Micro-grid group dispatching method and system based on hierarchical regulation and elastic load response

Abstract

The application discloses a micro-grid group scheduling method and system based on hierarchical regulation and elastic load response, and belongs to the technical field of power system collaborative optimization and energy transaction, and comprises the following steps: obtaining the power generation potential of the power generation side and the power consumption regulation potential of the demand side based on the operation constraints of the power generation side and the response constraints of the demand side; constructing a micro-grid group hierarchical interactive coupling model based on execution rules and information interaction constraints; constructing a market transaction strategy model based on transaction modes, bidding mechanisms and income distribution mechanisms; improving the particle swarm optimization algorithm through model predictive control; and obtaining an optimal scheduling scheme by using the optimized particle swarm optimization algorithm based on the power generation potential of the power generation side, the power consumption regulation potential of the demand side, the micro-grid group interactive coupling model and the market transaction strategy model. The technical problem that the existing technology is difficult to improve the feasibility of the scheduling scheme is solved.

Description

Technical Field

[0001] This invention relates to the field of power system collaborative optimization and energy trading technology, specifically to a microgrid group dispatching method and system based on hierarchical control and flexible load response. Background Technology

[0002] Against the backdrop of a clean and low-carbon energy transition, the application of distributed renewable energy in microgrids is becoming increasingly widespread. As the core carrier integrating distributed power sources, energy storage devices, and controllable loads, obtaining highly adaptable dispatch schemes for microgrid clusters is crucial for improving energy utilization efficiency and promoting the consumption of new energy. However, existing technologies still face two major bottlenecks that severely restrict the feasibility of dispatch schemes: Firstly, there is a pain point of insufficient depth in demand-side resource integration. Traditional microgrid cluster collaborative dispatch systems have long focused on the static optimization and allocation of generation-side resources, oversimplifying the dynamic response characteristics of demand-side loads. This leads to an underestimation of the potential adjustment capabilities of transferable and load-reducing loads, forcing the grid's energy balance to rely excessively on passive adjustments by the generation side. This not only exacerbates the dispatch pressure and operating costs on the generation side but also significantly restricts the overall flexibility and anti-disturbance capabilities of the system. Secondly, the inherent intermittency, randomness, and volatility of distributed renewable energy make the operating state of microgrid clusters highly dynamic. Existing optimization algorithms are mostly based on deterministic scenarios or simplified probabilistic models, lacking accurate quantitative characterization and real-time response mechanisms for multi-source uncertainties. Therefore, improving the feasibility of dispatch schemes remains a technical challenge that existing technologies struggle to solve. Summary of the Invention

[0003] To address the technical problem that existing technologies struggle to improve the feasibility of dispatching schemes, this invention provides a microgrid group dispatching method and system based on hierarchical control and flexible load response. It improves the particle swarm optimization (PSO) algorithm through model predictive control to obtain an optimized PSO algorithm that adapts to the intermittent, stochastic, and fluctuating characteristics of distributed renewable energy. Furthermore, by incorporating the power generation adjustment potential on the generation side and the electricity consumption adjustment potential on the demand side, a microgrid group interaction coupling model, and a market-based trading strategy model, the optimized PSO algorithm is used to obtain the optimal dispatching scheme. This solves the technical problem of existing technologies' inability to improve the feasibility of dispatching schemes.

[0004] To address the aforementioned technical problems, this invention provides a microgrid group scheduling method based on hierarchical control and flexible load response, comprising the following steps:

[0005] The potential for power generation regulation on the generation side and power consumption regulation on the demand side are obtained based on the power generation side operation constraints and the demand side response constraints, respectively.

[0006] A hierarchical interactive coupling model for microgrid groups is constructed based on execution rules and information interaction constraints.

[0007] A market-based trading strategy model is constructed based on trading methods, bidding mechanisms, and profit distribution mechanisms.

[0008] An optimized particle swarm optimization algorithm is obtained by improving the particle swarm optimization algorithm through model predictive control.

