Electrochemical energy storage robust configuration method and system for supporting new energy base delivery

Abstract

The invention relates to an electrochemical energy storage robust configuration method and system for supporting new energy base delivery, and belongs to the technical field of power system planning. The method comprises the following steps: constructing an energy storage construction cost model based on an energy storage power upper limit and an energy storage capacity upper limit; constructing a power grid operation cost model based on the thermal power generating unit output, the thermal power generating unit carbon emission, the predicted new energy output and the actual new energy output; constructing a direct current delivery channel model based on the output of the thermal power generating unit, the actual new energy output, the energy storage discharge power and the energy storage charge power; an electrochemical energy storage robust configuration model supporting new energy base delivery is constructed based on the model, and a Nash bargaining game model and B-Camp are combined; and a CG algorithm is used to solve the optimal solution of the objective function of the robust configuration model, and an energy storage investment and construction scheme and an operation scheme are obtained. According to the method and system, the utilization efficiency of new energy power generation can be effectively improved, the economical efficiency of flexible resource allocation is improved, and carbon emission is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning, and in particular relates to a robust configuration method for electrochemical energy storage supporting the transmission of new energy bases. Background Art

[0002] With the construction of large-scale new energy bases focusing on "Shagohuang", the energy outflow from the bases has brought new challenges to the safe and stable operation of the power system. This is because the power balance is more difficult due to the random volatility of new energy output over multiple time scales and the lack of flexible adjustment resources, which makes it difficult to stably deliver new energy.

[0003] Energy storage has the advantages of flexible regulation and rapid response. It is an important support for the safe and stable operation of the power system and a key link in building a new power system. The construction of the Shagohuang wind, photovoltaic and storage integrated project is an important measure to accelerate the green transformation of energy development methods and the construction of a new energy system. How to reasonably set the planning and construction of energy storage under typical operating scenarios with a high proportion of new energy according to the conventional energy scale of the power grid is a key issue that needs to be solved urgently. Summary of the invention

[0004] In view of the above analysis, the present invention aims to provide a robust configuration method for electrochemical energy storage to support the transmission of new energy bases. With planning and operation as the basic ideas, the energy storage investment and construction plan and operation plan are solved by considering the maximization of the utilization rate of the DC transmission channel of the new energy base, thereby improving the utilization efficiency of new energy power generation.

[0005] On the one hand, the present invention provides a robust configuration method for electrochemical energy storage supporting the transmission of new energy bases, which specifically includes the following steps:

[0006] The energy storage construction cost model is constructed based on the energy storage power upper limit and energy storage capacity upper limit; the grid operation cost model is constructed based on the output of thermal power units, carbon emissions of thermal power units, uncertain output of new energy and actual output of new energy; the DC transmission channel model is constructed based on the output of thermal power units, actual output of new energy, energy storage discharge power and energy storage charging power;

[0007] Based on the energy storage construction cost model, the grid operation cost model and the DC transmission channel model, an energy storage robust optimization configuration model is constructed. The objective function of the energy storage robust optimization configuration model is constructed with the goal of minimizing the total cost of energy storage construction cost and grid operation cost and minimizing the remaining capacity of the DC transmission channel.

[0008] Based on the flexibility constraints of DC transmission channels, node balance constraints, thermal power operation constraints, energy storage planning constraints and energy storage operation constraints, the optimal solution of the objective function is sought to obtain the energy storage investment and construction plan and operation plan.

[0009] Furthermore, the flexibility constraints of the DC transmission channel include upper and lower limit constraints on the transmission power of the DC transmission channel, power constraints of the DC transmission channel, flexible power adjustment rate constraints of the DC transmission channel, flexible adjustment times of the DC transmission channel throughout the day, transmission power operation time constraints and adjacent time period adjustment constraints.

