Multi-energy coordinated optimization method and device based on port electricity load characteristics

Abstract

The invention relates to the technical field of multi-energy complementation and optimal configuration, and discloses a multi-energy coordinated optimization method and device based on port electricity load characteristics. The method specifically comprises the following steps: acquiring a load high-resolution power parameter and a renewable resource supply power parameter; matching the load high-resolution electric power parameter with the renewable resource supply electric power parameter, determining a matching matrix of the electric power required by the load and the renewable resource supply electric power, and determining a power supply resource allocation strategy of the multi-energy energy storage system according to the matching matrix; and based on the objective function of the power supply resource allocation strategy, performing coordinated optimization on the power supply resource allocation strategy, and determining a dynamic optimization strategy. The power supply resource allocation strategy is coordinated and optimized through the objective function considered in the invention, so that the power supply cost and the waste of power supply resources can be reduced, and the stability and reliability of the port energy system are enhanced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of multi - energy complementarity and optimal allocation, and particularly to a multi - energy coordination and optimization method and device based on the electrical load characteristics of ports. Background Art

[0002] With the continuous increase in the global demand for clean energy and the diversification of power supply systems, optimizing the output of hybrid energy based on the load characteristics of ports has become a key research focus.

[0003] As a key node in the global supply chain, ports have unique energy demand characteristics, such as operating around the clock without interruption, sudden load fluctuations caused by container cranes, and strict requirements for reliability in cold chain facilities.

[0004] In related technologies, solutions and optimization models for supplementary power supply have been proposed for integrated clean energy systems with specific load characteristics of ports. However, there are fundamental defects in their solutions and optimization models, such as high power supply costs and unstable output. Summary of the Invention

[0005] To solve the above - mentioned technical problems, the present disclosure provides a multi - energy coordination and optimization method based on the electrical load characteristics of ports, including the following steps: Obtain high - resolution power parameters of the load and power parameters of renewable resource supply; match the high - resolution power parameters of the load and the power parameters of renewable resource supply, determine a matching matrix of the power required by the load and the power supplied by renewable resources, and determine a power supply resource allocation strategy for the multi - energy energy storage system according to the matching matrix; based on the objective function of the power supply resource allocation strategy, coordinate and optimize the power supply resource allocation strategy to determine a dynamic optimization strategy.

[0006] Further, the determination of the matching matrix of the power required by the load and the power supplied by renewable resources includes: Extract load characteristics based on the high - resolution power parameters of the load, and extract renewable energy operation characteristics based on the power parameters of renewable resource supply; determine a first interaction relationship between the load characteristics and the renewable energy operation, and determine a coupling model of the load and the renewable energy according to the first interaction relationship; use the coupling model to match the load characteristics and the renewable energy operation characteristics to determine a matching matrix of the power required by the load and the power supplied by renewable resources.

[0007] Further, the determination of the power supply resource allocation strategy for the multi - energy energy storage system according to the matching matrix includes: Determine the load characteristic type based on the high-resolution power parameters of the load; determine the characteristic quantization indexes of the load characteristic type and the power supply parameters of the renewable resources; determine the tidal energy based on the solar energy and wind energy in the historical meteorological data; use the tidal energy as the supplementary power supply during the low electricity consumption period, match the supplementary power supply and the characteristic quantization indexes through the matching matrix, and determine the power supply resource allocation strategy of the multi-energy energy storage system.

[0008] Further, the determining the load characteristic type based on the high-resolution power parameters of the load includes: Analyze the high-resolution power parameters of the load, determine the operating conditions of the load, the equipment energy consumption, and the second interaction relationship between the environmental factors and the equipment; determine the load characteristic type according to the second interaction relationship.

[0009] Further, the determining the characteristic quantization indexes of the load characteristic type and the power supply parameters of the renewable resources includes: Analyze the load characteristic type, determine the load mode quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index; determine the second interaction relationship between the load mode quantization index, the load time regularity quantization index, the load power consumption fluctuation quantization index and the power supply parameters of the renewable resources; analyze and quantify the second interaction relationship through spectral coherence to obtain the characteristic quantization indexes of the renewable resources.

[0010] Further, the using the tidal energy as the supplementary power supply during the low electricity consumption period, matching the supplementary power supply and the characteristic quantization indexes through the matching matrix, and determining the power supply resource allocation strategy of the multi-energy energy storage system includes: Obtain the determined load mode quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index; based on the load mode quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index, determine the lithium-ion battery configuration parameters, the capacitor model parameters of the multi-energy energy storage system, and the time parameters for using the tidal energy as the supplementary power supply during the low electricity consumption period; determine the power supply resource allocation strategy of the multi-energy energy storage system according to the lithium-ion battery configuration parameters, the capacitor model parameters, and the time parameters.

[0011] Further, the coordinating and optimizing the power supply resource allocation strategy based on the objective function of the power supply resource allocation strategy to determine the dynamic optimization strategy includes: Adaptive reference points and dynamic adjustment of Pareto front resolution for port scale based on the third-generation non-dominated sorting genetic algorithm, and determine the time weight based on the time discount effect of the third-generation non-dominated sorting genetic algorithm; determine the mutual conflict mechanism between the objectives of the objective function; according to the adjusted Pareto front resolution and the time weight, weigh the mutual conflict mechanism between the objectives, dynamically optimize the objective function, and determine the dynamic optimization strategy of the power supply resource allocation strategy.

[0012] Further, the objective function includes one or more of a levelized cost of electricity objective function, a carbon dioxide emissions objective function, and an unsupplied electricity quantity objective function.

[0013] Further, the objective function further includes constraint conditions; The constraint conditions include one or more of power balance constraints, generator operation limit constraints, energy storage dynamic characteristic constraints, renewable energy penetration constraints, and coupling constraints between equipment and energy.