[0009] Based on the potential for power generation on the generation side, the power consumption regulation potential on the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model, the optimal scheduling scheme is obtained using an optimized particle swarm optimization algorithm.

[0010] Preferably, the step of obtaining the power generation and demand regulation potential based on power generation side operating constraints and demand side response constraints respectively includes:

[0011] Based on the operational constraints of the power generation side, a power generation side unit operation model covering wind turbines, photovoltaic units, micro gas turbines, and energy storage systems is constructed, and power generation on the power generation side is obtained based on the power generation side unit operation model;

[0012] A demand-side resource response model is constructed based on demand-side response constraints and price elasticity matrix, and the demand-side electricity consumption adjustment potential is obtained based on the demand-side resource response model.

[0013] Preferably, the generation-side operation constraints include at least power balance constraints; the demand-side response constraints include transferable load time constraints, total transferable load constraints, and load reduction constraints.

[0014] Preferably, the execution rules include: issuing the optimal scheduling scheme through the control layer; executing the optimal scheduling scheme through the response layer and feeding back the real-time operating status of the microgrid group to the control layer;

[0015] The information interaction constraints include transmission power constraints and transmission loss constraints.

[0016] Preferably, the trading methods include day-ahead trading and real-time adjustment trading; the bidding mechanism includes determining the bidding priority based on the marginal cost of each microgrid and the proportion of renewable energy output; and the revenue distribution mechanism includes distributing trading revenue according to the contribution of each microgrid to the microgrid cluster.

[0017] Preferably, the step of improving the particle swarm optimization algorithm through model predictive control to obtain an optimized particle swarm optimization algorithm includes:

[0018] By introducing the prediction time domain and control time domain of model predictive control, the long-period search of the particle swarm optimization algorithm is decomposed into a short-period search. The dynamic prediction constraints of model predictive control are embedded into the search process of the particle swarm optimization algorithm, thereby obtaining an optimized particle swarm optimization algorithm.

[0019] Preferably, the step of using an optimized particle swarm optimization algorithm to obtain the optimal scheduling scheme based on the power generation potential on the generation side, the power consumption regulation potential on the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model includes:

[0020] The search boundary is constructed by leveraging the potential for power generation on the generation side and power consumption regulation on the demand side. Search constraint rules are constructed using a microgrid group interaction coupling model and a market-based trading strategy model. Based on the search boundary and search constraint rules, the optimal scheduling scheme is obtained using an optimized particle swarm optimization algorithm.

[0021] Preferably, the step of obtaining the optimal scheduling scheme using the optimized particle swarm optimization algorithm through search boundaries and search constraint rules includes:

[0022] The search objectives of the particle swarm optimization algorithm are to maximize the renewable energy absorption rate, minimize operating costs, maximize trading revenue, and maximize voltage stability. Under these objectives, the optimal scheduling scheme is obtained through search boundaries and search constraint rules.

[0023] By adopting the above technical solution, the present invention has the following advantages:

[0024] By separately quantifying the output adjustment space under generation-side operational constraints and the flexible adjustment capability under demand-side response constraints, this approach breaks the dependence of traditional dispatching on single generation-side resources, fully releasing the adjustment value of transferable and load-reducing capacity. It provides a richer resource allocation dimension for dispatching schemes, avoiding the difficulty in implementing schemes due to a single resource dimension. Through an optimized particle swarm optimization algorithm improved by model predictive control, it can adapt to the intermittency, randomness, and volatility of distributed renewable energy, thereby obtaining dispatching schemes suitable for distributed renewable energy. This effectively solves the pain point of the disconnect between schemes and actual operating conditions under traditional deterministic algorithms, improving the adaptability of schemes under complex operating conditions. Thus, it solves the technical problem that existing technologies struggle to improve the feasibility of dispatching schemes.

[0025] The microgrid group hierarchical interactive coupling model clarifies the boundaries of rights and responsibilities and the collaborative logic of each subject in the microgrid group through clear execution rules and information interaction constraints. It avoids the problems of chaotic information interaction and delayed command execution in traditional scheduling, improves the synergy between global optimization goals and local response actions, and reduces the communication cost and execution loss of the grid group scheduling.

