Hybrid energy storage system planning and operation joint optimization method, system, equipment and medium

Abstract

The present invention discloses a hybrid energy storage system planning and operation joint optimization method, system, equipment and medium, which relates to the technical field of energy storage systems, including: based on the historical operation data of the distribution network, determining the typical normal operation scenario, with minimizing the economic cost of the distribution network as the first objective function; simulating the line status under different wind speeds to generate a typical fault scenario, with the minimum weighted value of load reduction power during the fault duration as the second objective function; using the two objective functions as the game participants in the Nash equilibrium, and the configuration strategy and operation strategy of the hybrid energy storage system as the decision variables, to obtain the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system, as well as the operation strategy in normal scenarios and fault scenarios. The planning configuration strategy of the fixed energy storage system and the mobile energy storage system is realized by combining the normal scenario with the fault scenario.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a method, system, equipment and medium for joint optimization of planning and operation of a hybrid energy storage system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Energy storage systems have played a key role in the sustainable development of distribution networks and energy transformation in recent years. Stationary energy storage systems (SESS) are currently the most widely used form, but they have certain limitations in adjusting the access location to adapt to changes in demand. Mobile energy storage systems (MESS) can complete scheduling tasks that originally required multiple stationary energy storage systems in multiple scenarios. They are suitable for dealing with seasonal load fluctuations and imbalances in the supply and demand of renewable energy, thereby promoting the effective use of new energy.

[0004] With the intensification of climate change and the frequent occurrence of extreme weather events, the importance of energy storage systems in providing emergency power supply has become increasingly prominent, especially mobile energy storage systems, which can quickly respond to temporary or decentralized load restoration needs, such as local power outages or emergency rescue situations caused by extreme weather, and can be quickly deployed and moved to the required location. Therefore, the reasonable configuration of fixed energy storage systems and mobile energy storage systems in distribution networks has become an important issue.

[0005] Existing studies have planned energy storage systems in terms of economy and flexibility, but have not conducted in-depth analysis of the combination of normal scenarios and fault scenarios to consider the planning and configuration strategies of fixed and mobile energy storage systems. The goal of configuring energy storage systems is to ensure that the distribution network can not only operate economically and reliably under normal operating conditions, but also improve the resilience of the distribution network when facing faults or extreme weather events with high losses and low probability. Therefore, it is necessary to consider how to effectively balance these two conflicting goals and constraints to achieve the best configuration plan and distribution network operation strategy in multi-dimensional scenarios. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a hybrid energy storage system planning and operation joint optimization method, system, equipment and medium, which realizes the planning and configuration strategy of fixed energy storage system and mobile energy storage system by combining normal scenarios and fault scenarios, and introduces the Nash equilibrium method to realize the coordinated consideration of the economy and operation flexibility of the distribution network.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a hybrid energy storage system planning and operation joint optimization method, comprising:

[0009] Based on the historical operation data of the distribution network, a typical normal operation scenario is determined, and under the typical normal operation scenario, minimizing the economic cost of the distribution network is taken as the first objective function;

[0010] Simulate the line status under different wind speeds to generate typical fault scenarios. In the typical fault scenarios, the second objective function is to minimize the weighted value of load reduction power during the fault duration.

[0011] The first objective function and the second objective function are used as the game participants in the Nash equilibrium, the minimum value of the two objective functions is used as the critical value in the Nash equilibrium, and the configuration strategy and operation strategy of the hybrid energy storage system are used as decision variables to construct the Nash negotiation game objective function.

[0012] By solving the objective function of the Nash negotiation game, the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system are obtained, as well as the operation strategies in normal scenarios and fault scenarios.

[0013] As an optional implementation, the first objective function includes the annual average investment cost, operation and maintenance cost, energy storage life loss cost and distribution network operation cost of the fixed energy storage system and the mobile energy storage system.

[0014] As an optional implementation method, in a typical scenario of normal operation, energy storage configuration constraints and distribution network operation constraints are used as constraints;

[0015] Energy storage configuration constraints include energy storage capacity constraints and location constraints;

[0016] The distribution network operation constraints include: charging and discharging power constraints, energy level constraints and charge state constraints of fixed energy storage systems and mobile energy storage systems, as well as active power, reactive power, voltage and exchange power constraints between the distribution network and the main power grid.

