Isolated multi-capable micro-grid comprehensive cooperative scheduling method containing V2G

Abstract

The application discloses a kind of V2G containing island multi-energy microgrid comprehensive coordination scheduling method, comprising the following steps: constructing island multi-energy microgrid architecture containing V2G interaction and obtaining typical day prediction data;Establish the operation mathematical model of each regulation and control equipment in microgrid and system power balance model;Build dimensionless equal weight composite scheduling objective function considering operation cost, carbon emission, net load peak valley difference and unit output smoothness;Iterative solution is carried out to composite scheduling objective function using perception constraint hybrid moss growth optimization algorithm, and the optimal day-ahead scheduling scheme of microgrid is output;Its beneficial effects are that the island mode source and load fluctuation sensitive problem is effectively alleviated, and the system regulation capacity and operation feasibility are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching, specifically to a comprehensive collaborative dispatching method for islanded multi-energy microgrids including V2G. Background Technology

[0002] With the continuous improvement of renewable energy penetration and terminal electrification levels, multi-energy microgrids are showing great promise in industrial park energy supply. However, in islanded operation mode, microgrids lose the power exchange support with the main grid, and system power balance can only rely on local conventional generating units and limited energy storage devices, making them extremely sensitive to the randomness of wind and solar power output at the source and the volatility of load at the load end. Especially in scenarios with a high proportion of renewable energy access, drastic fluctuations in net load can easily lead to supply and demand imbalances, resulting in severe wind and solar curtailment or forced shelving of important loads, seriously threatening the safe and stable operation of islanded microgrids.

[0003] Meanwhile, aggregated electric vehicle fleets, as a highly promising flexible resource, can provide microgrids with significant spatiotemporal regulation capabilities through V2G (Vehicle-to-Grid) technology. However, in existing scheduling strategies, V2G resources are often simply treated as static loads or charged and discharged solely for economic gain, lacking deep synergy with the multi-energy coupling characteristics within the microgrid. This disordered or locally optimized charging and discharging behavior not only fails to effectively leverage the peak-shaving and valley-filling potential of electric vehicles but may even exacerbate the peak load pressure during peak load periods due to clustered charging, further worsening the power balance dilemma of islanded systems.

[0004] Furthermore, existing research on microgrid optimization scheduling largely focuses on grid-connected scenarios or a single economic operation objective, lacking a comprehensive and coordinated mechanism that balances economy, low carbon emissions, and operational stability in islanded scenarios. Single-objective economic scheduling often comes at the cost of sacrificing carbon emission allowances or exacerbating unit wear, making it difficult to adapt to the comprehensive requirements of multi-dimensional operational indicators under a "dual-carbon" background. Meanwhile, islanded microgrid scheduling with V2G is essentially a complex optimization problem that is high-dimensional, nonlinear, and highly coupled with constraints. When simultaneously considering multiple physical constraints such as centralized energy storage state of charge constraints, minimum travel demand constraints for V2G fleets, interruptible load response constraints, and conventional unit ramp rate constraints, traditional mathematical programming methods are prone to getting trapped in local optima when dealing with non-convex nonlinear models. Ordinary intelligent optimization algorithms generally face bottlenecks such as difficulty in generating feasible solutions, fragile constraint handling mechanisms, and susceptibility to boundary violations, resulting in a lack of feasibility for the final output scheduling scheme in engineering implementation. Summary of the Invention

[0005] This invention proposes a comprehensive coordinated scheduling method for islanded multi-energy microgrids including V2G, comprising: S1. Construct an islanded multi-energy microgrid architecture with V2G interaction to obtain typical daily scheduling cycles and typical daily forecast data of aggregated electric vehicle fleets; S2. Establish the operation mathematical model of each control device in the islanded multi-energy microgrid and the active power balance constraint model of the system. The operation mathematical model includes the operation constraint model of micro gas turbine and fuel cell, the operation mathematical model of centralized battery energy storage system and the response model of aggregated electric vehicle fleet. S3. Construct a dimensionless, weighted composite scheduling objective function that integrates multiple operational indicators, wherein the multiple operational indicators include the calculation of cost and carbon emission indicators of the objective function, the calculation of peak-valley difference and smoothness indicators of the objective function, and the dimensionless, weighted composite scheduling objective function. S4. The dimensionless, weighted composite scheduling objective function is iteratively solved using the perceptually constrained hybrid moss growth optimization algorithm to output the optimal day-ahead scheduling scheme for the microgrid.

[0006] Furthermore, S1 specifically includes: in islanded operation mode, the microgrid does not exchange power with the upper-level main grid, and relies on local conventional units and energy storage devices to maintain power balance; the typical daily scheduling cycle is divided into 24 equal-length time periods, and the acquired forecast data includes the forecast values ​​of photovoltaic generator output power, wind turbine generator output power, and park base load forecast data for each time period; the aggregated electric vehicle fleet connects to the microgrid through charging stations and aggregators, and participates in charging and discharging during vehicle parking to achieve V2G interaction.

[0007] Furthermore, the active power balance constraint model of the system in S2 satisfies the following: active power balance constraints for each time period of the system, wherein the sum of the predicted output power of photovoltaic and wind power, the output active power of micro gas turbine and fuel cell, the net active power injected into the AC bus by the centralized battery energy storage system, the net active power injected into the AC bus by the aggregated electric vehicle fleet, and the predicted value of the base load and the power reduction of the interruptible load is zero.