[0026] By introducing the potential for power generation regulation on the power generation side and the power consumption regulation potential on the demand side, a microgrid group interaction coupling model, and a market-based trading strategy model, and using an optimized particle swarm algorithm to obtain the optimal scheduling scheme, the system fully integrates the real characteristics of both supply and demand sides, solving the problem of balancing the interests of various participants and ultimately affecting the effective implementation of transactions.

[0027] This invention also provides a microgrid group dispatching system based on hierarchical regulation and flexible load response, applicable to the aforementioned microgrid group dispatching method based on hierarchical regulation and flexible load response, comprising:

[0028] The source-load potential acquisition module is used to acquire the power generation potential of the generation side and the power consumption regulation potential of the demand side based on the power generation side operation constraints and the demand side response constraints, respectively.

[0029] The hierarchical interactive coupling model construction module is used to construct a hierarchical interactive coupling model for microgrid groups based on execution rules and information interaction constraints.

[0030] The market-based trading strategy model building module is used to build market-based trading strategy models based on trading methods, bidding mechanisms, and profit distribution mechanisms.

[0031] The algorithm optimization module is used to improve the particle swarm optimization algorithm through model predictive control to obtain an optimized particle swarm optimization algorithm.

[0032] The optimal scheduling scheme acquisition module is used to obtain the optimal scheduling scheme based on the power generation potential of the generation side, the power consumption regulation potential of the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model, using an optimized particle swarm algorithm.

[0033] By adopting the above technical solution, the present invention has the following advantages:

[0034] This paper improves the particle swarm optimization (PSO) algorithm by incorporating model predictive control to obtain an optimized PSO algorithm that can adapt to the intermittent, random, and volatile characteristics of distributed renewable energy. Furthermore, by introducing the potential for power generation regulation on the generation side and demand side, a microgrid group interaction coupling model, and a market-based trading strategy model, the optimized PSO algorithm is used to obtain the optimal scheduling scheme. This solves the technical problem that existing technologies struggle to improve the feasibility of scheduling schemes. Attached Figure Description

[0035] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0036] Figure 1 This is a flowchart illustrating the microgrid group scheduling method based on hierarchical control and flexible load response of the present invention.

[0037] Figure 2 This is a comparison chart of the power interaction results of microgrid groups in the microgrid group scheduling method based on hierarchical regulation and flexible load response of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0039] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the figures; the process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0040] Example 1:

[0041] like Figure 1 As shown, the microgrid group scheduling method based on hierarchical control and flexible load response includes the following steps:

[0042] S1: Obtain the power generation potential on the generation side and the power consumption regulation potential on the demand side based on the power generation side operation constraints and the demand side response constraints, respectively.

[0043] As an optional embodiment, obtaining the power generation and demand regulation potential based on power generation side operating constraints and demand side response constraints respectively includes:

[0044] Based on the operational constraints of the power generation side, a power generation side unit operation model covering wind turbines, photovoltaic units, micro gas turbines, and energy storage systems is constructed, and power generation on the power generation side is obtained based on the power generation side unit operation model;

[0045] A demand-side resource response model is constructed based on demand-side response constraints and price elasticity matrix, and the demand-side electricity consumption adjustment potential is obtained based on the demand-side resource response model.

[0046] Specifically, the generation-side operation constraints include at least power balance constraints; the demand-side response constraints include transferable load time constraints, total transferable load constraints, and load reduction constraints.

[0047] Specifically, the power balance constraint is as follows: ;in, Let be the output power of the photovoltaic unit at time t. Let be the output power of the wind turbine at time t. For the output electrical power of the micro gas turbine, , These are the charging power and discharging power of the energy storage system, respectively. Let be the electrical load demand at time t. This refers to the interaction power between microgrid groups;

[0048] The time constraints for transferable electrical loads are: ;

[0049] The total amount of transferable electrical load is constrained as follows: ;

[0050] The electrical load constraint that can be reduced is: ;

[0051] in, Let be the transferable electrical load of the i-th microgrid during time period t. This is the proportional coefficient for transferable electrical loads. Let be the base electrical load of the i-th microgrid during time period t. To optimize the scheduling cycle, Let be the amount of electrical load that can be reduced in the i-th microgrid during time period t. To reduce the maximum proportional factor of the electrical load, Let t be the electrical load of the i-th microgrid during time period t.

