Energy storage optimization configuration method and system for extreme weather

Abstract

The present application belongs to the technical field of energy storage optimization, in order to solve the problem of inaccurate energy storage optimization under extreme weather, a kind of energy storage optimization configuration method and system for extreme weather are proposed, the energy storage investment cost, typical day operation cost and extreme day risk cost are unified into typical day-extreme day multi-scenario energy storage planning model, linear programming AC power flow approximation model is used for linear modeling of AC network, and the planning problem is converted into mixed integer linear programming solution;A price clearing model based on security constrained economic dispatch model is constructed, and the marginal price of each node at each time is calculated, and the energy storage planning and price clearing are solved jointly, and the energy storage optimization configuration result under the coupling condition of extreme weather and electricity price market is obtained.The present application considers the power supply safety and economic efficiency of power market under extreme weather, effectively reduces the risk of load shedding under extreme weather, and improves the economic efficiency of energy storage optimization configuration.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy storage optimization, and particularly relates to an energy storage optimization configuration method and system for extreme weather. BACKGROUND

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

[0003] At present, the installed capacity of new energy such as wind and light is rapidly increasing, and building a new type of power system mainly based on new energy has become an important direction for future development. At the same time, climate change has made extreme weather events in some areas increasingly frequent, which has greatly threatened the adequate supply of the power system. As a backup energy source, energy storage has the characteristics of flexible adjustment and rapid response, and can solve the power supply and demand balance problem in such areas caused by extreme weather, and improve the resilience of the power system to extreme weather events. However, only considering the impact of extreme weather on the configuration of energy storage will increase a large amount of redundant energy storage to deal with small probability risk events, resulting in a significant increase in overall cost and difficulty in ensuring economic efficiency. With the continuous advancement of power market reform, node electricity prices fluctuate in time and space with load and new energy output. Energy storage can take advantage of price differences to carry out time and space arbitrage and improve system operation economy. Therefore, for new power systems that are vulnerable to extreme weather, it is of great significance to consider both the impact of extreme weather and the factors of the power market to build an energy storage optimization model with extreme weather safety and operation economy.

[0004] For the problem of extreme weather risk of new energy high proportion power system, the existing methods mainly develop from two aspects of energy storage configuration and emergency dispatch. At the planning level, energy storage is usually considered as a key resource to enhance the resilience of the power grid, and the system supply gap is reduced by optimizing the configuration of energy storage. For example, for the power supply interruption problem of distribution network, a two-stage energy storage site selection and capacity determination model is constructed to enhance the system resilience on the basis of controlling the operation cost. Another method optimizes the joint of network enhancement and energy storage configuration to improve the post-disaster recovery ability of the system. At the operation and dispatch level, it focuses on dealing with typhoons, cold waves and other extreme weather through emergency dispatch. For example, a multi-stage disaster-resistant optimization model is constructed for typhoon influence to improve system flexibility. There are also methods that use electric vehicles as flexible resources for distribution network power supply, and optimize the charging and discharging path to improve the recovery speed in extreme events.

[0005] To depict the economic benefits of energy storage in power systems, most existing methods are based on power markets. For example, a storage arbitrage model considering the uncertainty of locational marginal price (LMP) is constructed to optimize the storage scheduling scheme. Another method compares the differences in storage scheduling optimization results under two scenarios: with and without considering the impact of storage charging and discharging. However, the above methods are based on existing storage structures and do not involve storage capacity optimization.

[0006] For the optimization configuration method of energy storage under extreme weather, the existing research at least has the following shortcomings: (1) The existing research on energy storage configuration can effectively improve the emergency power supply capacity of the system, but is usually used to optimize pre-disaster and post-disaster scheduling strategies, and the economic description is weak, and it is still difficult to answer the optimal configuration problem of energy storage under extreme risk. (2) In the existing research on improving the economy of energy storage configuration, the electricity price is usually regarded as an exogenous given parameter, and the feedback effect of energy storage investment decision on the temporal and spatial distribution of electricity price is not considered. SUMMARY

[0007] To overcome the above shortcomings of the prior art, the present application provides an energy storage optimization configuration method and system for extreme weather, which improves the safety and economy of new energy high proportion power system under extreme weather and electricity price fluctuation conditions.

[0008] To achieve the above purpose, the present application adopts the following technical solutions:

[0009] In a first aspect, the present application provides an energy storage optimization configuration method for extreme weather, comprising:

[0010] Considering the operating characteristics of the power system under extreme weather conditions, an extreme weather load loss risk assessment index is constructed to quantify the impact of extreme weather on the safety and power supply reliability of the power system;

[0011] On the basis of comprehensively considering the investment cost of energy storage, the operating cost of typical days and the risk cost of extreme days, a target function is constructed to minimize the total cost of the power system, and a typical day-extreme day multi-scenario energy storage planning model is established; wherein, the extreme weather risk measurement result is introduced into the extreme day risk cost;

[0012] The alternating current flow constraint of the typical day-extreme day multi-scenario energy storage planning model is linearized by using a linear programming alternating current flow approximation model to linearize the non-linear relationship, and the original non-linear programming problem is converted into a mixed integer linear programming problem, and the site selection, capacity configuration and operation strategy of the energy storage are jointly optimized;

[0013] An electricity price clearing model based on a security constrained economic dispatch model is constructed to calculate the marginal electricity price trajectory of each node and each period under the premise of meeting the unit output constraint, the power grid safety constraint and the standby constraint;

[0014] The price clearing model and the typical day-extreme day multi-scenario energy storage planning model are solved simultaneously to obtain the energy storage optimization configuration result under the coupling condition of extreme weather and the electricity price market.

