A Multi-Objective Collaborative Optimization Control Method for New Energy Power Stations and Energy Storage Systems

Abstract

This invention discloses a multi-objective collaborative optimization control method for new energy power plants and energy storage systems, belonging to the field of new energy optimization control technology. It constructs a deeply coupled collaborative optimization framework with multiple time scales, objectives, and elements. This framework achieves joint optimization control of wind farms, photovoltaic power plants, energy storage systems, and conventional generator units through a full-process design of "objective modeling - scenario adaptation - algorithm solving - closed-loop execution." It formulates a scenario-adaptive dynamic interaction strategy based on source-grid-storage to fully explore the regulation potential of wind power, photovoltaics, energy storage, and conventional generator units; and employs a multi-objective optimization algorithm to solve for the real-time optimal control allocation value, forming a closed-loop coordinated control. This invention solves the problems of poor adaptability and insufficient source-grid-storage resource coordination in traditional single-objective optimization, significantly improving the new energy absorption rate and system stability, and reducing operating costs.

Description

Technical Field

[0001] This invention relates to the field of new energy optimization control technology, and in particular to a multi-objective collaborative optimization control method for new energy power plants and energy storage systems. It is applicable to the operation optimization of regional power systems and new energy bases with a high proportion of new energy access, and can effectively solve the problems of system safety and stability and absorption caused by the volatility and uncertainty of new energy, thereby improving the overall operational economy. Background Technology

[0002] With the increasing penetration rate of intermittent renewable energy sources such as wind and solar power in the power system, maximizing the absorption of new energy while controlling operating costs under the premise of ensuring the safe and stable operation of the system has become a core challenge for high-proportion renewable energy power systems. Renewable energy generation is significantly affected by meteorological conditions, exhibiting intermittent, fluctuating, and uncertain characteristics, which easily leads to difficulties in maintaining system power balance, causing problems such as excessive frequency deviation, excessive voltage fluctuation, and line power flow exceeding limits, threatening the safe operation of the power grid. Traditional power system optimization control often focuses solely on economic efficiency, failing to fully consider the demand for renewable energy absorption and system security constraints, resulting in severe wind and solar curtailment. Existing control strategies mostly adopt fixed modes, lacking the coordinated utilization of multiple resources such as power generation, grid, and storage, and cannot adapt to the dynamic changes in renewable energy output and load demand. Furthermore, the loose connection between optimization control at different time scales, the disconnect between day-ahead dispatch and real-time operation due to forecasting errors, and the insufficient release of the flexible adjustment capabilities of energy storage systems all constrain the safe, efficient, and economical operation of high-proportion renewable energy power systems. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-objective collaborative optimization control method for new energy power plants and energy storage systems, in order to solve the coordination problems between safety and stability, new energy consumption and economy faced by the system operation under high proportion of new energy access. By constructing a multi-timescale collaborative control system and a dynamic interaction mechanism between source, grid and storage, the method can achieve efficient utilization of new energy, safe and stable operation of the system and precise control of operating costs.

[0004] To address the aforementioned issues, this invention provides a multi-objective collaborative optimization control method for new energy power plants and energy storage systems. Its core lies in establishing a deeply coupled collaborative optimization framework involving multiple time scales, objectives, and elements. This framework, through a full-process design of "objective modeling - scenario adaptation - algorithm solving - closed-loop execution," achieves joint optimization control of wind farms, photovoltaic power plants, energy storage systems, and conventional generator sets. The multi-objective optimization model is the foundation of this invention, containing three core objective functions that characterize system operation requirements from three dimensions: system safety, new energy utilization, and economic operation. A dynamic interaction strategy for source-grid-storage resources enables the system to adaptively adjust its control logic based on real-time operating scenarios. This strategy first collects data such as new energy output, load demand, and equipment operating status through the system monitoring module to identify the current scenario type; then, it clarifies the adjustment priority and control logic of each resource based on scenario characteristics; finally, it transmits the optimized power allocation value obtained through the algorithm solution to each device through the control command issuance module, achieving a collaborative response of source-grid-storage resources. This adaptive strategy fully taps the adjustment potential of each resource, avoids the limitations of fixed strategies, and improves the system's adaptability to complex operating conditions.

[0005] The specific technical solution adopted by this invention is as follows:

[0006] A multi-objective collaborative optimization control method for new energy power plants and energy storage systems includes the following steps:

[0007] Step 1: Establish a multi-objective optimization model that includes the objective function of system safety and stability, the objective function of maximizing the absorption of new energy sources, and the objective function of safe and economical production, and set multiple constraint conditions consisting of power balance constraints, equipment capacity constraints, and safe operation constraints;

[0008] Step 2: Formulate a dynamic interaction strategy for source, grid and storage resources. This strategy identifies the current operating scenario type based on the collected real-time system operation status data, and adaptively adjusts the coordination control logic and adjustment priority of wind power, photovoltaic, energy storage, conventional generator sets and grid equipment according to the characteristics of the identified scenario type.

[0009] Step 3: Employ a multi-objective, multi-constraint optimization algorithm to solve the multi-objective optimization model for the current scenario type, in order to obtain the real-time optimal control allocation value. The solution process includes first transforming the multi-objective problem into a single-objective problem using a fuzzy satisfaction trade-off decision method, and then using an improved genetic algorithm to iteratively solve the single-objective problem.

