A power plant-multiple energy storage collaborative optimization method, device and equipment

Abstract

This invention provides a method, apparatus, and equipment for the coordinated optimization of power generation stations and diversified energy storage. The method includes: modeling components of a hybrid power system to quantify the indirect costs of intermittent renewable energy; constructing a multi-objective optimization model incorporating economic operating costs, pollution emission control, and voltage safety and stability; establishing a set of relevant constraints, designing a tiered energy storage dispatch strategy, and performing multi-scale power balancing; and solving the multi-objective optimization model using an improved bacterial foraging algorithm to determine the optimal power generation plan. This invention achieves an optimal power generation plan that balances economic, environmental, and stable operation requirements, effectively mitigating renewable energy fluctuations and solving the problem of existing technologies' difficulty in achieving multi-objective coordinated optimization. It provides an effective technical approach to improving the overall operational performance of novel hybrid power systems.

Description

Technical Field

[0001] This invention relates to the field of power generation and energy storage system optimization technology, and in particular to a method, apparatus and equipment for collaborative optimization of power generation stations and diversified energy storage. Background Technology

[0002] With the acceleration of the global energy transition, the penetration rate of renewable energy sources such as wind power and photovoltaics in the power system continues to increase. However, the inherent intermittency and strong volatility of renewable energy pose significant challenges to the real-time power balance and safe and stable operation of the power grid, making traditional dispatching models unsustainable. Currently, although utilizing energy storage technology to mitigate the fluctuations of renewable energy sources has become an industry consensus, existing technical solutions still have significant shortcomings. First, at the energy storage application level, most research and practice are still limited to using a single type of energy storage (such as battery energy storage) or a simple combination of "battery + pumped hydro storage," lacking a refined, hierarchical coordination mechanism based on the dynamic response characteristics of different energy storage technologies. This results in the inability to fully leverage the advantages of pumped hydro storage—large capacity, low cost, and suitability for energy-type dispatching—and the advantages of battery energy storage—fast response speed, high precision, and suitability for power-type support—leading to poor overall system regulation economics.

[0003] Secondly, in setting optimization objectives, traditional scheduling models often take the lowest total system operating cost or the lowest coal consumption for thermal power as the single core objective, overemphasizing economic efficiency. This approach ignores the increasingly important issue of grid voltage stability under the high proportion of renewable energy integration. Treating voltage deviation and pollution emissions only as constraints rather than optimization objectives fails to achieve the best balance between economy, environmental protection, and safety at the Pareto optimal frontier, thus limiting the overall benefits of scheduling results.

[0004] Finally, regarding the solution algorithm, power grid optimal scheduling is a complex problem characterized by high dimension, nonlinearity, and strong constraints. Traditional mathematical programming algorithms (such as linear programming and quadratic programming) and standard intelligent algorithms (such as basic genetic algorithms and particle swarm optimization) are prone to getting trapped in local optima when dealing with such multi-objective problems. It is difficult to balance convergence accuracy and computational efficiency, and it is impossible to guarantee the acquisition of a high-quality scheduling strategy set.

[0005] There is currently no effective solution to the problem that existing related technologies cannot achieve multi-objective collaborative optimization. Summary of the Invention

[0006] This invention provides a method, apparatus, and equipment for the coordinated optimization of power generation stations and diversified energy storage, which addresses the shortcomings of existing related technologies in achieving multi-objective coordinated optimization.

[0007] In a first aspect, the present invention provides a method for synergistic optimization of power plant-diversified energy storage, comprising: Based on component modeling of electric hybrid systems, the indirect costs of intermittent renewable energy are quantified. Construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. Establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balance. An improved bacterial foraging algorithm is used to solve the multi-objective optimization model to determine the optimal power generation plan.

[0008] According to the present invention, a power plant-diversified energy storage synergistic optimization method is provided, wherein the indirect costs include thermal power costs, wind power intermittent costs, photovoltaic costs, battery energy storage costs, and pumped storage costs.