[0052] The price elasticity matrix describes the variation of load with electricity price using the self-elasticity coefficient (the impact of electricity price on load during the same period) and the mutual elasticity coefficient (the cross-influence of electricity price at different periods). The variation pattern is as follows: ,in, This is a matrix showing changes in electricity consumption. The elasticity coefficient matrix, For changes in electricity prices, This is the initial electricity price.

[0053] S2: Construct a hierarchical interactive coupling model for microgrid groups based on execution rules and information interaction constraints.

[0054] As an optional embodiment, the execution rules include: issuing the optimal scheduling scheme through the control layer; executing the optimal scheduling scheme through the response layer and feeding back the real-time operating status of the microgrid group to the control layer;

[0055] The information interaction constraints include transmission power constraints and transmission loss constraints.

[0056] The control layer is specifically located in the regional energy control center, responsible for receiving operational data from each microgrid (such as renewable energy output and load status), generating global coordinated dispatch instructions and transaction reference prices. The response layer includes each microgrid and user load; that is, the real-time operational status of the microgrid group includes actual output, load regulation, etc. By clarifying the boundaries of rights and responsibilities and the coordination logic of each entity within the microgrid group, the problems of chaotic information exchange and delayed instruction execution in traditional dispatching are avoided, improving the coordination between global optimization goals and local response actions, and reducing the communication costs and execution losses of grid group dispatching.

[0057] By constraining transmission power, the power exchange between microgrids is ensured to not exceed the maximum carrying capacity of the lines, i.e. , Let be the interactive electrical power between microgrid a and microgrid b at time t. The maximum power limit for the interaction between microgrids a and b is set to ensure the safety and stability of the microgrid group interaction by limiting the power interaction between microgrids. The transmission loss constraint is specifically as follows: ,in Let be the input power at time t. Let be the output power at time t. The power loss factor per 100m. To address the transmission distance, by considering the impact of distance on energy transmission, the calculated values ​​of the scheduling scheme that are not calculated due to neglecting losses are avoided from deviating from the actual values, thereby improving the overall energy utilization efficiency and reducing energy waste caused by transmission losses.

[0058] S3: Construct a market-oriented trading strategy model based on trading methods, bidding mechanisms, and profit distribution mechanisms.

[0059] Specifically, the trading methods include day-ahead trading and real-time adjustment trading; the bidding mechanism includes determining the bidding priority based on the marginal cost of each microgrid and the proportion of renewable energy output; and the revenue distribution mechanism includes distributing trading revenue according to the contribution of each microgrid to the microgrid cluster.

[0060] Understandably, day-ahead trading refers to each microgrid submitting its electricity and bid prices based on renewable energy output forecasts and load forecasts; real-time adjustment trading refers to revising trading plans according to actual output and load changes. By first planning ahead through day-ahead trading to ensure the orderliness of trading, and then correcting deviations through real-time adjustment trading, the feasibility of trading is guaranteed. This approach balances the efficiency of market-based trading with the uncertainties of renewable energy output and load demand, ultimately achieving a balance between planning and reality.

[0061] Understandably, the marginal costs of each microgrid include electricity costs and energy storage costs. Key indicators for evaluating the contribution of each microgrid to the microgrid cluster include adjustable capacity, response speed, and renewable energy absorption. The stronger the microgrid's regulation capability, the more timely its response, and the more renewable energy it absorbs, the more trading revenue it receives. Through the revenue distribution mechanism, each microgrid is incentivized to proactively improve its regulation capability, accelerate its response speed, and absorb more renewable energy, ultimately promoting the efficient coordination and low-carbon operation of the entire microgrid cluster. The expression for the trading revenue is:

[0062] ;

[0063] This represents the transaction revenue of the i-th microgrid. For the total revenue of the microgrid cluster, Let be the contribution coefficient of the i-th microgrid. The number of microgrids participating in the transaction. This represents the contribution coefficient of the k-th microgrid.

