Wind and light storage capacity planning method considering land resource constraint and energy storage ship

Abstract

The invention belongs to the technical field of new energy system planning and optimization, and provides a wind and light storage capacity planning method considering land resource constraints and an energy storage ship, and the method comprises the steps: building an explicit mapping relation between land attributes and unit area development cost through constructing a terrain, geology and ecology three-dimensional land classification index system; precise economic evaluation under land constraint is realized; an energy storage ship is incorporated into a double-layer optimization framework, and through coupling of a lower-layer dynamic scheduling model and upper-layer capacity planning, land resource rigid constraints are broken through, and the energy storage adjustment elasticity of the system is improved; and an upper-layer capacity planning-lower-layer scheduling optimization collaborative mechanism is designed, multi-target balance of minimum annual comprehensive economic cost, maximum land utilization rate and minimum energy storage scheduling cost is realized, and a quantifiable and landing planning scheme is provided for the wind and light storage project in the land limited area.

Description

Technical Field

[0001] This invention belongs to the field of new energy system planning and optimization technology, specifically relating to a wind, solar and energy storage capacity planning method that takes into account land resource constraints and energy storage vessels. Background Technology

[0002] Currently, research on planning models related to wind, solar, and energy storage has yielded some results. However, existing research still has two significant shortcomings: First, with the large-scale expansion of renewable energy into onshore and near-shore areas, especially in island regions with limited land resources, land heterogeneity is increasingly constraining system layout and economic viability. Unfortunately, existing planning models do not pay enough attention to this issue, focusing more on improving equipment-level configuration efficiency while neglecting the crucial impact of land stratification differences on the overall system layout and economic feasibility. Second, the system-level optimization framework integrating land stratification characteristics and energy storage dispatch flexibility is not yet complete. Current energy storage systems are still mainly deployed as fixed onshore facilities, failing to fully utilize the strong dispatchability and spatial flexibility of energy storage vessels, which significantly limits the system's adjustment capabilities under complex spatial resource conditions.

[0003] Currently, significant progress has been made in the research of the two-layer optimization model for wind, solar, and energy storage systems. However, there is still room for improvement in the above research, specifically: (1) As renewable energy projects are being promoted on a large scale in onshore and nearshore areas, especially in island areas with limited land resources, the constraints of land heterogeneity on system layout and economy are becoming increasingly prominent. Unfortunately, existing planning models do not pay enough attention to this issue, focusing more on improving the efficiency of equipment configuration, but neglecting the important impact of land classification differences on the overall optimization and economic feasibility of the system. (2) An effective system-level optimization framework that integrates land classification characteristics and energy storage scheduling flexibility has not yet been established. Currently, energy storage systems are still mainly deployed on land in a fixed manner, failing to fully utilize the advantages of energy storage vessels with strong dispatchability and spatial flexibility, which limits the system's adjustment capability under complex spatial resource conditions. The above problems lead to difficulties in the implementation of wind, solar, and energy storage projects in land-constrained areas, poor economic performance, and insufficient operational stability. There is an urgent need for a two-layer optimization technology that takes into account both land classification constraints and the flexibility of energy storage vessels to solve the problem of resource-space-efficiency synergy. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a wind-solar-storage capacity planning method that considers land resource constraints and energy storage vessels, thereby resolving the issues in the prior art. The technical solution adopted by this invention is as follows: A wind-solar-storage capacity planning method considering land resource constraints and energy storage vessels includes the following steps: Step 1: Collect basic data for land classification; Step 2: Based on the basic data, construct a land classification indicator system and the relationship between land attributes and costs: Step 3: Construct a two-level programming model: Step 4: Solve the bilevel programming model based on the improved Hippo optimization algorithm; Step 5: Based on the solution results of Step 4, output the wind, solar and energy storage capacity planning scheme.

[0005] Furthermore, step 1 includes: collecting basic data of the target area and constructing a structured database, including land basic data, geological data, and ecological data.

[0006] Furthermore, step 2 includes: Step 201: Construct a land classification index system: Based on topographic conditions, geological conditions, and ecological conditions, classify land suitability levels; among which topographic conditions include slope and surface curvature; geological conditions include soil bearing capacity and rock exposure rate; ecological conditions include vegetation cover and proximity to ecological protection zones. Step 202, Constructing the relationship between land attributes and costs: Constructing the relationship between topographic conditions and development costs, the relationship between geological conditions and development costs, the relationship between ecological constraints and compensation costs, and the relationship between comprehensive development costs.

[0007] Furthermore, step 202 includes: Step 2021, Establishing the relationship between terrain conditions and development costs: Classify the difficulty of terrain development based on different slope grades, and assign corresponding slope treatment costs per unit area. And construct the surface curvature correction cost function, the formula is: ; in, This represents the cost of surface curvature correction required per unit area. This represents the surface curvature value. This is the correction cost coefficient corresponding to unit curvature; Step 2022, Establishing the relationship between geological conditions and development costs: Classify the difficulty of foundation treatment based on soil bearing capacity level, and set the corresponding foundation bearing capacity treatment cost per unit area. A rock processing cost model is constructed, with the following formula: ; in, This represents the cost of rock processing per unit area. R represents the rock treatment cost required per unit area initially; m represents the rock exposure rate; and m represents the growth coefficient. Step 2023, Establishing the Relationship between Ecological Constraints and Compensation Costs: Ecological compensation levels are divided based on vegetation cover, and corresponding unit area compensation cost standards are established; an environmental monitoring cost model is also constructed, expressed as follows: ; in, This represents the cumulative environmental monitoring cost. denoted as the average annual environmental monitoring cost per unit area, and n represents the total operating years of the project. Step 2024, Construction of Comprehensive Development Cost Relationships: Based on the relationships between topographic conditions and development costs, geological conditions and development costs, and ecological constraints and compensation costs, a comprehensive development cost model per unit area is constructed, expressed as follows: ; in, Indicates the cost of slope treatment; Cost of surface curvature correction per unit area; Cost of foundation bearing capacity treatment; This refers to the depth of the foundation treatment. The cost of rock processing per unit area; To accumulate environmental monitoring costs.

