Ladder optimization scheduling method for grid-connected power of wind and light storage micro-grid

Abstract

The invention discloses a step optimization scheduling method for grid-connected power of a wind and light storage micro-grid, and the method comprises the steps: S1, obtaining typical day wind and light data in the wind and light storage micro-grid, calculating the wind and light output, and calculating the net load of a system; s2, on the premise that the net load of the system is met, constructing a capacity configuration optimization model for calculating stepped grid-connected power; s3, carrying out optimization solution on the capacity configuration optimization model, and further determining stepped grid-connected power; and S4, calculating a difference value between the stepped grid-connected power and the real-time grid-connected power, calling the hybrid energy storage system according to the difference value to compensate for output fluctuation of wind and light power generation, further updating the operation state of the energy storage system, and outputting a stepped grid-connected result. Through the method, the deterministic grid-connected power can be transmitted to the power grid, and the system cost can be increased under the condition that the wind and light abandoning rate and the load power shortage rate of the system are basically unchanged, or the load power shortage rate can be effectively reduced under the condition that the system cost is basically unchanged, so that the power supply reliability of the system is improved.

Description

Technical Field

[0001] This method belongs to the technical field of wind-solar grid connection, and specifically relates to a stepped optimization scheduling method for the grid-connected power of a wind-solar-storage microgrid. Background Art

[0002] At the current technical level, it cannot be effectively cured through methods such as accurate prediction and optimized scheduling of wind-solar power output.

[0003] The typical research methods for suppressing the random volatility of wind-solar mainly include the following categories: accurately predicting wind-solar power through meta-heuristic algorithms such as deep learning; suppressing wind-solar fluctuations with the assistance of various energy storage devices; comprehensively considering the spatio-temporal complementarity of multiple energy sources such as wind-solar to reduce wind-solar volatility.

[0004] Although the above typical research methods can reduce the volatility of the grid-connected power of wind-solar to a certain extent, they cannot fundamentally solve the problem of the volatility of the grid-connected output power of wind-solar. The fundamental reason is that under the premise of "connecting as much as possible and connecting early if possible", the main purpose of the current research methods is to meet the electricity demand of local loads or plant loads, and the grid-connected power of the wind-solar microgrid is not included in the whole process of optimization, resulting in a large volatility in the grid-connected power of the wind-solar microgrid, which not only affects the enthusiasm of the power grid to preferentially dispatch the output of the wind-solar microgrid, deteriorates the power supply stability of the power grid, but also causes a large amount of wind and light abandonment phenomena. Summary of the Invention

[0005] Aiming at the above deficiencies in the prior art, the stepped optimization scheduling method for the grid-connected power of a wind-solar-storage microgrid provided by the present invention solves the problem of the negative impact on power grid scheduling caused by the power fluctuation of renewable energy during grid connection.

[0006] In order to achieve the above invention purpose, the technical solution adopted by the present invention is: a stepped optimization scheduling method for the grid-connected power of a wind-solar-storage microgrid, including the following steps:

[0007] S1. Obtain the typical daily wind-solar data in the wind-solar-storage microgrid, calculate the wind-solar power output, and then calculate the system net load;

[0008] S2. On the premise of meeting the system net load, construct a capacity configuration optimization model for calculating the stepped grid-connected power;

[0009] The capacity configuration optimization model is a single-objective function with the lowest system cost or a multi-objective function with the lowest system cost and load power outage rate;

[0010] S3. Optimize and solve the capacity configuration optimization model, and then determine the stepped grid-connected power;

[0011] S4. Calculate the difference between the stepped grid-connected power and the real-time grid-connected power, and call the hybrid energy storage system according to the difference to compensate for the output fluctuations of wind and light power generation, thereby updating the operating state of the energy storage system and outputting the stepped grid-connected result.

[0012] Further, in the step S1, the wind turbine model for calculating the wind turbine output is expressed as:

[0013]

[0014] In the formula, P w (t) represents the wind power output at time t, P nwt represents the rated power of the wind turbine, v in represents the cut-in wind speed, v n represents the rated wind speed, v out represents the cut-out wind speed, and v(t) represents the wind speed;

[0015] Under standard test conditions, the output power of the photovoltaic array is:

[0016]

[0017] In the formula, P S (t) represents the photovoltaic output at time t, P npv represents the rated power of the photovoltaic array under standard test conditions, I c (t) and T c (t) respectively represent the irradiance and the photovoltaic cell temperature at time t, I N and T N respectively represent the irradiance and the ambient temperature under standard test conditions, and λ represents the power temperature coefficient of the photovoltaic cell;

[0018] The system net load is expressed as:

[0019] Pjload(t) = P load (t) - P w (t) * E wt - P s (t) * E pv

[0020] In the formula, Pjload(t) is the system net load at time t; P load (t) is the load output power at time t; E wt is the installed capacity of the wind turbine; E pv is the installed capacity of the photovoltaic.

[0021] Further, in step S2, according to the system net load, when the stepped grid-connected power at different stepped durations is output by changing the unit capacity, the load power shortage rate and the wind and light curtailment rates remain unchanged, thereby realizing stepped grid connection; the capacity configuration optimization model is a target function with the lowest system cost.

[0022] According to the system net load, when the number of units is guaranteed to be the optimal capacity unit configuration, the system load power shortage rate and the wind and light curtailment rates change with the stepped duration, thereby realizing stepped grid connection; the capacity configuration optimization model is a multi-objective function with the lowest system cost and load power shortage rate.

[0023] Further, the capacity configuration optimization model of the single-objective function is expressed as:

[0024] minf 1 =C 1 +C 2 +C 3 +C 4

[0025] The capacity configuration optimization model of the multi-objective function is expressed as:

[0026] minf 1 =C 1 +C 2 +C 3 +C 4

[0027]

[0028] In the formula, f 1 represents the annual comprehensive cost of the system, C 1 represents the system investment cost, C 2 represents the system operation and maintenance cost, C 3 represents the remaining fluctuation penalty cost, C 4 represents the load loss compensation cost, f lp represents the load power shortage rate, P load (t) represents the load output power at time t, P gs (t) represents the power that the system can supply at time t, and T represents the time length within a cycle.

