Water, wind, light and diesel storage multi-energy complementary off-grid system capacity optimization configuration method based on particle swarm optimization

Abstract

The invention discloses a water, wind, light and diesel storage multi-energy complementary off-grid system capacity optimization configuration method based on a particle swarm optimization algorithm, and the method comprises the steps: constructing a capacity optimization configuration model which comprises an upper layer model and a lower layer model; the upper-layer model takes minimization of the total cost of the whole life cycle of the system as a target and takes the planning capacity of each unit in the water, wind, light and diesel storage multi-energy complementary off-grid system as a decision variable; the lower-layer model takes system operation benefit maximization in a scheduling period as a target, and takes real-time output of each unit in the water, wind, light and diesel storage multi-energy complementary off-grid system as a decision variable; carrying out collaborative iteration solution on the capacity optimization configuration model based on a particle swarm optimization algorithm; and outputting an optimal capacity configuration scheme which minimizes the total cost of the whole life cycle of the system. According to the method, combination of water, wind, light and diesel storage multiple power supplies is considered, and the problems that a current multi-energy complementary off-grid system energy model is incomplete, hour-level time scale characteristics under the annual time span are ignored, the optimization target is single, and reliability quantization is insufficient are solved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system capacity configuration technology, and more specifically to a method for optimizing the capacity configuration of off-grid multi-energy complementary systems based on particle swarm optimization algorithm, involving hydropower, wind power, solar power, diesel power, and energy storage. Background Technology

[0002] Off-grid energy systems are a key technology for solving power supply problems in remote areas, islands, mountainous regions, and special application scenarios (such as border outposts and mining camps). Among them, multi-energy complementary systems that combine hydropower, photovoltaic (PV), wind power, diesel generators, and energy storage have become a hot topic in current research and application because they can comprehensively utilize multiple renewable energy sources and effectively improve power supply reliability and economy.

[0003] In the field of off-grid energy system capacity optimization and configuration research, some progress has been made, enabling the construction of various energy-complementary system architectures and the application of various optimization algorithms for capacity configuration research. However, in-depth analysis reveals numerous problems and shortcomings in existing technologies. These problems mainly manifest in incomplete energy models, insufficient consideration of hourly timescale characteristics over an annual time span, a singular optimization objective with inaccurate reliability quantification, and a lack of coordinated optimization between water resources and energy systems.

[0004] For example, the invention patent application CN118554425A, entitled "Optimal Power Generation Decision Balancing System for Power Plants with Multi-Subject Coupling of Electricity, Carbon, and Green Certificates," demonstrates that diesel generator sets offer greater operational flexibility than thermal power units in off-grid environments. Due to start-stop restrictions, thermal power units cannot promptly compensate for power shortages when renewable energy output fluctuates significantly. Furthermore, the adjustable capacity range of thermal power units is smaller than that of diesel generator sets. In off-grid systems with lower load demand, thermal power units may not be able to provide suitable installed capacity, or even if they can, they may suffer from poor economic efficiency.

[0005] For example, the invention patent application with publication number CN107834601A, entitled "A Method for Optimizing Capacity Configuration of Independent Microgrid Systems Considering Flexible Loads," focuses on wind-solar-storage microgrids. However, since the power sources used are only wind and solar energy, which are subject to fluctuations and uncertainties, power deficits typically occur in most scenarios. Furthermore, load systems in actual off-grid environments are highly sensitive to power deficits, which affects the system's power supply stability.

[0006] A typical off-grid multi-energy complementary system (hydropower, wind power, solar power, diesel power, and energy storage) is designed to provide a continuous, stable, and economical power supply to the load by integrating the stability and dispatchability of hydropower, the randomness and volatility of wind and solar power, the emergency backup capability of diesel generators, and the energy time-shifting and power support functions of energy storage systems. However, one of the core challenges of such systems lies in their capacity optimization configuration—that is, how to scientifically determine the installed capacity of various power sources and energy storage devices to minimize the system's total life-cycle cost while meeting power supply reliability requirements.