[0008] Furthermore, the operating constraint model of the micro gas turbine and fuel cell in S2 satisfies the following constraints: upper and lower limits of output of the micro gas turbine and fuel cell and ramp rate constraint.

[0009] Furthermore, the operational mathematical model of the centralized battery energy storage system in S2 satisfies the following constraints: the state of charge update constraint and the upper and lower limits of the charging and discharging power constraint of the centralized battery energy storage system.

[0010] Furthermore, the response model of the aggregated electric vehicle fleet in S2 satisfies: a recursive constraint on the state of charge of the aggregated electric vehicle fleet as equivalent to a generalized battery, and a minimum travel demand constraint satisfied at the moment the vehicle leaves.

[0011] Furthermore, the calculation of the cost and carbon emission indicators of the objective function in S3 satisfies the following: the original operating cost and original carbon emission are compressed to a preset dimensionless range using a nonlinear saturated mapping function to obtain the mapped operating cost indicator and the mapped carbon emission indicator.

[0012] Furthermore, the calculation of the peak-valley difference and smoothness index of the objective function in S3 satisfies the following: the original net load peak-valley difference index is calculated based on the equivalent net load taking into account interruptible load and V2G charging and discharging; the original unit output smoothness index is calculated based on the combined output of the micro gas turbine and fuel cell; and the normalized net load peak-valley difference index and the mapped unit output smoothness index are obtained by compressing them to a preset dimensionless interval using a nonlinear saturated mapping function.

[0013] Furthermore, the dimensionless, weighted composite scheduling objective function in S3 satisfies the following: the mapped operating cost index, the mapped carbon emission index, the normalized net load peak-valley difference index, and the mapped unit output smoothness index are added with equal weights, and a penalty for breach of contract is superimposed to form the final optimization objective function.

[0014] Furthermore, the hybrid moss growth optimization algorithm with perceptual constraints in S4 satisfies the following: wind direction determination, spore diffusion search, dual propagation search, adaptive differential evolution enhancement, cryptic mechanism, local search of elite individual particle swarms, and hierarchical constraint processing are performed under the moss growth optimization framework. The hierarchical constraint processing includes composite penalty function, state repair of centralized energy storage system and electric vehicle aggregate, and slope limit smoothing repair of conventional unit.

[0015] This invention proposes a comprehensive collaborative scheduling method for isolated multi-energy microgrids with V2G. It constructs a dimensionless, weighted composite scheduling objective function that integrates multiple operating indicators and solves it using a perceptual constraint-based hybrid moss growth optimization algorithm. This method effectively alleviates the problems of insufficient coordination of multiple energy sources, lack of a unified optimization framework for multiple operating indicators, and insufficient ability to generate feasible solutions under complex constraints in the comprehensive scheduling process of isolated multi-energy microgrids with electric vehicle-grid interaction. It significantly improves the economy, low carbon emissions, and operational stability of the microgrid. Attached Figure Description

[0016] Figure 1 A schematic diagram of an islanded multi-energy microgrid system containing V2G; Figure 2 Here is a flowchart of the integrated collaborative scheduling method; Figure 3 A schematic diagram of the algorithm for optimizing hybrid moss growth under perceived constraints; Figure 4 This is a schematic diagram of the day-ahead scheduling method for an islanded multi-energy microgrid with V2G interaction proposed in this invention. Detailed Implementation

[0017] refer to Figure 4 This invention proposes a day-ahead dispatching method for islanded multi-energy microgrids with V2G interaction, comprising: S1. Construct an islanded multi-energy microgrid architecture with V2G interaction to obtain typical daily scheduling cycles and typical daily forecast data of aggregated electric vehicle fleets.

[0018] The islanded multi-energy microgrid architecture with V2G interaction is a system structure that does not exchange power with the upper-level main grid in islanded operation mode, and relies on local conventional units and energy storage devices to maintain power balance. The typical daily forecast data is the output power forecast of photovoltaic generators, wind turbine generators, and park base load forecast data obtained by dividing the typical daily scheduling cycle into 24 equal-length time periods. The aggregated electric vehicle fleet connects to the microgrid through charging stations and aggregators, and participates in charging and discharging during vehicle parking to achieve V2G interaction.

[0019] Specifically, such as Figure 1 (A schematic diagram of an islanded multi-energy microgrid system including V2G is shown.) The microgrid is connected to a common AC bus and includes photovoltaic generators, wind turbines, micro gas turbines, fuel cells, a centralized battery energy storage system, and a fleet of aggregated electric vehicles connected through charging stations and aggregators; the system satisfies the active power balance equation at all times. in, This is the scheduling time slot number, with values ​​of 1, 2, ... , is a dimensionless quantity; This represents the total number of time periods in a typical daily schedule, with a value of 24, and is a dimensionless quantity. For microgrids in The predicted output power of photovoltaic power for the time period, in kilowatts; The predicted wind power output of the microgrid during time period t, in kilowatts. The output active power of the micro gas turbine during time period t is expressed in kilowatts. The active power output of the fuel cell during time period t is expressed in kilowatts. The net active power injected into the AC bus of the centralized battery energy storage system during time period t, in kilowatts; The net active power injected into the AC bus by the aggregated electric vehicle fleet during time period t, expressed in kilowatts; This represents the predicted base load for time period t, in kilowatts. The power reduction of interruptible load during time period t is expressed in kilowatts.