[0015] In a second aspect, the present application provides an energy storage optimization configuration system for extreme weather, comprising:

[0016] The extreme weather load risk assessment index construction module is configured to consider the operation characteristics of the power system under extreme weather conditions, and construct an extreme weather load loss risk assessment index for quantifying the influence of extreme weather on the safety and power supply reliability of the power system.

[0017] The energy storage optimization configuration modeling module is configured to construct a target function with the minimum total cost of the power system as the target on the basis of comprehensively considering the energy storage investment cost, typical day operation cost and extreme day risk cost, and establish a typical day-extreme day multi-scenario energy storage planning model; wherein the extreme weather risk measurement result is introduced into the extreme day risk cost.

[0018] The energy storage optimization configuration solving module is configured to linearize the nonlinear relationship by using a linear programming alternating current flow approximation model for the alternating current flow constraint of the typical day-extreme day multi-scenario energy storage planning model, convert the original nonlinear programming problem into a mixed integer linear programming problem, and jointly optimize the site selection, capacity configuration and operation strategy of the energy storage.

[0019] The price clearing module is configured to construct a price clearing model based on a security constrained economic dispatch model, and calculate the marginal electricity price trajectory of each node and each period under the premise of meeting the unit output constraint, the power grid safety constraint and the reserve constraint.

[0020] The energy storage planning and price clearing simultaneous solving module is configured to solve the price clearing model and the typical day-extreme day multi-scenario energy storage planning model simultaneously to obtain the energy storage optimization configuration result under the coupling condition of extreme weather and the electricity price market.

[0021] In a third aspect, the present application provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein when the computer instructions are run by the processor, the method of the first aspect is completed.

[0022] In a fourth aspect, the present application provides a computer readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method of the first aspect is completed.

[0023] The above one or more technical solutions have the following beneficial effects:

[0024] In the application, the energy storage investment cost, typical day operation cost and extreme day risk cost are uniformly included in the typical day-extreme day multi-scenario energy storage planning model, the alternating current flow approximation model of linear programming is used to linearly model the alternating current network, and the planning problem is converted into a mixed integer linear programming for solving; at the same time, a price clearing model based on a security constrained economic dispatch model is constructed, the marginal price of each node at each time period is calculated, the energy storage planning and the price clearing are jointly solved, and the energy storage optimization configuration result under the coupling condition of extreme weather and electricity price market is obtained. The application can simultaneously consider the power supply safety under extreme weather and the economy of the electricity market, effectively reduce the risk of load shedding under extreme weather, improve the economy of the energy storage optimization configuration, and is suitable for the medium and long term planning and operation decision of the high proportion of new energy power system.

[0025] In the application, in the joint iterative solving of the energy storage planning and the price clearing, the Anderson acceleration strategy is introduced, the iteration number is significantly reduced, and the price and investment are prevented from oscillating between different schemes.

[0026] The advantages of the additional aspects of the application will be partially given in the following description, partially will become obvious from the following description, or will be known by the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0027] The drawings accompanying the specification of the application form a part of the specification and serve to further illustrate the application, the illustrative embodiments of the application and the description thereof serve to explain the application without imposing undue limitation on the application.

[0028] Figure 1 It is a new energy output power change schematic diagram under two types of extreme weather in the embodiment one of the application; wherein, (a) represents an extreme static stability scenario; (b) is an extreme climbing scenario;

[0029] Figure 2 It is a power spot market typical clearing structure schematic diagram in the embodiment one of the application;

[0030] Figure 3 It is an iteration process schematic diagram of the joint solution of the energy storage planning and the price clearing in the embodiment one of the application;

[0031] Figure 4 It is a structure schematic diagram of the energy storage optimization configuration system facing extreme weather and electricity price market in the embodiment one of the application. DETAILED DESCRIPTION

[0032] It should be pointed out that the following detailed description is all exemplary, and aims to provide further description of the application. Unless otherwise specified, all technical and scientific terms used in this paper have the same meaning as that generally understood by ordinary skilled in the art to which the application belongs.

[0033] It is to be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application.

[0034] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0035] Embodiment one

[0036] The embodiment discloses an energy storage optimization configuration method for extreme weather, comprising:

[0037] An energy storage optimization configuration method for extreme weather, characterized in that it comprises:

[0038] Considering the operation characteristics of the power system under extreme weather conditions, an extreme weather loss load risk assessment index for quantifying the influence of extreme weather on the safety and power supply reliability of the power system is constructed;

[0039] On the basis of comprehensively considering the energy storage investment cost, typical day operation cost and extreme day risk cost, a target function is constructed to minimize the total cost of the power system, and a typical day-extreme day multi-scenario energy storage planning model is established; wherein, the extreme weather risk measurement result is introduced into the extreme day risk cost;

[0040] The linear programming AC power flow approximation model is used to linearize the non-linear relationship of the AC power flow constraint of the typical day-extreme day multi-scenario energy storage planning model, the original non-linear programming problem is converted into a mixed integer linear programming problem, and the site selection, capacity configuration and operation strategy of the energy storage are jointly optimized;

[0041] A price clearing model based on a security constrained economic dispatch model is constructed, and the marginal price trajectory of each node and each period is calculated under the premise of meeting the unit output constraint, the power grid safety constraint and the standby constraint;

[0042] The price clearing model and the typical day-extreme day multi-scenario energy storage planning model are solved simultaneously to obtain the energy storage optimization configuration result under the coupling condition of extreme weather and the electricity price market.