[0014] A multi-energy coordinated optimization device based on the port electricity load characteristics, adopting the multi-energy coordinated optimization method based on the port electricity load characteristics as described in any one of the above, the device includes: An acquisition module for acquiring high-resolution power parameters of the load and power supply parameters of renewable resources; a determination module for matching the high-resolution power parameters of the load and the power supply parameters of renewable resources, determining a matching matrix of the power required by the load and the power supplied by renewable resources, and determining a power supply resource allocation strategy for the multi-energy energy storage system according to the matching matrix; an optimization module for coordinately optimizing the power supply resource allocation strategy based on the objective function of the power supply resource allocation strategy to determine a dynamic optimization strategy.

[0015] The embodiments of the present disclosure have the following technical effects: The multi-energy coordinated optimization method based on the port electricity load characteristics provided by this application acquires high-resolution power parameters of the load and power supply parameters of renewable resources, so that through the determined matching matrix of the power required by the load and the power supply parameters of renewable resources, the dynamic characteristics of port equipment are linked to the operating characteristics of renewable energy from a mechanism, without relying on specific data sets, better matching the operating characteristics of renewable energy with the needs of port equipment, being able to increase the proportion of renewable energy in port energy supply, reduce dependence on traditional fossil energy, and promote the development of ports towards green and low-carbon directions, meeting the requirements of sustainable development. Coordinately optimizing the power supply resource allocation strategy based on the objective function of the power supply resource allocation strategy can consider multiple objectives, so that the obtained dynamic optimization strategy has a lower cost, stable power supply, and reduced carbon dioxide emissions, thereby enhancing the stability and reliability of the port energy system. Description of the Drawings

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 is a flowchart of a multi - energy coordinated optimization method based on the port electricity load characteristics provided by an embodiment of the present disclosure; Figure 2 is a flowchart of a method for determining a power supply resource allocation strategy provided by an embodiment of the present disclosure; Figure 3 is a three - dimensional surface diagram of the annual electricity demand provided by an embodiment of the present disclosure; Figure 4 is a pie chart of the load energy consumption ratio generated from Internet of Things sensor data provided by an embodiment of the present disclosure; Figure 5 is an analysis diagram using a clustering algorithm provided by an embodiment of the present disclosure; Figure 6 is a schematic diagram for analyzing the load electricity fluctuation quantization index using kernel density estimation provided by an embodiment of the present disclosure; Figure 7 is a schematic diagram of a multi - energy coordinated optimization device based on the port electricity load characteristics provided by an embodiment of the present disclosure. Specific Embodiments

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of this disclosure.

[0019] Global maritime transportation undertakes more than 80% of international trade volume, which has compelled the port energy system to undergo an urgent transformation.

[0020] As a key node in the global supply chain, ports have unique energy demand characteristics, manifested as round - the - clock uninterrupted operation, sudden load fluctuations brought about by container cranes, and strict requirements for reliability in cold chain facilities. In recent years, the energy demand driven by automation in ports has increased by 17%. However, due to the dependence on traditional diesel generators, the emissions per TEU - km have not decreased.

[0021] In the current related technologies, the development of port energy systems mainly focuses on combining renewable energy with traditional generators, but there are limitations, which may reduce the power generation of renewable energy. It is also proposed to combine offshore wind energy with a hydrogen storage system, which makes the output of the turbine unstable. Considering comprehensively, there are the following three systematic blind spots in the related technologies.

[0022] (1) Load characteristic analysis often simplifies complex industrial processes into static curves; (2) Resource modeling often ignores local meteorological interactions; (3) There is a continuous problem of time series disconnection in the planning and operation stages.

[0023] Based on the above technical problems, the present disclosure proposes a multi-energy coordination optimization method and device based on the port electricity load characteristics. Through a cyber-physical driven device-energy coupling model, a supply-demand mismatch matrix of the high-resolution operation data of the load and the renewable resource data is determined, which links the dynamic characteristics of port equipment with the operation characteristics of renewable energy from the mechanism. Without relying on specific data sets, it has better adaptability and generalization ability for port equipment and energy systems under different working conditions and different environmental conditions, and can be more reliably applied to actual port operation scenarios. Optimize the power supply resource allocation strategy based on a multi-objective optimization model, and determine a dynamic optimization strategy for the coordinated output power supply of a multi-energy energy storage system, considering multiple important objectives. Thus, the obtained dynamic optimization strategy for the coordinated output power supply of the multi-energy energy storage system has a lower cost, stable power supply, and reduces carbon dioxide emissions, thereby enhancing the stability and reliability of the port energy system.

[0024] Figure 1 It is a flowchart of the multi-energy coordination optimization method based on the port electricity load characteristics provided by an embodiment of the present disclosure. See Figure 1 , which specifically includes the following steps: In step S11, obtain high-resolution power parameters of the load and power supply parameters of renewable resources.

[0025] In the embodiment of the present disclosure, the obtained high-resolution power parameters of the load can be the high-resolution power parameters of the target automated terminal in the past two years. It includes crane motor current (for example, 3000 samples per second), the thermal cycle of refrigerated containers obtained through infrared imaging, and the ship-shore power curve. The renewable resource data can be the power supply parameters converted from renewable resources such as solar energy and wind energy.

[0026] In the present disclosure, filters can also be applied to remove the data values of abnormal high-resolution power parameters of the load and power supply parameters of renewable resources, and synchronize the timestamps.

[0027] In step S12, match the high-resolution power parameters of the load and the power parameters of the renewable resource supply, determine the matching matrix of the power required by the load and the power supplied by the renewable resources, and determine the power supply resource allocation strategy of the multi-energy energy storage system according to the matching matrix.

