Mobile energy storage dispatching method for multi-scenario power auxiliary services

Abstract

The present invention relates to a mobile energy storage scheduling method for multi-scenario power auxiliary services, and belongs to the technical field of optimized scheduling of distribution network energy storage systems. The steps include: establishing a mobile energy storage spatiotemporal transfer model and a charging and discharging model based on the dynamic transfer characteristics of mobile energy storage and the energy characteristics of energy storage; establishing a revenue model for mobile energy storage participating in multi-scenario power auxiliary services according to the trading rules of the power auxiliary service market; establishing a two-layer optimization scheduling model for mobile energy storage, wherein the outer optimization model aims to maximize the daily net income of the mobile energy storage system, and the inner optimization model aims to optimize the peak shaving and valley filling and reactive power voltage regulation effects, supplemented by corresponding constraints, to determine the optimal scheduling plan for mobile energy storage. The optimized coordinated scheduling of mobile energy storage and distribution networks is solved, and while improving the utilization rate of mobile energy storage, it has better economic benefits and ensures the safe and reliable operation of the distribution network. It has the advantages of being scientific and reasonable, highly applicable, highly reliable, and having good effects.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized dispatching of energy storage systems in distribution networks, and in particular to a mobile energy storage dispatching method for multi-scenario power auxiliary services. Background Art

[0002] As of the first half of 2023, the installed capacity of renewable energy in China exceeded 1.3 billion kilowatts, reaching 1.322 billion kilowatts, an increase of 18.2% year-on-year, historically exceeding coal-fired power, accounting for about 48.8% of my country's total installed capacity. Among them, hydropower installed capacity is 418 million kilowatts, wind power installed capacity is 389 million kilowatts, photovoltaic power generation installed capacity is 470 million kilowatts, and biomass power generation installed capacity is 43 million kilowatts. Wind power and photovoltaic power generation are growing rapidly. In the first half of the year, the country's renewable energy generation reached 1.34 trillion kilowatt-hours, of which wind power and photovoltaic power generation reached 729.1 billion kilowatt-hours, an increase of 23.5% year-on-year. The scale of renewable energy grid-connected power generation continues to increase. The large grid fluctuations caused by its intermittent output, poor power quality, and poor grid flexibility adjustment capabilities are important challenges that the power system needs to deal with. Mobile energy storage has attracted much attention due to its advantages such as strong flexibility, fast response speed and wide coverage. Reasonable planning of the coordinated dispatching capabilities of mobile energy storage in different application scenarios will greatly improve the utilization rate and expected economic benefits of mobile energy storage, and is of great significance to improving the flexible adjustment capabilities and operational reliability of the distribution network.

[0003] When considering the application of mobile energy storage in power auxiliary service dispatch, the existing technology has too single application scenario, and only considers common application modes such as peak shaving and valley filling, voltage regulation, etc. In the existing research, the following issues need to be further explored: 1) When modeling the revenue model of mobile energy storage participating in power auxiliary service, the investment return risk of the investment subject is not considered; 2) Considering the multi-scenario power auxiliary service involving mobile energy storage, the coupling modeling between scenarios is less considered; 3) The optimization goal of the coordinated optimization dispatch scheme is too single, and only considering the economic efficiency makes it difficult to reflect the regulation potential of the mobile energy storage system in reactive voltage regulation, emergency power supply, etc. Summary of the invention

[0004] The purpose of the present invention is to provide a mobile energy storage dispatching method for multi-scenario power auxiliary services, which solves the problems existing in the prior art, such as the unclear revenue model of the auxiliary service market without considering the participation of mobile energy storage, the low utilization rate of mobile energy storage in a single scenario, and the application potential of the active output of mobile energy storage in peak shaving and the reactive output in shifting voltage fluctuations. The method solves the problem of optimized coordinated dispatching of mobile energy storage and distribution network, realizes the improvement of the utilization rate of mobile energy storage, makes it have better economic benefits and ensures the safe and reliable operation of the distribution network.

[0005] The above-mentioned object of the present invention is achieved by the following technical solutions:

[0006] The mobile energy storage dispatching method for multi-scenario power auxiliary services includes the following steps:

[0007] Step 1: Construction of mobile energy storage model based on dynamic transfer characteristics and energy storage characteristics:

[0008] According to the design idea of ​​lithium batteries in electric vehicles, physical and mathematical modeling of mobile energy storage is carried out. First, the energy storage timing characteristics and spatial transfer characteristics of energy storage equipment and mobile devices are considered at the same time; secondly, the state of charge constraints, charge and discharge constraints and mobile state constraints of mobile energy storage are considered, and a mobile energy storage spatiotemporal transfer and charge and discharge model based on dynamic transfer characteristics and energy storage energy characteristics is constructed.