[0064] S4: Improve the particle swarm algorithm by using model predictive control to obtain an optimized particle swarm algorithm.

[0065] As an optional embodiment, the step of improving the particle swarm optimization algorithm through model predictive control to obtain an optimized particle swarm optimization algorithm includes:

[0066] By introducing the prediction time domain and control time domain of model predictive control, the long-period search of the particle swarm optimization algorithm is decomposed into a short-period search. The dynamic prediction constraints of model predictive control are embedded into the search process of the particle swarm optimization algorithm, thereby obtaining an optimized particle swarm optimization algorithm.

[0067] Model predictive control (MMC) is an advanced control method based on predictive models, rolling optimization, and feedback correction. Its core logic is to use models to predict the future, optimize step-by-step, and correct in real time. It is suitable for handling complex, dynamic, and multi-constraint systems. Particle swarm optimization (PSO) is a global optimization algorithm based on swarm intelligence. Its core logic simulates the collective cooperative behavior of flocks of birds foraging and schools of fish migrating, with multiple particles autonomously searching the solution space to find the global optimum. Considering the inherent intermittency, randomness, and volatility of distributed renewable energy, the operating state of microgrid clusters exhibits highly dynamic and nonlinear characteristics. While PSO possesses global optimization capabilities, it is essentially a static optimization method. Its optimization process generates a scheduling scheme based on initial prediction data in a single step, lacking a real-time perception and response mechanism for dynamic changes during operation. This leads to deviations between the algorithm-generated scheduling scheme and the actual operating state, resulting in problems such as insufficient practicality of the scheme and system imbalance. Therefore, the particle swarm optimization (PSO) algorithm is improved and optimized by introducing model predictive control (MMDC). On one hand, the concepts of prediction and control time domains from MMDC are introduced, breaking down the original long-cycle scheduling search task into multiple consecutive short-cycle search units. This allows the algorithm to focus on the actual operating conditions of the current period for refined optimization. On the other hand, the dynamic prediction constraints of MMDC are embedded into the PSO search process. By integrating real-time dynamic prediction information of renewable energy output and load demand, dynamically updated constraint boundaries are provided for the PSO optimization process. This improvement deeply couples the PSO optimization process with the dynamic operating state of the system, effectively offsetting the prediction bias caused by the uncertainty of distributed energy resources. This ensures that the scheduling scheme can adapt to changes in operating conditions in real time, fundamentally improving the robustness of the algorithm and the practical feasibility of the scheduling scheme.

[0068] By introducing adaptive weights, the search process avoids getting stuck in local optima. Among these, inertia weights... , , These are the maximum and minimum inertia weights, respectively. This represents the current iteration number. The maximum number of iterations is represented by w. Understandably, the larger w is, the more likely the particle is to continue moving in its previous direction, exhibiting a stronger ability to explore new regions, but it is prone to missing the optimal solution; the smaller w is, the more easily the particle is drawn to the group's optimal / individual optimal direction, resulting in faster convergence, but it is prone to local optima. Introducing adaptive weights solves both the problems of overexploration and slow convergence caused by fixed large weights, and the problems of overly fast convergence and getting stuck in local optima caused by fixed small weights.

[0069] S5: Based on the potential for power generation on the generation side, the power consumption regulation potential on the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model, the optimal scheduling scheme is obtained using an optimized particle swarm algorithm.

[0070] As an optional embodiment, the method of obtaining the optimal scheduling scheme using an optimized particle swarm optimization algorithm based on the power generation potential on the generation side, the power consumption regulation potential on the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model includes:

[0071] The search boundary is constructed by leveraging the potential for power generation on the generation side and power consumption regulation on the demand side. Search constraint rules are constructed using a microgrid group interaction coupling model and a market-based trading strategy model. Based on the search boundary and search constraint rules, the optimal scheduling scheme is obtained using an optimized particle swarm optimization algorithm.