[0028] In the embodiments of the present disclosure, the load curve of the load can be determined according to the high-resolution power parameters of the load, so as to match the curve corresponding to the power parameters of the renewable resource supply with the load curve, and obtain the matching matrix of the power required by the load and the power supplied by the renewable resources.

[0029] Among them, the matching matrix can also be understood as the difference matching matrix of the power required by the load and the power supplied by the renewable resources, or the matching matrix can be called the mismatch matrix.

[0030] Furthermore, the power supply resource allocation strategy of the multi-energy energy storage system can be formulated by analyzing the cross-correlation relationship between the matching matrix, the characterized load curve and the irradiance of renewable energy.

[0031] In step S13, based on the objective function of the power supply resource allocation strategy, coordinate and optimize the power supply resource allocation strategy to determine the dynamic optimization strategy.

[0032] In the embodiments of the present disclosure, the objective function is pre-constructed in the present disclosure and can adjust 12 decision variables. For example, from the photovoltaic tilt angle to the ramp rate of the liquefied natural gas generator, while balancing objectives such as minimizing energy costs and carbon dioxide intensity. And the enhanced third-generation non-dominated sorting genetic algorithm is used to determine the dynamic optimization strategy for the coordinated output power supply of the multi-energy energy storage system. And 20% of the population of the multi-objective optimization model is initialized according to the configurations analyzed by the data analysis tool.

[0033] The multi-energy coordinated optimization method based on the load characteristics of port electricity consumption provided by the embodiments of the present disclosure obtains the high-resolution power parameters of the load and the power parameters of the renewable resource supply, so as to connect the dynamic characteristics of port equipment with the operating characteristics of renewable energy from the mechanism through the determined matching matrix of the power required by the load and the power supplied by the renewable resources, without relying on specific data sets, better matching the operating characteristics of renewable energy with the needs of port equipment, being able to increase the proportion of renewable energy in port energy supply, reduce the dependence on traditional fossil energy, and promote the development of ports towards green and low-carbon directions, meeting the requirements of sustainable development. Optimizing the power supply resource allocation strategy based on the objective function of the power supply resource allocation strategy can consider multiple objectives, so that the obtained dynamic optimization strategy for the coordinated output power supply of the multi-energy energy storage system has a low cost, stable power supply, and reduced carbon dioxide emissions, thereby enhancing the stability and reliability of the port energy system.

[0034] Further, in the embodiments of the present disclosure, load characteristics can be extracted based on high-resolution power parameters of the load, and operating characteristics of renewable energy can be extracted based on power supply parameters of renewable resources; thereby determining a first interaction relationship between the load characteristics and the operation of renewable energy, and according to the first interaction relationship, determining a coupling model of the load and renewable energy. Further, the coupling model is used to match the load characteristics and the operating characteristics of renewable energy to determine a matching matrix of the power required by the load and the power supplied by renewable resources. Among them, the load characteristics may be a characterized load curve.

[0035] In the embodiments of the present disclosure, by analyzing the load characteristics of the port, two dimensions of load data classification and feature quantification can be determined. Through the integration of high-precision measured data and advanced statistical methods, the complex coupling relationship among the equipment operation dynamics, environmental factors, and energy demand patterns can be systematically analyzed, thereby determining the power supply resource allocation strategy. Figure 2 It is a flowchart of a method for determining a power supply resource allocation strategy provided by an embodiment of the present disclosure. Refer to Figure 2 and its implementation manners include the following embodiments.

[0036] In step S21, the load characteristic type is determined based on the high-resolution power parameters of the load.

[0037] In the embodiments of the present disclosure, the high-resolution power parameters of the load can be analyzed to determine the operating conditions of the load, the equipment energy consumption, and the second interaction relationship between the environmental factors and the equipment, thereby determining the load characteristic type according to the second interaction relationship.

[0038] In the present disclosure, the loads in the port have typical multi-scale heterogeneity characteristics. Therefore, different high-resolution power parameters of the load have different load characteristics, corresponding to different characteristic types. Figure 3 It is a three-dimensional surface diagram of the annual power demand provided by an embodiment of the present disclosure. Refer to Figure 3 , based on the power parameters with a time resolution of 15 minutes for 730 days from 2019 to 2021, the three-dimensional surface of the annual power demand (month × date × power) reconstructed by cubic spline interpolation clearly presents the following load characteristics: (1) The diurnal peak-valley characteristics of the load, manifested as the peak power demand (6.8 - 8.2 MW) being strictly synchronized with the ship berthing plan.

[0039] (2) The seasonal difference characteristics of the load, manifested as the base load in winter being 23% higher than that in summer due to cold chain demand.

[0040] (3) The characteristics of the impact of extreme load events, manifested as a 35 - 40% sudden drop in the load during the typhoon passing period from July to September.

[0041] Using a window length The seasonal difference characteristics of the load are analyzed by the Singular Spectrum Analysis (SSA) of days, the dominant trend component affecting the seasonal difference characteristics of the load is determined, and the dominant trend component is affected by the long-term influence of port infrastructure. It is also possible to determine that the seasonal residual of the seasonal difference characteristics of the load is strongly correlated with the monsoon humidity cycle.

[0042] By using the Isolation Forest algorithm to perform anomaly detection on the seasonal difference characteristics of the load, it is determined that the load trough period (from July to September) caused by typhoons shows a load reduction of 35% to 40%.

[0043] In the present disclosure, the load can be an electrical equipment such as a gantry crane, a cold chain facility, a ship, etc. Figure 4 It is a pie chart showing the proportion of load energy consumption generated by the Internet of Things sensor data provided by the embodiments of the present disclosure. See Figure 4 , a pie chart generated based on the Internet of Things sensor data with a sampling frequency of 3 kHz for 18 months, analyzes the proportion of energy consumption of each load, including: The proportion of the dominant energy consumption of the gantry crane (38±5%), where the power curve has a trapezoidal characteristic, and when using container hoisting, the motor current suddenly rises from 0 to 3.8 MW within 12 seconds and lasts for a power plateau period of 45±8 seconds.