[0009] Step 2: Consider the construction of a multi-scenario power auxiliary service revenue model involving mobile energy storage:

[0010] Through portfolio theory, the operating benefits and risk benefits of mobile energy storage in different markets are evaluated, and the capacity of mobile energy storage applied to different markets is optimized, in order to maximize the application benefits under a certain risk level. The expected rate of return and risk calculation of each market for multi-scenario power auxiliary services are analyzed and calculated, and an optimal profit model for multi-scenario power auxiliary services involving mobile energy storage is constructed.

[0011] Portfolio theory in a narrow sense mainly refers to Markowitz portfolio theory, which includes two important contents: mean-variance analysis method and portfolio efficient frontier model, which aims to maximize expected returns in uncertain risks and returns. v p and risks s p The risk-return function (the theoretical risk coefficient in portfolio theory in a broad sense) is:

[0012]

[0013] Where: w is the risk-return ratio; w 0 is the expected deviation value. l is the risk value coefficient of the investment decision maker, usually ranging from 1 to 3. l =2 is medium risk return type, l >2 is high risk and high return type, l <2 means low risk and low return.

[0014] Benefits of mobile energy storage participating in the electric energy market I pav,t The risk is caused by the peak and valley difference of electricity prices in different periods of the electricity market. s pav,t(The market risk coefficient of mobile energy storage participating in the electric energy market refers to) t The standard deviation of the expected electricity market price at any given moment:

[0015]

[0016] Where: C pav,t for a certain moment t Real-time electricity prices; C pav It is the average value of the electricity market price in one day; C pav,t,i for t The historical electricity price of the electricity market at that moment i price; m for t The total number of historical electricity prices in the electricity market at each moment.

[0017] Benefits of mobile energy storage participating in the backup ancillary service market I ras,t Risks from the uncertainty of the reserve price increase or decrease in the reserve ancillary service market s ras,t (The market risk coefficient of mobile energy storage participating in the backup ancillary service market refers to) t The expected standard deviation of the standby ancillary service price at each moment, the expected rate of return and risk calculation formula of the standby ancillary service market are:

[0018]

[0019] Where: C ras,t For the moment t the price of ancillary services; C c,t For the moment t Energy purchase costs; C ras,t,i for t The historical price data of the time reserve ancillary service market i A price.

[0020] Step 3: Establish a two-layer optimization dispatch model for mobile energy storage:

[0021] A two-layer optimization dispatch model for mobile energy storage to participate in multi-scenario power auxiliary services is established. The outer optimization model aims to maximize the daily net income of mobile energy storage, with the capacity constraints of mobile energy storage allowed to be installed in the distribution network, the flow balance constraints, the node voltage constraints and the maximum transmission power constraints of the line as constraints, and the decision variables are the time-series active output of mobile energy storage. The inner optimization model takes the peak shaving and valley filling and the optimal reactive voltage regulation effect as the objective function, with the mobile energy storage charging and discharging power, charge state change and operating status as constraints, and the decision variables are the access nodes and time-series reactive output of mobile energy storage.

[0022] Reactive voltage sensitivity can accurately reflect the change of node voltage after reactive power is injected. N For a distribution network with a node, the node voltage amplitude change Δ V With active sequence Δ P and reactive sequence Δ Q The relationship between them satisfies:

[0023]

[0024] Where: Δ V P is the change in node voltage amplitude caused by the change in active power; Δ V Q is the change in node voltage amplitude caused by reactive power change; is the active voltage sensitivity matrix; is the reactive voltage sensitivity matrix; Δ V for The change in node voltage amplitude.

[0025] Determine the unit reactive power compensation amount. The size of the compensation amount will only affect the size of the sensitivity value, and will not affect the size relationship between the sensitivities. The corresponding voltage increment is calculated, and the corrected reactive voltage sensitivity matrix is:

[0026]

[0027] Where: Δ V′ is the change in node voltage amplitude after correction.

[0028] In order to reflect the nodes in the system j The impact of this node on the overall system voltage when reactive power is injected can be f QV Sum by column, and express the node as follows j Static comprehensive reactive voltage correction sensitivity when reactive power is injected N S,j .

[0029]

[0030] Where: f 0,j For Node j The initial reactive voltage sensitivity, f ij For Node j Injected reactive power Δ Q j Post node j For Node i Reactive voltage sensitivity; N is the number of nodes.

[0031] When the reactive output of mobile energy storage participates in voltage regulation, it should be adjusted according to the actual voltage regulation requirements of each node. Since the voltage of different nodes deviates from the rated voltage value to different degrees, the voltage deviation factor Δ is introduced U i Improve the sensitivity of static comprehensive reactive voltage correction.

[0032]

[0033] Where: U i , U i,N Node i The actual voltage value and rated voltage value; n、m is the number of rows and columns of the reactive voltage sensitivity matrix.