[0007] Therefore, how to effectively solve the problems of incomplete energy models, neglect of hourly time scale characteristics under annual time spans, single optimization objectives and insufficient reliability quantification in current multi-energy complementary off-grid systems has become an urgent problem for those skilled in the art. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide a capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm to overcome or at least partially solve the above problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for capacity optimization configuration of off-grid multi-energy complementary systems based on particle swarm optimization algorithm, including: Construct a capacity optimization configuration model, which includes an upper-level model and a lower-level model; The upper-level model aims to minimize the total cost of the system throughout its entire life cycle, and uses the planned capacity of each unit in the off-grid system that is a multi-energy complementary system of water, wind, solar, diesel, and storage as the decision variable. The lower-level model aims to maximize the system's operational efficiency within the scheduling cycle, and uses the real-time output of each unit in the off-grid system that is a multi-energy complementary system of water, wind, solar, diesel, and storage as the decision variable. The capacity optimization configuration model is solved collaboratively and iteratively based on the particle swarm optimization algorithm. During the iteration process, the planned capacity scheme of the upper-level model is passed to the lower-level model for evaluation, and the system operation benefits output by the lower-level model are fed back to the upper-level model to calculate the total cost of the system's entire life cycle. The particle swarm is then updated based on the total cost of the system's entire life cycle. When the iteration meets the termination condition, the output is the planned capacity scheme that minimizes the total cost of the system's entire life cycle, which is taken as the optimal capacity configuration scheme.

[0010] Furthermore, the objective function of the upper-level model is expressed as:

[0011]

[0012]

[0013]

[0014]

[0015]

[0016] in, C The total cost of the system's entire lifecycle; This refers to the initial investment cost of the system. For system operation and maintenance costs; The replacement cost of damaged equipment during the system's lifecycle; Costs incurred by external energy sources for the system; For environmental benefits; This refers to the installed capacity of the photovoltaic unit; The installed capacity of the wind power unit; The installed capacity of the hydropower unit; The installed capacity of the energy storage unit; The installed capacity of the diesel engine unit; This refers to the initial investment cost of the photovoltaic unit; This refers to the initial investment cost of the wind power unit; This represents the initial investment cost of the hydroelectric unit; This refers to the initial investment cost of the energy storage unit; This refers to the initial investment cost of the diesel engine unit; This is the depreciation factor; The operating and maintenance costs of the photovoltaic unit; The operating and maintenance costs of the wind power unit; The operating and maintenance costs of the hydropower unit; The operating and maintenance costs of the energy storage unit; The operating and maintenance costs of the diesel engine unit; For equipment z The initial investment cost; For equipment z The capital recovery coefficient; m This refers to the total number of devices; Cost of purchasing diesel fuel; for t The amount of fuel consumed during the period; For the first i The environmental benefit coefficient of each unit, and i= {Photovoltaic unit, wind power unit, hydropower unit, energy storage unit, diesel engine unit}; For the first iAnnual output of each unit; This is a penalty term for the probability of insufficient power. This is a penalty for abandoning electricity.

[0017] Furthermore, the constraints of the upper-level model include: annual output constraints of each unit, remaining power constraints of the energy storage unit, and energy storage output constraints.

[0018] Furthermore, the objective function of the lower-level model is expressed as:

[0019]

[0020]

[0021]

[0022] in, F This represents the system's operational efficiency value. For the environmental benefits of wind and solar energy integration and energy storage units; For the operating cost of energy storage units; The equivalent environmental cost of pollutants released by diesel power generation; This refers to the environmental benefit coefficient for energy storage. The photovoltaic environmental benefit coefficient; This refers to the environmental benefit coefficient of wind power. For the lower-level model t The charging power value of the energy storage unit at any time; for t The power of the photovoltaic unit during the time period; for t The power output of the wind power unit during the time period; The unit operating cost of the energy storage unit; The charging and discharging efficiency of the energy storage unit; T r The duration; W This refers to the annual power generation of the diesel generator. For diesel generator No. j Emission coefficients of various pollutants; X The types of pollutants; For the first j The environmental value of these pollutants; For the first j Fines imposed for certain pollutants.

[0023] Furthermore, the constraints of the lower-level model include: system power balance constraints, wind, solar, hydro, and diesel power output constraints, diesel generator set power ramp rate constraints, and diesel generator set start-stop constraints.

[0024] Furthermore, the cooperative iterative solution of the capacity optimization configuration model based on the particle swarm optimization algorithm specifically includes: The position of each particle in the particle swarm represents a set of candidate schemes for the planned capacity; Each particle represents a candidate scheme for the planning capacity, which is then input into the lower-level model and solved to obtain the corresponding system operating benefits. The system operation benefits are input into the upper-level model to calculate the total system lifecycle cost under the proposed capacity candidate scheme. Based on the total system lifecycle cost, the individual historical best position of the particles and the global historical best position of the particle swarm are updated to realize the updating of particle speed and position.

[0025] Furthermore, the particle swarm optimization algorithm uses a random inertia weight strategy to update the velocity and position of particles, and the inertia weight values ​​are adjusted based on a random distribution.

[0026] Furthermore, the lower-level model is a mixed-integer programming model, which is solved using Cplex.