[0072] Specifically, the step of obtaining the optimal scheduling scheme using the optimized particle swarm optimization algorithm through search boundaries and search constraint rules includes:

[0073] The search objectives of the particle swarm optimization algorithm are to maximize the renewable energy absorption rate, minimize operating costs, maximize trading revenue, and maximize voltage stability. Under these objectives, the optimal scheduling scheme is obtained through search boundaries and search constraint rules.

[0074] Under the influence of model predictive control, this scheme can also verify the following indicators through pilot operation by inputting microgrid group equipment parameters, load data, electricity pricing mechanism and new energy output prediction data: new energy absorption rate, operating cost, transaction revenue and system voltage stability; and adjust and optimize the parameters of particle swarm algorithm and optimize the strategy based on the verification results.

[0075] Taking a microgrid cluster in a certain city's industrial park as an example, this microgrid cluster comprises three independent microgrids (denoted as W1, W2, and W3), covering distributed wind power, photovoltaic units, micro gas turbines, energy storage systems, and three types of loads: industrial, commercial, and residential. The optimized dispatch cycle is 24 hours (divided into 24 time periods, each lasting 1 hour). The equipment parameters, load characteristics, and market environment parameters of each microgrid are configured with reference to the actual operating data of the region. The core parameters are shown in Table 1 below:

[0076] Table 1. Core Parameter Configuration Table

[0077]

[0078] Furthermore, to verify the advantages of the technical solution of the present invention, three sets of comparative solutions were set up. The core differences between each set of solutions and the correlation with the technical features of the present invention are shown in Table 2 below:

[0079] Table 2. Correlation Table between the Invention and Comparative Solutions

[0080]

[0081] Furthermore, the operating results of a single microgrid (W1, W2, W3) were first measured:

[0082] (1) Results of W1 execution

[0083] Option 1: W1 uses a micro gas turbine as the core power source, supplemented by wind and solar power units. The energy storage system has a weak regulation function, and during peak hours (10:00-12:00), it needs to purchase 20-25kW of electricity from the external grid to meet the basic load demand.

[0084] Option 2: By adjusting the price elasticity matrix, 8kW of load can be transferred from peak hours to off-peak hours, and 3kW of load can be reduced. The output of micro gas turbines can be reduced to 12-16kW, and the external power purchase can be reduced to 12-15kW. However, due to the lack of energy mutual assistance, the amount of wind / solar curtailment will still reach an average of 2kWh per day.

[0085] Option 3: Based on demand response, through a hierarchical control architecture (the control layer issues coordinated instructions, and the response layer executes them), W1 delivers 7-10kW of electricity to W3 during peak hours, the energy storage charging and discharging range is increased to 5-8kW, the output of the micro gas turbine is further reduced to 10-14kW, the external power purchase is only 5-8kW, and the amount of wind / solar curtailment is reduced to below 0.5kW·h.

[0086] (2) Results of W2 execution

[0087] The W2 wind turbine has a more balanced output, as shown in Option 3:

[0088] Demand Response: Store surplus wind and solar power during off-peak hours and release 4-6kW during peak hours, combined with 5kW of load transferable capacity, to reduce peak-hour electricity purchases;

[0089] Energy sharing: Purchase 3-5kW of electricity from W3 between 17:00 and 20:00 (peak load) to avoid overloading the micro gas turbine;

[0090] Operational stability: The fluctuation range of the equivalent load curve decreased from ±15% in Scheme 1 to ±8%.

[0091] (3) Results of W3 execution

[0092] W3 uses wind turbines as its core energy source, as described in Scheme 3:

[0093] Layered control: The response layer provides real-time feedback on load fluctuations, and the control layer coordinates W1 and W2 for energy replenishment to avoid purchasing electricity from external sources at high prices;

[0094] Demand response: The load can be reduced by 2-3kW, and with the energy storage discharge of 4-5kW, the peak-hour electricity purchase can be reduced from 15-18kW in Option 1 to 5-7kW;

[0095] New energy consumption: The wind and solar consumption rate will increase from 72% in Scheme 1 to 91%.

