Operation scheduling system considering high-proportion new energy power grid

Abstract

The invention relates to the technical field of power system operation scheduling, in particular to an operation scheduling system considering a high-proportion new energy power grid. According to the technical scheme, the operation scheduling system considering the high-proportion new energy power grid comprises a superior power grid, a power distribution operator and load aggregators, and is characterized in that the power distribution operator is connected with the multiple load aggregators to jointly form a multi-zone-area power distribution network framework, the framework comprises multiple zone areas, and the multiple load aggregators are connected with the superior power grid. Each transformer area comprises a plurality of distributed renewable energy power generation units, energy storage units and interaction units, energy control is carried out on the new energy power generation units, the energy storage units and the interaction units through the energy management unit, peak load shifting and efficient energy utilization are achieved, the operation cost is reduced, and comprehensive benefits are improved. The comprehensive benefits of the power grid can be improved to the maximum extent. The operation cost of the system is obviously reduced, the network loss of the power grid is reduced, the voltage stability is optimized, and the utilization rate of renewable energy sources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation dispatching, and in particular to an operation dispatching system for a power grid taking into account a high proportion of new energy. Background Art

[0002] With the development of new energy technologies, the proportion of renewable energy such as wind power and photovoltaics in the power grid is increasing. The volatility and uncertainty of these renewable energy sources pose challenges to the stability and economy of the power grid. Traditional power grid dispatching methods are usually not suitable for dealing with the complex problems brought about by such a high proportion of renewable energy. Therefore, there is an urgent need for an operation and dispatching system that can effectively handle a high proportion of renewable energy power grids to achieve efficient energy utilization, reduce operating costs and improve the overall benefits of the system. Summary of the invention

[0003] The present invention provides an operation dispatching system for a power grid with a high proportion of new energy, which is characterized by optimizing the operation dispatching of the power grid through a multi-level and multi-time scale dispatching strategy combined with a particle swarm optimization algorithm to improve the dispatching accuracy and system stability.

[0004] The solution of the present invention to solve the above technical problems is as follows:

[0005] An operation and dispatching system for a power grid with a high proportion of new energy sources, including a superior power grid, a distribution operator and a load aggregator, is characterized in that: the distribution operator is connected to multiple load aggregators to jointly form a multi-station distribution network architecture, which includes multiple stations, each of which contains multiple distributed renewable energy power generation units, energy storage units and interactive units. The energy management unit is used to control the energy of the new energy power generation units, energy storage units and interactive units to achieve peak shaving and valley filling and efficient energy utilization, so as to reduce operating costs and improve comprehensive benefits.

[0006] Based on the above technical solution, the present invention can also be improved as follows.

[0007] Furthermore, the energy management unit can adopt multi-time scale scheduling strategies, including day-ahead scheduling and real-time scheduling, according to the volatility, intermittency and uncertainty of distributed renewable energy such as wind power and photovoltaic power, so as to improve scheduling accuracy and system stability; the day-ahead scheduling strategy utilizes the day-ahead forecast values ​​of renewable energy and load, with the goal of optimizing economic benefits, minimizing network losses, maximizing renewable energy consumption, and minimizing voltage deviation, and first establishes a day-ahead scheduling model.

[0008] Objective function 1: The optimization goal is to minimize the operating cost of the active distribution network, as shown in the formula.

[0009] minC=C pcc +C FL +CMR +C DERG

[0010] Where C is the lowest total operating cost of the distribution network during the entire dispatching cycle; C pcc The cost of purchasing electricity from the upper grid; C FL Compensation cost for flexible load dispatch; C MR is the dispatching cost of distributed power grid; C DERG Penalty costs for abandoning wind and solar power for renewable energy.

[0011] Considering that the energy storage device, capacitor bank and energy storage device are all owned by the power grid, only their maintenance costs are calculated. The main economic costs come from the cost of purchasing electricity from the upper power grid, the compensation cost of dispatching flexible load resources on the demand side, and the cost of connecting and operating each dispatchable unit.