[0044] The proportion of energy consumption of the cold chain facility (29±3%), and the thermodynamic characteristics of the cold chain facility conform to the constant temperature hysteresis phenomenon described by the lumped capacitance equation.

[0045] The shore power demand law of the port (accounting for 19% of the total load) is strongly correlated with the ship tonnage and shows a bimodal distribution.

[0046] It should be noted that environmental factors will cause non-linear disturbances in the load energy consumption. The influence mechanism is that when the crosswind speed exceeds 10 m / s, the operating speed of the crane of the gantry crane will decrease. At the same time, the tidal surge exceeding 1.5 meters will interfere with the shore power connection, resulting in a power outage of 2 to 4 minutes for the multi-energy energy storage system, and a peak in the load energy consumption will be triggered when the power supply is restored.

[0047] In summary, different high-resolution power parameters of the load correspond to different load characteristic types, and relevant steps are further executed according to the load characteristic types.

[0048] In step S22, the load characteristic type and the characteristic quantization index of the power supply parameters of the renewable resources are determined.

[0049] In the embodiments of the present disclosure, the original data needs to be converted into executable design parameters, that is, quantization indexes.

[0050] Furthermore, the load characteristic types can be analyzed by advanced statistical decomposition methods to determine the load pattern quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index. Thus, the third interaction relationship between the load pattern quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index and the power parameters of renewable resource supply can be determined. The third interaction relationship is analyzed and quantified through spectral coherence to obtain the characteristic quantization index of renewable resources.

[0051] Among them, the third interaction relationship is a dynamic association. Taking the load of a gantry crane as an example, the operation of the gantry crane and gusts show 0.85 coherence in the frequency band of 0.1 - 0.3 Hz.

[0052] Furthermore, the K-means++ clustering algorithm can be used to analyze the standardized daily load curve to obtain the following load characteristic types. Figure 5 It is an analysis diagram of using the clustering algorithm provided by an embodiment of the present disclosure. Refer to Figure 5 , Cluster 1 is the load double-peak characteristic under the regular operation mode, including the load peak characteristic type at 10:00 (7.1 MW) and the load peak characteristic type at 15:00 (6.8 MW), and the load double-peak characteristic is synchronized with the arrival time of Panamax ships.

[0053] The load characteristic type of Cluster 2 is the load characteristic type in which the midnight load may suddenly increase to a certain value in the emergency operation mode (for example, the transportation demand of refrigerated drugs), and this load characteristic type is a transient load characteristic type that cannot be captured by the monthly average load analysis.

[0054] The load characteristic type of Cluster 3 is the load characteristic type in which the load shows periodic fluctuations in the meteorological disturbance mode. For example, the 2-hour periodic fluctuation is due to the coordinated deceleration of gantry cranes in heavy rain.

[0055] Among them, the determined load time regularity quantization index includes using the daily imbalance coefficient , with a value range of 0.38 (high fluctuation day) to 0.62 (stable operation day), and it is significantly negatively correlated with the energy storage cycle depth demand.

[0056] Figure 6 It is a schematic diagram of analyzing the load power consumption fluctuation quantization index using kernel density estimation provided by an embodiment of the present disclosure. Refer to Figure 6 , using kernel density estimation to analyze the load power consumption fluctuation quantization index The analysis results show that its peaky and heavy-tailed distribution characteristics reveal two typical fluctuation modes: high-frequency peak fluctuations and medium-period fluctuations.

[0057] In step S23, tidal energy is determined based on historical meteorological data of solar energy and wind energy.

[0058] In step S24, tidal energy is used as a supplement to supply power during the low electricity consumption period. The supplementary power supply and characteristic quantization indexes are matched through a matching matrix to determine the power supply resource allocation strategy.

[0059] In the embodiment of the present disclosure, after determining tidal energy by using historical meteorological data, tidal energy can be modeled as a supplementary energy source during the low electricity consumption period, so that the supplementary energy can be subjected to energy conversion to determine the supplementary power supply. Further, the determined load mode quantization index, load time regularity quantization index, and load power consumption fluctuation quantization index are obtained; based on the load mode quantization index, load time regularity quantization index, and load power consumption fluctuation quantization index, the lithium-ion battery configuration parameters, capacitor model parameters of the multi-energy energy storage system, and the time parameter for using tidal energy as the supplementary power supply during the low electricity consumption period are determined. Thus, according to the lithium-ion battery configuration parameters, capacitor model parameters, and time parameters, the power supply resource allocation strategy of the multi-energy energy storage system is determined. Among them, the power supply resource allocation strategy includes hybrid power supply of renewable energy and power generation equipment. It can not only reduce energy waste and improve power supply reliability, but also has remarkable effects in realizing sustainable port energy management.

[0060] Further, for the lithium-ion battery configuration, a derating factor of 1.8 times the manufacturer's cycle life needs to be set, and it can be based on the rain flow counting method, and the damage degree is the rated power of unit i, which is convenient for adapting to frequent shallow charge and discharge cycle conditions. The super capacitor selection is designed with a capacity to cover 95% of the load peak (200kW / ms change rate). The time parameter for using tidal energy as the supplementary power supply during the low electricity consumption period is the time period with a 2.3-hour time lag between photovoltaic power generation and the gantry crane load. Tidal energy is introduced for supplementary supply (night utilization rate of 18%), and a demand-side management strategy is implemented to adjust non-critical loads to the photovoltaic power generation period.