[0009] According to the power plant-diversified energy storage synergistic optimization method provided by the present invention, the intermittent cost of wind power and the cost of photovoltaic power are both composed of three parts: direct cost, penalty cost and backup cost.

[0010] According to the present invention, a collaborative optimization method for power generation stations and diversified energy storage is provided, which constructs a multi-objective optimization model including economic operating costs, pollution emission control, and voltage safety and stability, including: The total cost is determined based on the costs of thermal power, intermittent wind power, photovoltaic power, battery energy storage, and pumped hydro storage. The total emissions of sulfur oxides and nitrogen oxides are determined based on the operating conditions of the thermal power units. Based on the rated voltage and actual operating voltage of each node in the power hybrid system, determine the total voltage deviation of all nodes.

[0011] According to the present invention, a power plant-diversified energy storage collaborative optimization method is provided, wherein the relevant constraint set includes power balance constraints, upper and lower limit constraints of power of thermal generators, upper and lower limit constraints of system bus voltage, power limit constraints of wind turbines, photovoltaic output power limit constraints, pumped storage system constraints, and battery energy storage charging state constraints.

[0012] According to the present invention, a power plant-diversified energy storage collaborative optimization method is provided, which designs a hierarchical energy storage dispatch strategy, including: The response conditions for battery energy storage and pumped hydro storage are determined based on the fluctuation amplitude discrimination criterion. Power allocation is performed on battery energy storage and pumped hydro storage separately using the indicator function.

[0013] According to the present invention, a collaborative optimization method for power generation stations and diversified energy storage is provided, which uses an improved bacterial foraging algorithm to solve the multi-objective optimization model and determine the optimal power generation plan, including: An adaptive step size mechanism and an enhanced group cooperation effect function are introduced for global search; The optimal power generation plan is determined by selecting the optimal compromise solution based on fuzzy decision-making.

[0014] Secondly, the present invention also provides a power plant-diversified energy storage synergistic optimization device, comprising: Modules are used for component modeling based on hybrid power systems to quantify the indirect costs of intermittent renewable energy. The processing module is used to construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. The configuration module is used to establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balancing. The optimization module is used to solve the multi-objective optimization model using an improved bacterial foraging algorithm to determine the optimal power generation plan.

[0015] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power plant-diversified energy storage synergistic optimization method as described in the first aspect above.

[0016] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power plant-diversified energy storage synergistic optimization method as described in the first aspect above.

[0017] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the power plant-diversified energy storage collaborative optimization method as described in the first aspect above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The power plant-diversified energy storage collaborative optimization method provided by this invention quantifies and models the uncertain costs caused by the intermittency and volatility of wind and solar power output, incorporating them into the overall operational economic analysis, thereby more realistically reflecting the actual cost of grid connection of new energy sources. This method significantly improves convergence accuracy and search efficiency by introducing adaptive step size and a group collaboration mechanism, obtaining the optimal power generation plan that balances economic, environmental, and stable operation requirements. It effectively mitigates the fluctuations in new energy sources and solves the problem of existing related technologies' difficulty in achieving multi-objective collaborative optimization, providing an effective technical approach to improve the comprehensive operational performance of new hybrid power systems. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the power plant-diversified energy storage synergistic optimization method provided by the present invention; Figure 2 This is a structural block diagram of the power plant-diversified energy storage synergistic optimization device provided by the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] This invention provides a method for synergistic optimization of power plant and diversified energy storage. Figure 1 This is a flowchart of the power plant-diversified energy storage synergistic optimization method provided by the present invention, such as... Figure 1 As shown, the method includes the following steps: Step S101: Based on component modeling of the electric hybrid system, quantify the indirect costs of intermittent renewable energy. Step S102: Construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. Step S103: Establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balance. Step S104: The improved bacterial foraging algorithm is used to solve the multi-objective optimization model to determine the optimal power generation plan.

