An ecological environment-based scheduling optimization method for water, wind, light and scenery complementation

Abstract

The application discloses a kind of based on ecological environment's water wind sight storage complementation's scheduling optimization method, belong to electric power system and renewable energy technology field, including: collection and analysis regional energy resources and environmental constraint basic data, clear general operation constraint and ecological environment constraint condition;Establish multi-objective function, calculate channel electric quantity and objective function value;Using non-dominated sorting genetic algorithm to generate multi-objective optimization scheduling rule, construct and iterate simulation scheduling model, obtain Pareto solution set, select compromise scheme, determine scheduling rule, output water wind sight storage integrated scheduling optimal solution set and decision under ecological environmental protection constraint;Analysis optimal solution set, draw multi-objective trade-off relationship diagram, output optimal scheme.The application can meet multiple ecological environment protection requirements such as fish breeding period, bird migration period, plant critical growth period and the like under the premise, give consideration to system economic benefit, power abandonment rate, channel utilization rate and ecological protection loss electric quantity and the like multi-objective collaborative optimization.

Description

Technical Field

[0001] This invention relates to the fields of power systems and renewable energy technology, and more specifically, to a scheduling optimization method based on ecological environment-integrated hydropower, wind power, solar power and energy storage. Background Technology

[0002] As my country's energy structure rapidly transforms towards green and low-carbon development, the proportion of new energy sources, primarily wind and solar power, in the power grid's installed capacity is increasing. This makes the impact of wind and solar power on the operation of the power system increasingly prominent. Utilizing the flexible adjustment capabilities of hydropower stations and energy storage facilities to mitigate the fluctuations in new energy sources is an effective way to improve the level of new energy consumption.

[0003] Large-scale wind and solar power development will have certain environmental impacts on the terrestrial ecosystems surrounding photovoltaic facilities and on birds in the wind power areas. Traditional hydropower operation and scheduling will also be affected by wind and solar power scheduling, leading to corresponding changes in the environmental impact on rivers. In particular, the irregularity of wind and solar power generation results in irregularities in hydropower flow, especially in river sections with important fish habitats, where sudden rises and falls in water levels can impact fish populations. Therefore, it is crucial and necessary to scientifically implement water, wind, solar, and storage scheduling based on environmental constraints to form a rational scheduling method that is environmentally friendly.

[0004] Existing dispatching methods mainly focus on complementary operation and dispatching in medium- and long-term or extreme situations from the perspectives of economic benefits and grid stability, in order to achieve power generation revenue and grid stability. However, they lack complementary dispatching strategies that consider ecological carrying capacity or ecological sustainability.

[0005] In reality, with the gradual strengthening of ecological and environmental protection policies and awareness, complementary operation and scheduling must also adhere to the principle of ecological priority. For example, during the fish breeding season, hydropower stations need to control the daily fluctuations of downstream water levels to protect aquatic life; wind farms need to limit output during periods of active bird migration to reduce the risk of bird strikes; and photovoltaic power stations can actively limit power generation by adjusting the tilt angle of the panels during specific plant growth periods to reduce the shading impact on the surface ecosystem. There is a certain contradiction between the related ecological protection requirements and the economic and reliability goals of the system. This invention proposes a complementary scheduling method for hydropower, wind power, solar power, and energy storage from the perspective of environmental constraints, which can maximize the dynamic balance between current power generation revenue and environmental protection. Summary of the Invention

[0006] The main objective of this invention is to disclose a scheduling optimization method based on the complementary relationship between water, wind, solar, and storage in the ecological environment, comprising: The basic data collected includes regional energy resources and environmental constraints. The energy resources data includes hydrological and wind and solar resources of hydropower stations. The environmental constraints data includes the downstream water level fluctuation limits during the fish breeding season at the cascade hydropower station dam site, specific protection periods in areas with frequent bird activity, and key growth periods for plants in ecologically sensitive terrestrial areas.

