CN-121461284-B - Multi-microgrid energy collaborative optimization method and system

Abstract

The invention provides a multi-microgrid energy collaborative optimization method and system, which comprises the steps of S1, establishing an energy collaborative network topological structure comprising a plurality of microgrid nodes, S2, constructing an energy balance model based on real-time power demand, new energy output prediction and power grid dispatching instructions, S3, forming an optimization function with the aim of minimizing the total running cost of a multi-microgrid system, S4, solving the optimization function by adopting an improved beluga optimization algorithm to obtain an energy dispatching scheme of each microgrid in each period, S5, regulating and controlling an energy storage device, a charging pile and adjustable load in each microgrid according to the energy dispatching scheme, and coordinating the energy flow direction and the transmission power among the microgrids, S6, monitoring the actual running state and the energy interaction effect of each microgrid, and re-executing S2 to S5 based on real-time data when the prediction deviation is detected to exceed a preset threshold. The invention can realize energy coordination among multiple micro networks, improve new energy consumption capability, reduce operation cost and enhance economy and stability of power grid operation.

Inventors

• LV QINGWEN
• HE LINRU
• LIANG XIN
• Ban Yanling

Assignees

• 广西华蓝数智科技有限公司

Dates

Publication Date

20260512

Application Date

20251104

Claims (8)