[0008] Furthermore, step 3 includes: Step 301, Upper-level model construction, wind, solar and reservoir capacity planning under land constraints, including: Objective function construction: The objective function is to minimize the annual comprehensive economic cost and maximize the land utilization rate. The formula is as follows: ; In the formula: F represents the overall objective function, This represents the annual comprehensive economic cost. Indicates land utilization rate; Annual comprehensive economic cost The calculation formula is: ; In the formula: This represents the average annual cost of wind power configuration capacity. This represents the average annual cost of photovoltaic power generation configuration capacity. This represents the average annual cost of the energy storage configuration capacity. This represents the average annual cost of land occupied by the combined capacity of wind, solar, and energy storage systems; where: ; ; ; In the formula: This represents the average annual cost of constructing wind power capacity. This represents the average annual operation and maintenance cost of the wind power configuration capacity; For wind power capacity, Represents the construction cost per unit capacity of wind power and the average annual operation and maintenance cost, respectively; Y represents the service life; r represents the discount rate; where: ; ; ; In the formula: This represents the average annual cost of constructing photovoltaic (PV) system capacity. This represents the average annual operation and maintenance cost of the photovoltaic (PV) configuration capacity. For photovoltaic power generation capacity, These represent the construction cost per unit capacity of photovoltaic power and the average annual operation and maintenance cost, respectively; where: ; ; ; In the formula, and These represent the average annual construction costs for onshore energy storage equipment and energy storage vessels, respectively. These represent the average annual operation and maintenance costs of onshore energy storage equipment and energy storage ships, respectively, based on their respective configuration capacities. and This represents the energy storage capacity on land and the energy storage capacity on ships; where: ; In the formula, These represent the capacity configuration of wind, solar, and energy storage, and the land area occupied per unit capacity, respectively. This indicates the total land area occupied by wind, solar and reservoir properties; land utilization rate The calculation formula is: ; Step 302, Lower-level model construction: Constructing an energy storage ship scheduling model, including: Construct an objective function that minimizes scheduling cost, as shown in the formula: ; In the formula, This indicates that the dispatch cost of energy storage ships is the lowest. This indicates the unit electricity price. Charging efficiency and discharging efficiency and These represent the charging and discharging power of the energy storage vessel at time t, respectively.

[0009] Furthermore, in step 301, the constraints include: Installation and capacity range constraints: ; In the formula: and These represent the upper and lower limits of wind power installed capacity, respectively. and These represent the upper and lower limits of photovoltaic power generation installed capacity, respectively. and These represent the upper and lower limits of energy storage capacity, respectively. and These represent the upper and lower limits of the installed capacity of energy storage ships, respectively. Minimum configuration requirements for energy storage ; In the formula, This indicates the minimum proportion of energy storage in the installed capacity of new energy sources, corresponding to the minimum energy storage ratio required for new energy power stations in each province and region. Total area constraint: ; In the formula, This indicates the land area occupied by wind power. This indicates the land area occupied by photovoltaic power generation. This indicates the land area occupied by onshore energy storage. This indicates the area of ​​land resources on the island that can be used to build wind, solar and energy storage projects; Suitability constraints: ; In the formula, This indicates the maximum land area on the island that can be used to build wind power projects. This indicates the maximum land area on the island that can be used to build a photovoltaic project.

[0010] Furthermore, in step 302, the constraints include: System power constraints: ; In the formula, Let represent the output power of photovoltaic and wind power at time t, and the charging and discharging power of energy storage and energy storage ship at time t, respectively. Charge and discharge constraints: ; In the formula, , and , These represent the minimum and maximum power for charging and discharging onshore energy storage, respectively. , and , These represent the minimum and maximum power for charging and discharging the ship's storage, respectively. State of charge constraints: ; ; In the formula, and These represent the minimum and maximum values ​​of onshore energy storage SOCs, respectively; and These represent the minimum and maximum values ​​of the ship's storage SOC, respectively; and Let SOC represent the values ​​of land-based storage and ship-based storage at time t, respectively. ; ; In the formula, and These represent the ship's nuclear power storage status at time t and t-1, respectively. and These represent the charging and discharging power of the ship's storage at time t; and These represent the onshore energy storage nuclear power plant status at time t and t-1, respectively; and Let represent the charging and discharging power of the land storage at time t; ; ; In the formula, and These represent the soc values ​​at time t for land-based storage and ship-based storage, respectively. and The capacity of land-based and ship-based storage; Constraints on new energy output: ; ; In the formula, and These represent the minimum and maximum wind power output, respectively. and These represent the minimum and maximum photovoltaic output, respectively.

[0011] Furthermore, step 4 includes: Step 401, Coding Design: Real number coding is used, with each hippopotamus individual corresponding to one set of candidate wind-solar-storage capacity planning schemes; where, the hippopotamus individual location vector = [ In the formula, For wind power capacity, For photovoltaic power generation capacity, For terrestrial energy storage capacity, For ship energy storage capacity; Step 402, Population initialization: Generate a uniformly distributed initial hippopotamus population within the constraints of the decision variables; Step 403: Three-stage iterative optimization, each iteration includes three stages: updating water area location, defending against predators, and escaping from predators; Step 404: Couple the two-level programming model; Step 405, Elite Retention and Stopping Conditions: Includes: Step 4051, Elite Preservation: Merge the parent and offspring populations to form a population of size 2. N Temporary populations; sorted by fitness, before selection. N Each individual serves as the parent of the next generation; Step 4052, Stopping condition: Iteration stops when either of the following conditions is met: the maximum number of iterations T is reached; or, the change in the optimal fitness of the elite population over 20 consecutive generations is less than a threshold. Step 406: Output the optimal solution.