[0029] Further, the system investment cost includes the wind power cost C 1_wt , the photovoltaic cost C 1_pv , the power energy storage cost C 1_H and the capacity energy storage cost C 1_se , which are respectively expressed as:

[0030]

[0031] Wherein, P wt (t) and P pv (t) respectively represent the wind power and photovoltaic power outputs, k wt.2 , k pt.2 , k H.2 , k se.2 are the operation and maintenance costs per unit capacity of wind power, photovoltaic, liquid hydrogen, and superconductivity. P H (t) represents the output power of the liquid hydrogen energy storage at time t, and P se (t) represents the output power of the superconducting energy storage at time t. n represents the number of time nodes in a cycle;

[0032] The remaining fluctuation penalty cost C 3 is expressed as:

[0033]

[0034] Wherein, represents the wind curtailment penalty coefficient, represents the photovoltaic curtailment penalty coefficient, ρ wt , ρ pv respectively represent and the corresponding wind curtailment and photovoltaic curtailment penalty coefficients. P curt wind (t), P curt pv (t) respectively represent and the wind curtailment and photovoltaic curtailment powers in the corresponding time period t. Δt represents the time corresponding to the wind curtailment and photovoltaic curtailment powers;

[0035] The load shedding compensation cost is expressed as:

[0036]

[0037] Wherein, ρ loss represents the load shedding penalty coefficient; P curt loss represents the electricity quantity purchased from the power grid in time period t.

[0038] Further, the constraint conditions of the capacity configuration optimization model include the wind-solar-storage installed capacity constraint, the power balance constraint, the energy storage capacity constraint, and the stepped power constraint of the wind-solar output, which are respectively expressed as:

[0039]

[0040] Wherein, E w tmin , E pv min , E H min , E semin Respectively represent the minimum installed capacity of wind power, photovoltaic, liquid hydrogen and superconducting; E wt max 、E pv max 、E H max 、E se max is the maximum installed capacity of each unit, E wt 、E pv 、E H 、E se Respectively represent the minimum installed capacity of wind power, photovoltaic, liquid hydrogen and superconducting;

[0041]

[0042] Where P d Indicates load power, P load Indicates the grid-connected power, P loss Indicates the abandoned wind and solar power, P curt Indicates the load loss power, P wt , P pv , P H and P se They are respectively represented as wind turbine output power, photovoltaic output power, liquid hydrogen energy storage output power and superconducting energy storage output power;

[0043]

[0044] In the formula, S H min , S se min Respectively represent the minimum value of liquid hydrogen and superconducting energy storage capacity, S H max , S se max Respectively represent the maximum value of liquid hydrogen and superconducting energy storage capacity, S H (t) and S se (t) represent the amount of liquid hydrogen and superconducting energy storage respectively;

[0045]

[0046] Where P se Indicates the maximum power that the energy storage can output during rapid response. They represent the charging power and discharging power of the energy storage at time t, P pr (t) represents the output power of the energy storage with stepped grid-connected power.

[0047] Furthermore, in step S3, when a grid-connected method is adopted in which the capacity of the unit is changed to output a stepped grid-connected power with different step durations, a capacity configuration model of a single objective function is solved to output the stepped grid-connected power;

[0048] When the grid-connected method of changing the step duration to output the stepped grid-connected power under the optimal capacity unit configuration is adopted, the capacity configuration model of the multi-objective function is solved to obtain the optimal capacity unit configuration, and the stepped grid-connected power is output under the optimal capacity unit configuration.

[0049] Furthermore, the step S4 is specifically as follows:

[0050] S41, calculating the difference between the current stepped grid-connected power and the real-time grid-connected power;

[0051] S42, judging whether the difference is less than 0;

[0052] If yes, proceed to step S43;

[0053] If not, proceed to step S44;

[0054] S43, storing excess electric energy through an energy storage device, and determining whether the energy storage current exceeds the limit or does not meet the power constraint condition;

[0055] If so, the wind and solar power abandonment rates are calculated, and then electricity is abandoned;

[0056] If not, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding step-by-step grid connection results are output;

[0057] S44, using the excess grid-connected power to supplement the shortfall of the stepped grid-connected power, and determining whether the excess grid-connected power meets the valley filling requirement;

[0058] If so, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding step-by-step grid-connected results are output;

[0059] If not, call the energy storage device to supplement the stepped grid-connected power again, and determine whether the stepped grid-connected power requirements are met; if so, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding stepped grid-connected results are output; if not, return to step S3 and re-optimize and solve the capacity optimization configuration model.

[0060] Furthermore, in step S41, the difference between the stepped grid-connected power and the real-time grid-connected power is:

[0061] P pr (t) = P Ld (t)-P Net (t)

[0062]

[0063] P Net (t) = P w (t)+P s (t)-P load (t)

[0064] Where P Ld (t) represents the stepped grid-connected power output after adjustment by the energy storage system at time t, P Net (t) represents the real-time grid-connected power output by the system at time t, P w (t) represents the unit fan output power, P s (t) represents the unit photovoltaic output power, P load (t) represents the load power, and n represents the number of time intervals within the step time range.

[0065] The beneficial effects of the present invention are:

[0066] 1) The present invention proposes a new scheme for the grid-connected power of stepped wind, solar and storage systems. Through the coordinated optimization of the energy storage system, the system can output deterministic stepped power with different durations to the power grid on the premise of meeting the local load. The system can deliver high-quality, predictable and high-quality electric energy to the power grid through the coordination of energy storage units and the optimization calculation of the optimization algorithm on the premise of meeting the local load or plant load.

