Harbor energy station risk planning method and system under energy flow-logistics coupling

Abstract

The invention relates to a port energy station risk planning method and system under energy flow-logistics coupling. The method comprises the following steps: constructing a port multiple uncertainty model; based on the port multi-uncertainty model, adopting a conditional value-at-risk theory to quantitatively analyze a wind and light energy abandoning risk brought by multi-uncertainty to port energy station operation simulation, and establishing a wind and light energy abandoning risk constraint; constructing a port energy station risk planning model considering multiple uncertainties based on the wind and light energy abandoning risk constraint, wherein the port energy station risk planning model takes the minimum annual comprehensive cost as an objective function; and solving the port energy station risk planning model. Compared with the prior art, the method can fully consider the influence of multiple uncertainties of the port energy flow side and the logistics side on the clean energy abandoning risk, and integrates the risk measurement in the planning process to achieve the quantitative analysis of the risk preference and risk loss in the planning decision, thereby properly avoiding the wind and light energy abandoning risk of the port energy station.

Description

Technical Field

[0001] The present invention relates to the technical field of port energy station planning, and in particular to a port energy station risk planning method and system under energy flow-logistics coupling. Background Art

[0002] In the context of the current global energy structure transformation and increasing awareness of environmental protection, ports, as important hubs for international trade and logistics, have an important role to play in promoting sustainable development, reducing environmental pollution and enhancing national competitiveness. In particular, the application of clean energy such as wind and solar power has become a key path for port transformation and upgrading, and the penetration rate of clean energy such as wind and solar power in ports has also increased year by year.

[0003] Considering the intermittent and volatile nature of clean energy output and the randomness of conventional multi-energy load demand, the "source-load" supply and demand balance of port energy stations faces severe challenges. The excess clean energy output in some periods makes it impossible to consume it in time, and the problem of wind and solar power abandonment is particularly significant. For example, patent application CN115577880A discloses a port energy management optimization method, including: in the day-ahead scheduling stage, a day-ahead economic scheduling model and constraints for the port area are established with the purpose of minimizing the overall operating cost of the port area for one day; the day-ahead scheduling results are obtained based on the day-ahead economic scheduling model and constraints; in the intraday adjustment stage, a prediction model and constraints for future working conditions within the day are established with the purpose of reducing the power changes of the main power grid; the intraday adjustment results are obtained based on the prediction model and constraints for future working conditions within the day; the day-ahead scheduling results are corrected based on the intraday adjustment results to improve the energy autonomy of the port. The above method can handle the uncertainty of new energy power.

[0004] However, in addition to the uncertainty on the energy side, the arrival time and cargo volume of ships are key considerations for ports to formulate logistics operation plans. Their uncertainties may have a profound impact on the resource allocation and energy consumption of logistics links such as ship berthing, quay crane loading and unloading, container truck transportation, yard crane stacking, cold chain storage, etc., and to a certain extent, it also exacerbates the contradiction between energy supply and demand. Existing studies usually ignore the impact of logistics system uncertainty on port logistics energy consumption. There is an urgent need for a port energy station risk planning method that considers multiple uncertainties under energy flow-logistics coupling. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a port energy station risk planning method and system under energy flow-logistics coupling, which can properly avoid the risk of wind and solar power abandonment in port energy stations on the energy flow side and the logistics side.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A risk planning method for a port energy station under energy flow-logistics coupling includes the following steps:

[0008] Construct multiple uncertainty models for ports, including wind and solar power output forecast error uncertainty model, multi-energy load forecast error uncertainty model, ship arrival time deviation uncertainty model and ship cargo capacity deviation uncertainty model;

[0009] Based on the port multiple uncertainty model, the conditional risk value theory is used to quantitatively analyze the wind and solar power abandonment risks brought by multiple uncertainties to the port energy station operation simulation, and establish wind and solar power abandonment risk constraints;

[0010] Based on the wind and solar power abandonment risk constraints, a port energy station risk planning model considering multiple uncertainties is constructed. The port energy station risk planning model takes the minimization of annual comprehensive costs as the objective function. The annual comprehensive costs include annual investment costs, annual operating costs and annual abandonment risk costs.

[0011] Solve the port energy station risk planning model to obtain the optimal port energy station planning solution.