[0010] Furthermore, the power constraint of the DC transmission channel includes limiting the annual utilization hours of the DC transmission channel based on the DC transmission power and the upper limit of the DC transmission power, which is expressed as:

[0011]

[0012] Among them, t represents the time period, and T is the total number of time periods in the whole year; is the DC transmission power in period t; represents the upper limit of DC transmission power in period t; γ represents the minimum utilization hours of the transmission channel throughout the year.

[0013] Furthermore, the power flexible adjustment rate constraints of the DC transmission channel include:

[0014]

[0015] Where t represents the time period, is the DC transmission power in period t; and Both are 0 / 1 variables, representing the upward and downward power regulation states of DC transmission in period t; Δv max The maximum value of the adjustment rate.

[0016] Furthermore, the external power operation time constraint is expressed as:

[0017]

[0018] Where t and τ represent time periods, T is the total number of time periods throughout the year, and H represents the stable duration required for DC transmission power; and Both are 0 / 1 variables, representing the upward and downward power regulation states of DC transmission in period t.

[0019] Furthermore, the adjacent time period adjustment constraint includes indicating that the adjacent time periods cannot be adjusted in the reverse direction, which is expressed as:

[0020]

[0021] in, and Both are 0 / 1 variables, representing the upward and downward power regulation states of DC transmission in period t.

[0022] Furthermore, the objective function is expressed as:

[0023]

[0024] Among them, C total is the total cost; C Inv is the energy storage construction cost; C Ope is the operating cost of the power grid system; L rate is the remaining capacity ratio of the outbound channel; π r is the proportion of typical days r in a year; t represents the time period; is the upper limit of DC transmission power during period t; is the DC transmission power in period t;

[0025]

[0026] Among them, r, t, g, n, and b are the subscripts of typical day, time period, thermal power unit, new energy power station, and energy storage respectively; c g is the operating cost coefficient of thermal power unit; c e is the carbon emission cost coefficient; c cur P is the penalty coefficient for wind and solar power abandonment; i,t,r is the output of the i-th thermal power unit; E i,t,t is the carbon emission of the i-th thermal power unit; and P n,t,t are the uncertain output of renewable energy and the actual output of renewable energy on a typical day, respectively; U is the set of renewable energy outputs in uncertain scenarios; v∈Ξ is a 0 / 1 variable; p∈Ψ is a continuous variable;

[0027] C P and C E are the cost coefficients of power and capacity of energy storage, respectively; and are the upper power limit and capacity limit of energy storage respectively; T life is the useful life of energy storage; s is the investment discount rate; and are the discharge power and charging power of energy storage respectively.

[0028] Furthermore, a typical daily operation simulation model of new energy uncertainty is established when solving the objective function. The new energy uncertainty set is expressed as:

[0029]

[0030] Among them, U is the set of new energy outputs in uncertain scenarios; Contribute to the uncertainty of new energy; Forecast new energy output for a typical day; Forecast deviation for new energy output; and As an auxiliary variable, it describes whether the output of new energy reaches the boundary of the uncertain set; Γ T Unsure budget for time.

[0031] Furthermore, based on the bargaining game model and the improved column and constraint generation algorithm BC&CG, the objective function is solved to obtain the energy storage investment and construction plan and the operation plan. The investment and construction plan includes the investment and construction quantity of energy storage, the power upper limit of energy storage, and the capacity upper limit of energy storage; the operation plan includes the charging state and discharging state of each energy storage in each period of a typical day and the charging power and discharging power of each energy storage.

[0032] On the other hand, the present invention also provides an electrochemical energy storage robust configuration system for supporting the transmission of new energy bases, comprising:

[0033] Model management module, used to build energy storage construction cost model based on energy storage power upper limit and energy storage capacity upper limit; build power grid operation cost model based on thermal power unit output, thermal power unit carbon emissions, new energy uncertain output and actual new energy output; build DC transmission channel model based on thermal power unit output, actual new energy output, energy storage discharge power and energy storage charging power;

[0034] An objective function construction module is used to construct an objective function with the goal of minimizing the total cost of energy storage construction cost and grid operation cost and minimizing the remaining capacity of the DC transmission channel;

[0035] The model solving module is used to find the optimal solution to the objective function based on the flexibility constraints of the DC transmission channel, node balance constraints, thermal power operation constraints and energy storage operation constraints, and obtain the energy storage investment and construction plan and operation plan.