[0027] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm, which has the following beneficial effects: Existing capacity optimization configuration is usually applied to grid-connected multi-energy complementary systems, while the present invention considers the combination of multiple power sources such as water, wind, solar, diesel and storage, which can cope with more complex off-grid environments and can effectively solve the problems of incomplete energy models, neglect of hourly time scale characteristics under the annual time span, single optimization target and insufficient reliability quantification of current multi-energy complementary off-grid systems. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the capacity optimization configuration model structure framework provided in the embodiments of the present invention.

[0030] Figure 2 This is a schematic diagram illustrating the solution process of the capacity optimization configuration model based on the stochastic inertial weighted particle swarm algorithm provided in this embodiment of the invention.

[0031] Figure 3 This is a schematic diagram of the load demand curve provided in an embodiment of the present invention.

[0032] Figure 4 This is a schematic diagram of water flow rate and ambient temperature provided in an embodiment of the present invention.

[0033] Figure 5 This is a schematic diagram of the annual 8760-hour wind speed curve provided in an embodiment of the present invention.

[0034] Figure 6 This is a schematic diagram of the annual 8760-hour light intensity curve provided in an embodiment of the present invention.

[0035] Figure 7 This is a schematic diagram of annualized cost breakdown provided in an embodiment of the present invention.

[0036] Figure 8 This is a schematic diagram showing the output breakdown of each power source provided in an embodiment of the present invention.

[0037] Figure 9 This is a schematic diagram of the charge state of an energy storage system provided in an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] This invention discloses a method for capacity optimization configuration of off-grid multi-energy complementary systems based on particle swarm optimization algorithm, such as... Figure 1 As shown, it includes: Construct a capacity optimization configuration model, which includes an upper-level model and a lower-level model; The upper-level model aims to minimize the total cost of the system throughout its entire life cycle, and uses the planned capacity of each unit in the off-grid system that is a multi-energy complementary system of water, wind, solar, diesel, and storage as the decision variable. The lower-level model aims to maximize the system's operational efficiency within the scheduling cycle, and uses the real-time output of each unit in the off-grid system that is a multi-energy complementary system of water, wind, solar, diesel, and storage as the decision variable. The capacity optimization configuration model is solved collaboratively and iteratively based on the particle swarm optimization algorithm. During the iteration process, the planned capacity scheme of the upper-level model is passed to the lower-level model for evaluation, and the system operation benefits output by the lower-level model are fed back to the upper-level model to calculate the total cost of the system's entire life cycle. The particle swarm is then updated based on the total cost of the system's entire life cycle. When the iteration meets the termination condition, the output is the planned capacity scheme that minimizes the total cost of the system's entire life cycle, which is taken as the optimal capacity configuration scheme.

[0040] The method provided by the present invention will now be described in detail.

[0041] 1. Off-grid multi-energy complementary system: water, wind, solar, diesel, and storage It includes hydropower units, wind power units, photovoltaic units, diesel engine units, and energy storage units.

[0042] 2. Capacity Optimization Configuration Model: The capacity optimization configuration model has a two-layer structure, in which: (1) Upper-level model: The upper-level model aims to minimize the total cost over the entire system's lifecycle. It considers energy storage output constraints, as well as the annual output limits of supporting hydropower, wind power, photovoltaic power, diesel engines, and energy storage equipment. It also incorporates the planned capacity parameters of the equipment as decision variables. Specifically: 1) The objective function of the upper-level model is expressed as:

[0043] in, C The total cost of the system's entire lifecycle; This refers to the initial investment cost of the system. For system operation and maintenance costs; The replacement cost of damaged equipment during the system's lifecycle; Costs incurred for external energy sources (diesel) in the system; For environmental benefits; ① Initial investment cost of the system :

[0044]

[0045] in, The installed capacity of the photovoltaic unit is expressed in MW. The installed capacity of the wind power unit is expressed in MW. The installed capacity of the hydropower unit is expressed in MW. The installed capacity of the energy storage unit is expressed in MW. The installed capacity of the diesel engine unit is expressed in MW. The initial investment cost of the photovoltaic unit is expressed in yuan / kW. The initial investment cost of the wind power unit is RMB / kW; The initial investment cost of the hydropower unit is expressed in yuan / kW. The initial investment cost of the energy storage unit is RMB / kW; The initial investment cost of the diesel engine unit is RMB / kW; This is the depreciation factor; k The discount rate;N For the system's lifecycle; ②System operation and maintenance costs ;

[0046] in, The operating and maintenance cost of the photovoltaic unit is expressed in yuan / kW. The operating and maintenance cost of the wind power unit is RMB / kW; The operating and maintenance cost of the hydropower unit is RMB / kW; The operating and maintenance cost of the energy storage unit is expressed in yuan / kW. The operating and maintenance cost of the diesel engine unit is expressed in yuan / kW. ③ Replacement cost of damaged equipment during the system's lifecycle ; Equipment replacement costs over its lifecycle are determined by both the initial investment and the equipment's lifespan. Because equipment failure is unpredictable, these costs are typically calculated annualized over the system's expected lifespan.