[0012] Furthermore, the wind and solar power output prediction error uncertainty model is:

[0013]

[0014] Where: are the prediction error, prediction benchmark value and true value of wind and solar power output at time t respectively; μ WT&PV and σ WT&PV are the expectation and standard deviation of wind and solar power output prediction errors, respectively.

[0015] Furthermore, the multi-energy load forecast error uncertainty model is:

[0016]

[0017] Where: are the prediction error, prediction benchmark value and true value of conventional multi-energy load of the port respectively; μ load and σ load are the expectation and standard deviation of the conventional multi-energy load forecasting error of the port, respectively.

[0018] Furthermore, the uncertainty model of the ship arrival time deviation is:

[0019]

[0020] Where: Δt arrive ,t arrive , are the deviation value, planned value and actual value of the ship arrival time respectively; λ is also the expectation and standard deviation of the deviation distribution of the ship arrival time.

[0021] Furthermore, the uncertainty model of the ship cargo capacity deviation is:

[0022]

[0023] Where: ΔTEU, TEU, are the deviation value, planned value and actual value of the ship's cargo capacity respectively; μ TEU and σ TEU are the expected and standard deviation of the ship cargo capacity deviation, respectively.

[0024] Furthermore, the annual energy abandonment risk cost is expressed as:

[0025]

[0026] Where: C loss is the annual risk cost of energy abandonment; ΔP PV,s,t , ΔP WT,s,t is the abandoned solar and wind power in a typical scenario s period t; c PV 、c WT They are the penalty costs for abandoned solar power and abandoned wind power respectively; CVaR PV CVaR WT are the corresponding conditional risk values ​​of abandoned solar power and abandoned wind power respectively; ε1 and ε2 are the weight coefficients corresponding to the expected loss value and the CVaR tail risk value respectively, and ε1+ε2=1; d, Typical day numbers and corresponding sets constructed for clustering; t, is the time period number and corresponding set within the scheduling period; Δt is the time period duration.

[0027] Furthermore, the wind and solar power abandonment risk constraint is:

[0028]

[0029] Z middle ≥0

[0030] Z middle ≥(ΔP PV,d,t Δt+ΔP WT,d,t Δt)-VaR cur

[0031] CVaR(ΔP PV,d,t Δt+ΔP WT,d,t Δt)≤q CVaR

[0032] Where: CVaR(ΔP PV,d,t Δt+ΔPWT,d,t Δt) is the wind and solar energy abandonment CVaR; VaRcur is the wind and solar energy abandonment VaR, that is, the wind and solar energy abandonment quantile; Z middle is a non-negative intermediate variable introduced during linearization; β is a given confidence level; s is the index of different scene numbers; P S is the probability of scene s appearing; q CVaR is the CVaR threshold of wind and solar power curtailment; Δt is the duration of the time period.

[0033] Furthermore, the constraints of the port energy station risk planning model also include upper and lower limit constraints on the number of energy equipment, power balance constraints, energy equipment operation constraints, logistics equipment operation constraints and annual carbon emission limit constraints.

[0034] Furthermore, when solving the risk planning model of the port energy station, it is decomposed into an investment decision main problem and several operation simulation sub-problems for iterative solution. The investment risk weight is introduced into the investment decision main problem, and the transformation function is introduced in the process of solving the operation simulation sub-problem to linearize the wind and solar power abandonment risk constraint.

[0035] The present invention also provides a port energy station risk planning system under energy flow-logistics coupling, including one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method described above.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. Considering the intermittent and volatile nature of clean energy output and the randomness of conventional multi-energy load demand, the "source-load" supply and demand balance of port energy stations faces severe challenges. The excess clean energy output in some periods makes it impossible to absorb it in time, and the problem of wind and solar power abandonment is particularly significant. In addition, the arrival time and cargo volume of ships are key considerations for ports to formulate logistics operation plans. Their uncertainty may have a profound impact on the resource allocation and energy consumption of logistics links such as ship berthing, quay crane loading and unloading, container truck transportation, yard crane stacking, cold chain storage, etc., and to a certain extent, it also exacerbates the contradiction between energy supply and demand. Multiple uncertainties on the energy flow side and the logistics side together constitute the source of risk in the planning of port energy stations, posing a huge test for the safe, stable and economic operation of energy stations. The present invention studies the probability distribution characteristics of multiple uncertain factors such as wind and solar power prediction errors, conventional multi-energy load prediction errors, ship arrival time deviations, and ship cargo capacity deviations, and constructs multiple uncertainty models on the port energy flow side and the logistics side. It can conduct in-depth research on the operational risks that multiple uncertainties bring to the planning scheme of port energy stations, and realize quantitative analysis of risk preferences and risk losses in planning decisions, thereby properly avoiding the risk of wind and solar power abandonment in port energy stations.

