Multi-time scale energy system optimization scheduling method based on electric vehicle V2G response

Abstract

The invention provides a multi-time scale energy system optimization scheduling method based on electric vehicle V2G response, and relates to the technical field of intelligent power grids, a day-ahead scheduling model is constructed, and scheduling is carried out by optimizing an objective function and constraint conditions of electric power, thermodynamic balance and electric vehicle electric quantity state. A day is divided into a plurality of same time periods, and an intra-day optimization model is formulated. And based on the travel path and the charging behavior of the electric vehicle, establishing a travel chain model, predicting the charging demands of different time periods and regions, generating a transition probability matrix, estimating the overall charging demand, and adjusting the charging demand in the intraday optimization model. And finally, according to the charging power of the electric vehicle, the power grid electricity price signal and the battery state, a charging and discharging demand response model is constructed, and the charging power of the electric vehicle is optimized by taking the minimization of the total operation cost as a target, so that the multi-stage linkage energy system optimization scheduling is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and specifically to a multi-time scale energy system optimal scheduling method based on the V2G response of electric vehicles. Background Technique

[0002] With the acceleration of the global energy transformation, the integrated energy system (IES) has become a key technical direction for building a low-carbon and clean energy structure due to its ability to efficiently integrate various energy forms such as electricity, heat, cold energy, and natural gas. Through multi-energy complementarity and collaborative optimization, the IES can effectively improve the energy utilization efficiency and reduce the system operation cost. However, the volatility and uncertainty in system operation are still the main challenges faced by current optimal scheduling. At the same time, the rapid development of electric vehicles has brought revolutionary changes to the transportation field and also provided a new opportunity for the optimal scheduling of the integrated energy system. As a flexible resource with mobile energy storage characteristics, through vehicle-to-grid (V2G) technology, EVs can achieve bidirectional energy flow, not only providing auxiliary services such as peak shaving and valley filling, reserve capacity, and frequency regulation for the power grid, but also significantly enhancing the consumption capacity of renewable energy and providing support for the access of new energy sources such as wind power and photovoltaic power. Therefore, studying the collaborative optimization of EVs and IESs is of great significance for alleviating the contradiction between energy supply and demand, enhancing the system operation flexibility, and achieving carbon emission reduction goals.

[0003] Multi-time scale optimization methods have been widely applied in the integrated energy system. Day-ahead optimization formulates the overall scheduling plan through a lower time resolution, and the main goal is global operation economy; intraday rolling optimization dynamically adjusts the strategy with a higher time resolution to cope with the fluctuations in renewable energy output and load changes. Some studies have initially combined the scheduling optimization of EVs in the IES. However, the current research still has limitations. First, most studies regard EVs as static load models and fail to combine their dynamic behavior characteristics of the travel chain, making it difficult to accurately predict their charging and discharging demands; second, there is a lack of real-time adjustment methods for EV charging and discharging behaviors in the intraday rolling optimization stage, and the flexibility of EVs has not been fully utilized to enhance the economy and flexibility of the system; finally, the design of multi-time scale collaborative strategies for V2G technology is still in its infancy, especially the problem of how to combine the EV travel chain with the scheduling requirements of the IES has not been deeply studied; some studies have optimized the layout and service efficiency of charging stations by combining travel chain models, but their application research in multi-time scale scheduling is still relatively limited. To address the above problems, this paper proposes a multi-time scale optimal scheduling framework that combines the EV travel chain and V2G collaboration, aiming to improve the economy, flexibility, and new energy consumption capacity of the IES.

[0004] The above information disclosed in the background technique section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization scheduling method for a multi-time-scale energy system based on the V2G response of electric vehicles to solve the problems raised in the above background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An optimization scheduling method for a multi-time-scale energy system based on the V2G response of electric vehicles, the specific steps include:

[0008] Step 1: Divide a day into several identical time periods, generate an objective function with the minimum total system cost as the optimization goal, and construct a day-ahead scheduling model with power balance, heat balance, and the SOC of electric vehicles as constraint conditions;

[0009] Step 2: Generate an objective function with the minimum intra-day total cost as the optimization goal, and construct an intra-day optimization model with the ramp limit values of gas turbines and gas boilers as constraint conditions;

[0010] Step 3: Based on the travel paths and charging behaviors of electric vehicles, construct a travel chain model, predict the charging probability of electric vehicles in specific time periods and regions, and construct a transfer probability matrix between different destinations of vehicles. Based on the travel chain model and the day-ahead scheduling model, estimate the overall charging demand of electric vehicles, and adjust the intra-day charging demand based on the travel chain model and the intra-day optimization model;

[0011] Step 4: Construct a charging and discharging demand response model for electric vehicles based on the charging power of electric vehicles, grid electricity price signals, battery states of electric vehicles, and parking durations, and take the total operating cost as the objective function. Under the condition of minimizing the total operating cost, adjust the charging and discharging demand response model of electric vehicles to optimize the charging power of electric vehicles.