[0047]

[0048] in, For equipment z The initial investment cost; For equipment z The capital recovery coefficient; m This refers to the total number of devices; For the service life of device z; ④ Costs generated by external energy sources (diesel) ;

[0049] in, Cost of purchasing diesel fuel; for t The amount of fuel consumed during a given period; in this embodiment of the invention, it can be... t The time period is set to one year; ⑤ Environmental benefits ;

[0050] in, For the first i The environmental benefit coefficient of each unit, and i= {Photovoltaic unit, wind power unit, hydropower unit, energy storage unit, diesel engine unit}; For the first i Annual output of each unit; This is a penalty term for the probability of insufficient power (LPSP). This is a penalty for abandoning electricity.

[0051] 2) The constraints of the upper-level model include: ①Annual output constraints for each unit:

[0052] in, Provide annual output for photovoltaic units; This represents the lower limit of the annual output of the photovoltaic unit. This is the annual output limit for photovoltaic units; Annual output for wind power units; This is the lower limit of the annual output of the wind power unit; This is the annual output limit for wind power units; Provide annual output for the hydropower unit; This represents the lower limit of the annual output of the hydropower unit. This is the annual output limit for hydropower units; Annual output for energy storage units; This represents the lower limit of the annual output of the energy storage unit. This is the annual output limit for the energy storage unit; For the annual output of the diesel engine unit; This is the lower limit of the annual output of the diesel engine unit; This is the annual output limit for the diesel engine unit; ② Remaining power constraints of energy storage units:

[0053] in, The remaining power of the energy storage unit during time period t; This is the lower limit of the remaining power of the energy storage unit; This is the upper limit of the remaining power of the energy storage unit; typically, the battery takes... =0.1~0.2, =0.8~0.9. Meanwhile, to ensure charging and discharging can begin immediately, an initial value of the remaining charge is typically used. = =0.5~0.6.

[0054] ③ Energy storage output constraints:

[0055]

[0056]

[0057] in, Let be a function of time t, representing the upper-level model.t The charging power of the energy storage unit during the time period, in kW; for t Maximum allowable charging power of energy storage unit during the time period, kW; Let be a function of time t, representing the upper-level model. t Discharge power of the energy storage unit during the time period, kW; This indicates the charging status of the energy storage unit. A value of 1 indicates that it is in the charging state, and a value of 0 indicates that it is not in the charging state. This represents the discharge state of the energy storage unit. A value of 1 indicates that it is in the discharge state, and a value of 0 indicates that it is not in the discharge state. for t Maximum allowable discharge power of energy storage unit during the time period, kW; The maximum continuous charging power of the energy storage unit, kW; The maximum continuous discharge power of the energy storage system is expressed in kW; Δ t For time intervals; E C This refers to the rated capacity of the energy storage unit; δ The self-discharge rate of the energy storage unit; η d For discharge efficiency, and η d The value ranges from 0 to 1; (2) Lower-level model: The lower-level model aims to maximize the system's operational efficiency within the scheduling cycle. It considers system power balance constraints, wind, solar, hydro, and diesel output constraints, diesel generator power ramp-up rate constraints, and diesel generator start-up and shutdown constraints, using the real-time operational output of each unit as the decision variable. Specifically: 1) The objective function of the lower-level model is expressed as:

[0058] in, F This represents the system's operational efficiency value. For the environmental benefits of wind and solar energy integration and energy storage units; For the operating cost of energy storage units; The equivalent environmental cost of releasing pollutants from diesel generators; ① Environmental benefits of wind and solar energy integration and energy storage units :

[0059] in, This refers to the environmental benefit coefficient for energy storage. The photovoltaic environmental benefit coefficient; This refers to the environmental benefit coefficient of wind power. For the lower-level model tThe charging power value of the energy storage unit at any time; for t The power of the photovoltaic unit during the time period; for t The power output of the wind power unit during the time period; ② Operating costs of energy storage units :

[0060] in, The unit operating cost of the energy storage unit is expressed in yuan / kW. The charging and discharging efficiency of the energy storage unit; T r The duration; ③ The equivalent environmental cost of pollutants released by diesel power generation :

[0061] in, W This refers to the annual power generation of the diesel generator. For diesel generator No. j Emission coefficients of various pollutants; X The types of pollutants; For the first j The environmental value of these pollutants; For the first j The fines imposed for each type of pollutant are listed in Table 1. The environmental assessment standards for pollutants in the power industry are shown in Table 1.