[0020] In a specific implementation scenario, such as Figure 2(Flowchart of the integrated collaborative scheduling method) The scheduling cycle takes a typical day and is divided into 24 equal-length time periods. The input data for each time period includes the predicted output power of photovoltaic generator sets, the predicted output power of wind turbine generator sets, and the predicted basic load of the park. The electric vehicle fleet participates in charging and discharging during the parking period and meets the minimum state of charge requirement before leaving.

[0021] S2. Establish the operational mathematical model of each control device in the islanded multi-energy microgrid and the active power balance constraint model of the system. The operational mathematical model includes the operational constraint model of the micro gas turbine and fuel cell, the operational mathematical model of the centralized battery energy storage system, and the response model of the aggregated electric vehicle fleet.

[0022] The operational mathematical models of each control device include operational constraint models for micro gas turbines and fuel cells, state-of-charge update models for centralized battery energy storage systems, and response models for aggregated electric vehicle fleets; the system power balance model is the active power balance equation satisfied by the system at each time period.

[0023] Specifically, the micro gas turbine and fuel cell satisfy the upper and lower limits of output and the ramp-up constraint, and are designed to meet the following conditions: Let represent the active power output of a micro gas turbine or fuel cell during time period t, then satisfying the following condition: as well as ,in, This represents the active power output of a conventional generating unit during time period t, expressed in kilowatts. This represents the active power output of a conventional generating unit during the t-1 time period, expressed in kilowatts. This represents the lower limit of the active power output of a conventional generating unit, expressed in kilowatts. This represents the upper limit of the active power output of a conventional generating unit, in kilowatts. The threshold for the unit's downward ramp rate, in kilowatts; The ramp-up rate limit for the generator unit is given in kilowatts; the centralized battery energy storage system is described using a state-of-charge (SOC) model, and the net storage power meets the following requirements. The recursive equation for the state of charge of the energy storage system is as follows: and satisfy ,in, Let be the actual discharge power of the energy storage system during time period t, expressed in kilowatts, and satisfying the following conditions: ; Let be the actual charging power of the energy storage system during time period t, expressed in kilowatts, and satisfying the following conditions: ; State of charge of the centralized battery energy storage system at time t, expressed as a percentage; The state of charge of the centralized battery energy storage system at time t+1 is expressed as a percentage. The charging efficiency of the energy storage system is a dimensionless quantity. Δt represents the discharge efficiency of the energy storage system, which is a dimensionless quantity; Δt represents the duration of a single scheduling period, in hours. This refers to the rated capacity of the energy storage system, expressed in kilowatt-hours. The minimum state of charge allowed for a centralized battery energy storage system, expressed as a percentage; The maximum permissible state of charge for a centralized battery energy storage system is expressed as a percentage; the aggregated electric vehicle fleet is equivalent to a generalized battery, with a net injected power of [missing information]. Its recursive equation for the state of charge is: At the moment the vehicle leaves Prior to this, the minimum travel demand constraint must be met. ,in, To aggregate the actual discharge power of the electric vehicle fleet during time period t, in kilowatts, and satisfying the following conditions: ; The actual charging power of the electric vehicle fleet during time period t is aggregated, expressed in kilowatts, and satisfies the following conditions: ; The state of charge of the electric vehicle fleet at time t is expressed as a percentage. The state of charge of the electric vehicle fleet at time t+1 is expressed as a percentage. The charging efficiency of the fleet is a dimensionless quantity. Let be the discharge efficiency of the vehicle fleet, and be a dimensionless quantity. This represents the equivalent total available capacity of the fleet, in kilowatt-hours. The preset departure time of the convoy from the microgrid is a dimensionless quantity. The minimum state of charge threshold required to meet fleet travel planning needs is expressed as a percentage.

[0024] In one specific implementation scenario, the system does not exchange power with the upper-level main grid. Various internal energy units operate collaboratively under a unified scheduling framework to ensure supply and demand balance and operational safety in each time period. The output range and timing adjustment speed of conventional units are controlled within the allowable range to avoid affecting the safe operation and service life of equipment due to excessively rapid power changes in adjacent time periods. The charging and discharging behavior of the energy storage system and its energy change process are uniformly incorporated into the timing scheduling framework. The aggregated electric vehicle fleet, as a flexible resource with both energy storage and load characteristics, is uniformly incorporated into the optimized scheduling.

[0025] S3. Construct a dimensionless, weighted composite scheduling objective function that integrates multiple operational indicators, including the calculation of cost and carbon emission indicators of the objective function, the calculation of peak-valley difference and smoothness indicators of the objective function, and the dimensionless, weighted composite scheduling objective function.