[0038] 2. The present invention quantitatively analyzes the wind and solar power abandonment risks brought by multiple uncertainties to the port energy station operation simulation based on the conditional risk value theory, and realizes the risk planning of the port energy station considering multiple uncertainties under the coupling of energy flow and logistics. It strives to minimize the cost of planning schemes at the economic level, and reduces the possibility of energy stations facing high wind and solar power abandonment risks at the risk level. It can effectively improve the clean energy penetration rate of the port while also improving the clean energy absorption capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the risk planning process of a port energy station corresponding to an embodiment of the present invention;

[0040] Figure 2 A comparison chart of wind and solar power consumption rates of a port energy station under different scenarios of an embodiment of the present invention;

[0041] Figure 3 It is a cumulative probability distribution curve diagram of wind and solar power abandonment under different confidence levels in an embodiment of the present invention;

[0042] Figure 4 It is a curve diagram of the cumulative probability distribution of wind and solar power abandonment under different CVaR thresholds in an embodiment of the present invention;

[0043] Figure 5 This is a relationship diagram between the weight coefficient of an embodiment of the present invention and the average annual wind and solar power abandonment energy and annual planning costs. DETAILED DESCRIPTION

[0044] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0045] Example 1

[0046] Multiple uncertainties on the energy flow side and the logistics side together constitute the risk source of port energy station planning, which poses a huge challenge to the safe, stable and economic operation of the energy station. In order to solve the above technical problems, this embodiment provides a risk planning method for port energy stations under energy flow-logistics coupling. The method takes into account the multiple uncertainties on the energy flow side and the logistics side, and integrates risk measurement into the planning process to achieve quantitative analysis of risk preferences and risk losses in planning decisions, thereby properly avoiding the risk of wind and solar power abandonment in port energy stations.

[0047] like Figure 1 As shown, the method comprises the following steps:

[0048] Step S1: Study the probability distribution characteristics of wind and solar power prediction error, conventional multi-energy load prediction error, ship arrival time deviation, and ship cargo capacity deviation, and establish a port multiple uncertainty model, including a wind and solar power output prediction error uncertainty model, a multi-energy load prediction error uncertainty model, a ship arrival time deviation uncertainty model, and a ship cargo capacity deviation uncertainty model;

[0049] Step S2: Based on the port multiple uncertainty model, the conditional risk value theory is used to quantify the wind and solar power abandonment risks brought by multiple uncertainties to the port energy station operation simulation, establish wind and solar power abandonment risk constraints, and take the minimum annual comprehensive cost as the objective function to construct a port energy station risk planning model considering multiple uncertainties. The annual comprehensive cost includes annual investment cost, annual operation cost and annual abandonment risk cost;

[0050] Step S3: Solve the planning model to obtain the optimal port energy station planning scheme, including the selection of energy equipment for the port energy station and the configuration quantity of each energy equipment.

[0051] In step S1, the multiple uncertainty models of each port are as follows:

[0052] (1) Wind and solar power output prediction error uncertainty model

[0053]

[0054] Where: are the prediction error, prediction benchmark value and true value of wind and solar power output at time t respectively; μ WT&PV and σ WT&PV are the expectation and standard deviation of wind and solar power output prediction errors, respectively.

[0055] (2) Multi-energy load forecasting error uncertainty model

[0056]

[0057] Where: are the prediction error, prediction benchmark value and true value of conventional multi-energy load of the port respectively; μ load and σ load are the expectation and standard deviation of the conventional multi-energy load forecasting error of the port, respectively.

[0058] (3) Uncertainty model of ship arrival time deviation

[0059]

[0060] Where: Δt arrive ,t arrive , are the deviation value, planned value and true value of the ship arrival time respectively; the expectation and standard deviation of this distribution are both λ.