[0012] Furthermore, the objective function is:

[0013] f1 = f grid + f c + f gas + f op

[0014] wherein, f1 represents the total system cost, f gird represents the grid interaction cost, f c represents the carbon trading cost, f gas represents the cost for users to purchase natural gas, f op represents the equipment operation and maintenance cost;

[0015] The power balance constraint condition is:

[0016] Pload,t +P bat,c,t +P EV,c,t +P CCS,t +P LA2user,t

[0017] =P wind,t +P tV,t +P grid,buy,t +P bat,d,t +P orc,t +P GT,t

[0018] where P load,t represents the grid load at time period t, t represents the index of the time period, and t ∈ [1, T], where T represents the number of time periods in a day, P bat,c,t represents the battery charging power at time period t, P EV,c,t represents the electric vehicle charging power at time period t, P CCS,t represents the carbon capture equipment power at time period t, P LA2user,t represents the power sold to the grid at time period t, P wind,t represents the wind power generation power at time period t, P PV,t represents the photovoltaic power generation power at time period t, P grid,buy,t represents the grid power supply power at time period t, P bat,d,t represents the battery discharge power at time period t, P orc,t represents the waste heat boiler power generation power at time period t, P GT,t represents the gas turbine power generation power at time period t;

[0019] The thermal balance constraint is:

[0020] P heat,t +H bat,c,t +H AC,t =H GT,t +H GB,t +H eb,t +H bat,d,t +H whb,t

[0021] where P heat,t represents the heat load at time period t, H bat,c,t represents the charging efficiency of the thermal energy storage at time period t, H AC,t represents the heat consumption power of the absorption refrigeration at time period t, H GT,t represents the heat supply power of the gas turbine at time period t, H GB,t represents the heat supply power of the gas boiler at time period t, H eb,t represents the heat supply power of the electric boiler at time period t, H bat,d,t represents the discharging efficiency of the thermal energy storage at time period t, H whb,tThe heating power of the waste heat boiler representing the time period t.

[0022] The SOC constraint conditions are:

[0023]

[0024]

[0025] And satisfy SOC min ≤SOC bat,t ≤SOC max 、SOC min ≤SOC k,t ≤SOC max ;

[0026] Among them, SOC bat,t represents the remaining battery capacity at time period t, SOC bat,t-1 represents the remaining battery capacity of the battery at time period t - 1, η c represents the charging efficiency of the battery, η d represents the discharging efficiency of the battery, SOC k,t represents the remaining battery capacity of the k-th electric vehicle within the time period t, k represents the index of the electric vehicle, SOC k,t-1 represents the remaining battery capacity of the k-th electric vehicle within the time period t - 1, P EV,c,t,k represents the charging power of the k-th electric vehicle within the time period t, P bat,d,t,k represents the discharging power of the k-th electric vehicle within the time period t, SOC min represents the minimum state of charge of the battery, which is the lowest battery power limit to prevent over-discharge, SOC max represents the maximum state of charge of the battery, which represents the highest battery power limit to prevent over-charging.

[0027] Furthermore, the formula for generating the grid interaction cost is

[0028]

[0029] Among them, and respectively represent the power purchase and power selling of the system within the time period t, and respectively represent the unit power purchase and power selling prices within the time period t;

[0030] The formula for generating the user's natural gas purchase cost is:

[0031]

[0032] Among them, It represents the unit gas purchase price of natural gas within the time period t. It represents the heat supply power of the gas turbine within the time period t, η GT It represents the efficiency of the gas turbine, LHV gas It represents the lower heating value of natural gas. It represents the heat supply power of the gas boiler within the time period t, η GB It represents the efficiency of the gas boiler;

[0033] The formula for generating the operation and maintenance costs of the equipment is:

[0034]

[0035] Among them, It represents the unit loss cost of charge and discharge of the battery pack within the time period t. It represents the discharge power of the battery within the time period t. It represents the charging power of the battery within the time period t. u represents the index of the equipment type, U represents the total number of equipment, D u It represents the unit operation and maintenance cost of equipment u. It represents the operation power of equipment u within the time period t. The equipment includes gas turbines, electric boilers, waste heat boilers, absorption refrigerators, electric refrigerators, energy storage systems, electric vehicle charging and discharging stations, and power grids.

[0036] Furthermore, the principle for constructing the intraday optimization model is:

[0037] The objective function is:

[0038]

[0039] Among them, f2 represents the total intraday cost, f grid_in,t It represents the intraday grid interaction cost within the time period t, f to_in,t It represents the intraday planned change penalty cost compared with the day-ahead plan within the time period t, f gas_in,t It represents the intraday short-term gas purchase cost of the gas network within the time period t, f op_in,t It represents the intraday short-term operation and maintenance cost within the time period t, △T represents the length of the time period, f c_in,t It represents the intraday stepped carbon trading cost within the time period t;

[0040] The constraint conditions are:

[0041] -△P max ≤P t+1 -P t ≤△P max

[0042] Among them, △P max It represents the maximum ramp rate limit value of the equipment, Pt Represents the power of the device during time period t.

[0043] Furthermore, the formula for generating the intraday grid interaction cost is:

[0044]

[0045] Wherein, Represents the intraday electricity purchase power during time period t, Represents the intraday electricity sale power during time period t;

[0046] The formula for generating the intraday gas network short - term gas purchase cost is:

[0047]

[0048] Wherein, H GT_in Represents the heating power of the intraday gas turbine, H GB_in Represents the heating power of the intraday gas boiler;

[0049] The formula for generating the intraday short - term operation and maintenance cost is:

[0050]

[0051] Wherein, Represents the intraday battery discharge power during time period t, Represents the intraday battery charging power during time period t, Represents the intraday device operation power during time period t;

[0052] The formula for generating the intraday planned change penalty cost compared with the day - ahead plan is:

[0053] f to_in [[ID=5--]]=f punish_e +f punish_h +f punish_c

[0054] Wherein, f punish_e Represents the penalty cost for the loading of each part of the power grid, f punish_h Represents the penalty cost for the adjustment amount of each part of the heat network, f punish_c Represents the penalty cost for the adjustment amount of each part of the cold network;

[0055] f punish_e =△P bat ·μ bat +△P GT ·μ GT +△P grid ·μ grid

[0056]

[0057]

[0058]

[0059] Among them, △P bat , △P GT , △P grid They represent the total amount of adjustment of battery, gas turbine and grid interaction power relative to the day-ahead dispatch plan, μ bat 、μ GT 、μ grid Represent the penalty adjustment coefficients of battery, gas turbine and grid interaction, P bat (t), P GT (t), P grid (t) represents the electric power of the battery, gas turbine, and grid interaction in time period t during the day-ahead scheduling phase, P bat_0 (t), P GT_0 (t), P grid_0 (t) represents the electric power of the battery, gas turbine, and grid interaction during time period t within a day, and |·| represents the absolute value of the difference.