[0026] Specifically, the mapped operating cost and mapped carbon emissions are indicators obtained by compressing the original operating cost and original carbon emissions to a preset dimensionless range using a nonlinear saturated mapping function; the normalized net load peak-valley difference is an indicator obtained by calculating the original net load peak-valley difference based on the equivalent net load considering interruptible load and V2G charging and discharging, and then compressing it to a preset dimensionless range using a nonlinear saturated mapping function; the mapped unit output smoothness is an indicator obtained by calculating the original unit output smoothness based on the combined output of micro gas turbines and fuel cells, and then compressing it to a preset dimensionless range using a nonlinear saturated mapping function; and the dimensionless weighted composite scheduling objective function is the final optimization objective function formed by adding the above four types of indicators with equal weights and superimposing constraint penalties.

[0027] Specifically, the mapping operating cost and mapping carbon emissions are compressed to the (0, 1) interval using a nonlinear saturated mapping function, and the mapping formula is as follows: ,in, This represents the dimensionless operating cost or carbon emission index after mapping, and is a dimensionless quantity. This represents the corresponding original operating cost or original total carbon emissions, where the original operating cost is in yuan and the original total carbon emissions are in kilograms; α is the corresponding scale parameter, which is dimensionless; the equivalent net load taking into account interruptible load and V2G charging and discharging is defined as... The original net load peak-to-valley difference index is calculated as follows: ,in, This is the original net load peak-to-valley difference index, in kilowatts; The equivalent net load after the interaction between interruptible load and V2G is taken into account for time period t, and the unit is kilowatt; The maximum equivalent net load over the entire scheduling cycle, expressed in kilowatts; The minimum equivalent net load over the entire scheduling cycle, expressed in kilowatts; based on the combined output of a microturbine and a fuel cell. The original unit output smoothness index was calculated as follows: ,in, This is the original unit output smoothness index, in kilowatts; This represents the combined total output power of conventional generating units during time period t, expressed in kilowatts. This represents the combined total output power of conventional generating units during the t-1 time period, expressed in kilowatts. This represents the total number of time periods in a typical daily schedule, with a value of 24, and is a dimensionless quantity. To avoid positive numbers with a denominator of zero, the unit is kilowatt; the original net load peak-valley difference index... and the original unit output smoothness index The normalized net load peak-to-valley difference index was obtained by compressing the load to the (0, 1) interval using a nonlinear saturation mapping function. And the output smoothness index of the mapping unit Its mapping formula is as well as ,in, The normalized net load peak-to-valley difference index is a dimensionless quantity. The output smoothness index of the mapping unit is a dimensionless quantity. The scale ratio parameter corresponding to the peak-valley difference index is a dimensionless quantity. The scale ratio parameter corresponding to the smoothness index is a dimensionless quantity; the dimensionless, weighted composite scheduling objective function is defined as follows: ,in, The objective function value is a dimensionless composite scheduling function with equal weights, and is a dimensionless quantity. To map the operating cost indicator, it is a dimensionless quantity; To map carbon emission indicators, which are dimensionless quantities; after comprehensively considering the penalties for default, the final optimization objective function is expressed as follows: ,in, To optimize the objective function value, it is a dimensionless quantity; Let be the total penalty function value of an individual, and be a dimensionless quantity.

[0028] In a specific implementation scenario, by uniformly mapping the original indicators to the same order of magnitude, a basis is provided for the subsequent construction of a composite objective function with equal weights. The smaller the net load peak-valley difference index, the smoother the net load fluctuation and the better the peak shaving and valley filling effect of the system. The smaller the unit output smoothness index, the smoother the combined output curve of the micro gas turbine and fuel cell, which is conducive to reducing the ramp-up pressure of the unit. Thus, economy, low carbon emissions, peak shaving and valley filling capabilities and unit smooth operation requirements can be uniformly incorporated into a single optimization objective.

[0029] S4. The dimensionless, weighted composite scheduling objective function is iteratively solved using the perceptually constrained hybrid moss growth optimization algorithm to output the optimal day-ahead scheduling scheme for the microgrid.

[0030] The aforementioned perceptually constrained hybrid moss growth optimization algorithm is an algorithm that performs wind direction determination, spore diffusion search, dual propagation search, adaptive differential evolution enhancement, cryptic mechanism, local search of elite individual particle swarms, and hierarchical constraint processing within the moss growth optimization framework. The output microgrid optimal day-ahead scheduling scheme includes the output power sequence of micro gas turbines, the output power sequence of fuel cells, the charging and discharging power sequence of centralized battery energy storage systems, the charging and discharging power sequence of aggregated electric vehicle fleets, and the interruptible load reduction power sequence for each time period.