[0023] This method first constructs a hybrid system model incorporating renewable energy sources such as wind power and solar power, as well as flexible resources such as thermal power, battery storage, and pumped storage. Based on this, a multi-objective optimization model is established with three optimization objectives: minimizing total system operating cost, minimizing pollutant emissions, and optimizing grid node voltage deviation. Specifically, the uncertainty costs arising from the intermittency and volatility of wind and solar power output are quantified and modeled, incorporating them into the overall operational economic analysis to more realistically reflect the actual cost of integrating new energy sources into the grid. Finally, an improved Modified Bacteria Foraging Optimization Algorithm (MBFA) is used for solving the problem. By introducing an adaptive step size and a group collaboration mechanism, the convergence accuracy and search efficiency are significantly improved, obtaining the optimal power generation plan that balances economic, environmental, and stable operation requirements. This effectively mitigates the fluctuations in new energy sources and solves the problem of existing related technologies struggling to achieve multi-objective collaborative optimization, providing an effective technical approach to improve the overall operational performance of novel hybrid power systems.

[0024] In some embodiments, indirect costs include thermal power costs, wind power intermittency costs, photovoltaic costs, battery energy storage (BES) costs, and pumped hydro storage (PHS) costs. Wind power intermittency costs and photovoltaic costs each consist of three parts: direct costs, penalty costs, and backup costs.

[0025] Specifically, in step S101, power information of each unit, as well as wind speed, solar irradiance prediction data, and load prediction data are acquired. Based on component modeling of the hybrid system of wind power, photovoltaic, thermal power, battery energy storage, and pumped storage, the indirect costs of intermittent renewable energy, including penalty costs and reserve costs, are quantified. BES operation and maintenance costs are calculated through an annualized investment model, and PHS costs cover start-up and shutdown losses and pump energy consumption.

[0026] For example, the cost model for thermal power is as follows:

[0027] in, This indicates the total number of thermal power units within the system. i Indicates the serial number of the thermal power generation unit. Indicates the first i The power generation cost function of a thermal power unit Indicates the first i The output power of each thermal power generation unit , and This represents the cost coefficient.

[0028] The expression for the intermittent cost model of wind power is as follows:

[0029] in, This indicates the total number of wind turbine units in the system. j Indicates the wind power unit serial number. Indicates the first j Total intermittent operating cost of a single wind power unit Indicates direct costs, Indicates the cost of punishment. This indicates the cost of backup.

[0030] The formula for calculating direct costs is as follows:

[0031] in, Indicates the first j Direct cost coefficient of typhoon generator units Indicates the first j The output power of each wind power generation unit.

[0032] The formula for calculating the cost of punishment is as follows:

[0033] in, This represents the penalty cost coefficient. Indicates the first j The planned output power of each wind power generation unit This indicates the actual usable power of the wind power generation unit. This represents the probability density function of wind power.

[0034] The formula for calculating reserve costs is as follows:

[0035] in, This represents the reserve cost coefficient.

[0036] The expression for the photovoltaic cost model is as follows:

[0037] in, This indicates the total number of photovoltaic power generation units in the system. k Indicates the serial number of the solar cell unit. Indicates the first k Total intermittent operating cost of a photovoltaic power generation unit Indicates direct costs, Indicates the cost of punishment. This indicates the cost of backup.

[0038] The expression for direct cost is as follows:

[0039] in, Indicates the first k The direct cost coefficient of a single photovoltaic power generation unit. Indicates the first k Output power of each photovoltaic power generation unit.

[0040] The expression for the penalty cost is as follows:

[0041] in, This represents the penalty cost coefficient. Indicates the first k The planned output power of each photovoltaic power generation unit Indicates the actual usable power of the power generation unit. This represents the power probability density function.

[0042] The expression for reserve cost is as follows:

[0043] in, This represents the reserve cost coefficient.

[0044] The expression for the battery energy storage cost model is as follows:

[0045] in, This indicates the total number of battery energy storage units in the system. l Indicates the serial number of the battery energy storage unit. Indicates the first l Annualized full lifecycle operation and maintenance cost of a single battery energy storage unit Indicates the number of batteries. This represents the annual maintenance cost per unit capacity. Indicates the battery's rated capacity. Indicates the average upgrade rate. Represents the average interest rate. b Indicates battery life. This indicates the total design lifespan in years for battery energy storage throughout its entire lifecycle.