[0060] f pun i sh_h =△H WHB μ WHB +△H GB μ GB +△H HS μ HS

[0061]

[0062]

[0063]

[0064] Among them, △H WHB , △H GB , △H HS They represent the total amount of adjustment of waste heat boiler, gas boiler and heat storage tank compared with the day-ahead dispatch plan, μ WHB 、μ GB 、μ HS They represent the penalty adjustment coefficients of waste heat boiler, gas boiler and heat storage tank respectively, H WHB (t), H GB (t), H HS (t) represents the interactive thermal power of the waste heat boiler, gas boiler and heat storage tank in the day-ahead scheduling period t, P WHB_0 (t), H GB_0(t), H HS_0 (t) represent the interactive heat powers of the waste heat boiler, gas boiler, and heat storage tank during the intraday time period t, respectively.

[0065] f punish_c =△Q AC ·μ ac +△Q AR ·μ ar

[0066]

[0067]

[0068] Among them, △Q AC , △Q AR represent the total adjustment amounts of the absorption chiller and the electric chiller compared with the day-ahead scheduling plan, respectively, and μ ac , μ ar represent the penalty adjustment coefficients of the absorption chiller and the electric chiller, respectively, and Q AC (t), Q AR (t) represent the interactive cooling powers of the absorption chiller and the electric chiller during the day-ahead scheduling stage in time period t, and Q AC_0 (t), Q AR_0 (t) represent the interactive cooling powers of the absorption chiller and the electric chiller during the intraday time period t, respectively.

[0069] Furthermore, the principle on which the travel chain model is constructed is as follows:

[0070] Based on the Markov chain theory, construct the transfer probability matrix of vehicles between different destinations:

[0071]

[0072] Among them, P ij represents the probability that a vehicle transfers from destination i to destination j, where i and j represent the indices of destinations, and i, j ∈ [1, n], and n represents the number of destinations;

[0073] In day-ahead scheduling, based on the travel chain prediction model, estimate the overall charging demand of electric vehicles and establish a power balance equation. The formula is as follows:

[0074]

[0075] Among them, P load represents the total load of the power grid system, k represents the index of the electric vehicle, K represents the number of electric vehicles in a specific area, P charge,k represents the charging power of the kth electric vehicle, and P renewable represents the power generation of renewable energy, including wind power and photovoltaic power, Pgrid Represents the grid power supply

[0076] In the intraday rolling optimization, based on the real-time updated travel chain data, the intraday charging demand is adjusted, and the formula is as follows:

[0077]

[0078] Among them, f deviation Represents the adjustment amount of the intraday charging demand, P real-time,t Represents the actual intraday charging power in time period t, P day-ahead,t Represents the day-ahead predicted charging power in time period t.

[0079] Furthermore, the charge and discharge demand response model of the electric vehicle is:

[0080] P EV,c (t) = γ·f[SOC k (t), β(t), P EV,parking +(1 - γ)·P day-ahead,t

[0081] Among them, P EV,c (t) represents the charging power of the electric vehicle in time period t, SOC k (t) represents the battery power of the kth electric vehicle in time period t, β(t) represents the grid electricity price signal in time period t, P EV,parking Represents the parking duration of the electric vehicle, f[SOC k (t), β(t), P EV,parking represents the real-time intraday charging power affected by the battery power, grid electricity price signal and parking duration, γ represents the weight coefficient of the real-time intraday charging power, and (1 - γ) represents the weight coefficient of the day-ahead predicted charging power;

[0082] Taking the total operating cost as the objective function, the formula is as follows:

[0083]

[0084] Among them, f3 represents the total operating cost, G V2G (t) represents the V2G discharge cost.

[0085] Compared with the prior art, the beneficial effects of the present invention are:

[0086] When constructing the day-ahead scheduling model of the present invention, the total system cost is used as the objective function, and the power balance, heat balance, and SOC state of electric vehicles are considered as constraint conditions, ensuring that not only the basic operation requirements of the power grid can be met during the scheduling process, but also the situation of power shortage or surplus can be avoided; the day is divided into several identical time periods for intraday optimization. By setting the minimum of the intraday total cost as the objective function and adding the ramp limit values of gas turbines and gas boilers, the real-time scheduling ability of power generation resources is further optimized, ensuring the flexible response ability of the system to peak-valley changes. This process reduces the system's dependence on traditional energy sources, increases the proportion of renewable energy used, and thus promotes environmental protection and the sustainable use of energy.