1. 1. The multi-microgrid energy collaborative optimization method is characterized by comprising the following steps of: The method comprises the steps of S1, establishing an energy cooperative network topological structure comprising a plurality of micro grid nodes, wherein each micro grid node comprises a distributed photovoltaic device, a wind power device, an energy storage device and a charging pile, and the micro grid nodes realize bidirectional electric energy flow through an energy interaction interface; s2, constructing a multi-micro-grid energy balance model integrating a demand response mechanism and carbon transaction constraints based on real-time power demand, new energy output prediction and power grid dispatching instructions, wherein the demand response mechanism dynamically adjusts energy storage charging and discharging strategies in each micro-grid according to time-of-use power price and load characteristics, and the carbon transaction constraints limit energy interaction scale based on carbon emission quota of each micro-grid; S3, establishing a comprehensive objective function comprising electricity purchasing cost, carbon transaction cost, equipment operation and maintenance cost and energy transmission loss cost by taking the total running cost of the multi-microgrid system as a target, and introducing energy complementary income items among the microgrids to form a multi-objective optimization function considering economy and environmental protection; s4, solving the multi-objective optimization function by adopting an improved white whale optimization algorithm fused with a self-adaptive weight adjustment and elite retention strategy to obtain an optimal energy scheduling scheme of each micro-grid in each period, wherein the optimal energy scheduling scheme comprises energy storage charging and discharging power, energy interaction power among the micro-grids and charging pile power distribution; S5, according to the optimal energy scheduling scheme, the energy storage device, the charging pile and the adjustable load in each micro-grid are regulated and controlled in real time through an energy management system, and meanwhile, the energy flow direction and the transmission power between the micro-grids are coordinated, so that the cooperative operation of a plurality of micro-grid systems is realized; S6, monitoring the actual running state and the energy interaction effect of each micro-grid, and re-executing the steps S2 to S5 based on real-time data when the prediction deviation is detected to exceed a preset threshold value, so as to realize the dynamic optimization adjustment of multi-micro-grid energy coordination; In step S1, a dynamic weight allocation mechanism is adopted to determine the cooperative priority of each micro-grid node, and the cooperative priority is calculated specifically by the following formula: Wherein, the Is the cooperative weight coefficient of the mg-th micro-grid; renewable energy installed capacity for the mg-th microgrid; The total capacity of all the renewable energy sources of all the micro-networks in the system is installed; the energy storage capacity of the mg-th micro-grid; the total energy storage capacity of all micro-networks in the system is calculated; the communication delay time between the mg-th micro-grid and the energy coordination center is set; Is a reference communication delay time; 、 、 the weight is assigned a factor which satisfies ; When a demand response mechanism is established in step S2, an adaptive response depth adjustment model is adopted to dynamically adjust the response degree of each micro-grid through the following formula: Wherein, the The demand response depth of the mg-th micro-grid at the t moment; the depth of response is the reference demand; adjusting the coefficient in response to the sensitivity; The total load power of the power grid at the moment t; Triggering a threshold power for demand response; Normalizing parameters for load power; is the response capacity coefficient of the mg-th micro-net at the time t.
2. 2. The multi-microgrid energy collaborative optimization method according to claim 1, wherein S5 is characterized in that according to the optimal energy scheduling scheme, energy storage devices, charging piles and adjustable loads in each microgrid are regulated and controlled in real time through an energy management system, and meanwhile energy flow directions and transmission power among the microgrids are coordinated, so that collaborative operation of the multi-microgrid system is realized, and the method comprises the following steps: according to the optimal energy scheduling scheme and the time-sharing electricity price information of the current time period, judging the charge and discharge modes of the energy storage devices in each micro-grid, controlling the energy storage charge in the time period when the electricity price is lower than a first preset price, and controlling the energy storage discharge in the time period when the electricity price is higher than a second preset price; Monitoring real-time power load of each micro-grid transformer, and controlling the energy storage device to discharge or the photovoltaic device to reduce power output when detecting that the load power is close to the capacity set value of the transformer, so that the capacity of the transformer is kept within a safe operation range; when the total power requirement of the charging piles exceeds the available power capacity of the micro-grid, dynamically adjusting the charging power of each charging pile or suspending part of the operation of the charging piles according to a preset first priority principle; and when the output power of the distributed power supply is insufficient, the energy storage device is controlled to discharge to supplement power supply.
3. 3. The multi-microgrid energy collaborative optimization method according to claim 2, wherein the step S6 is characterized in that the actual running state and the energy interaction effect of each microgrid are monitored, when the prediction deviation exceeding the preset threshold is detected, the steps S2 to S5 are re-executed based on real-time data, and the step of realizing the dynamic optimization adjustment of the multi-microgrid energy collaborative is realized, and comprises the following steps: Monitoring the load power change rate of each micro-grid in real time, and controlling the energy storage device to discharge to stabilize load fluctuation when the load power change rate exceeds a preset threshold value, so as to reduce impact on a power grid; when detecting that a high-power short-time load demand occurs in the micro-grid, the energy storage device is preferentially called to discharge for power supplement, overload of a transformer is avoided, and dynamic expansion of system capacity is realized; according to the real-time change of the power factor of each micro gateway port, controlling the energy storage PCS to continuously adjust reactive power output, and maintaining the power factor of the system within a reasonable range; and comparing and analyzing the actual operation data and the prediction data of each micro-grid, and when the deviation exceeds the allowable range, recalculating the optimization parameters and updating the scheduling strategy.