[0043] The energy storage investment cost, typical day operation cost and extreme day risk cost are uniformly included in the typical day-extreme day multi-scenario energy storage planning model, a linear alternating current flow approximation model of linear programming is used for linear modeling of the alternating current network, and the planning problem is converted into a mixed integer linear programming for solving; meanwhile, a price clearing model based on a security constrained economic dispatch model is constructed, the marginal price of each node at each time period is calculated, the energy storage planning and the price clearing are jointly solved, and the energy storage optimization configuration result under the coupling condition of extreme weather and electricity price market is obtained. The application can simultaneously consider the power supply safety under extreme weather and the economy of the electricity market, effectively reduce the risk of load shedding under extreme weather, improve the economy of energy storage optimization configuration, and is suitable for long-term planning and operation decision of high proportion of new energy power system.

[0044] A kind of energy storage optimization configuration method for extreme weather proposed in the embodiment is described in detail as follows:

[0045] S1: based on historical meteorological data and new energy output data, an identification method of two typical extreme weather scenarios, extreme static stable scenario and extreme climbing scenario, is proposed, and a typical extreme weather scenario set containing various extreme working conditions is constructed;According to the characteristics of new energy output and system operation state under each typical extreme weather scenario, the loss of load index is extracted, and the extreme weather loss of load risk evaluation index for describing the influence degree of extreme weather is formed.

[0046] In step S1, the extreme weather event refers to a meteorological event with high intensity, long duration and significant influence on new energy output. To describe the variation characteristics of new energy output under different types of extreme weather, the extreme weather scenario is divided into two types, extreme static stable scenario and extreme climbing scenario, and the new energy output power variation under the two scenarios is shown in Figure 1 . Figure 1 In the figure, the horizontal coordinate t represents time, and the vertical coordinate represents the active power output of the new energy station; P max is the rated maximum output of the new energy station, α is a power reduction coefficient between 0 and 1, represents the time length of the influence of extreme weather. Figure 1 In the figure, (a) extreme static stable scenario is used to describe the working condition that new energy output is maintained at a low level for a long time under the influence of large-scale low wind speed or persistent cloud cover; Figure 1 In the figure, (b) extreme climbing scenario is used to describe the working condition that new energy output is sharply reduced within a few minutes to a few hours under the action of thunderstorm, sandstorm and other extreme weather.

[0047] After identifying the two sets of extreme weather scenarios, to quantify the power supply risk of the system under extreme weather, the embodiment takes the cut-load power of each node and each period as the basis, multiplies it by the corresponding cut-load cost coefficient, and sums it up in space and time to construct an extreme weather loss-of-load risk assessment index D LS The calculation method is shown in formula (1).

[0048] (1)

[0049] In the formula, Dk,t,n represents the cut-load power of node n in period t under scenario k, Dk,t,n is the unit cut-load cost of node n. The extreme weather loss-of-load risk assessment index D LS The larger the value, the higher the risk of loss of load faced by the system under the corresponding extreme weather scenario, which provides a quantitative basis for subsequent energy storage optimization configuration.

[0050] Based on the above extreme weather loss-of-load risk assessment index, the power system operation constraints are constructed under each type of extreme weather scenario. The operation constraints include: unit segmented active power output and upper and lower limit constraints, unit ramping capability constraints, wind and photovoltaic market side output and curtailment constraints, wind and photovoltaic non-market side output and curtailment constraints, energy storage charging and discharging power and state constraints, node active / reactive power balance constraints, node voltage amplitude and phase angle constraints, line flow constraints, and node cut-load upper and lower limit constraints.

[0051] S2: Based on the extreme weather loss-of-load risk assessment index, a typical day-extreme day multi-scenario energy storage planning model is constructed, the extreme weather risk measurement result is introduced into the objective function or the constraint condition, the energy storage site selection decision, the capacity configuration decision and the charging and discharging operation strategy are comprehensively considered, and a multi-scenario joint optimization model containing investment cost, operation cost and cut-load cost under extreme weather is established.

[0052] Among them, the extreme day is the ramping scenario or the static stable scenario identified from the annual new energy output scenario. The typical day is several typical scenarios obtained by clustering a large number of normal operation scenarios, and the two are used together for energy storage planning.

[0053] In step S2, based on the typical day and extreme day scenarios and their risk indexes obtained in step S1, a typical day-extreme day multi-scenario energy storage planning model is constructed. The objective function of the typical day-extreme day multi-scenario energy storage planning model minimizes the total cost of the power system on the basis of comprehensively considering the energy storage investment cost, the typical day operation cost and the extreme day risk cost, and the objective function is shown in formula (2).

[0054] (2)

[0055] wherein, C Inv is the investment cost of energy storage; C Ope is the typical day operation cost; C LS is the extreme day risk cost. is the weight coefficient, which is used to reflect the relative importance of the typical day operation cost and the extreme day risk cost in the total target, and is determined according to the measured occurrence probability of each scenario.