[0012] Furthermore, step 402 includes: Step 4021, Position Vector Generation: The j-th dimension position of each hippopotamus individual is randomly generated, using the following formula: ; In the formula, For the first The first individual hippopotamus Dimensional position; For the first Lower and upper limits of each decision variable; , is a uniformly distributed random number; Step 4021, Constraint Screening and Population Construction; If a certain hippopotamus individual satisfies: , , Then the individual will be regenerated; it will satisfy the constraints. The individual hippos are grouped as follows: Population matrix, formula: ; In the formula Population size.

[0013] Furthermore, step 403 includes: Step 4031, Water Location Update: Hippos are divided into two categories: males, females, and juveniles, including: Step 40311, Individual Classification and Optimization Objective: forward N Two individuals were male hippos; back N Two individuals were a female and a juvenile hippopotamus; Step 40312, Position Update Formula: Male hippopotamus: ; ; in: The distance between the current individual and the dominant hippopotamus. For population The mean, , The parameter is random. Female and juvenile hippos: ; ; in: , Let T be the number of iterations, and T be the maximum number of iterations. Step 40313, update rule: If the fitness of the new position is greater than or equal to that of the original position, replace the original position; otherwise, keep the original position. Step 4032: Simulate the hippopotamus' defensive behavior in response to predators, defining the predator as the individual with the lowest fitness in the current population, including: Step 40321, Predator location definition: ; Step 40322, Individual Defense Adjustment: Calculate the distance between each individual and the predator. ; If the land costs exceed the budget: ;reduce or ; If energy storage is insufficient: ;Increase ; If scheduling costs are too high: Large; Adjustment ; Step 40323: Update the rule: If the adjusted position satisfies the constraints and the fitness is improved, replace the original position; otherwise, regenerate the position. Step 4033: Simulate the hippopotamus escaping to a safe area, and perform local fine-tuning on the feasible solutions selected in the first two stages, using the local update formula: ; In the formula The safe zone near the current individual; : , is a random number; Step 404 includes: Step 4041, Input from upper to lower levels: For each individual hippopotamus Substitute these parameters into the lower-level model as known parameters. Step 4042, Lower-level scheduling solution: Solve the lower-level objective function. The minimum scheduling cost corresponding to this capacity scheme is obtained. And simultaneously satisfy power balance and SOC constraints; Step 4043, Feedback from lower to upper layers: Substitute the data into the upper-level model to update the individual fitness.

[0014] The present invention has the following beneficial effects: This invention offers significant advantages in renewable energy planning. Through an innovative land grading mechanism and multi-objective optimization method, it effectively improves land resource utilization efficiency, significantly increasing the development and utilization rate of medium- and low-cost Class II-III land, while achieving zero development of Class IV high-cost land. It is particularly suitable for island and coastal areas with limited land resources. The system architecture is scientifically and rationally designed, incorporating energy storage vessels into system scheduling optimization, effectively reducing wind and solar curtailment rates, significantly improving the state of charge (SOC) fluctuation characteristics of onshore energy storage equipment, and enhancing the long-term stability of system operation. It exhibits strong ecological integration, incorporating key ecological parameters such as vegetation cover and proximity to ecological protection zones into the planning constraint system, achieving synergistic advancement of renewable energy development and ecological environmental protection, and providing technical support for energy transition in ecologically sensitive areas. The planning model demonstrates strong synergy and innovation, with a deeply coupled mechanism between upper-level capacity planning and lower-level scheduling optimization, breaking through the technical bottleneck of the disconnect between planning and operation in traditional planning methods. Through refined spatial resource allocation and deep synergy with system operation, it achieves multi-dimensional optimization goals of resources, space, ecology, and operation in renewable energy planning, providing a new technical path for the scientific planning and sustainable development of renewable energy systems in island and coastal areas. Attached Figure Description

[0015] Figure 1 Here is the flowchart for the HO algorithm. Detailed Implementation

[0016] The following will be based on embodiments of the present invention. Figure 1 The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0017] This invention aims to provide a wind, solar, and energy storage capacity planning method that considers land resource constraints and energy storage vessels. Specific objectives include: constructing a three-dimensional land classification index system based on topography, geology, and ecology; establishing an explicit mapping relationship between land attributes and development costs per unit area to achieve accurate economic assessment under land constraints; incorporating energy storage vessels into a two-layer optimization framework, and through the coupling of the lower-layer dynamic scheduling model with the upper-layer capacity planning, breaking through the rigid constraints of onshore resources and improving the system's energy storage regulation flexibility; and designing a collaborative mechanism of "upper-layer capacity planning - lower-layer scheduling optimization" to achieve a multi-objective equilibrium of "minimum annual comprehensive economic cost, maximum land utilization rate, and minimum energy storage scheduling cost," providing quantifiable and feasible planning solutions for wind, solar, and energy storage projects in land-constrained areas.

[0018] Specifically, this invention proposes a wind-solar-storage capacity planning method that considers land resource constraints and energy storage vessels. This method, while meeting constraints, optimizes resource utilization and maximizes economic benefits based on multiple factors. The invention achieves two-layer optimization of the wind-solar-storage system through a five-step process: data preparation, land classification, model construction, algorithm solution, and scheme generation. The specific steps are as follows: Step 1: Data collection and preparation of basic land classification data.

