Space granularity power system optimization method, system and related device

Abstract

The invention discloses a space granularity power system optimization method and system and a related device, and belongs to the technical field of power system management, and the method comprises the steps: obtaining related data of a power system; performing clustering analysis on related data of the power system by adopting an efficient spatio-temporal data processing method based on a k-means clustering technology to obtain key features; constructing an ESONE model by using a mixed integer linear programming method, inputting the key features into the ESONE model, and performing model decomposition and iterative solution on the ESONE model to obtain an optimal power system planning scheme; based on the optimal electric power system planning scheme, the influence of the electric vehicle fleet on the whole electric power supply network under different charging strategies is evaluated, the overall benefit difference of different charging schemes on scale electric power supply and transmission infrastructures is compared, and a more reasonable electric vehicle charging strategy is obtained. According to the method, the investment of a power system on power generation and power transmission infrastructures can be determined, and operation scheduling and power flow can be optimized per hour.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system management, and in particular relates to a spatial granularity power system optimization method, system and related devices. Background Art

[0002] Electric vehicles (EVs) are widely considered as a potential alternative to polluting internal combustion engine vehicles (ICEVs). Compared to ICE vehicles, EVs are powered by electric motors, avoid tailpipe emissions, and are more energy efficient throughout their use. However, a significant barrier to widespread adoption of EVs is limited driving range, leading to a perception that EVs may not reach their destination due to insufficient driving range, known as range anxiety. New technologies and commercial solutions are being developed to address the challenges of energy transition, such as balancing issues, energy and resource transportation, new supply and value chains, and decentralized generation. In conjunction with the above-mentioned changes in the energy system, mathematical models are increasingly being used to investigate possible pathways in increasingly complex decision spaces. Expectations of such models are high; they must capture technical details, short- and long-term timescales, both supply and demand sides, political and social requirements, market dynamics, fine-scale spatiality, representativeness, uncertainty, selectivity, and more. While models clearly cannot (and need not) capture all descriptive aspects in all situations, transparency of model capabilities, assumptions, and data is essential. However, despite a large amount of activity, there remains a gap in combining long-term power supply planning based on technically detailed bottom-up models with the ability to address location-dependent transformations such as transport electrification. Importantly, some energy sources and sinks (i.e., distributed solar generation or electric vehicle (EV) charging) are highly site-specific. In the context of transport electrification, common research questions include the optimal charging / discharging of EV batteries, the construction of charging infrastructure, or vehicle-to-grid technologies for advanced grid management.

[0003] Power system models play a vital role in strategic planning and decision-making for energy transition. Despite the continuous improvement in the complexity and diversity of existing technologies, combining high resolution in space and time with long-term planning remains a challenge. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a spatial granularity power system optimization method, system and related devices. The present invention proposes a spatial granularity power system optimization model ESONE model. The ESONE model adopts the mixed integer linear programming (MILP) method, which can not only determine the investment of the power system in power generation and transmission infrastructure, but also optimize the operation scheduling and power flow on an hourly basis. The model ensures the feasibility of calculation through unique data clustering technology, combined with model decomposition and iterative solution process.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a spatial granularity power system optimization method, comprising: Obtain relevant data of the power system; An efficient spatiotemporal data processing method based on k-means clustering technology is used to cluster the relevant data of the power system and obtain key features; The ESONE model is constructed using the mixed integer linear programming method. The key features are input into the ESONE model, and the optimal power system planning scheme is obtained by model decomposition and iterative solution of the ESONE model. The objective function of the ESONE model is to minimize the total system cost, including the capital expenditure of power supply and transmission capacity expansion and the total system operating cost. Based on the optimal power system planning scheme, evaluate the impact of electric vehicle fleets on the entire power supply network under different charging strategies, compare the overall benefits of different charging schemes on large-scale power supply and transmission infrastructure, and obtain a more reasonable electric vehicle charging strategy; The more reasonable electric vehicle charging strategy is output to guide the actual power system planning and management.

[0006] As a further improvement of the present invention, the acquisition of relevant data of the power system includes information on power generation and transmission infrastructure of the power system, power load demand in different regions, electric vehicle charging behavior data, electric vehicle cost data and environmental factors; As a further improvement of the present invention, the ESONE model is based on the power system optimization ESO framework to include high spatial and temporal resolution.