[0056] Investment cost of energy storage C Inv is obtained by multiplying the energy storage capacity and power of each candidate node by the corresponding unit investment cost and summing them up, and is used to represent the initial construction cost of the newly built energy storage station, and the calculation method is shown in formula (3):

[0057] (3)

[0058] wherein, and represent the unit capacity and unit power investment cost of the energy storage system respectively, and represent the total investment capacity and investment power of the energy storage system, is the set of newly built energy storage stations in the system.

[0059] Typical day operation cost C Ope Under the conditions of typical day load and new energy output, the fuel consumption and start-stop cost of thermal power units, the market side output of wind power and photovoltaic, and the penalty cost of abandoned electricity, etc. are comprehensively considered, and each typical scenario is weighted and summed according to the occurrence probability, and the expression is shown in formula (4):

[0060] (4)

[0061] wherein, is the probability of the occurrence of the i-th typical day scenario, k is the unit operation cost of the thermal power unit in the i-th typical day scenario, is the thermal power unit output of the i-th typical day scenario at the j-th time point, g is the unit operation cost of the thermal power unit in the i-th typical day scenario, j is the thermal power unit output of the i-th typical day scenario at the j-th time point, is the unit abandoned electricity penalty cost of the wind power, g is the unit abandoned electricity penalty cost of the photovoltaic, j k t w v ​​​​​​​​The abandoned electricity quantity of wind and light entering the market respectively; The unit penalty cost of wind and light not entering the market at the source, which is usually higher than and to encourage the system to preferentially consume non-market new energy.

[0062] Extreme day risk cost C LS The extreme weather load loss risk index defined in step S1 is introduced into the planning model. The load shedding amount under each extreme day scenario is multiplied by the scenario occurrence probability and the unit load shedding cost and summed up to form the extreme day risk cost term, and its calculation method is shown in formula (5).

[0063] (5)

[0064] In the formula, is the probability of the occurrence of the k extreme day scenario, D LS is the extreme weather load loss risk assessment index.

[0065] On the basis of the above objective function, the typical day-extreme day multi-scenario energy storage planning model further sets the following constraint conditions:

[0066] (1) Investment constraint: through binary location variable and upper and lower limits of energy storage capacity and power, the construction of energy storage power station at each candidate node and the maximum configurable capacity and power are limited, and the upper limit of the total number of newly built energy storage power stations in the system is set, which is shown in formula (6).

[0067] (6)

[0068] In the formula, x n is the construction decision variable of the energy storage station at the candidate node, when the energy storage is configured at the node n x n = 1, otherwise x n = 0; , are the energy storage s planned energy storage energy capacity and charge and discharge power; , are the maximum energy capacity and maximum power upper limit allowed by a single energy storage site; is the upper limit of the number of energy storage sites allowed by the system.

[0069] (2) Typical day constraint: under the typical day scenario, it is required that under the premise of meeting the power system operation constraint model in step S1, the load shedding amount of each node at each time period is zero.​

[0070] (3) Extreme day constraints: In the extreme static stability scenario and the extreme ramping scenario, the power system operation constraint model in step S1 is also satisfied, and each node is allowed to have a cut load amount in the range of 0 to the total load of each node in the extreme day scenario.

[0071] Through the objective function and the constraint condition, a trade-off between typical day economy and extreme day safety can be achieved, and an optimal site and capacity configuration scheme of energy storage in multiple scenarios is obtained.

[0072] S3: For the AC power flow constraint in the typical day-extreme day multi-scenario energy storage planning model, a linear programming AC power flow approximation model (LPAC) is used to linearize the non-linear relationship of voltage phase angle, active power flow, etc., and the original non-linear programming problem is converted into a mixed integer linear programming (MILP) problem, so as to improve the solving efficiency and convergence stability under the conditions of multiple scenarios and multiple variables.

[0073] In step S3, in order to reduce the solving complexity of the multi-scenario energy storage planning model while considering the AC network constraint, the AC power flow equation in the typical day-extreme day multi-scenario energy storage planning model constructed in step S2 is linearized based on LPAC.

[0074] Specifically, by using conventional means such as small-angle approximation and piecewise linear approximation of cosine function, the non-linear relationship between active power, reactive power flow and node voltage deviation, phase angle difference is converted into a linear relationship, and a linear power flow constraint model based on LPAC is established.

[0075] The person skilled in the art can give the specific form of the linear power flow equation according to the existing LPAC method.

[0076] After replacing the original AC power flow constraint with the LPAC linear power flow constraint, the multi-scenario energy storage planning model in step S2 is converted into a MILP model containing only linear equality / inequality constraints and integer decision variables, which can be directly solved by an existing MILP solver for global optimization, thereby improving the solvability and computational efficiency of the multi-scenario energy storage optimization configuration problem while ensuring the accuracy of power flow calculation.

[0077] S4: On the basis of considering the unit segmented bidding, the participation of partial wind power and photovoltaic power stations in the spot market clearing, and the influence of joint dispatching of existing and newly-built energy storage on the node marginal price (LMP), a price clearing model based on a security-constrained economic dispatch (SCED) model is constructed; under the premise of satisfying the unit output constraint, the power grid safety constraint and the reserve constraint, multi-period price clearing is performed on the given scene set to obtain the LMP trajectory of each node and each period.