[0061] (4) Uncertainty model of ship cargo capacity deviation

[0062]

[0063] Where: ΔTEU, TEU, are the deviation value, planned value and actual value of the ship's cargo capacity respectively; μ TEU and σ TEU are the expected and standard deviation of the ship cargo capacity deviation, respectively.

[0064] The risk planning model of the port energy station considering multiple uncertainties under the energy flow-logistics coupling described in step S2 is established as follows:

[0065] a. Objective function

[0066] The risk planning model of the port energy station takes the minimization of the annual comprehensive cost as the optimization goal, where the annual comprehensive cost mainly includes the annual investment cost, annual operating cost and annual energy abandonment risk cost.

[0067] (1) Annual investment cost

[0068] The annual investment cost refers to the purchase cost of various energy equipment required to be selected in the planned energy station. It is obtained by calculating the sum of the annual values ​​of the investment costs of all energy equipment to be selected in the planning scheme, as shown in formula (9):

[0069]

[0070] Where: C inv is the annual investment cost of the port energy station; ω and W are the type index and set of the energy equipment to be selected, including CHP (Combined Heat and Power), GB (Gas Boiler), EB (Electric Boiler), EC (Electronic Commutation), AC (Alternating Current), EES (Energy Storage System), WT (Wind Turbine), and PV (Photovoltaic); m and M are the number index and set of a certain type of energy equipment to be selected; c ω,m is the investment cost of the mth energy equipment to be selected; is the configuration status of the mth energy device to be selected, a 0-1 variable, which is 1 when the energy device to be selected is configured, otherwise it is 0; Rω,m is the service life of the mth energy equipment to be selected; r is the discount rate.

[0071] (2) Annual operating costs

[0072] The annual operating cost includes the annual energy purchase cost and the annual carbon emission cost, as shown in formula (10).

[0073]

[0074] Among them, the calculation method of annual energy purchase cost is shown in (11).

[0075]

[0076] Where: d, Typical day numbers and corresponding sets constructed for clustering; t, is the time period number and corresponding set within the scheduling period; ξ d is the number of days with typical type d days throughout the year; Δt is the duration of the time period.

[0077] The calculation method of annual carbon emission cost is shown in formula (12).

[0078]

[0079] Where: is the unit carbon price; The carbon emission factor of electricity purchased from the upper power grid; It is the carbon emission factor of the combustion of natural gas purchased from the upstream gas grid.

[0080] (3) Annual risk cost of energy abandonment

[0081] The annual energy abandonment risk cost consists of two parts: the expected loss value and the product of the CVaR tail risk value and its corresponding weight coefficient.

[0082]

[0083] Where: ΔP PV,s,t , ΔP WT,s,t is the abandoned solar and wind power in a typical scenario s period t; c PV 、c WT are the penalty fees for abandoning solar power and wind power respectively; p s is the probability of occurrence of typical scenario s; CVaR PV CVaR WT are the conditional risk values ​​corresponding to abandoned solar power and abandoned wind power respectively; ε1 and ε2 are the weight coefficients corresponding to the expected loss value and the CVaR tail risk value respectively, and ε1+ε2=1, where ε2<0.05 indicates that the investment decision maker is risk-seeking, and ε2>0.1 indicates that the investment decision maker is risk-averse.

[0084] b. Constraints

[0085] The constraints of the port energy station risk planning model considering multiple uncertainties include upper and lower limits of energy equipment quantity, power balance constraints, energy equipment operation constraints, logistics equipment operation constraints, annual carbon emission limit constraints, and wind and solar power abandonment risk constraints based on CVaR theory. The wind and solar power abandonment risk constraints are:

[0086]

[0087] Z middle ≥0 (15)

[0088] Z middle ≥(ΔP PV,s,t Δt+ΔP WT,s,t Δt)-VaR cur (16)

[0089] CVaR(ΔP PV,s,t Δt+ΔP WT,s,t Δt)≤q CVaR (17)

[0090] Where: CVaR(ΔP PV,s,t Δt+ΔP WT,s,t Δt) is the CVaR of wind and solar power curtailment; VaR cur is the VaR of wind and solar power abandonment, i.e., the wind and solar power abandonment quantile; Z middle is a non-negative intermediate variable introduced during linearization; β is a given confidence level; s is the index of different scene numbers; P S is the probability of scene s appearing; q CVaR It is the CVaR threshold of wind and solar power curtailment.