[0067] 2) The present invention is adapted to different application scenarios and proposes two different methods for outputting stepped grid-connected electric energy. Different methods for outputting stepped grid-connected power can be selected according to actual conditions;

[0068] 3) Compared with the traditional "surplus power grid-connected" model, the stepped grid-connected power proposed in the present invention can deliver deterministic grid-connected power to the power grid, and can increase the system cost while ensuring that the system's wind and solar power abandonment rate and load power shortage rate remain basically unchanged, or effectively reduce the load power shortage rate while ensuring that the system cost remains basically unchanged, thereby improving the power supply reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Flow chart of the step-by-step optimization scheduling method for wind, solar, and energy storage microgrid grid-connected power provided by the present invention.

[0070] Figure 2 This is a curve diagram of the SOC change of the system hybrid energy storage in scenario 1 provided by the present invention.

[0071] Figure 3 This is a diagram of the stepped grid-connected power output in scenario 2 provided by the present invention.

[0072] Figure 4 This is a distribution diagram of the Pareto non-inferior solution set provided by the present invention.

[0073] Figure 5 This is the energy storage output power diagram provided by the present invention.

[0074] Figure 6 This is the relationship coupling diagram of scenario 1 provided by the present invention.

[0075] Figure 7 This is the load power failure rate fitting curve provided by the present invention.

[0076] Figure 8 This is the wind and solar power abandonment rate fitting curve provided by the present invention. DETAILED DESCRIPTION

[0077] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0078] The present invention provides a step-by-step optimization scheduling method for wind, solar, and energy storage microgrid grid-connected power. Figure 1 As shown, the following steps are included:

[0079] S1. Obtain typical daily wind and solar data in the wind, solar and energy storage microgrid, calculate the wind and solar output, and then calculate the net load of the system;

[0080] S2. On the premise of meeting the net load of the system, a capacity configuration optimization model for calculating the step-by-step grid-connected power is constructed;

[0081] The capacity configuration optimization model is a single objective function with the lowest system cost or a multi-objective function with the lowest system cost and load power outage rate;

[0082] S3. Optimize and solve the capacity configuration optimization model, and then determine the step-by-step grid-connected power;

[0083] S4. Calculate the difference between the stepped grid-connected power and the real-time grid-connected power, and call the hybrid energy storage system based on the difference to compensate for the output fluctuation of wind and solar power generation, and then update the operating status of the energy storage system and output the stepped grid-connected result.

[0084] The present invention provides a solution to make the wind-solar grid-connected output have deterministic steps. According to the changing characteristics of wind-solar output, with the assistance of energy storage equipment, under the premise of meeting the local load, through optimized scheduling, hybrid energy storage is used to correct the wind-solar grid-connected power in real time, so that its output power presents a fixed step wave, thereby reducing the impact of wind-solar fluctuations on grid scheduling. When the system is connecting wind-solar power to the grid, it is forced to regulate the output power of wind-solar power generation, so that the wind-solar power station reports the grid-connected power generation plan to the dispatching department a certain time in advance after meeting the factory load (or local load), and promises that the power station can output a definite and stable grid-connected power similar to a "step wave" shape to the grid through the coordination and help of hybrid energy storage in a future period of time; at the same time, the power grid can use real-time electricity prices and penalty measures to encourage power stations to output electricity according to the prepared grid-connected power, thereby effectively solving the problem of wind-solar grid connection.

[0085] In the embodiment of the present invention, in order to solve the fluctuation problem caused by large-scale wind and solar grid connection to the power grid, a wind, solar and storage ladder translation system architecture is established, which is mainly composed of two parts: the power generation system and the hybrid energy storage system. The power generation system is composed of wind power units and photovoltaic units, and the hybrid energy storage system is composed of supercapacitors and batteries. In the system optimization process, the hybrid energy storage system has two functions: when the wind and solar output inside the microgrid is not enough to meet the load demand, the hybrid energy storage unit outputs electric energy to supply power to the load; when the system connects the grid-connected electric energy to the grid on a large scale, the hybrid energy storage unit exerts its flexible scheduling ability to connect the grid-connected power output to a stable ladder wave, thereby ensuring the stable operation of the power grid.

[0086] Based on this, in step S1 of this embodiment, the wind turbine generator model for calculating the wind turbine output is expressed as:

[0087]

[0088] Where P w (t) represents the wind power output power at time t, P nwt Indicates the rated power of the wind turbine, v in represents the cut-in wind speed, v n Indicates rated wind speed, v out represents the cut-out wind speed, v(t) represents the wind speed;

[0089] Under standard test conditions, the output power of the photovoltaic array is:

[0090]

[0091] Where P S (t) represents the photovoltaic output power at time t, P npv Indicates the rated power of the photovoltaic array under standard test conditions, I c (t) and Tc (t) represent the irradiance and photovoltaic cell temperature at time t, I N and T N They represent the irradiance and ambient temperature under standard test conditions, respectively, and λ represents the photovoltaic cell power temperature coefficient;

[0092] The net load of the system is expressed as:

[0093] Pjload(t)=P load (t)-P w (t)*E wt -P s (t)*E pv

[0094] Where Pjload(t) is the net load of the system at time t; P load (t) is the load output power at time t; E wt is the installed capacity of the wind turbine; E pv Photovoltaic installed capacity.

[0095] In step S21 of the embodiment of the present invention, in order to achieve the universality and adaptability of the grid-connected power laddering, two different laddering schemes are provided in this embodiment, and corresponding capacity configuration optimization models are constructed for them. The capacity configuration optimization model in this embodiment is a single objective function with the lowest system cost or a multi-objective function with the lowest system cost and load power shortage rate.

[0096] Specifically, according to the net load of the system, when the step-by-step grid-connected power with different step durations is output by changing the unit capacity, the load power shortage rate and the wind and solar power abandonment rate remain unchanged, thereby realizing the step-by-step grid-connected power. The capacity configuration optimization model is an objective function with the lowest system cost.

