Multi-stage planning method and device for oil-electric hybrid energy station, electronic equipment and medium

Abstract

This application relates to the field of hybrid electric vehicle (HEV) station planning technology, and particularly to a multi-stage planning method, device, electronic equipment, and medium for HEV stations. The method includes: calculating the balanced allocation of HEV station charging demand at different planning locations under any planning stage based on a rational charging demand allocation strategy for vehicle users; performing capacity planning for the HEV station under multiple planning stages in conjunction with constraints that the planning must meet, resulting in a multi-stage planning model for the HEV station; and decoupling and iterating the multi-stage planning model based on the fixed-point theorem and the net cost of dismantling refueling facilities, resulting in an optimized multi-stage planning scheme for the HEV station. This solves the problems of related technologies ignoring user rationality in vehicle charging demand allocation, leading to discrepancies between the actual charging and refueling demand allocation and the demand distribution used in planning, thus reducing the economic efficiency of the planning scheme.

Description

Technical Field

[0001] This application relates to the field of planning technology for hybrid electric power stations, and in particular to a multi-stage planning method, device, electronic equipment and medium for hybrid electric power stations. Background Technology

[0002] Promoting electric vehicles and gradually reducing the use of traditional gasoline-powered vehicles has become the future development direction of the automotive industry. As the penetration rate of electric vehicles increases and the use of gasoline-powered vehicles gradually decreases, the utilization rate of many existing gas station facilities will gradually decline. Traditional gas station service providers can gradually reduce the configuration of refueling facilities at some gas stations and install high-power fast charging facilities and energy storage systems, transforming them into hybrid energy stations integrating refueling and fast charging services.

[0003] The relevant technology determines the location and time when each electric vehicle needs to be charged on the highway, and uses a shared nearest neighbor clustering algorithm to determine the address of the fast charging station based on the spatiotemporal distribution prediction results of the electric vehicle charging load; it also uses queuing theory to determine the number of chargers in the fast charging station, which can largely meet the charging needs of electric vehicles.

[0004] However, most related technologies ignore the rationality of vehicle users' decisions when choosing charging destinations, resulting in a gap between the actual charging demand allocation and the demand distribution adopted in the planning, thereby reducing the economic efficiency of the planning scheme. Summary of the Invention

[0005] This application provides a multi-stage planning method, device, electronic equipment, and medium for hybrid electric energy stations to address the problems in related technologies that ignore user rationality in the allocation of vehicle energy replenishment demand, resulting in a gap between the actual charging and refueling demand allocation and the demand distribution adopted during planning, thereby reducing the economic efficiency of the planning scheme.

[0006] The first aspect of this application provides a multi-stage planning method for hybrid electric power stations, comprising the following steps: calculating the balanced allocation result of the energy replenishment demand of the hybrid electric power station at different planning locations under any planning stage based on a rational energy replenishment demand allocation strategy of vehicle users; determining the constraints that the planning should meet based on the balanced allocation result; using the constraints to perform capacity planning for the hybrid electric power station under multiple planning stages to obtain a multi-stage planning model for the hybrid electric power station; and decoupling and iterating the multi-stage planning model based on the fixed point theorem and the net cost of dismantling refueling facilities to obtain a multi-stage planning optimization scheme for the hybrid electric power station.

[0007] Optionally, in one embodiment of this application, the calculation of the balanced allocation result of the energy replenishment demand of the hybrid electric vehicle station at different planning locations under any planning stage based on the rational energy replenishment demand allocation strategy of vehicle users includes: obtaining the total number of vehicles, electric vehicle penetration rate, demand generation probability, and station demand capture ratio of the hybrid electric vehicle station; calculating the charging or refueling energy replenishment demand in each grid cell of the planning area under any planning stage based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and station demand capture ratio; determining the non-objective optimization problem based on the charging or refueling energy replenishment demand in each grid cell of the planning area under any planning stage; equivalently transforming the non-objective optimization problem into a convex optimization problem; and solving the convex optimization problem based on the rational decision-making of users when choosing a energy replenishment destination to obtain the balanced allocation result of energy replenishment demand.

[0008] Optionally, in one embodiment of this application, the step of calculating the charging or refueling demand in each grid cell within the planning area under any planning stage based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and station capture demand ratio includes: equivalently concentrating the energy replenishment demand of each grid cell within the planning area at the center point of the planning area, and constructing each hybrid electric power station at the center of the grid cell; continuously allocating the energy replenishment demand of each grid cell to one or more hybrid electric power stations, so that each hybrid electric power station captures the charging or refueling demand of one or more grid cells.

[0009] Optionally, in one embodiment of this application, the constraints include energy station operation constraints, planning constraints, and equilibrium allocation results. The step of using the constraints to perform capacity planning for the hybrid energy station under multiple planning stages to obtain a multi-stage planning model for the hybrid energy station includes: obtaining the optimization objective of the hybrid energy station and the operator's costs and benefits; determining an objective function based on the optimization objective and the operator's costs and benefits, and constraining the objective function using the operation constraints, planning constraints, and equilibrium allocation results; calculating the planned capacity of refueling facilities, charging facilities, and energy storage facilities in the hybrid energy station under multiple planning stages using the objective function; and establishing a multi-stage planning model for the hybrid energy station based on the planned capacity.