[0034] Considering the voltage conditions of each node under network reconstruction and different distributed power output conditions, combined with the time-space flexibility of mobile energy storage, the above-mentioned improved static comprehensive reactive voltage correction sensitivity is extended to the dynamic comprehensive reactive voltage correction sensitivity, and the reactive voltage sensitivity at each moment in the dispatching cycle is calculated. N S,j,t Perform calculations.

[0035]

[0036] Where: Ω olt,t ,Ω llt,t They are t The set of nodes whose actual voltage value is higher or lower than the rated voltage value at any moment; f i,j,t , f k,j,t They are t Time Node j Injected reactive power Δ Q j Post node j For Node i ,node kReactive voltage sensitivity; Δ U i,t , Δ U k,t They are t Time Node i ,node k The voltage deviation factor.

[0037] Mobile energy storage is mostly a truck-mounted energy storage system with strong dynamic transfer capabilities. The energy storage system is mostly composed of electrochemical batteries such as lithium iron phosphate and lithium titanate, which have the advantages of fast charging and discharging rates and strong response capabilities. The above characteristics of mobile energy storage provide a guarantee for its rapid switching in different application scenarios.

[0038] When mobile energy storage participates in the multi-scenario power auxiliary market, the risks in the market may lead to the failure of mobile energy storage bidding. Therefore, it is necessary to quantify the relevant profit model and expected risk of the mobile energy storage system. The feasibility of using price prediction errors to represent the risk of market price changes has been confirmed in relevant studies.

[0039] The described two-layer optimization scheduling model for mobile energy storage, the outer optimization scheduling model aims at the problem of low utilization of mobile energy storage systems in a single scenario, and proposes a scheduling strategy for mobile energy storage participating in multi-scenario power auxiliary services with the goal of maximizing daily net profit; the inner optimization model considers the mobile energy storage system as a dispatchable resource to cope with the problem of further increase in load peak-to-valley differences caused by the access of a large number of distributed power sources, and considers the potential for regulating distribution network voltage fluctuations by the reactive output of mobile energy storage, thereby improving the voltage quality of the distribution network.

[0040] The beneficial effects of the present invention are: solving the optimization and coordinated scheduling of mobile energy storage and distribution networks, achieving the goal of improving the utilization rate of mobile energy storage while making it have better economic benefits and ensuring the safe and reliable operation of the distribution network. It has the advantages of scientific rationality, strong applicability, high reliability and good effect. In view of the existing research, in the design of mobile energy storage, most of the structures of fuel vehicles equipped with lithium batteries are adopted. However, considering the pollution of the exhaust gas and noise generated by fuel to the environment and the continuous development of power electronics technology, the structure of electric vehicles equipped with lithium batteries will gradually become the mainstream. The present invention uses the design idea of ​​electric vehicles equipped with lithium batteries to carry out physical modeling and mathematical modeling of mobile energy storage systems. In view of the problem that the profit rules of mobile energy storage participating in the power auxiliary service market are imperfect and unsound, the present invention proposes the construction of a multi-scenario profit model for mobile energy storage participating under the current power auxiliary market trading rules, and quantifies the operating benefits and risk benefits of mobile energy storage in different markets based on the portfolio theory, so as to maximize the application benefits under a certain risk level. The present invention proposes a two-layer optimization scheduling model for mobile energy storage. The outer optimization scheduling model aims at the problem of low utilization of mobile energy storage in a single scenario, and proposes a scheduling strategy for mobile energy storage to participate in multi-scenario power auxiliary services with the goal of maximizing daily net profit; the inner optimization model considers mobile energy storage as a dispatchable resource to cope with the problem of further increase in load peak-to-valley difference caused by the access of a large number of distributed power sources, and considers the regulation potential of mobile energy storage reactive output to smooth the voltage fluctuations of the distribution network, thereby improving the voltage quality of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide further understanding of the present invention and constitute a part of this application. The illustrative examples of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0042] Figure 1 This is a flow chart of the mobile energy storage system of the present invention participating in multi-scenario power auxiliary services;

[0043] Figure 2 A schematic diagram of the mobile energy storage system of the present invention for improving the voltage quality of the distribution network;

[0044] Figure 3 It is a schematic diagram of the net load power curve effect before and after the mobile energy storage system of the present invention participates in peak shaving and valley filling. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] See also Figure 1 to Figure 3 As shown, the mobile energy storage scheduling method for multi-scenario power auxiliary services of the present invention includes the following steps: establishing a mobile energy storage spatiotemporal transfer model and a charge and discharge model based on the dynamic transfer characteristics of mobile energy storage and the energy characteristics of energy storage; establishing a revenue model for mobile energy storage participating in multi-scenario power auxiliary services according to the power auxiliary service market trading rules; establishing a two-layer optimization scheduling model for mobile energy storage, wherein the outer optimization model aims to maximize the daily net income of the mobile energy storage system, and the inner optimization model aims to optimize the peak shaving and valley filling and reactive power regulation effects, and with the assistance of corresponding constraints, determines the optimal scheduling plan for mobile energy storage. The present invention solves the problem of low utilization of mobile energy storage in a single scenario, so that mobile energy storage has better economic benefits while improving utilization and ensuring safe and reliable operation of the distribution network.