[0012] Objective function 2: In order to reduce the burden on the upper power grid, reduce the standby power and increase the utilization of equipment, the minimum fluctuation of the tie line power is taken as the target optimization:

[0013]

[0014] P in the formula PCC (t) is the exchange power of the tie line at the time, P avgpcc (t) is the average power of the tie line during the dispatch period. nT24 is the entire dispatch period.

[0015] Objective function 3: Minimize active network loss. When the DG processing of the power system is too large to meet the load consumption of the area, it will also feed back power to the upper power grid. At this time, it will cause long-distance transmission of electricity, resulting in increased losses. Active network loss is also a criterion for measuring power loss. The active loss of the network is equivalent to the minimum sum of the power injected by all nodes in the system, as shown in the following formula:

[0016]

[0017] The formula nbus is the number of nodes; V i is the voltage amplitude of node i; G ij , B ij are branch conductance and susceptance respectively; θ ijis the phase angle difference between nodes i and j; Γ is the set of nodes connected to the system j. Objective function 4: Minimum node voltage offset. Distributed power sources are connected to the active distribution network. Due to the volatility of renewable distributed power sources, the power quality of the system is greatly affected. The node voltage should be guaranteed to be within a reasonable fluctuation range during scheduling. The actual voltage of each node in the power grid is compared with the standard voltage. It is expected that when renewable energy is connected, some voltage margin will be left to reduce the possibility of voltage crossing the line. This paper takes the minimum deviation between the actual voltage and the rated voltage as the optimization goal as shown in the formula:

[0018]

[0019] Where V i (t) is the voltage amplitude of the ith node at time t, and nbus is the number of nodes.

[0020] Constraints:

[0021] 1) Flexible load constraints:

[0022] The main temperature control load constraints are:

[0023]

[0024] Electric vehicle charging stations must ensure that the total charging capacity remains unchanged throughout the entire scheduling period.

[0025]

[0026] In the formula It represents the power consumption of the original load i at time t. The power demand of load i at time t after participating in demand-side response.

[0027] The constraints of energy storage devices include storage energy constraints, SOC upper and lower limit constraints, and charge and discharge constraints per unit time.

[0028]

[0029] In the formula They represent the maximum charge and discharge capacity of the energy storage in time period t, with discharge taking negative value and charge taking positive value. i (t) represents the energy storage charge state, which is equal to the ratio of the remaining power to the maximum power. During the entire charging and discharging process, the energy storage system is prevented from overcharging and discharging, and its SOC is kept within a certain range. Indicates the minimum and maximum limit values ​​of the charge of the i-th energy storage device. In order to facilitate the next stage of scheduling, the energy storage battery is kept at the end Greater than a set value

[0030] The optimal dispatching scheme is solved through the particle swarm algorithm, and the dispatching plan for the day-ahead flexible load is released; the real-time dispatching strategy is based on more accurate intraday short-term forecast data to correct the day-ahead dispatching results, and performs rolling optimization at intervals of 15 minutes to ensure that the system responds to changes in renewable energy and load in real time and reduce the impact of forecast errors on dispatching.

[0031] The short-term forecast period of the intraday plan is 4 hours, the rolling cycle is 15 minutes, and the control period is 15 minutes. The intraday correction strategy mainly includes the following steps:

[0032] At the current time k, based on a certain prediction model, predict the power value of the system in 16 time periods. Based on the optimization model, obtain the control instructions for the future k+1, k+2, ..., k+16 times;

[0033] Only the [k, k+1] period is controlled, and the state of each unit of the load in the substation obtained at this moment is input as the initial value of the [k+1, k+2] period;

[0034] Repeat the above steps, and so on, to optimize backwards.

[0035] The daily optimization of distribution network load takes into account both economic cost and tie line change value. The objective function is as follows:

[0036]

[0037] Where, F2 is the objective function value of the intraday optimization of the distribution network; FP is the economic cost of intraday operation; Pgrid1 and Pgrid2 are the optimized values ​​of the tie line power in the day-ahead and intraday stages respectively; μ is the weight coefficient of the tie line power change.