[0062] Table 1: Pollutant Emission Coefficients of Different Energy Equipment

[0063] (2) The constraints of the lower-level model include: ①System power balance constraints:

[0064] in, for t Photovoltaic unit power during the time period; for t The power output of the wind power unit during the time period; for t The power output of the hydroelectric unit during the specified time period; for t The power of the diesel engine unit during the time period; For the lower-level model t Discharge power value of the energy storage unit at any time; for t The power of the load during the time period; For the lower-level modelt The charging power value of the energy storage unit at any time; ② Output constraints of wind, solar, hydro, and diesel engines:

[0065] in, For wind power units in t The upper limit of power generation at any given time; for Photovoltaic units in t The upper limit of power generation at any given time; For hydroelectric units in t The upper limit of power generation at any given time; For diesel engine unit in t The upper limit of power generation at any given time; ③ Diesel generator set power ramp rate constraint:

[0066] in, For the first i The maximum downhill gradient of the diesel generator set; For the first i The maximum uphill gradient of the diesel generator set; For the first i A diesel generator set in t Output power at any given moment; For the first i A diesel generator set in t Output power at time -1; ④ Diesel generator set start-stop constraints: To ensure the economical and reliable operation of diesel generators and to minimize the impact of frequent start-stop cycles on their service life, the following requirements are generally met:

[0067] in, This refers to the operating time of the diesel generator set; This refers to the minimum permissible operating time for diesel generator sets.

[0068] (3) The collaborative relationship between the upper-level model and the lower-level model: The solutions for each current optimal capacity configuration scheme obtained from the upper layer are passed to the lower layer. The lower layer then performs daily-scale optimization based on the current optimal capacity configuration scheme from the upper layer. Taking into account the day's weather conditions such as light intensity, water volume, and wind speed, the optimal daily operating revenue is calculated. Multiplying the optimal daily operating revenue obtained from the lower layer by the planned number of operating days yields the minimum total cost for the upper layer on an annual time scale.

[0069] 3. Solve the capacity optimization configuration model collaboratively and iteratively based on the particle swarm optimization algorithm: The multi-energy complementary off-grid system two-layer capacity optimization configuration model constructed in this invention, as a two-layer mixed integer programming model, suffers from nested upper and lower layers, making it difficult to solve effectively using general solution methods such as boundary search or penalty function methods. Particle Swarm Optimization (PSO), by simulating the collective behavior of flocks of birds or schools of fish, endows the search process with swarm intelligence, enabling it to better find the optimal solution. It possesses both good adaptability and powerful global search capabilities, making it better suited for handling high-dimensional problems.

[0070] Traditional particle swarm optimization (PSO) algorithms suffer from drawbacks in practical applications, including susceptibility to local optima, slow search speed in later stages, and poor robustness. To address these issues, researchers have proposed an improved PSO algorithm based on stochastic inertia weights. This improved PSO algorithm abandons the linearly decreasing strategy for inertia weights, as this can lead to convergence difficulties if a suitable inertia weight value cannot be found early on. The stochastic weight-based PSO algorithm utilizes the random distribution of inertia weights to adjust them, allowing them to reach better values ​​later on. This reduces the likelihood of stagnation in the adaptation function, maintains population diversity, and improves the algorithm's global search capability. Therefore, this paper selects the stochastic inertia weight PSO algorithm to solve and analyze the upper-level model. The lower-level model is a mixed-integer programming model, solved using Cplex.

[0071] During the iterative solution process, the position of each particle in the particle swarm represents a set of candidate schemes for the planned capacity. The planned capacity candidate scheme represented by each particle is input into the lower-level model and solved to obtain the corresponding system operation benefits. The system operation benefits are input into the upper-level model to calculate the total system lifecycle cost under the planned capacity candidate schemes. Based on the total system lifecycle cost, the individual historical best position of the particle and the global historical best position of the particle swarm are updated to realize the updating of the particle speed and position. For example, the capacities of five types of generators—water, wind, solar, diesel, and storage—are represented as five particles, and upper and lower limits are set for each particle to constrain them within a reasonable range. Then, 100 groups of the five particles with different positions and velocities are set. First, the particles are substituted into the upper-level model to solve whether the particles satisfy the upper-level constraints. Then, the particles that satisfy the upper-level constraints are substituted into the lower-level model to solve. The optimal solution obtained from the lower-level model is taken and substituted into the upper-level model to solve for the solution of the upper-level model. This result is recorded as the result of one iteration of the two-level model. Based on this, the 100 groups of particles are updated according to their positions and velocities, and then the next iteration is performed. By performing multiple iterations with a large number of particles and continuously increasing the number of iterations, the optimal solution is continuously approached, thereby finding the optimal solution.