4. 4. The multi-microgrid energy collaborative optimization method according to claim 3, wherein S2, the step of constructing a multi-microgrid energy balance model integrating a demand response mechanism and carbon transaction constraints based on real-time power demand, new energy output prediction and power grid dispatching instructions, comprises the following steps: the method comprises the steps of collecting power demand data of each micro-grid, power generation power prediction data of new energy equipment and scheduling instruction data issued by a superior power grid in real time, establishing a unified data time synchronization mechanism and a data quality check mechanism, and ensuring consistency of various data in time dimension and precision requirements; Based on time-of-use electricity price signals and historical load characteristic curves of all micro networks, dynamic charge-discharge response strategies of the energy storage device are formulated, energy storage and charge power is increased in electricity price valley time periods to store low-cost electric energy, and energy storage and discharge power is increased in electricity price peak time periods to reduce electricity purchasing cost, and meanwhile, technical constraints and service life of energy storage equipment are considered; According to the carbon emission policy and the carbon emission quota distributed by each micro-grid, a carbon emission accounting system is established, the actual carbon emission of each micro-grid is compared with the quota, when the carbon emission of a certain micro-grid is close to the upper limit of the quota, the electricity purchasing scale of the micro-grid from the power grid is limited or the use proportion of clean energy is required to be increased, and the overall carbon emission control of the system is realized through the carbon quota transaction among the micro-grids; Integrating a demand response mechanism and carbon transaction constraint into a traditional supply-demand balance equation, establishing a comprehensive energy balance constraint model considering electricity price response, carbon emission limitation and new energy output fluctuation, and ensuring that each micro-grid realizes supply-demand dynamic balance on the premise of meeting environmental protection requirements; when conflict occurs among a plurality of constraint conditions, a constraint priority coordination mechanism is established, the power supply and demand safety constraint is guaranteed preferentially, the carbon emission constraint is considered, the economical constraint is optimized finally, and the resolvability and practicality of the constraint model are guaranteed through constraint relaxation and compensation strategies.
5. 5. The method of optimizing energy cooperation of multiple micro-grid according to claim 4, wherein S1, establishing an energy cooperation network topology structure comprising a plurality of micro-grid nodes, each micro-grid node comprising a distributed photovoltaic device, a wind power device, an energy storage device and a charging pile, each micro-grid node realizing bidirectional flow of electric energy through an energy interaction interface, comprises: A unified internal architecture is configured for each micro-grid node, and the unified internal architecture comprises an inverter access point of a distributed photovoltaic device, a converter access point of a wind power device, a bidirectional converter access point of an energy storage device and an alternating current-direct current conversion access point of a charging pile, wherein each device is uniformly regulated and controlled through a local energy management unit; A standardized energy interaction interface is deployed among all the micro grid nodes, and comprises a power transmission channel and an information communication channel, wherein the power transmission channel is responsible for realizing bidirectional electric energy flow among micro grids, and the information communication channel is responsible for transmitting energy scheduling instructions and state feedback information; A unified multi-microgrid communication protocol is formulated, a data exchange format, a communication time sequence and a fault processing mechanism among the microgrid nodes are specified, and the nodes can share key operation information such as load prediction, power generation plans and energy storage states in real time; according to the geographic position, the electric distance and the load characteristic of each micro-grid, establishing a physical connection relation and a logical connection relation among the micro-grids to form a redundant network topological structure supporting multipath energy transmission; And (3) distributing a unique network identifier for each micro-grid node, setting initial energy interaction authority and transmission capacity limit value, and establishing a device parameter database and an operation state monitoring mechanism of each node.
6. 6. The method of claim 5, wherein S3, with the goal of minimizing the total running cost of the multi-microgrid system, establishes a comprehensive objective function including electricity purchase cost, carbon transaction cost, equipment operation and maintenance cost and energy transmission loss cost, and introduces energy complementary benefit items among the microgrids to form a multi-objective optimization function considering economy and environmental protection, comprising: Respectively establishing a cost calculation matrix of an electricity purchase cost item, a carbon transaction cost item, an equipment operation and maintenance cost item and an energy transmission loss cost item, wherein the electricity purchase cost item is based on time-sharing electricity prices of each period and electricity purchase requirements of each micro-grid, the carbon transaction cost item is based on carbon emission and carbon price fluctuation, the equipment operation and maintenance cost item is based on operation duration and maintenance frequency of each equipment, and the energy transmission loss cost item is based on transmission distance and line loss rate between the micro-grids; Establishing an energy complementary benefit assessment mechanism between micro-networks, and identifying energy supply and demand time difference and space distribution difference between the micro-networks by analyzing load time