[0038] The constraints of intraday scheduling are consistent with those of day-ahead scheduling, and the economically optimal scheduling result is given by the particle swarm optimization algorithm.

[0039] Furthermore, the day-ahead dispatch strategy includes multiple objective functions, including minimizing the total operating cost of the distribution network, minimizing the power fluctuation of the interconnection line, minimizing the active network loss, minimizing the node voltage offset, and corresponding constraints, such as flexible load constraints, energy storage device constraints, network operation constraints, etc.

[0040] The network operation constraints include:

[0041] (1) System power flow constraints:

[0042]

[0043] in: Inject active and reactive power into the tie line of node i at time t; It is the output active and reactive power of distributed power source; For active and reactive loads; is the reactive power of capacitor switching; θ ij is the voltage phase angle difference between nodes i and j; V i (t), V j (t) voltage amplitude of nodes i and j; G ij , B ij are branch conductance and susceptance respectively; nbus is the number of branches.

[0044] (2) Upper and lower limits of active and reactive power of tie lines:

[0045]

[0046] (3) Upper and lower limits of reactive power compensation device:

[0047]

[0048] It represents the reactive power currently dispatched by the i-th capacitor bank at time t, N min 、N max The maximum and minimum number of capacitor groups for the ith capacitor.

[0049] 3) Node voltage and line power constraints:

[0050]

[0051] V i (t) represents the actual node voltage value of the i-th node at time t; are the upper and lower limits of the node voltage; Maximum allowed line power.

[0052] 4) Upper and lower limits of DG active and reactive output:

[0053]

[0054] It represents the current dispatching reactive power of the i-th DG. It represents the current dispatched active power of the i-th DG.

[0055] First, the distribution network is scheduled for the next 24 hours. The 24 hours are divided into 24 points, and the forecast values ​​of load, wind power, and photovoltaic power are also one value per hour, and it is assumed that the output remains unchanged within one hour. The distribution network dispatching center obtains the day-ahead forecast data, as well as the dispatchable flexible load capacity for each period on the day-ahead time scale in LA, and adds the wind power and photovoltaic forecast output as the maximum constraint condition to the system dispatching. The particle swarm multi-objective algorithm is used to solve the Pareto solution set that satisfies the constraint adjustment. After discussion, the distribution network operator selects a better optimization plan, determines the day-ahead flexible load dispatching plan, and completes the day-ahead dispatching plan.

[0056] Furthermore, the specific steps of the particle swarm algorithm include: initializing the particle swarm, calculating the fitness value, updating the particle speed and position, and judging whether the termination condition is met; wherein the fitness value is calculated according to the objective function, and the updating formula of the particle speed and position is determined according to the principle of the particle swarm algorithm.

[0057] Furthermore, the objective function of the real-time dispatching strategy includes the weighted sum of the economic cost of daily operation and the change in the power of the interconnection line, and the economically optimal real-time dispatching result is obtained through a particle swarm optimization algorithm.

[0058] Furthermore, the specific steps of the particle swarm optimization algorithm include: initializing the particle swarm, calculating the fitness value, updating the particle speed and position, and judging whether the termination condition is met; wherein the fitness value is calculated according to the objective function, and the updating formula of the particle speed and position is determined according to the principle of the particle swarm optimization algorithm.

[0059] Furthermore, the load aggregators conduct energy transactions through a bidding mechanism and are divided into consumer load aggregators and provider load aggregators. Consumer load aggregators participate in bidding by changing the electricity purchase price, and provider load aggregators provide energy to consumers with higher bids based on priority factors.

[0060] Furthermore, the specific rules of the bidding mechanism include: the consumer load aggregator proposes the purchase price and the purchase amount of electricity according to its own demand and market price; the provider load aggregator proposes the sales price and the sales amount of electricity according to its own power generation capacity and market price; the system determines the transaction price and the transaction amount according to the priority factors and bidding strategy.