[0010] Step 4: Based on the optimal control allocation value, perform closed-loop coordinated control on wind farms, photovoltaic power plants, energy storage systems and grid equipment, and dynamically correct control commands based on the operating status feedback from the real-time monitoring system to eliminate control deviations.

[0011] Furthermore, the determination of the system safety and stability objective function aims to minimize the risks of frequency deviation, voltage deviation, and line transmission power exceeding limits during system operation. The determination method includes: comprehensively evaluating the frequency deviation of each node in the system, the voltage deviation of each node, and the transmission power of each transmission line, and applying a penalty term to the power portion exceeding the preset transmission limit. By setting corresponding weight coefficients for each deviation and penalty term, they are weighted and summed to quantify the overall safety and stability level of the system.

[0012] Furthermore, the determination of the objective function for maximizing the absorption of new energy sources aims to maximize the actual power generation of new energy power plants while suppressing the impact of power output fluctuations on the system. The determination method includes: accumulating the actual power output of each new energy power plant during the optimization period as the basic absorption amount; simultaneously, calculating the absolute value of the deviation between the actual power output and the predicted power output, and multiplying the absolute value of the deviation by a smoothing penalty coefficient to form a fluctuation penalty term; finally, subtracting the fluctuation penalty term from the basic absorption amount to obtain the objective function value, and maximizing the function value through optimization.

[0013] Furthermore, the determination of the safe and economical production objective function aims to minimize the total cost of system operation. The determination method includes: summing up the power generation costs of all conventional generator units, the charging and discharging operation costs of all energy storage systems, the curtailment penalty costs of all new energy power stations, and the violation penalty costs of system safety constraints during the optimization period; wherein, the power generation cost is related to the unit output and the power generation cost coefficient, the charging and discharging operation cost is related to the charging and discharging power and the operation cost coefficient, and the curtailment penalty cost is related to the amount of curtailed power and the curtailment penalty coefficient.

[0014] Furthermore, the power balance constraint requires that at any given time, the total power generation in the system equals the total power consumption; wherein, the total power generation includes the sum of the output of all conventional generator sets, the sum of the actual output of all new energy power plants, and the sum of the discharge power of all energy storage systems; the total power consumption includes the system load, the sum of the charging power of all energy storage systems, and the system network loss.

[0015] Furthermore, the equipment capacity constraints include: for conventional generator sets, their output must be between their minimum and maximum technical output, and the rate of change of output per unit time must meet their upward and downward ramp rate constraints; for new energy power plants, their actual output must be greater than or equal to zero and not greater than the smaller of their predicted output and maximum available power; for energy storage systems, their charging and discharging power must be within the rated power range, their state of charge must be between the allowable minimum and maximum capacity, and they must meet energy conservation constraints.

[0016] Furthermore, the safety operation constraints are used to ensure that key parameters of the power grid operate within a safe range, specifically including: node frequency constraints, requiring that the actual frequency of any node in the system is between a preset safe lower limit and an upper limit; node voltage constraints, requiring that the actual voltage of any node is between a preset safe lower limit and an upper limit; and line power flow constraints, requiring that the absolute value of the transmission power of any transmission line is not greater than the transmission limit of that line.

[0017] Furthermore, the scenario types include normal scenarios, renewable energy surge scenarios, peak load scenarios, and equipment failure scenarios. A normal scenario refers to a situation where the amplitude of both renewable energy output fluctuations and load fluctuations does not exceed their respective preset normal operating fluctuation thresholds, and the line power flow, frequency, and node voltage within the system are all within preset safe operating ranges, with no equipment failure signals. A renewable energy surge scenario refers to a situation where the actual total output of renewable energy power plants exceeds a specific multiple of the difference between the current system load and the minimum output of conventional generator units, and the duration of this state exceeds a preset time threshold. A peak load scenario refers to a situation where the actual total load of the system exceeds a preset threshold proportion of the total rated output of conventional generator units, or, based on load forecasting results, the total system load will reach the preset threshold proportion of the total rated output of conventional generator units within a preset future time window. An equipment failure scenario refers to a situation where a generator trip is detected, or the line power flow exceeds a specific proportion of the transmission limit, or the node frequency and voltage exceed safe ranges.

[0018] Furthermore, the method for adjusting the coordination control logic includes:

[0019] Under normal circumstances, the control objective is to prioritize the absorption of new energy sources and smooth out fluctuations. The adjustment priority is, in order, new energy power plants, energy storage systems, and conventional generator sets. The specific control logic is to use the energy storage system to charge and discharge according to the fluctuations in the output of new energy sources.

[0020] In the scenario of large-scale renewable energy generation, the control objective is to maximize absorption and ensure safety. The adjustment priority is, in order, energy storage system, conventional generator set, and renewable energy power station. The specific control logic is to instruct the energy storage system to charge at full power, and the conventional generator set to reduce its output to the minimum technical output. If there is still a surplus of renewable energy at this time, the renewable energy power station is given a curtailment instruction in order of curtailment cost from low to high. The curtailment ratio is adjusted periodically until power balance is achieved.