[0072] Next, the solution process for the capacity optimization configuration model based on the stochastic inertia weighted particle swarm optimization algorithm will be explained in detail. For example... Figure 2As shown: Step 1: Initialize system parameters: Set the unit parameters for each unit, including photovoltaic, wind power, hydropower, energy storage, and diesel engines, to build the operating model of each power source; Step 2: Input raw data for hydropower, wind power, and solar power to simulate the output of clean energy units based on a specific geographical location; specifically: Meteorological data, including wind speed and solar radiation, for a specific region over a year was generated using PVsyst. The data collection granularity was one hour, meaning raw meteorological data was collected hourly, yielding meteorological data points for wind speed and solar radiation in the region. Hydropower data was obtained by retrieving local hydrological data to acquire information such as minimum flow and maximum head in the local watershed. This raw data was then imported into MATLAB software, and relevant mathematical models were used to generate hydropower, wind power, and photovoltaic power data based on the acquired raw data.

[0073] Step 3: Define the variables for capacity optimization configuration. Set hydropower capacity, wind power capacity, photovoltaic capacity, diesel engine capacity, and energy storage capacity as particles, and initialize the position and velocity of the particles. That is, randomly generate the initial capacity that meets the constraints of the upper-level optimization model. Each population represents a capacity configuration scheme for a device. Step 4: Update the velocity and position of particles in the particle swarm based on the stochastic inertial weighted particle swarm algorithm; Step 5: Transmit the capacity configuration results from the upper layer to the lower layer. The lower layer model uses Cplex in Matlab to solve the problem. Then, the optimal solution obtained from the lower layer is fed back to the upper layer to determine whether the termination condition is met. If not, there is no optimal solution and the process returns to Step 4. If the condition is met, there is an optimal solution. The output optimal solution is the optimized configuration scheme of the device to be determined. Then, it is substituted into the objective function of the upper layer to obtain the desired value.

[0074] Next, a specific embodiment will be used to describe in detail the capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm provided by the present invention.

[0075] 1. Define the structure and application targets of the water-wind-solar-diesel-storage system: Considering that the purpose of constructing a hydropower, wind power, solar power, diesel power, and energy storage system is to solve the power supply problem in remote areas, islands, mountains, and special application scenarios (such as border outposts and mining camps), this invention selects a river basin in Northwest China as the example object of this study (hereinafter referred to as the target research river basin). This region is high-altitude and cold, sparsely populated, rich in mineral resources, and has power supply problems. At the same time, it has abundant hydropower, wind power, and solar power resources, so it is suitable to install hydropower units, wind power units, photovoltaic units, diesel engine units, and energy storage units in this region.

[0076] The capacity optimization configuration method for the constructed multi-energy complementary off-grid system of water, wind, solar, diesel and storage is analyzed for the capacity optimization configuration of the instance object.

[0077] Based on the above Figure 2 The two-layer capacity optimization configuration model established by the solution process of the capacity optimization configuration model based on the stochastic inertial weighted particle swarm algorithm is used to perform simulation analysis using MATLAB after obtaining the original data of local hydropower, wind power, and solar power.

[0078] Based on PVsyst, local meteorological data such as wind speed and solar radiation can be generated for one year. The time granularity of meteorological data collection is 1 hour, meaning that raw meteorological data is collected at one point per hour, resulting in meteorological data points for wind speed and solar radiation in a specific area. Hydropower data can be obtained by examining local hydrological data and other information to acquire information such as the minimum flow and maximum head of the local watershed. Then, these raw data are imported into MATLAB software, and relevant mathematical models are used to generate hydropower, wind power, and photovoltaic power output data based on the acquired raw data.

[0079] A schematic diagram of load forecasting based on industrial development in the target watershed is shown below. Figure 3 As shown.

[0080] Specific raw data on hydropower include, for example: Figure 4 The water flow rate and ambient temperature are shown.

[0081] The local annual wind speed curve for 8760 hours is as follows: Figure 5 As shown, by Figure 5 It can be seen that the local wind speed has a weak regularity and exhibits strong fluctuations.

[0082] The local annual 8760-hour light intensity curve is as follows Figure 6 As shown, by Figure 6 It can be seen that the local solar radiation has a strong regularity and exhibits seasonal differences.

[0083] 2. Set the initial parameters for the capacity optimization configuration model: Based on the architecture strategy of the capacity optimization configuration model, a corresponding mathematical model is built using MATLAB. Commonly used hydropower, wind power, photovoltaic, diesel engines, and energy storage equipment are selected for the construction of the power supply model. The unit parameters of each unit of photovoltaic, wind power, hydropower, energy storage, and diesel engines are determined, and the specific values ​​of the parameters required for the capacity optimization configuration model are determined. All kinds of parameters are input into the corresponding mathematical model built in MATLAB to initialize the system parameters.