[0007] We will sort out and analyze the basic data on energy resources and environmental constraints, conduct constraint modeling, and clarify the general operational constraints and ecological and environmental constraints. Among them, the general operational constraints include transmission channel constraints, water balance constraints, and upper and lower limits of power plant output and capacity constraints; the ecological and environmental constraints include the maximum peak-shaving output of hydropower dispatch to meet the daily fluctuation requirements of downstream water level within a specific period, and the output limits of wind power and photovoltaic power to meet the needs of plant growth and bird migration within a specific period. Furthermore, the transmission channel constraint is used to adjust the power output of hydropower, wind power, solar power, and energy storage based on the transmission channel to meet the daily occupied rated power of the transmission channel; the water balance constraint is used based on the hydropower inflow and reservoir regulation; the upper and lower limits of power station output and capacity constraint are used to consider the constraints of the power station's expected output, minimum power generation output, ecological base load, and unit maintenance arrangements.

[0008] A multi-objective function is established based on general operational constraints and ecological and environmental constraints. The actual power consumption information of the channel is calculated, and the objective function value corresponding to the actual power consumption information of the channel is output. Furthermore, a non-dominated sorting genetic algorithm is used as the optimization engine to generate multi-objective optimization scheduling rules and construct a simulated scheduling model; Among them, the simulation scheduling model is used to simulate the scheduling situation during the scheduling cycle of water-wind-solar-storage complementary projects; The scheduling rules are based on a typical year as the data basis for the scheduling cycle, including the power generation output of hydropower stations, the power generation output of wind farms and photovoltaic power stations, and the charging and discharging output of energy storage facilities.

[0009] The simulation scheduling model is iterated repeatedly to obtain the Pareto solution set, a compromise scheme is selected, the scheduling rules are determined, and the optimal Pareto solution set and scheduling decision of the integrated water, wind, solar and storage scheduling rules under the constraints of ecological and environmental protection are output. Among them, the simulated scheduling model sets parameters for the non-dominated sorting genetic algorithm and performs repeated iterations to form a Pareto optimal solution set and scheduling decision; Furthermore, a compromise solution is selected from the Pareto solution set, and the final scheduling decision is determined and output according to the scheduling rules corresponding to the compromise solution. The scheduling decision includes: Pareto optimal solution set data file (decision variables, objective function values), scheduling plan table or scheduling instructions (time, power plant output plan). Furthermore, a non-dominated sorting genetic algorithm is used to identify the set of strategy parameters that performs better on the multi-objective function. The crowding degree and the uniformity of the distribution of the solution set are compared to form the optimal Pareto solution set.

[0010] Analyze the Pareto optimal solution set, use visualization technology to draw a trade-off diagram among multiple objectives, select a compromise solution from the Pareto optimal solution set according to the requirements of ecological and environmental protection policies, determine the final optimization scheduling rules, and output the optimal solution under the constraints of ecological and environmental protection management needs. The trade-off graph is used to transform the Pareto optimal solution set into intuitive visual information. Specifically, each solution in the Pareto optimal solution set consists of a continuous set of power generation combinations of each power station during the scheduling cycle.

[0011] Based on existing principles of hydropower, wind power, solar power, and energy storage scheduling, this invention further incorporates ecological and environmental protection constraints, providing a multi-objective scheduling optimization method for hydropower, wind power, solar power, and energy storage systems that considers multiple environmental constraints. This method can achieve synergistic optimization of multiple objectives, including total system revenue, total curtailment rate, system power shortage rate, and ecological protection, while ensuring that specific ecological protection requirements are met. This provides a scheduling basis for complementary scheduling of hydropower, wind power, solar power, and energy storage systems under ecological and environmental protection constraints. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the scheduling optimization method based on the complementary relationship between water, wind, solar and storage in the ecological environment, provided by an embodiment of the present invention. Detailed Implementation

[0013] The specific implementation of the present invention will now be described in detail with reference to the accompanying drawings.