[0096] Furthermore, power interaction within the microgrid group is performed, and the comparison of the interaction results is shown in the figure below. Figure 2 As shown, through the energy mutual assistance mechanism of the hierarchical control architecture, the interactive power of the microgrid group exhibits the following characteristics (with a 24-hour cycle):

[0097] During off-peak hours (0:00-6:00): Each microgrid mainly balances its own power, with the interaction power approaching zero, and the energy storage system is charged in a concentrated manner (W1 charging 5kW, W2 charging 4kW, W3 charging 3kW).

[0098] During the flat period (7:00-9:00, 13:00-17:00): W2 supplies 2-4kW of power to W1 (W2 has surplus wind and solar power), and W3 supplies 1-2kW to W2, with smooth fluctuations in the power exchange.

[0099] During peak hours (10:00-12:00, 18:00-20:00): the interaction power is significantly improved, and the transmission loss is calculated according to the formula in claim 3 of the patent. When the distance between W1 and W3 is 500m, the loss rate is 0.5%, which meets the constraint requirements.

[0100] This example compares the optimization effects of three schemes using four key indicators: operating cost, renewable energy integration cost, carbon emission cost, and total cost. The data is derived from the example's operational statistics, as shown in Table 3 below.

[0101] Table 3. Effect Comparison Table

[0102]

[0103] This invention solves the problem of poor coordination in microgrid groups through a hierarchical control architecture, and energy mutual assistance reduces the overall electricity purchase cost of the grid group by 15-20%; by quantifying the load adjustment potential through a price elasticity matrix, the peak-valley load difference is reduced by 30%, reducing the adjustment pressure on the generation side; and by improving the optimization algorithm, the renewable energy absorption rate is increased to 90%, and the carbon emission cost is reduced by more than 60%, achieving a multi-objective balance of "economy-low carbon-stability".

[0104] Example 2:

[0105] This embodiment also provides a microgrid group dispatching system based on hierarchical control and flexible load response, applicable to the aforementioned microgrid group dispatching method based on hierarchical control and flexible load response, including:

[0106] The source-load potential acquisition module is used to acquire the power generation potential of the generation side and the power consumption regulation potential of the demand side based on the power generation side operation constraints and the demand side response constraints, respectively.

[0107] The hierarchical interactive coupling model construction module is used to construct a hierarchical interactive coupling model for microgrid groups based on execution rules and information interaction constraints.

[0108] The market-based trading strategy model building module is used to build market-based trading strategy models based on trading methods, bidding mechanisms, and profit distribution mechanisms.

[0109] The algorithm optimization module is used to improve the particle swarm algorithm through model predictive control to obtain an optimized particle swarm algorithm;

[0110] The optimal scheduling scheme acquisition module is used to obtain the optimal scheduling scheme based on the power generation potential of the generation side, the power consumption regulation potential of the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model, using an optimized particle swarm algorithm.

[0111] The specific embodiments described above are preferred embodiments of the microgrid group scheduling method and system based on hierarchical control and flexible load response of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A microgrid group dispatching method based on hierarchical control and flexible load response, characterized in that, Includes the following steps: The potential for power generation regulation on the generation side and power consumption regulation on the demand side are obtained based on the power generation side operation constraints and the demand side response constraints, respectively. A hierarchical interactive coupling model for microgrid groups is constructed based on execution rules and information interaction constraints. A market-based trading strategy model is constructed based on trading methods, bidding mechanisms, and profit distribution mechanisms. An optimized particle swarm optimization algorithm is obtained by improving the particle swarm optimization algorithm through model predictive control. Based on the potential for power generation on the generation side, the power consumption regulation potential on the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model, the optimal scheduling scheme is obtained using an optimized particle swarm algorithm. The improvement of the particle swarm optimization algorithm through model predictive control to obtain an optimized particle swarm optimization algorithm includes: By introducing the prediction time domain and control time domain of model predictive control, the long-period search of the particle swarm algorithm is decomposed into a short-period search, and the dynamic prediction constraints of model predictive control are embedded into the search process of the particle swarm algorithm, thereby obtaining an optimized particle swarm algorithm. The optimal scheduling scheme is obtained using an optimized particle swarm optimization algorithm, based on the power generation potential on the generation side, the power consumption regulation potential on the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model. This includes: The search boundary is constructed by the potential for power generation on the power generation side and power consumption regulation on the demand side. Search constraint rules are constructed by the microgrid group interaction coupling model and the market-based trading strategy model. The optimal scheduling scheme is obtained by using the optimized particle swarm algorithm based on the search boundary and search constraint rules. The trading methods include day-ahead trading and real-time adjustment trading; the bidding mechanism includes determining the bidding priority based on the marginal cost of each microgrid and the proportion of renewable energy output; the revenue distribution mechanism includes distributing trading revenue according to the contribution of each microgrid to the microgrid cluster.