[0078] In step S4, the SCED-based price clearing model is constructed. On the basis of the energy storage configuration scheme and the power grid parameters obtained in steps S2 and S3, this step is used to depict the clearing mechanism of the spot power market and calculate the node marginal price (LMP) trajectory of each node and each period, which specifically includes:

[0079] (1) Spot market mode and clearing mechanism: a centralized clearing mode based on the node marginal price is adopted, and the system operator organizes the clearing according to the generation side bidding and load prediction. The conventional thermal power unit is described by using a segmented linear cost curve, the output of new energy such as wind power and photovoltaic power is divided into a market entry part participating in the spot market clearing and a non-market entry part modeled according to a fixed output, the market entry part is set with a wind power and photovoltaic power curtailment penalty cost in the objective function, and the non-market entry part is used as a fixed injection to participate in power balance; at the same time, the charging and discharging power, state constraint and load shedding constraint of the existing and newly-built energy storage are also included in the price clearing model, and the marginal price LMP of each node and each period is calculated under the premise of satisfying the unit output constraint, the reserve constraint and the network safety constraint. The hierarchical clearing of the power spot market is shown in Figure 2 .

[0080] (2) SCED-based power clearing model: under the above market mode, the SCED-based power clearing model is constructed, the sum of the bidding cost of each power generating unit and new energy station and the load shedding penalty cost is used as the objective function, and the minimum total clearing cost of the system is realized, which can be expressed by formula (7).

[0081] (7)

[0082] In the formula, is the bidding of the thermal power unit g in the first j segment, is the winning power of the thermal power unit g in the first j segment at the k time in the t scene; , respectively are wind farms w market side generation offer and winning bid at t time t; , respectively are photovoltaic power stations v market side generation offer and winning bid at t time t; , respectively are wind and photovoltaic non-market side generation offer and output power at n time t at node i; t , respectively are unit load shedding penalty cost and load shedding amount at node i. n (3) LMP calculation: based on the SCED clearing result of LPAC, take the node net injection power as decision variable and the total system operation cost as objective. According to the physical meaning of LMP, define the LMP of node in given time period as the marginal cost of unit injection power to the objective function under the condition of keeping other constraints unchanged.

[0083] Therefore, based on the SCED price clearing model of formula (7), construct the Lagrange function on the objective function and various constraints. And take the partial derivative of the node net injection power, the relationship between the node LMP and the shadow price of system power balance constraint and the shadow price of each line power upper and lower limit constraint can be obtained, which can be expressed by formula (8).

[0084]

[0085] (8)

[0086] In the formula, is the dual multiplier of node power balance constraint, , respectively are the dual multipliers of line constraint active power flow upper and lower limit constraint, is the active power flow of line m to the linear sensitivity coefficient of node n injection power.

[0087] From the above formula, it can be seen that LMP can be decomposed into electricity price and network congestion price. is the system marginal generation cost when ignoring line constraints, and the second term reflects the correction of line flow constraints and network congestion to node price. When the system has no line congestion, the dual multipliers of each line satisfy = ​​=0, all nodes have the same LMP. When the system experiences line congestion, the LMP of the node connected to the congested line is different from the LMP of other nodes, while the LMP of the node connected to the non-congested line is the same as the LMP of the relaxed node.

[0088] S5: Solve the electricity price clearing model and the typical-extreme-day multi-scenario energy storage planning model simultaneously: Under the given LMP trajectory, solve the typical-extreme-day multi-scenario energy storage planning model to obtain energy storage site selection, capacity and operation strategy; then use the updated energy storage operation strategy as an adjustable resource input to the electricity price clearing model to recalculate the LMP trajectory; use the fixed-point iterative method with Anderson acceleration to solve the final energy storage optimization configuration result.

[0089] In step S5, the energy storage planning and electricity price clearing systems are solved simultaneously. To reflect the impact of electricity price changes on energy storage investment and operation decisions, and to characterize the feedback effect of energy storage on nodal electricity prices, this step proposes a method for solving the energy storage planning and electricity price clearing systems simultaneously.

[0090] (1) Construct an objective function for energy storage planning that takes into account electricity price revenue.

[0091] Without considering electricity price feedback, the total system cost target of the typical day-extreme day multi-scenario energy storage planning model is shown in formula (2). Therefore, an energy storage revenue term under electricity price signals is introduced based on this. C Rev Its calculation form is shown in formula (9).

[0092] (9)

[0093] In the formula, , Typical daytime scenarios k energy storage s exist t The energy storage discharge power and charging power at any given time. For nodes n exist t LMP at any given time , Typical daytime scene and extreme daytime scene respectively k The corresponding weights.

[0094] The objective function for energy storage planning, after considering electricity price feedback, is adjusted as shown in formula (10). While comprehensively weighing investment costs, typical daily operating costs, and extreme daily risk costs, the goal is to maximize energy storage revenue driven by electricity prices.

[0095] (10)

[0096] (2) Construct the iteration fixed point framework between energy storage planning and price clearing.