[0046] The expression for the pumped storage cost model is as follows:

[0047] in, This indicates the cost of pumped storage. This indicates the total number of pumped storage units in the system. m Indicates the serial number of the pumped storage unit. T This indicates the total pumped storage time. and These represent the pump start-up and shutdown costs, and These represent the time periods under consideration. t The binary variable representing the on / off state of the internal pump. Indicates the time period t The internal pump is on; otherwise... ; Indicates the time period t The internal pump is in the off state; otherwise... .

[0048] A penalty function is introduced to ensure that system variables do not exceed limits during the optimization process. Penalty function It consists of two parts, used to check the deviation between line power flow and bus voltage. When the constraints are limited to the upper and lower limits, The value is zero. The penalty function is expressed as follows:

[0049]

[0050]

[0051] in, and These represent the penalties for exceeding line power limits and voltage limits, respectively. Indicates rated power. and These represent the maximum rated power and the minimum rated power, respectively. V n Indicates the rated voltage. and These represent the maximum rated voltage and the minimum rated voltage, respectively. abs [] indicates the absolute value operation, and sign() indicates the sign function. M pf This represents a coefficient or correction factor related to the power factor.

[0052] The power output of a wind farm is directly proportional to wind speed. The intermittency of wind speed is simulated using the Weibull probability density function and the Weibull cumulative distribution function, as shown in the following formula:

[0053]

[0054]

[0055] in, P jj Indicates the first j The actual active power output of the typhoon wind turbine at actual wind speeds. This indicates the rated power of fan j. v j Indicates wind speed. These are the cut-in, rated, and cut-out wind speeds, respectively. When the actual wind speed reaches or exceeds the cut-in wind speed, the fan starts and gradually generates power output. When the actual wind speed reaches or exceeds the cut-out wind speed, in order to protect the safety of the fan equipment, the fan will stop running and the power output will drop to zero. k and c These are the shape and scale parameters of the Weibull distribution, respectively. Indicates wind turbine j The value of the Weibull probability density function corresponding to the output power. Indicates wind turbine j The value of the Weibull cumulative distribution function corresponding to the output power.

[0056] We establish the functional relationship between photovoltaic output and solar radiation, as well as the bimodal probability density function for modeling solar irradiance variation, to perform photovoltaic power probability analysis. The specific formulas are as follows:

[0057]

[0058] in, Represented by solar radiation s The average power output of the photovoltaic system. This indicates the rated power output of the photovoltaic system. A coefficient related to solar irradiance. This represents real-time solar irradiance. The reference solar irradiance under standard test conditions. This is a temperature correction factor coupled with irradiance. This is the actual average temperature. This is the reference temperature under standard test conditions; Indicates solar irradiance s The corresponding probability density function; These represent the shape parameters of the Weibull distribution, corresponding to the shapes of the two Weibull distributions respectively; Let be the scale parameter of the Weibull distribution, corresponding to the scales of the two Weibull distributions respectively. This represents the weighting coefficient of the first unimodal Weibull distribution in a bimodal Weibull mixture distribution.

[0059] In some embodiments, step S102 involves constructing a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. This includes: determining the total cost based on the costs of thermal power, wind power intermittency, photovoltaic power, battery energy storage, and pumped storage; determining the total emissions of sulfur oxides and nitrogen oxides based on the operating conditions of thermal power units; and determining the sum of voltage deviations of all nodes based on the rated voltage and actual operating voltage of each node in the power hybrid system.

[0060] For example, the expression for total cost is as follows:

[0061] in, This represents the total cost. Hazardous emissions of sulfur oxides and nitrogen oxides are expressed as:

[0062] in, This indicates the total emissions of sulfur oxides and nitrogen oxides. Indicates the first i The unit power emission intensity coefficient of Taiwan thermal power units Indicates the first i The output of Taiwan's thermal power units, , and This represents the emission coefficient of thermal power units. and The coefficient represents the nonlinear emission correction term.