[0097] According to the net load of the system, when the configuration of ensuring the number of units to be the optimal capacity is adopted, the system load power shortage rate and the wind and solar power abandonment rate change with the change of the step duration, thereby realizing step-by-step grid connection; the capacity configuration optimization model is a multi-objective function with the lowest system cost and load power shortage rate.

[0098] In this embodiment of the present invention, the capacity configuration optimization model of wind and solar power output under different durations is constructed through the above two stepped schemes, and the coupling relationship between the step length and the system parameters is given to verify the stepped optimization effect, providing a basis for subsequent analysis.

[0099] Based on this, the capacity configuration optimization model of the single objective function in this embodiment is expressed as:

[0100] minf 1 =C 1 +C 2 +C3 +C 4

[0101] The capacity configuration optimization model of multi-objective function is expressed as:

[0102] minf 1 =C 1 +C 2 +C 3 +C 4

[0103]

[0104] In the formula, f 1 represents the annual comprehensive system cost, C 1 represents the system investment cost, C 2 represents the system operation and maintenance cost, C 3 represents the remaining volatility penalty cost, C 4 represents the loss of load compensation cost, f lp Indicates the load power failure rate, P load (t) represents the load output power at time t, P gs (t) represents the power that the system can supply at time t, and T represents the length of time in one cycle.

[0105] Among them, the system investment cost includes the wind power cost C 1_wt , Photovoltaic cost C 1_pv , power storage cost C 1_H and capacity storage cost C 1_se , which are respectively expressed as:

[0106]

[0107]

[0108] In the formula, P wt (t), P pv (t) represent wind power and photovoltaic power output, respectively, k wt.2 , k pt.2 , k H.2 , k se.2 is the unit capacity operation and maintenance cost of wind power, photovoltaic power, liquid hydrogen, and superconducting power, P H (t) represents the output power of liquid hydrogen energy storage at time t, P se (t) represents the output power of the superconducting energy storage at time t, and n represents the number of time nodes in a cycle;

[0109] In actual operation, when the fluctuation of wind and solar power is large, if hybrid energy storage cannot completely eliminate the fluctuation, in order to ensure grid stability, the system abandons wind and solar power. In order to reflect the additional costs caused by this, the residual penalty cost C is introduced. 3 It is expressed as:

[0110]

[0111] In the formula, represents the wind abandonment penalty coefficient, represents the light abandonment penalty coefficient, ρ wt , pv Respectively and Corresponding to the penalty coefficient for wind and solar power abandonment, P curt wind (t), P curt pv (t) respectively represent and The corresponding wind and solar power abandoned in period t, Δt represents the time corresponding to the abandoned wind and solar power;

[0112] Among them, the system's wind and solar curtailment rate represents the efficiency of renewable energy utilization in the system, that is, the proportion of wind energy and solar energy that the system fails to effectively utilize, and its expression is as follows:

[0113]

[0114] In the formula, the wind and solar power abandonment rate is an important indicator for evaluating the utilization efficiency of renewable energy. The lower the wind and solar power abandonment rate of the system, the higher the utilization efficiency of renewable energy.

[0115] In actual operation, due to the volatility of wind and solar, if the wind-solar-storage system cannot meet the local load demand, it is necessary to purchase electricity from the power grid. A load loss penalty cost corresponding to the power purchase cost is established to describe the additional cost required for power purchase, which is expressed as:

[0116]

[0117] In the formula, ρ loss represents the load loss penalty coefficient; P curt loss Represents the amount of electricity purchased from the grid during period t.

[0118] Load power failure rate f lp It reflects the system reliability. When the gap between system output and load demand is larger, the load power shortage rate is higher and the reliability is lower.

[0119] In an embodiment of the present invention, the constraints of the capacity configuration optimization model include wind, solar and storage installed capacity constraints, power balance constraints, energy storage capacity constraints and wind and solar output step power constraints;

[0120] Among them, the wind, solar and storage installed capacity constraints are expressed as:

[0121]

[0122] In the formula, E w tmin 、E pv min 、E H min 、E se min Respectively represent the minimum installed capacity of wind power, photovoltaic, liquid hydrogen and superconducting; E wt max 、E pv max 、E H max 、E se max is the maximum installed capacity of each unit, E wt 、E pv 、E H 、E se Respectively represent the minimum installed capacity of wind power, photovoltaic, liquid hydrogen and superconducting;

[0123] After the local load is met and the energy storage SOC reaches the upper limit, when the actual grid-connected power available from wind and solar power is greater than the expected value of the grid-connected power, the system generates power abandonment, and its power balance constraint is:

[0124]

[0125] Where P d Indicates load power, P load Indicates the grid-connected power, P loss Indicates the abandoned wind and solar power, P curt Indicates the load loss power, P wt , P pv , P H and P se They are respectively represented as wind turbine output power, photovoltaic output power, liquid hydrogen energy storage output power and superconducting energy storage output power;

[0126] The energy storage capacity constraint is expressed as:

[0127]

[0128] In the formula, S H min , S semin Respectively represent the minimum value of liquid hydrogen and superconducting energy storage capacity, S H max , S se max Respectively represent the maximum value of liquid hydrogen and superconducting energy storage capacity, S H (t) and S se (t) represent the amount of liquid hydrogen and superconducting energy storage respectively;

[0129] Since storage energy cannot release all energy quickly, when using energy storage devices to connect to the grid in a step-by-step manner, it is necessary to limit the charging and discharging power of LIQHYSMES according to the duration of a single step. The step-by-step power constraint of wind and solar output is:

[0130]

[0131] Where P se Indicates the maximum power that the energy storage can output during rapid response. They represent the charging power and discharging power of the energy storage at time t, P pr (t) represents the output power of the energy storage with stepped grid-connected power.