[0036] The present invention can achieve at least one of the following beneficial effects:

[0037] By maximizing the utilization rate of the DC transmission channel of the new energy base, that is, minimizing the remaining capacity of the DC transmission channel, and minimizing the total cost of energy storage construction cost and grid operation cost, a robust model considering the uncertainty of new energy is constructed, and the energy storage investment and construction plan and operation plan are solved. Through reasonable planning of energy storage construction and operation, support is provided for the safe and stable operation of the power grid system, while further improving the utilization rate of the base's new energy.

[0038] By utilizing the strategic interactions in game theory to guide the iterative process of the C&CG algorithm, in each iteration, participants can adjust their strategies based on the behavior of each other to achieve better solutions, thereby improving the efficiency and effectiveness of the solution.

[0039] Other features and advantages of the present invention will be described in the following description, and some advantages may become apparent from the description, or may be understood by practicing the present invention. The purpose and other advantages of the present invention may be realized and obtained through the contents particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0041] Figure 1 is a flow chart of the method of the present invention;

[0042] Figure 2 It is the initial DC transmission curve after configuring the new energy capacity and thermal power installed capacity;

[0043] Figure 3 The typical daily operation results of different examples in the second embodiment of the method;

[0044] Figure 4 This is a diagram showing the output of each energy source under different new energy prediction errors in method embodiment 2;

[0045] Figure 5 This is the new energy consumption situation under different energy storage configuration capacities of method embodiment 3. DETAILED DESCRIPTION

[0046] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0047] Method Example 1

[0048] A specific embodiment of the present invention discloses a robust configuration method for electrochemical energy storage supporting the transmission of new energy bases, which specifically includes the following steps:

[0049] Step S01, constructing an energy storage construction cost model based on the energy storage power upper limit and the energy storage capacity upper limit; constructing a power grid operation cost model based on the output of thermal power units, the carbon emissions of thermal power units, the uncertain output of new energy and the actual new energy output; constructing a DC transmission channel model based on the output of thermal power units, the actual new energy output, the energy storage discharge power and the energy storage charging power;

[0050] Step S02: constructing an energy storage robust optimization configuration model based on the energy storage construction cost model, the power grid operation cost model and the DC transmission channel model, and constructing an objective function of the energy storage robust optimization configuration model with the goal of minimizing the total cost of energy storage construction cost and power grid operation cost and minimizing the remaining capacity of the DC transmission channel;

[0051] Step S03, based on the flexibility constraints of the DC transmission channel, the node balance constraints, the thermal power operation constraints, the planned energy storage constraints and the energy storage operation constraints, the optimal solution of the objective function is obtained to obtain the energy storage investment and construction plan and the operation plan; wherein, the flexibility constraints of the DC transmission channel include the upper and lower limit constraints of the DC transmission channel transmission power, the power constraints of the DC transmission channel, the power flexible adjustment rate constraints of the DC transmission channel, the number of flexible adjustments of the DC transmission channel throughout the day, the transmission power operation time constraints and the adjacent time period adjustment constraints.

[0052] In this embodiment, by maximizing the utilization rate of the DC transmission channel of the new energy base, that is, minimizing the remaining capacity of the DC transmission channel, and minimizing the total cost of energy storage construction cost and grid operation cost, a robust optimization configuration model for electrochemical energy storage that supports the transmission of new energy bases considering the uncertainty of new energy is constructed, and the energy storage investment and construction plan and operation plan are solved. Through reasonable planning of energy storage construction and operation, support is provided for the safe and stable operation of the power grid system, while further improving the utilization rate of the base's new energy.