[0061] Furthermore, the method for determining the priority factor includes: determining the priority factor based on the credit rating, historical transaction records, load characteristics and other factors of the consumer load aggregator; determining the priority factor based on the power generation capacity, proportion of renewable energy, cost and other factors of the provider load aggregator.

[0062] Furthermore, it also includes a monitoring and evaluation module, which is used to monitor the operating status of the system in real time, evaluate the execution effect of the scheduling strategy, and adjust the scheduling strategy according to the evaluation results to improve the stability and economic performance of the system; the specific functions of the monitoring and evaluation module include: data collection and processing, status monitoring and early warning, scheduling strategy evaluation, and optimization suggestion generation.

[0063] The present invention provides an operation and dispatching system for a power grid with a high proportion of new energy.

[0064] Beneficial effects:

[0065] Improve grid stability:

[0066] In a power grid with a high proportion of renewable energy, the natural fluctuations of wind power and photovoltaic power generation will bring about unpredictability in power supply. In order to cope with this uncertainty, the operation and dispatching system of the present invention adopts a multi-time scale dispatching strategy, including day-ahead dispatching and real-time dispatching. Day-ahead dispatching is optimized based on the forecast data of renewable energy and load to ensure the stable operation of the power grid within the forecast range; while real-time dispatching is dynamically adjusted based on the latest short-term forecast data and actual operating conditions. This dual dispatching mechanism effectively improves the power grid's ability to respond to fluctuations in renewable energy, reduces the occurrence of power grid failures and unstable events, and thus improves the overall stability and reliability of the power grid.

[0067] Optimize energy use:

[0068] The energy management unit in the system achieves efficient use of energy by comprehensively managing new energy power generation units (such as wind power and photovoltaics), energy storage units (such as battery energy storage) and interactive units (such as load control). Specifically, the system balances the load demand and power generation capacity of the power grid through peak shaving and valley filling technology, avoiding energy waste. For example, when wind power or photovoltaic power generation is at its peak, the system can store excess electricity in the energy storage unit; and when power generation is at its low point, the stored electricity can be released, thereby improving energy utilization efficiency. This move not only reduces dependence on traditional fossil energy, but also optimizes the consumption of renewable energy.

[0069] Reduce operating costs:

[0070] The system uses optimization techniques such as particle swarm optimization to optimize economic benefits in day-ahead scheduling. Specific goals include minimizing the total operating cost of the distribution network, minimizing tie line power fluctuations, minimizing active network losses, and minimizing node voltage offsets. This optimized scheduling scheme reduces costs in grid operations, for example, reducing additional costs due to grid power fluctuations and reducing the risk of equipment damage due to voltage instability. In addition, the real-time scheduling strategy further reduces the economic cost of operation by optimizing actual operating data to reduce additional expenses caused by prediction errors.

[0071] Promoting fair and efficient market mechanisms:

[0072] The load aggregator bidding mechanism of the present invention effectively promotes the fairness and efficiency of the energy market. Consumer load aggregators can propose electricity purchase prices and electricity purchase amounts according to their own needs and market prices, while provider load aggregators propose electricity sales prices and electricity sales amounts according to their power generation capacity and market prices. This bidding mechanism not only improves the transparency of the market, but also encourages all parties to provide more competitive prices. The setting of priority factors (such as credit rating, historical transaction records, load characteristics, etc.) ensures the fairness of transactions, makes the allocation of market resources more reasonable, and thus promotes the efficient operation of the market.

[0073] Improve system adaptability:

[0074] The introduction of the monitoring and evaluation module enables the system to monitor the operating status of the power grid in real time and evaluate the execution effect of the dispatching strategy. This module has functions such as data collection and processing, status monitoring and early warning, dispatching strategy evaluation, and optimization suggestion generation. Through real-time data collection, the system can quickly identify potential problems and issue early warnings, and adjust the dispatching strategy in time to meet new challenges. For example, if the load of a certain area is detected to exceed expectations, the system can automatically adjust the dispatching plan to ensure the stable operation of the power grid. This dynamic adjustment mechanism significantly improves the adaptability and responsiveness of the system, thereby ensuring the continuous and efficient operation of the power grid.