[0063] The objective function for minimizing voltage deviation is expressed as:

[0064] in, This represents the sum of voltage deviations across all nodes. Indicates the first b The rated voltage of each node, Indicates the first b The actual operating voltage of each node This represents the total number of grid nodes in the power system that participate in the voltage deviation optimization assessment.

[0065] In step S103, the relevant constraint set includes power balance constraints, upper and lower limit constraints of power of thermal generator, upper and lower limit constraints of system bus voltage, power limit constraints of wind turbine generator, photovoltaic output power limit constraints, pumped storage system constraints, and battery energy storage charging state constraints.

[0066] For example, the expression for the power balance constraint is as follows:

[0067]

[0068] in, Indicates the first i The output active power of the thermal power generating unit Indicates the first j The output active power of the typhoon turbine generator set. Indicates the first k The output active power of each photovoltaic power generation unit Indicates the first s Net active power output of each energy storage unit s Indicates the serial number of the diversified energy storage unit. This indicates the total number of diverse energy storage units within the system; This indicates the total active power loss of the system. This represents the total active power load of the system; Indicates the first i The reactive power output of the thermal power generating unit Indicates the first j The reactive power output of the typhoon turbine generator set Indicates the first k The reactive power output of each photovoltaic power generation unit; This represents the total reactive power loss of the system. This represents the total reactive load of the system.

[0069] The expressions for the upper and lower power limits of the heat generator are as follows:

[0070]

[0071] in, Indicates the first i Minimum power output of each thermal power unit Indicates the first i The maximum power output of each thermal power generator unit; Indicates the first i The lower limit of reactive power of each thermal power unit Indicates the first i The upper limit of reactive power of a thermal power unit.

[0072] The expressions for the upper and lower limits of the system bus voltage are as follows:

[0073] in, and These represent the minimum and maximum values ​​of the bus voltage, respectively. This indicates the bus voltage.

[0074] The expression for the power limit constraint of wind turbine generators is as follows:

[0075]

[0076] in, Indicates the first j The maximum rated power output of each wind turbine unit and They represent the first j The minimum and maximum reactive power of each wind turbine unit.

[0077] The expression for the photovoltaic output power limit constraint is as follows:

[0078] in, Indicates the first k The maximum rated power of each solar cell.

[0079] For pumped storage, the start-stop states of the pumps and generators are represented by binary variables, and the expressions for the constraints of the pumped storage system are as follows:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] in, Indicates time t The Inner Canon m The water pumped into the upper reservoir This represents the efficiency coefficient during the pumping process. This indicates pumping power, which is the power used to pump water from the lower reservoir to the upper reservoir. This indicates the density of water. g Represents gravitational acceleration. Indicates at time t The effective head height between the upper and lower reservoirs at that time; Indicates the time of the upper reservoir t The amount of water stored in +1 Indicates the attenuation coefficient. Indicates time t Inland flow m The water flowing from the generating unit into the reservoir; Indicates the first m The upper limit of the maximum pumping flow rate or maximum pumping power of a pumping device per unit time; This indicates the total number of pumped storage hydroelectric power units, i.e., the upper limit for the number of units. Indicates the first m Taiwan pumped storage unit t A binary variable representing the operating status of the generator under real-time power generation conditions; Indicates the first m Each power generation device is in t Power generation at any given moment This represents the efficiency coefficient during the power generation process.

[0086] Within a given time period, the state of charge (SOC) of a battery must meet upper and lower limits. The change in SOC is determined by factors such as self-discharge, initial SOC, charge / discharge power, and efficiency. The battery's state of charge and its changes are shown below:

[0087] in, and These represent the minimum and maximum states of charge of the battery, respectively. This indicates the initial SoC value of the battery energy storage. This indicates the battery's self-discharge rate. Indicates battery charge / discharge efficiency. Indicates the rated energy of the battery system. Indicates the battery's charging and discharging power. This indicates the duration of a single scheduling period in power system dispatch optimization.