[0053] Specifically, in step S01, the energy storage construction cost model is constructed based on the energy storage power upper limit and the energy storage capacity upper limit and is expressed as:

[0054]

[0055] Among them, C Inv is the energy storage construction cost; C P and C E are the cost coefficients of the power and capacity of energy storage, respectively; b is the subscript representing energy storage; and are the upper limit of energy storage power and energy storage capacity respectively; T life is the useful life of energy storage; s is the investment discount rate.

[0056] Specifically, in step S01, a power grid operation cost model is constructed based on the output of thermal power units, carbon emissions of thermal power units, uncertain output of new energy, and actual output of new energy, and is expressed as:

[0057]

[0058] Among them, C Ope is the grid operation cost; U is the set of renewable energy output in uncertain scenarios; v∈Ξ is a 0 / 1 variable, Ξ is a set of 0 / 1 variables; p∈Ψ is a continuous variable, Ψ is a set of continuous variables; r, t, g, n are the subscripts of typical days, time periods, thermal power units and renewable energy power stations respectively; π r is the proportion of typical days r in a year; c g is the operating cost coefficient of thermal power unit; ce is the carbon emission cost coefficient; c cur P is the penalty coefficient for wind and solar power abandonment; i,t,r is the output of the ith thermal power unit on a typical day; E i,t,t is the carbon emissions of the ith thermal power unit on a typical day; and P n,t,t They are the uncertain output of renewable energy and the actual output of renewable energy on a typical day respectively.

[0059] Specifically, in step S01, a DC transmission channel model is constructed based on the output of thermal power units, actual new energy output, energy storage discharge power, and energy storage charging power, and is expressed as:

[0060]

[0061] in, is the DC transmission power in period t; and are the discharge power and charging power of energy storage respectively.

[0062] Specifically, in step S02, an objective function is constructed with the goal of minimizing the total cost of energy storage construction cost and grid operation cost and minimizing the remaining capacity of the DC transmission channel, which is expressed as:

[0063]

[0064] Among them, C total is the total cost; L rate is the remaining capacity ratio of the delivery channel; is the upper limit of DC transmission power in period t.

[0065] Specifically, in step S03, the flexibility constraints of the DC transmission channel include upper and lower limit constraints on the transmission power of the DC transmission channel, power constraints of the DC transmission channel, power flexible adjustment rate constraints of the DC transmission channel, flexible adjustment times of the DC transmission channel throughout the day, transmission power operation time constraints and adjacent time period adjustment constraints.

[0066] Furthermore, the upper and lower limits of the DC transmission power are expressed as:

[0067]

[0068] in is the lower limit of DC transmission power in period t.

[0069] Furthermore, the power constraints of the DC transmission channel include limiting the annual utilization hours of the DC transmission channel based on the DC transmission power and the upper limit of the DC transmission power, which can be expressed as:

[0070]

[0071] Among them, t represents the time period, and T is the total number of time periods in the whole year; is the DC transmission power in period t; represents the upper limit of DC transmission power in period t; γ represents the minimum utilization hours of the transmission channel throughout the year.

[0072] Furthermore, the power flexible adjustment rate constraints of the DC transmission channel include:

[0073]

[0074] Where t represents the time period, is the DC transmission power in period t; and are all 0 / 1 variables, representing the upward and downward power regulation states of DC transmission in period t (1 means regulation occurs, 0 means no regulation occurs); Δv max The maximum value of the adjustment rate.

[0075] Furthermore, the constraint on the number of flexible adjustments of the DC transmission channel throughout the day is expressed as:

[0076]

[0077] Among them, X max Indicates the maximum number of external DC regulation times.

[0078] Furthermore, the external power operation time constraint is expressed as:

[0079]

[0080] Where t and τ represent time periods, T is the total number of time periods throughout the year, and H represents the stable duration required for the DC transmission power.