[0017] As an optional implementation, the process of generating a typical fault scenario includes:

[0018] Assuming that all lines are initially in normal operation, determine the interruption probability of lines in the distribution network under a specified wind speed;

[0019] Generate random numbers uniformly distributed in the range (0, 1), the number of which corresponds to the number of lines;

[0020] For each line, the interruption probability is compared with the corresponding random number. If the interruption probability is less than the random number, the corresponding line is interrupted; otherwise, the line remains connected;

[0021] After traversing all the lines in the distribution network and all the specified wind speeds, several fault scenarios are generated at each specified wind speed using the non-sequential Monte Carlo method, and the number of fault scenarios is reduced using the reverse scenario reduction method, thereby obtaining a typical fault scenario at the specified wind speed.

[0022] As an optional implementation, the second objective function is:

[0023]

[0024] in, is the active power restored by the load at node i at time t; is the loss value under load active recovery power; η is the highest risk cost that satisfies the confidence interval β; y s represents the sth sample in the random variable y, that is, the sth typical fault scenario; NS is the total number of typical fault scenarios; represents the probability of occurrence of typical failure scenario s; v s is an intermediate auxiliary variable.

[0025] As an optional implementation method, the energy storage configuration constraints under typical fault scenarios include energy storage capacity constraints and location constraints; the distribution network operation constraints include active load recovery power and reactive load recovery power constraints, and the maximum capacity constraints and voltage constraints of line transmission in the distribution network.

[0026] As an optional implementation method, the Nash negotiation game objective function is: Among them, F B is the result of Nash equilibrium; s=(s p ,s q )∈S is the critical point of negotiation breakdown, S is the set of benefits of all game parties; F J and F T It is the minimum economic efficiency and the minimum elasticity.

[0027] In a second aspect, the present invention provides a hybrid energy storage system planning and operation joint optimization system, comprising:

[0028] A first target determination module is configured to determine a typical normal operation scenario based on historical operation data of the distribution network, and in the typical normal operation scenario, minimize the economic cost of the distribution network as the first objective function;

[0029] The second target determination module is configured to simulate the line status under different wind speeds to generate a typical fault scenario. In the typical fault scenario, the second target function is to minimize the weighted value of load reduction power within the fault duration.

[0030] A Nash equilibrium module is configured to use the first objective function and the second objective function as game participants in Nash equilibrium, the minimum value of the two objective functions as the critical value in Nash equilibrium, and the configuration strategy and operation strategy of the hybrid energy storage system as decision variables to construct a Nash negotiation game objective function;

[0031] The solution module is configured to obtain the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system as well as the operation strategies in normal scenarios and fault scenarios by solving the objective function of the Nash negotiation game.

[0032] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is completed.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention proposes a joint optimization method for the planning and operation of a hybrid energy storage system, which considers the balance between economic and elastic targets, and realizes the planning and configuration strategy of fixed energy storage systems and mobile energy storage systems through the combination of normal scenarios and fault scenarios. In normal scenarios, the multi-scenario probability method is used to consider the economic efficiency of distribution network operation; in fault scenarios, the conditional risk value is used to consider the impact of tail extreme risks that may be caused by typhoons, and a non-sequential Monte Carlo simulation method is used to generate typical fault scenarios. The objective functions under normal operation and fault scenarios are defined as economic and elastic indicators, respectively. The configuration strategy and operation strategy of the hybrid energy storage system are used as decision variables. On this basis, a Nash negotiation model of the economy and elasticity of the distribution network is established, and the Nash equilibrium method is introduced to achieve the coordinated consideration of the two. The economy and operational elasticity of the distribution network are improved by optimizing the configuration of the capacity, location and operation strategy of fixed energy storage and mobile energy storage.

[0036] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0038] Figure 1 A flow chart of a method for joint optimization of planning and operation of a hybrid energy storage system provided in Example 1 of the present invention;

[0039] Figure 2 A schematic diagram of the relationship between the wind speed contour line and the vulnerability curve provided in Example 1 of the present invention;

[0040] Figure 3 A flowchart of generating typical fault scenarios based on the non-sequential Monte Carlo sampling method provided in Example 1 of the present invention;

[0041] Figure 4 A schematic diagram of the power purchased by the distribution network and the power price under a normal scenario in the case analysis provided in Example 1 of the present invention;

[0042] Figure 5 This is a schematic diagram of voltage fluctuations under a fault scenario in the case analysis provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "comprise" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0047] Example 1

[0048] In order to solve the problem of optimal configuration of energy storage systems in normal operation scenarios and fault operation scenarios of distribution networks, this embodiment provides a hybrid energy storage system planning and operation joint optimization method, which considers the balance between economy and elasticity to obtain the optimal configuration strategy and operation strategy of fixed energy storage systems and mobile energy storage systems in the distribution network.