[0091] In step S3, the Benders decomposition algorithm is used to effectively solve the risk planning model of the port energy station, and the original problem is decomposed into a main problem and several sub-problems for iterative solution. The investment risk weights ε1 and ε2 are introduced into the main problem of investment decision-making to balance the economy and risk of the planning scheme. The transformation function linearization wind and solar power abandonment risk constraints are introduced in the process of solving the running simulation sub-problems. In a specific embodiment, the investment risk weights ε1 and ε2 are selected according to the risk preferences of investors to achieve a balance between economy and risk.

[0092] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0093] To verify the effectiveness of the above method, this embodiment selects a port improvement example for analysis. The port terminal has a shoreline of 750m long, with a total of 8 shore power supplies, 15 quay cranes, 35 electric container trucks, 20 yard cranes and 1,000 refrigerated containers deployed. The parameters of each logistics equipment are shown in Table 1. The port plans to plan an energy station. The energy equipment to be planned includes CHP, GB, EB, AC, EC, EES, WT, and PV. The main parameters are shown in Table 2. The service life of the energy station planning and the selected energy equipment are both set to 20 years, and the discount rate is set to 5%. The penalty fee for the unit abandonment of wind and light in the port is 0.45 yuan / kWh, the given confidence level is 85%, the risk value threshold of the wind and light abandonment condition is 2MWh, the expected loss weight ε1=0.1, and the tail risk weight ε2=0.9. It is assumed that the wind and solar power output prediction error follows a normal distribution with a mean of 0 and a variance of 0.09 times the basic value; the conventional multi-energy load prediction error follows a normal distribution with a mean of 0 and a variance of 0.09 times the basic value; it is assumed that the ship arrival time deviation follows an Erlang first-order distribution with an expectation of 0.1 and a variance of 0.01.

[0094] In order to analyze the impact of wind and solar power abandonment risks caused by multiple uncertainties on the planning and operation simulation of port energy stations, three types of scenario comparisons are set up, as shown in Table 3, where "√" means that the condition is taken into account and "×" means that the condition is not taken into account.

[0095] Table 1 Logistics equipment parameters

[0096]

[0097] Table 2 Parameters of energy equipment to be configured

[0098]

[0099]

[0100] Table 3 Comparison of port energy station planning scenarios considering multiple uncertainties

[0101]

[0102] The planning costs of port energy stations considering multiple uncertainties under different scenarios are shown in Table 4. Scenario I does not consider the risk control of wind and solar energy abandonment, and the annual investment cost is the lowest, indicating that the port sacrifices its adaptability to the risk of wind and solar energy abandonment in exchange for the comprehensive cost advantage of the planning scheme. Since this scenario does not take into account the risk control of energy abandonment, if the port energy station encounters extreme wind and solar output fluctuations during actual operation, it may cause more serious economic losses. After considering the risk control of wind and solar energy abandonment in Scenario II, the annual investment cost and annual operating cost both increased, but the annual energy abandonment cost decreased significantly by 68.13%, indicating that at this time, investment decision makers prefer to increase economic costs to reduce the operating risks of port energy stations, thereby achieving efficient use of clean energy. In this scenario, investment decision makers show a preference for accepting the increase in the comprehensive cost of the planning scheme in exchange for the safe operation of the energy station, which is sustainable. Scenario III considers the risk control of wind and solar power abandonment but does not consider the adjustable timing of energy consumption in all aspects of logistics. Compared with Scenario II, the annual operating costs and annual abandonment costs have increased, and the annual comprehensive costs have increased by 2.28%. This further shows that considering the adjustable timing of energy consumption in all aspects of port logistics can promote the consumption of clean energy to a certain extent and reduce the operating costs of energy stations.