[0010] Optionally, in one embodiment of this application, the step of decoupling and iterating the multi-stage planning model based on the net cost of dismantling refueling facilities to obtain a multi-stage planning optimization scheme for the hybrid energy station includes: determining whether the refueling machine modification cost in the hybrid energy station is positive; if the refueling machine modification cost is negative, calculating the minimum number of refueling machines required to meet refueling demand in the current planning stage and all previous planning stages, and calculating the capacity of the charger in each planning stage to obtain a multi-stage planning optimization scheme for the hybrid energy station; if the refueling machine modification cost is positive, solving for the optimal capacity of the charger and energy storage facilities in the current planning stage, and determining whether the number of refueling machines in the previous planning stage meets preset resource constraints. If it does, the number of refueling machines is used as the planned number in the current planning stage; otherwise, the refueling machines and chargers are jointly optimized until all planning stages are completed to obtain a multi-stage planning optimization scheme for the hybrid energy station.

[0011] A second aspect of this application provides a multi-stage planning device for a hybrid electric vehicle (HEV) station, comprising: a calculation module for calculating the balanced allocation result of HEV station replenishment demand at different planning locations under any planning stage, based on a rational replenishment demand allocation strategy for vehicle users; a planning module for determining the constraints that the planning should satisfy based on the balanced allocation result, and using the constraints to perform capacity planning for the HEV station under multiple planning stages to obtain a multi-stage planning model for the HEV station; and a processing module for decoupling and iterating the multi-stage planning model based on the fixed point theorem and the net cost of dismantling refueling facilities to obtain a multi-stage planning optimization scheme for the HEV station.

[0012] Optionally, in one embodiment of this application, the calculation module is further configured to obtain the total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio of the hybrid energy station; calculate the charging or refueling demand in each grid cell within the planning area under any planning stage based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio; determine the non-objective optimization problem based on the charging or refueling demand in each grid cell within the planning area under any planning stage; and equivalently transform the non-objective optimization problem into a convex optimization problem. Based on the user's decision-making rationality when choosing a refueling destination, the convex optimization problem is solved to obtain the balanced allocation result of the refueling demand.

[0013] Optionally, in one embodiment of this application, the calculation module is further configured to equivalently concentrate the energy replenishment demand of each grid cell within the planning area at the center point of the planning area, and construct each hybrid electric power station at the center of the grid cell; continuously distribute the energy replenishment demand of each grid cell to one or more hybrid electric power stations, so that each hybrid electric power station captures the charging or refueling energy replenishment demand of one or more grid cells.

[0014] Optionally, in one embodiment of this application, the constraints include energy station operation constraints, planning constraints, and equilibrium allocation results. The planning module is further used to obtain the optimization objective of the hybrid energy station and the operator's costs and benefits; determine the objective function based on the optimization objective and the operator's costs and benefits, and constrain the objective function using the operation constraints, planning constraints, and equilibrium allocation results; calculate the planned capacity of refueling facilities, charging facilities, and energy storage facilities in the hybrid energy station under multiple planning stages using the objective function, and establish a multi-stage planning model of the hybrid energy station based on the planned capacity.

[0015] Optionally, in one embodiment of this application, the processing module is further configured to determine whether the cost of retrofitting the fuel dispensers in the hybrid energy station is positive; if the cost of retrofitting the fuel dispensers is negative, the minimum number of fuel dispensers required to meet the refueling demand in the current planning stage and all previous planning stages is calculated, and the capacity of the chargers in each planning stage is calculated to obtain a multi-stage planning optimization scheme for the hybrid energy station; if the cost of retrofitting the fuel dispensers is positive, the optimal capacity of the chargers and energy storage facilities in the current planning stage is solved, and the number of fuel dispensers in the previous planning stage is considered to meet the preset resource constraints. If the constraints are met, the number of fuel dispensers is used as the planned number in the current planning stage; otherwise, the fuel dispensers and chargers are jointly optimized until all planning stages are completed to obtain a multi-stage planning optimization scheme for the hybrid energy station.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-stage planning method for hybrid power stations as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the multi-stage planning method for hybrid power stations as described in the above embodiments.

[0018] Therefore, this application has at least the following beneficial effects:

[0019] This application's embodiments can calculate the equilibrium state of energy demand allocation among hybrid energy stations based on a rational energy demand allocation strategy for vehicle users. This is more in line with user decision-making rationality and can yield a more reasonable planning scheme. By combining the constraints that the planning should meet, capacity planning is performed on hybrid energy stations under multiple planning stages to obtain a multi-stage planning model for hybrid energy stations. This adapts to the long-term trend of electric vehicles replacing fuel vehicles. Based on the fixed-point theorem, the multi-stage planning model is decoupled and iterated according to the net cost of dismantling refueling facilities to obtain a multi-stage planning optimization scheme for hybrid energy stations. Thus, it solves the problems of related technologies ignoring user rationality in vehicle energy demand allocation, resulting in a gap between the actual charging and refueling demand allocation results and the demand distribution adopted during planning, which reduces the economy of the planning scheme.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a flowchart of a multi-stage planning method for a hybrid power station according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of the structure of a hybrid power station provided according to an embodiment of this application;

[0024] Figure 3 This is a schematic diagram illustrating the piecewise linearization processing of the maximum queuing time constraint according to an embodiment of this application;

[0025] Figure 4 The flowchart shows the decoupled iterative solution algorithm for the multi-stage planning model of the hybrid power station provided in the embodiments of this application.