[0097] Since the LMP trajectory is needed as the price input in the typical day-extreme day multi-scenario energy storage planning model, and the SCED clearing model is affected by the energy storage site selection and capacity decision, a mutual dependence relationship is formed between the two. To obtain a self-consistent joint solution, this step adopts a fixed point iteration framework: given a node LMP trajectory, solve the energy storage planning model considering the price benefit to obtain the energy storage site selection and capacity decision; inject the updated energy storage investment and operation strategy into the SCED clearing model as an adjustable resource to re-clear the power market and obtain a new LMP trajectory; compare the differences between the new and old LMP trajectories and the energy storage investment scheme as the fixed point residual to guide the next iteration update.

[0098] (3) Fixed point iteration solution based on Anderson acceleration.

[0099] To improve the convergence speed and stability of the above iteration process, the Anderson acceleration strategy is introduced on the basis of the traditional simple fixed point iteration.

[0100] Specifically, in the kth iteration, the current energy storage site selection and capacity, power investment scheme is represented as shown in equation (11), and the fixed point iteration is updated.

[0101] (11)

[0102] In the formula, is the node LMP trajectory of the kth iteration, k is the construction decision variable of the upper energy storage station of the kth iteration, n and represent the investment capacity and investment power of the kth iteration energy storage. k s For the current investment scheme , first use the price clearing model to obtain the time-of-use price trajectory, and then solve the typical day-extreme day multi-scenario energy storage planning model under the price condition to obtain a new investment candidate

[0103] . And the corresponding residual is recorded as the following formula:

[0104]

[0105] (12)

[0106] In the formula, r (k) is used to measure the difference between the new investment candidate and the current investment scheme.

[0107] ​​​​1) Damped iteration step.

[0108] To ensure convergence stability, first a simple damped update is used to get an intermediate solution , as shown in the following equation:

[0109] (13)

[0110] where is an adaptive damping factor, which can be appropriately reduced when residual oscillation or iteration divergence tendency occurs.

[0111] 2) Anderson acceleration step.

[0112] On the basis of the damped update, a first-order Anderson acceleration is introduced to improve the convergence speed, and the difference between the residuals of the adjacent two steps is denoted as

[0113] (14)

[0114] Then the Anderson coefficient α (k) is estimated as

[0115] (15)

[0116] where the numerator is the inner product of the transpose of the residual difference and the current residual , and the denominator is the square of the norm of the residual difference plus a very small ε to prevent division by zero; and α (k) is limited to the interval to avoid over extrapolation. The superscript T denotes the transpose.

[0117] Using the coefficient α (k) the Anderson candidate solution is constructed as shown in equation (17), that is, on the basis of the original fixed point step x (k) + r (k) , a correction is made along Δ r (k) , which is equivalent to "straightening" the convergence trajectory, thereby accelerating the convergence.

[0118] (16)

[0119] 3) Convex combination update.

[0120] The new energy storage investment scheme is a convex combination of the vector damping solution and the Anderson solution, as shown in the following equation:

[0121] (17)

[0122] In the formula, β For Anderson weights, This is a pre-defined upper bound. If Anderson significantly improves convergence, a larger bound can be used. β If the effect is not good, you can... β Setting it to 0 degenerates into a pure damping iteration, ensuring robustness.

[0123] To measure the convergence of the fixed-point iteration, a new energy storage investment scheme is obtained. z ES,(k+1) Then, simultaneously assess the changes in electricity price trends and energy storage investment plans. When both are less than the preset tolerance... ε π , ε ES When the iteration converges, the current energy storage planning scheme and the electricity price curve are considered to be the desired planning-electricity price joint optimization result.

[0124] The above-mentioned energy storage planning-electricity price clearing simultaneous solution process can be used as follows: Figure 3 The process can be summarized as follows: First, input the grid parameters, typical day and extreme day scenarios, and set the initial LMP trajectory; under the current energy storage investment scheme, solve the SCED clearing model to obtain the LMP trajectory, and then map it into the electricity price signal according to the market pricing rules and input it into the energy storage planning model to obtain the new energy storage site selection and capacity decision; then calculate the change magnitude of the electricity price trajectory and the investment scheme, and update the iteration direction based on the damping-Anderson strategy; if the change magnitude is greater than the preset convergence threshold, proceed to the next iteration; otherwise, output the finally converged energy storage investment scheme and its corresponding node LMP trajectory, realizing the simultaneous solution of energy storage planning and electricity price clearing.

[0125] The embodiment first constructs a plurality of scenes such as typical days, extreme static stability, and extreme climbing based on historical meteorological data and new energy output data, in combination with output duration, fluctuation amplitude, and climbing rate, and counts load shedding levels to form an extreme weather load shedding risk assessment index for representing system safety. On this basis, the investment cost and operation cost of energy storage and the extreme weather risk cost are uniformly included in a multi-scenario energy storage site selection and capacity configuration model, linear power flow is used for linear modeling of alternating current networks, and the planning problem is converted into a mixed integer linear programming problem for solving. At the same time, a multi-period electricity price clearing model based on SCED is constructed, considering segmented bidding of conventional units, segmented market entry of wind and light, and the cost of abandoned electricity and operation constraints of existing / new energy storage, to calculate the marginal electricity price LMP of each node at each time period. Further, the alternating fixed point iteration and Anderson acceleration strategy are used to iterate between the "energy storage planning-electricity price clearing" to obtain a joint convergence solution of the energy storage configuration scheme and the electricity price trajectory. Compared with existing methods, the embodiment can simultaneously consider the power supply safety and market economy under extreme weather, effectively reduce the extreme weather load shedding risk, improve the economy of energy storage optimization configuration, and is suitable for medium and long term planning and operation decision of high proportion of new energy power system.