[0061] In the embodiment of the present disclosure, the resolution of the Pareto front can also be dynamically adjusted based on the adaptive reference point of the third-generation non-dominated sorting genetic algorithm and the port scale, and the time weight can be determined based on the time discount effect of the third-generation non-dominated sorting genetic algorithm. Further determine the mutual conflict mechanism between the objectives of the objective function. Thus, according to the adjusted resolution of the Pareto front and the time weight, the mutual conflict mechanism between the objectives is weighed, the objective function is dynamically optimized, and the dynamic optimization strategy for the coordinated output power supply of the multi-energy energy storage system is determined.

[0062] In the embodiment of the present disclosure, multiple objective functions and their corresponding constraint conditions can be determined according to the balance of economy, environmental sustainability, and operation reliability. Among them, the constraint conditions include physical constraints and / or operation constraints.

[0063] Among them, the levelized cost of electricity (LCOE) target function is determined according to the balance of economy, the carbon dioxide emissions target function is determined according to environmental sustainability, and the unsupplied electricity target function is determined according to operation reliability.

[0064] Among them, the expression of the levelized cost of electricity (LCOE) target function is as follows:

[0065] In the formula, represents the minimum value of the levelized cost of electricity; represents the investment cost in the y th year, including the costs of LNG units, energy storage systems, photovoltaics, etc. represents the operation cost in the y th year, including operation costs such as fuel, maintenance, and grid interaction. represents the maintenance cost in the y th year. represents the total electricity supply in the y th year. r represents the discount rate (usually taken as 5 - 8%). N represents the system life cycle (such as 20 years).

[0066] The numerator of this formula sums up the life cycle costs, while the denominator discounts the supplied energy, thereby penalizing systems with large energy losses due to high intermittency. This formula expression essentially tends to postpone the investment in energy storage technologies, which is consistent with the price prediction trend of lithium - ion batteries. At the same time, it penalizes the situation of excessive energy curtailment by reducing the supplied electricity.

[0067] The expression of the carbon dioxide emissions target function is as follows:

[0068] In the formula, the minimum value of carbon dioxide emissions, representing the carbon emissions on the power generation side represents the carbon intensity (kg - CO2 / kWh) of the P i,t th generator set, represents the output power (kW) of the generator set at time period t, the implicit carbon emissions on the energy storage side j represents the unit - cycle implicit carbon emissions (kg - CO2 / kWh) of the energy storage system represents the energy storage j change in the state of charge at time period t, T represents the time period, G represents the total number of generator sets, S represents the total number of energy storage systems.

[0069] The full - life - cycle carbon emission accounting of this formula covers direct emissions (generator operation) and indirect emissions (implied carbon costs for energy storage device charging and discharging), avoids carbon leakage problems, has a refined time scale, quantifies dynamic carbon emissions with an hourly time step (t), and accurately matches the port load fluctuations. It is compatible with the Scope3 standard by including upstream emissions such as energy storage device manufacturing and transportation through parameters, and conforms to the international carbon accounting system.

[0070] Exemplarily, taking the constraint linkage as an example, when the photovoltaic power output is insufficient, the model preferentially schedules the LNG unit (such as a high - efficiency gas turbine) with a lower carbon emission intensity instead of directly purchasing electricity.

[0071] The expression of the power shortage target function is as follows:

[0072] In the formula, represents the minimum value of the power shortage, R t represents the renewable energy power generation, and Δt is the time interval from 10 to 15 minutes. L t represents the total load demand (kW) at time period t, represents the energy storage system j at time period t discharge power (kW), T represents the time period, G represents the total number of generator sets, S represents the total number of energy storage systems.

[0073] This formula includes risk - aversion design. Through an asymmetric operator, it only punishes power supply shortages ( L t > total power supply) and ignores excess power generation. It can also guarantee critical loads, directly quantify the expected energy not served (EENS), and is strictly linked to the requirements of port operation continuity. It also includes time granularity optimization, that is, 15 - minute - level calculation to match the port operation cycle (such as the ship berthing window).

[0074] Exemplarily, taking the constraint linkage mechanism as an example, when a typhoon causes R t to drop suddenly, the model preferentially compensates for the gap through energy storage discharge and fast - start units.

[0075] The mechanism of conflicting objectives includes the conflict between economy and environmental protection as well as the conflict between reliability and economy as well as the conflict between reliability and economy.

[0076] Economy and environmental friendliness The contradiction can be understood as that minimizing the LCOE tends to select low-cost fossil energy (such as LNG), but it will push up carbon emissions. If the proportion of renewable energy is forced to increase, the system investment cost (CAPEX) will increase by 15 - 25%.

[0077] Reliability and economy The contradiction can be understood as that reducing the EENS requires configuring excess energy storage (such as a 30% expansion of lithium batteries), resulting in an 8 - 12% increase in the LCOE. However, energy storage redundancy can reduce high - cost power outage penalties (typical value: 100,000 - 500,000 yuan per time).

[0078] In the embodiments of the present disclosure, the adaptive reference point of the third - generation non - dominated sorting genetic algorithm can dynamically adjust the Pareto front resolution according to the port scale. For example, a small port (<100MW) can have 50 reference points, and a large port (≥100MW) can have 100 reference points. Based on this, while the calculation efficiency is increased by 40%, the diversity of the solution set can be maintained.

[0079] The time - discounting effect based on the third - generation non - dominated sorting genetic algorithm introduces time - dependent weights, making the reliability investment in the near future more advantageous than long - term storage, which can lead to a rapid decline in battery costs.