[0132] In the embodiment of the present invention, in step S3, when a grid-connected method of changing the unit capacity to output a stepped grid-connected power with different step durations is adopted, a capacity configuration model of a single objective function is solved to output the stepped grid-connected power;

[0133] When the grid-connected method of changing the step duration to output the stepped grid-connected power under the optimal capacity unit configuration is adopted, the capacity configuration model of the multi-objective function is solved to obtain the optimal capacity unit configuration, and the stepped grid-connected power is output under the optimal capacity unit configuration.

[0134] In a specific example of this embodiment, taking the objective function of the capacity configuration model as a multi-objective function as an example, a multi-objective particle swarm optimization algorithm with an elite strategy is used to perform multi-objective optimization on the constructed capacity configuration optimization model to obtain a Pareto non-inferior solution set;

[0135] The Euclidean distance is used to calculate the distance between each non-inferior solution in the Pareto non-inferior solution set, and the number of wind and solar units corresponding to the minimum distance is taken as the optimal capacity solution of the system, thereby determining the optimal capacity ratio of wind and solar units and energy storage equipment.

[0136] Specifically, the multi-objective particle swarm optimization algorithm of the elite strategy is as follows:

[0137] 1) Randomly generate an initial population of size N, and define the elites as the best M particles in the population. Since the elites are the best particles in the population, the system keeps the elites unchanged during the update iteration and only updates the non-elite particles.

[0138] 2) When updating speed and position, the system uses elite particles to guide the learning of non-elite particles. At the same time, two elite particles are selected to compete, and the winner replaces the global optimal variable, and the loser replaces the iterative optimal variable to continue guiding particle learning.

[0139] 3) Obtain the Pareto non-inferior solution set through selection, crossover and mutation operations, and extract the Pareto optimal solution set from the non-inferior solution set, and then update the adaptive network of non-inferior solutions. Then, repeat the second step and run in a loop.

[0140] 4) When the number of iterations is reached or the accuracy requirement is met, the loop ends and the Pareto non-inferior solution set is output.

[0141] When determining the optimal number of wind and solar power units based on the Pareto non-inferior solution set, in order to ensure that the weight of each parameter on the result is the same, the normalized Euclidean distance calculation method of each particle is as follows:

[0142]

[0143] In the formula, y m (k) represents the total system cost corresponding to the particle, y i,max represents the maximum total system cost in the Pareto solution set obtained by the system, x m (k) represents the load power shortage rate corresponding to the particle, x i,max Represents the maximum load power outage rate in the Pareto solution set.

[0144] Step S4 in the embodiment of the present invention is specifically as follows:

[0145] S41, calculating the difference between the current stepped grid-connected power and the real-time grid-connected power;

[0146] S42, judging whether the difference is less than 0;

[0147] If yes, proceed to step S43;

[0148] If not, proceed to step S44;

[0149] S43, storing excess electric energy through an energy storage device, and determining whether the energy storage current exceeds the limit or does not meet the power constraint condition;

[0150] If so, the wind and solar power abandonment rates are calculated, and then electricity is abandoned;

[0151] If not, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding step-by-step grid connection results are output;

[0152] S44, using the excess grid-connected power to supplement the shortfall of the stepped grid-connected power, and determining whether the excess grid-connected power meets the valley filling requirement;

[0153] If so, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding step-by-step grid-connected results are output;

[0154] If not, call the energy storage device to supplement the stepped grid-connected power again, and determine whether the stepped grid-connected power requirements are met; if so, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding stepped grid-connected results are output; if not, return to step S3 and re-optimize and solve the capacity optimization configuration model.

[0155] In step S41 of this embodiment, the difference between the stepped grid-connected power and the real-time grid-connected power is:

[0156] P pr (t) = P Ld (t)-P Net (t)

[0157]

[0158] P Net (t) = P w (t)+P s (t)-P load (t)

[0159] Where P Ld (t) represents the stepped grid-connected power output after adjustment by the energy storage system at time t, P Net (t) represents the real-time grid-connected power output by the system at time t, P w (t) represents the unit fan output power, P s (t) represents the unit photovoltaic output power, P load (t) represents the load power, and n represents the number of time intervals within the step time range.

[0160] In one embodiment of the present invention, a data simulation case of the above-mentioned stepwise optimization scheduling scheme is provided.

[0161] In this embodiment, the actual wind speed, light intensity, temperature and load on a typical day in a certain place are selected as the input of the model, and a simulation analysis is performed.

[0162] Considering that the present invention adopts a new scheme of real-time output of stepped grid-connected power, in order to analyze the corresponding demand of the hybrid energy storage system for system power changes, the present invention first uses the interpolation method to pre-process the typical daily data. Two data points are inserted into every two data points to obtain typical daily data with a time interval of 5 minutes. At the same time, in order to unify the data and facilitate calculation, the wind and solar power output and load data are normalized to obtain the corresponding normalized value data.

[0163] In order to explore the impact of different methods of achieving stepped effects on the power grid, two different scenarios for achieving stepped effects are proposed to compare the advantages and disadvantages of the stepped and non-stepped grid-connected power models, and to analyze their practicality under different step wave durations.

[0164] 1) Scenario 1: Simulate the traditional surplus power grid-connected model and establish a single-objective step-by-step model with the total system cost as the objective function; analyze the impact of different step durations on the system by changing the step time length.

[0165] 2) Scenario 2: Combined optimization of wind, solar and storage. A multi-objective step-by-step model is established with the total system cost and load power shortage rate as the objective function. The optimal unit capacity is fixed and the step time length is changed to analyze the impact of different step time lengths on the system.

[0166] The system optimizes the scheduling so that the system output meets the load demand, and then further processes the remaining electric energy in a step-by-step manner before connecting to the grid. For the convenience of comparison, the step time lengths in this embodiment are set to 15 minutes, 30 minutes, and 60 minutes respectively and compared with the non-step-by-step model, so that the results are more intuitive.

[0167] In order to meet the above scenario requirements and analyze the simulation results, the key system parameters required are shown in Table 1.