sequence characteristics and renewable energy source output complementarity of each micro-network so as to convert complementary effects into quantifiable economic benefit items; setting dynamic weight coefficients for each cost item and benefit item according to the running targets and policy guidance of the multi-microgrid system, wherein the economical weight is adjusted according to electricity price fluctuation and market environment, and the environmental protection weight is set according to carbon emission reduction targets and policy incentives; Establishing an association relation between each cost item and each benefit item and a system operation constraint condition, and ensuring that the equipment operation parameters, the energy transmission power and the energy storage state change of each micro-grid in the objective function optimization process meet the technical constraint and the safety constraint; And taking each cost item as a minimum target and a complementary benefit item as a maximum target, and constructing a unified multi-target optimization function by combining the set weight coefficients to realize coordination and unification of economic cost minimization and environmental protection benefit maximization.
7. 7. The multi-microgrid energy collaborative optimization method according to claim 6, wherein S4 is characterized in that the multi-objective optimization function is solved by adopting an improved beluga optimization algorithm integrating adaptive weight adjustment and elite retention strategy to obtain an optimal energy scheduling scheme of each microgrid in each period, and the method comprises the following steps: according to the equipment parameters and operation constraints of each micro-grid, generating multidimensional decision variable individuals representing energy storage charging and discharging power, energy interaction power among the micro-grids and charging pile power distribution, constructing an initial population meeting constraint conditions, and distributing fitness evaluation indexes for each individual; According to the iterative process of the algorithm and/or the convergence state of the population, the exploration weight and the development weight in the white whale algorithm are dynamically adjusted, specifically, when the iterative times are less than one third of the preset total iterative times, and/or when the population individual fitness dispersion degree is higher than the preset convergence threshold value, the exploration weight is increased to expand the search range, when the iterative times exceed two thirds of the preset total iterative times, and/or when the population individual fitness dispersion degree is lower than the preset convergence threshold value, the development weight is increased to refine and optimize, and the balance of global search and local optimization is realized; In each iteration process, a plurality of elite individuals with optimal fitness in the current population are identified and reserved, and excellent characteristics of the elite individuals are transmitted to the next generation population through crossover and mutation operation, so that excellent solutions are prevented from being lost in the evolution process; Dividing the algorithm solving process into a coarse searching stage and a fine searching stage, adopting a large-step position updating strategy to quickly position an optimal solution area in the coarse searching stage, and adopting a small-step local searching strategy to accurately solve an optimal scheduling scheme in the fine searching stage; When the algorithm meets the convergence condition or reaches the maximum iteration number, the decision variable value corresponding to the optimal individual is extracted and converted into a specific scheduling instruction of each micro-grid in each period, wherein the specific scheduling instruction comprises a charging and discharging power set value of each energy storage device, an energy exchange power instruction among the micro-grids and a power distribution scheme of each charging pile.
8. 8. A multi-microgrid energy co-optimization system for performing the multi-microgrid energy co-optimization method according to any one of claims 1 to 7, comprising a microgrid node and a server; The server is configured to: Establishing an energy cooperative network topology structure comprising a plurality of micro-grid nodes, wherein each micro-grid node comprises a distributed photovoltaic device, a wind power device, an energy storage device and a charging pile, and each micro-grid node realizes bidirectional flow of electric energy through an energy interaction interface; Based on real-time power demand, new energy output prediction and power grid dispatching instructions, constructing a multi-micro-grid energy balance model integrating a demand response mechanism and carbon transaction constraint, wherein the demand response mechanism dynamically adjusts energy storage charging and discharging strategies in each micro-grid according to time-of-use electricity price and load characteristics, and the carbon transaction constraint limits the energy interaction scale based on carbon emission quota of each micro-grid; the method comprises the steps of establishing a comprehensive objective function comprising electricity purchasing cost, carbon transaction cost, equipment operation and maintenance cost and energy transmission loss cost by taking the total running cost of a multi-microgrid system as a target, introducing energy complementation benefit items among the microgrids, and forming a multi-target optimization function considering economy and environmental protection; Solving the multi-objective optimization function by adopting an improved white whale optimization algorithm fused with a self-adaptive weight adjustment and elite retention strategy to obtain an optimal energy scheduling scheme of each micro-grid in each period, wherein the optimal energy scheduling scheme comprises energy storage charging and discharging power, energy interaction power among the micro-grids and charging pile power distribution; According to the optimal energy scheduling scheme, an energy storage device, a charging pile and an adjustable load in each micro-grid are regulated and controlled in real time through an energy management system, and meanwhile, the energy flow direction and the transmission power between the micro-grids are coordinated, so that the cooperative operation of a plurality of micro-grid systems is realized; And (3) monitoring the actual running state and the energy interaction effect of each micro-grid, and re-executing the steps S2 to S5 based on real-time data when the prediction deviation is detected to exceed a preset threshold value, so as to realize the dynamic optimization adjustment of the energy coordination of the multiple micro-grids.