2. The microgrid group dispatching method based on hierarchical control and flexible load response according to claim 1, characterized in that, The method of obtaining the power generation and demand regulation potential based on power generation side operation constraints and demand side response constraints respectively includes: Based on the operational constraints of the power generation side, a power generation side unit operation model covering wind turbines, photovoltaic units, micro gas turbines, and energy storage systems is constructed, and power generation on the power generation side is obtained based on the power generation side unit operation model; A demand-side resource response model is constructed based on demand-side response constraints and price elasticity matrix, and the demand-side electricity consumption adjustment potential is obtained based on the demand-side resource response model.

3. The microgrid group dispatching method based on hierarchical control and flexible load response according to claim 2, characterized in that, The generation-side operation constraints include at least power balance constraints; the demand-side response constraints include transferable load time constraints, total transferable load constraints, and load reduction constraints.

4. The microgrid group dispatching method based on hierarchical control and flexible load response according to claim 1, characterized in that, The execution rules include: issuing the optimal scheduling scheme through the control layer; executing the optimal scheduling scheme through the response layer and feeding back the real-time operating status of the microgrid group to the control layer; The information interaction constraints include transmission power constraints and transmission loss constraints.

5. The microgrid group dispatching method based on hierarchical control and flexible load response according to claim 1, characterized in that, The trading methods include day-ahead trading and real-time adjustment trading; the bidding mechanism includes determining the bidding priority based on the marginal cost of each microgrid and the proportion of renewable energy output; the revenue distribution mechanism includes distributing trading revenue according to the contribution of each microgrid to the microgrid cluster.

6. The microgrid group dispatching method based on hierarchical control and flexible load response according to claim 1, characterized in that, The step of obtaining the optimal scheduling scheme using the optimized particle swarm optimization algorithm through search boundaries and search constraint rules includes: The search objectives of the particle swarm optimization algorithm are to maximize the renewable energy absorption rate, minimize operating costs, maximize trading revenue, and maximize voltage stability. Under these objectives, the optimal scheduling scheme is obtained through search boundaries and search constraint rules.

7. A microgrid group dispatching system based on hierarchical control and flexible load response, applicable to the microgrid group dispatching method based on hierarchical control and flexible load response as described in any one of claims 1-6, characterized in that, include: The source-load potential acquisition module is used to acquire the power generation potential on the generation side and the power consumption regulation potential on the demand side based on the power generation side operation constraints and the demand side response constraints, respectively. The hierarchical interactive coupling model construction module is used to construct a hierarchical interactive coupling model for microgrid groups based on execution rules and information interaction constraints. The market-based trading strategy model building module is used to build market-based trading strategy models based on trading methods, bidding mechanisms, and profit distribution mechanisms. The algorithm optimization module is used to improve the particle swarm algorithm through model predictive control to obtain an optimized particle swarm algorithm; The optimal scheduling scheme acquisition module is used to obtain the optimal scheduling scheme based on the power generation potential of the generation side, the power consumption regulation potential of the demand side, the microgrid group interaction coupling model, and the market-based trading strategy model, using an optimized particle swarm algorithm.

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