[0088] The design of a tiered energy storage scheduling strategy includes: determining the response conditions of battery energy storage and pumped hydro storage based on the fluctuation amplitude discrimination criterion; and allocating power to battery energy storage and pumped hydro storage respectively by combining the indicator function.

[0089] In this embodiment, the wind speed and solar irradiance probability density functions across multiple prediction intervals are considered to obtain the output power of wind and solar power. The system-on-a-chip (SoC) and power supply system (PHS) status of the BES are monitored to address imbalances between generation and load. Using the BES can mitigate short-term power fluctuations (less than 10%) in clean energy sources, enabling rapid response and improving power density. When the difference between the estimated and actual output of renewable energy or the difference between generation and load exceeds 10%, the PHS is activated for regulation. Through a tiered scheduling strategy, the characteristics of different types of energy storage systems are rationally utilized to achieve economical and efficient operation of the energy storage system while ensuring power balance.

[0090] For example, the tiered energy storage dispatch strategy makes judgments based on the fluctuation amplitude discrimination criterion, as shown in the following formula:

[0091] in, Indicates the magnitude of system power imbalance. Indicates actual power. This indicates the predicted power.

[0092] The battery energy storage response conditions are:

[0093] in, This indicates the system's rated reference power.

[0094] The response conditions for pumped storage are:

[0095] Energy storage power allocation is as follows:

[0096]

[0097] in, Indicates the battery's energy storage capacity. This indicates the rated maximum charge and discharge power of the battery energy storage system. I Indicates an indicator function, Indicates pumped storage capacity. This indicates the rated maximum power generation capacity of the pumped storage system.

[0098] The above objective function is then subjected to multi-objective weighting, and the specific formula is as follows:

[0099] in, F Represents the objective function value. This represents the weight coefficients, and their sum is 1. This indicates the reference values ​​for each target.

[0100] In some embodiments, step S104 involves using an improved bacterial foraging algorithm to solve a multi-objective optimization model and determine the optimal power generation plan. This includes: introducing an adaptive step size mechanism and an enhanced group cooperation effect function for global search, significantly improving global search capability and convergence accuracy; and selecting the optimal compromise solution based on fuzzy decision-making to determine the optimal power generation plan.

[0101] For example, set the algorithm parameters and randomly initialize the bacterial locations:

[0102] in, Indicates the initial position of the bacteria. This represents the lower bound of the values ​​that the decision variable can take. This represents the upper limit of the values ​​that the optimization decision variable can take. rand () represents a random number generation function that follows a uniform distribution over an interval; It includes all generator output and energy storage state variables. This indicates the output active power of the first thermal power generating unit. This indicates the output active power of the second thermal power generating unit. Indicates the first The output active power of a thermal power generating unit; SOC represents the state of charge of the battery energy storage system. W u This indicates the amount of water stored in the reservoir of a pumped storage system.

[0103] Next, chemotaxis is performed for each bacterium. i In each chemotactic step j In this context, the mathematical formulas for fitness and position updates are as follows:

[0104]

[0105] in, Represents the fitness function. This represents the economic objective function. Indicates the first i The bacteria in the first j The position vector of each chemotaxis step. The function represents the penalty function for constraint violation. This indicates an adaptive step size (which decreases with iteration). This represents a random direction vector.

[0106] Next, we introduce the population cooperation effect function, which guides bacteria to gather in the nutrient-rich zone (optimal solution) while avoiding overcrowding. The population cooperation effect function is as follows:

[0107] in, This represents the group cooperation effect function. S This represents the total size of the bacterial population in the improved bacterial foraging algorithm. This represents the coefficient of attraction correlation. The weighting parameter represents the attraction effect. This represents the coefficient of exclusivity. The weighting parameter represents the repulsive effect. This represents the position vector of the bacterial individual whose group cooperative effect is currently to be calculated.

[0108] At the end of each generation, the cells are sorted by health and then split and eliminated.

[0109] in, Indicates the first i The health function value of a bacterium. Indicates the first i The bacteria in the first j The fitness function values ​​for each chemotaxis step. Then, with probability... p ed Randomly relocate bacteria while avoiding overcrowding.