Description

Multi-microgrid energy collaborative optimization method and system Technical Field The invention relates to the technical field of new energy, in particular to a multi-microgrid energy collaborative optimization method and system. Background With the global energy crisis, distributed energy systems with renewable energy (such as wind power and photovoltaic) distributed power generation as a core are rapidly developed. Micro-grids are receiving wide attention as an important technical means for effectively integrating distributed energy, improving energy utilization efficiency and enhancing regional power supply reliability. The micro-grid can be regarded as a small power generation and distribution system comprising various distributed power supplies, energy storage systems, loads and control devices, and can realize grid-connected operation or independent operation. Due to the limited scale, the limited resource types and capacity and the limited regulation capability of a single micro-grid, the renewable energy source output in the single micro-grid has obvious randomness, volatility and intermittence, and the uncertainty exists in the requirement of user load, so that the single micro-grid is difficult to balance source load power fluctuation during independent operation, the problems of power supply reliability reduction, high operation cost and the like can be faced, and particularly when a power supply or load fault occurs in the micro-grid, the safe and stable operation of the single micro-grid faces a great challenge. To address the limitations of single microgrid systems, the concept of multiple microgrid systems has evolved. The multi-microgrid system refers to a complex energy system which connects a plurality of microgrids with independent operation capability together through a public coupling point and is connected into a power distribution network in a geographical similar region. However, the existing multi-micro-grid system has the defects in the aspect of energy coordination among the multi-micro-grids, so that the new energy consumption capability is low, the overall operation cost of the system is high and is not stable, and the power grid operation is unstable. Disclosure of Invention Based on the problems, the invention provides a multi-micro-grid energy collaborative optimization method and a multi-micro-grid energy collaborative optimization system, which are characterized in that an improved beluga algorithm is combined to solve through establishing a multi-micro-grid collaborative optimization framework and a multi-dimensional constraint model, so that intelligent energy collaboration among the multi-micro-grids is realized, new energy consumption capability is remarkably improved, the overall operation cost of the system is reduced, and the economical efficiency and stability of power grid operation are enhanced. In view of this, an aspect of the present invention proposes a multi-microgrid energy collaborative optimization method, including: The method comprises the steps of S1, establishing an energy cooperative network topological structure comprising a plurality of micro grid nodes, wherein each micro grid node comprises a distributed photovoltaic device, a wind power device, an energy storage device and a charging pile, and the micro grid nodes realize bidirectional electric energy flow through an energy interaction interface; s2, constructing a multi-micro-grid energy balance model integrating a demand response mechanism and carbon transaction constraints based on real-time power demand, new energy output prediction and power grid dispatching instructions, wherein the demand response mechanism dynamically adjusts energy storage charging and discharging strategies in each micro-grid according to time-of-use power price and load characteristics, and the carbon transaction constraints limit energy interaction scale based on carbon emission quota of each micro-grid; S3, establishing a comprehensive objective function comprising electricity purchasing cost, carbon transaction cost, equipment operation and maintenance cost and energy transmission loss cost by taking the total running cost of the multi-microgrid system as a target, and introducing energy complementary income items among the microgrids to form a multi-objective optimization function considering economy and environmental protection; s4, solving the multi-objective optimization function by adopting an improved white whale optimization algorithm fused with a self-adaptive weight adjustment and elite retention strategy to obtain an optimal energy scheduling scheme of each micro-grid in each period, wherein the optimal energy scheduling scheme comprises energy storage charging and discharging power, energy interaction power among the micro-grids and charging pile power distribution; S5, according to the optimal energy scheduling scheme, the energy storage device, the charging pile and the adjustable load in each micro-g