[0019] The system collects basic data of the target area (such as islands) and constructs a structured database, specifically covering: land basic data, including topographic data obtained based on a digital elevation model (DEM), containing slope and surface curvature data with a resolution of no less than 30m; geological data, obtained through standard penetration tests and static cone penetration tests to obtain soil bearing capacity data, combined with field surveys to obtain rock exposure rate data; ecological data, obtained through remote sensing inversion based on normalized vegetation index (NDVI) to obtain vegetation cover data, and combined with the vector boundary of the ecological protection zone to determine a buffer zone of 1-5km; wind, solar, and energy storage technical and economic data, including equipment costs including wind power installation costs, photovoltaic installation costs, onshore energy storage costs, and energy storage vessel costs including construction and operation and maintenance costs, and operating parameters covering annual utilization hours of wind and solar power, energy storage charging and discharging efficiency, self-discharge rate (μ), discount rate (r), and project operating years (Y); load and policy data, including the target area's average annual load curve, minimum new energy storage ratio, and upper limit of land development suitability (maximum suitable ground for wind power, maximum suitable ground for photovoltaic).

[0020] Step 2: Constructing the relationship between land grading indicator system and costs: Based on the data from step 1, a land classification index system is constructed, and a quantitative mapping between each index and the development cost per unit area is established: Step 201: Construction of a land classification indicator system; Land suitability is classified into levels based on three dimensions: topographical conditions, geological conditions, and ecological conditions. Topographical conditions include slope and surface curvature; geological conditions include soil bearing capacity and rock exposure rate; and ecological conditions include vegetation cover and proximity to ecological reserves. Details are as follows:

[0021] Step 202: Constructing the relationship between land attributes and costs; Step 2021: Constructing the relationship between terrain conditions and development costs; To quantify the impact of terrain slope on development costs, terrain development difficulty is categorized according to different slope grades, and corresponding slope treatment costs per unit area are assigned. The specific grading standards and cost parameters are shown in the table below:

[0022] Surface curvature, as an important micro-geomorphic factor affecting site flatness, has an absolute value exceeding 0.05m. -1 This will significantly increase construction difficulty and land reclamation costs. Therefore, a surface curvature correction cost function is constructed as follows: ; in, This represents the cost of surface curvature correction per unit area (RMB / ㎡). The value of surface curvature (unit: m) -1 ), This is the correction cost coefficient corresponding to unit curvature. This function is used to further refine the impact of terrain complexity on development costs based on slope, improving the accuracy and scientific rigor of economic assessments.

[0023] Step 2022: Constructing the relationship between geological conditions and costs; To quantify the impact of foundation bearing capacity on project cost, the difficulty of foundation treatment is classified according to soil bearing capacity level, and a corresponding cost per unit area for foundation bearing capacity treatment is set. The specific grading standards and corresponding cost parameters are shown in the table below:

[0024] This grading system reflects the engineering measures required under different bearing capacity conditions and their corresponding economic inputs, and is an important basis for assessing the impact of geological conditions on development costs.

[0025] When the rock exposure rate R When the rock exposure exceeds a critical value (30%), special treatments such as blasting or crushing are required during construction, and the treatment cost increases exponentially with the degree of rock exposure. Therefore, a rock treatment cost model is constructed as follows: ; in, This represents the cost of rock processing per unit area. R represents the initial rock treatment cost per unit area, R represents the rock exposure rate, and m represents the growth coefficient. This function can effectively reflect the nonlinear impact of rock exposure degree on development economics, providing quantitative support for cost prediction and engineering decision-making under complex geological conditions.

[0026] Step 2023: Constructing the relationship between ecological constraints and compensation costs; To reflect the potential impact of development activities on the ecological environment, ecological compensation levels are classified based on vegetation cover, and corresponding compensation cost standards per unit area are established. The specific classifications are as follows:

[0027] This compensation mechanism aims to quantify vegetation disturbance and ecosystem service loss caused by land development, and to provide economic regulation tools for ecological protection and coordinated project implementation.

[0028] If the project site is located within a buffer zone (1–5 km) surrounding an ecological protection zone, continuous environmental monitoring is required during the project's operation to assess its potential impact on the ecosystem. The corresponding environmental monitoring cost model expression is as follows: ; in, This represents the cumulative environmental monitoring cost (in yuan). Let be the average annual environmental monitoring cost per unit area (RMB / ㎡·year), and n be the total operating years of the project. This function helps to assess the long-term environmental management investment associated with the development of ecologically adjacent areas.

[0029] Step 2024: Establishing a comprehensive development cost relationship; Based on the relationships between topographic conditions and development costs, geological conditions and development costs, and ecological constraints and compensation costs, a comprehensive development cost model per unit area is constructed by integrating multi-dimensional cost factors such as topography, geology, and ecology. The expression is as follows: ; in, Indicates the cost of slope treatment; Cost of surface curvature correction; Cost of foundation bearing capacity treatment; The depth of the foundation treatment is taken as 3m in this paper; Cost of rock processing; This model system integrates the impacts of major natural and anthropogenic factors during the development of wind, solar, and energy storage projects, providing a scientific and quantitative analytical tool for economic feasibility assessments under different land types. It addresses the costs associated with ecological monitoring.

[0030] Step 3: Constructing the two-level programming model: Step 301: Upper-level model construction: Wind, solar and energy storage capacity planning under land constraints; (1) Construction of the objective function: The objective function is to minimize the annual comprehensive economic cost and maximize the land utilization rate. The formula is as follows: ; In the formula: F represents the overall objective function, This represents the annual comprehensive economic cost. The land utilization rate is expressed by the following formula: Minimize annual overall economic cost target: ; In the formula: This represents the average annual cost of wind power configuration capacity. This represents the average annual cost of photovoltaic power generation configuration capacity. This represents the average annual cost of the energy storage configuration capacity. This represents the average annual cost of land occupied by the combined capacity of wind, solar, and energy storage systems.