[0080] In the present disclosure, the dynamic optimization strategy for the coordinated output power supply of the multi - energy energy storage system can be as follows: In the medium term (2025 - 2030), an LNG + energy storage hybrid scheme (carbon emission / cost balance point) can be adopted, and in the long term (2030+), a photovoltaic + tidal energy + short - term energy storage (relying on the decline of technology costs) can be adopted.

[0081] In the present disclosure, physical and / or operating constraint conditions ensure the feasibility of the scheme, including: equipment physical limitations, operating safety boundaries, and special requirements for port operations. Among them, the equipment physical limitation can be that the ramp rate of the LNG unit ≤15MW / min, the operating safety boundary can be that the SOC working window of the energy storage is 20 - 95%, and the special requirement for port operations can be the continuous power supply guarantee of shore power. Based on the above requirements, the determined constraint conditions include one or more of the power balance constraint, generator operation limit constraint, energy storage dynamic characteristic constraint, renewable energy penetration constraint, and equipment - energy coupling constraint.

[0082] Among them, the expression of the power balance constraint is as follows:

[0083] In the formula, represents the charging power (kW) of the energy storage system j at time period t, indicates that this equation holds for all time periods t and represents the renewable energy curtailment, which is limited to not exceeding t the renewable energy generation R t by 5%. This constraint not only ensures the balance between supply and demand but also incorporates strategic energy curtailment measures to prevent grid instability during load spikes caused by cranes.

[0084] The expression of the generator operation limit constraint is as follows:

[0085] where represents the minimum output power (kW) of unit , represents the maximum output power (kW) of unit , represents the output power of unit at time period t - 1, that is, the output power of the previous time period of time period t, represents the maximum allowable output power change rate of unit i , represents the time interval.

[0086] This equation stipulates the upper and lower limits of the generation capacity and restricts the power ramp rate. Due to the lag effect of the turbocharger, diesel generators face more stringent ramp rate limits (2 MW / min), and this characteristic is modeled by a first-order delay transfer function where the time constant τ = 8 seconds.

[0087] The expression of the energy storage dynamic characteristic constraint is as follows:

[0088] where represents the change in the state of charge of the energy storage at time period t - 1, represents the charging efficiency of the energy storage system j , represents the discharging efficiency of the energy storage system j , represents the rated capacity of the energy storage system j , represents the discharging power of the energy storage system j , represents the charging power of the energy storage system j .

[0089] This equation models the change in state of charge (SOC) through efficiency factors (charge efficiency η ch = 95%, discharge efficiency η dis = 98%), sets a safety margin to prevent lithium metal precipitation, and limits the charge and discharge rates. The time-coupling relationship, i.e., today's charging affects tomorrow's discharging, requires multi-period optimization, which demands that the second-generation non-dominated sorting genetic algorithm has the ability to handle 12,208 variables within a 24-hour time range.

[0090] The expression for the renewable energy penetration constraint is as follows:

[0091] This equation enables the optimizer to balance policy compliance and economic reality.

[0092] The expression for the coupling constraint between the device and energy is as follows:

[0093] In the equation, represents the power of the crane at time t M represents the number of rotating components in the crane, m is the index for traversing the rotating components, ranging from 1 to M , representing different rotating components, represents the angular velocity of the rotating component m , represents the angular acceleration of the rotating component m , represents the angle of the crane motor, , are torque constants determined according to the manufacturer's specifications.

[0094] This constraint directly links mechanical operations with power demand, transforming the crane scheduling problem into a power trajectory optimization problem.

[0095] This disclosure causally integrates the physical characteristics of the device and the energy flow, and co-optimizes the device operation and power scheduling. At the same time, the three-objective (i.e., three objective functions) structure promotes explicit trade-off analysis, which is beneficial for the decision-making of stakeholders.

[0096] The embodiments of this disclosure can also verify the optimal configuration scheme through a large number of Monte Carlo simulations. The simulation process integrates probability models such as ship fuel transportation delays, random evolution of typhoon paths, and accelerated degradation of photovoltaic modules. The resilience evaluation indicators include the load recovery time during grid outages, the real-time state of charge (SOC) of the energy storage system, and the reproduction of historical major fault events using digital twin technology.​​​​​​

[0097] The effectiveness of this disclosure can be verified through a comparative analysis of three scenarios: Scenario S1 (a baseline scenario dominated by diesel power generation), Scenario S2 (a photovoltaic-wind energy-storage hybrid scenario), and Scenario S3 (an optimized system scenario). The three-objective Pareto front quantifies the inherent trade-off relationships among the levelized cost of energy (LCOE), carbon intensity, and reliability.

[0098] Among them, Scenario S3 has an advantage in the solution space, with both its levelized cost of energy and carbon dioxide emissions being reduced compared to Scenario S1.

[0099] This performance benefits from energy arbitrage in terms of time: the lithium-ion battery energy storage system transfers 38% of the surplus photovoltaic power during the day to the peak electricity consumption period of the crane at night, thus reducing the dependence on liquefied natural gas peaking units. However, the concave shape of the Pareto surface indicates that when the energy storage capacity exceeds 45 megawatt-hours, the benefits will gradually decline and the costs will gradually increase, exceeding the current valuation of carbon credits.

[0100] Although Scenario S2 has lower emissions, its levelized cost of energy has increased. This (higher-cost) situation stems from the static scheduling method of Scenario S2, which fails to match the photovoltaic power generation with the low-load period of the crane. Scenario S3 overcomes this limitation by deliberately delaying the utilization time of photovoltaic power generation for a certain period.

[0101] The energy storage system plays a dual role: the lithium-ion battery absorbs the load peaks caused by the crane, while the vanadium redox flow battery provides the ability to cope with typhoons and can continuously supply power to critical loads during historical power outages. This hybrid energy storage strategy reduces the dependence on diesel power generation, mainly during the recovery stage after typhoons, and the liquefied natural gas peaking units operate selectively, avoiding the inefficient low-load operation state commonly existing in Scenario S1.