[0168] Table 1: Key system parameters

[0169]

[0170] For scenario 1:

[0171] Taking into account the volatility and randomness of wind and solar power output, in order to avoid the impact of extreme situations such as continuous low output or high peak value of wind and solar power on the final capacity configuration and result analysis of the system, this paper adopts the walrus algorithm to optimize the energy storage capacity and stepped data.

[0172] In order to explore the advantages and impacts of different methods of implementing laddering on the system, the present invention adopts two different scenarios to implement laddering operation. Scenario 1 is to verify the popularity and adaptability of laddering. Scenario 1 uses the walrus optimization algorithm to optimize the total system cost as the objective function. The obtained data is shown in Table 2.

[0173] Table 2: System Optimization Indexes for Scenario 1

[0174]

[0175] Table 2 shows the simulation results of the stepped grid-connected power model with the total system cost as the objective function in Scenario 1. Under the condition of meeting the load demand, as the length of the stepped wave time becomes longer, to meet certain requirements for stepped grid-connected power output, the installed capacity of the hybrid energy storage increases, and the total system cost increases by 46,300 yuan, 117,100 yuan, and 421,100 yuan in sequence. The load power shortage rate and the curtailment rate of wind and light are basically unchanged compared with the optimization results of the non-stepped system.

[0176] The load power shortage rate and the curtailment rate of wind and light respectively represent the power supply capacity of the system and the efficiency of the system to absorb renewable energy. In the stepped optimization model, on the premise of meeting the local load demand, the system comprehensively analyzes and considers the grid-connected stepped wave power to obtain the expected value of the grid-connected stepped wave power. As the length of the stepped time increases, within a stepped time range, the system needs to call more energy storage to smooth the grid-connected power, so the required energy storage capacity is larger, and the total system cost increases accordingly. During the stepping process, since the energy storage capacity can meet the load and grid-connected demands, the load power shortage rate and the curtailment rate of wind and light remain unchanged.

[0177] After system optimization, the change curves of the SOC of the system's hybrid energy storage under different stepped time intervals are as Figure 2 shown.

[0178] As Figure 2 shown, in the non-stepped grid-connected power model shown in a), the system uses the wind-solar-storage combined system to meet the local load demand and directly feeds the remaining power into the grid. While in the stepped grid-connected power model, the system uses the hybrid energy storage to step the grid-connected power to make the grid-connected power output a stable stepped wave. As the stepped time length increases, to ensure the stable output of the system, the energy storage mobilizes more power to step the grid-connected power, so the change depth of the SOC value of the hybrid energy storage increases.

[0179] The system outputs the stepped grid-connected power as Figure 3 shown. It can be seen from Figure 3 that under the premise of meeting the load demand, as the stepped time length increases, the stepped grid-connected power output by the system is more stable, and the fluctuation of the grid-connected power received by the power grid is smaller.

[0180] For Scenario 2:

[0181] In Scenario 2, with the total system cost and the load power outage rate as the objective functions, the walrus optimization algorithm is used for multi-objective optimization. To increase the algorithm convergence speed and reduce the situation of the system falling into local convergence, the population size is set to 200 and the maximum number of iterations is set to 100 in the present invention. The corresponding Pareto non-dominated solution set is obtained as Figure 4 shown.

[0182] As Figure 3 can be seen, with the decrease of the system cost, the load power outage rate rises accordingly. When the system cost decreases, the number of wind-solar-storage combined units decreases accordingly, and the power supply capacity of the system to the load decreases correspondingly, resulting in the increase of the system power outage rate.

[0183] Figure 3 Each Pareto non-dominated solution set in

[0184] corresponds to a set of optimal capacity allocation results of wind, light, and storage respectively. The present invention calculates the position parameters of each non-dominated solution using the Euclidean distance, and the point with the smallest parameter is set as the optimal capacity solution of the system. The blue asterisk in the figure is the position of the optimal solution in the Pareto solution set. The capacity configuration of the corresponding units is as follows: the capacities of wind power, photovoltaic, supercapacitor, and battery are 3500kW, 5900kW, 390kW, and 550kW respectively.

[0185] Different capacity configurations of wind-solar-storage will also have a greater impact on the grid-connected power of the system. In Scheme 2, it is set that under different step durations, the wind-solar-storage all maintain the optimal capacity unchanged. On this basis, a comparative analysis of the grid-connected power and the characteristics of system parameter indicators is carried out to demonstrate the impact of the stepwise time length on the system.

[0186] As shown in the figure, the longer the grid-connected step time length, the more drastic the change of the hybrid energy storage SOC value. As the time for the system to output a stable step wave increases, the energy storage needs to dispatch more stepwise grid-connected power. And the shorter the stepwise time length, in order to ensure the stable output of the system power in the short term, the charge and discharge frequency of the hybrid energy storage is higher, and the change of the SOC is more obvious.

[0187] When stepping the grid-connected power, the output power of the hybrid energy storage is as Figure 5 shown.

[0188] As Figure 4 can be seen, from 0 to 4 and from 8 to 19, the system calls the hybrid energy storage to shift the grid-connected power to make the grid-connected power output a stable trapezoidal wave. And from 4 to 8 and from 19 to 24, the system wind-solar output is not enough to meet the load demand, and at this time there is no grid-connected power and the output of the hybrid energy storage is 0.

[0189] The system capacity optimization results under Scenario 2 are shown in Table 3;

[0190] Table 3: System Capacity Optimization Results for Scenario 2

[0191]

[0192] As can be seen from Table 3, when the optimal capacities of wind, light, and storage remain unchanged, as the stepped grid connection duration increases, the system's load power shortage rate shows a downward trend, while the wind and light curtailment rates show an upward trend. When the stepped time lengths are 0 min, 15 min, 30 min, and 60 min respectively, the system's load power shortage rates decrease by 0.22%, 0.17%, and 0.07% in sequence; when the stepped time lengths are 15 min, 30 min, and 60 min respectively, the wind and light curtailment rates increase by 0.07% and 0.03% respectively as the stepped duration increases, but they are still lower than the wind and light curtailment rates under the non-stepped model. It can be seen that the stepped time length has a negative correlation with the load power shortage rate and a positive correlation with the wind and light curtailment rates.