[0047] Embodiment 1:

[0048] See also Figure 1 to Figure 3 As shown, a mobile energy storage scheduling method for multi-scenario power auxiliary services in this embodiment includes the following steps:

[0049] Step 1: Construction of mobile energy storage system model based on dynamic transfer characteristics and energy storage characteristics:

[0050] At present, most designs of mobile energy storage are based on the structure of fuel vehicles equipped with lithium batteries. However, considering the pollution caused by exhaust gas and noise generated by fuel and the continuous development of power electronics technology, the structure of electric vehicles equipped with lithium batteries will gradually become the mainstream. Therefore, the present invention will carry out physical modeling and mathematical modeling of mobile energy storage according to the design idea of ​​electric vehicles equipped with lithium batteries.

[0051] Mobile energy storage mainly consists of two parts: energy storage equipment and mobile equipment. Energy storage equipment is generally composed of lithium battery packs and connected to the DC link of back-to-back voltage source converters through DC / DC converters. The DC / DC converter can adjust the charging and discharging power according to the system setting value. Compared with fixed energy storage, mobile energy storage can be flexibly connected to the grid between nodes, improving the flexibility of operation.

[0052] In the modeling of mobile energy storage, it is necessary to consider the timing characteristics of energy storage and its spatial transferability, as shown below.

[0053] 1.1) Charge and discharge power constraints

[0054]

[0055] Where: Mobile energy storage t Time period at node i The charging and discharging active power at Mobile energy storage t Time period at node i The charging and discharging reactive power at Mobile energy storage at the nodes i The decision variable for whether to charge or discharge is and , and They are respectively the charging and discharging active and reactive power ratings of mobile energy storage.

[0056] 1.2) State of Charge Constraints

[0057]

[0058] Where: Δ t is the time step; l t is the decision variable for whether the mobile energy storage is to be moved; E per The amount of electricity consumed per unit distance for mobile energy storage transfer; They are the charging and discharging efficiency of mobile energy storage; They are respectively the charge and discharge flags of mobile energy storage; v 0 is the driving speed of the mobile energy storage; and They are the upper and lower limits of the state of charge during the operation of mobile energy storage; For mobile energy storage t State of charge during the time period; and They respectively represent the charge state of the mobile energy storage at the start and end of the operation cycle.

[0059] 1.3) Mobile state constraints

[0060] The mobile state constraints of mobile energy storage are as follows:

[0061]

[0062] Where: oh k,t is the location coordinate of the mobile energy storage. It can only be located at a certain node in a certain period of time. tAt the moment k When it is at the node, its value is 1, otherwise it is 0; K The total number of nodes that mobile energy storage needs to go to for charging and discharging.

[0063] Step 2: Consider the construction of a multi-scenario power auxiliary service revenue model involving mobile energy storage:

[0064] 2.1) Peak-to-valley arbitrage benefits

[0065] When using time-of-use electricity prices, the energy storage system can charge during low electricity price periods and discharge during peak electricity price periods, thereby shifting the load and achieving peak-valley arbitrage. Arbitrage income over the entire life cycle f 1 is:

[0066]

[0067] Where: S 1 is the profit of peak-to-valley arbitrage within one day; i A certain time of the day; Δ t i for i The state duration of the moment is taken as 15 minutes; D is the annual operation days of energy storage, which is taken as 300 days; p time,i is the electricity price at the time; P dis,t , P ch,i Energy storage i Actual charging and discharging power at all times; S dis,i , S ch,i Energy storage i The charging and discharging status at all times; k r is the compound interest coefficient for the entire life cycle.

[0068] 2.2) Demand Management Benefits

[0069] Based on the two-part electricity price policy, the monthly electricity bills paid by large industrial users should include two parts: basic electricity charges and electricity charges. Electricity charges can be calculated based on time-of-use electricity prices, and basic electricity charges are calculated based on the user's maximum demand. Optimizing the configuration of the energy storage system can reasonably reduce the maximum load value required by the user and reduce the electricity costs of energy storage users. Benefits of demand management throughout the life cycle f 2 is:

[0070]

[0071] Where: S 2 is the basic electricity bill saved by energy storage users each month;p b is the basic capacity electricity charge; P md The maximum load power value of the user before the energy storage is installed; P d It is the maximum demand value reported by the user after installing energy storage.