[0031] ; ; ; In the formula: This represents the average annual cost of constructing wind power capacity. This represents the average annual operation and maintenance cost of the wind power configuration capacity; For wind power capacity (KW), represents the construction cost per unit capacity of wind power and the average annual operation and maintenance cost, respectively; Y represents the service life; and r represents the discount rate.

[0032] ; ; ;

[0033] In the formula: This represents the average annual cost of constructing photovoltaic (PV) system capacity. This represents the average annual operation and maintenance cost of the photovoltaic (PV) configuration capacity. Photovoltaic power generation capacity (KW), These represent the construction cost per unit capacity of photovoltaic power and the average annual operation and maintenance cost, respectively. ; ; ; In the formula, and This represents the average annual cost of constructing the capacity of energy storage equipment and energy storage ships. This represents the average annual operation and maintenance cost of the energy storage equipment and the energy storage vessel configuration capacity. and This indicates the energy storage capacity on land and the energy storage capacity on ships (kWh).

[0034] ; In the formula, These represent the capacity configuration of wind, solar, and energy storage, and the land area occupied per unit capacity, respectively.

[0035] land utilization rate The calculation formula is: ; (2) In step 301, the constraints of the upper-level optimization model mainly consider the constraints of the installed capacity range of new energy and energy storage, as well as the constraints of land resources. The constraints of the upper-level model include: Installation and capacity range constraints: ; In the formula: and These represent the upper and lower limits of wind power installed capacity, respectively. and These represent the upper and lower limits of photovoltaic power generation installed capacity, respectively. and These represent the upper and lower limits of energy storage capacity, respectively. and These represent the upper and lower limits of the installed capacity of energy storage ships, respectively.

[0036] Minimum energy storage configuration requirements constraints: ; In the formula, This indicates the minimum proportion of energy storage in the installed capacity of new energy sources, corresponding to the minimum energy storage ratio required for new energy power stations in each province and region.

[0037] Total area constraint: ; In the formula, This indicates the land area occupied by wind power. This indicates the land area occupied by photovoltaic power generation. This indicates the land area occupied by onshore energy storage. This indicates the area of ​​land resources on the island that can be used to build wind, solar and energy storage projects.

[0038] Suitability constraints: ; In the formula, This indicates the maximum land area on the island that can be used to build wind power projects. This indicates the maximum land area on the island that can be used to build a photovoltaic project.

[0039] Step 302: Lower-level model construction, constructing an energy storage ship scheduling model, including: (1) Objective function: Construct the objective function, which is to minimize the scheduling cost. The formula is: ; In the formula, This indicates that the dispatch cost of energy storage ships is the lowest. This indicates the unit electricity price. Charging efficiency and discharging efficiency and These represent the charging and discharging power of the energy storage vessel at time t, respectively.

[0040] (2) Step 302: The constraints of the lower-level model include: System power constraints: ; In the formula, Let represent the output power of photovoltaic and wind power at time t, and the charging and discharging power of energy storage and energy storage ship at time t, respectively.

[0041] Energy storage constraints include: Charge and discharge constraints: ; In the formula, , and , These represent the minimum and maximum power for charging and discharging onshore energy storage, respectively. , and , These represent the minimum and maximum power for charging and discharging the ship's storage, respectively. State of charge constraints: ; ; In the formula, and These represent the minimum and maximum values ​​of onshore energy storage SOCs, respectively; and These represent the minimum and maximum values ​​of the ship's storage SOC, respectively; and These represent the soc values ​​at time t for land-based and ship-based storage, respectively.

[0042] ; ; In the formula, and These represent the ship's nuclear power storage status at time t and t-1, respectively. and These represent the charging and discharging power of the ship's storage at time t. and These represent the onshore energy storage nuclear power plant status at time t and t-1, respectively; and and represent the charging and discharging power of the land storage at time t, respectively.

[0043] ; ; In the formula, and These represent the soc values ​​at time t for land-based storage and ship-based storage, respectively. and The capacity of land-based and ship-based storage.

[0044] Constraints on new energy output: ; ; In the formula, and These represent the minimum and maximum wind power output, respectively. and These represent the minimum and maximum photovoltaic output, respectively. Step 4: Solve using the Hippo Optimization Algorithm (HO) The Hippopotamus Optimization (HO) algorithm simulates the three-stage natural behavior of a hippopotamus—"adjusting its location in the water area, defending against predators, and escaping threats"—to efficiently solve a two-layer optimization model for wind-solar-storage systems. This algorithm addresses multi-objective (minimizing annual comprehensive economic cost and maximizing land utilization) and multi-constraint (installed capacity, land resources, and energy storage configuration) scenarios. Through an iterative mechanism of "global exploration - local refinement - elite retention," it avoids the "closed competition" problem of traditional genetic algorithms, improving the economy and feasibility of the optimal solution. The specific steps are as follows: Step 401: Coding Design; Based on the characteristics of decision variables in wind and solar storage capacity planning, real-number coding is adopted. Each "hippopotamus individual" corresponds to one set of candidate planning schemes, and its position vector (chromosome) structure is directly related to the decision variables, ensuring that the physical meaning of the variables is clear and convenient for continuous space optimization: Hippopotamus individual position vector = [ In the formula, For wind power capacity, For photovoltaic power generation capacity, For terrestrial energy storage capacity, For ship energy storage capacity.