[0026] Figure 5 This is a schematic diagram of a planning area rasterization processing method provided according to an embodiment of this application;

[0027] Figure 6 This is a block diagram of a multi-stage planning device for a hybrid power station according to an embodiment of this application;

[0028] Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0029] Explanation of reference numerals in the attached diagram: Calculation module-100, Planning module-200, Processing module-300, Memory-701, Processor-702, Communication interface-703. Detailed Implementation

[0030] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0031] The following description, with reference to the accompanying drawings, outlines a multi-stage planning method, apparatus, electronic device, and storage medium for hybrid electric power stations according to embodiments of this application. Addressing the problems mentioned in the background section, this application provides a multi-stage planning method for hybrid electric power stations. This method calculates the equilibrium state of energy demand allocation among hybrid power stations based on a rational energy demand allocation strategy for vehicle users, which better aligns with user decision-making rationality and yields a more reasonable planning scheme. By combining the constraints that the planning should satisfy, capacity planning is performed on hybrid electric power stations under multiple planning stages, resulting in a multi-stage planning model for hybrid electric power stations. This model adapts to the long-term trend of electric vehicles replacing gasoline vehicles. Based on the fixed-point theorem, the multi-stage planning model is decoupled and iterated according to the net cost of dismantling refueling facilities, resulting in an optimized multi-stage planning scheme for hybrid electric power stations. This solves the problems of related technologies ignoring user rationality in vehicle energy demand allocation, leading to discrepancies between the actual charging and refueling demand allocation results and the demand distribution used in the planning, thus reducing the economic efficiency of the planning scheme.

[0032] Specifically, Figure 1 This is a flowchart illustrating a multi-stage planning method for a hybrid power station provided in an embodiment of this application.

[0033] like Figure 1 As shown, the multi-stage planning method for this hybrid power station includes the following steps:

[0034] In step S101, the balanced allocation result of the energy replenishment demand of the hybrid energy station at different planning locations under any planning stage is calculated based on the rational energy replenishment demand allocation balance strategy of vehicle users.

[0035] In this application embodiment, the hybrid energy station integrating refueling and fast charging services can be simply referred to as an energy station, such as... Figure 2 As shown, the station is equipped with refueling facilities, fast charging facilities, and a battery energy storage system to reduce peak loads and buffer the impact of fast charging loads. The transformers at the energy station are either dedicated or public transformers. The planning of all facilities within the energy station is carried out by a specialized operator.

[0036] At each planning stage, the spatiotemporal distribution of electric vehicle charging demand and gasoline vehicle refueling demand will change due to the increasing penetration rate and driving range of electric vehicles. Because multi-stage planning has a long timescale, it is difficult to accurately predict changes in the traffic network topology and traffic flow. This application's embodiment, by considering the rationality of vehicle users' choices of refueling destinations, calculates the equilibrium state of refueling demand distribution among hybrid energy stations, which is more in line with user decision-making rationality and can yield a more reasonable planning scheme.

[0037] In one embodiment of this application, a balanced allocation strategy for the energy replenishment demand of hybrid electric vehicles (HEVs) at different planning locations under any planning stage is calculated based on a rational energy replenishment demand allocation strategy for vehicle users. This includes: obtaining the total number of vehicles at the HEV, the electric vehicle penetration rate, the demand generation probability, and the site's demand capture ratio; calculating the charging or refueling demand in each grid cell within the planning area under any planning stage based on the total number of vehicles, the electric vehicle penetration rate, the demand generation probability, and the site's demand capture ratio; determining a non-objective optimization problem based on the charging or refueling demand in each grid cell within the planning area under any planning stage; effectively transforming the non-objective optimization problem into a convex optimization problem; and solving the convex optimization problem based on the rational decision-making of users when choosing their energy replenishment destination to obtain the balanced allocation result of the energy replenishment demand.

[0038] This application embodiment can calculate the energy replenishment demand for charging or refueling at different locations under a certain planning stage by obtaining parameters such as the total number of vehicles at hybrid energy stations, electric vehicle penetration rate, demand generation probability, and the proportion of demand captured by the station. Based on the rational decision of users pursuing the lowest cost when choosing a replenishment destination, the balanced distribution result of energy replenishment demand is obtained by solving a non-objective optimization problem or a convex optimization problem.

[0039] Specifically, within the planning area, the charging or refueling demand in each grid cell during a certain planning phase is related to the following parameter: the total number of vehicles in planning phase s. electric vehicle penetration rate Probability of generating demand for refueling or fast charging and The proportion of refueling or fast charging demand that can be captured at the site and Let D be the energy replenishment requirement of each grid cell at the current time point in time period t. i,0,t The total number of vehicles is Therefore, the refueling and fast charging demands that need to be met by the stations in grid cell i under the planning stage s can be expressed as:

[0040]

[0041]

[0042] in, and This indicates the ratio of the energy replenishment demand in the planning phase s to the current energy replenishment demand.

[0043] Furthermore, embodiments of this application can specify the charging and refueling needs of each grid cell within the gridded region. and All are considered as continuous variables that can be continuously allocated to multiple energy stations. Let the energy replenishment demand of grid cell i be the amount of energy allocated to energy station k. The allocation of energy replenishment demand should satisfy the following:

[0044]

[0045]

[0046]

[0047] in, K and i meet the charging and refueling needs of each energy station. max These represent the number of energy stations and grid cells, respectively. When choosing a charging or refueling destination, vehicle users, based on rational decision-making, will choose the station with the lowest cost. Generally, the costs users consider when making a charging or refueling decision consist of time costs (driving time, queuing time, refueling service time, etc.) and refueling fees. Driving time is calculated as the Manhattan distance between the average driving speed and the grid cell. Estimated queuing time The following formula can be used to calculate:

[0048]

[0049] in, The base queuing time is J, which is an adjustable coefficient that can usually be determined based on the refueling service duration. This refers to the capacity of the energy replenishment facilities. Service time and replenishment costs are related to the energy required. Taking electric vehicle users as an example, the cost they consider when making a charging decision can be expressed as:

[0050]

[0051] Where, π time v is the cost per unit of time. ave p is the average driving speed. acs E0 represents energy consumption per unit distance, e0 represents initial energy replenishment requirement, and P represents energy consumption per unit distance. k rt For rated charging power, π chgThe charging cost per unit of electricity. The four terms in equation (7) are the travel time cost, queuing time cost, service time cost, and recharging cost, respectively. This can be rearranged and expressed as a linear relationship between travel distance and queuing time, i.e.