[0049] like Figure 1 As shown, specifically including:

[0050] Based on the historical operation data of the distribution network, a typical normal operation scenario is determined, and under the typical normal operation scenario, minimizing the economic cost of the distribution network is taken as the first objective function;

[0051] Simulate the line status under different wind speeds to generate typical fault scenarios. In the typical fault scenarios, the second objective function is to minimize the weighted value of load reduction power during the fault duration.

[0052] The first objective function and the second objective function are used as the game participants in the Nash equilibrium, the minimum value of the two objective functions is used as the critical value in the Nash equilibrium, and the configuration strategy and operation strategy of the hybrid energy storage system are used as decision variables to construct the Nash negotiation game objective function.

[0053] By solving the objective function of the Nash negotiation game, the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system are obtained, as well as the operation strategies in normal scenarios and fault scenarios.

[0054] In this embodiment, based on the historical operation data of the distribution network (including historical data of photovoltaic and load), the impact of different weather changes on photovoltaic output and the impact of seasons and holidays on load fluctuations are taken into account. A two-stage clustering method combined with the k-means algorithm is adopted to generate typical scenarios of normal operation by identifying representative scenarios, thereby achieving a more reliable and adaptive configuration.

[0055] In this embodiment, under a typical scenario of normal operation, a multi-scenario probability method is used to comprehensively consider the economic model of the distribution network, with minimizing the economic cost of the distribution network as the first objective function, to achieve the optimal economic configuration of the fixed energy storage system and the mobile energy storage system, flexibly guarantee the load demand in different seasonal scenarios, and maximize economic benefits.

[0056] Specifically:

[0057] The first objective function f c It includes the following four parts: 1) The average annual investment cost of fixed energy storage system and mobile energy storage system f1 inv , 2) Operation and maintenance costs f2 o&m , 3) Energy storage life loss cost 4) Distribution network operating costs Includes the cost of buying and selling electricity.

[0058]

[0059]

[0060]

[0061] Among them, D κ is the number of days in a year corresponding to scenario κ; N κ is the total number of typical scenarios for normal operation; i and N sess is the index and set of SESS; j and N mess is the index and set of SESS; Λ sess and Λ mess is the capital recovery factor for SESS and the capital recovery factor for MESS, which converts the current investment cost into an equivalent annual payment stream over the planning period; r sess and r mess are the discount rates of SESS and MESS respectively; and are the lifespans of SESS and MESS respectively (in years); and is the unit energy capacity cost of SESS and MESS; and Cost is the unit power capacity cost of SESS and MESS installations; is the energy capacity of the SESS / MESS installed at node i / j; is the power capacity of the SESS / MESS installed at node i / j.

[0062]

[0063]

[0064]

[0065] Among them, C o&m is the operation and maintenance cost factor; is the cost coefficient of energy storage life loss; and is the charging power and discharging power of SESS at node i / j in scenario κ at time t; and is the charging power and discharging power of the MESS at node i / j in scenario κ at time t; and is the power input and output flow of the common coupling point in scenario κ at time t; and are the power input and output flows at the point of common coupling (PCC) at time t; and are the purchase price and sale price of electricity at time t; Δt is the duration of a time period; T is a time set; K is a set of typical scenarios for normal operation.

[0066] In this embodiment, in a typical normal operation scenario, energy storage configuration constraints and distribution network operation constraints are used as constraint conditions; specifically:

[0067] Energy storage configuration constraints include constraints on energy storage capacity and location:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] Among them, NumS is the number of SESS configurations; is the flag bit of the SESS installed at node i in scenario κ; is the lower / upper limit of the SESS energy capacity; is the lower / upper limit of SESS power; is the maximum number of SESSs; NumM is the number of configured MESSs; is the flag bit of the MESS installed at node j in scenario κ; is the lower / upper limit of MESS energy capacity; is the lower / upper limit of MESS power.