[0045] Step 402: Population initialization; Generate a uniformly distributed initial "hippo population" within the constraints of the decision variables to ensure that the initial candidate solutions satisfy hard constraints and avoid invalid search, including: Step 4021: Position vector generation; The j-th dimension position (corresponding to the decision variable) of each hippopotamus individual is randomly generated, as shown in the formula: ; In the formula, For the first The first individual hippopotamus Dimensional position (corresponding decision variable); For the first Lower and upper limits of each decision variable; , which are uniformly distributed random numbers to ensure the initial population dispersion.

[0046] Step 4021: Constraint screening and population construction; Eliminate initial individuals that violate hard constraints: If a hippopotamus individual satisfies: , , (Minimum energy storage configuration constraint), then regenerate the individual; construct the population matrix: [the population matrix will then be constructed based on the constraints]. The individual hippos are grouped as follows: Population matrix (6 dimensions for decision variables): ; In the formula Population size; Step 403: Three-stage iterative optimization; Each iteration goes through three stages: updating the location of the water area (exploration), defending against predators (exploration), and escaping from predators (utilization), to achieve dynamic optimization of candidate solutions; Step 4031: Update water area location; Simulating the gathering and dispersal behavior of hippos in water, different decision variables were optimized for two groups of individuals: males, females, and juveniles.

[0047] Step 40311: Individual classification and optimization objectives; forward N Two individuals were male hippos: Optimizing "wind power + onshore energy storage" ( Since both directly depend on land, the cost of land grading needs to be considered. Explore low-cost combinations; back N Two individuals were female and juvenile hippos: Optimizing "photovoltaic + energy storage ships" ( Photovoltaics rely on land, while energy storage ships are not bound by land, reflecting "waterway flexibility".

[0048] Step 40312: Position update formula; Male hippopotamus ( Adjustment): "Dominant Hippo" (the individual with the highest current fitness, i.e., the temporary optimal solution) Adjusting wind power and onshore energy storage capacity, and exploring economical land matching: ; ; in: This represents the distance between the current individual and the dominant hippopotamus (fitness difference). For population The mean, , The parameter is random.

[0049] Female / juvenile hippopotamus Adjustment): Refer to "mother individual" (the top 20% of the population in terms of fitness): ; ; in: ( (where T is the number of iterations and T is the maximum number of iterations). T When the value is >0.6, the simulated photovoltaic system moves away from its "motherland" (exploring new land). T When the value is ≤0.6, simulate the energy storage vessel approaching the "mother" (using flexible scheduling); Step 40313: Update the rules; If the fitness of the new position is greater than or equal to that of the original position, replace the original position; otherwise, retain the original position.

[0050] Step 4032: Defend against predators (Exploration Phase); Simulating hippopotamus defensive behavior against predators, the "predator" is defined as the individual with the lowest fitness in the current population. Threat avoidance is achieved by adjusting variables, including: Step 40321: Predator location definition; That is, the option with the lowest fitness.

[0051] Step 40322: Individual Defense Adjustment: Calculate the "distance" between each individual and the predator. ,like If the value is too small (the solution is close to a suboptimal one), adjust the variables using Levy flight, focusing on optimizing variables that are prone to violating constraints: If the land exceeds the budget ( ):reduce or (Reduce land occupation); If energy storage is insufficient ( ):Increase (Energy storage vessels are flexible and require no land). If the scheduling cost is too high ( Large): Adjustment (Increase ship capacity to reduce fluctuations in charging and discharging power).

[0052] Step 40323: Update the rules: If the adjusted position satisfies the constraints and the fitness is improved, replace the original position; otherwise, regenerate the position.

[0053] Step 4033: Escape the Threat (Exploitation Phase): Simulating a hippopotamus escaping to a safe area, the feasible solutions selected in the first two stages are locally fine-tuned using a local update formula: ; In the formula The "safe zone" near the current individual; : Use random numbers to ensure small fine-tuning amplitudes (utilizing local optima).

[0054] Step 404: Two-layer model coupling: the linkage between HO and lower-level scheduling; Step 4041: Input from upper layer to lower layer; For each individual hippopotamus Substitute these parameters into the lower-level scheduling model as known parameters; Step 4042: Solve the lower-level scheduling problem; Solve the lower-level objective function The minimum scheduling cost corresponding to this capacity scheme is obtained. And simultaneously satisfy power balance and SOC constraints; Step 4043: Feedback from lower to upper layers; Will Substitute the fitness function of the upper layer to update the individual fitness, and use the fitness to guide the three-stage iteration of HO.

[0055] Step 405: Elite Retention and Stopping Conditions; Step 4051: Elite Retention; Merge the parent population (before iteration) and the offspring population (after iteration) to form a population of size 2. NTemporary populations; sorted by fitness, before selection. N The individual with the highest fitness is selected as the parent of the next generation; "non-dominated solutions" are specifically reserved: if multiple individuals cannot dominate each other (e.g., ... Small medium, Medium but All of them (large) are included in the elite population, forming the Pareto optimal solution set.

[0056] Step 4052: Stop condition; Iteration stops when any of the following conditions are met: 1. Reach the maximum number of iterations T; 2. The change in optimal fitness of an elite population over 20 consecutive generations is less than a threshold (e.g., (i.e., solution convergence).

[0057] Step 406: Output the optimal solution; Based on the requirements, the optimal solution is selected from the Pareto optimal solution set to obtain the planned capacity, and the optimal solution is output.

[0058] Step 5: Planning strategy generation; Based on the optimal solution in Step 4, output the wind-solar-storage capacity configuration scheme: wind power capacity Photovoltaic power generation capacity terrestrial energy storage capacity Ship energy storage capacity Land use plan: development area and comprehensive cost of various land types; Energy storage dispatch plan: typical daily charge and discharge strategies and SOC change curves for energy storage ships and onshore energy storage; Economic efficiency and operation indicators: annual comprehensive economic cost, land utilization rate, wind and solar curtailment rate, and power supply reliability.