[0075] Maximize comprehensive benefits:

[0076] The present invention can maximize the comprehensive benefits of the power grid by comprehensively applying the above-mentioned technical measures. This includes significantly reducing the operating cost of the system, reducing the network loss of the power grid, optimizing voltage stability, and improving the utilization rate of renewable energy. For example, by reducing operating costs and network losses, the economic benefits of the power grid have been significantly improved; by optimizing voltage stability, the risk of equipment failure has been reduced and the service life of the equipment has been increased; by improving the utilization rate of renewable energy, the development of green energy has been promoted and the dependence on traditional energy has been reduced. In general, this system improves the overall operating efficiency and economic benefits of the power grid through precise scheduling and optimization, and provides strong support for the sustainable development of future new energy power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The drawings described herein are used to provide further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0078] In the attached picture:

[0079] Figure 1 It is a schematic diagram of the structural framework of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] See also Figure 1 As shown, the embodiments provided by the present invention are:

[0082] Embodiment 1

[0083] High-proportion wind and solar power generation dispatching in urban distribution networks

[0084] System Configuration:

[0085] Substation setting: The urban power grid is divided into 10 substations, each of which is equipped with multiple wind power generation units, photovoltaic power generation units and energy storage units.

[0086] Dispatch strategy: Implement multi-time scale dispatch strategy, including day-ahead dispatch and real-time dispatch. Day-ahead dispatch is based on weather and load forecasts, while real-time dispatch is adjusted based on actual wind and solar power generation and load changes.

[0087] Scheduling execution:

[0088] Day-ahead dispatch: The system uses particle swarm optimization algorithm to calculate the optimal day-ahead dispatch plan, optimize power generation and energy storage configuration, reduce grid operation costs, and ensure maximum utilization of new energy.

[0089] Real-time dispatch: In actual operation, the system is adjusted every 15 minutes based on the latest short-term forecast data and actual power generation data. During real-time dispatch, the system dynamically adjusts the charging and discharging strategy of the energy storage unit to cope with the volatility of wind and solar power generation.

[0090] Effect evaluation:

[0091] Enhanced stability: Grid stability has been significantly improved, with power supply instability incidents caused by fluctuations in wind and photovoltaic power generation reduced by 50%.

[0092] Cost reduction: Operating costs are reduced by 15%, mainly due to optimized energy storage utilization and reduced network losses.

[0093] The consumption rate of new energy has increased: the consumption rate of renewable energy has increased from 40% to 45%.

[0094] Embodiment 2

[0095] High-proportion energy storage and load aggregator dispatch in industrial parks

[0096] System Configuration:

[0097] Substation setting: The industrial park is divided into 5 substations, each of which is equipped with energy storage equipment and load aggregators.

[0098] Bidding mechanism: Load aggregators are divided into consumer load aggregators and provider load aggregators, and energy transactions are carried out through a bidding mechanism. Consumers propose the purchase price and quantity of electricity, and providers propose the sale price and quantity of electricity.

[0099] Each consumer load aggregator participates in the bidding by changing the power purchase price t, thereby controlling the energy of its own transactions with other load aggregators to minimize its own economic cost. Participants can choose any value in the strategy set. The set of all feasible power purchase price strategies for participant i is defined as:

[0100]

[0101] in, and They represent the maximum and minimum electricity purchase prices of participant i respectively. It is the price at which load aggregators purchase electricity from the distribution network; It is the price at which the load aggregator sells electricity to the distribution network. The bid price of each load aggregator is between the purchase and sale prices of the distribution network, and the bid range is consistent. This definition ensures that the benefits of energy transactions between the load aggregator in the area and the adjacent load aggregator are greater than the benefits of transactions with the distribution network, so that energy is first transferred within the load aggregator in the area.