[0007] As a further improvement of the present invention, the ESONE model is:

[0008] in as follows:

[0009] In the formula, For start-up costs, is the operating cost, including fuel costs, efficiency, variable operating costs, carbon prices, CO2 transport and storage costs, For fixed operating costs, Energy storage operating costs These are the cost of electricity input, the cost of operating the transmission grid, and possible penalties for not meeting electricity demand.

[0010] Weighting factors for regional EV demand is calculated as follows: the hourly charging profile given as the percentage of the fleet that charges daily is combined with the annual EV demand to provide the annual hourly EV demand a and the cluster time set c, ;

[0011]

[0012] parameter is a {0, 1} matrix indicating whether zone z contains the EV demand in year a. represents the initial population of region z in the baseline year; the weight factor of regional electric vehicle demand Multiplying with the growing fleet of electric vehicles The relevant annual demand is exogenously added to the hourly demand profiles of each region; the hourly specific charging profiles are

[0013] In the formula, a : planning period, t: time period, c: clustering of compressed time sets, z: spatial region, i: technology, ig: power generation technology, ic: conventional power generation technology, ir: intermittent renewable technology, is: energy storage technology, iz: geographically restricted technology, inoz: geographically unrestricted technology, Δ a : the step size of the planning year, DIni,z: the number of available units of technology i at a=1; DMaxi: the maximum number of units of technology i; DZMaxi,z: the maximum number of units of technology i in region z; Desi: the nominal capacity per unit of technology i; BRi: the construction rate of technology i; LTIni: the lifetime of the initial capacity of technology i at a=1; LTi: the lifetime of technology i; TEi∗: the characteristics of technology i; Pmin: the minimum power output as a percentage of the nominal capacity.

[0014] As a further improvement of the present invention, the iterative solution of the ESONE model is solved using GAMS software and CPLEX solver, and the ESONE model is decomposed into multiple planning periods along the time dimension and solved one by one in the iterative process; each iteration contains two planning periods, the decisions of the first planning period are considered fixed, and the decisions of the second planning period can be adjusted.

[0015] As a further improvement of the present invention, the iterative solution process is as follows: Define a rolling horizon iteration, define an iteration set h ∈ H, each iteration h contains a set of active planning periods a; Initialize the dynamic set aa of iteration h as the planning period set. Set the first planning period aa_f and the last planning period aa_l; Solve the model, calculate the solution that minimizes the total system cost tsc, and initialize the dynamic set Dini for the next iteration based on the solution; For each iteration h, update the iteration set aa to the set of next planning periods; repeat until all planning periods are covered; Each iteration consists of two planning periods; in the first planning period, the system design decisions are fixed; the optimal solution in the second planning period is used to initialize the next iteration; Validation results were obtained through comparison with existing data and previous studies; Analyze the model results, evaluate the impact of electric vehicles on the power system supply and transmission capacity requirements, and the impact of different charging strategies, and ultimately obtain the optimal power system planning solution.

[0016] In a second aspect, the present invention provides a spatial granularity power system optimization system, comprising: An acquisition module, used for acquiring relevant data of the power system; Cluster analysis module, used to perform cluster analysis on relevant data of power system by using efficient spatiotemporal data processing method based on k-means clustering technology to obtain key features; Iterative solution module, used to construct ESONE model using mixed integer linear programming method, input key features into ESONE model, and obtain the optimal power system planning scheme by model decomposition and iterative solution of ESONE model; the objective function of ESONE model is to minimize the total system cost, including capital expenditure for power supply and transmission capacity expansion and total system operating cost; The evaluation and comparison module is used to evaluate the impact of electric vehicle fleets on the entire power supply network under different charging strategies based on the optimal power system planning scheme, compare the overall benefit differences of different charging schemes on the scale of power supply and transmission infrastructure, and obtain a more reasonable electric vehicle charging strategy; The strategy output module is used to output the more reasonable electric vehicle charging strategy for guiding the actual power system planning and management.

[0017] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the spatial granularity power system optimization method when executing the computer program.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the spatial granularity power system optimization method is implemented.

[0019] In a fifth aspect, the present invention provides a computer program product, wherein the computer program product comprises computer instructions, wherein the computer instructions instruct a computer to execute the spatial granularity power system optimization method.