[0059] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, alterations, or substitutions made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for wind-solar-storage capacity planning considering land resource constraints and energy storage ships, characterized in that, The method comprises the following steps: Step 1: Collecting basic data for land classification; Step 2: Based on the basic data, constructing a land classification index system, land attribute and cost relationship: Step 3: Constructing a bi-level programming model: Step 4: Solving the bi-level programming model based on the improved hippo optimization algorithm; Step 5: Based on the solving result of step 4, outputting the wind and light storage capacity planning scheme.

2. The method for wind-solar-storage capacity planning considering land resource constraints and storage ships according to claim 1, characterized in that, Step 1 includes: collecting the basic data of the target area and constructing a structured database, including land basic data, geological data, and ecological data. 3.The method of claim 1, wherein, Step 2 includes: Step 201, constructing a land classification index system: based on the terrain condition, geological condition, and ecological condition, dividing the land suitability level; wherein the terrain condition includes slope and surface curvature; the geological condition includes soil bearing capacity and rock exposure rate; the ecological condition includes vegetation coverage and ecological protection area proximity; Step 202, constructing a land attribute and cost relationship: constructing a terrain condition and development cost relationship, a geological condition and development cost relationship, a geological condition and cost relationship, an ecological constraint and compensation cost relationship, and a comprehensive development cost relationship.

4. The method for wind-solar-storage capacity planning considering land resource constraints and storage ships according to claim 3, characterized in that, Step 202 includes: Step 2021, terrain condition and development cost relationship construction: according to different slope grade, the terrain development difficulty is divided, and the corresponding unit area slope processing cost is given ; and a surface curvature correction cost function is constructed, the formula is: ； wherein, represents the ground surface curvature correction cost required per unit area, is the ground surface curvature value, is the correction cost coefficient corresponding to the unit curvature; Step 2022, geological condition and development cost relationship construction: according to the soil bearing capacity grade, the foundation treatment difficulty is divided, and the corresponding unit area foundation bearing capacity treatment cost is set ; and the rock treatment cost model is constructed, and the formula is as follows: ； wherein, represents the rock treatment cost required per unit area, represents the initial rock treatment cost required per unit area R represents the rock exposure rate, and m represents a growth coefficient; Step 2023, constructing an ecological constraint and compensation cost relationship: dividing the ecological compensation level according to the vegetation coverage, establishing the corresponding unit area compensation cost standard; and constructing an environmental monitoring cost model, the expression is as follows: ； wherein, represents the cumulative environmental monitoring cost, is the average annual environmental monitoring cost per unit area, and n is the total operating life of the project. Step 2024, comprehensive development cost relationship construction: based on the relationship between the terrain condition and the development cost, the relationship between the geological condition and the development cost, the relationship between the ecological constraint and the compensation cost, a unit area comprehensive development cost model is constructed, and the expression is as follows: ; wherein, represents a slope treatment cost; represents a ground surface curvature correction cost per unit area; represents a ground bearing capacity treatment cost; represents a ground treatment depth; represents a rock treatment cost per unit area; represents a cumulative environmental monitoring cost.

5. The method for wind-PV-storage capacity planning considering land resource constraints and storage ships according to claim 4, characterized in that, Step 3 includes: Step 301, constructing an upper model, wind and light storage capacity planning under land constraint, including: Objective function construction: the objective function is the minimum annual comprehensive economic cost and the maximum land utilization rate, the formula is: ； F = total objective function, represents the annual comprehensive economic cost, represents the land utilization rate; Annualized economic cost The formula is: ； In the formula: represents the annual average cost of the wind power configuration capacity, represents the annual average cost of the photovoltaic power generation configuration capacity, represents the annual average cost of the energy storage configuration capacity, represents the annual average cost of the land occupied by the wind-solar-storage configuration capacity; wherein: ； ； ； In the formula: represents the average annual cost of wind power configuration capacity construction, represents the average annual operation and maintenance cost of wind power configuration capacity; is the wind power capacity, respectively represents the construction cost and the average annual operation and maintenance cost of unit wind power capacity; Y represents the service life; r represents the discount rate; wherein: ； ； ； In the formula: represents the annual average cost of photovoltaic configuration capacity construction, represents the annual average operation and maintenance cost of photovoltaic configuration capacity; is the photovoltaic power generation capacity, respectively represents the construction cost and the annual average operation and maintenance cost of photovoltaic unit capacity; wherein: ； ； ； wherein, and respectively represent the average annual cost of land-based energy storage and energy storage ship configuration capacity construction, respectively represent the average annual operation and maintenance cost of land-based energy storage and energy storage ship configuration capacity; and represent land-based energy storage capacity and ship-based energy storage capacity; wherein: ； In the formula, respectively represent the capacity configuration of wind-solar-storage and the area of land occupied by unit capacity, represents the total land area occupied by wind-solar-storage; Land use efficiency The calculation formula is: ； Step 302, constructing a lower model, constructing a storage energy ship scheduling model, including: Constructing an objective function, the objective function is the minimum scheduling cost, the formula is: ； In the formula, denotes the minimum scheduling cost of the energy storage ship, denotes the unit electricity price, charging efficiency and discharging efficiency, and denote the charging and discharging power of the energy storage ship at time t, respectively.

