f < SOCmin, let SOC f = SOCmin.

[0125] Among them, SOC max is the maximum state of charge of the energy storage, and SOCmin is the minimum state of charge of the energy storage; the programming method adopted is as follows:

[0126]

[0127]

[0128] 6) Repeat the iteration until the maximum number of iterations is reached

[0129] Repeat 2), 3), 4), 5) until the maximum number of iterations Iter is reached.

[0130] 7) Output the final population optimal function value gbest as the result of the particle swarm algorithm. gbest is an array representing the best charge-discharge plan of the energy storage device.

[0131] Step 6, analyze and statistically process the final data to obtain the final result

[0132] According to the result P BESS,t calculated by the particle swarm algorithm, the real-time power balance formula can be combined:

[0133] P WT,t + P PV,t + P Grid,t + P BESS,t = P Load,t

[0134] At this time, P WT,t and PPV,t P Load,t P BESS,t The main grid power P in hour t can be obtained. Grid,t .

[0135] And Price Buy,t It takes into account prices at different times of day, such as peak, off-peak, and valley periods.

[0136]

[0137]

[0138] Price Buy,t With P Grid,t Multiplying these two figures yields the cost of purchasing electricity from the main grid. The levelized cost per kilowatt-hour (LCOE) allows us to calculate the total cost of green electricity for the entire year. The total cost of supplying electricity for the year is the sum of the annual cost of purchasing electricity from the main grid and the cost of green electricity.

[0139] Based on the charging and discharging amount of the energy storage device in each period, as well as the charging and discharging efficiency of the energy storage device, the charging and discharging loss of the energy storage device can be calculated. Multiplying the charging and discharging loss of the energy storage device in each period by the electricity cost and then summing them up, the annual loss cost of the energy storage device can be obtained.

[0140] Equipment operation and maintenance costs are generally a fixed percentage multiplied by the investment in the equipment itself. Wind power, photovoltaic, and energy storage equipment each have different operation and maintenance cost coefficients.

[0141] The total annual cost can be obtained by adding up the annual electricity purchase cost, energy storage device loss cost, and equipment operation and maintenance cost.

[0142] Step 7: Calculate the green electricity technical indicators based on the results of the particle swarm optimization calculation.

[0143] Because selling electricity to the grid is permitted, when the amount of electricity generated by new energy sources exceeds the load consumption, and energy storage devices are unable to absorb it, this will result in the curtailment of new energy power. There are two ways to calculate the annual curtailment of new energy power:

[0144] The first method is to calculate hourly using the following formula:

[0145] P abd,t =P WT,t +P PV,t +P BESS,t -P Load,t

[0146] In the above formula, P abd,t This represents the amount of renewable energy wasted in hour t. For all P values ​​greater than 0... abd,t By summing them up, we can obtain the total amount of abandoned electricity for the whole year.

[0147] The second method is the summation method.

[0148] P abd,sum = WT, + PV, + Grid, -Load,

[0149]

[0150]

[0151]

[0152]

[0153] In the above formula, P abd,sum This represents the amount of renewable energy power that was wasted throughout the year. WT, P represents the total annual wind power generation. PV, P represents the total photovoltaic power generation for the entire year. Grid, P represents the total amount of electricity purchased from the grid throughout the year. Load, This represents the total annual load power consumption.

[0154] Calculate the curtailment rate of renewable energy:

[0155]

[0156] In the above formula, γ abd This refers to the curtailment rate of renewable energy.

[0157] Calculate the project's annual green electricity ratio:

[0158]

[0159] In the above formula, γ green This represents the percentage of green electricity used throughout the year.

[0160] The capacity configurations for wind power, solar power, and energy storage in the example are as follows:

[0161]

[0162] The photovoltaic power generation from January to December is as follows:

[0163]

[0164]

[0165] The wind power generation from January to December is as follows:

[0166]

[0167] Referring to Figure 6, the main grid electricity purchases from January to December are as follows:

[0168] Monthly Power Generation (100 Million kWh) 15.88 75.05 26.07 84.98 35.91 94.92 43.95 105.09 54.02 116.02 64.97 127.10 surface

[0169] Referring to Figure 7, the proportion of renewable energy power generation from January to December.

[0170]

[0171] Referring to Figure 8, the curtailment rate of renewable energy from January to December.

[0172]

[0173]

[0174] Step 8: Evaluate the planning scheme of the source-grid-load-storage project based on the new energy technology indicators.

[0175] Example 2

[0176] Referring to Figure 9, a year-round 8760-hour operation evaluation device for a power generation, grid, load, and storage project is characterized by comprising:

[0177] The input module is used to collect basic data, including the capacity configuration of wind power and photovoltaic power, and hourly wind power and photovoltaic power output and load information throughout the year;

[0178] The calculation module stores the objective function and constraints, and is used to solve the objective function and constraints according to the particle swarm algorithm to obtain the optimal charging and discharging plan for the energy storage device.

[0179] The output module is used to calculate new energy technical indicators based on basic data and the optimal charging and discharging plan of the energy storage device. The new energy technical indicators include grid power purchase cost, new energy curtailment amount, new energy curtailment rate, and new energy power ratio.

[0180] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0181] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] This invention, based on the characteristics of the integrated operation of wind power, photovoltaics, energy storage, power grid, and load, considers the charging and discharging plans of energy storage devices in each time period during the operation of a source-grid-load-storage project. With the objective of minimizing the sum of annual electricity purchase cost, energy storage device loss cost, and equipment operation and maintenance cost, and considering power balance constraints, operational constraints of wind power, photovoltaics, power grid, and energy storage equipment, and state of charge (SOC) constraints of energy storage, a particle swarm optimization algorithm-based simulation method for the 8760-hour annual operation of source-grid-load-storage is proposed. Ultimately, it can output economic indicators for any time period and the entire year, as well as technical indicators such as renewable energy curtailment rate and renewable energy power ratio for source-grid-load-storage projects.