[0072] 2.3) Demand response benefits

[0073] Electricity demand response refers to encouraging users to change their load power consumption during peak hours of the power grid through preferential policies such as electricity prices and subsidies, thereby alleviating the pressure on the power grid and obtaining corresponding subsidy benefits. This paper aims to analyze the economic feasibility of users participating in power auxiliary services. Assuming that the demand-side response time is between 19:00 and 21:00 on a certain day of a month, the agreed demand response benefits are f 3 is:

[0074]

[0075] Where: For the p The electricity price for load interruption caused by participation in demand response; s p For the p The electricity price standard corresponding to the duration of the secondary regulation; v p is the response speed coefficient; P DSM,p For the p The reported power value of the agreed response for the second participation; p de is the number of demand responses.

[0076] 2.4) Emergency power supply benefits

[0077] When an emergency power outage occurs in the power grid, user-side energy storage can participate in the auxiliary services of emergency power supply, provide safe power quality for important loads, and serve as an emergency backup power supply when the city power is interrupted, thereby improving the user's power supply reliability and reducing the user's power outage losses.

[0078] Assuming that the emergency power supply time is any time period on a certain day of the month, the user is notified 1 hour in advance, and the user obtains benefits after participating in the power supply f 4 is:

[0079]

[0080] Where: s q For the q The compensation electricity price for the units participating in the emergency power supply; P EMS,q For the qThe reported power value of the emergency power supply; q em The number of times participated in emergency power supply.

[0081] Step 3: Establish a two-layer optimization scheduling model for mobile energy storage system:

[0082] Firstly, a two-layer optimization scheduling model is constructed which comprehensively considers the economic efficiency and reliability of mobile energy storage operation. Secondly, the timing access nodes of mobile energy storage are calculated through the dynamic comprehensive reactive voltage correction sensitivity. Then, the reactive timing output of mobile energy storage is determined according to the reactive voltage regulation target. Finally, the optimal scheduling scheme of mobile energy storage is determined.

[0083] 3.1) External economic operation model

[0084] The outer optimization model aims to maximize the daily net profit of mobile energy storage, with the capacity constraints of mobile energy storage allowed to be installed in the distribution network, node voltage constraints, line power constraints and system power balance constraints as constraints, and the decision variables are the time-series active output sequence of mobile energy storage.

[0085] 3.1.1) Objective Function

[0086] The objective function of the outer optimization model can be expressed as:

[0087]

[0088] Where: F The daily net income of mobile energy storage; f 1 is the peak-to-valley arbitrage profit; f 2 is the demand management benefit; f 3 is the demand response benefit; f 4 is the income from emergency power supply; f MES,inv , f MES,o&m They are the daily investment cost and daily operation and maintenance cost of mobile energy storage; T life The equivalent operating life of mobile energy storage; f truck,h For the h Unit moving cost of mobile energy storage; E ESS,h , P ESS,h Respectively h Rated capacity and rated power of mobile energy storage; f E , f P They are the unit capacity and unit power investment costs of the energy storage system respectively; H The amount of mobile energy storage dispatched daily;f labor The driver labor cost per truck per day; f m It is the maintenance cost per unit power of mobile energy storage.

[0089] 3.1.2) Constraints

[0090] 1) Constraints on the mobile energy storage capacity allowed to be installed in the distribution network

[0091]

[0092] Where: P ESS,max The maximum capacity of mobile energy storage allowed to be connected to the distribution network; H is the amount of mobile energy storage.

[0093] 2) Node voltage constraints

[0094]

[0095] Where: V i,t for t Time Node i The voltage amplitude; V i,max , V i,min Node i The upper and lower limits of voltage.

[0096] 3) Line power constraints

[0097]

[0098] Where: S l,t for t Timeline l The transmission power of S l,max For Line l The maximum value of the allowed transmission power.

[0099] 4) System power balance constraints

[0100]

[0101] Where: P grid,t , P DG,t , P load,t They are t The grid active power, distributed power source active output, and load active power at all times; Ploss,t , P MES,loss,t Before and after mobile energy storage is connected t Active power loss of the system at all times; Q grid,t , Q DG,t , Q load,t They are t The grid reactive power, distributed power source reactive output, and load reactive power at all times; Q MES,h,t for t Moment h Reactive power output of mobile energy storage; Q MES,loss,t After the mobile energy storage is connected t Reactive power loss of the system at all times.

[0102] 3.2) Inner layer timing optimization model

[0103] The inner optimization model aims to minimize the standard deviation of equivalent load and the overall voltage offset at each moment, with mobile energy storage power constraints, charge state constraints, and operating state constraints as constraints. The decision variables are the access nodes of mobile energy storage and the time-series reactive power output sequence.