[0081] Furthermore, the adjacent time period adjustment constraint includes indicating that the adjacent time periods cannot be adjusted in the opposite direction, which is expressed as:

[0082]

[0083] Specifically, in step S03, the node balance constraint is expressed as:

[0084]

[0085] Among them, P g,t,r Output for thermal power units; P n,t,r Contribute to practical new energy; are the discharge power and charging power of energy storage respectively; It is the DC output power.

[0086] Specifically, in step S03, the thermal power operation constraints include upper and lower output limits, unit start and stop time constraints, start and stop cost constraints, and unit ramp constraints.

[0087] The output upper and lower limit constraints are expressed as:

[0088]

[0089] Among them, u g,t,r The operating status of the thermal power unit is a 0 / 1 variable, 1 means running, 0 means not running; and They are the upper and lower limits of the output of thermal power units respectively.

[0090] The start and stop time constraints of the unit are expressed as:

[0091]

[0092] in, and are the continuous start and stop time of thermal power units; T on,g and T off,g They are the minimum continuous start / stop time of thermal power units respectively.

[0093] The startup and shutdown cost constraint is expressed as:

[0094]

[0095] in, and They are start-up and shutdown costs respectively; and are the start-up and shutdown cost coefficients respectively.

[0096] The unit climbing constraint is expressed as:

[0097] -r i down Δt≤P g,t,r (t)-P g,t,r (t-1)≤r i up Δt;

[0098] Among them, r i down and r i up They are the up and down climbing rates of the unit respectively.

[0099] Specifically, in step S03, the energy storage planning constraints include constraints on the planned construction quantity of energy storage, and constraints on the upper limit of energy storage power and capacity.

[0100] Specifically, in step S03, the energy storage operation constraints include energy storage charging and discharging power constraints, energy storage charging and discharging state constraints, energy storage power change constraints in adjacent time periods, and energy storage equipment operation power constraints.

[0101] The energy storage charging and discharging power constraint is expressed as:

[0102]

[0103] in,

[0104] They are 0 / 1 variables for the energy storage discharge / charge working state, 1 means it is in working state, and 0 means it is not in working state.

[0105] The energy storage charging and discharging state constraints are expressed as:

[0106]

[0107] The energy storage capacity change constraint in adjacent time periods is expressed as:

[0108]

[0109] Among them, e b,t,r The amount of electricity stored for energy storage; η c and η d are the charging and discharging power of energy storage respectively.

[0110] The power constraint of energy storage equipment operation is expressed as:

[0111]

[0112] in, is the lower limit of energy storage capacity; Δe b,t,r is the change in the amount of electricity provided by the energy storage; T is the total number of time periods, and t0 is the initial time period.

[0113] Specifically, in step S03, a typical daily operation simulation model of new energy uncertainty is established as a new energy output uncertainty constraint when solving the objective function. The new energy uncertainty set is expressed as:

[0114]

[0115] Among them, U is the set of new energy outputs in uncertain scenarios; Contribute to the uncertainty of new energy; Forecast new energy output for a typical day; Forecast deviation for new energy output; and As an auxiliary variable, it describes whether the output of new energy reaches the boundary of the uncertain set. It is a 0 / 1 variable, 1 means yes, 0 means no; ΓT Unsure budget for time.

[0116] Specifically, in step S03, the objective function is solved based on the Nash bargaining game model and the improved column and constraint generation algorithm BC&CG.

[0117] Specifically, the objective function is a dual objective function, and the problem of the dual objective function is converted into a Nash bargaining game model, and is solved by a parameterized KKT condition method.

[0118] Specifically, the objective function is piecewise linearized, and the optimization problem of the objective function can be expressed as the following dual-objective linear programming without loss of generality:

[0119]

[0120] Among them, J, K, j, k, c1, c2 are constant coefficient matrices and vectors respectively, and x is a continuous variable. According to the Nash bargaining game model, the two objectives in formula (1), "total cost" and "DC transmission channel utilization rate", can be regarded as two virtual players, which jointly determine a bargaining solution through negotiation to solve the optimization configuration problem. Formula (1) has a unique Nash bargaining solution that conforms to the Nash axiom.