[0087] The present invention can also better understand users' charging habits and demand patterns by analyzing the travel paths and charging behaviors of electric vehicles, accurately predict the charging probability in specific time periods and regions, and then improve the resource allocation efficiency of charging stations and reduce the charging waiting time. This not only improves the charging experience of users, but also maximizes the utilization of power resources. By constructing a transition probability matrix, the charging demands between different destinations can be more reasonably allocated. This dynamic scheduling ability can help grid operators flexibly adjust the charging strategy during the intraday rolling optimization process to cope with the changes in actual charging demands. By constructing a charging and discharging demand response model for electric vehicles, based on charging power, grid electricity price signals, the battery state of electric vehicles, and parking duration, the charging and discharging strategies of electric vehicles are optimized, comprehensively considering multi-dimensional factors such as the battery state and parking duration of electric vehicles, making the charging strategy more comprehensive and reasonable. By optimizing the charging and discharging strategy, the value of electric vehicles as adjustable load resources can be maximized without affecting users' travel, improving the economic efficiency of the power system; optimizing the charging and discharging demand response model enables electric vehicles to better adapt to the fluctuations of renewable energy generation, provides greater flexibility for the power grid, and promotes the use and access of renewable energy. Brief Description of the Drawings

[0088] Figure 1 It is a schematic flow chart of the method of the embodiment of the present invention;

[0089] Figure 2 It is a comparison chart of electricity purchase under day-ahead and intraday scheduling of the embodiment of the present invention;

[0090] Figure 3 It is a comparison chart of gas purchase under day-ahead and intraday scheduling of the embodiment of the present invention;

[0091] Figure 4 It is a comparison chart of the output power change of gas turbines under day-ahead and intraday scheduling of the embodiment of the present invention;

[0092] Figure 5 It is a comparison chart of the power consumption change of the carbon capture system under day-ahead and intraday scheduling of the embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0094] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0095] Example:

[0096] See also Figures 1 to 5 , the present invention provides a technical solution:

[0097] A multi-time-scale energy system optimization scheduling method based on electric vehicle V2G response, specifically comprising the following steps:

[0098] Step 1: Divide a day into several equal time periods, generate an objective function with the minimum total system cost as the optimization goal, and construct a day-ahead scheduling model with power balance, thermal balance, and electric vehicle SOC as constraints;

[0099] In this embodiment, the principles for constructing the day-ahead scheduling model are as follows:

[0100] The objective function is:

[0101] f1=f grid +f c +f gas +f op

[0102] Among them, f1 represents the total system cost, f gird represents the grid interaction cost, f c represents the carbon trading cost, f gas represents the cost of natural gas purchased by the user, f op Indicates equipment operation and maintenance costs;

[0103]

[0104] Among them, \(t\) represents the index of the time period, and \(T\) represents the number of time periods in a day. and respectively represent the electricity purchase and sale power of the system within the time period \(t\). and respectively represent the unit electricity purchase and sale prices within the time period \(t\).

[0105]

[0106] Among them, represents the unit gas purchase price of natural gas within the time period \(t\). represents the heat supply power of the gas turbine within the time period \(t\), and \(\eta\) GT represents the efficiency of the gas turbine, and \(LHV\) gas represents the lower heating value of natural gas. represents the heat supply power of the gas boiler within the time period \(t\), and \(\eta\) GB represents the efficiency of the gas boiler.

[0107]

[0108] Among them, represents the unit loss cost of charge and discharge of the battery pack. represents the discharge power of the battery within the time period \(t\). represents the charge power of the battery within the time period \(t\), and \(u\) represents the index of the equipment type, and \(D\) u represents the unit operation and maintenance cost of the equipment \(u\). represents the operation power of the equipment \(u\) within the time period \(t\).

[0109] \(f\) op reflects the operation cost and maintenance cost of all equipment in the system during scheduling. reflects the loss cost of battery charge and discharge. reflects the operation cost and maintenance cost of other equipment in the system.

[0110] The power balance constraint condition is:

[0111] \(P\) load,t +\(P\) bat,c,t +\(P\) EV,c,t +\(P\) CCS,t +\(P\) LA2user,t

[0112] =\(P\) wind,t +\(P\) PV,t +\(P\) grid,buy,t +\(P\) bat,d,t +\(P\) orc,t +\(P\) GT,t

[0113] Among them, P load,t represents the grid load in time period t, t represents the index of the time period, and t ∈ [1, T], where T represents the number of time periods in a day. P bat,c,t represents the battery charging power in time period t, P EV,c,t represents the electric vehicle charging power in time period t, P CCS,t represents the carbon capture equipment power in time period t, P LA2user,t represents the power sold to the grid in time period t, P wind,t represents the wind power generation power in time period t, P PV,t represents the photovoltaic power generation power in time period t, P grid,buy,t represents the grid power supply power in time period t, P bat,d,t represents the battery discharge power in time period t, P orc,t represents the waste heat boiler power generation power in time period t, P GT,t represents the gas turbine power generation power in time period t;

[0114] The thermal balance constraint condition is:

[0115] P heat,t +H bat,c,t +H AC,t =H GT,t +H GB,t +H eb,t +H bat,d,t +H whb,t

[0116] Among them, P heat,t represents the heat load in time period t, H bat,c,t represents the charging efficiency of the thermal energy storage in time period t, H AC,t represents the heat consumption power of the absorption refrigeration in time period t, H GT,t represents the heat supply power of the gas turbine in time period t, H GB,t represents the heat supply power of the gas boiler in time period t, H eb,t represents the heat supply power of the electric boiler in time period t, H bat,d,t represents the discharge efficiency of the thermal energy storage in time period t, H whb,t represents the heat supply power of the waste heat boiler in time period t.