[0126] Embodiment two

[0127] As shown in Figure 4 , the purpose of the embodiment is to provide an energy storage optimization configuration system for extreme weather, comprising:

[0128] An extreme weather scene recognition module is configured to analyze output fluctuation characteristics at different time scales based on historical meteorological data and new energy output data, and divide extreme weather into two categories of extreme static stability scene and extreme climbing scene.

[0129] An extreme weather load risk assessment index construction module is configured to consider the operation characteristics of the power system under extreme weather conditions, and construct an extreme weather load shedding risk assessment index for quantifying the influence of extreme weather on the safety and power supply reliability of the power system.

[0130] An energy storage optimization configuration modeling module is configured to construct a target function with the minimum total cost of the power system as the target based on comprehensive consideration of the investment cost of energy storage, the operation cost of typical days, and the extreme day risk cost, and establish a typical day-extreme day multi-scenario energy storage planning model; wherein the extreme weather risk measurement result is introduced into the extreme day risk cost.

[0131] The energy storage optimization configuration solving module is configured to linearize the non-linear relationship by using a linear programming alternating current flow approximation model for the alternating current flow constraint of the typical day-extreme day multi-scenario energy storage planning model, convert the original non-linear programming problem into a mixed integer linear programming problem, and jointly optimize the site selection, capacity configuration and operation strategy of the energy storage.

[0132] The electricity price clearing module is configured to construct an electricity price clearing model based on a security constrained economic dispatch model.

[0133] The node marginal electricity price calculation module is configured to calculate the marginal electricity price trajectory of each node and each period under the premise of satisfying the unit output constraint, the power grid security constraint and the reserve constraint.

[0134] The energy storage planning and electricity price clearing joint solving module is configured to jointly solve the electricity price clearing model and the typical day-extreme day multi-scenario energy storage planning model to obtain the energy storage optimization configuration result under the coupling condition of extreme weather and electricity price market.

[0135] In more embodiments, there are also provided:

[0136] An electronic device includes a memory and a processor, and computer instructions stored on the memory and run on the processor, when the computer instructions are run by the processor, the method described in Embodiment I is completed. For brevity, this will not be repeated here.

[0137] It should be understood that in the embodiments, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-to-program gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0138] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, and a part of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0139] A computer readable storage medium for storing computer instructions, when the computer instructions are executed by a processor, the method described in Embodiment I is completed.

[0140] The method in the embodiment one can be directly embodied as being completed by a hardware processor or being completed by a combination of hardware and software modules in the processor. The software modules can be located in a storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, or the like. The storage medium is located in a memory, and the processor reads information in the memory and completes the steps of the above method in combination with the hardware. To avoid repetition, no further detailed description is given here.

[0141] A computer program product includes a computer program, which, when executed by a processor, implements the method described in the embodiment one.

[0142] The present application also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer executable instructions, for example, instructions embodied in program modules, executed by devices at the destination, real or virtual processors, to perform processes / methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In various embodiments, the functions of the program modules can be combined or divided as desired in various embodiments. Machine executable instructions for program modules can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote storage media.

[0143] Computer program code for carrying out operations of the present application can be written in one or more programming languages. These computer program codes can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, so that the program codes, when executed by the computer or other programmable data processing apparatus, cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program codes can be executed entirely on a computer, partially on a computer, as a standalone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0144] In the context of the present application, computer program code or related data can be carried by any suitable carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals can include electrical, optical, radio, sound or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0145] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the present embodiment can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0146] Although the specific embodiments of the present application are described above in combination with the drawings, it is not a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the scope of protection of the present application.

Claims

1. A method for optimizing configuration of energy storage for extreme weather, characterized in that, include: Considering the operating characteristics of power systems under extreme weather conditions, an extreme weather load shedding risk assessment index is constructed to quantify the impact of extreme weather on power system security and power supply reliability. Based on a comprehensive consideration of energy storage investment costs, typical day operating costs, and extreme day risk costs, an objective function is constructed with the goal of minimizing the total cost of the power system, and a multi-scenario energy storage planning model is established for typical days and extreme days; among them, the results of extreme weather risk measurement are incorporated into the extreme day risk cost. For the AC power flow constraints of the multi-scenario energy storage planning model for typical days and extreme days, a linear programming AC power flow approximation model is used to linearize the nonlinear relationship, transforming the original nonlinear programming problem into a mixed integer linear programming problem, and jointly optimizing the energy storage site selection, capacity configuration and operation strategy. A price clearing model based on a security-constrained economic dispatch model is constructed. Under the premise of satisfying unit output constraints, grid security constraints, and reserve constraints, the marginal price trajectory of each node and each time period is calculated. By simultaneously solving the electricity price clearing model and the multi-scenario energy storage planning model for typical and extreme days, the optimal energy storage configuration results under the coupled conditions of extreme weather and the electricity price market are obtained, specifically: Given the current energy storage site selection and capacity configuration scheme, the marginal electricity price trajectory of each node is calculated by calling the electricity price clearing model; Using the updated marginal electricity price trajectory as the electricity price signal, the energy storage planning model for typical days and extreme days is re-solved to obtain a new energy storage site selection and capacity configuration scheme. Whether convergence is determined by whether the change in the electricity price trajectory and the change in the energy storage investment variable between two adjacent iterations are less than a preset threshold. If the convergence condition is not met, the iteration continues until convergence is achieved, and finally the energy storage investment scheme and its corresponding node marginal electricity price trajectory are obtained.