[0103] Table 4 Comparison of planning costs of port energy stations under different scenarios

[0104]

[0105] Table 5 shows the planning schemes of port energy stations under different scenarios. In scenario I, in terms of equipment type selection, GB and EB are not configured. This is because CHP has the advantage of lower operating costs in heating and can effectively reduce the heating costs of energy stations. Compared with scenario I, scenario II increases the configuration capacity of CHP, GB, and EC, and scenario III increases the configuration capacity of GB, ES, and EES, indicating that the planning schemes of scenarios II and III are more conservative, and the configuration capacity of energy conversion and storage equipment has increased, that is, at the expense of a certain degree of economic efficiency in exchange for effective control of the risk of wind and solar energy abandonment, reducing the high operating risk of energy stations that may be faced. Compared with scenario II and scenario III, scenario III does not consider the adjustable time sequence of energy consumption in the entire logistics link, which requires additional costs, that is, to reduce the risk of wind and solar energy abandonment, the EES capacity configuration is increased, indicating that in this scenario, when the wind and solar power output is excessive, the energy station chooses to temporarily store part of the energy in the ESS instead of directly abandoning it, thereby effectively reducing the amount of wind and solar abandonment and improving the utilization efficiency of clean energy in the port.

[0106] Table 5 Number of energy equipment configurations in port energy stations under different scenarios

[0107]

[0108] The wind and solar power consumption rates of port energy stations considering multiple uncertainties under different scenarios are as follows: Figure 2 As shown. Through comparative analysis, it can be seen that the port wind and solar power consumption rate is the highest under scenario II, and there are two reasons. On the one hand, the adjustable time sequence of energy consumption in all links of port logistics can realize the transfer of logistics energy load on a time scale. When the wind and solar power output is sufficient, the relevant logistics energy load can be increased in a timely manner, and when the wind and solar power output is low, the relevant logistics operations can be reduced or postponed, thereby promoting the consumption of clean energy. On the other hand, after considering the risk control of wind and solar power abandonment, investment decision makers introduce the wind and solar power abandonment CVaR threshold. When simulating the operation of the port energy station, they will make full use of various energy conversion and storage equipment to quickly respond to wind and solar power fluctuations to reduce the risk loss of wind and solar power abandonment and improve the instant absorption capacity of the energy station. In addition, by comparing scenario I, scenario III with scenario II, it can be found that the adoption of wind and solar power abandonment risk control measures has a more significant impact on improving the wind and solar power absorption level of the port energy station than considering the adjustable time sequence of energy consumption in all links of logistics. This shows that although considering the adjustable timing of energy consumption in all links of logistics can promote the consumption of clean energy to a certain extent, when facing the risk of wind and solar power abandonment brought about by multiple uncertainties, introducing conditional risk value to control the risk of wind and solar power output fluctuations, and configuring energy conversion and storage equipment with sufficient capacity to quickly respond to changes in wind and solar power, plays a more critical role in improving the wind and solar power consumption rate in ports.

[0109] In order to deeply analyze the impact of wind and solar power abandonment CVaR on the planning results of port energy stations, this paper explores the three aspects of different confidence levels, risk thresholds and weight coefficients.

[0110] (1) Impact of energy abandonment risk on planning results at different confidence levels

[0111] Based on scenario II, the CVaR confidence levels are 80%, 85%, 90%, and 95%, respectively. The energy station planning costs are calculated at each confidence level, and the results are shown in Table 6.

[0112] As can be seen from Table 6, as the confidence level increases, the planning cost of the port energy station planning scheme also shows an increasing trend. This shows that when investment decision makers choose a higher confidence level to pursue more stringent wind and solar power abandonment risk control, they need to bear higher tail risk costs. However, excessive pursuit of high confidence levels may lead to excessively high total planning costs, which may affect the economy and feasibility of the planning scheme. Therefore, in order to achieve safety and stability during operation while taking into account the economy of the planning scheme, investment decision makers may consider appropriately lowering the confidence level. By investing more economic costs, such as increasing the capacity configuration of energy station equipment, the risk of wind and solar power abandonment caused by multiple uncertainties during operation can be avoided, and the sensitivity to high operating risks can be reduced as much as possible, thereby reducing the additional costs caused by tail risks.