[0117] At the same time, to prevent overcharging or over-discharging, the SOC of the electric vehicle also needs to be maintained within a reasonable range:

[0118]

[0119] And it satisfies SOC min ≤SOC bat,t ≤SOC max 、SOC min ≤SOCk,t ≤SOC max ;

[0120] Among them, SOC bat,t represents the remaining battery power at time period t, SOC bat,t-1 represents the remaining battery power at time period t-1, η c represents the charging efficiency of the battery, η d represents the discharging efficiency of the battery, SOC k,t represents the remaining battery power of the kth electric vehicle within time period t, SOC k,t-1 represents the remaining battery power of the kth electric vehicle within time period t-1, P EV,c,t,k represents the charging power of the kth electric vehicle within time period t, P bat,d,t,k represents the discharging power of the kth electric vehicle within time period t, SOC min represents the minimum state of charge of the battery, which is the lowest battery power limit to prevent over-discharging, SOC max represents the maximum state of charge of the battery, which represents the highest battery power limit to prevent over-charging, and SOC bat,t 、SOC bat,t-1 、η c 、η d represents the average of all electric vehicles in a specific area.

[0121] Step 2: Generate an objective function with the minimum total cost within a day as the optimization goal, and use the ramp rate limit values of gas turbines and gas boilers as constraint conditions to construct an intra-day optimization model;

[0122] In this embodiment, the principle for constructing the intra-day optimization model is:

[0123] The objective function is:

[0124]

[0125] Among them, f2 represents the total intra-day cost, f grid_in,t represents the intra-day grid interaction cost at time period t, f to_in,t represents the cost of penalty for changes in the intra-day plan compared to the day-ahead plan at time period t, f gas_in,t represents the short-term gas purchase cost in the gas grid at time period t, f op_in,t represents the short-term operation and maintenance cost at time period t, △T represents the length of the time period, f c_in,t represents the intra-day stepped carbon trading cost at time period t;

[0126] The formula for generating the intra-day grid interaction cost is:

[0127]

[0128] Among them, represents the on - day purchased electric power within the time period t, represents the on - day sold electric power within the time period t;

[0129] The formula for generating the short - term gas network gas purchase cost within a day is:

[0130]

[0131] Among them, H GT_in represents the heat supply power of the gas turbine within a day, H GB_in represents the heat supply power of the gas boiler within a day;

[0132] The formula for generating the short - term operation and maintenance cost within a day is:

[0133]

[0134] Among them, represents the battery discharge power within the time period t, represents the battery charging power within the time period t, represents the equipment operation power within the time period t;

[0135] The formula for generating the penalty cost for the change between the on - day plan and the day - ahead plan is:

[0136] f to_in = f punish_e + f punish_h + f punish_c

[0137] Among them, f punish_e represents the penalty cost for the loading of each part of the power grid, f punish_h represents the penalty cost for the adjustment amount of each part of the heat network, f punish_c represents the penalty cost for the adjustment amount of each part of the cold network;

[0138] f punish_e = △P bat ·μ bat + △P GT ·μ GT + △P grid ·μ prid

[0139]

[0140]

[0141]

[0142] Among them, △Pbat , △P GT , △P grid respectively represent the total adjustment amounts of the battery, gas turbine, and grid interaction power with respect to the day-ahead scheduling plan, μ bat , μ GT , μ grid respectively represent the penalty adjustment coefficients of the battery, gas turbine, and grid interaction, P bat (t), P GT (t), P grid (t) represent the electric powers of the battery, gas turbine, and grid interaction during the time period t in the day-ahead scheduling stage, P bat_0 (T), P Gt_0 (t), P grid_0 (T) respectively represent the electric powers of the battery, gas turbine, and grid interaction during the time period t within the day, and |·| represents taking the absolute value of the difference.

[0143] f pun i sh_h = △h WHB ·μ WHB + △H GB ·μ GB + △H HS ·μ HS

[0144]

[0145]

[0146]

[0147] Among them, △H WHN , △H GB , △H Hs respectively represent the total adjustment amounts of the waste heat boiler, gas boiler, and heat storage tank within the day compared with the day-ahead scheduling plan, μ WHB , μ GB , μ HS respectively represent the penalty adjustment coefficients of the waste heat boiler, gas boiler, and heat storage tank, H WHB (t), H GB (t), H HS (t) respectively represent the interactive heat powers of the waste heat boiler, gas boiler, and heat storage tank during the time period t in the day-ahead scheduling stage, P WHB_0 (t), H GB_0 (t), H HS_0 (t) respectively represent the interactive heat powers of the waste heat boiler, gas boiler, and heat storage tank during the time period t within the day.

[0148] f pun i sh_c = △QAC · μ ac + △Q AR · μ ar

[0149]

[0150]

[0151] Among them, △Q AC and △Q AR respectively represent the total adjustment amounts of the absorption chiller and the electric chiller compared with the day-ahead scheduling plan, μ ac and μ ar respectively represent the penalty adjustment coefficients of the absorption chiller and the electric chiller, Q AC (t) and Q AR (t) represent the interactive cooling powers of the absorption chiller and the electric chiller in time period t of the day-ahead scheduling stage, Q AC_0 (t) and Q AR_0 (t) respectively represent the interactive cooling powers of the absorption chiller and the electric chiller in the intraday time period t.

[0152] The constraint conditions are:

[0153] - △P max ≤ P t+1 - P t ≤ △P max

[0154] Among them, △P max represents the maximum ramp limit value of the device, and P t represents the power of the device at time t.