2. The method for optimal configuration of energy storage for extreme weather, as claimed in claim 1, wherein, An extreme weather load shedding risk assessment index is constructed to quantify the impact of extreme weather on power system security and power supply reliability. Specifically: ； wherein, is an extreme weather load loss risk assessment index; is the unit load shedding cost of node n, is the load shedding power of scenario k at node n in period t.

3. The energy storage optimization configuration method for extreme weather as described in claim 1, characterized in that, The constraints of the typical day-extreme day multi-scenario energy storage planning model include investment constraints, typical day constraints, and extreme day constraints; the investment constraints include the energy storage capacity and charging / discharging power constraints of the energy storage plan and the total number of energy storage sites allowed to be built by the power system. The typical daily constraint is that, under the premise of satisfying the power system operation constraint model, the load shedding amount of each node in each time period is zero. The extreme day constraint allows each node to experience a load shedding amount within a set range under extreme day scenarios, provided that the power system operation constraint model is satisfied.

4. The energy storage optimization configuration method for extreme weather as described in claim 1, characterized in that, For the AC power flow constraints of the multi-scenario energy storage planning model for typical and extreme days, a linear programming AC power flow approximation model is used to linearize the nonlinear relationships, transforming the original nonlinear programming problem into a mixed-integer linear programming problem, specifically: For the voltage amplitude and phase angle variables at the two ends of each line, the nonlinear relationship between active and reactive power flow and node voltage deviation and phase angle difference is transformed into a linear relationship. By replacing the original AC power flow constraints with linear programming power flow constraints, the typical day-extreme day multi-scenario energy storage planning model is transformed into a mixed integer linear programming problem containing only linear equality or inequality constraints and integer decision variables.

5. The energy storage optimization configuration method for extreme weather as described in claim 1, characterized in that, A price clearing model based on a security-constrained economic dispatch model is constructed, specifically as follows: Conventional thermal power units are described using piecewise linear cost curves. The output of new energy sources is divided into the market entry portion that participates in the spot market clearing and the non-market entry portion modeled according to fixed output. For the market entry portion, a penalty cost for curtailment of wind and solar power is set in the objective function, and for the non-market entry portion, it participates in power balance as a fixed injection. With the goal of minimizing the sum of the bidding costs of each generator unit and new energy power station and the load shedding penalty cost, a power clearing model based on a safety-constrained economic dispatch model is constructed.

6. The energy storage optimization configuration method for extreme weather as described in claim 1, characterized in that, The Anderson acceleration strategy is introduced. In each iteration, the residuals of several iterations and the corresponding energy storage investment schemes are recorded. A weighted candidate solution is constructed using a linear combination of the residuals, and the weighted candidate solution is corrected and updated once to obtain a new energy storage investment scheme and the corresponding node marginal electricity price trajectory.

7. An energy storage optimization configuration system for extreme weather, characterized in that, include: The extreme weather load risk assessment index construction module is configured to: consider the operating characteristics of the power system under extreme weather conditions, and construct an extreme weather load loss risk assessment index to quantify the impact of extreme weather on the safety and reliability of power supply. The energy storage optimization configuration modeling module is configured to: construct an objective function with the goal of minimizing the total cost of the power system, based on a comprehensive consideration of energy storage investment costs, typical day operating costs, and extreme day risk costs; and establish a multi-scenario energy storage planning model for typical days and extreme days; wherein, the results of extreme weather risk measurement are incorporated into the extreme day risk cost. The energy storage optimization configuration solution module is configured to: linearize the nonlinear relationship of the AC power flow constraints of the energy storage planning model for typical days to extreme days using a linear programming AC power flow approximation model, transform the original nonlinear programming problem into a mixed integer linear programming problem, and jointly optimize the energy storage site selection, capacity configuration and operation strategy; The electricity price clearing module is configured to: construct an electricity price clearing model based on a security-constrained economic dispatch model, and calculate the marginal electricity price trajectory for each node and each time period under the premise of satisfying unit output constraints, grid security constraints and reserve constraints; The module for simultaneously solving energy storage planning and electricity price clearing is configured to: solve the electricity price clearing model and the multi-scenario energy storage planning model for typical days and extreme days simultaneously, to obtain the optimal energy storage configuration results under the coupled conditions of extreme weather and the electricity price market, specifically: Given the current energy storage site selection and capacity configuration scheme, the marginal electricity price trajectory of each node is calculated by calling the electricity price clearing model; Using the updated marginal electricity price trajectory as the electricity price signal, the energy storage planning model for typical days and extreme days is re-solved to obtain a new energy storage site selection and capacity configuration scheme. Whether convergence is determined by whether the change in the electricity price trajectory and the change in the energy storage investment variable between two adjacent iterations are less than a preset threshold. If the convergence condition is not met, the iteration continues until convergence is achieved, and finally the energy storage investment scheme and its corresponding node marginal electricity price trajectory are obtained.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

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

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