[0031] Specifically, such as Figure 3 (A schematic diagram of the perceptual constraint-based hybrid moss growth optimization algorithm is shown.) The wind direction determination determines the population evolution direction based on the positional relationship between the current best moss individual and the dominant moss individual in the population, thus obtaining the wind direction vector. The spore diffusion search was used to simulate the diffusion process of moss spores under steady and turbulent wind conditions, and combined with the split-axis weight vector. Generate candidate solutions whose updates satisfy: when hour, ;when hour, ,in, Candidate individual location vectors generated for spore dispersal search; For the first The generation Individual moss location vectors; Let be the diffusion step size under steady wind conditions, and be a dimensionless quantity. Let be the diffusion step size under turbulent wind conditions, and be a dimensionless quantity. The axis-weighted vector representing the magnitude of the adjustment inertia of each physical device in the microgrid is a dimensionless quantity. is the wind direction vector, which is a dimensionless quantity; is a random number distributed in the interval [0, 1], and is a dimensionless quantity; The preset wind condition threshold is a dimensionless quantity; the dual propagation search is used to simulate the sexual and vegetative reproduction of mosses, allowing candidate individuals to locally develop near the current optimal moss individual, and its update satisfies: when hour, ;when hour, ,in, The first generation of double propagation search Position vectors of candidate individuals; For the first The current optimal moss individual position vector; For individual activators; Index of the decision variable dimension in the individual location vector; The first of the candidate individual position vectors generated by the dual propagation search Dimensional components; The first position vector of the current optimal moss individual Dimensional components; This is the local propagation step size; The first wind direction vector Dimensional components; For distribution in Random numbers within a range; A preset threshold for determining the reproductive method is used; based on the dual propagation search, a SHADE-type current-to-pbest / 1 mutation strategy based on historical success parameters is adopted to further enhance candidate individuals, whose mutation vectors satisfy... ,in, For the vector of mutated individuals; It is one of the preferred individual position vectors in the current population with a predetermined proportion of individuals ranked high in fitness; is the adaptive variation factor, and is a dimensionless quantity; This is the location vector of a moss individual taken from the current population; The vector represents the location of a moss individual taken from the current population or an external archive, and is distinct from the current individual; the final trial individual vector generated after crossover is... satisfy ,in, The first element of the final trial individual vector generated after the crossover. Dimensional components; The first individual vector of the variant Dimensional components; The first of the candidate individual position vectors generated by the dual propagation search Dimensional components; For each dimension Independently generated Uniformly distributed random numbers within an interval; A randomly selected dimension index is used to ensure at least one dimension mutation occurs; crossover probability. The system adaptively updates based on the set of historical successful parameters; a hidden mechanism is used to record multiple historical positions of the same moss individual during continuous iterations, and when a preset number of records is reached or a preset judgment condition is met, the current position is replaced by the historical best record of that moss individual. When the global optimal fitness is continuously... The improvement within a generation is less than When the population is deemed to have entered a stagnant state, a predetermined proportion of low-vitality moss individuals after the fitness ranking are restarted, provided that the restarted individuals meet the following criteria. ,in, This is the new individual position vector obtained after restarting; The individual position vector is generated based on the opposition learning mechanism; This is an individual position vector generated based on the Latin hypercube sampling mechanism; This represents the current optimal moss individual position vector; These are the proportional weight coefficients of each term in the restart formula, satisfying... and ; The threshold for determining a stalled continuous iterative algebraic step is a dimensionless quantity. The fitness improvement threshold is a dimensionless quantity; every preset generation, a particle swarm local search is performed on the top predetermined proportion of elite moss individuals in the current population, and its update formula satisfies: ,in, This is the updated local search velocity vector; This is the local search velocity vector before the update; The inertial weight for local search is a dimensionless quantity. and The learning factor is a dimensionless quantity. and It is a random vector whose elements are distributed in the interval [0, 1], and it is a dimensionless quantity; For the first The historical best position vector of each individual is the historical best position vector of the i-th individual; This represents the current globally optimal moss individual position vector; For the position vector of the next generation of elite individuals, For the first The generation Individual moss location vectors Layered constraint processing includes composite penalty functions, specifically satisfying... ,in, Let be the total penalty function value for an individual, and be a dimensionless quantity; where, Let be the location vector of any individual moss to be assessed for constraints or repaired, i.e., the microgrid dispatch decision variable vector; and These are the index numbers for inequality constraints and equality constraints, respectively. For the first microgrid system Inequality constraint functions; For the first microgrid system One equality constraint function; and The first The inequality constraints and the first Excess penalty squared term for each equality constraint; For the individual's total penalty function value; and These are the corresponding penalty weight coefficients; the conventional unit slope limiting smoothing repair satisfies: ,in, This is the power sequence vector after slope smoothing repair, in kilowatts; This is the original power sequence vector before repair, in kilowatts; This is the corresponding moving average power sequence vector, in kilowatts; For smoothing weights, which are dimensionless quantities; the state restoration of the aggregated energy storage system and electric vehicle includes smoothing adjustment and zero-mean correction of the aggregated power sequence to suppress continuous unilateral charging or discharging and meet the corresponding state of charge constraints.

[0032] In one specific implementation scenario, differentiated searches are conducted based on the different adjustment inertia of conventional generating units, energy storage systems, electric vehicle aggregates, and interruptible loads. The constraint violation degree of candidate solutions is converted into a unified penalty value through a composite penalty function and superimposed into the final optimization objective. By using slope-limiting smoothing repair, the abrupt changes between adjacent time periods are weakened while maintaining the overall trend of the power sequence, making it easier for micro gas turbines and fuel cells to meet the ramping constraints. By combining penalty function guidance with structured repair, the ability to generate feasible solutions is improved and the engineering feasibility of candidate scheduling trajectories is enhanced. The output optimal day-ahead scheduling scheme achieves integrated collaborative scheduling of islanded multi-energy microgrids with V2G under the premise of satisfying system power balance, conventional generating unit operation constraints, energy storage and electric vehicle state of charge constraints, and minimum travel demand constraints.