[0110] The algorithm terminates under the following conditions:

[0111] in, Representation Algorithm Number k During iterations, the globally optimal fitness function value within the population is... This represents the precision threshold for algorithm convergence. k This indicates the current iteration number of the algorithm. k max This indicates the maximum number of iterations allowed by the algorithm.

[0112] Finally, the optimal compromise solution is selected based on fuzzy decision-making. After multiple runs of MBFA, 20 non-dominated solutions are obtained, which constitute the Pareto front. For each solution and each goal (q=1,2,3) Calculate membership degrees and select the optimal compromise solution:

[0113]

[0114]

[0115] in, Indicates the first m The Pareto nondominated solution is at the th... q Membership values ​​on each optimization objective and They represent the objectives in the Pareto solution set, respectively. q The minimum and maximum values, Indicates the first q The weighting coefficients of each optimization objective. This represents the optimal compromise solution with the highest comprehensive membership degree selected based on the fuzzy decision-making method. The optimal power generation plan is determined by selecting the solution with the highest comprehensive membership degree.

[0116] In summary, this method constructs a power plant-diversified energy storage joint system architecture by specifically combining wind power, photovoltaic power, thermal power, battery energy storage, and pumped hydro storage and clarifying their collaborative operation relationships. It transforms the intermittency and volatility of wind and photovoltaic power into penalty and reserve costs, incorporating them into the overall economic objective function to achieve integrated optimization, breaking through traditional processing models and quantifying and internalizing uncertainty. Based on this, a multi-objective optimization model is constructed with economic efficiency, environmental friendliness, and voltage stability as objectives, pursuing a multi-dimensional balance of the Pareto optimal frontier. Finally, an improved bacterial foraging algorithm with adaptive step size and a group collaboration mechanism solves the convergence and local optimum problems of high-dimensional nonlinear problems, and a BES / PHS hierarchical coordinated scheduling strategy based on power imbalance amplitude is proposed, clarifying the energy storage division of labor and cooperation mechanism to fully leverage technological advantages.

[0117] The present invention also provides a power plant-diversified energy storage synergistic optimization device. The power plant-diversified energy storage synergistic optimization device provided by the present invention will be described below. The power plant-diversified energy storage synergistic optimization device described below and the power plant-diversified energy storage synergistic optimization method described above can be referred to in correspondence. Figure 2 This is a structural block diagram of the power plant-diversified energy storage synergistic optimization device provided by the present invention, as shown below. Figure 2 As shown, the device includes: Module 201 is used for component modeling based on electric hybrid systems to quantify the indirect costs of intermittent renewable energy. Processing module 202 is used to construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. The configuration module 203 is used to establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balancing. The optimization module 204 is used to solve the multi-objective optimization model using an improved bacterial foraging algorithm to determine the optimal power generation plan.

[0118] In its application, this device first constructs a hybrid system model incorporating renewable energy sources such as wind and solar power, as well as flexible resources including thermal power, battery storage, and pumped hydro storage. Based on this, a multi-objective optimization model is established with three optimization objectives: minimizing total system operating cost, minimizing pollutant emissions, and optimizing grid node voltage deviation. Specifically, the uncertainty costs arising from the intermittency and volatility of wind and solar power output are quantified and modeled, incorporating them into the overall operational economic analysis to more realistically reflect the actual cost of integrating new energy sources into the grid. Finally, an improved bacterial foraging algorithm is used for solving the problem. By introducing adaptive step size and a group cooperation mechanism, the convergence accuracy and search efficiency are significantly improved, obtaining the optimal power generation plan that balances economic, environmental, and stable operation requirements. This effectively mitigates the fluctuations in new energy sources and solves the problem of existing related technologies struggling to achieve multi-objective collaborative optimization, providing an effective technical approach to improve the comprehensive operational performance of novel hybrid power systems.