[0155] Step 3: Based on the travel routes and charging behaviors of electric vehicles, construct a travel chain model, predict the charging probabilities of electric vehicles in specific time periods and regions, construct a transfer probability matrix between different destinations of the vehicles, estimate the overall charging demand of electric vehicles based on the travel chain model and the day-ahead scheduling model, and adjust the intraday charging demand based on the travel chain model and the intraday optimization model;

[0156] The charging demand of the travel chain mainly depends on: the SOC at the start of the trip: the initial battery charge; the travel distance: which determines the power consumption of each trip; the parking time: which determines whether the charging window demand can be met; the charging facilities at the destination: whether there are charging conditions; through the time and space characteristics of the travel chain, predict the charging probabilities of electric vehicles in specific time periods and regions, and thus convert them into the load impact on the power grid, and construct a transfer probability matrix between different destinations of the vehicles.

[0157] In this embodiment, the principle on which the travel chain model is constructed is:

[0158] Construct a transfer probability matrix for vehicles between different destinations based on the Markov chain theory:

[0159]

[0160] Among them, P ij represents the probability that the vehicle transfers from destination i to destination j. i and j represent the indices of the destinations, and i, j ∈ [1, n], where n represents the number of destinations;

[0161] Each row of the transfer probability matrix satisfies Obtain the distribution probability of electric vehicles at different destinations through the transfer probability matrix, and predict the spatio-temporal distribution of charging demand.

[0162] In day-ahead scheduling, based on the trip chain prediction model, estimate the overall charging demand of electric vehicles, and establish a power balance equation. The formula is as follows:

[0163]

[0164] Among them, P load represents the total load of the power grid system, k represents the index of the electric vehicle, K represents the number of electric vehicles, P charge,k represents the charging power of the k-th electric vehicle, p renewable represents the power generation of renewable energy, including wind power and photovoltaic power, p grid represents the power supply of the power grid;

[0165] In intraday rolling optimization, based on the real-time updated trip chain data, adjust the intraday charging demand. The formula is as follows:

[0166]

[0167] Among them, f deviation represents the adjustment amount of the intraday charging demand, p real-time,t represents the actual intraday charging power at time period t, p day-ahead,t represents the day-ahead predicted charging power at time period t.

[0168] Step 4: Construct a charging and discharging demand response model for electric vehicles based on the charging power of electric vehicles, grid electricity price signals, battery status of electric vehicles, and parking duration. Taking the total operating cost as the objective function, adjust the charging and discharging demand response model of electric vehicles under the condition of minimizing the total operating cost, and optimize the charging power of electric vehicles.

[0169] In this implementation, the charging and discharging demand response model of electric vehicles is:

[0170] P EV,c (t) = γ·f[SOCk (t), β(t), P EV,parking +(1 - γ)·P day-ahead,t

[0171] Among them, P EV,c (t) represents the charging power of the electric vehicle in time period t, SOC k (t) represents the battery power of the k-th electric vehicle in time period t, β(t) represents the grid electricity price signal in time period t, P EV,parking represents the parking duration of the electric vehicle, f[SOC k (t), β(t), P EV,parking represents the real-time in-day charging power affected by the battery power, grid electricity price signal, and parking duration. γ represents the weight coefficient of the real-time in-day charging power, and (1 - γ) represents the weight coefficient of the day-ahead predicted charging power;

[0172] γ is used to balance the relationship between real-time demand response and day-ahead scheduling plan. In this scheme, real-time demand response is dominant, and γ = 0.6.

[0173] f[SOC k (t), β(t), P EV,parking aims to dynamically adjust the charging and discharging power of the electric vehicle according to the battery state, grid electricity price signal, and parking time. If the SOC of the battery is low, the electric vehicle is charged. If the SOC of the battery is high, the electric vehicle is discharged or the charging power is reduced; when the grid electricity price signal is high, the electric vehicle reduces the charging power or discharges to reduce the grid load. When the electricity price is low, the electric vehicle increases the charging power to utilize the low electricity price period for charging; if the parking time is long, the electric vehicle has enough time for charging and discharging. If the parking time is short, the electric vehicle can only perform partial charging or discharging. The specific formula is:

[0174]

[0175] Among them, P max represents the maximum charging and discharging power of the electric vehicle, SOC max represents the maximum SOC of the battery, usually taken as 100%, SOC threshold represents the SOC threshold for determining whether the vehicle needs to be charged. When it is lower than SOC threshold , charging is required. When it is higher than SOC threshold , discharging can be selected. β max represents the maximum value of the grid electricity price, T max represents the maximum staying time of the electric vehicle at the destination; when the battery needs to be charged, the lower the SOC, the lower the electricity price, and the longer the parking time, the greater the charging power; when the battery needs to be discharged, the higher the SOC, the higher the electricity price, and the longer the parking time, the greater the discharging power.

[0176] Taking the total operating cost as the objective function, the formula is as follows:

[0177]

[0178] Among them, f3 represents the total operating cost, and G V2G (t) represents the V2G discharge cost;

[0179] The V2G discharge cost includes the discharge loss of the electric vehicle and the fees that the power grid needs to pay. With the goal of minimizing the total operating cost f3, the charging and discharging demand response model is adjusted. The intraday scheduling takes every 4 hours as a cycle. Within each cycle, the charging and discharging power is adjusted with the goal of the lowest total operating cost. After each cycle is completed, the time is advanced forward to enter the next cycle.