[0020] The beneficial effects of the present invention compared with the prior art are as follows: The present invention extracts key features from a large amount of data through cluster analysis, making power system planning more scientific and accurate. The ESONE model adopts a mixed integer linear programming method, which can comprehensively consider multiple factors, including capital expenditures, operating costs, etc., so as to obtain the optimal power system planning scheme. This process can evaluate the impact of different charging strategies on the power supply network, providing flexibility and diversity for the formulation of electric vehicle charging strategies. By minimizing the total system cost, including capital expenditures for power supply and transmission capacity expansion and total system operating costs, the economy and sustainability of the power system are improved. The output of a more reasonable electric vehicle charging strategy can be directly used to guide the actual power system planning and management, providing strong support for the optimized operation of the power system. The principles and advantages of this power system planning and management process lie in its data-driven, optimized decision-making, flexibility, cost-effectiveness and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flow chart of spatial granularity power system optimization provided by the present invention; Figure 2 This is the charging load demand distribution diagram of the present invention, (a) is the total charging load demand distribution in each time period, (b) is the charging load demand distribution of residents in each time period, (c) is the charging load demand distribution of the working area in each time period, (d) is the charging load demand distribution of other functional areas in each time period, (e) is the charging load demand distribution of the social rest area in each time period, and (f) is the charging load demand distribution of the shopping and entertainment area in each time period; Figure 3The four figures are examples of clustering profiles for electricity demand, onshore wind power, offshore wind power, and solar energy availability, respectively; Figure 4 Representation of the workflow and software tools used by ESONE for the present invention; Figure 5 This is a curve chart showing the expansion of power system generation capacity from 2015 to 2050. DETAILED DESCRIPTION

[0023] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0024] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0026] join Figure 1 The first object of the present invention is to propose a spatial granularity power system optimization method, comprising: S100, obtaining relevant data of the power system; S200, uses an efficient spatiotemporal data processing method based on k-means clustering technology to cluster the relevant data of the power system and obtain key features; S300, uses mixed integer linear programming method to build ESONE model, inputs key features into ESONE model, and obtains the optimal power system planning scheme by model decomposition and iterative solution of ESONE model; the objective function of ESONE model is to minimize the total system cost, including capital expenditure of power supply and transmission capacity expansion and total system operating cost; S400, based on the optimal power system planning scheme, evaluates the impact of electric vehicle fleets on the entire power supply network under different charging strategies, compares the overall benefits of different charging schemes on the scale of power supply and transmission infrastructure, and obtains a more reasonable electric vehicle charging strategy; S500: Output the more reasonable electric vehicle charging strategy to guide actual power system planning and management.

[0027] The present invention obtains relevant data from the power system, including but not limited to power load, power generation capacity, capacity and loss of transmission lines, power market prices, etc. Cluster analysis is performed on these data using an efficient spatiotemporal data processing method based on k-means clustering technology. The purpose of cluster analysis is to extract key features from a large amount of data, which can represent the main behavior or mode of the power system. The ESONE model is constructed using a mixed integer linear programming method. The key features obtained by cluster analysis are used as input, and the optimal power system planning scheme is obtained through model decomposition and iterative solution. The objective function of the ESONE model aims to minimize the total system cost, which includes capital expenditures for power supply and transmission capacity expansion, as well as the total system operating cost. Based on the optimal power system planning scheme, the impact of electric vehicle fleets on the entire power supply network under different charging strategies is evaluated. Compare the overall benefit differences of different charging schemes on power supply and transmission infrastructure, including grid load balance, transmission line utilization, power market impact, etc. According to the evaluation results, a more reasonable electric vehicle charging strategy is output.

[0028] Further, the present invention proposes the ESONE model, a spatial granular power system optimization model that aims to solve the problems of the existing technology. The ESONE model adopts a mixed integer linear programming (MILP) method, which can not only determine the investment of the power system in power generation and transmission infrastructure, but also optimize the operation scheduling and power flow on an hourly basis. The model ensures computational feasibility through a unique data clustering technique, combined with model decomposition and iterative solution process. In this application, the present invention extends the power system optimization (ESO) framework, which is a set of mixed integer linear programs, to include high spatial and temporal resolution. The present invention introduces the ESONE model as part of the ESO framework. In addition, the present invention proposes an efficient spatiotemporal data processing technique based on k-means clustering and specific analysis developed as part of this work, as well as a rolling horizontal solution technique to achieve computational tractability combining long-term planning with short-term operational details. The present invention demonstrates the ESONE model by evaluating the impact of the electric vehicle revolution on the expansion of supply and transmission capacity, carbon emissions and cost requirements. The impact of electric vehicle fleets under different charging strategies and regional load demand profiles is estimated and externally integrated into the ESONE model. Suboptimal and optimal (aka smart) charging behaviors are compared with regard to their impact on scaled electricity supply and transmission infrastructure.