[0084] 3. Solve the problem using a multi-energy complementary off-grid system model that integrates hydropower, wind power, solar power, diesel fuel, and energy storage: Hydropower capacity, wind power capacity, photovoltaic capacity, diesel engine capacity, and energy storage capacity were each set as particles to optimize the capacity configuration of a multi-energy complementary off-grid system based on particle swarm optimization algorithm. The MATLAB output results are shown in Table 2.

[0085] Table 2 Optimal Capacity Configuration Results

[0086] 4. Calculation and analysis of capacity optimization configuration results for off-grid multi-energy complementary systems based on particle swarm optimization algorithm.

[0087] After obtaining the capacity optimization configuration results, system performance indicators were analyzed. The system power shortage probability was found to be 5.00%. The analysis shows that due to excessive fluctuations in renewable energy output, and the inability of hydropower output to maintain system power balance, diesel engines, limited by ramp-up conditions, may not reach ideal output within a short period, leading to system power shortages. To reduce the system power shortage probability, the curtailment penalty for wind and solar power needs to be reduced. The wind and solar curtailment rate was found to be 30.09%, indicating abundant clean resources in the area and a promising future for the promotion and application of renewable energy units. The diesel engine utilization rate was found to be 68.52%, indicating that diesel generators bear the base load during both dry and normal water periods, demonstrating that diesel units provide crucial support for the stable power supply of the off-grid system.

[0088] Cost analysis was performed on the obtained capacity optimization configuration results. The specific costs are shown in Table 3. Due to the small installed capacity of energy storage, the energy storage degradation cost is approximately 0. Since the system has new energy units, it has considerable environmental benefits of RMB 1,006,102. The present value of the total system cost, based on investment cost, operation and maintenance cost, equipment replacement cost, fuel cost, environmental benefits, etc., and considering additional penalty factors, is approximately RMB 717.17 million. It can be seen that the constructed multi-energy complementary off-grid system capacity optimization configuration method has achieved multi-objective synergy of economy, environmental protection and reliability in cost analysis.

[0089] Table 3 Cost Breakdown Analysis Table

[0090] The percentage of each cost item in the system, for example Figure 7 As shown, it is clear that the system has the highest one-time investment cost, with 49% coming from the initial investment. This indicates that the system is a capital-intensive system. Although the high initial investment brings significant financial pressure, it results in a substantial reduction in fuel costs in the later stages, which is in line with the economic principle of renewable energy: "high capital expenditure and low operating costs".

[0091] The output status of each power source is as follows: Figure 8As shown, wind and solar power, as the most promising renewable energy sources, form an important component of off-grid integrated systems; hydropower plays a crucial role in regulating wind and solar power fluctuations and can improve the stability of the system's power supply; the coordinated control strategy of diesel engines and energy storage ensures that the system can operate smoothly even when hydropower, wind and solar power generation is insufficient.

[0092] The charge state of the energy storage system is as follows Figure 9 As shown, the energy storage system undertakes the consumption of clean energy when the output of clean energy exceeds the load demand, and mainly participates in the system's power balance during the high-water season.

[0093] In summary, this invention considers that the hydro-wind-solar-diesel-storage multi-energy complementary off-grid system is a joint operation of hydropower, wind power, photovoltaic, diesel engines, and energy storage equipment. Due to the fluctuations and uncertainties in wind and solar power output, their output share in the multi-energy complementary off-grid system is limited. Hydropower plays an important role in regulating wind and solar power fluctuations and meeting load demands. Utilizing diesel engines and energy storage equipment can greatly improve the system's operating benefits and the clean energy consumption rate. This invention optimizes the capacity configuration of the constructed multi-energy complementary system from multiple perspectives, including economy, environmental protection, and reliability. It verifies the superiority of the hydro-wind-solar-diesel-storage multi-energy complementary off-grid system in solving power supply problems in remote areas, islands, mountainous areas, and special application scenarios (such as border outposts and mining camps), demonstrating the broad application prospects of the capacity optimization configuration method for hydro-wind-solar-diesel-storage multi-energy complementary off-grid systems. Compared with existing technologies, the power unit combination of this invention is more complete, has hourly time scale characteristics over an annual time span, and the selection of the objective function considers a more comprehensive range of factors.