[0021] In peak load scenarios, the control objective is to ensure power supply to the load and maintain stability. The adjustment priority is energy storage system, new energy power station, and conventional generator set. The specific control logic is to instruct the energy storage system to discharge at maximum power, while instructing the new energy power station to increase output. The conventional generator set gradually increases output according to the ramp rate limit. When all conventional generator sets reach maximum output and still cannot meet the load, the standby peak shaving unit is started.

[0022] In equipment failure scenarios, the control objective is to quickly isolate the fault and restore stability. The adjustment priority is, in order, energy storage system, conventional generator sets, and new energy power plants. The specific control logic is as follows: When a generator trips, the energy storage system immediately discharges to supplement the output gap of the tripped generator set, while instructing other conventional generator sets to increase their output. After the output stabilizes, the energy storage system gradually reduces the discharge power. When a line overload occurs, the new energy power plants connected to the line are instructed to reduce their output, and the energy storage system adjusts the charging and discharging direction until the line power flow drops to a safe range. When the frequency / voltage exceeds the limit, the energy storage system participates in primary frequency / voltage regulation first, conventional generator sets participate in secondary regulation, and new energy power plants adjust their output according to the instructions to restore the frequency / voltage to within the safe threshold.

[0023] Furthermore, the closed-loop coordinated control process includes: generating specific control commands from the optimal control allocation value and sending them to the actuators of the wind farm, photovoltaic power station, energy storage system, and conventional generator set via a real-time control bus; the actuators adjusting their operating parameters according to the commands; simultaneously, the system monitoring module collecting real-time operating status data of each device after executing the commands and feeding it back to the scene recognition module; if the feedback status data deviates from the target status, the process of scene recognition, optimization solution, and command correction is re-triggered to form a dynamic closed-loop correction until the system operating status meets the target requirements.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] (1) This invention achieves coordinated optimization of multiple operational objectives, effectively resolving the conflict between safety, energy consumption, and economy. It is no longer limited to the traditional single economic objective, but rather incorporates multiple previously mutually restrictive operational demands into a unified decision-making framework by establishing an optimization model that includes three objective functions: system safety and stability, maximizing renewable energy consumption, and safe and economical production. This method can intelligently seek the optimal balance between maximizing renewable energy consumption and minimizing operating costs, based on the weights and priorities of different scenarios, while ensuring grid safety and stability. This fundamentally solves the technical challenge of coordinating multiple objectives in high-proportion renewable energy scenarios.

[0026] (2) Significantly improves the renewable energy consumption rate and the system's adaptability to dynamic changes. The dynamic interaction strategy of source-grid-storage resources designed in this invention can accurately identify various typical scenarios such as normal operation, high renewable energy generation, peak load, and equipment failure based on real-time operating status. For different scenarios, this method can adaptively adjust the coordination control logic and adjustment priority of wind, solar, storage, and conventional units. For example, in the scenario of high renewable energy generation, the system will automatically prioritize instructing energy storage to charge and conventional units to reduce output, creating maximum space for renewable energy to be connected to the grid. This "time-sensitive" intelligent control method breaks the limitations of the traditional fixed mode, greatly improves the system's adaptability to the strong fluctuations of renewable energy and the uncertainty of load, thereby significantly reducing the wind and solar curtailment rate.

[0027] (3) The potential for coordinated regulation of multiple resources (source, grid, and storage) has been deeply explored, enhancing the flexibility and reliability of system operation. This invention achieves deep synergy among diverse heterogeneous resources through joint optimization control of wind power, photovoltaics, energy storage, and conventional units. Under different scenarios, the roles and functions of each resource are dynamically optimized, giving full play to the rapid response capability of energy storage, the reliable support capability of conventional units, and the regulation potential of new energy sources. For example, in the event of equipment failure, the energy storage system is given the highest priority to provide emergency power support. This refined coordination mechanism not only improves the overall regulation flexibility of the system but also provides key stability assurance for the system in emergency situations, significantly enhancing the operational reliability of the power grid.

[0028] (3) It ensures the scientific nature of the optimization decision and the accuracy of real-time control, effectively overcoming the impact of prediction errors. This invention adopts a two-step method of "multi-objective transformation - intelligent algorithm solution," which can scientifically and efficiently solve complex multi-constraint optimization problems. More importantly, this invention designs a closed-loop coordinated control mechanism based on real-time state feedback. This mechanism can dynamically correct control commands to eliminate the deviation between actual operation and optimization objectives, effectively solving the control disconnect problem caused by uncertainties such as new energy prediction errors. This seamless closed loop from optimization to execution ensures that the theoretically optimal decision can be accurately implemented in the actual system, guaranteeing the authenticity and superiority of the control effect. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1This is a flowchart illustrating a multi-objective collaborative optimization control method for a new energy power station and energy storage system as described in this invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0032] like Figure 1 As shown, this invention provides a multi-objective collaborative optimization control method for new energy power plants and energy storage systems, the specific implementation of which is as follows:

[0033] The core of this invention lies in constructing a full-process collaborative optimization control framework, encompassing target modeling, scenario adaptation, algorithm solving, and closed-loop execution. This method first characterizes the complex requirements of system operation by establishing a refined multi-objective optimization model. Then, it adaptively adjusts the control strategy based on the real-time operating scenario. Next, it uses an efficient optimization algorithm to solve for the optimal control scheme. Finally, a closed-loop control mechanism ensures the precise execution of the scheme.