[0104] 3.2.1) Objective function construction

[0105] 1) Peak shaving and valley filling objective function

[0106] The inner layer considers the active output regulation capability of mobile energy storage. Mobile energy storage can be used as a power source to supply power to the grid, reducing the peak of the load curve, or as a load to absorb power from the grid, raising the trough of the load curve. Therefore, the minimization of the equivalent load standard deviation is selected as the objective function of the inner layer peak shaving and valley filling.

[0107]

[0108] Where: f P is the standard deviation of equivalent load; P netload,t for t Equivalent load at the moment; P av It is the average value of the equivalent load during the entire dispatching period after the mobile energy storage is connected to the distribution network.

[0109] 2) Reactive voltage regulation objective function

[0110] In order to quantitatively reflect the ability of mobile energy storage to improve the system voltage level, the t Time Node iFeeder voltage indicators of voltage quality I VQ,I,t , minimizing the overall voltage deviation at each moment is taken as the objective function of reactive voltage regulation.

[0111]

[0112] 3.2.2) Constraints

[0113] Assume h The access location of the mobile energy storage is determined according to the reactive voltage sensitivity. i Mobile energy storage in t The output at each moment should satisfy the following constraints.

[0114] 1) Mobile energy storage power constraints

[0115]

[0116] Where: S MES,h For the h Rated apparent power of the vehicle's mobile energy storage.

[0117] 2) State of charge constraints

[0118]

[0119] Where: S SOC,h,t for t The state of charge of mobile energy storage at all times; S SOC,max , S SOC,min They are the upper and lower limits of the mobile energy storage charge state respectively; S SOC,h,0 , S SOC,h,24 They are the charge states of the mobile energy storage at the beginning and end of the dispatch period respectively.

[0120] 3) Operation status constraints

[0121]

[0122] Where: m c,h,t , m d,h,t They are t Moment h The charging and discharging status flag variables of mobile energy storage vehicles, h When the mobile energy storage is in charging state m c,h,t =1, m d,h,t=0,th h When the mobile energy storage is in the discharge state, m c,h,t =0, m d,h,t =1, No. h When the vehicle's mobile energy storage is in charging or moving state, m c,h,t = m d,h,t =0.

[0123] 3.3) Model solution method and process

[0124] The scheduling problem of mobile energy storage has high-dimensional and nonlinear characteristics. Determining the access nodes of mobile energy storage at different times based on the dynamic comprehensive reactive voltage correction sensitivity can avoid using optimization algorithms to encode the accessible nodes in the distribution network, which can effectively reduce the coding dimension.

[0125] The present invention adopts a two-layer optimization method to perform loop feedback optimization on the scheduling problem of mobile energy storage. First, based on the above-mentioned sensitivity calculation results, the node with the highest sensitivity at each moment is selected as the access node of the mobile energy storage. Then, the time-series active and reactive output of the mobile energy storage is encoded according to multiple application scenarios, and Harris Hawks optimization (HHO) is used to solve it. HHO has a strong global search capability in the early stage and a strong local search capability in the middle and late stages, which can avoid the traditional optimization algorithm from falling into the trouble of local optimal solution when solving such problems. Among them, the goal of using the reactive output of mobile energy storage for voltage regulation is set to minimize the node voltage offset of the distribution system at each moment, and finding a feasible solution means jumping out of the loop optimization process. Finally, the obtained time-series reactive output sequence of the mobile energy storage is fed back to the outer model. After optimization and solution, the result of reactive voltage regulation using mobile energy storage is shown in the figure below. Figure 2 As shown in the figure, the voltage fluctuation is limited to the allowable range in the intraday optimization dispatch; the result of using mobile energy storage to reduce the peak and fill the valley is shown in the figure Figure 3 As shown in the figure, the peak-to-valley difference of load power is significantly reduced after optimization.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made to the present invention shall be included in the protection scope of the present invention.