[0014] like Figure 1 As shown, the main objective of this invention is to disclose a scheduling optimization method based on the complementary relationship between water, wind, solar, and storage in the ecological environment, comprising: Step S100 involves collecting basic data on regional energy resources and environmental constraints, and selecting the area where integrated hydropower, wind power, solar power, and energy storage systems are located as the complementary dispatch range, including: Basic data on the energy resources, exploitable resources, and planned installed capacity of corresponding hydropower stations, wind power stations, photovoltaic power stations, and energy storage power stations; Data on surrounding ecological and environmental factors, including: aquatic ecology (fish breeding season, fish adaptability to water level fluctuations) and terrestrial ecology (areas with frequent bird activity and specific protection periods, sensitive terrestrial ecological areas and key growth periods of endemic protected plants) downstream of the hydropower station dam site; Ecological and environmental constraints include: downstream water level fluctuation limits during the fish breeding season at the cascade hydropower dam site, setting a maximum daily fluctuation limit for the downstream water level and correspondingly setting a maximum allowable hydropower output limit for that period; specific protection periods in areas with frequent bird activity, setting a maximum allowable number of wind turbines or a limit for that period in the wind farm area; and key growth periods for plants in ecologically sensitive terrestrial areas, setting a maximum allowable output limit for adjusting the tilt angle of photovoltaic power stations during that period.

[0015] Step S110: Review and analyze energy resource data and environmental constraint data, conduct constraint condition modeling, and clarify general operational constraints and ecological environment constraint conditions; Specifically, a typical year is used as the scheduling cycle to form corresponding intraday scheduling rules; 1) The variable factors within the dispatch rules include: the power generation output S of the hydropower station in each time period, the power generation output F of the wind farm and G of the photovoltaic power station in each time period, and the charging and discharging output C of the energy storage facility. The total power generation = S + F + G + C.

[0016] 2) General operational constraints include: Transmission channel constraints: The maximum transmission power Tmax of the integrated complementary simulation is the rated transmission capacity of the transmission channel. During intraday dispatch, the power generation output is adjusted according to the transmission channel to try to meet the requirement of occupying as much of the rated power of the transmission channel as possible during the day. When the dispatch output exceeds Tmax, the power generation output needs to be reduced. Water balance constraints: The scheduling method is determined based on the hydropower inflow and reservoir storage, according to the reservoir's own regulation capacity. Monthly water volume is mainly matched and coordinated based on inflow and the average monthly wind and solar power output to avoid concentrated wind and solar curtailment due to large hydropower output; intraday water volume is mainly utilized within the hydropower adjustable range according to the peak and valley time sequence of the receiving end area to minimize the intraday peak-shaving pressure of hydropower. Upper and lower limits of power plant output and capacity constraints: These mainly consider constraints such as the expected output, minimum power generation output, ecological base load, and unit maintenance schedule of each power plant.

[0017] 3) Ecological and environmental constraints include: Limitation on downstream water level fluctuations during the fish breeding season downstream of the cascade hydropower station dam site: Set the maximum allowable daily fluctuation of the downstream water level ΔHmax to limit the peak-shaving output capacity ΔSmax of the hydropower station; Specific protection periods for areas with frequent bird activity: Set the maximum allowable number of wind turbines or the limit ΔFmax for wind farms in the area during the specified period; Key growth period for plants in ecologically sensitive terrestrial areas: Set the maximum allowable output limit ΔGmax for adjusting the tilt angle of photovoltaic power stations during this period.

[0018] Step S120: Based on general operational constraints and ecological and environmental constraints, establish a multi-objective function using hydrological and meteorological data for each time period within the current scheduling cycle. The specific steps are as follows: Step 1: Set the specific date and time, and automatically determine whether the corresponding ecological protection rules are triggered; Step 2: If it is within the period specified in the relevant ecological protection rules, the actual output of hydropower, wind power, and photovoltaic power during that period shall be forcibly limited to not exceeding the preset limit; Step 3: Simulate the system's operation status over time periods, calculate relevant indicators such as the actual amount of water, wind, and solar power transmitted through the channel, the amount of power wasted, the amount of power lost due to channel limitations, and the amount of power lost for ecological protection, and output a multi-objective function (F1, F2, F3, F4). The specific objective function values ​​include: maximizing the total amount of electricity sent to the grid through the channel (F1 Max), minimizing the total regional curtailment rate (F2 Min), maximizing the channel utilization rate (F3 Max), and minimizing the amount of electricity lost for ecological protection (F4 Min).

[0019] Step S130: The non-dominated sorting genetic algorithm (NSGA-II) is used as the optimization engine to generate multi-objective optimization scheduling rules and construct a simulation scheduling model; Specifically, the multi-objective function (F1, F2, F3, F4) and the simulation scheduling model are combined to form a closed-loop framework of "parameter-simulation-optimization".