[0193] In the stepped model under Scenario 2, while meeting the local load demand, the system considers the wind and light output and the charge and discharge status of the energy storage, and performs stepped optimization on the grid connection power to obtain the expected value of the stepped power. When the grid connection power is greater than the expected power, the energy storage absorbs the excess power and discards the part that exceeds the energy storage SOC limit or power limit; when the grid connection power is less than the expected power, the energy storage releases electrical energy, and the system uses the energy storage to make up for the grid connection power shortage, so that the grid connection power outputs a stable stepped wave.

[0194] In the embodiments of the present invention, an analysis of the system law of the stepped time length is also provided in the above process.

[0195] As can be seen from Table 2, in Scenario 1, there is a linear relationship between the system stepped time length and the total system cost. Further analyzing its configuration results, the least squares method is used to fit the change power between the total system cost and the stepped time length under different time lengths, and the results are as Figure 6 shown.

[0196] As Figure 6 shown, in Scenario 1, the total system cost is in a proportional relationship with the stepped time length, and the polynomial fitting result of the total system cost is:

[0197] y = 2.02 * 10 -2 x 2 - 0.412x + 942.62

[0198] The variation laws of the system power shortage rate and the wind and light curtailment rate under different stepped time lengths in Scenario 2 are as Figure 7 and Figure 8 shown; as Figure 7As shown, the coefficient of determination R2 = 0.9939, and the fitting effect is good. The polynomial fitting result of the total system cost is: y = 8.55*10 -5 x 2 -9.3*10 -3 x + 16.34, as Figure 8 shown, the coefficient of determination R2 = 0.9999, achieving a perfect fit. The fitting result of the system's curtailment rate of wind and light is: y = -5.07*10 -5 x 2 + 5.2*10 -3 x + 17.32.

[0199] Compare the stepped models of Scenario 1 and Scenario 2 above. The stepped model of Scenario 1 increases the capacity of the hybrid energy storage unit, increases the system cost, outputs a stepped wave with a longer stepped time length and greater stability, and ensures that the curtailment rate of wind and light and the load power shortage rate of the system remain unchanged. The stepped model of Scenario 2 fixes the unit capacity. When the stepped time length changes, the total system cost remains unchanged, and the curtailment rate of wind and light and the load power shortage rate change with the stepped time length. The two scenarios are respectively adapted to the situations of outputting stepped grid-connected electric energy under different conditions.

[0200] Therefore, compared with the traditional "surplus power grid connection" model, the stepped grid-connected power proposed in the present invention can transmit deterministic grid-connected power to the power grid by means of the stepped model, and can increase the system cost while ensuring that the curtailment rate of wind and light and the load power shortage rate of the system remain basically unchanged, or effectively reduce the load power shortage rate while ensuring that the system cost remains basically unchanged, thereby improving the power supply reliability of the system.

[0201] In the present invention, specific embodiments are used to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

[0202] Those of ordinary skill in the art will realize that the embodiments described here are for helping readers understand the principle of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations without departing from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A stepwise optimization dispatching method for wind, solar, and energy storage microgrid grid-connected power, characterized in that: The following steps are involved: S1. Obtain typical daily wind and solar data in the wind, solar and energy storage microgrid, calculate the wind and solar output, and then calculate the net load of the system; S2. On the premise of meeting the net load of the system, a capacity configuration optimization model for calculating the step-by-step grid-connected power is constructed; The capacity configuration optimization model is a single objective function with the lowest system cost or a multi-objective function with the lowest system cost and load power outage rate; S3. Optimize and solve the capacity configuration optimization model, and then determine the step-by-step grid-connected power; S4. Calculate the difference between the stepped grid-connected power and the real-time grid-connected power, and call the hybrid energy storage system based on the difference to compensate for the output fluctuation of wind and solar power generation, and then update the operating status of the energy storage system and output the stepped grid-connected result.

2. The stepwise optimization dispatching method for wind, solar, and energy storage microgrid grid-connected power according to claim 1 is characterized in that: In step S1, the wind turbine generator model for calculating the wind turbine output is represented by: Where P w (t) represents the wind power output power at time t, P nwt Indicates the rated power of the wind turbine, v in represents the cut-in wind speed, v n Indicates rated wind speed, v out represents the cut-out wind speed, v(t) represents the wind speed; Under standard test conditions, the output power of the photovoltaic array is: Where P S (t) represents the photovoltaic output power at time t, P npv Indicates the rated power of the photovoltaic array under standard test conditions, I c (t) and T c (t) represent the irradiance and photovoltaic cell temperature at time t, I N and T N They represent the irradiance and ambient temperature under standard test conditions, respectively, and λ represents the photovoltaic cell power temperature coefficient; The net load of the system is expressed as: Pjload(t)=P load (t)-P w (t)*E wt -P s (t)*E pv Where Pjload(t) is the net load of the system at time t; P load (t) is the load output power at time t; E wt is the installed capacity of the wind turbine; E pv Photovoltaic installed capacity.

3. The stepwise optimization dispatching method for wind, solar, and energy storage microgrid grid-connected power according to claim 1 is characterized in that: In step S2, according to the net load of the system, when the step-by-step grid-connected power with different step durations is output by changing the unit capacity, the load power shortage rate and the wind and solar power abandonment rate remain unchanged, thereby realizing step-by-step grid connection; the capacity configuration optimization model is an objective function with the lowest system cost; According to the net load of the system, when the configuration of ensuring the number of units to be the optimal capacity is adopted, the system load power shortage rate and the wind and solar power abandonment rate change with the change of the step duration, thereby realizing step-by-step grid connection; the capacity configuration optimization model is a multi-objective function with the lowest system cost and load power shortage rate.