[0034] Step 1: Establishing a multi-objective optimization model

[0035] This invention first establishes a multi-objective optimization model, which details three core objective functions and is supplemented with complete constraints, each corresponding to a different core requirement for system operation.

[0036] 1. System safety and stability objective function

[0037] The objective function aims to minimize the risks of frequency deviation, voltage deviation, and excessive power transmission on transmission lines during system operation, ensuring system safety from the core dimension of power grid operating parameters. Its determination method involves comprehensively evaluating the frequency deviation of each node, the voltage deviation of each node, and the transmission power of each transmission line within the system. A penalty term is applied to the power exceeding the preset transmission limit. By assigning corresponding weight coefficients to each deviation and penalty term, they are weighted and summed to quantify the overall safety and stability level of the system. Its mathematical expression is:

[0038]

[0039] in, This represents the frequency deviation of the i-th node at time t. The rated frequency of the power grid is 50Hz. This value reflects the difference between the actual frequency of the node and the rated frequency. When the deviation exceeds the safety threshold, the frequency regulation mechanism will be triggered. This represents the voltage deviation of the i-th node at time t, reflecting the difference between the actual voltage and the rated voltage of the node. This represents the line transmission power of the k-th line at time t; As a penalty term, the max() function applies the penalty only if the line power... Exceeding the transmission limit Only when this condition is met is the item positive, thus penalizing the risk of exceeding the limit; This indicates the transmission limit of the line, which is determined by the conductor cross-sectional area, heat resistance rating, and safety and stability standards of the line. , and The weighting coefficient has a value range of [0,1] and satisfies the following conditions: The system can be dynamically adjusted according to operational needs; for example, it can be improved during power grid maintenance. Focusing on preventing overload of key power lines, increasing [efforts] during peak electricity consumption periods. To enhance frequency stability control; N is the total number of system nodes, covering generation nodes, load nodes, and interconnection nodes; T is the length of the optimization period, which can be varied according to control requirements at different time scales.

[0040] 2. Objective function for maximizing the absorption of new energy sources

[0041] The objective function aims to maximize renewable energy generation while mitigating system impact by penalizing output fluctuations, thus achieving a balance between renewable energy utilization efficiency and system stability. Its determination method is as follows: The actual output of each renewable energy plant during the optimization period is accumulated to form the base absorption capacity; simultaneously, the absolute value of the deviation between this actual output and the predicted output is calculated, and this absolute value is multiplied by a smoothing penalty coefficient to form a fluctuation penalty term; finally, the fluctuation penalty term is subtracted from the base absorption capacity to obtain the objective function value. The expression for this objective function is:

[0042]

[0043] in, The actual output of the j-th renewable energy power station at time t is collected by the real-time power monitoring device of the power station and directly reflects the actual power generation capacity of renewable energy. The sum of the actual outputs of all renewable energy power stations is the basic absorption capacity. The weighted sum of the deviations between the actual output and the predicted output is the fluctuation penalty term. This represents the corresponding predicted output, which is obtained by fusing numerical weather prediction data with machine learning models. The prediction duration is divided into long-term, medium-term, and short-term depending on the time scale, and the prediction error decreases as the prediction duration shortens; M represents the number of new energy power stations, each of which can be an independent wind farm or photovoltaic power station. To smooth out the deviation between actual and predicted power output, the penalty coefficient is set within the range of [0.1, 1.0]. A larger coefficient results in a more severe penalty for the discrepancy between actual and predicted power output. This coefficient can be set according to the system's adjustment capabilities; for example, it can be appropriately reduced when the energy storage capacity is large. Prioritize ensuring the amount of energy consumed; increase capacity when energy storage is relatively small. To reduce the impact of fluctuations; the negative sign before the function is to transform the objective of "maximizing absorption" into "minimizing negative absorption", which makes it easier to solve in a unified manner with other minimization objective functions.

[0044] 3. Objective function for safe and economical production

[0045] This objective function comprehensively considers various costs during system operation, aiming to achieve economically optimal control under safety constraints. Its determination method is as follows: summing the generation costs of all conventional generator units, the charging and discharging operation costs of all energy storage systems, the curtailment penalty costs of all renewable energy plants, and the violation penalty costs of system safety constraints within the optimization period. The expression for this objective function is:

[0046]

[0047] in, This represents the output of the kth conventional generator unit. Conventional units include thermal power units, hydropower units, etc., which mainly provide base load power and regulation capabilities. The power generation cost coefficient is calculated based on fuel prices, power generation efficiency, and operation and maintenance costs, while the cost of hydropower units is mainly operation and maintenance costs, which are usually lower than those of thermal power units. and These represent the charging power and discharging power of the l-th energy storage system, respectively, during the charging process. It is positive during the discharge process. A positive value indicates that the energy storage system cannot charge and discharge simultaneously at the same time. ; and These are the corresponding operating cost coefficients, including battery cycle life loss costs and energy loss costs during charging and discharging. This represents the amount of abandoned electricity at the m-th renewable energy power station, which is the difference between the predicted output and the actual absorption output of the station. It is generated when the system cannot fully absorb renewable energy. The larger the amount of abandoned electricity, the lower the utilization efficiency of renewable energy. The curtailment penalty cost coefficient is determined by national new energy policy requirements, investment recovery costs of new energy projects, and environmental benefit losses. It is usually set to a higher value to incentivize consumption. VIOL(t) is the system safety constraint violation amount at time t, which is obtained by quantifying the degree of violation of constraints such as frequency, voltage, and line power flow. It is set to 0 when there is no violation. To constrain the penalty coefficient for violations, the unit is yuan, and the value is much larger than other cost coefficients, so as to ensure that safety constraints are satisfied first during the optimization process and avoid system security risks caused by pursuing economic efficiency.