[0020] Step S140: By setting the parameters of the Non-Dominated Sorting Genetic Algorithm (NSGA-II), the simulated scheduling model is iterated repeatedly until the preset maximum number of iterations is reached; Specifically, during the iteration process, the non-dominated sorting genetic algorithm (NSGA-II) is used to identify the set of policy parameters that performs better on the multi-objective function, and the uniformity of the solution set is ensured by comparing the crowding degree, ultimately forming the Pareto optimal solution set; In the Pareto optimal solution set, each solution consists of a continuous set of power generation combinations of each power station (hydropower station, wind farm, photovoltaic power station, energy storage facility) within the scheduling cycle; each solution represents a set of scheduling rules that achieve the best trade-off among four objectives: economic benefits (F1), system operating efficiency (F2), power supply reliability (F3), and ecological protection level (F4). Specifically, the scheduling rules refer to the use of a clear and operable set of instructions in the complementary coordination of hydropower, wind power, solar power and energy storage to determine the power generation allocation, power generation time and connection method of each power source (hydropower station, wind farm, photovoltaic power station and energy storage) in the system within a certain period of time. Among them, the optimal trade-off is a dynamic decision-making process based on the current external conditions and situation. According to time, environmental policy requirements, external conditions and real-time meteorological forecast information, the solution most suitable for the current situation is selected from the Pareto solution set, and the current optimal dispatch rule is determined according to the specific power generation dispatch plan corresponding to the Pareto solution. For example, during the next 24 to 72 hours, in the ecologically sensitive period, with an emphasis on ecological protection, the dispatch instructions selected after Pareto optimization are as follows: hydropower stations operate at a constant flow rate, maintaining stable output without peak shaving; wind farms increase power generation during the day and limit power generation at night; photovoltaic power stations limit power generation by adjusting the angle of photovoltaic panels; energy storage absorbs wind and photovoltaic power during the day and undertakes peak shaving tasks at night; during the non-ecologically sensitive period or when there are no environmental constraints, the Pareto-optimized dispatch instructions are as follows: during the next 24 to 72 hours, wind farms and photovoltaic power stations are fully connected to the grid; energy storage and hydropower stations generate electricity at their maximum peak shaving capacity.

[0021] Step S150: Analyze the Pareto optimal solution set and draw a diagram of the trade-offs among multiple objectives. Step S160: Output the optimal solution under the constraints of ecological and environmental protection management requirements. The specific steps are as follows: Step 1: Use visualization techniques to draw a diagram of the trade-offs between multiple objectives (such as a Pareto front diagram) to transform the Pareto optimal solution set into intuitive visual information; Step 2: Based on the focus of environmental protection policies and the requirements for environmental protection implementation, select a compromise solution from the Pareto solution set that best meets the current needs of ecological and environmental protection management. Step 3: Based on the scheduling rules corresponding to the compromise solution, determine the final optimized operation rules that take into account multiple ecological protections.

[0022] This invention achieves multi-objective collaborative optimization scheduling of a hydro-wind-solar-storage complementary system under multiple ecological and environmental constraints. By systematically modeling various ecological protection requirements, such as water level fluctuations during fish breeding seasons, power output limitations during bird migration seasons, and power output limits during key plant growth periods, as constraints and optimization objectives of the scheduling model, the relationship between power generation and environmental protection is quantified. This provides transparent and comprehensive data support for scientific decision-making, making scheduling decisions more eco-friendly. A "parameter-simulation-optimization" framework is adopted, combined with a non-dominated sorting genetic algorithm (NSGA-II) to achieve collaborative optimization and closed-loop feedback. The generated scheduling rules effectively balance ecological protection and power generation benefits and can be directly used to guide actual system operation. Under different ecological and environmental protection requirements, the system can quickly switch to the corresponding optimized operation mode based on the Pareto solution set, maximizing the system's economic benefits, renewable energy absorption level, and channel utilization rate, providing a reliable decision-making basis for integrated hydro-wind-solar-storage scheduling under ecological and environmental protection constraints.