[0052]

[0053] Where c1 to c3 are cost coefficients, and c3 is a constant term that is independent of the charging destination decision.

[0054] Furthermore, when considering the rationality of user decision-making, the distribution of the energy replenishment demand of each grid cell among different energy stations should, in addition to satisfying equations (3)-(5), also satisfy:

[0055]

[0056]

[0057] Where, α i,s,t With β i,s,t This represents the minimum cost for selecting each energy station as the refueling and charging destination for the refueling and charging needs in grid cell i. The above formula is a complementary relaxation constraint, ensuring that users will only choose the station with the lowest cost as their refueling destination.

[0058] Given the energy replenishment demand of each grid cell within a given area and the facility capacity of each energy station, the distribution of energy replenishment demand among the stations can be obtained by solving the non-objective optimization problem composed of equations (3)-(5) and (9)-(10), and can be equivalently transformed into a convex optimization problem. For each planning stage s and time period t, the balanced distribution of fast charging and refueling demand can be obtained by solving the following optimization problems respectively:

[0059]

[0060]

[0061] in, and These are the coefficients corresponding to the decision-making costs for fast charging and refueling needs, respectively.

[0062] In step S102, the constraints that the planning should meet are determined based on the balanced allocation results. The capacity planning of the hybrid energy station under multiple planning stages is carried out using the constraints to obtain a multi-stage planning model for the hybrid energy station.

[0063] It is understood that the embodiments of this application can optimize the net present value of maximizing revenue or minimizing cost, satisfy constraints such as energy station operation constraints, planning constraints, and balanced allocation results, and carry out capacity planning for refueling facilities, charging facilities, energy storage systems, etc. in hybrid energy stations under multiple planning stages, to a multi-stage planning model for hybrid energy stations, in order to adapt to the long-term trend of electric vehicles gradually replacing fuel vehicles.

[0064] In one embodiment of this application, the constraints include energy station operation constraints, planning constraints, and equilibrium allocation results. The constraints are used to perform capacity planning for the hybrid energy station under multiple planning stages, resulting in a multi-stage planning model for the hybrid energy station. This includes: obtaining the optimization objective of the hybrid energy station and the operator's costs and benefits; determining the objective function based on the optimization objective and the operator's costs and benefits, and constraining the objective function using the operation constraints, planning constraints, and equilibrium allocation results; calculating the planned capacity of refueling facilities, charging facilities, and energy storage facilities in the hybrid energy station under multiple planning stages using the objective function; and establishing a multi-stage planning model for the hybrid energy station based on the planned capacity.

[0065] In this embodiment, the hybrid energy station operator needs to optimize the decision-making regarding the number of refueling devices, fast chargers, on-site energy storage capacity, and transformer capacity of each energy station under each planning stage s∈{1,2,L,S}. The optimization objective is to maximize revenue or minimize cost through net present value (NPV). The operator's costs and revenues mainly include the investment costs of refueling, fast charging, and energy storage equipment at each stage. Maintenance costs And revenue from providing refueling and fast charging services The objective function is expressed as follows:

[0066]

[0067] in, The discount rate is r. d The discount factor for present value in the planning stage 's' is given, where 's' is the number of planning stages. The expressions for the costs or benefits of each component are as follows:

[0068]

[0069]

[0070]

[0071]

[0072] in, and This refers to the number of chargers, energy storage power and capacity, transformer capacity, and number of refueling machines that energy station k will have during the planning stage. cg / ess,p / ess,e / tf / fu and m cg / ess,p / ess,e / tf / fu The unit initial investment cost and maintenance cost for each facility, and For the revenue of units providing refueling and charging services, and For electricity purchase and sales prices, The power purchased and sold is denoted by Δt, where Δt is the duration of a single time period, and Δs is the number of years in a single planning phase.

[0073] The constraints that the planning in this application embodiment should meet mainly include energy station operation constraints, planning constraints, and balanced allocation results, which are detailed below:

[0074] 1) Constraints on the operation of energy stations: These include constraints on power balance within the station and the operation of energy storage systems.

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] in, and These represent the vehicle charging power and the energy storage charging and discharging power, respectively, 1 / μ cg For average charging service time, P rt Rated charging power, For energy storage, η sc / sd For energy storage charging and discharging efficiency, λ ess For the maximum depth of discharge of energy storage, The maximum grid access capacity is given by equation (18). Equation (19) is the power balance constraint within the station. Equation (20) is the charging power obtained based on the demand. Equation (21) is the energy storage operation constraint. Equation (22)-(24) gives the upper and lower limits of energy storage power, energy and power purchased and sold. Equation (25) indicates that the transformer capacity should not exceed the grid access capacity limit.