[0180] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0181] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0182] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A multi-scale energy system optimal scheduling method based on the V2G response of electric vehicles, characterized in that, The specific steps include: Step 1: Divide a day into several identical time periods, generate an objective function with the minimum total system cost as the optimization goal, and construct a day-ahead scheduling model with power balance, heat balance, and electric vehicle SOC as constraint conditions; Step 2: Generate an objective function with the minimum intraday total cost as the optimization goal, and construct an intraday optimization model with the ramp limit values of gas turbines and gas boilers as constraint conditions; Step 3: Based on the travel paths and charging behaviors of electric vehicles, construct a travel chain model, predict the charging probability of electric vehicles in specific time periods and regions, and construct a transfer probability matrix between different destinations of vehicles. Based on the travel chain model and the day-ahead scheduling model, estimate the overall charging demand of electric vehicles, and adjust the intraday charging demand based on the travel chain model and the intraday optimization model; Step 4: Construct a charging and discharging demand response model for electric vehicles based on the charging power of electric vehicles, grid electricity price signals, battery status of electric vehicles, and parking duration, and take the total operating cost as the objective function. Under the condition of minimizing the total operating cost, adjust the charging and discharging demand response model of electric vehicles to optimize the charging power of electric vehicles.

2. The method according to claim 1, wherein: The principle for constructing the day-ahead scheduling model in Step 1 is: The objective function is: f1 = f grid + f c + f gas + f op Among them, f1 represents the total system cost, f gird represents the grid interaction cost, f c represents the carbon trading cost, f gas represents the cost of users purchasing natural gas, f op represents the equipment operation and maintenance cost; The power balance constraint condition is: P load,t +P bat,c,t +P EV,c,t +P CCS,t +P LA2user,t = P wind,t + P PV,t + P grid,buy,t + P bat,d,t + P orc,t + P GT,t Among them, P load,t represents the grid load in time period t, where t represents the index of the time period and t ∈ [1, T], and T represents the number of time periods in a day. P bat,c,t represents the battery charging power in time period t, P EV,c,t represents the electric vehicle charging power in time period t, P CCS,t represents the carbon capture equipment power in time period t, P LA2user,t represents the power selling in time period t, P wind,t represents the wind power generation power in time period t, P PV,t represents the photovoltaic power generation power in time period t, P grid,buy,t represents the grid power supply in time period t, P bat,d,t represents the battery discharging power in time period t, P orc,t represents the waste heat boiler power generation in time period t, P GT,t represents the gas turbine power generation in time period t; The heat balance constraint condition is: P heat,t +H bat,c,t +H AC,t =H GT,t +H GB,t +H eb,t +H bat,d,t +H whb,t Among them, P heat,t represents the heat load in time period t, H bat,c,t represents the charging efficiency of the heat energy storage in time period t, H AC,t represents the heat consumption power of the absorption refrigeration in time period t, H GT,t represents the heat supply power of the gas turbine in time period t, H GB,t represents the heat supply power of the gas boiler in time period t, H eb,t represents the heat supply power of the electric boiler in time period t, H bat,d,t represents the discharging efficiency of the heat energy storage in time period t, H whb,t represents the heat supply power of the waste heat boiler in time period t. The SOC constraint condition is: and satisfy SOC min ≤SOC bat,t ≤SOC max 、SOC min ≤SOC k,t ≤SOC max ; Among them, SOC bat,t represents the remaining battery charge at time period t, and SOC bat,t-1 represents the remaining battery charge at time period t - 1, and η c represents the charging efficiency of the battery, and η d represents the discharging efficiency of the battery, and SOC k,t represents the remaining battery charge of the k-th electric vehicle within time period t, where k represents the index of the electric vehicle, and SOC k,t-1 represents the remaining battery charge of the k-th electric vehicle within time period t - 1, and P EV,c,t,k represents the charging power of the k-th electric vehicle within time period t, and P bat,d,t,k represents the discharging power of the k-th electric vehicle within time period t, and SOC min represents the minimum state of charge of the battery, which is the lowest battery charge limit to prevent over-discharging, and SOC max represents the maximum state of charge of the battery, which represents the highest battery charge limit to prevent over-charging.

3. The multi-time scale energy system optimal scheduling method based on the V2G response of electric vehicles according to claim 2, wherein: The formula for generating the grid interaction cost is Among them, and respectively represent the power purchase and power selling of the system within the time period t, and respectively represent the unit power purchase price and power selling price within the time period t; The formula for generating the user's natural gas purchase cost is: Among them, represents the unit gas purchase price of natural gas within the time period t, represents the heat supply power of the gas turbine within the time period t, η GT represents the efficiency of the gas turbine, LHV gas represents the low calorific value of natural gas, represents the heat supply power of the gas boiler within the time period t, η GB represents the efficiency of the gas boiler; The formula for generating the equipment operation and maintenance cost is: Among them, represents the unit loss cost of charge and discharge of the battery pack within the time period t, represents the discharge power of the battery within the time period t, represents the charging power of the battery within the time period t, u represents the index of the equipment type, U represents the total number of equipment, D u represents the unit operation and maintenance cost of equipment u, represents the operating power of equipment u within the time period t, and the equipment includes gas turbines, electric boilers, waste heat boilers, absorption chillers, electric chillers, energy storage systems, electric vehicle charging and discharging stations, and power grids.

4. The optimal scheduling method for a multi-time scale energy system based on the V2G response of an electric vehicle according to claim 3, wherein: The principle for constructing the intraday optimization model in Step 2 is: The objective function is: Among them, f2 represents the total intraday cost, and f grid_in,t represents the intraday grid interaction cost during time period t, and f to_in,t represents the cost of penalty for the change in the intraday plan compared to the day-ahead plan during time period t, and f gas_in,t represents the short-term gas purchase cost in the intraday gas network during time period t, and f op_in,t represents the short-term operation and maintenance cost during time period t, △T represents the length of the time period, and f c_in,t represents the stepped carbon trading cost during time period t; The constraint condition is: -△P max ≤P t+1 -P t ≤△P max Among them, △P max represents the maximum ramp limit value of the device, and P t represents the power of the device in the time period t.