[0076] Distribution network operation constraints:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] in, is the energy level of the SESS of node i at time t in scenario κ; is the energy level of the MESS of node j at time t in scenario κ; is the energy level of the SESS of node i at time t+1 in scenario κ; and represent the energy levels of the SESS of node i at the initial and end times in scenario κ, respectively; is the energy level of the MESS of node j at time t+1 in scenario κ; and Respectively represent the energy level of the MESS of node j at the initial and end times in scenario κ; η ch is the charging efficiency of energy storage; η dis is the discharge efficiency of energy storage; SOC min and SOC max It is the minimum and maximum value of the SOC (State of Charge) of the energy storage; is the SOC of the SESS of node i at time t in scenario κ; is the SOC of the MESS of node j at time t in scenario κ; SOC ini Indicates the initial SOC of energy storage; Ppv m,κ,t represents the predicted output of PV at time t in scenario κ of node m; represents the actual output of photovoltaic power generation at time t in the scenario κ with m access points; l and Φ DN is the index and set of branches in the distribution network; l(m,:) / l(:,m) means that branch l starts / ends at node m; P l,κ,t and Q l,κ,t is the active power and reactive power on branch l at time t in scenario κ; and is the active load and reactive load of node m in scenario κ at time t; μ κ,t The load fluctuation coefficient of scenario κ at time t; U m,κ,t is the voltage of node m at time t in scenario κ; U k,κ,t is the voltage of node k at time t in scenario κ; r l and x l Respectively represent the resistance and reactance on branch l; U min and U max Indicates the lower and upper limits of voltage; P pcc,κ,t Represents the exchange power between the distribution network and the main grid. When P pcc,κ,t ≥0, the main grid transmits power to the distribution network, otherwise, the distribution network absorbs power from the main grid; is the maximum permissible power flow limited by the transformer; δ re is the percentage of reverse power flow allowed.

[0094] In this embodiment, the line status under different wind speeds is simulated, and the impact of typhoons on the elasticity of the distribution network is considered. For each wind speed scenario, the probability density function of the wind speed contour line is used to determine the probability of occurrence of the wind speed scenario, and a limited number of typical fault scenarios are randomly generated using a non-sequential Monte Carlo sampling method.

[0095] In typhoon weather, the cause of line failure is excessive wind speed. In this embodiment, the impact of typhoon on distribution network lines is studied by combining the wind speed contour line under extreme weather and the vulnerability curve of the distribution line. The vulnerability curve of the distribution line is used to calculate the failure probability of the line at different wind speeds. The vulnerability curve describes the failure probability of the distribution line with wind speed as the independent variable.

[0096] The line fault model is:

[0097]

[0098] Among them, p wind is the failure probability of the line under strong winds, V represents the maximum wind speed that can be tolerated, and v is the actual wind speed.

[0099] When the actual wind speed is lower than the maximum wind speed that can be tolerated, the line operates normally; when the actual wind speed exceeds twice the maximum wind speed that can be tolerated, the line is completely damaged; when the actual wind speed is between the two, the probability of damage increases exponentially with the increase of wind speed. Figure 2 Shown is the relationship between wind speed contours and fragility curves.

[0100]

[0101] Among them, π outageline,l is the interruption probability of distribution line l; typhoon speed f v is the line failure probability extracted from the brittleness curve; A d It is the typhoon spatiotemporal propagation factor in the corresponding area.

[0102] The state of the lines in the distribution network during a typhoon can be stochastically modeled through a set of scenarios. This embodiment uses non-sequential Monte Carlo simulation technology. The number of fault scenarios increases with the number of lines. Incorporating so many scenarios into the stochastic optimization problem provided for resilience assessment requires a lot of calculations, and the computational complexity of solving the stochastic problem becomes very high. To solve this problem, a scenario clustering method is used to reduce the solution scale and shorten the operation time while maintaining the accuracy of the computational performance.

[0103] Formula (32) shows that the probability of the fault scenario is obtained by calculating the interruption probability and normal operation probability of each distribution line: The non-sequential Monte Carlo simulation will obtain a set of distribution network line conditions, separate each line according to the interruption condition and normal operation, and then calculate and multiply them according to the interruption probability of each line to obtain the probability of the fault scenario. The generated probability of the fault scenario will be used for subsequent scenario clustering.

[0104]

[0105] Among them, π s is the probability of failure scenario s; Π status＝0 Indicates interrupt status; status＝1 Indicates normal operating status.