[0119] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 301, a communication interface 302, a memory 303, and a communication bus 304. The processor 301, communication interface 302, and memory 303 communicate with each other via the communication bus 304. The processor 301 can call logical instructions from the memory 303 to execute a power plant-diversified energy storage collaborative optimization method. This method includes: Based on component modeling of electric hybrid systems, the indirect costs of intermittent renewable energy are quantified. Construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. Establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balance. An improved bacterial foraging algorithm was used to solve the multi-objective optimization model and determine the optimal power generation plan.

[0120] Furthermore, the logical instructions in the aforementioned memory 303 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the power plant-diversified energy storage collaborative optimization method provided by the above methods, the method including: Based on component modeling of electric hybrid systems, the indirect costs of intermittent renewable energy are quantified. Construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. Establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balance. An improved bacterial foraging algorithm was used to solve the multi-objective optimization model and determine the optimal power generation plan.

[0122] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power plant-diversified energy storage synergistic optimization method provided by the methods described above, the method comprising: Based on component modeling of electric hybrid systems, the indirect costs of intermittent renewable energy are quantified. Construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. Establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balance. An improved bacterial foraging algorithm was used to solve the multi-objective optimization model and determine the optimal power generation plan.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for synergistic optimization of power plant-diversified energy storage, characterized in that, include: Based on component modeling of electric hybrid systems, the indirect costs of intermittent renewable energy are quantified. Construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. Establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balance. An improved bacterial foraging algorithm is used to solve the multi-objective optimization model to determine the optimal power generation plan.

2. The power plant-diversified energy storage synergistic optimization method according to claim 1, characterized in that, The indirect costs include thermal power costs, wind power intermittent costs, photovoltaic costs, battery energy storage costs, and pumped storage costs.

3. The power plant-diversified energy storage synergistic optimization method according to claim 2, characterized in that, Both the intermittent cost of wind power and the cost of photovoltaic power consist of three parts: direct cost, penalty cost, and backup cost.

4. The power plant-diversified energy storage synergistic optimization method according to claim 3, characterized in that, Construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability, including: The total cost is determined based on the costs of thermal power, intermittent wind power, photovoltaic power, battery energy storage, and pumped hydro storage. The total emissions of sulfur oxides and nitrogen oxides are determined based on the operating output of the thermal power units. Based on the rated voltage and actual operating voltage of each node in the power hybrid system, determine the total voltage deviation of all nodes.

5. The power plant-diversified energy storage synergistic optimization method according to claim 1, characterized in that, The relevant constraint set includes power balance constraints, upper and lower limit constraints on the power of the thermal generator, upper and lower limit constraints on the system bus voltage, power limit constraints on the wind turbine, photovoltaic output power limit constraints, pumped storage system constraints, and battery energy storage charging state constraints.

6. The power plant-diversified energy storage synergistic optimization method according to claim 1, characterized in that, Design a tiered energy storage dispatch strategy, including: The response conditions for battery energy storage and pumped hydro storage are determined based on the fluctuation amplitude discrimination criterion. Power allocation is performed on battery energy storage and pumped hydro storage separately using the indicator function.

7. The power plant-diversified energy storage synergistic optimization method according to claim 1, characterized in that, An improved bacterial foraging algorithm is used to solve the multi-objective optimization model to determine the optimal power generation plan, including: An adaptive step size mechanism and an enhanced group cooperation effect function are introduced for global search; The optimal power generation plan is determined by selecting the optimal compromise solution based on fuzzy decision-making.

8. A power plant-diversified energy storage synergistic optimization device, characterized in that, include: Modules are used for component modeling based on hybrid power systems to quantify the indirect costs of intermittent renewable energy. The processing module is used to construct a multi-objective optimization model that includes economic operating costs, pollution emission control, and voltage safety and stability. The configuration module is used to establish a set of relevant constraints, design a hierarchical energy storage scheduling strategy, and perform multi-scale power balancing. The optimization module is used to solve the multi-objective optimization model using an improved bacterial foraging algorithm to determine the optimal power generation plan.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the power plant-diversified energy storage collaborative optimization method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power plant-diversified energy storage collaborative optimization method as described in any one of claims 1 to 7.

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