[0102] flow.

[0103] Benefit function: The benefit function of the participant is defined as:

[0104]

[0105] For participant i, the optimal bidding strategy is:

[0106]

[0107] Where Ci represents the set {C1,...,C i-1 ,C i+1 ,...,C I}.

[0108] The access of renewable distributed energy and flexible loads has made the power grid develop towards diversification. It is unreasonable to use only a single goal as the optimization decision for the power grid optimization goal. A comprehensive standard of multiple parties should be adopted to evaluate the advantages and disadvantages of the power grid operation so that the various optimization goals of the power grid can be balanced.

[0109] Scheduling execution:

[0110] Day-ahead dispatch: The system uses a particle swarm algorithm to develop a day-ahead dispatch plan based on historical data and forecast information to ensure that energy storage equipment can effectively support demand during peak grid load periods.

[0111] Real-time dispatch: The real-time dispatch strategy is updated every hour and adjusted according to actual load changes and energy storage status. The bidding mechanism of load aggregators ensures fair allocation and optimal utilization of resources.

[0112] Effect evaluation:

[0113] Improved economic benefits: The energy transaction costs of the park were reduced by 20%, thanks to the optimized bidding mechanism and energy storage management to improve transaction efficiency.

[0114] Improved load response speed: The load response speed is improved by 30%, and the impact of load fluctuations on the power grid is reduced through effective energy storage scheduling.

[0115] Improved energy storage utilization: Energy storage equipment utilization increased by 25% due to real-time scheduling that optimized energy storage charging and discharging strategies.

[0116] Embodiment 3

[0117] High-proportion photovoltaic power generation dispatching in rural power grids

[0118] System Configuration:

[0119] Substation setting: The rural power grid is divided into 8 substations, each of which contains multiple photovoltaic power generation units and energy storage units.

[0120] Monitoring and evaluation module: The system includes a monitoring and evaluation module, which is used to monitor the operation status of the power grid in real time and evaluate the effectiveness of the dispatching strategy.

[0121] Scheduling execution:

[0122] Day-ahead dispatching: Based on the photovoltaic power generation forecast data and load forecast data, the system uses the particle swarm optimization algorithm to formulate a day-ahead dispatching plan to ensure efficient use of photovoltaic power generation resources and balanced grid load.

[0123] Real-time dispatch: The system adjusts the dispatch strategy every 30 minutes based on real-time photovoltaic power generation data and load changes. The monitoring module collects data in real time, generates optimization suggestions and adjusts strategies to cope with instantaneous changes in photovoltaic power generation.

[0124] Effect evaluation:

[0125] Improved photovoltaic power generation utilization rate: The photovoltaic power generation utilization rate increased by 35%, and the photovoltaic power generation capacity was maximized through effective energy storage scheduling and load management.

[0126] Improved grid stability: Grid stability has been significantly improved, with grid instability incidents caused by photovoltaic power generation fluctuations reduced by 40%.

[0127] Reduced operating costs: Operating costs were reduced by 18%, mainly due to optimized energy storage management and reduced grid dispatch costs.

[0128] The above embodiments demonstrate the specific application and effects of the present invention in different power grid environments, and improve the stability, economy and comprehensive benefits of the power grid through precise scheduling strategies and optimization algorithms.

[0129] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended to include all changes within the meaning and scope of the equivalent elements of the claims. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it should be understood that although this specification is described in accordance with the implementation mode, not each implementation mode contains only one independent technical solution. This narrative mode of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. An operation and dispatching system for a power grid with a high proportion of new energy sources, comprising a superior power grid, a distribution operator and a load aggregator, characterized in that: The distribution operator is connected with multiple load aggregators to form a multi-area distribution network architecture, which includes multiple areas. Each area contains multiple distributed renewable energy power generation units, energy storage units and interactive units. The energy management unit controls the energy of the new energy power generation units, energy storage units and interactive units to achieve peak shaving and valley filling and efficient energy utilization, so as to reduce operating costs and improve comprehensive benefits.