[0106] like Figure 3 As shown in Figure 2, the process of generating typical failure scenarios using the non-sequential Monte Carlo sampling method is as follows:

[0107] (1) It is assumed that all lines were connected before the typhoon occurred, that is, they were all in normal operation.

[0108] (2) Determine the outage probability of all lines in the distribution network.

[0109] (3) Generate a series of random numbers Rand(n) uniformly distributed in the range of (0, 1), where n represents the nth random number; wherein a set of random numbers is generated when each scenario is generated, and the number of random numbers corresponds to the number of distribution lines in the distribution network.

[0110] (4) For each distribution line, compare the outage probability P(i) with the corresponding random number Rand(n); if the outage probability is less than the random number, the corresponding line is disconnected; otherwise, the line remains connected. Each line state is represented by a number 0 or 1, where 0 indicates outage and 1 indicates connection. By comparing the outage probability and the corresponding random numbers of all lines, a scenario consisting of all distribution line states is generated.

[0111] (5) After traversing all the lines in the distribution network and all the specified wind speeds, 20,000 fault scenarios were generated at each specified wind speed using the non-sequential Monte Carlo method. Then, the number of fault scenarios was reduced to 20 groups using the reverse scenario reduction method, that is, the clustering algorithm was used to obtain the typical fault scenarios at that wind speed.

[0112] In this embodiment, under typical fault scenarios, the Conditional Value at Risk (CVaR) is used to consider the impact of the tail extreme risk that may be caused by typhoon disasters, and is used to evaluate and control potential risks. A second objective function is established to minimize the weighted value of load reduction power within the fault duration under multiple typical fault scenarios, and the fault condition constraints are considered. By configuring fixed energy storage systems and mobile energy storage systems and flexibly dispatching mobile energy storage systems, the elasticity of the distribution network is maximized.

[0113] First, the objective function under a single typical fault scenario is:

[0114]

[0115] Among them, Ω T is the set of failure periods; is the load active power at node i at time t; is the active power restored by the load at node i at time t; w i is the weight.

[0116] The fault scenario model based on conditional value at risk has important application value in dealing with the resilience problem of distribution networks. CVaR is a risk management tool that is usually used to quantify and manage extreme losses that may occur at a specific confidence level. This metric has been widely used in the financial field to assess and control potential risk exposure. In recent years, it has also been gradually applied to risk assessment and management of power systems.

[0117] Assume that x represents a vector of decision variables, y represents a vector of random variables, and y represents the loss function of the portfolio. The probability that y does not exceed a given limit θ (which is the value at risk at that confidence level) is expressed as follows:

[0118] P{f(x,y)≤θ} (34);

[0119] If the probability density function of y is For a given confidence level β (representing a set confidence interval), the value at risk (VaR) is:

[0120] VaR β =min{θ:P{f(x,y)≤θ}≥β} (35);

[0121]

[0122] ∫[f(x,y)-VaR β ] + =max{f(x,y)-VaR β ,0} (37);

[0123] If the random variable is discretized and represented by a series of scenarios, the CVaR is approximated by:

[0124]

[0125] The CVaR of the load recovery amount after the distribution network is hit by typhoon weather is selected as the optimization target, and a distribution network load recovery model based on CVaR is established. According to the solution method of discrete CVaR and the derivation of the minimum weighted value of load reduction power as the target, the second objective function based on CVaR is obtained as follows:

[0126]

[0127] Where η represents the risk cost VaR, which refers to the highest risk value that satisfies the confidence interval β; y s represents the sth sample in the random variable y, that is, the sth typical fault scenario; NS is the total number of typical fault scenarios; represents the probability of occurrence of typical failure scenario s; v s is an intermediate auxiliary variable; It is the loss value of load active power recovery.

[0128] In this embodiment, the energy storage configuration constraints in the typical fault scenario are the same as those in the typical normal operation scenario, and the distribution network operation constraints are:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] in, and is the active load recovery power and reactive load recovery power of node m at time t in scenario κ; It represents the maximum capacity that can be transmitted by line l in the distribution network; l,κ,t Indicates whether branch l in scenario κ fails at time t.

[0136] The voltage constraint after line fault relaxation using the large M method is expressed as:

[0137]

[0138]

[0139] Where M represents a sufficiently large positive number; z l,t Indicates whether branch l fails at time t, z l,t =1 means that line l is still in normal connection state at time t, z l,t =0 means that line l is disconnected at time t due to the influence of typhoon; r mk and x mk are the resistance and reactance values ​​respectively.