Claims

1. A mobile energy storage dispatching method for multi-scenario power auxiliary services. Features: The following steps are involved: Step 1: Construction of mobile energy storage model based on dynamic transfer characteristics and energy storage characteristics; Step 2: Consider the construction of a multi-scenario power auxiliary service revenue model involving mobile energy storage: The operating benefits and risk benefits of mobile energy storage in different markets are evaluated through portfolio theory. Portfolio theory refers to Markowitz portfolio theory, which includes: mean-variance analysis method and portfolio effective frontier model, based on expected rate of return. v p and the theoretical risk factor in portfolio theory σ p The risk-return function is: ； Where: w is the risk-return ratio; w 0 is the expected deviation value; λ is the risk value coefficient of the investment decision maker, ranging from 1 to 3. λ =2 is medium risk return type, λ >2 is high risk and high return type, λ <2 means low risk and low return; Benefits of mobile energy storage participating in the electric energy market I pav,t Due to the peak and valley difference of electricity prices in different periods of the electricity market, the market risk coefficient of mobile energy storage in the electricity market is σ pav,t From t The standard deviation of the expected electricity market price at any given moment: ； ； Where: C pav,t for a certain moment t Real-time electricity prices; C pav It is the average value of the electricity market price in one day; C pav,t,i for t The historical electricity price of the electricity market at that moment i price; m for t The total number of historical electricity prices in the electricity market at each moment; Benefits of mobile energy storage participating in the backup ancillary service market I ras,t The uncertainty of the reserve price increase or decrease in the reserve ancillary service market, and the market risk coefficient of mobile energy storage in the reserve ancillary service market σ ras,t for t The expected standard deviation of the standby ancillary service price at each moment, the expected rate of return and risk calculation formula of the standby ancillary service market are: ； ； Where: C ras,t For the moment t the price of ancillary services; C c,t For the moment t Energy purchase costs; C ras,t,i for t The historical price data of the time reserve ancillary service market i price; Step 3: Establish a two-layer optimization dispatch model for mobile energy storage: 3.1) External economic operation model The outer optimization model aims to maximize the daily net income of mobile energy storage, with the capacity constraints of mobile energy storage allowed to be installed in the distribution network, node voltage constraints, line power constraints and system power balance constraints as constraints, and the decision variables are the time-series active output sequence of mobile energy storage; 3.1.1) Objective Function ； Where: F The daily net income of mobile energy storage; f 1 To earn peak-to-valley arbitrage profits; f 2 To provide demand management benefits; f 3 for demand response benefits; f 4 Profit from emergency power supply; f MES,inv , f MES,o&m They are the daily investment cost and daily operation and maintenance cost of mobile energy storage; T life The equivalent operating life of mobile energy storage; f truck,h For the h Unit moving cost of mobile energy storage; E ESS,h , P ESS,h Respectively h Rated capacity and rated power of mobile energy storage; f E , f P They are the unit capacity and unit power investment costs of the energy storage system respectively; H is the amount of mobile energy storage; f labor The driver labor cost per truck per day; f m The unit power maintenance cost of mobile energy storage; 3.1.2) Constraints 1) Constraints on the mobile energy storage capacity allowed to be installed in the distribution network ； Where: P ESS,max The maximum capacity of mobile energy storage allowed to be connected to the distribution network; H is the amount of mobile energy storage; 2) Node voltage constraints ； Where: V i,t for t Time Node i The voltage amplitude of V i,max , V i,min Node i The upper and lower limits of voltage; 3) Line power constraints ； Where: S l,t for t Timeline l The transmission power of S l,max For Line l The maximum value of the transmission power allowed; 4) System power balance constraints ； Where: P grid,t , P DG,t , P load,t They are t The grid active power, distributed power source active output, and load active power at all times; P MES,loss,t for t Active power loss of the system at all times; Q grid,t , Q DG,t , Q load,t They are t The grid reactive power, distributed power source reactive output, and load reactive power at all times; Q MES,h,t for t Moment h Reactive power output of mobile energy storage; Q MES,loss,t for t Reactive power loss of the system at all times; 3.2) Inner layer timing optimization model The inner optimization model aims to minimize the standard deviation of equivalent load and the overall voltage deviation at each moment, with the power constraint, state of charge constraint and operation state constraint of mobile energy storage as constraints, and the decision variables are the access nodes of mobile energy storage and the time sequence reactive power output sequence; 3.2.1) Objective function construction 1) Peak shaving and valley filling objective function ； Where: f P is the standard deviation of equivalent load; P netload,t for t Equivalent load at the moment; P av It is the average value of the equivalent load during the entire dispatching period after the mobile energy storage is connected to the distribution network; 2) Reactive voltage regulation objective function In order to quantitatively reflect the ability of mobile energy storage to improve the system voltage level, the t Time Node i Feeder voltage indicators of voltage quality I VQ,I,t , taking the minimization of the overall voltage deviation at each moment as the objective function of reactive voltage regulation; ； 3.2.2) Constraints 1) Mobile energy storage power constraints ； Where: S MES,h For the h Rated apparent power of mobile energy storage of the vehicle; 2) State of charge constraints ； Where: S SOC,h,t for t The state of charge of mobile energy storage at all times; S SOC,max , S SOC,min They are the upper and lower limits of the mobile energy storage charge state respectively; S SOC,h,0 , S SOC,h,24 They are the charge states of the mobile energy storage at the beginning and end of the dispatch period respectively; 3) Operation status constraints ； Where: μ c,h,t , μ d,h,t They are t Moment h The charging and discharging status flag variables of mobile energy storage vehicles, h When the mobile energy storage is in charging state μ c,h,t =1, μ d,h,t =0,th h When the mobile energy storage is in the discharge state, μ c,h,t =0, μ d,h,t =1, No. h When the mobile energy storage vehicle is in motion μ c,h,t = μ d,h,t =0.