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] When discussing future power system planning, the choice of generation capacity, investment timing, geographic location, and the need for transmission network reinforcement with or without considering passenger electrification are critical issues. To achieve this goal, the present invention extends the existing power system optimization (ESO) framework, which is a collection of mixed integer linear programs (MILP) models, and is now able to handle data with higher spatial and temporal resolution. As part of the ESO framework, the present invention introduces the ESONE model, which is a power system optimization model with high temporal and spatial resolution, which is able to optimize the power system's generation and transmission infrastructure at the hourly level.

[0031] In order to process large-scale spatiotemporal data and maintain computational feasibility, the present invention develops efficient spatiotemporal data processing technology based on k-means clustering and specific analysis. In addition, the present invention also adopts rolling horizontal solution technology, which enables the model to handle long-term planning and short-term operational details at the same time.

[0032] Through the ESONE model, we evaluate the impact of the electric vehicle revolution on power system supply and transmission capacity requirements, carbon emissions, and costs. We consider different electric vehicle charging strategies and regional load demand scenarios and integrate these factors into the ESONE model. By comparing the potential impacts of suboptimal and optimal (smart) charging behaviors on scaled power supply and transmission infrastructure, we are able to provide insights into the selection of electric vehicle charging strategies and the timing of grid reinforcement. Overall, this study provides a comprehensive framework for evaluating and planning the development of future power systems, especially in the context of the increasing popularity of electric vehicles. Through this meticulous analysis, we can better understand how to balance generation resources, optimize grid infrastructure, and ensure the sustainability and flexibility of the power system.

[0033] The specific solutions provided in the embodiments of this application are as follows: 1) Data collection: First, you need to obtain relevant data, including but not limited to: Information on the generation and transmission infrastructure of the electric power system.

[0034] Electricity load demand in different areas.

[0035] Data on electric vehicle (EV) charging behavior, including charging strategies, charging times and locations.

[0036] Cost data, such as investment cost, operating cost, etc.

[0037] Environmental factors, such as carbon emission limits.

[0038] 2) Data preprocessing: The raw data is clustered using an efficient spatiotemporal data processing method based on k-means clustering technology. This data clustering technique helps reduce the amount of data and retain key features for subsequent calculations.

[0039] 3) Modeling and solving: The ESONE model is constructed using the mixed integer linear programming (MILP) method. This model can not only determine the best investment decisions for the power system in power generation and transmission infrastructure, but also optimize operational scheduling and power flow distribution at the hourly level. The model decomposition and iterative solution process ensure that even large-scale problems can be effectively solved.

[0040] 4) Evaluation and Integration: Evaluate the impact of electric vehicle fleets on the entire power supply network under different charging strategies, and incorporate these impacts as external inputs into the ESONE model. Compare the overall benefits of different charging solutions (suboptimal and optimal / smart) on the scale of power supply and transmission infrastructure.

[0041] Result analysis and application: The final output results can be used to guide actual power system planning and management, such as determining where new power generation facilities should be added or the existing grid structure should be improved to cope with possible changes in demand in the future; it can also help formulate more reasonable electric vehicle charging strategies and promote the achievement of energy conservation and emission reduction goals.

[0042] Among them, data collection is the starting point of the entire research or project. For power system planning and management, the data that needs to be collected include but are not limited to: information on power generation and transmission infrastructure, which is the basis for understanding the current status and future planning of the power system, including the installed capacity of various power plants, the layout and capacity of the transmission network, etc. Power load demand. The power load demand in different regions is an important basis for power system scheduling and planning. It is necessary to collect historical power consumption data and forecast data in each region. Electric vehicle (EV) charging behavior data. With the popularization of electric vehicles, the impact of their charging behavior on the power system is becoming increasingly significant. It is necessary to collect data such as charging strategies, charging time and location of electric vehicles to evaluate their impact on the power system. Cost data, including investment costs and operating costs, are important considerations for formulating power system planning and policies. Environmental factors, such as carbon emission restrictions, are important references for formulating environmental protection policies and plans for power systems.