[0033] Furthermore, S1 specifically includes: in islanded operation mode, the microgrid does not exchange power with the upper-level main grid, and relies on local conventional units and energy storage devices to maintain power balance; the typical daily scheduling cycle is divided into 24 equal-length time periods, and the acquired forecast data includes the forecast values ​​of photovoltaic generator output power, wind turbine generator output power, and park base load forecast data for each time period; the aggregated electric vehicle fleet connects to the microgrid through charging stations and aggregators, and participates in charging and discharging during vehicle parking to achieve V2G interaction.

[0034] The islanded operation mode is an operation mode that does not exchange power with the upper-level main grid; the typical daily forecast data is time-series forecast information covering photovoltaic, wind power and load; the V2G interaction is the aggregation of charging and discharging behavior of electric vehicle fleets participating in unified scheduling through aggregators during parking.

[0035] Specifically, since there is no real-time support from the upper-level power grid, various energy units within the microgrid need to operate collaboratively under a unified scheduling framework to ensure supply and demand balance and operational safety at different times.

[0036] In a specific implementation scenario, the microgrid system structure is established according to the islanded operation scenario, the typical daily scheduling cycle and the forecast information for each time period are determined, and the aggregated electric vehicle fleet is connected to the microgrid to form V2G resources that can participate in scheduling, so as to provide an input basis for the subsequent operation model establishment and comprehensive scheduling solution.

[0037] Furthermore, the active power balance constraint model of the system in S2 satisfies the following: active power balance constraints for each time period of the system, wherein the sum of the predicted output power of photovoltaic and wind power, the output active power of micro gas turbine and fuel cell, the net active power injected into the AC bus by the centralized battery energy storage system, the net active power injected into the AC bus by the aggregated electric vehicle fleet, and the predicted value of the base load and the power reduction of the interruptible load is zero.

[0038] The active power balance constraint is an equation constraint that ensures the balance between power generation and consumption in the microgrid at different times.

[0039] Specifically, the output of renewable energy, the output of conventional units, the interaction power of energy storage and electric vehicles, and the load reduction are all constrained in the power balance equation.

[0040] In one specific implementation scenario, the microgrid relies on local equipment to maintain power balance, and all power variables participate in the supply and demand balance calculations for each time period.

[0041] Furthermore, the operating constraint model of the micro gas turbine and fuel cell in S2 satisfies the following constraints: upper and lower limits of output of the micro gas turbine and fuel cell and ramp rate constraint.

[0042] The upper and lower limits of output power are constraints that limit the extreme values ​​of the unit's output power; the ramp rate constraint is a constraint that limits the range of power variation between adjacent time periods.

[0043] Specifically, by setting upper and lower limits and ramp rate limits, the output range and timing adjustment speed of conventional units are controlled within the allowable range.

[0044] In a specific implementation scenario, it is necessary to avoid affecting the safe operation and service life of equipment due to rapid power changes in adjacent time periods.

[0045] Furthermore, the operational mathematical model of the centralized battery energy storage system in S2 satisfies the following constraints: the state of charge update constraint and the upper and lower limits of the charging and discharging power constraint of the centralized battery energy storage system.

[0046] The state of charge update constraint describes the recursive relationship between the energy of the energy storage system and the charge / discharge power; the upper and lower limits of the charge / discharge power constraint are constraints that limit the extreme values ​​of the charge / discharge power of the energy storage system.

[0047] Specifically, the net energy storage power meets The state of charge is updated according to the recursive equation and is restricted to the minimum and maximum allowable states of charge.

[0048] In a specific implementation scenario, the above model integrates the charging and discharging behavior of the energy storage system and its energy change process into a unified time-series scheduling framework.

[0049] Furthermore, the response model of the aggregated electric vehicle fleet in S2 satisfies: a recursive constraint on the state of charge of the aggregated electric vehicle fleet as equivalent to a generalized battery, and a minimum travel demand constraint satisfied at the moment the vehicle leaves.

[0050] The recursive state of charge constraint describes the recursive relationship between the energy of the aggregated electric vehicle fleet and the charging and discharging power; the minimum travel demand constraint is the bottom-line constraint to ensure that the state of charge of the vehicle meets the travel requirements when it leaves.

[0051] Specifically, the net power injection of the fleet is The state of charge is updated according to the recursive equation and satisfies the condition of being greater than or equal to the minimum state of charge threshold before departure.

[0052] In one specific implementation scenario, the aggregated electric vehicle fleet is incorporated into the optimized scheduling as a flexible resource that simultaneously possesses energy storage and load characteristics.

[0053] Furthermore, the calculation of the cost and carbon emission indicators of the objective function in S3 satisfies the following: the original operating cost and original carbon emission are compressed to a preset dimensionless range using a nonlinear saturated mapping function to obtain the mapped operating cost indicator and the mapped carbon emission indicator.

[0054] Wherein, the nonlinear saturation mapping function is of the form of The function is used to eliminate dimensional differences and avoid the dominance of extreme values.

[0055] Specifically, the original operating costs consist of the operating costs of conventional units, energy storage loss costs, electric vehicle compensation costs, and interruptible load compensation costs; the original carbon emissions consist of the total carbon emissions from micro gas turbines and fuel cells.

[0056] In a specific implementation scenario, the original indices corresponding to different physical quantities are uniformly mapped to the same order of magnitude using a mapping formula, providing a foundation for the subsequent construction of a composite objective function with equal weights.