[0084] 2) Planning Constraints: Since hybrid energy stations are transitioning from existing gas stations to fast charging stations, the number of refueling facilities within the station should monotonically increase, while the number of fast charging facilities and the capacity of energy storage and transformers should monotonically decrease, i.e., satisfying the following constraints:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] On the other hand, the capacity of each facility during the planning process will be limited by various resources (such as investment funds, land, etc.), which can be expressed as the following linear inequality constraint:

[0091]

[0092] Among them, l cg / ess,p / ess,e / tf / fu L represents the amount of resources used per unit of each facility. k This refers to the total amount of resources. Each site should meet certain service capacity requirements during planning; the capacity of each site should ensure that queuing times do not exceed a certain level, i.e.

[0093]

[0094]

[0095] in, The maximum allowed waiting time for charging and refueling. Since queuing time is usually non-linear, equation (31) can be piecewise linearized. When the energy demand is known, if the number of facilities is continuously variable, then the queuing time is a convex function with respect to the number of facilities. Therefore, in the embodiments of this application, the non-linear constraints of equations (32)-(33) can be replaced by the following set of linear inequality constraints.

[0096]

[0097] in, and b j cg / fu That is, the slope and intercept of each linear inequality constraint expression. A piecewise linearization method is illustrated as follows: Figure 3 As shown in the figure. Taking charging demand as an example, the relationship between queuing time and the number of facilities at a certain demand level is as follows. Figure 3As shown. Treating the number of facilities as a continuous variable, its integer portion can be obtained through interpolation. Taking the queuing time when the number of facilities is an integer as a reference point, a certain linear expression is a straight line passing through the two points corresponding to the integer number of facilities and their queuing times at both ends of the segment.

[0098] 3) Balanced distribution results: When users choose a destination for refueling, they will choose the station with the lowest cost due to rationality. Therefore, the charging and refueling demand obtained by each station in the gridded area should meet the constraints composed of equations (3)-(5) and (9)-(10), or nest the convex optimization problem shown in equations (11) and (12).

[0099] In step S103, based on the fixed point theorem, the multi-stage planning model is decoupled and iterated according to the net cost of dismantling the refueling facilities to obtain the multi-stage planning optimization scheme for the hybrid energy station.

[0100] The embodiments of this application can utilize a decoupled iterative solution algorithm to achieve efficient solution of the multi-stage planning model of hybrid energy stations, which helps to solve the planning problem of the transition from traditional refueling service providers to integrated energy service providers.

[0101] The multi-stage planning model mentioned in the above embodiments contains complementary relaxation constraints as shown in equations (9)-(10), which are difficult to solve. Therefore, based on the fixed point theorem, the embodiments of this application can decouple the number of refueling and fast charging facilities at the energy station and iteratively solve the corresponding multi-stage planning optimization strategy by judging whether the net cost of dismantling the refueling facility is positive.

[0102] Specifically, for the planning phase s, the decision on the number of refueling and fast charging facilities at each energy station is as follows: The refueling and fast charging demand captured by each energy station is The above planning model can then be broken down into the following two sub-processes:

[0103] 1) Site capacity optimization process. This process optimizes the configuration capacity of refueling and fast charging facilities, in-station energy storage systems, etc., at each site under a given site energy replenishment demand. This process corresponds to the capacity optimization model shown in equations (13)-(34) above. Let the mapping relationship corresponding to this process be denoted as If in the capacity optimization model, the capacity of each facility is x s All are considered continuous variables. Since the capacity optimization model after piecewise linearization is a linear programming problem, the mapping... It is a continuous mapping.

[0104] 2) Energy replenishment demand balancing process. This process, for a given site capacity, calculates the balanced distribution of refueling or fast charging demand among the various sites. This process actually corresponds to the convex optimization problem shown in equations (3)-(5), (9)-(10), or (11)(12) above. In this embodiment, the mapping relationship corresponding to this process can be recorded as follows: If the facility capacity of each site is x s Treating it as a continuous variable is because queuing time strictly and monotonically decreases with increasing facility capacity. When the capacity of a certain station increases by a tiny amount, its queuing time should also decrease by a tiny amount, thereby attracting demand from other stations to that station, leading to a slight change in the demand captured by each station. Therefore, the mapping... It can also be viewed as a continuous mapping.

[0105] Further consider the composite mapping of the corresponding mappings of the two sub-processes mentioned above. Mapping It is x s The mapping to itself, which is also a continuous mapping, allows the original problem to be modeled as a fixed-point problem. According to Brouwer's fixed-point theorem, since the facility quantity decision variable x... s The feasible region is a non-empty compact convex set in Euclidean space, and the mapping For a continuous mapping, satisfying fixed point x s Yes, it exists. Based on this, embodiments of this application can use an iterative method to obtain the fixed point, that is, to iteratively solve the demand equilibrium allocation and optimal capacity planning problem until it converges to the fixed point.

[0106] It should be noted that the Brouwer fixed point theorem in this application provides the existence of a fixed point but does not guarantee its uniqueness. Therefore, there may be multiple fixed points, but in an engineering sense, one of the fixed points can still be found through iteration.

[0107] In one embodiment of this application, a multi-stage planning model is decoupled and iterated based on the net cost of dismantling refueling facilities to obtain a multi-stage planning optimization scheme for a hybrid energy station. This includes: determining whether the cost of retrofitting refueling machines in the hybrid energy station is positive; if the cost of retrofitting refueling machines is negative, calculating the minimum number of refueling machines required to meet refueling demand in the current planning stage and all previous planning stages, and calculating the capacity of chargers in each planning stage to obtain a multi-stage planning optimization scheme for the hybrid energy station; if the cost of retrofitting refueling machines is positive, solving for the optimal capacity of chargers and energy storage facilities in the current planning stage, and determining whether the number of refueling machines in the previous planning stage meets preset resource constraints. If it does, the number of refueling machines is used as the planned number in the current planning stage; otherwise, joint optimization is performed on the refueling machines and chargers until all planning stages are completed to obtain a multi-stage planning optimization scheme for the hybrid energy station.