[0094] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0095] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A capacity optimization configuration method for off-grid multi-energy complementary systems (hydro, wind, solar, diesel, and storage) based on particle swarm optimization algorithm, characterized in that: include: Construct a capacity optimization configuration model, which includes an upper-level model and a lower-level model; The upper-level model aims to minimize the total cost of the system throughout its entire life cycle, and uses the planned capacity of each unit in the off-grid system that is a multi-energy complementary system of water, wind, solar, diesel, and storage as the decision variable. The lower-level model aims to maximize the system's operational efficiency within the scheduling cycle, and uses the real-time output of each unit in the off-grid system that is a multi-energy complementary system of water, wind, solar, diesel, and storage as the decision variable. The capacity optimization configuration model is solved collaboratively and iteratively based on the particle swarm optimization algorithm. During the iteration process, the planned capacity scheme of the upper-level model is passed to the lower-level model for evaluation, and the system operation benefits output by the lower-level model are fed back to the upper-level model to calculate the total cost of the system's entire life cycle. The particle swarm is then updated based on the total cost of the system's entire life cycle. When the iteration meets the termination condition, the output is the planned capacity scheme that minimizes the total cost of the system's entire life cycle, which is taken as the optimal capacity configuration scheme.

2. The capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm as described in claim 1, characterized in that, The objective function of the upper-level model is expressed as: in, C The total cost of the system's entire lifecycle; This refers to the initial investment cost of the system. For system operation and maintenance costs; The replacement cost of damaged equipment during the system's lifecycle; Costs incurred by external energy sources for the system; For environmental benefits; This refers to the installed capacity of the photovoltaic unit; The installed capacity of the wind power unit; The installed capacity of the hydropower unit; The installed capacity of the energy storage unit; The installed capacity of the diesel engine unit; This refers to the initial investment cost of the photovoltaic unit; This refers to the initial investment cost of the wind power unit; This represents the initial investment cost of the hydroelectric unit; This refers to the initial investment cost of the energy storage unit; This refers to the initial investment cost of the diesel engine unit; This is the depreciation factor; The operating and maintenance costs of the photovoltaic unit; The operating and maintenance costs of the wind power unit; The operating and maintenance costs of the hydropower unit; The operating and maintenance costs of the energy storage unit; The operating and maintenance costs of the diesel engine unit; For equipment z The initial investment cost; For equipment z The capital recovery coefficient; m This refers to the total number of devices; Cost of purchasing diesel fuel; for t The amount of fuel consumed during the period; For the first i The environmental benefit coefficient of each unit, and i= {Photovoltaic unit, wind power unit, hydropower unit, energy storage unit, diesel engine unit}; For the first i Annual output of each unit; This is a penalty term for the probability of insufficient power. This is a penalty for abandoning electricity.

3. The capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm as described in claim 1, characterized in that: The constraints of the upper-level model include: annual output constraints of each unit, remaining power constraints of the energy storage unit, and energy storage output constraints.

4. The capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm as described in claim 1, characterized in that, The objective function of the lower-level model is expressed as: in, F This represents the system's operational efficiency value. For the environmental benefits of wind and solar energy integration and energy storage units; For the operating cost of energy storage units; The equivalent environmental cost of pollutants released by diesel power generation; This refers to the environmental benefit coefficient for energy storage. The photovoltaic environmental benefit coefficient; This refers to the environmental benefit coefficient of wind power. For the lower-level model t The charging power value of the energy storage unit at any time; for t The power of the photovoltaic unit during the time period; for t The power output of the wind power unit during the time period; The unit operating cost of the energy storage unit; The charging and discharging efficiency of the energy storage unit; T r The duration; W This refers to the annual power generation of the diesel generator. For diesel generator No. j Emission coefficients of various pollutants; X The types of pollutants; For the first j The environmental value of these pollutants; For the first j Fines imposed for certain pollutants.

5. The capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm as described in claim 1, characterized in that, The constraints of the lower-level model include: system power balance constraints, wind, solar, hydro, and diesel power output constraints, diesel generator set power ramp rate constraints, and diesel generator set start-stop constraints.

6. The capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm as described in claim 1, characterized in that, The cooperative iterative solution of the capacity optimization configuration model based on the particle swarm optimization algorithm specifically includes: The position of each particle in the particle swarm represents a set of candidate schemes for the planned capacity; Each particle represents a candidate scheme for the planning capacity, which is then input into the lower-level model and solved to obtain the corresponding system operating benefits. The system operation benefits are input into the upper-level model to calculate the total system lifecycle cost under the proposed capacity candidate scheme. Based on the total system lifecycle cost, the individual historical best position of the particles and the global historical best position of the particle swarm are updated to realize the updating of particle speed and position.

7. The capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm as described in claim 1, characterized in that, The particle swarm optimization algorithm uses a random inertia weight strategy to update the velocity and position of particles, and the inertia weight values ​​are adjusted based on a random distribution.

8. The capacity optimization configuration method for off-grid multi-energy complementary systems based on particle swarm optimization algorithm as described in claim 1, characterized in that, The lower-level model is a mixed integer programming model, which is solved using Cplex.

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