[0048] 4. Multiple constraints

[0049] To ensure the physical feasibility and security of the optimization results, the model is designed with multiple constraints, including power balance constraints, equipment capacity constraints, and safe operation constraints.

[0050] (1) Power balance constraint

[0051] The power balance constraint requires that at any given time, the total generating power in the system (the sum of conventional units, new energy sources, and energy storage discharge) equals the total power consumed (the sum of system load, energy storage charging, and system network losses), that is:

[0052]

[0053] in, The system load at time t represents the load forecasting system, which is obtained by combining historical load data, meteorological data and socio-economic activity forecasts. It is divided into types such as residential load, industrial load and commercial load. The system network loss at time t represents the loss of electricity during transmission and transformation, mainly including line resistance loss, transformer copper loss, and iron loss.

[0054] (2) Equipment capacity constraints

[0055] Equipment capacity constraints limit the technical parameters of various types of equipment, including: upper and lower limits of output and ramp rate constraints for conventional generator sets; constraints that the output of new energy power stations is not negative and does not exceed the predicted or available power; and constraints on the charging and discharging power, state of charge (SOC), and energy conservation of energy storage systems.

[0056] Conventional generator set constraints include upper and lower output limits and gradeability constraints. The upper and lower output limits are as follows:

[0057]

[0058] in and These are the minimum and maximum technical outputs of the kth conventional generator unit, respectively.

[0059] The gradient rate constraint is:

[0060]

[0061] in and These are the downward ramp rate and upward ramp rate of the kth conventional generator unit, respectively, reflecting the maximum range of output adjustment per unit time. The ramp rate of thermal power units is usually lower due to the thermal inertia of the boiler, while the ramp rate of hydropower units is higher and the adjustment is more flexible. This constraint ensures that the unit output adjustment will not cause equipment damage or parameter overrun due to excessive speed.

[0062] The output constraint of new energy power stations is

[0063]

[0064] in The maximum available power of the m-th renewable energy power station is equal to the product of the station's installed capacity and the real-time meteorological condition correction coefficient. When the wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the maximum available power of the wind farm is zero. When the solar intensity is lower than the threshold, the maximum available power of the photovoltaic power station decreases. The lower limit of the constraint is zero because renewable energy power stations cannot absorb power from the grid, and the actual output cannot be negative.

[0065] Energy storage system constraints encompass four dimensions: charging, discharging, capacity, and energy conservation, ensuring the safe and efficient operation of the energy storage system. The charging and discharging power constraints are as follows:

[0066]

[0067]

[0068] in and These are the maximum charging power and maximum discharging power of the l-th energy storage system, respectively, determined by the rated power of the energy storage converter. Typically, the charging power and discharging power are equal. The capacity constraint is:

[0069]

[0070] in Let be the State of Capacity (SOC) of the l-th energy storage system at time t. and These are the minimum capacity and the maximum capacity, respectively.

[0071] The energy conservation constraint is:

[0072]

[0073] in and These are charging efficiency and discharging efficiency, respectively. Lithium batteries typically have high energy storage efficiency, and this constraint reflects the change in energy storage capacity with the charging and discharging process.

[0074] (3) Safety operation constraints

[0075] Safety operation constraints ensure that key power grid parameters are within safe limits, including node frequency constraints, node voltage constraints, and line power flow constraints. These parameters are required to be within preset safe upper and lower limits.

[0076] Node frequency constraint is

[0077]

[0078] in Let be the actual frequency of the i-th node at time t. and These are the lower and upper limits for frequency safety, respectively.

[0079] Node voltage constraints are

[0080]

[0081] in Let be the actual voltage of the i-th node at time t. and These are the lower and upper limits of voltage safety, typically a percentage range of the rated voltage.

[0082] Line power flow constraints are The line transmission limit in the system's safety and stability objective function is consistent, ensuring that the line will not trip due to power overload.

[0083] Step Two: Formulating a Dynamic Interaction Strategy for Source-Network-Storage Resources

[0084] This invention enables adaptive control based on system operating scenarios, fully tapping the regulation potential of wind power, photovoltaic, energy storage, conventional generator sets and grid equipment, and ensuring the accurate achievement of system operating objectives under different scenarios.

[0085] 1. Scene recognition

[0086] The scene identification process involves real-time data collection through the system monitoring module. Data types include renewable energy output data, load data, equipment operating status data, and fault signals. Based on the data-driven scene identification, the triggering conditions for four typical scenarios are as follows:

[0087] The normal scenario is that the output of new energy sources fluctuates little, the load fluctuates little, there are no equipment fault signals, and the power flow, frequency, and voltage of the lines are all within the safe range. That is, the amplitude of the output fluctuation of new energy sources and the load fluctuation do not exceed their respective preset normal operation fluctuation thresholds, and the power flow, frequency, and node voltage of the lines in the system are all within the preset safe operating range, and there are no equipment fault signals.