[0113] Table 6 Comparison of energy station planning costs at different confidence levels

[0114]

[0115] The cumulative probability distribution curves of wind and solar power abandonment under different confidence levels are as follows: Figure 3 As shown in the figure, with 80%, 85%, 90%, and 95% confidence levels as boundaries, it can be found that the cumulative probability of wind and solar power abandonment is higher in the tail, that is, the shaded part can effectively show the tail characteristics of wind and solar power abandonment. According to the conditional risk value theory, the wind and solar power abandonment CVaR under a given confidence level is actually the expected value of the abandonment VaR corresponding to the confidence level. Only considering VaR may ignore the moments with low probability of occurrence and large abandonment energy, while CVaR can pay attention to the points beyond the confidence level range, which can more comprehensively reflect the tail characteristics of abandonment risk, indicating that CVaR effectively compensates for the limitations of VaR in measuring tail risks. As investment decision makers continue to adjust the confidence level, the steepness of the tail distribution of wind and solar power abandonment risk also changes accordingly. The higher the confidence level, the more obvious the steepness of the tail, that is, the greater the fluctuation of the wind and solar power abandonment value, indicating that in rare cases, there may be wind and solar power abandonment that is significantly higher than the average. On the contrary, when the confidence level decreases, the cumulative probability curve of wind and solar power abandonment becomes flat in the tail area, indicating that in rare cases, the fluctuation range of wind and solar power abandonment is relatively small; at this time, the tail risk boundary shifts to the right, the covered abandonment range expands, the tolerance of investment decision makers for abandonment risk increases, and the requirements for preventing extreme risks are relatively relaxed. Investment decision makers can choose the appropriate confidence level according to their own risk preferences, so as to determine the control strength of the tail risk of wind and solar power abandonment.

[0116] (2) Impact of energy abandonment risk on planning results under different energy abandonment CVaR thresholds

[0117] Based on Scenario II, the CVaR thresholds are set at 1MWh, 2MWh, and 4MWh respectively. The energy station planning costs are calculated at each energy abandonment threshold, and the results are shown in Table 7.

[0118] Table 7 Comparison of energy station planning costs under different CVaR thresholds

[0119]

[0120] As shown in Table 7, as the CVaR threshold increases, the total planning cost of the port energy station also shows an increasing trend. This is because investment decision makers choose a higher CVaR threshold to focus on greater potential losses and assess higher-level risks, and more economic costs are required to make the planning scheme better able to withstand more extreme risk events.

[0121] The cumulative probability distribution curve of wind and solar power abandonment under different CVaR thresholds is as follows: Figure 4 As shown in the figure, with the 85% confidence level as the boundary, it can be seen that different CVaR thresholds affect the tail shape of the cumulative probability distribution curve of wind and solar power abandonment. As the CVaR threshold increases, the tail distribution of wind and solar power abandonment becomes steeper, indicating that the investment decision makers can accept the risk of the planning scheme more highly at this time, which means that the investment decision makers are willing to bear greater economic losses to deal with possible extreme abandonment situations. On the contrary, when the CVaR threshold is set lower, the tail of the curve is relatively flat, reflecting that the investment decision makers are cautious about tail risks. Investment decision makers can choose the appropriate CVaR threshold according to their own risk preferences, so as to determine the intensity of control over the tail risk of wind and solar power abandonment.

[0122] (3) Impact of energy abandonment risk on planning results under different weight coefficients

[0123] Depend on Figure 5 It can be seen that with the increase of the risk preference weight coefficient, the average annual wind and solar energy abandonment gradually decreases, the corresponding operation risk gradually decreases, and the planned annual comprehensive cost gradually increases. This shows that at this time, the investment decision makers hope to avoid risks and increase the weight coefficient to strengthen risk control, that is, to reduce the system operation risk by sacrificing certain economic costs. Therefore, in the planning process of port energy stations, investment decision makers can control the risk of wind and solar energy abandonment by increasing the weight coefficient, but they also need to pay attention to balancing the economy and risk of planning methods. They can deeply explore the historical wind and solar output fluctuation characteristics of the planned port area, the characteristics of user energy demand and its changing trends, and reasonably select the weight coefficient. At the economic level, strive to minimize the cost of the planning scheme, and at the risk level, reduce the possibility of energy stations facing high wind and solar energy abandonment risks, effectively improve the port's clean energy penetration rate, and improve the clean energy absorption capacity, and avoid unnecessary energy waste.

[0124] Example 2

[0125] The present invention also provides a port energy station risk planning system under energy flow-logistics coupling, comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the port energy station risk planning method under energy flow-logistics coupling as described in Example 1.