[0140] In this embodiment, based on the two objective functions constructed above, a Nash negotiation model that considers the balance between the economy and resilience of the distribution network is established. The coordinated consideration of the two is achieved through the Nash equilibrium method. The configuration strategy and operation strategy of the hybrid energy storage system are used as decision variables. Through the Nash negotiation model, the economy and resilience of the distribution network can be used as independent game participants to more realistically and effectively seek equilibrium solutions, which not only helps to improve the economic efficiency of the distribution network, but also improves the resilience and reliability of the system when facing different faults and challenges.

[0141] Nash equilibrium is a core concept in game theory, which describes a state in which no single player can gain greater benefits by unilaterally changing its strategy. In the optimization problem of distribution networks, different players (such as grid operators, electricity suppliers, consumers) may have different goals and interests. Nash equilibrium provides a strategy that allows a relatively stable solution to be reached by balancing these different interests while taking into account the behaviors of different players and external changes.

[0142] In this embodiment, two game participants are considered at the same time: system economy and system elasticity, making the optimal configuration scheme of energy storage more comprehensive and reliable. C1 represents the configuration economy of energy storage under normal conditions, and C2 represents the configuration of energy storage for elastic demand of distribution network under fault conditions. The goal of the two participants is to maximize their respective benefits, and the decision variables are the configuration strategy and operation strategy of the hybrid energy storage system.

[0143] By using the Nash equilibrium theory, we can find the optimal strategy combination between C1 and C2, so that each game participant cannot obtain higher benefits by changing their decision. The Nash equilibrium point represents the maximization of the overall benefits of the system. The Nash negotiation model satisfies the linear transformation independence, and its model is expressed as:

[0144]

[0145] Among them, F B is the result of Nash equilibrium; w p (x) and w q (x) is the profit of both players; s = (s p ,s q )∈S is the critical point of negotiation breakdown. If the two parties fail to reach an agreement under this condition, the negotiation breaks down. S is the set of benefits of all game parties.

[0146] The setting of the negotiation breakdown critical point reflects the minimum acceptance condition of both parties when they cannot reach an agreement. The objective function aims to make the target combination of economy and resilience as far away from this breakdown point as possible, so as to achieve equilibrium. Therefore, in this embodiment, the value of the negotiation breakdown critical point is taken as the minimum value of the economy of the objective function F J and elastic minimum F T , and then the Nash negotiation game objective function of the distribution network in normal and fault scenarios is obtained:

[0147]

[0148] In this embodiment, the Nash negotiation game objective function is solved by an improved fast non-dominated sorting genetic algorithm, and finally the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system as well as the operation strategies in normal scenarios and fault scenarios are obtained.

[0149] The improved fast non-dominated sorting genetic algorithm (NSGA-II) has been widely used in multi-objective optimization problems due to its efficient optimization capabilities. An adaptive mechanism is added to the original NSGA-II framework, making the algorithm more efficient and flexible when facing constraints, and can automatically adjust the probability of crossover and mutation according to the constraint violations in the current population. When the algorithm detects that a high proportion of individuals violate the constraints, it will increase the probability of mutation to explore new feasible solutions; otherwise, it will reduce the probability of mutation to maintain the stability of the population. By improving the NSGA-II algorithm, the optimal configuration of fixed mobile energy storage in the distribution network can be found more efficiently.

[0150] This embodiment uses the IEEE-33 node system model to simulate and verify the above method. NumS and NumM are both 2, SESS1 and SESS2 represent two pre-configured fixed energy storage systems, MESS1 and MESS2 represent two pre-configured mobile energy storage systems; among them, the voltage reference value is 12.66kV, the power reference value is 10MW, and the system load is (3715+j2340)kV·A. The upper limit of the capacity of SESS and MESS is 3MW, and the power investment is fixed at 50% of the capacity investment. Assume that the typhoon in this case occurs at 10:00 on a rainy day in summer, and the fault recovery time is 7 hours.

[0151] The simulation analysis results are divided into two parts. The first part corresponds to the analysis of energy storage optimization configuration and optimization scheduling under normal scenarios, and the second part corresponds to the analysis of energy storage optimization configuration and optimization scheduling under extreme weather scenarios. SESS1 access node 7 (1104kwh), SESS2 access node 30 (707kwh); MESS1 (806kwh), MESS2 (1053kwh).