[0043] Data preprocessing is the processing of raw data to make it more suitable for subsequent analysis and modeling. In power system planning, it is an effective method to cluster raw data using an efficient spatiotemporal data processing method based on k-means clustering technology. The principle of this method is to reduce the amount of data and maintain key features by grouping similar data points into one category. This not only reduces the complexity of subsequent calculations, but also improves the accuracy and efficiency of the model.

[0044] In power system planning, mixed integer linear programming (MILP) is a commonly used modeling method. The MILP model can comprehensively consider multiple aspects of the power system, such as power generation, transmission, and power consumption, as well as multiple cost factors such as investment and operation, to develop the optimal power system planning scheme.

[0045] The ESONE model is a power system optimization model based on the MILP method. This model can not only determine the best investment decision for the power system in power generation and transmission infrastructure, but also optimize the operation scheduling and power flow distribution at the hourly level. Through model decomposition and iterative solution process, it can ensure that even large-scale problems can be effectively solved.

[0046] In power system planning, it is very important to evaluate the impact of electric vehicle fleets on the entire power supply network under different charging strategies. This requires incorporating the charging behavior of electric vehicles as external input into the ESONE model to evaluate its impact on the power system. By comparing the overall benefits of different charging schemes (such as suboptimal and optimal / intelligent) on the scale of power supply and transmission infrastructure, a more reasonable electric vehicle charging strategy can be formulated, thereby promoting the achievement of energy conservation and emission reduction goals.

[0047] The final output results can be used to guide the actual planning and management of power systems. For example, it can determine where new power generation facilities should be added or the existing grid structure should be improved to cope with possible changes in demand in the future; at the same time, it can also help formulate more reasonable electric vehicle charging strategies. The application of these results can not only improve the economy and reliability of the power system, but also promote energy conservation, emission reduction and the realization of sustainable development goals.

[0048] The entire power system planning and management process is a complex and systematic process that requires comprehensive consideration of multiple factors and data. Through scientific data collection, preprocessing, modeling and solving, evaluation and integration, and result analysis and application, the optimal power system planning scheme can be formulated to provide strong support for the sustainable development of the power system.

[0049] Objective function: The objective function is defined to minimize the total system cost, including capital expenditures for power supply and transmission capacity expansion, and total system operating costs. The objective function is to minimize the total system cost, including capital expenditures for power supply and transmission capacity expansion, and total system OPEX, where the years between the 5-year planning interval a are approximated in a piecewise linear manner (the difference between the OPEX in a and a)1. By introducing the weight factor to account for the end-of-the-horizon effect, a factor that accounts for a small fraction of the technology lifetime compared to the modeling time horizon.

[0050]

[0051] in as follows:

[0052] In the formula, a : planning period, a∈A={1,...,Aend}, year; t: time period, t∈T={1,...,Tend}, hours; c: clustering of time set compression, c∈C={1,...,Cend}; z: spatial region, z∈Z={1,...,Zend}; i: technology, i∈I={1,...,Iend}; ig: power generation technology, ig⊆I; ic: Conventional power generation technology, I c ⊆ I ; ir: Intermittent Renewable Technologies, I r ⊆ I ; is:Energy Storage Technology,I s ⊆ I ; iz: Geographically restricted technology, I z ⊆ I ; inoz: Geographically independent technology, inoz ∈{ I ∖ iz}; Liz,z′: If regions z and z′ z ' is connected, it is 1, otherwise it is 0; NoLiz,z′: If regions z and z′ z 'If not connected, it is 1, otherwise it is 0; Δ a : The step length of the planning year, year; DIni,z: the number of available units of technology i when a=1; DMaxi: maximum number of units of technology i; DZMaxi,z: the maximum number of units of technology i in zone z; Desi: nominal capacity per unit of technology i, MW / unit; BRi: construction rate of technology i, unit / year; LTIni: lifetime of initial capacity of technology i at a=1, years; LTi: lifespan of technology i, years; TEi∗: characteristics of technology i, different units; Pmin: minimum power output as a percentage of nominal capacity; AVr i,z,c,t : Availability factor of technology ir, %-MW; SD c,t,z,a : The power demand of the system at hour t in year a, MWh; EVD a : Additional demand for the national electric fleet in year a, MWh; Wlq z,a : Weight factor of electric vehicle fleet demand share in region z in year a, %; EVDc,t,a : hourly regional electric fleet demand for cluster c at time t in year a, MWh; WFC: weight factor of temporal cluster c; WFA i,a : Calculate the weight factor of capital expenditure according to the life of technology i and year a; PL a,z : peak load in time range T per year a, MW; CM: capacity margin, %-MW; RM: absolute reserve margin, %-MW; WR: dynamic reserve of wind power, %-MW; SI: minimum system inertia requirement, MW s; Pmax : Maximum power output as a percentage of nominal capacity, %-MW; Cmax: Maximum capacity provided as a percentage of nominal capacity, %-MW; RP: reserve potential as a percentage of nominal capacity, %-MW; IP: Inertia potential as a percentage of nominal capacity, %-MW; Ems: emission rate, tco2 / MWh; UtilMin: minimum utilization as a percentage of nominal capacity, %-MW; MAia: maturity parameter of technology i - the year in which technology i can be deployed; CAPEXi: investment cost of technology i, £ / MW; OPEXia: operating cost of technology i in year a, £ / MWh; OPEXSU: startup cost of technology i, £ / h / unit; OPEXNL: fixed operating cost of technology i when operating in any mode, £ / h / unit; TCAPEX: investment cost of transmission line, £ / MW / km; TOPEX: operating cost of transmission lines, £ / MW; Distz,z′: region z and z′ z ′, km; TLDist: transmission losses, %-MWh / km; TDes: capacity unit size of the transmission line, MW; TIniz,z′: Region z and z′ z Initial transmission capacity between ′, MW; In the formula, For start-up costs, For operating costs (including fuel costs, efficiency, variable operating costs, carbon prices, CO2 transportation and storage costs), For fixed operating costs, Energy storage operating costs These are the cost of electricity input, the cost of operating the transmission grid, and possible penalties for not meeting electricity demand.

[0053] The increase in the electric vehicle fleet can significantly impact the capacity and transmission requirements and operation of the power system. EVs are modeled as additional regional demand while simulating specific load (i.e. charging) profiles. Data on annual energy demand growth as a function of the size of the EV fleet and the daily charging profile are provided by the grid. In particular, two different EV models are implemented in this work to study three main scenarios including EVs. The following formula represents the annual demand growth uniformly increased in the daily profile, e.g. with continuous charging every hour. Weighting factors for regional EV demand The calculation method of is shown in the formula. The hourly charging profile given as the percentage of the fleet charged daily is combined with the annual EV demand (TWh) to provide the annual hourly (t) EV demand a and cluster time set c, .

[0054]

[0055]

[0056] parameter is a {0, 1} matrix indicating whether zone z contains the EV demand in year a. represents the initial population of area z in the baseline year. This invention does not consider the change in population distribution within the planning time range. The weight factor of regional electric vehicle demand Multiplying with the growing fleet of electric vehicles The relevant annual demand (TWh) is exogenously added to the hourly demand profile for each region. The formula below represents the hourly specific charging profile.

[0057]

[0058] Model Solution: The model was run on an x86_64 machine with 24 CPUs using GAMS 25.0.3 software and CPLEX 12.8.0 solver, typically using 6 threads. The model was decomposed into multiple planning periods (typically 5 years) along the time dimension and solved one by one in an iterative process. Each iteration contains two planning periods, the decisions of the first planning period are considered fixed, while the decisions of the second planning period can be adjusted. The iterative solution process is as follows: Define a rolling horizon iteration: Define a set of iterations h ∈ H, each iteration h contains a set of active planning periods a.

[0059] Initialization: Initialize the dynamic set aa of iteration h as the set of planning periods. Set the first planning period aa_f and the last planning period aa_l.

[0060] Solution model: Solve the MILP problem ESONE and minimize the total system cost tsc. Initialize the dynamic set DIni for the next iteration based on the solution.

[0061] Update iteration: For each iteration h, update the iteration set aa to the set of next planning periods. Repeat until all planning periods are covered.

[0062] Iterative solution process: Each iteration consists of two planning periods. In the first planning period, the system design decisions are fixed. The optimal solution of the second planning period is used to initialize the next iteration.

[0063] Model validation: Verify the accuracy and reliability of the model by comparing it with existing data and previous studies.

[0064] Results Analysis: Analyze the model results to assess the impact of EVs on the power system supply and transmission capacity requirements, as well as the impact of different charging strategies.