[0183] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for evaluating the operation of a power-source-grid-load-storage project throughout the year, characterized in that, Includes the following steps: Step 1: Based on the wind power and solar power capacity configuration determined by the source-grid-load-storage project planning scheme, and the hourly wind power and solar power output throughout the year, construct the hourly annual power generation of wind power and solar power; based on the load information, construct the hourly annual load power consumption. Step 2: Construct constraints based on the real-time power balance principle. Step 3: Construct an objective function, which aims to minimize the total annual cost. The total annual cost is obtained by summing the annual electricity purchase cost, energy storage device loss cost, and equipment operation and maintenance cost. The annual electricity purchase cost is calculated by multiplying the electricity purchase price by the purchased electricity volume. The energy storage device loss cost is calculated by multiplying the energy storage device charging and discharging losses by the electricity consumption cost. Operation and maintenance costs are calculated by multiplying the installed capacity of the equipment by the corresponding operation and maintenance coefficient; the equipment includes wind power equipment, photovoltaic equipment, and energy storage equipment; Step 4: Set the number of swarms and the number of iterations for the particle swarm algorithm; Step 5: Use the particle swarm algorithm to solve the system of equations consisting of constraints and objective functions to obtain the optimal charging and discharging plan for the energy storage device; Step 6: Calculate the new energy technical indicators based on the annual hourly power generation of wind and photovoltaic, the annual hourly load power consumption, and the optimal charging and discharging plan for the energy storage device. The new energy technical indicators include grid power purchase cost, new energy curtailment, new energy curtailment rate, and new energy power ratio; Step 7: Evaluate the source-grid-load-storage project planning scheme based on the new energy technical indicators.

2. The method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour, as described in claim 1, is characterized in that... In step 2, the constraints include power balance constraints, wind power equipment operation constraints, photovoltaic equipment operation constraints, grid equipment operation constraints, and energy storage charge rate constraints.

3. The method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour, as described in claim 1, is characterized in that... In step 4, the number of groups is 200, and the number of iterations is 10~15.

4. The method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour, as described in claim 1, is characterized in that... Step 5 includes the following steps: Step 5.1: Initialize all particles. Each particle is an array composed of the number of hours in the current month, and each number represents the charging and discharging plan of the energy storage device at this moment; Step 5.2: Calculate the objective function for all particles to obtain the objective function values ​​for different particles; Step 5.3: Take the objective function value with the smallest objective function value as the population optimal function value for this round, and the particle corresponding to this optimal function value is the optimal particle for this round; Step 5.4: Generate the N particles for the next round using the following formula: in, This represents the velocity of the f-th particle during the update. This represents the velocity of the f-th particle at this moment. Represents the self-learning factor, where rand is a random number between 0 and 1. This represents the best historical position of the f-th particle. This is the array for the f-th particle in this iteration. The population learning factor is represented by gbest, which is the historical best particle position of the population. Step 5.5: Determine the position of each particle. If the position or velocity of a particle exceeds the limit, pull it back to the boundary. Step 5.6: Repeat steps 5.2 to 5.5 until the maximum number of iterations is reached. Step 5.7: Output the final optimal particle position of the population as the result of the particle swarm optimization algorithm, which is the optimal charging and discharging plan for the energy storage device.

5. The method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour, as described in claim 1, is characterized in that... In step 6, the power grid purchase cost is the sum of the product of the main grid power per hour throughout the year and the electricity price per hour.

6. The method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour, as described in claim 1, is characterized in that... In step 6, the amount of abandoned renewable energy is calculated using either hourly calculation or summation method. The calculation formula for the hourly calculation method is as follows: in, For wind power in hour t, For the photovoltaic power in hour t, Let t be the power of the energy storage device in hour t. Let be the power consumption of the load in hour t; the summation calculation method is as follows: in, This represents the amount of renewable energy power that was wasted throughout the year. Represents the total amount of wind power generated throughout the year. Represents the total photovoltaic power generation for the entire year. This represents the total amount of electricity purchased from the power grid throughout the year. This represents the total annual load power consumption.

7. The method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour, as described in claim 1, is characterized in that... In step 6, the renewable energy curtailment rate The calculation method is as follows: in, This represents the amount of renewable energy power that was wasted throughout the year. Represents the total amount of wind power generated throughout the year. This represents the total photovoltaic power generation for the entire year.

8. The method for evaluating the operation of a source-grid-load-storage project throughout the year for each hour, as described in claim 1, is characterized in that... In step 6, the proportion of new energy power... The calculation method is as follows: in, This represents the amount of renewable energy power that was wasted throughout the year. Represents the total amount of wind power generated throughout the year. Represents the total photovoltaic power generation for the entire year. This represents the total annual load power consumption.

9. A year-round hourly operation evaluation device for source-grid-load-storage project planning, used to implement the method described in claim 1, characterized in that, include: The input module is used to collect basic data, including the capacity configuration of wind power and photovoltaic power, and hourly wind power and photovoltaic power output and load information throughout the year. The processing module stores the objective function and constraints, and is used to solve the objective function and constraints according to the particle swarm optimization algorithm to obtain the optimal charging and discharging plan for the energy storage device. The output module is used to calculate the new energy technical indicators based on the basic data and the optimal charging and discharging plan for the energy storage device. The new energy technical indicators include the grid purchase cost, the amount of new energy curtailment, the new energy curtailment rate, and the proportion of new energy power.

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