[0152] Figure 4 This is a schematic diagram of the power purchase and electricity price of the distribution network under normal scenarios. Under normal scenarios, after the introduction of the energy storage system to the distribution network, the power purchase cost is significantly optimized according to the time-of-use electricity price strategy. The energy storage system stores electricity during periods of low electricity prices and releases it during periods of high electricity prices, thereby effectively balancing demand and cost. This strategy takes advantage of electricity price fluctuations and reduces the purchase of electricity during peak hours. Compared with the traditional distribution network that purchases electricity on demand, the introduction of the energy storage system has significantly reduced the cost of purchasing electricity, demonstrated the economic benefits under changes in electricity prices, helped to reduce the burden on the power grid, and improved energy efficiency, providing an effective solution for the economic operation and energy management of the distribution network.

[0153] Figure 5This is a schematic diagram of voltage fluctuations under fault scenarios. In the face of large-scale faults or extreme weather events, it is crucial to ensure that the voltage fluctuations of the distribution network remain within a reasonable range to ensure the stable operation and reliable power supply of the power grid. Energy storage, especially mobile energy storage, can respond quickly when the voltage drops to help stabilize the voltage level. Dynamic scheduling of mobile energy storage can achieve the optimal allocation of energy in time and space dimensions and make full use of the scheduling flexibility of MESS. The recovery rate of important loads in the later stage of the fault is relatively high, reaching more than 90% overall. By optimizing the scheduling of emergency resources, the proportion of system load loss can be significantly reduced.

[0154] In this embodiment, first, under normal scenarios, a multi-scenario probability method is used to comprehensively consider the economic model of the annual investment and operation of the distribution network; under fault scenarios, a non-sequential Monte Carlo simulation method is used to generate disaster scenarios, and the conditional risk value is used to consider the impact of tail extreme risks that may be caused by typhoons. Secondly, the objective functions under normal operation and fault scenarios are established, which are economic and elastic indicators, respectively. The installation nodes and configuration capacities of fixed energy storage systems and mobile energy storage systems are used as decision variables. On this basis, a negotiation model for the economy and elasticity of the distribution network is established, and the coordinated consideration of the two is achieved through the Nash equilibrium method. Finally, the model is applied to the IEEE 33-node system. The results show that the economic cost-effectiveness and elasticity improvement of the distribution network can be effectively balanced.

[0155] Example 2

[0156] This embodiment provides a hybrid energy storage system planning and operation joint optimization system, including:

[0157] A first target determination module is configured to determine a typical normal operation scenario based on historical operation data of the distribution network, and in the typical normal operation scenario, minimize the economic cost of the distribution network as the first objective function;

[0158] The second target determination module is configured to simulate the line status under different wind speeds to generate a typical fault scenario. In the typical fault scenario, the second target function is to minimize the weighted value of load reduction power within the fault duration.

[0159] A Nash equilibrium module is configured to use the first objective function and the second objective function as game participants in Nash equilibrium, the minimum value of the two objective functions as the critical value in Nash equilibrium, and the configuration strategy and operation strategy of the hybrid energy storage system as decision variables to construct a Nash negotiation game objective function;

[0160] The solution module is configured to obtain the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system as well as the operation strategies in normal scenarios and fault scenarios by solving the objective function of the Nash negotiation game.

[0161] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0162] In further embodiments, there is also provided:

[0163] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0164] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0165] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0166] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0167] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0168] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0169] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A hybrid energy storage system planning and operation joint optimization method, characterized in that: include: Based on the historical operation data of the distribution network, a typical normal operation scenario is determined, and under the typical normal operation scenario, minimizing the economic cost of the distribution network is taken as the first objective function; Simulate the line status under different wind speeds and determine the line failure probability to generate a typical fault scenario. In the typical fault scenario, the second objective function is to minimize the weighted value of load reduction power during the fault duration. The first objective function and the second objective function are used as the game participants in the Nash equilibrium, the minimum value of the two objective functions is used as the critical value in the Nash equilibrium, and the configuration strategy and operation strategy of the hybrid energy storage system are used as decision variables to construct the Nash negotiation game objective function. By solving the objective function of the Nash negotiation game, the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system as well as the operation strategies in normal scenarios and fault scenarios are obtained; The first objective function includes the average annual investment cost, operation and maintenance cost, energy storage life loss cost and distribution network operation cost of fixed energy storage systems and mobile energy storage systems; The second objective function is: ； in, is the active power restored by the load at node i at time t; is the loss value of load active power recovery; To satisfy the confidence interval The highest risk cost; represents the sth sample in the random variable y, that is, the sth typical fault scenario; NS is the total number of typical fault scenarios; represents the probability of occurrence of typical failure scenario s; v s is an intermediate auxiliary variable; In a typical scenario of normal operation, the energy storage configuration constraints and distribution network operation constraints are used as constraints; Energy storage configuration constraints include energy storage capacity constraints and location constraints; The distribution network operation constraints include: charging and discharging power constraints, energy level constraints and charge state constraints of fixed energy storage systems and mobile energy storage systems, as well as active power, reactive power, voltage and exchange power constraints between the distribution network and the main grid; The energy storage configuration constraints under typical fault scenarios include energy storage capacity constraints and location constraints; the distribution network operation constraints include active load recovery power constraints and reactive load recovery power constraints, and the maximum capacity constraints and voltage constraints of line transmission in the distribution network.

2. A hybrid energy storage system planning and operation joint optimization method according to claim 1, characterized in that: The process of generating typical failure scenarios includes: Assuming that all lines are initially in normal operation, determine the interruption probability of lines in the distribution network under a specified wind speed; Generate random numbers uniformly distributed in the range (0, 1), and the number of random numbers corresponds to the number of lines; For each line, the interruption probability is compared with the corresponding random number. If the interruption probability is less than the random number, the corresponding line is interrupted; otherwise, the line remains connected; After traversing all the lines in the distribution network and all the specified wind speeds, several fault scenarios are generated at each specified wind speed using the non-sequential Monte Carlo method, and the number of fault scenarios is reduced using the reverse scenario reduction method, thereby obtaining a typical fault scenario at the specified wind speed.

3. A hybrid energy storage system planning and operation joint optimization method as claimed in claim 1, characterized in that: The objective function of the Nash negotiation game is: ;in, is the result of Nash equilibrium; is the critical point of negotiation breakdown, S is the set of benefits of all game parties; and It is the minimum economic efficiency and the minimum elasticity.

4. A hybrid energy storage system planning and operation joint optimization system, characterized in that: include: A first target determination module is configured to determine a typical normal operation scenario based on historical operation data of the distribution network, and in the typical normal operation scenario, minimize the economic cost of the distribution network as the first objective function; The second target determination module is configured to simulate the line status under different wind speeds to generate a typical fault scenario. In the typical fault scenario, the second target function is to minimize the weighted value of load reduction power within the fault duration. A Nash equilibrium module is configured to use the first objective function and the second objective function as game participants in Nash equilibrium, the minimum value of the two objective functions as the critical value in Nash equilibrium, and the configuration strategy and operation strategy of the hybrid energy storage system as decision variables to construct a Nash negotiation game objective function; A solution module is configured to obtain the installation nodes and configuration capacity of the fixed energy storage system and the mobile energy storage system and the operation strategies in normal scenarios and fault scenarios by solving the objective function of the Nash negotiation game; The first objective function includes the average annual investment cost, operation and maintenance cost, energy storage life loss cost and distribution network operation cost of fixed energy storage systems and mobile energy storage systems; The second objective function is: ； in, is the active power restored by the load at node i at time t; is the loss value of load active power recovery; To satisfy the confidence interval The highest risk cost; represents the sth sample in the random variable y, that is, the sth typical fault scenario; NS is the total number of typical fault scenarios; represents the probability of occurrence of typical failure scenario s; v s is an intermediate auxiliary variable; In a typical scenario of normal operation, the energy storage configuration constraints and distribution network operation constraints are used as constraints; Energy storage configuration constraints include energy storage capacity constraints and location constraints; The distribution network operation constraints include: charging and discharging power constraints, energy level constraints and charge state constraints of fixed energy storage systems and mobile energy storage systems, as well as active power, reactive power, voltage and exchange power constraints between the distribution network and the main grid; The energy storage configuration constraints under typical fault scenarios include energy storage capacity constraints and location constraints; the distribution network operation constraints include active load recovery power constraints and reactive load recovery power constraints, and the maximum capacity constraints and voltage constraints of line transmission in the distribution network.

5. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 3 is completed.

6. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 3.

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