[0088] The scenario of a new energy power plant is when the actual total output of the new energy power plant exceeds a certain multiple of the difference between the current load of the system and the minimum output of the conventional generator set, and this state lasts for a long time (e.g., the duration exceeds a preset time threshold).

[0089] The peak load scenario is when the actual total load of the system exceeds the total rated output of conventional generator units by a relatively high proportion (i.e., a certain preset threshold proportion), or when it is predicted that the total load of the system will reach the same preset threshold proportion of the total rated output of conventional generator units within a preset future time window.

[0090] Equipment failure scenarios include monitoring unit tripping, line power flow exceeding a specific proportion of transmission limits, or node frequency or voltage exceeding safe ranges.

[0091] 2. Strategy matching and control logic adjustment

[0092] Based on the identified scenario type, the system invokes preset collaborative control logic to adaptively adjust the adjustment priority, adjustment target, and control parameters of each resource.

[0093] Under normal circumstances, the control objective is "prioritizing the absorption of new energy sources + smoothing out fluctuations + maintaining economic operation". The adjustment priority from high to low is new energy power plants, energy storage systems, and conventional generator sets. The specific control logic is to monitor the fluctuations in new energy output in real time. When the fluctuation exceeds the threshold, the energy storage system adjusts its charging and discharging according to the direction of the fluctuation: when the output suddenly increases, the energy storage charges to absorb the excess power; when the output suddenly decreases, the energy storage discharges to make up for the power gap. After the fluctuation is eliminated, the energy storage returns to standby mode.

[0094] In scenarios involving large-scale renewable energy generation, the control objectives are "maximizing renewable energy consumption + reducing curtailment + ensuring safety." The adjustment priorities, from highest to lowest, are energy storage systems, conventional generator sets, and renewable energy power plants. The specific control logic is as follows: First, all energy storage systems are instructed to operate at maximum charging power while monitoring the remaining capacity of the energy storage. Once the energy storage reaches its maximum capacity, conventional generator sets are instructed to reduce their output to the minimum technical output. If there is still a surplus of renewable energy at this time, curtailment instructions are issued to renewable energy power plants in order of curtailment cost from lowest to highest. The curtailment ratio is adjusted periodically until power balance is achieved.

[0095] During peak load scenarios, the control objectives are "ensuring power supply to the load + maintaining system stability + controlling costs". The adjustment priorities, from highest to lowest, are energy storage systems, renewable energy power plants, and conventional generator sets. The specific control logic is as follows: before the peak load arrives, the energy storage system is instructed to enter standby mode and start maximum discharge during peak hours; at the same time, the renewable energy power plants are instructed to increase their output as much as possible; the conventional generator sets gradually increase their output according to the ramp rate limit. When the conventional units have all reached their maximum output and still cannot meet the load, the standby peak-shaving units are started to ensure that there is no gap in the power supply to the load.

[0096] In equipment failure scenarios, the control objectives are "rapidly isolating the fault + ensuring system safety + restoring stable operation." The adjustment priorities, from high to low, are energy storage systems, conventional generator sets, and new energy power plants. The specific control logic is adjusted according to the fault type: When a generator trips, the energy storage system immediately discharges to supplement the output gap of the tripped generator set, while instructing other conventional generator sets to increase their output. After the output stabilizes, the energy storage gradually reduces its discharge power. When a line overload occurs, the energy plant connected to the line is instructed to reduce its output, and the energy storage system adjusts its charging and discharging direction until the line power flow drops to a safe range. When the frequency / voltage exceeds the limit, the energy storage system participates in primary frequency / voltage regulation first, conventional generator sets participate in secondary regulation, and new energy power plants adjust their output according to the instructions to ensure that the frequency / voltage quickly recovers to within the safe threshold.

[0097] 3. Solving multi-objective, multi-constraint optimization algorithms

[0098] This invention uses a multi-objective, multi-constraint optimization algorithm to solve the real-time optimal control allocation value under various typical supply scenarios. The algorithm first transforms the multi-objective problem into a single-objective problem through fuzzy satisfaction trade-off decision-making, and then uses an improved genetic algorithm for iterative solution to ensure the feasibility and optimality of the solution.

[0099] This invention employs a two-step method of "multi-objective transformation - intelligent algorithm solution". First, the multi-objective problem is transformed into a single-objective problem, and then the optimal solution is obtained by improving the genetic algorithm, so as to ensure the feasibility, optimality and diversity of the solution.

[0100] The first step is a multi-objective transformation, which adopts a trade-off decision-making method based on fuzzy satisfaction, specifically:

[0101] First, we solve for the minimum value of each objective function. and maximum value , (i=1,2,3), where This is the minimum value obtained when optimizing the i-th objective function alone. This represents the maximum possible value of the objective function within the feasible region.

[0102] Then, each objective function is normalized to the [0,1] interval, and the normalization formula is as follows:

[0103]

[0104] After normalization The smaller the value, the higher the satisfaction level with the goal.