[0108] Understandably, in the aforementioned optimization model, refueling facilities, fast-charging facilities, and energy storage systems are only coupled within planning constraints, and refueling demand and fast-charging demand are independent of each other. Therefore, it is possible to decouple the capacity setting of refueling facilities from that of fast-charging facilities. Examining the objective function term related to facility capacity, its cost includes both initial fixed investment cost and subsequent operation and maintenance cost. Since fast-charging facilities and energy storage systems are expansion projects, the cost of their added capacity is necessarily positive; however, refueling facilities are dismantling projects. Although the cost of dismantling facilities is positive, it may save future maintenance costs, so whether its net cost is positive needs to be discussed.

[0109] When the cost of retrofitting refueling pumps in a hybrid electric energy station is positive, operators tend to postpone the removal of refueling facilities as much as possible. That is, operators will only remove refueling facilities when faced with resource constraints preventing fast-charging infrastructure from meeting requirements. For example... Figure 4 As shown, the embodiments of this application can adopt a forward-reverse approach. Starting from the first planning stage, the optimal capacity of fast charging facilities and energy storage systems is calculated without considering the impact of refueling facilities. Then, it is checked whether the current capacity of refueling facilities is crowding out fast charging facilities, i.e., violating resource constraints. If no constraints are exceeded, the capacity planning scheme for the current stage is obtained. If some sites have resource constraints exceeded, the capacity of refueling facilities is re-optimized based on the resource constraints of the relevant sites, until the capacity determination work of the last planning stage is completed.

[0110] When the cost of retrofitting fuel dispensers in a hybrid electric power station becomes negative, operators will tend to dismantle the fuel dispensing facilities as early as possible to save costs. In this case, the fuel dispensing facilities can be decoupled from other facilities. For example... Figure 4As shown, starting from the last planning stage, the system iteratively advances backward to obtain the configuration scheme that minimizes the total number of refueling facilities while satisfying the refueling demand of the current stage and the capacity reduction relationship with the next planning stage. The number of refueling facilities obtained for each stage is treated as a fixed parameter, and the system iteratively advances backward from the first planning stage to obtain capacity schemes for fast charging facilities, energy storage systems, etc., that satisfy the charging demand of the current stage and the capacity increase relationship with the previous stage, until the capacity optimization of all planning stages is completed.

[0111] In one embodiment of this application, the charging or refueling demand in each grid cell within the planning area under any planning stage is calculated based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and the demand capture ratio of the stations. This includes: equivalently concentrating the refueling demand of each grid cell within the planning area at the center point of the planning area, and constructing each hybrid electric energy station at the center of the grid cell; continuously allocating the refueling demand of each grid cell to one or more hybrid electric energy stations, so that each hybrid electric energy station captures the charging or refueling demand of one or more grid cells.

[0112] like Figure 5 As shown, the embodiments of this application can adopt a method of gridding the planning area, which can equivalently concentrate the energy replenishment demand of each grid unit at its center point, and assume that each energy station is also built at the center of the grid unit. The energy replenishment demand of each grid unit can be continuously distributed to one or more energy stations. Each energy station can capture the demand of one or more grid units, so that operators need to predict the distribution of refueling and fast charging demand at each stage, and optimize the facility capacity at different planning stages according to the demand distribution to achieve the optimal economic goal.

[0113] The multi-stage planning method for hybrid energy stations proposed in this application calculates the equilibrium state of energy demand allocation among hybrid energy stations based on a rational energy demand allocation strategy for vehicle users. This approach better aligns with user decision-making rationality and yields a more reasonable planning scheme. By combining the constraints that the planning should satisfy, capacity planning is performed on hybrid energy stations across multiple planning stages, resulting in a multi-stage planning model for hybrid energy stations. This model adapts to the long-term trend of electric vehicles replacing gasoline vehicles. Based on the fixed-point theorem, the multi-stage planning model is decoupled and iterated according to the net cost of dismantling refueling facilities, resulting in an optimized multi-stage planning scheme for hybrid energy stations. This solves the problems of related technologies ignoring user rationality in vehicle energy demand allocation, leading to discrepancies between the actual charging and refueling demand allocation results and the demand distribution used in the planning, thus reducing the economic efficiency of the planning scheme.

[0114] Next, referring to the accompanying drawings, a multi-stage planning device for a hybrid power station based on an embodiment of this application is described.

[0115] Figure 6 This is a block diagram of a multi-stage planning device for a hybrid electric power station according to an embodiment of this application.

[0116] like Figure 6 As shown, the multi-stage planning device 10 for the hybrid electric energy station includes: a calculation module 100, a planning module 200, and a processing module 300.

[0117] The calculation module 100 is used to calculate the balanced allocation result of the energy replenishment demand of the hybrid electric energy station at different planning locations under any planning stage, based on the rational energy replenishment demand allocation balance strategy of vehicle users; the planning module 200 is used to determine the constraints that the planning should meet based on the balanced allocation result, and to perform capacity planning for the hybrid electric energy station under multiple planning stages using the constraints to obtain a multi-stage planning model for the hybrid electric energy station; the processing module 300 is used to decouple and iterate the multi-stage planning model based on the fixed point theorem and the net cost of dismantling the refueling facilities to obtain a multi-stage planning optimization scheme for the hybrid electric energy station.