2. The mobile energy storage scheduling method for multi-scenario power auxiliary services according to claim 1, Features: The construction of the mobile energy storage model based on dynamic transfer characteristics and energy storage characteristics as described in step 1 is specifically as follows: physical modeling and mathematical modeling of mobile energy storage are performed; first, the energy storage timing characteristics and spatial transferability characteristics of energy storage devices and mobile devices are considered simultaneously; second, the state of charge constraints, charge and discharge constraints, and mobility constraints of mobile energy storage are considered, and a mobile energy storage spatiotemporal transfer and charge and discharge model based on dynamic transfer characteristics and energy storage characteristics is constructed.

3. The mobile energy storage scheduling method for multi-scenario power auxiliary services according to claim 1, Features: The establishment of a two-layer optimization dispatch model for mobile energy storage as described in step 3 is specifically: A two-layer optimization dispatching model for mobile energy storage to participate in multi-scenario power auxiliary services is established; the outer optimization model aims to maximize the daily net income of mobile energy storage, with the capacity constraints of mobile energy storage allowed to be installed in the distribution network, power flow balance constraints, node voltage constraints and line maximum transmission power constraints as constraints, and the decision variables are the time-series active output of mobile energy storage; the inner optimization model takes the peak shaving and valley filling and the optimal reactive voltage regulation effect as the objective function, with the mobile energy storage charging and discharging power, charge state change and operating status as constraints, and the decision variables are the access nodes of mobile energy storage and the time-series reactive output; Reactive voltage sensitivity can accurately reflect the change of node voltage after reactive power is injected; N For a distribution network with a node, the node voltage amplitude change Δ V With active sequence Δ P and reactive sequence Δ Q The relationship between them satisfies: ； Where: Δ V P is the change in node voltage amplitude caused by the change in active power; Δ V Q is the change in node voltage amplitude caused by reactive power change; is the active voltage sensitivity matrix; is the reactive voltage sensitivity matrix; Δ V is the change in node voltage amplitude; Determine the unit reactive power compensation amount, calculate the corresponding voltage increment, and the corrected reactive power voltage sensitivity matrix is: ； Where: Δ V′ is the modified node voltage amplitude change; Will φ QV Sum by column, and express the node as follows j Static comprehensive reactive voltage correction sensitivity when reactive power is injected N S,j ; ； Where: φ 0,j For Node j The initial reactive voltage sensitivity, φ ij For Node j Injected reactive power Δ Q j Post node j For Node i Reactive voltage sensitivity, N is the number of nodes; When the reactive output of mobile energy storage participates in voltage regulation, it is adjusted according to the actual voltage regulation requirements of each node and a voltage deviation factor Δ is introduced. U i Improve the sensitivity of static comprehensive reactive voltage correction; ； Where: U i , U i,N Node i The actual voltage value and rated voltage value; n、m is the number of rows and columns of the reactive voltage sensitivity matrix; The improved static comprehensive reactive voltage correction sensitivity is extended to the dynamic comprehensive reactive voltage correction sensitivity, and the reactive voltage sensitivity at each moment in the dispatching cycle is calculated. N S,j,t Perform calculations; ； Where: Ω olt,t ,Ω llt,t They are t The set of nodes whose actual voltage value is higher or lower than the rated voltage value at any moment; φ i,j,t , φ k,j,t They are t Time Node j Injected reactive power Δ Q j Post node j For Node i ,node k Reactive voltage sensitivity; Δ U i,t , Δ U k,t They are t Time Node i ,node k The voltage deviation factor.

4. According to the mobile energy storage scheduling method for multi-scenario power auxiliary services as described in claim 1, Features: Mobile energy storage is a structure in which a truck is equipped with an energy storage system, which is composed of lithium iron phosphate or lithium titanate electrochemical batteries.

5. According to the mobile energy storage scheduling method for multi-scenario power auxiliary services as described in claim 1, Features: Quantify the revenue model and expected risks of mobile energy storage systems when mobile energy storage participates in the multi-scenario power auxiliary market.

6. According to the mobile energy storage scheduling method for multi-scenario power auxiliary services as described in claim 1, Features: A two-layer optimization dispatch model for mobile energy storage is proposed. The outer optimization dispatch model aims to maximize daily net profit, and mobile energy storage participates in multi-scenario power auxiliary services. The inner optimization model uses mobile energy storage systems as dispatchable resources, and considers the potential for regulating distribution network voltage fluctuations by using the reactive output of mobile energy storage to improve the voltage quality of the distribution network.

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