[0105] Finally, a single-objective function is constructed by combining the satisfaction weight coefficients:

[0106]

[0107] in, , , Let be the satisfaction weight coefficients for each objective function, with values ​​ranging from [0,1], and satisfying... .

[0108] The second step involves solving the problem using intelligent algorithms. For the transformed single-objective optimization problem, an improved multi-objective non-dominated sorting genetic algorithm (NSGA-II) is used for iterative solving. Through population initialization, non-dominated sorting, reference point association, niche protection, and genetic operations (selection, crossover, mutation), a set of optimal solutions uniformly distributed on the Pareto front is finally obtained, providing decision-makers with diverse control options. Specifically, this includes:

[0109] Population initialization: a certain number of feasible solutions are randomly generated. Each solution corresponds to a set of control variables (such as the output of each unit, the charging and discharging power of energy storage, and the amount of new energy consumption). The solution is then verified to ensure that it meets all constraints, and infeasible solutions are eliminated.

[0110] Non-dominated sorting sorts the solutions in the population according to the objective function value, dividing them into different non-dominated levels, with the solutions at the top of the level being better.

[0111] Reference point association: Each solution is associated with a preset reference point to ensure that the solutions are evenly distributed in the target space;

[0112] The niche protection operation calculates the distance between the solution and the reference point and selects the solution with the smaller distance to enter the next generation of population, thus preventing high-quality solutions from being eliminated.

[0113] Iterative evolution involves repeated genetic operations such as selection, crossover, and mutation until the population converges, ultimately yielding a uniformly distributed set of optimal solutions.

[0114] Finally, the optimized power allocation values ​​obtained through the algorithm are transmitted to each device via the control command distribution module, enabling coordinated response of source, network, and storage resources. This adaptive strategy fully leverages the adjustment potential of each resource, avoids the limitations of fixed strategies, and enhances the system's adaptability to complex operating conditions.

[0115] Step 4: Execution of closed-loop coordinated control

[0116] 1. Command execution: Select an optimal control allocation value from the optimal solution set, generate specific control commands (such as the output value of each unit, the charging and discharging power of energy storage, etc.), and send them to the actuators of each device through the real-time control bus. The actuators adjust the operating parameters according to the commands.

[0117] 2. Status Feedback and Dynamic Correction: The system monitoring module collects real-time data on the actual operating status of the equipment after executing commands and feeds it back to the scene recognition and strategy matching module. If deviations are found after command execution (e.g., actual output does not reach the command value), the process of scene recognition, strategy adjustment, optimization solution, and command correction is re-triggered, forming a dynamic closed-loop correction mechanism until the system operating status meets the target requirements, thereby effectively eliminating control deviations and ensuring control accuracy.

[0118] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-objective collaborative optimization control method for new energy power plants and energy storage systems, characterized in that, Includes the following steps: Step 1: Establish a multi-objective optimization model that includes the objective function of system safety and stability, the objective function of maximizing the absorption of new energy sources, and the objective function of safe and economical production, and set multiple constraint conditions consisting of power balance constraints, equipment capacity constraints, and safe operation constraints; Step 2: Formulate a dynamic interaction strategy for source, grid and storage resources. This strategy identifies the current operating scenario type based on the collected real-time system operation status data, and adaptively adjusts the coordination control logic and adjustment priority of wind power, photovoltaic, energy storage, conventional generator sets and grid equipment according to the characteristics of the identified scenario type. The scenario types include normal scenario, new energy power generation scenario, peak load scenario, and equipment failure scenario. The normal scenario refers to a situation where the amplitude of new energy power output fluctuation and load fluctuation does not exceed their respective preset normal operation fluctuation thresholds, and the line power flow, frequency, and node voltage in the system are all within the preset safe operating range, and there are no equipment failure signals. The new energy power generation scenario refers to a situation where the actual total output of the new energy power station exceeds a specific multiple of the difference between the current load of the system and the minimum output of the conventional generator set, and the duration of this state exceeds a preset time threshold. The peak load scenario refers to the actual total load of the system exceeding a preset threshold ratio of the total rated output of conventional generator sets, or, according to the load forecast results, the total load of the system will reach the preset threshold ratio of the total rated output of conventional generator sets within a preset future time window. The equipment failure scenarios mentioned refer to the detection of unit tripping, or the line power flow exceeding a certain proportion of the transmission limit, or the node frequency or voltage exceeding the safe range. The methods for adjusting the coordination control logic include: Under normal circumstances, the control objective is to prioritize the absorption of new energy sources and smooth out fluctuations. The adjustment priority is, in order, new energy power plants, energy storage systems, and conventional generator sets. The specific control logic is to use the energy storage system to charge and discharge according to the fluctuations in the output of new energy sources. In the scenario of large-scale renewable energy generation, the control objective is to maximize absorption and ensure safety. The adjustment priority is, in order, energy storage system, conventional generator set, and renewable energy power station. The specific control logic is to instruct the energy storage system to charge at full power, and the conventional generator set to reduce its output to the minimum technical output. If there is still a surplus of renewable energy at this time, the renewable energy power station is given a curtailment instruction in order of curtailment cost from low to high. The curtailment ratio is adjusted periodically until power balance is achieved. In peak load scenarios, the control objective is to ensure power supply to the load and maintain stability. The adjustment priority is energy storage system, new energy power station, and conventional generator set. The specific control logic is to instruct the energy storage system to discharge at maximum power, while instructing the new energy power station to increase output. The conventional generator set gradually increases output according to the ramp rate limit. When all conventional generator sets reach maximum output and still cannot meet the load, the standby peak shaving unit is started. In equipment failure scenarios, the control objective is to quickly isolate the fault and restore stability. The adjustment priority is, in order, energy storage system, conventional generator sets, and new energy power plants. The specific control logic is as follows: When a generator trips, the energy storage system immediately discharges to supplement the output gap of the tripped generator set, while instructing other conventional generator sets to increase their output. After the output stabilizes, the energy storage system gradually reduces the discharge power. When a line overload occurs, the new energy power plants connected to the line are instructed to reduce their output, and the energy storage system adjusts the charging and discharging direction until the line power flow drops to a safe range. When the frequency / voltage exceeds the limit, the energy storage system participates in primary frequency / voltage regulation first, conventional generator sets participate in secondary regulation, and new energy power plants adjust their output according to the instructions to restore the frequency / voltage to within the safe threshold. Step 3: Employ a multi-objective, multi-constraint optimization algorithm to solve the multi-objective optimization model for the current scenario type, in order to obtain the real-time optimal control allocation value. The solution process includes first transforming the multi-objective problem into a single-objective problem using a fuzzy satisfaction trade-off decision method, and then using an improved genetic algorithm to iteratively solve the single-objective problem. Step 4: Based on the optimal control allocation value, perform closed-loop coordinated control on wind farms, photovoltaic power plants, energy storage systems and grid equipment, and dynamically correct control commands based on the operating status feedback from the real-time monitoring system to eliminate control deviations.