[0118] In one embodiment of this application, the calculation module 100 is further configured to obtain the total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio of the hybrid energy station, and calculate the charging or refueling demand in each grid cell within the planning area under any planning stage based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio; determine the non-objective optimization problem based on the charging or refueling demand in each grid cell within the planning area under any planning stage, and equivalently transform the non-objective optimization problem into a convex optimization problem; and solve the convex optimization problem based on the user's decision rationality when choosing a refueling destination to obtain the balanced distribution result of the refueling demand.

[0119] In one embodiment of this application, the calculation module 100 is further configured to equivalently concentrate the energy replenishment demand of each grid cell within the planning area at the center point of the planning area, and construct each hybrid electric power station at the center of the grid cell; continuously distribute the energy replenishment demand of each grid cell to one or more hybrid electric power stations, so that each hybrid electric power station captures the charging or refueling energy replenishment demand of one or more grid cells.

[0120] In one embodiment of this application, the constraints include energy station operation constraints, planning constraints, and equilibrium allocation results. The planning module 200 is further used to obtain the optimization objective of the hybrid energy station and the operator's costs and benefits; determine the objective function based on the optimization objective and the operator's costs and benefits, and constrain the objective function using the operation constraints, planning constraints, and equilibrium allocation results; calculate the planned capacity of refueling facilities, charging facilities, and energy storage facilities in the hybrid energy station under multiple planning stages using the objective function, and establish a multi-stage planning model of the hybrid energy station based on the planned capacity.

[0121] In one embodiment of this application, the processing module 300 is further used to determine whether the cost of retrofitting the fuel dispensers in the hybrid energy station is positive; if the cost of retrofitting the fuel dispensers is negative, the minimum number of fuel dispensers required to meet the refueling demand in the current planning stage and all previous planning stages is calculated, and the capacity of the chargers in each planning stage is calculated to obtain a multi-stage planning optimization scheme for the hybrid energy station; if the cost of retrofitting the fuel dispensers is positive, the optimal capacity of the chargers and energy storage facilities in the current planning stage is solved, and the number of fuel dispensers in the previous planning stage is used as the number planned in the current planning stage, based on whether the number of fuel dispensers in the previous planning stage meets the preset resource constraints. If it does, the number of fuel dispensers is used as the number planned in the current planning stage; otherwise, the fuel dispensers and chargers are jointly optimized until all planning stages are completed to obtain a multi-stage planning optimization scheme for the hybrid energy station.

[0122] It should be noted that the foregoing explanation of the multi-stage planning method embodiment for hybrid energy stations also applies to the multi-stage planning device for hybrid energy stations in this embodiment, and will not be repeated here.

[0123] The multi-stage planning device for hybrid energy stations proposed in this application calculates the equilibrium state of energy demand allocation among hybrid energy stations based on a rational energy demand allocation strategy for vehicle users. This is more in line with user decision-making rationality and can yield a more reasonable planning scheme. The device performs capacity planning for hybrid energy stations under multiple planning stages, combining the constraints that the planning should satisfy, resulting in a multi-stage planning model for hybrid energy stations. This model adapts to the long-term trend of electric vehicles replacing gasoline vehicles. Based on the fixed-point theorem, the multi-stage planning model is decoupled and iterated according to the net cost of dismantling refueling facilities, resulting in an optimized multi-stage planning scheme for hybrid energy stations. This solves the problems of related technologies ignoring user rationality in vehicle energy demand allocation, leading to discrepancies between the actual charging and refueling demand allocation results and the demand distribution used in the planning, thus reducing the economic efficiency of the planning scheme.

[0124] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0125] The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0126] When the processor 702 executes the program, it implements the multi-stage planning method for hybrid power stations provided in the above embodiments.

[0127] Furthermore, electronic devices also include:

[0128] Communication interface 703 is used for communication between memory 701 and processor 702.

[0129] The memory 701 is used to store computer programs that can run on the processor 702.

[0130] The memory 701 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0131] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0132] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0133] The processor 702 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0134] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-stage planning method for hybrid power stations.

[0135] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0136] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0137] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0138] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0139] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0140] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A multi-stage planning method for an oil-electric hybrid energy station, characterized in that, Includes the following steps: Based on a rational energy replenishment demand allocation strategy for vehicle users, the equilibrium allocation results of energy replenishment demand for hybrid electric power stations at different planning locations are calculated under any planning stage. Based on the balanced allocation results, the constraints that the planning should meet are determined, and the capacity planning of the oil-electric hybrid energy station is carried out under multiple planning stages using the constraints to obtain the multi-stage planning model of the oil-electric hybrid energy station. Based on the fixed point theorem, the multi-stage planning model is decoupled and iterated according to the net cost of dismantling the refueling facilities to obtain the multi-stage planning optimization scheme of the oil-electric hybrid energy station. The decoupling and iteration of the multi-stage planning model based on the net cost of dismantling the refueling facilities yields a multi-stage planning optimization scheme for the hybrid energy station, including: Determine whether the cost of retrofitting the fuel dispensers in the hybrid energy station is positive; If the cost of retrofitting the fuel dispensers is negative, then calculate the minimum number of fuel dispensers required to meet the refueling demand in the current planning stage and all previous planning stages, and calculate the capacity of the chargers in each planning stage to obtain the multi-stage planning optimization scheme for the hybrid energy station. If the cost of retrofitting the fuel dispensers is positive, then the optimal capacity of the chargers and energy storage facilities under the current planning stage is determined. Based on whether the number of fuel dispensers in the previous planning stage meets the preset resource constraints, if it does, then the number of fuel dispensers is used as the planned number for the current planning stage; otherwise, the fuel dispensers and chargers are jointly optimized until all planning stages are completed, thus obtaining the multi-stage planning optimization scheme for the hybrid energy station.