6. The method for wind-solar-storage capacity planning considering land resource constraints and storage ships according to claim 5, characterized in that, In step 301, the constraint conditions include: Installation and capacity range constraint: ； In the formulae: and respectively represent the upper and lower limits of the wind power installed capacity; and respectively represent the upper and lower limits of the photovoltaic power installed capacity; and respectively represent the upper and lower limits of the energy storage capacity; and respectively represent the upper and lower limits of the energy storage ship installed capacity; Storage energy minimum configuration demand constraint ； In the formula, represents the minimum proportion of energy storage to new energy installed capacity, corresponding to the minimum proportion of energy storage to new energy station in each province. Total area constraint: ； In the formula, represents the land resource area occupied by wind power, represents the land resource area occupied by photovoltaic, represents the land resource area occupied by land energy storage, represents the land resource area available for building wind-photovoltaic-energy storage projects on islands. Suitability constraint: ； wherein represents the maximum land area on the island that can be used to build a wind power project, represents the maximum land area on the island that can be used to build a photovoltaic project.

7. The method for wind-solar-storage capacity planning considering land resource constraints and storage ships according to claim 5, characterized in that, In step 302, the constraint conditions include: System power constraint: ； In the formula, respectively represent the output power of photovoltaic, wind power at time t and the charging and discharging power of energy storage and energy storage ship at time t. Charging and discharging constraint: ； wherein, , and , represent the minimum and maximum power for onshore energy storage charging and discharging, respectively, , and , represent the minimum and maximum power for ship storage charging and discharging, respectively; State of charge constraint: ； ； wherein, and respectively represent the minimum and maximum values of the onshore energy storage soc; and respectively represent the minimum and maximum values of the ship energy storage soc; and respectively represent the soc values of the onshore storage and the ship storage at time t. ； ； In the formula, and respectively represent the ship energy storage nuclear power state at time t and time t-1; and respectively represent the charge and discharge power of the ship storage at time t; and respectively represent the land energy storage nuclear power state at time t and time t-1; and respectively represent the charge and discharge power of the land storage at time t; ； ； In the formula, and soc values of the land storage and the ship storage at time t, respectively, and capacities of the land storage and the ship storage; New energy output constraint: ； ； wherein, and Pmin and Pmax represent the minimum and maximum values of the wind power output, respectively; and Pmin and Pmax represent the minimum and maximum values of the photovoltaic power output, respectively. 8.The method of claim 5, wherein, Step 4 includes: Step 401, encoding design: real number encoding is adopted, each river horse individual corresponds to a set of candidate wind and light storage capacity planning schemes; wherein the river horse individual position vector is [ ]; in the formula, is the wind power capacity, is the photovoltaic power generation capacity, is the land energy storage capacity, is the ship energy storage capacity; Step 402, population initialization: generating an initial hippo population with uniform distribution within the constraint range of decision variables; Step 403: three-stage iterative optimization, each iteration including water area position update, defense predator, and escape predator three stages; Step 404: coupling of bi-level programming model; Step 405, elite reservation and stopping condition: including: Step 4051, Elitism: Merge parent population with offspring population to form a temporary population of size 2 N N ; sort by fitness and select top individuals as parents for next generation;​ Step 4052, stopping condition: stopping iteration when any of the following conditions is met: reaching the maximum iteration number T; or, the optimal fitness of the elite population changes by less than a threshold value for 20 consecutive generations; Step 406, outputting the optimal solution.

9. The method for wind-solar-storage capacity planning considering land resource constraints and storage ships according to claim 8, characterized in that, Step 402 includes: Step 4021, position vector generation: the j-th dimension position of each hippo individual is randomly generated, the formula is: ； wherein is the position of the i-th hippo individual in the j-th dimension; is the position of the i-th hippo individual in the j-th dimension; is the position of the i-th hippo individual in the j-th dimension; is the lower bound, upper bound of the i-th decision variable; is the lower bound, upper bound of the i-th decision variable; is a uniformly distributed random number; Step 4021, constraint screening and population construction; if a certain hippo individual satisfies: , , ; then regenerate the individual; combine the hippo individuals satisfying the constraints into a population matrix, the formula is: ​ ； In the formula is the population size.

10. The method for wind-solar-storage capacity planning considering land resource constraints and storage ships according to claim 9, characterized in that, Step 403 includes: Step 4031, water area position update: hippo individuals are divided into male, female and juvenile individuals, including: Step 40311, individual classification and optimization target: Male hippopotamus N / 2 individuals are male hippos Back N / 2 individuals are female and young hippopotamus; Step 40312, position update formula: Male hippo: ； ； where: is the distance of the current individual to the fittest zebra, is the mean of the population , , is a random parameter; Female and juvenile hippo: ； ； wherein: , is the iteration number, T is the maximum iteration number; Step 40313, update rule: if the fitness of the new position ≥ the original position, replace the original position; otherwise, keep the original position; Step 4032: simulate the hippo's defense behavior against the predator, define the predator as the individual with the lowest fitness in the current population, including: Step 40321, define the position of the predator: ； Step 40322, adjust the individual's defense: calculating the distance of each individual from the predator ; If the land is overspent: ; reduce or ; If the energy storage is insufficient: ; increase ; If the scheduling cost is too high: large; adjust ; Step 40323: update rule: if the adjusted position meets the constraints and the fitness is improved, replace the original position; otherwise, generate a new position; Step 4033: simulate the hippo's escape to a safe area, fine-tune the feasible solutions screened out in the previous two stages, and use the local update formula: ； In the formula is a secure zone near the current individual; : is a random number; Step 404 includes: Step 4041, input from upper layer to lower layer: for each river hippo individual , substitute into the lower layer model as known parameters; Step 4042, lower layer scheduling solution: solving the lower layer objective function to obtain the minimum scheduling cost corresponding to the capacity scheme while meeting the power balance, SOC constraints; Step 4043, feedback from lower layer to upper layer: the Substitute into the upper model, update individual fitness.

Images