2. The method according to claim 1, characterized in that, The objective function for system safety and stability is determined to minimize the risks of frequency deviation, voltage deviation, and line transmission power exceeding limits during system operation. The determination method includes: comprehensively evaluating the frequency deviation of each node in the system, the voltage deviation of each node, and the transmission power of each transmission line, and applying a penalty term to the power portion exceeding the preset transmission limit. By setting corresponding weight coefficients for each deviation and penalty term, they are weighted and summed to quantify the overall safety and stability level of the system.

3. The method according to claim 1, characterized in that, The objective function for maximizing the absorption of new energy sources aims to maximize the actual power generation of new energy power plants while suppressing the impact of power output fluctuations on the system. The determination method includes: accumulating the actual power output of each new energy power plant during the optimization period as the basic absorption amount; simultaneously, calculating the absolute value of the deviation between the actual power output and the predicted power output, and multiplying this absolute value by a smoothing penalty coefficient to form a fluctuation penalty term; finally, subtracting the fluctuation penalty term from the basic absorption amount to obtain the objective function value, and maximizing this function value through optimization.

4. The method according to claim 1, characterized in that, The objective function for safe and economical production aims to minimize the total cost of system operation. Its determination method includes summing the power generation costs of all conventional generator units, the charging and discharging operation costs of all energy storage systems, the curtailment penalty costs of all new energy power plants, and the violation penalty costs of system safety constraints within the optimization period. Specifically, the power generation costs are related to the unit output and the power generation cost coefficient; the charging and discharging operation costs are related to the charging and discharging power and the operation cost coefficient; and the curtailment penalty costs are related to the amount of curtailed electricity and the curtailment penalty coefficient.

5. The method according to claim 1, characterized in that, The power balance constraint requires that at any given time, the total power generation in the system equals the total power consumption; wherein, the total power generation includes the sum of the output of all conventional generator sets, the sum of the actual output of all new energy power plants, and the sum of the discharge power of all energy storage systems; the total power consumption includes the system load, the sum of the charging power of all energy storage systems, and the system network loss.

6. The method according to claim 1, characterized in that, The equipment capacity constraints include: for conventional generator sets, their output must be between their minimum and maximum technical output, and the rate of change of output per unit time must meet their upward and downward ramp rate constraints; for new energy power plants, their actual output must be greater than or equal to zero and not greater than the smaller of their predicted output and maximum available power; for energy storage systems, their charging and discharging power must be within the rated power range, their state of charge must be between the allowable minimum and maximum capacity, and they must meet energy conservation constraints.

7. The method according to claim 1, characterized in that, The safety operation constraints are used to ensure that key parameters of the power grid operate within a safe range. Specifically, they include: node frequency constraints, which require that the actual frequency of any node in the system is between a preset lower and upper frequency safety limit; node voltage constraints, which require that the actual voltage of any node is between a preset lower and upper voltage safety limit; and line power flow constraints, which require that the absolute value of the transmission power of any transmission line is not greater than the transmission limit of that line.

8. The method according to claim 1, characterized in that, The closed-loop coordinated control process includes: generating specific control commands from the optimal control allocation values ​​and sending them to the actuators of the wind farm, photovoltaic power station, energy storage system, and conventional generator set via a real-time control bus; the actuators adjusting their operating parameters according to the commands; simultaneously, the system monitoring module collecting real-time operating status data of each device after executing the commands and feeding it back to the scene recognition module; if the feedback status data deviates from the target status, the process of scene recognition, optimization solution, and command correction is re-triggered to form a dynamic closed-loop correction until the system operating status meets the target requirements.

Images