[0102] Scenario S2 experienced multiple power outages due to relying only on a single type of energy storage device, while Scenario S1 incurred high fines due to fuel shortages, highlighting the disaster resistance of the optimized system.

[0103] The economic feasibility measured by the net present value (NPV), internal rate of return (IRR), and payback period further verifies the superiority of Scenario S3. The net present value of Scenario S3 is lower than that of Scenario S1. This economic advantage stems from load transfer incentives: energy storage arbitrage reduces the costs incurred due to rapid electricity demand every day and also reduces the power outage costs.

[0104] The levelized cost of energy (LCOE) of Scenario S3 is lower than that of the hybrid microgrid and the liquefied natural gas-solar power system. At the same time, its carbon dioxide emission intensity is close to that of the system mainly based on offshore wind power, and the capital intensity is relatively low.

[0105] In summary of the above embodiments, the present disclosure integrates the high-resolution load dynamic changes, the complementarity of multiple energy sources, and the transient response at the equipment level into a unified decision-making architecture, establishing a transformative framework for the optimization of port energy systems. Compared with the baseline scenario mainly based on diesel power generation, the optimized system reduces the levelized cost of energy, reduces carbon dioxide emissions, and still has reliability during extreme weather events.

[0106] Analysis shows that the emission reduction is due to the load shifting strategy, which utilizes the flexibility of crane scheduling and the thermal inertia of refrigerated containers, rather than relying on increasing renewable energy. This challenges the currently prevalent paradigm of decarbonization driven by installed capacity and provides an expandable blueprint for ports with limited land resources.

[0107] Based on the same principle as the method shown in Figure 1 The present disclosure also provides a multi-energy coordinated optimization device based on the characteristics of port electrical loads. Figure 7 It is a schematic diagram of the multi-energy coordinated optimization device based on the characteristics of port electrical loads provided by the embodiments of the present disclosure. Refer to Figure 7 As shown in, the multi-energy coordinated optimization device 700 based on the characteristics of port electrical loads may include: An acquisition module 701 for acquiring high-resolution power parameters of the load and power parameters of renewable resource supply; a determination module 702 for matching the high-resolution power parameters of the load and the power parameters of renewable resource supply, determining a matching matrix of the power required by the load and the power supplied by renewable resources, and determining a power supply resource allocation strategy for the multi-energy energy storage system according to the matching matrix; an optimization module 703 for coordinately optimizing the power supply resource allocation strategy based on the objective function of the power supply resource allocation strategy to determine a dynamic optimization strategy.

[0108] In the embodiments of the present disclosure, the determination module 702 is configured to extract load characteristics based on the high-resolution power parameters of the load, and extract renewable energy operation characteristics based on the power parameters of renewable resource supply; determine a first interaction relationship between the load characteristics and the renewable energy operation, and determine a coupling model of the load and the renewable energy according to the first interaction relationship; use the coupling model to match the load characteristics and the renewable energy operation characteristics to determine a matching matrix of the power required by the load and the power supplied by renewable resources.

[0109] In an embodiment of the present disclosure, a determination module 702 is configured to determine a load characteristic type based on the high-resolution power parameters of the load; determine characteristic quantization indexes of the load characteristic type and the power supply parameters of the renewable resources; determine tidal energy based on solar energy and wind energy in historical meteorological data; use the tidal energy as supplementary power supply during low electricity consumption periods, match the supplementary power supply and the characteristic quantization indexes through the matching matrix, and determine a power supply resource allocation strategy for the multi-energy energy storage system.

[0110] In an embodiment of the present disclosure, the determination module 702 is configured to analyze the high-resolution power parameters of the load, determine the operating conditions of the load, the equipment energy consumption, and the second interaction relationship between the environmental factors and the equipment; and determine the load characteristic type according to the second interaction relationship.

[0111] In an embodiment of the present disclosure, the determination module 702 is configured to analyze the load characteristic type and determine a load mode quantization index, a load time regularity quantization index, and a load power consumption fluctuation quantization index. Determine the third interaction relationship between the load mode quantization index, the load time regularity quantization index, the load power consumption fluctuation quantization index and the power supply parameters of the renewable resources; analyze and quantify the third interaction relationship through spectral coherence to obtain the characteristic quantization index of the renewable resources.

[0112] In the present disclosure, the determination module 702 is configured to obtain the determined load mode quantization index, load time regularity quantization index, and load power consumption fluctuation quantization index; determine the lithium-ion battery configuration parameters, capacitor model parameters of the multi-energy energy storage system, and the time parameter for using tidal energy as supplementary power supply during low electricity consumption periods based on the load mode quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index; and determine a power supply resource allocation strategy for the multi-energy energy storage system according to the lithium-ion battery configuration parameters, the capacitor model parameters, and the time parameter.

[0113] In an embodiment of the present disclosure, an optimization module 703 is configured to dynamically adjust the Pareto front resolution based on the adaptive reference point of the third-generation non-dominated sorting genetic algorithm and the port scale, and determine the time weight based on the time discount effect of the third-generation non-dominated sorting genetic algorithm; determine the mutual conflict mechanism between the objectives of the objective function; balance the mutual conflict mechanism between the objectives according to the adjusted Pareto front resolution and the time weight, dynamically optimize the objective function, and determine a dynamic optimization strategy for the power supply resource allocation strategy.

[0114] In the present disclosure, the objective function includes one or more of a levelized cost of electricity objective function, a carbon dioxide emissions objective function, and a power supply shortage objective function.

[0115] Furthermore, the multi-objective optimization model further includes constraint conditions; The constraint conditions include one or more of power balance constraint, generator operation limit constraint, energy storage dynamic characteristic constraint, renewable energy penetration constraint, and coupling constraint between equipment and energy.