2. According to claim 1, an operation and dispatching system for a power grid with a high proportion of new energy is characterized in that: The energy management unit can adopt multi-time scale scheduling strategies, including day-ahead scheduling and real-time scheduling, according to the volatility, intermittency and uncertainty of wind power and photovoltaic distributed renewable energy, so as to improve scheduling accuracy and system stability; the day-ahead scheduling strategy uses the day-ahead forecast values ​​of renewable energy and load, with the goal of optimizing economic benefits, minimizing network losses, maximizing renewable energy consumption, and minimizing voltage deviation, and solves the optimal scheduling scheme through the particle swarm algorithm, and publishes the scheduling plan of the day-ahead flexible load; The real-time dispatch strategy corrects the day-ahead dispatch results based on more accurate intraday short-term forecast data and performs rolling optimization at intervals of 15 minutes to ensure that the system responds to changes in renewable energy and load in real time and reduce the impact of forecast errors on dispatch.

3. According to claim 2, an operation and dispatching system considering a high proportion of new energy power grid is characterized in that: The day-ahead dispatch strategy includes multiple objective functions, including minimizing the total operating cost of the distribution network, minimizing the power fluctuation of the interconnection line, minimizing the active network loss, minimizing the node voltage deviation, and corresponding constraints, such as flexible load constraints, energy storage device constraints, and network operation constraints.

4. According to claim 3, an operation and dispatching system considering a high proportion of new energy power grid is characterized in that: The specific steps of the particle swarm algorithm include: initializing the particle swarm, calculating the fitness value, updating the particle speed and position, and judging whether the termination condition is met; wherein the fitness value is calculated according to the objective function, and the updating formula of the particle speed and position is determined according to the principle of the particle swarm algorithm.

5. According to claim 2, an operation and dispatching system for a power grid taking into account a high proportion of new energy, characterized in that: The objective function of the real-time dispatching strategy includes the weighted sum of the economic cost of daily operation and the change in tie line power, and the economically optimal real-time dispatching result is obtained through a particle swarm optimization algorithm.

6. According to claim 5, an operation and dispatching system for a power grid taking into account a high proportion of new energy, characterized in that: The specific steps of the particle swarm optimization algorithm include: initializing the particle swarm, calculating the fitness value, updating the particle speed and position, and judging whether the termination condition is met; wherein the fitness value is calculated according to the objective function, and the updating formula of the particle speed and position is determined according to the principle of the particle swarm optimization algorithm.

7. The operation and dispatching system of the power grid considering a high proportion of new energy according to claim 1, characterized in that: The load aggregators conduct energy transactions through a bidding mechanism and are divided into consumer load aggregators and provider load aggregators. Consumer load aggregators participate in bidding by changing the electricity purchase price, and provider load aggregators provide energy to consumers with higher bids based on priority factors.

8. The operation and dispatching system of the power grid considering a high proportion of new energy according to claim 7, characterized in that: The specific rules of the bidding mechanism include: consumer load aggregators propose electricity purchase prices and electricity purchase quantities based on their own demand and market prices; provider load aggregators propose electricity sales prices and electricity sales quantities based on their own power generation capacity and market prices; the system determines the transaction price and transaction quantity based on priority factors and bidding strategies.

9. The system according to any one of claims 8, characterized in that: The method for determining the priority factor includes: determining the priority factor according to the credit rating, historical transaction records, and load characteristic factors of the consumer load aggregator; and determining the priority factor according to the power generation capacity, renewable energy proportion, and cost factors of the provider load aggregator.

10. The system according to any one of claims 1 to 9, characterized in that: It also includes a monitoring and evaluation module, which is used to monitor the operating status of the system in real time, evaluate the execution effect of the scheduling strategy, and adjust the scheduling strategy according to the evaluation results to improve the stability and economic performance of the system; The specific functions of the monitoring and evaluation module include: data collection and processing, status monitoring and early warning, scheduling strategy evaluation, and optimization suggestion generation.

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