5. A multi-time scale energy system optimal scheduling method based on the V2G response of electric vehicles according to claim 4, characterized in that: The formula for generating the intraday grid interaction cost is: Among them, represents the in-day purchase electric power for time period t, represents the in-day sale electric power for time period t; The formula for generating the short-term gas network gas purchase cost within the day is: Among them, H GT_in represents the heat supply power of the gas turbine within a day, and H GB_in represents the heat supply power of the gas boiler within a day; The formula for generating the short-term operation and maintenance cost within the day is: Among them, represents the in-day battery discharge power during time period t, represents the in-day battery charging power during time period t, represents the in-day device operating power during time period t; The formula for generating the cost of handling changes in the intraday plan compared to the day-ahead plan is: f to_in = f punish_e + f punish_h + f punish_c Among them, f punish_e represents the penalty cost of the loading amount of each part of the power grid, and f punish_h represents the penalty cost of the adjustment amount of each part of the heat grid, and f punish_c represents the penalty cost of the adjustment amount of each part of the cold grid; f punish_e = ΔP bat · μ bat + ΔP GT · μ GT + ΔP grid · μ grid Among them, △P bat , △P GT , △P grid respectively represent the total adjustment amounts of the battery, gas turbine, and grid interactive power with respect to the day-ahead scheduling plan. μ bat , μ GT , μ grid respectively represent the penalty adjustment coefficients of the battery, gas turbine, and grid interaction. P bat (t), P GT (t), P grid (t) represent the electric powers of the battery, gas turbine, and grid interaction in the time period t of the day-ahead scheduling stage. P bat_0 (t), P GT_0 (t), P grid_0 (t) respectively represent the electric powers of the battery, gas turbine, and grid interaction in the intraday time period t. |·| represents taking the absolute value of the difference. f pun i sh_h = ΔH WHB · μ WHB + ΔH GB · μ GB + ΔH HS · μ HS Among them, △H WHB , △H GB , △H HS respectively represent the total adjustment amounts of the waste heat boiler, gas boiler, and heat storage tank compared with the day-ahead scheduling plan within a day. μ WHB , μ GB , μ HS respectively represent the penalty adjustment coefficients of the waste heat boiler, gas boiler, and heat storage tank. H WHB (t), H GB (t), H HS (t) respectively represent the interactive heat powers of the waste heat boiler, gas boiler, and heat storage tank during the time period t in the day-ahead scheduling stage. P WHB_0 (t), H GB_0 (t), H HS_0 (t) respectively represent the interactive heat powers of the waste heat boiler, gas boiler, and heat storage tank during the time period t within a day. f pun i sh_c = ΔQ AC · μ ac + ΔQ AR · μ aR Among them, △Q AC and △Q AR respectively represent the total adjustment amounts of the absorption chiller and the electric chiller compared with the day-ahead scheduling plan. μ ac and μ ar respectively represent the penalty adjustment coefficients of the absorption chiller and the electric chiller. Q AC (t) and Q AR (t) represent the interactive cooling powers of the absorption chiller and the electric chiller during the time period t in the day-ahead scheduling stage. Q AC_0 (t) and Q AR_0 (t) respectively represent the interactive cooling powers of the absorption chiller and the electric chiller during the intraday time period t.

6. The multi-time-scale energy system optimal scheduling method based on the V2G response of an electric vehicle according to claim 1, characterized in that: The principle for constructing the travel chain model in Step 3 is: Construct a transfer probability matrix between different destinations of vehicles based on the Markov chain theory: Among them, P ij represents the probability that the vehicle transfers from destination i to destination j, where i and j represent the indices of the destinations, and i, j ∈ [1, n], and n represents the number of destinations; In the day-ahead scheduling, estimate the overall charging demand of electric vehicles based on the travel chain prediction model, and establish a power balance equation. The formula is: Among them, P load represents the total load of the power grid system, k represents the index of the electric vehicle, K represents the number of electric vehicles in a specific area, and P charge,k represents the charging power of the k-th electric vehicle, and P renewable represents the power generation of renewable energy, including wind power and photovoltaic power, and P grid represents the power supply of the power grid; In the intraday rolling optimization, adjust the intraday charging demand based on the real-time updated travel chain data. The formula is: Among them, f deviation represents the adjustment amount of the intra-day charging demand, P real-time,t represents the intra-day actual charging power in time period t, P day-ahead,t represents the day-ahead predicted charging power in time period t.

7. A multi-time-scale energy system optimal scheduling method based on the V2G response of electric vehicles according to claim 6, characterized in that: The charging and discharging demand response model of electric vehicles is: P EV,c (t) = γ·f[SOC k (t), β(t), P EV,parking + (1 - γ)·P day-ahead,t Among them, P EV,c (t) represents the charging power of the electric vehicle in time period t, and SOC k (t) represents the battery charge of the k-th electric vehicle in time period t, β(t) represents the grid electricity price signal in time period t, and P EV,parking represents the parking duration of the electric vehicle, and f[SOC k (t), β(t), P EV,parking represents the real-time in-day charging power affected by the battery charge, grid electricity price signal, and parking duration. γ represents the weight coefficient of the real-time in-day charging power, and (1 - γ) represents the weight coefficient of the day-ahead predicted charging power; Taking the total operating cost as the objective function, the formula is: Among them, f3 represents the total operating cost, and G V2G (t) represents the V2G discharge cost.

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