[0057] Furthermore, the calculation of the peak-valley difference and smoothness index of the objective function in S3 satisfies the following: the original net load peak-valley difference index is calculated based on the equivalent net load taking into account interruptible load and V2G charging and discharging; the original unit output smoothness index is calculated based on the combined output of the micro gas turbine and fuel cell; and the normalized net load peak-valley difference index and the mapped unit output smoothness index are obtained by compressing them to a preset dimensionless interval using a nonlinear saturated mapping function.

[0058] The equivalent net load is the load after deducting the output of renewable energy and the power of flexible resource interaction; the original net load peak-valley difference index is the difference between the maximum and minimum values ​​of the equivalent net load within the dispatch cycle; and the original unit output smoothness index is an index that measures the degree of fluctuation in the combined output of conventional units in adjacent time periods.

[0059] Specifically, adopt To calculate the peak-to-valley difference, the following method is used: Calculate the smoothness and then perform nonlinear mapping separately.

[0060] In a specific implementation scenario, the smaller the net load peak-valley difference index, the better the system's peak shaving and valley filling effect, and the smaller the unit output smoothness index, the better it is to reduce the unit's ramp-up pressure.

[0061] Furthermore, the dimensionless, weighted composite scheduling objective function in S3 satisfies the following: the mapped operating cost index, the mapped carbon emission index, the normalized net load peak-valley difference index, and the mapped unit output smoothness index are added with equal weights, and a penalty for breach of contract is superimposed to form the final optimization objective function.

[0062] The equal-weighted addition involves linearly accumulating the four dimensionless indices by assigning them the same coefficient; the constraint violation penalty involves converting the degree of constraint violation of the candidate solution into a penalty value and adding it to the objective function.

[0063] Specifically, the composite objective function is: The final optimization objective function is: .

[0064] In a specific implementation scenario, economic efficiency, low carbon emissions, peak shaving and valley filling capabilities, and smooth unit operation requirements are all incorporated into a single optimization objective.

[0065] Furthermore, the hybrid moss growth optimization algorithm with perceptual constraints in S4 satisfies the following: wind direction determination, spore diffusion search, dual propagation search, adaptive differential evolution enhancement, cryptic mechanism, local search of elite individual particle swarms, and hierarchical constraint processing are performed under the moss growth optimization framework. The hierarchical constraint processing includes composite penalty function, state repair of centralized energy storage system and electric vehicle aggregate, and slope limit smoothing repair of conventional unit.

[0066] The wind direction determination is used to determine the direction of population evolution; the spore diffusion search is used to generate candidate solutions by combining the axis weight vector; and the hierarchical constraint processing is used to improve the ability to generate feasible solutions by combining penalty functions with structural repair.

[0067] Specifically, wind direction determination yields the wind direction vector. The spore diffusion search determines the threshold based on wind conditions and updates individuals using a different step-length formula. The dual propagation search determines the threshold based on the reproduction method and updates candidate individuals. The cryptic mechanism restarts when stagnation conditions are met. Particle swarm local search is used for refining elite individuals. The composite penalty function is implemented through... Calculations show that slope smoothing repair is achieved through... The execution and state repair processes perform smoothing adjustments and zero-mean correction on the aggregate power sequence.

[0068] In one specific implementation scenario, differentiated searches are conducted based on the adjustment inertia of different devices. By using a repair mechanism to weaken power mutations and suppress continuous unilateral charging and discharging, the engineering feasibility of candidate scheduling trajectories is improved.

[0069] To verify the effectiveness of the method of the present invention, this embodiment further sets up a basic scenario involving electric vehicles, a high-load scenario, a high-renewable energy scenario, and a low-renewable energy scenario, and selects MGO, GWO, tGSSA, and PSO as comparison algorithms, using operating cost, carbon emissions, smoothness, peak-valley difference, and comprehensive score as evaluation indicators for comparison. The comprehensive performance comparison results of different algorithms in the electric vehicle scenario are shown in Table 1.

[0070] Table 1. Comparison of overall performance of different algorithms in scenarios involving electric vehicles.

[0071] As shown in Table 1, the method of the present invention outperforms the comparative algorithm in terms of operating cost, carbon emissions, smoothness and peak-valley difference, and has the best overall performance.

[0072] To further verify the applicability of the method of the present invention under different operating conditions, tests were conducted in basic scenarios, high-load scenarios, high-renewable-energy scenarios, and low-renewable-energy scenarios. The comprehensive scores and rankings of the method of the present invention under different typical operating scenarios are shown in Table 2.

[0073] Table 2. Overall scores and rankings under different typical operating scenarios

[0074] As shown in Table 2, the method of the present invention maintains excellent overall performance in multiple typical operating scenarios, indicating that it has good adaptability to changes in load level and fluctuations in renewable energy output.

[0075] To further illustrate the feasibility of the method of the present invention under complex constraints, the supply and demand balance error and the abandonment ratio under conditions with and without electric vehicles were compared, and the results are shown in Table 3.

[0076] Table 3 Comparison of constraint satisfaction and supply-demand balance results under conditions including electric vehicles and those not including electric vehicles.