[0116] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A multi - energy coordinated optimization method based on the characteristics of port electrical loads, characterized in that, It includes the following steps: Obtain the high-resolution power parameters of the load and the power parameters supplied by renewable resources; Match the high-resolution power parameters of the load and the power parameters supplied by renewable resources, determine the matching matrix of the power required by the load and the power supplied by renewable resources, and determine the power supply resource allocation strategy of the multi-energy energy storage system according to the matching matrix; Based on the objective function of the power supply resource allocation strategy, coordinate and optimize the power supply resource allocation strategy to determine the dynamic optimization strategy.

2. The multi - energy coordinated optimization method based on the port electricity load characteristics according to claim 1, wherein, The determination of the matching matrix of the power required by the load and the power supplied by renewable resources includes: Extract the load characteristics based on the high-resolution power parameters of the load, and extract the operating characteristics of renewable energy based on the power parameters supplied by renewable resources; Determine the first interaction relationship between the load characteristics and the operation of renewable energy, and determine the coupling model of the load and renewable energy according to the first interaction relationship; Use the coupling model to match the load characteristics and the operating characteristics of renewable energy to determine the matching matrix of the power required by the load and the power supplied by renewable resources.

3. The multi - energy coordinated optimization method based on the port electricity load characteristics according to claim 1, characterized in that, The determination of the power supply resource allocation strategy of the multi-energy energy storage system according to the matching matrix includes: Determine the load characteristic type based on the high-resolution power parameters of the load; Determine the characteristic quantization indexes of the load characteristic type and the power parameters supplied by renewable resources; Determine tidal energy based on solar energy and wind energy in historical meteorological data; Use the tidal energy as the supplementary power supply during the low-power consumption period, match the supplementary power supply and the characteristic quantization indexes through the matching matrix, and determine the power supply resource allocation strategy of the multi-energy energy storage system.

4. The multi - energy coordinated optimization method based on the port electricity load characteristics according to claim 3, wherein, The determination of the load characteristic type based on the high-resolution power parameters of the load includes: Analyze the high-resolution power parameters of the load to determine the second interaction relationship between the operating conditions of the load, the equipment energy consumption, and the environment and the equipment; Determine the load characteristic type according to the second interaction relationship.

5. The multi - energy coordinated optimization method based on the characteristics of port electrical loads according to claim 3, wherein The determination of the characteristic quantization indexes of the load characteristic type and the power parameters supplied by renewable resources includes: Analyze the load characteristic type to determine the load mode quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index; Determine the third interaction relationship between the load mode quantization index, the load time regularity quantization index, the load power consumption fluctuation quantization index and the power parameters supplied by renewable resources; Analyze and quantify the third interaction relationship through spectral coherence to obtain the characteristic quantization index of renewable resources.

6. The multi - energy coordinated optimization method based on the port electricity load characteristics according to claim 3, wherein, The use of the tidal energy as the supplementary power supply during the low-power consumption period, matching the supplementary power supply and the characteristic quantization indexes through the matching matrix, and determining the power supply resource allocation strategy of the multi-energy energy storage system includes: Obtain the determined load mode quantization index, load time regularity quantization index, and load power consumption fluctuation quantization index; Based on the load pattern quantization index, the load time regularity quantization index, and the load power consumption fluctuation quantization index, determine the lithium-ion battery configuration parameters, capacitor model parameters of the multi-energy energy storage system, and the time parameters for using tidal energy as supplementary power supply during low electricity consumption periods. According to the lithium-ion battery configuration parameters, the capacitor model parameters, and the time parameters, determine the power supply resource allocation strategy of the multi-energy energy storage system.

7. The multi - energy coordinated optimization method based on the port electricity load characteristics according to claim 1, characterized in that, Based on the objective function of the power supply resource allocation strategy, coordinate and optimize the power supply resource allocation strategy to determine the dynamic optimization strategy, including: Adapting the reference point and dynamically adjusting the Pareto front resolution based on the port scale using the third-generation non-dominated sorting genetic algorithm, and determining the time weight based on the time discount effect of the third-generation non-dominated sorting genetic algorithm. Determine the mechanism of mutual conflict between the objectives of the objective function. According to the adjusted Pareto front resolution and the time weight, weigh the mechanism of mutual conflict between the objectives, dynamically optimize the objective function, and determine the dynamic optimization strategy of the power supply resource allocation strategy.

8. The multi - energy coordinated optimization method based on the port electricity load characteristics according to claim 1 or 7, characterized in that The objective function includes one or more of the levelized cost of electricity objective function, carbon dioxide emissions objective function, and power supply shortage objective function.

9. The multi - energy coordinated optimization method based on the port electrical load characteristics according to claim 1 or 7, characterized in that, The objective function also includes constraint conditions. The constraint conditions include one or more of power balance constraints, generator operation limit constraints, energy storage dynamic characteristic constraints, renewable energy penetration constraints, and equipment and energy coupling constraints.

10. A multi - energy coordinated optimization device based on the characteristics of port electrical loads, characterized in that, Using the multi-energy coordinated optimization method based on the port electricity load characteristics according to any one of claims 1-9, the device includes: An acquisition module for acquiring high-resolution power parameters of the load and power supply parameters of renewable resources. A determination module for matching the high-resolution power parameters of the load and the power supply parameters of renewable resources, determining the matching matrix of the power required by the load and the power supply of renewable resources, and determining the power supply resource allocation strategy of the multi-energy energy storage system according to the matching matrix. An optimization module for coordinating and optimizing the power supply resource allocation strategy based on the objective function of the power supply resource allocation strategy to determine the dynamic optimization strategy.

Images