[0065] The rolling horizon method effectively handles uncertainty and dynamic changes by decomposing the long-term planning problem into a series of short-term decision problems and re-optimizing at each decision point. This method is particularly suitable for power system planning, where decisions need to be made step by step and may be affected by factors such as previous decisions, technological progress, market demand and policy changes. With the support of advanced software and solvers such as GAMS and CPLEX, this process can be implemented efficiently and provide accurate decision support to decision makers.

[0066] Figure 2It shows (a) the total charging load demand distribution in each time period, (b) the charging load demand distribution of residents in each time period, (c) the charging load demand distribution of the working area in each time period, (d) the charging load demand distribution of other functional areas in each time period, (e) the charging load demand distribution of social rest areas in each time period, and (f) the charging load demand distribution of shopping and entertainment areas in each time period. It provides a basis for the establishment of the ESONE model.

[0067] Figure 3 An example of clustering profiles for electricity demand, onshore wind, offshore wind, and solar availability is shown for k=11; the original data profiles (thin colorful lines) are assigned to the kth cluster using the k-means algorithm. The cluster averages (dashed black lines) are smooth in nature, and the profiles selected best preserve the energy integrals within the respective clusters (solid lines with markers), representing the true nature of the underlying data.

[0068] Figure 4 A schematic summarizing these workflows and software links is shown, visualizing the three main phases of the workflow: pre-optimization (data preparation and processing), optimization, and post-optimization (data visualization and analysis). During the data preparation and spatial data processing phases, data is transferred between Excel and ArcGIS to transform it into a form usable by ESONE.

[0069] Figure 5 The figure shows the expansion of the power system's generation capacity from 2015 to 2050, based on four different models: ESONE (base case), ESONE-EV-const. (constant power demand model including electric vehicle demand), ESO-X-my (an optimization model) and ESO-X-my-EV (optimization model including electric vehicle demand). The figure uses two different axes to present the data: the left axis shows the installed generation capacity (in gigawatts GW), and the right axis shows the system-wide carbon intensity (in tons of carbon dioxide equivalent per megawatt hour tCO2 / MWh).

[0070] The second object of the present invention is to provide a spatial granularity power system optimization system, comprising: An acquisition module, used for acquiring relevant data of the power system; Cluster analysis module, used to perform cluster analysis on relevant data of power system by using efficient spatiotemporal data processing method based on k-means clustering technology to obtain key features; Iterative solution module, used to construct ESONE model using mixed integer linear programming method, input key features into ESONE model, and obtain the optimal power system planning scheme by model decomposition and iterative solution of ESONE model; The evaluation and comparison module is used to evaluate the impact of electric vehicle fleets on the entire power supply network under different charging strategies based on the optimal power system planning scheme, compare the overall benefit differences of different charging schemes on the scale of power supply and transmission infrastructure, and obtain a more reasonable electric vehicle charging strategy; The strategy output module is used to output the more reasonable electric vehicle charging strategy for guiding the actual power system planning and management.

[0071] The third object of an embodiment of the present invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the spatial granularity power system optimization method when executing the computer program.

[0072] A fourth objective of an embodiment of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the spatial granularity power system optimization method is implemented.

[0073] A fifth objective of an embodiment of the present invention is to provide a computer program product, wherein the computer program product comprises computer instructions, wherein the computer instructions instruct a computer to execute the spatial granularity power system optimization method.

[0074] 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.

[0075] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] The present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take 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.) containing computer-usable program codes.

[0077] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of 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 produce 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.

[0078] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A spatial granularity power system optimization method, characterized in that: include: Obtain relevant data of the power system; An efficient spatiotemporal data processing method based on k-means clustering technology is used to cluster the relevant data of the power system and obtain key features; The ESONE model is constructed using the mixed integer linear programming method. The key features are input into the ESONE model, and the optimal power system planning scheme is obtained by model decomposition and iterative solution of the ESONE model. The objective function of the ESONE model is to minimize the total system cost, including the capital expenditure of power supply and transmission capacity expansion and the total system operating cost. Based on the optimal power system planning scheme, evaluate the impact of electric vehicle fleets on the entire power supply network under different charging strategies, compare the overall benefits of different charging schemes on large-scale power supply and transmission infrastructure, and obtain a more reasonable electric vehicle charging strategy; The more reasonable electric vehicle charging strategy is output to guide the actual power system planning and management.