2. The method according to claim 1, characterized in that, The equilibrium allocation result of the energy replenishment demand of the hybrid electric power station at different planning locations under any planning stage, calculated by the rational energy replenishment demand allocation strategy based on vehicle user rationality, includes: The total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio of the hybrid energy station are obtained. Based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio, the charging or refueling demand in each grid cell within the planning area under any planning stage is calculated. Based on the charging or refueling demand in each grid cell within the planning area under any planning stage, a non-objective optimization problem is determined, and the non-objective optimization problem is equivalently transformed into a convex optimization problem. Based on the decision rationality of users when choosing a refueling destination, the convex optimization problem is solved to obtain the balanced distribution result of refueling demand.

3. The method according to claim 2, characterized in that, The calculation of charging or refueling demand in each grid cell within the planning area at any planning stage, based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio, includes: The energy replenishment needs of each grid cell within the planning area are equivalently concentrated at the center point of the planning area, and each hybrid power station is built at the center of the grid cell. The energy replenishment needs of each grid cell are continuously distributed to one or more hybrid electric power stations, so that each hybrid electric power station can capture the charging or refueling needs of one or more grid cells.

4. The method according to claim 1, characterized in that, The constraints include energy station operation constraints, planning constraints, and balanced allocation results. The process of using these constraints to perform capacity planning for the hybrid oil-electric energy station under multiple planning stages yields a multi-stage planning model for the hybrid oil-electric energy station, including: To obtain the optimization objectives of hybrid power stations and the costs and benefits for operators; The objective function is determined based on the optimization objective and the operator's costs and benefits, and the objective function is constrained by the operational constraints, planning constraints, and equilibrium allocation results. The planned capacity of refueling facilities, charging facilities, and energy storage facilities in the hybrid energy station under multiple planning stages is calculated using the objective function, and a multi-stage planning model of the hybrid energy station is established based on the planned capacity.

5. A multi-stage planning device for a hybrid oil-electricity energy station, characterized in that, include: The calculation module is used to calculate the balanced allocation of energy demand for hybrid electric power stations at different planning locations under any planning stage, based on a rational energy demand allocation strategy for vehicle users. The planning module is used to determine the constraints that the planning should meet based on the balanced allocation results, and to use the constraints to perform capacity planning for the hybrid energy station under multiple planning stages, thereby obtaining a multi-stage planning model for the hybrid energy station. The processing module is used to decouple and iterate the multi-stage planning model based on the fixed point theorem and the net cost of dismantling the refueling facilities, so as to obtain the multi-stage planning optimization scheme of the oil-electric hybrid energy station. The processing module is further used for: Determine whether the cost of retrofitting the fuel dispensers in the hybrid energy station is positive; If the cost of retrofitting the fuel dispensers is negative, then calculate the minimum number of fuel dispensers required to meet the refueling demand in the current planning stage and all previous planning stages, and calculate the capacity of the chargers in each planning stage to obtain the multi-stage planning optimization scheme for the hybrid energy station. If the cost of retrofitting the fuel dispensers is positive, then the optimal capacity of the chargers and energy storage facilities under the current planning stage is determined. Based on whether the number of fuel dispensers in the previous planning stage meets the preset resource constraints, if it does, then the number of fuel dispensers is used as the planned number for the current planning stage; otherwise, the fuel dispensers and chargers are jointly optimized until all planning stages are completed, thus obtaining the multi-stage planning optimization scheme for the hybrid energy station.

6. The apparatus according to claim 5, characterized in that, The computing module is further used for: The total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio of the hybrid energy station are obtained. Based on the total number of vehicles, electric vehicle penetration rate, demand generation probability, and site-captured demand ratio, the charging or refueling demand in each grid cell within the planning area under any planning stage is calculated. Based on the charging or refueling demand in each grid cell within the planning area under any planning stage, a non-objective optimization problem is determined, and the non-objective optimization problem is equivalently transformed into a convex optimization problem. Based on the decision rationality of users when choosing a refueling destination, the convex optimization problem is solved to obtain the balanced distribution result of refueling demand.

7. The apparatus according to claim 6, characterized in that, The computing module is further used for: The energy replenishment needs of each grid cell within the planning area are equivalently concentrated at the center point of the planning area, and each hybrid power station is built at the center of the grid cell. The energy replenishment needs of each grid cell are continuously distributed to one or more hybrid electric power stations, so that each hybrid electric power station can capture the charging or refueling needs of one or more grid cells.

8. The apparatus according to claim 5, characterized in that, The constraints include energy station operation constraints, planning constraints, and equilibrium allocation results. The planning module is further used for: To obtain the optimization objectives of hybrid power stations and the costs and benefits for operators; The objective function is determined based on the optimization objective and the operator's costs and benefits, and the objective function is constrained by the operational constraints, planning constraints, and equilibrium allocation results. The planned capacity of refueling facilities, charging facilities, and energy storage facilities in the hybrid energy station under multiple planning stages is calculated using the objective function, and a multi-stage planning model of the hybrid energy station is established based on the planned capacity.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the multi-stage planning method for hybrid power stations as described in any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the multi-stage planning method for hybrid power stations as described in any one of claims 1-4.

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