[0023] The above-disclosed embodiments are merely a few specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A scheduling optimization method based on ecological environment-integrated water, wind, solar, and storage complementarity, characterized in that, include: The basic data for collecting regional energy resources and environmental constraints includes hydrological and wind and solar resources of hydropower stations; environmental constraints include the downstream water level fluctuation limits during the fish breeding season at the cascade hydropower station dam site, the protection period for areas with frequent bird activity, and the key growth period for plants in ecologically sensitive terrestrial areas. The basic data on energy resources and environmental constraints were analyzed and sorted out. Constraint models were developed to clarify general operational constraints and ecological and environmental constraints. The general operational constraints include: transmission channel constraints, water balance constraints, and upper and lower limits on power plant output and capacity. The ecological and environmental constraints include: determining the maximum peak-shaving output of hydropower based on the daily fluctuation of downstream water levels during fish breeding seasons; determining the maximum permissible output of wind farms during protection periods in areas with frequent bird activity; and determining the maximum permissible output of photovoltaic power plants during the critical growth periods of plants in ecologically sensitive terrestrial areas. A multi-objective function is established based on general operational constraints and ecological and environmental constraints. It automatically determines whether corresponding ecological protection rules are triggered based on the set date and time. If it falls within the ecological protection rule period, the actual output of hydropower, wind power, and photovoltaic power during that period is forcibly limited to a preset limit. The system simulates the operating status of the system in each time period, calculates the actual electricity flow through the channel (hydropower, wind power, photovoltaic power, and energy storage), and outputs the objective function value corresponding to the actual electricity flow through the channel. The multi-objective function is constructed based on hydrological and meteorological data and includes maximizing total revenue, minimizing total wasted electricity, maximizing channel utilization, and minimizing ecological protection loss. The ecological protection loss refers to the electricity loss caused by limiting the output of hydropower, wind power, photovoltaic power, and energy storage due to ecological and environmental constraints during the protection period. A non-dominated sorting genetic algorithm is used as the optimization engine to generate multi-objective optimization scheduling rules and construct a simulation scheduling model. The simulation scheduling model is used to simulate the scheduling situation within the scheduling cycle of water, wind, solar and storage complementarity, and forms a "parameter-simulation-optimization" closed-loop framework with the multi-objective function. The simulation scheduling model is iterated repeatedly to obtain the Pareto solution set, and the optimal Pareto solution set and scheduling decision of the integrated water, wind, solar and storage scheduling rule under the constraints of ecological and environmental protection are output. Analyze the Pareto optimal solution set, draw a trade-off diagram among multiple objectives, select a compromise solution from the Pareto optimal solution set according to the requirements of ecological and environmental protection policies, determine the final optimized scheduling rules, and output the optimal solution under the constraints of ecological and environmental protection management needs; wherein, the trade-off diagram is used to transform the Pareto optimal solution set into intuitive visual information.

2. The scheduling optimization method based on ecological environment-integrated water, wind, solar, and storage as described in claim 1, characterized in that, The transmission channel constraints are used to adjust the power generation output of hydropower, wind power, solar power, and energy storage according to the transmission channel to meet the daily occupied rated power of the transmission channel; the water balance constraints are used according to the hydropower inflow and reservoir regulation; the upper and lower limits of power station output and capacity constraints are used to consider the power station's expected output, minimum power generation output, ecological base load, and unit maintenance arrangements.

3. The scheduling optimization method based on ecological environment-integrated water, wind, solar, and storage as described in claim 1, characterized in that, The simulated scheduling model forms a Pareto optimal solution set and scheduling decision by repeatedly iterating through the parameters of the non-dominated sorting genetic algorithm.

4. The scheduling optimization method based on ecological environment-integrated water, wind, solar, and storage as described in claim 1, characterized in that, The optimal Pareto solution set is identified by a non-dominated sorting genetic algorithm that identifies the set of strategy parameters that performs better on the multi-objective function, comparing crowding and ensuring the uniformity of the solution set distribution.

5. The scheduling optimization method based on ecological environment-integrated water, wind, solar, and storage as described in claim 1, characterized in that, Each solution in the Pareto optimal solution set comprises a continuous set consisting of the power output combinations of each power station during the scheduling cycle.

6. The scheduling optimization method based on ecological environment-integrated water, wind, solar, and storage as described in claim 1, characterized in that, The scheduling rules are based on a typical year as the data basis for the scheduling cycle, including the power generation output of hydropower stations, the power generation output of wind farms and photovoltaic power stations, and the charging and discharging output of energy storage facilities.

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