2. The spatial granularity power system optimization method according to claim 1, characterized in that: The obtained power system related data includes power generation and transmission infrastructure information of the power system, power load demand conditions in different regions, electric vehicle charging behavior data, electric vehicle cost data and environmental factors.

3. The spatial granularity power system optimization method according to claim 1, characterized in that: The ESONE model is based on the ESO framework for power system optimization to include high spatial and temporal resolution.

4. The spatial granularity power system optimization method according to claim 1, characterized in that: The ESONE model is: in as follows: In the formula, For start-up costs, is the operating cost, including fuel costs, efficiency, variable operating costs, carbon prices, CO2 transport and storage costs, For fixed operating costs, Energy storage operating costs These are the cost of electricity input, transmission grid operation costs, and possible penalties for not meeting electricity demand; Weighting factors for regional EV demand is calculated as follows: the hourly charging profile given as the percentage of the fleet that charges daily is combined with the annual EV demand to provide the annual hourly EV demand a and the cluster time set c, ; parameter is a {0, 1} matrix indicating whether zone z contains the EV demand in year a. represents the initial population of region z in the baseline year; the weight factor of regional electric vehicle demand Multiplying with the growing fleet of electric vehicles The relevant annual demand is exogenously added to the hourly demand profiles of each region; the hourly specific charging profiles are In the formula, a : planning period, t: time period, c: clustering of compressed time sets, z: spatial region, i: technology, ig: power generation technology, ic: conventional power generation technology, ir: intermittent renewable technology, is: energy storage technology, iz: geographically restricted technology, inoz: geographically unrestricted technology, Δ a : the step length of the planning year, DIni,z: the number of available units of technology i when a=1; DMaxi: maximum number of units of technology i; DZMaxi,z: the maximum number of units of technology i in zone z; Desi: nominal capacity per unit of technology i; BRi: construction rate of technology i; LTIni: lifetime of initial capacity of technology i at a = 1; LTi: lifetime of technology i; TEi∗: characteristics of technology i; Pmin: minimum power output as a percentage of nominal capacity.

5. The spatial granularity power system optimization method according to claim 1, characterized in that: The iterative solution of the ESONE model is solved using GAMS software and CPLEX solver. The ESONE model is decomposed into multiple planning periods along the time dimension and solved one by one in the iterative process; each iteration contains two planning periods, the decisions of the first planning period are considered fixed, and the decisions of the second planning period can be adjusted.

6. The spatial granularity power system optimization method according to claim 1, characterized in that: The iterative solution process is as follows: Define a rolling horizon iteration, define an iteration set h ∈ H, each iteration h contains a set of active planning periods a; The dynamic set aa of initialization iteration h is the planning period set; Set the first planning period aa_f and the last planning period aa_l; Solve the model, calculate the solution that minimizes the total system cost tsc, and initialize the dynamic set Dini for the next iteration based on the solution; For each iteration h, update the iteration set aa to the set of next planning periods; repeat until all planning periods are covered; Each iteration consists of two planning periods; in the first planning period, the system design decisions are fixed; the optimal solution in the second planning period is used to initialize the next iteration; Validation results were obtained through comparison with existing data and previous studies; Analyze the model results, evaluate the impact of electric vehicles on the power system supply and transmission capacity requirements, and the impact of different charging strategies, and ultimately obtain the optimal power system planning solution.

7. A spatial granularity power system optimization system, characterized in that: include: An acquisition module, used for acquiring relevant data of the power system; Cluster analysis module, used to perform cluster analysis on relevant data of power system by using efficient spatiotemporal data processing method based on k-means clustering technology to obtain key features; Iterative solution module, used to construct ESONE model using mixed integer linear programming method, input key features into ESONE model, and obtain the optimal power system planning scheme by model decomposition and iterative solution of ESONE model; The evaluation and comparison module is used to evaluate the impact of electric vehicle fleets on the entire power supply network under different charging strategies based on the optimal power system planning scheme, compare the overall benefit differences of different charging schemes on the scale of power supply and transmission infrastructure, and obtain a more reasonable electric vehicle charging strategy; The strategy output module is used to output the more reasonable electric vehicle charging strategy for guiding the actual power system planning and management.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the spatial granularity power system optimization method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the spatial granularity power system optimization method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising computer instructions, characterized in that: The computer instructions instruct the computer to execute the spatial granularity power system optimization method according to any one of claims 1-6.

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