4. The stepwise optimization dispatching method for wind, solar, and energy storage microgrid grid-connected power according to claim 3 is characterized in that: The capacity configuration optimization model of a single objective function is expressed as: minf1=C1+C2+C3+C4 The capacity configuration optimization model of multi-objective function is expressed as: minf1=C1+C2+C3+C4 In the formula, f1 represents the annual comprehensive system cost, C1 represents the system investment cost, C2 represents the system operation and maintenance cost, C3 represents the residual fluctuation penalty cost, C4 represents the loss of load compensation cost, and f lp Indicates the load power failure rate, P load (t) represents the load output power at time t, P gs (t) represents the power that the system can supply at time t, and T represents the length of time in one cycle.

5. The stepwise optimization dispatching method for wind, solar and energy storage microgrid grid-connected power according to claim 4 is characterized in that: The system investment cost includes the wind power cost C 1_wt , Photovoltaic cost C 1_pv , power storage cost C 1_H and capacity storage cost C 1_se , which are respectively expressed as: In the formula, P wt (t), P pv (t) represent wind power and photovoltaic power output, respectively, k wt.2 , k pt.2 , k H.2 , k se.2 is the unit capacity operation and maintenance cost of wind power, photovoltaic power, liquid hydrogen, and superconducting power, P H (t) represents the output power of liquid hydrogen energy storage at time t, P se (t) represents the output power of the superconducting energy storage at time t, and n represents the number of time nodes in a cycle; The remaining volatility penalty cost C3 is expressed as: In the formula, represents the wind abandonment penalty coefficient, represents the light abandonment penalty coefficient, ρ wt , pv Respectively and Corresponding to the penalty coefficient for wind and solar power abandonment, P curt wind (t), P curt pv (t) respectively represent and The corresponding wind and solar power abandoned in period t, Δt represents the time corresponding to the abandoned wind and solar power; The loss of load compensation cost is expressed as: In the formula, ρ loss represents the load loss penalty coefficient; P curt loss Represents the amount of electricity purchased from the grid during period t.

6. The stepwise optimization dispatching method for wind, solar and energy storage microgrid grid-connected power according to claim 4 is characterized in that: The constraints of the capacity configuration optimization model include wind, solar and storage installed capacity constraints, power balance constraints, energy storage capacity constraints and wind and solar output step power constraints, which are respectively expressed as: In the formula, E w tmin 、E pv min 、E H min 、E se min Respectively represent the minimum installed capacity of wind power, photovoltaic, liquid hydrogen and superconducting; E wt max 、E pv max 、E H max 、E se max is the maximum installed capacity of each unit, E wt 、E pv 、E H 、E se Respectively represent the minimum installed capacity of wind power, photovoltaic, liquid hydrogen and superconducting; Where P d Indicates load power, P load Indicates the grid-connected power, P loss Indicates the abandoned wind and solar power, P curt Indicates the load loss power, P wt , P pv , P H and P se They are respectively represented as wind turbine output power, photovoltaic output power, liquid hydrogen energy storage output power and superconducting energy storage output power; In the formula, S H min , S se min Respectively represent the minimum value of liquid hydrogen and superconducting energy storage capacity, S H max , S se max Respectively represent the maximum value of liquid hydrogen and superconducting energy storage capacity, S H (t) and S se (t) represent the amount of liquid hydrogen and superconducting energy storage respectively; Where P se Indicates the maximum power that the energy storage can output during rapid response. They represent the charging power and discharging power of the energy storage at time t, P pr (t) represents the output power of the energy storage with stepped grid-connected power.

7. The stepwise optimization dispatching method for wind, solar, and energy storage microgrid grid-connected power according to claim 4 is characterized in that: In step S3, when a grid-connected method is adopted in which the capacity of the unit is changed to output a stepped grid-connected power with different step durations, a capacity configuration model of a single objective function is solved to output the stepped grid-connected power; When the grid-connected method of changing the step duration to output the stepped grid-connected power under the optimal capacity unit configuration is adopted, the capacity configuration model of the multi-objective function is solved to obtain the optimal capacity unit configuration, and the stepped grid-connected power is output under the optimal capacity unit configuration.

8. The stepwise optimization dispatching method for wind, solar, and energy storage microgrid grid-connected power according to claim 4 is characterized in that: The step S4 is specifically as follows: S41, calculating the difference between the current stepped grid-connected power and the real-time grid-connected power; S42, judging whether the difference is less than 0; If yes, proceed to step S43; If not, proceed to step S44; S43, storing excess electric energy through an energy storage device, and determining whether the energy storage current exceeds the limit or does not meet the power constraint condition; If so, the wind and solar power abandonment rates are calculated, and then electricity is abandoned; If not, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding step-by-step grid connection results are output; S44, using the excess grid-connected power to supplement the shortfall of the stepped grid-connected power, and determining whether the excess grid-connected power meets the valley filling requirement; If so, the step optimization condition is met, the operating status of the energy storage system is updated, and the corresponding step-by-step grid connection result is output; If not, call the energy storage device to supplement the stepped grid-connected power again, and determine whether the stepped grid-connected power requirements are met; if so, the step optimization conditions are met, the operating status of the energy storage system is updated, and the corresponding stepped grid-connected results are output; if not, return to step S3 and re-optimize and solve the capacity optimization configuration model.

9. The stepwise optimization dispatching method for wind, solar, and energy storage microgrid grid-connected power according to claim 8 is characterized in that: In step S41, the difference between the stepped grid-connected power and the real-time grid-connected power is: P pr (t)=P Ld (t)-P Net (t) P Net (t)=P w (t)+P s (t)-P load (t) Where P Ld (t) represents the stepped grid-connected power output after adjustment by the energy storage system at time t, P Net (t) represents the real-time grid-connected power output by the system at time t, P w (t) represents the unit fan output power, P s (t) represents the unit photovoltaic output power, P load (t) represents the load power, and n represents the number of time intervals within the step time range.

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