[0126] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0127] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0132] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A risk planning method for a port energy station under energy flow-logistics coupling, characterized in that: The following steps are involved: Construct multiple uncertainty models for ports, including wind and solar power output forecast error uncertainty model, multi-energy load forecast error uncertainty model, ship arrival time deviation uncertainty model and ship cargo capacity deviation uncertainty model; Based on the port multiple uncertainty model, the conditional risk value theory is used to quantitatively analyze the wind and solar power abandonment risks brought by multiple uncertainties to the port energy station operation simulation, and establish wind and solar power abandonment risk constraints; Based on the wind and solar power abandonment risk constraints, a port energy station risk planning model considering multiple uncertainties is constructed. The port energy station risk planning model takes the minimization of annual comprehensive costs as the objective function. The annual comprehensive costs include annual investment costs, annual operating costs and annual abandonment risk costs. Solve the port energy station risk planning model to obtain the optimal port energy station planning solution.

2. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: The wind and solar power output prediction error uncertainty model is: Where: are the prediction error, prediction benchmark value and true value of wind and solar power output at time t respectively; μ WT&PV and σ WT&PV are the expectation and standard deviation of wind and solar power output prediction errors, respectively.

3. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: The multi-energy load forecast error uncertainty model is: Where: are the prediction error, prediction benchmark value and true value of conventional multi-energy load of the port respectively; μ load and σ load are the expectation and standard deviation of the conventional multi-energy load forecasting error of the port, respectively.

4. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: The uncertainty model of the ship arrival time deviation is: Where: Δt arrive ,t arrive , are the deviation value, planned value and actual value of the ship arrival time respectively; λ is also the expectation and standard deviation of the deviation distribution of the ship arrival time.

5. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: The uncertainty model of ship cargo capacity deviation is: Where: ΔTEU, TEU, are the deviation value, planned value and actual value of the ship's cargo capacity respectively; μ TEU and σ TEU are the expected and standard deviation of the ship cargo capacity deviation, respectively.

6. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: The annual energy abandonment risk cost is expressed as: Where: C loss is the annual risk cost of energy abandonment; ΔP PV,s,t , ΔP WT,s,t is the abandoned solar and wind power in a typical scenario s period t; c PV 、c WT They are the penalty costs for abandoned solar power and abandoned wind power respectively; CVaR PV CVaR WT They are the corresponding conditional risk values ​​of abandoned solar power and abandoned wind power respectively; ε1 and ε2 are the weight coefficients corresponding to the expected loss value and the CVaR tail risk value, respectively, and ε1+ε2=1; d, Typical day numbers and corresponding sets constructed for clustering; t, It is the time period number and corresponding set within the scheduling cycle; Δt is the time period duration.

7. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: The wind and solar power abandonment risk constraints are: Z middle ≥0 Z middle ≥(ΔP PV,d,t Δt+ΔP WT,d,t Δt)-VaR cur CVaR(ΔP PV,d,t Δt+ΔP WT,d,t Δt)≤q CVaR Where: CVaR(ΔP PV,d,t Δt+ΔP WT,d,t Δt) is the wind and solar power abandoned energy CVaR; VaR cur is the wind and solar power abandoned energy VaR, that is, the wind and solar power abandoned energy quantile; Z middle is a non-negative intermediate variable introduced during linearization; β is a given confidence level; s is the index of different scene numbers; P S is the probability of scene s appearing; q CVaR is the CVaR threshold for wind and solar power curtailment; Δt is the duration of the time period.

8. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: The constraints of the port energy station risk planning model also include upper and lower limit constraints on the number of energy equipment, power balance constraints, energy equipment operation constraints, logistics equipment operation constraints and annual carbon emission limit constraints.

9. The risk planning method for a port energy station under energy flow-logistics coupling according to claim 1 is characterized in that: When solving the risk planning model of the port energy station, it is decomposed into an investment decision main problem and several operation simulation sub-problems for iterative solution. The investment risk weight is introduced into the investment decision main problem, and the transformation function is introduced in the process of solving the operation simulation sub-problem to linearize the wind and solar power abandonment risk constraint.

10. A port energy station risk planning system under energy flow-logistics coupling, characterized in that: The system comprises one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method according to any one of claims 1 to 9.

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