[0077] As shown in Table 3, after introducing electric vehicles into the scheduling, the method of the present invention exhibits a lower load abandonment ratio in multiple typical scenarios, and the supply and demand balance error is significantly reduced in high-load scenarios. This indicates that the participation of electric vehicles as a flexible resource in scheduling helps to improve the system's adjustment capability and operational feasibility.

[0078] After the above steps, an optimal day-ahead scheduling scheme can be output. This optimal day-ahead scheduling scheme includes at least the output power sequences of micro gas turbines, fuel cells, centralized battery energy storage systems, aggregated electric vehicle fleets, and interruptible load reduction sequences for each time period, where each power sequence is in kilowatts. This allows for the comprehensive coordinated scheduling of isolated multi-energy microgrids, including V2G, while meeting system power balance, conventional unit operation constraints, energy storage and electric vehicle state-of-charge constraints, and minimum travel demand constraints.

[0079] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for integrated coordinated dispatching of islanded multi-energy microgrids including V2G, characterized in that, include: S1. Construct an islanded multi-energy microgrid architecture with V2G interaction to obtain typical daily scheduling cycles and typical daily forecast data of aggregated electric vehicle fleets; S2. Establish the operation mathematical model of each control device in the islanded multi-energy microgrid and the active power balance constraint model of the system. The operation mathematical model includes the operation constraint model of micro gas turbine and fuel cell, the operation mathematical model of centralized battery energy storage system and the response model of aggregated electric vehicle fleet. S3. Construct a dimensionless, weighted composite scheduling objective function that integrates multiple operational indicators, wherein the multiple operational indicators include the calculation of cost and carbon emission indicators of the objective function, the calculation of peak-valley difference and smoothness indicators of the objective function, and the dimensionless, weighted composite scheduling objective function. S4. The dimensionless, weighted composite scheduling objective function is iteratively solved using the perceptually constrained hybrid moss growth optimization algorithm to output the optimal day-ahead scheduling scheme for the microgrid.

2. The integrated coordinated dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, S1 specifically includes: In islanded operation mode, the microgrid does not exchange power with the upper-level main grid and relies on local conventional units and energy storage devices to maintain power balance; The typical daily scheduling cycle is divided into 24 equal-length time periods, and the obtained forecast data includes the forecast values ​​of photovoltaic generator output power, wind turbine output power, and park basic load for each time period. The aggregated electric vehicle fleet connects to the microgrid through charging stations and aggregators, and participates in charging and discharging while the vehicles are parked to achieve V2G interaction.

3. The integrated coordinated dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The active power balance constraint model of the system in S2 satisfies the following: active power balance constraints for each time period of the system, wherein the predicted output power of photovoltaic and wind power, the output active power of micro gas turbine and fuel cell, the net active power injected into the AC bus by the centralized battery energy storage system, the net active power injected into the AC bus by the aggregated electric vehicle fleet, and the sum of the predicted base load and the power reduction of interruptible load are zero.

4. The integrated collaborative dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The operational constraint model for the micro gas turbine and fuel cell in S2 satisfies the following constraints: upper and lower limits of output of the micro gas turbine and fuel cell, and ramp rate constraint.

5. The integrated coordinated dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The mathematical model for the operation of the centralized battery energy storage system in S2 satisfies the following constraints: state of charge update constraints and upper and lower limits of charge and discharge power constraints for the centralized battery energy storage system.

6. The integrated collaborative scheduling method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The response model of the aggregated electric vehicle fleet in S2 satisfies: the recursive constraint of the state of charge of the aggregated electric vehicle fleet as equivalent to a generalized battery, and the minimum travel demand constraint satisfied at the moment the vehicle leaves.

7. The integrated coordinated dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The calculation of cost and carbon emission indicators of the objective function in S3 satisfies the following: the original operating cost and original carbon emission are compressed to a preset dimensionless range by using a nonlinear saturated mapping function to obtain the mapped operating cost indicator and the mapped carbon emission indicator.

8. The integrated coordinated dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The calculation of the peak-valley difference and smoothness index of the objective function in S3 satisfies the following: the original net load peak-valley difference index is calculated based on the equivalent net load taking into account interruptible load and V2G charging and discharging; the original unit output smoothness index is calculated based on the combined output of micro gas turbine and fuel cell; and the normalized net load peak-valley difference index and the mapped unit output smoothness index are obtained by compressing them to a preset dimensionless interval using a nonlinear saturated mapping function.

9. The integrated coordinated dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The dimensionless, weighted composite scheduling objective function in S3 satisfies the following: the mapped operating cost index, the mapped carbon emission index, the normalized net load peak-valley difference index, and the mapped unit output smoothness index are added with equal weights, and a penalty for default is superimposed to form the final optimization objective function.

10. The integrated coordinated dispatch method for islanded multi-energy microgrids with V2G as described in claim 1, characterized in that, The hybrid moss growth optimization algorithm with perception constraints in S4 satisfies the following: wind direction determination, spore diffusion search, dual propagation search, adaptive differential evolution enhancement, cryptic mechanism, local search of elite individual particle swarms, and hierarchical constraint processing are performed under the moss growth optimization framework. The hierarchical constraint processing includes composite penalty function, state repair of centralized energy storage system and electric vehicle aggregate, and slope limit smoothing repair of conventional unit.

Images