[0121] Furthermore, the Nash bargaining solution of equation (1) is defined by the following optimization problem:

[0122]

[0123] Among them, the set S represents the Pareto frontier of equation (2), represents the no-protocol utility. Consider the following parameterized linear program:

[0124]

[0125] Among them, λ is the decision variable, and So The possible values ​​are In addition, the optimal values ​​of the two single objectives are and

[0126] It is difficult to solve the optimization problem (3) directly, because the Pareto frontier of equation (2) is difficult to express in the form of explicit constraints. Therefore, the parameterized KKT condition is used to describe the Pareto efficient set S.

[0127] The KKT conditions (Karush-Kuhn-Tucker Conditions) of problem (3) are:

[0128] λc1+(1-λ)c2+J T η+K T ξ=0 (4)

[0129] Kx=k (5)

[0130] 0≤j-Jx⊥η≥0 (6)

[0131] Among them, η and ξ are dual variables; j-Jx⊥η means η T (j-Jx)=0, that is, at most one of η and j-Jx is strictly greater than 0.

[0132] For each constraint in KKT regulation (6), a Boolean variable z is introduced. i , further linearize KKT condition (6):

[0133]

[0134] Where M is a very large constant; z represents a 0 / 1 variable; z i represents the set of 0 / 1 variables introduced; i represents the number of constraints.

[0135] Therefore, S can be characterized as the projection of KKT conditions (4), (5), and (6) onto the x subspace:

[0136]

[0137] Therefore, the bargaining problem (2) becomes the following mixed integer nonlinear programming (MINLP):

[0138]

[0139] In MINLP (9), λ' is also a decision variable. In the sense of Nash bargaining solution, solving MINLP (9) naturally obtains an optimal solution for coordinating total cost and DC transmission channel utilization.

[0140] Furthermore, according to the Nash axiom, problem (2) has a unique solution, so (9) also has a unique solution. However, in addition to the Nash product, the method of transforming the Pareto efficient solution set using parameterized KKT conditions can be extended to other optimization objectives, that is, other secondary objectives can be selected for the optimization problem (9) to select reasonable solutions from the Pareto efficient solutions.

[0141] Furthermore, the optimization problem (9) is divided into an upper layer and a lower layer for iterative solution. The upper layer plans the installed capacity of new energy, and the lower layer simulates the operation of the system considering the uncertainty of new energy in a typical day, where the uncertainty is characterized by a robust optimization method. For ease of analysis, it is expressed in matrix form:

[0142]

[0143] Among them, x is a 0 / 1 variable in the basic scenario, p and y are vectors of continuous variables, y is a vector of planned capacity variables, and p is a vector of operating variables; v is a 0 / 1 variable in the uncertain scenario, p u is the vector of continuous variable conventional units and energy storage under uncertain scenarios; is the uncertain output vector of the new energy source; A, B, D, E, H, F, G, R, a, b, c, g, h are constant coefficient matrices and vectors respectively. The specific algorithm solution process includes step C1 and step C2.

[0144] Step C1, outer C&CG iteration, decompose the problem into a main problem and sub-problems. The goal of the main problem is to minimize the total cost. The constraints include energy storage planning constraints, various operation constraints under basic scenarios, and C&CG cutting plane constraints returned in the sub-problems, as shown below:

[0145]

[0146] The x obtained from the main problem iter 、p iter (where the subscript iter indicates the number of iterations) is substituted into the subproblem to solve the subproblem with the goal of minimizing power imbalance under uncertain scenarios. This problem can be described as a max-min problem, where s is the slack variable introduced, representing the vector of safety violations to ensure that the subproblem has a feasible solution, as shown below:

[0147]

[0148] Among them, f and M are parameter matrices or vectors respectively;

[0149] By calling the inner C&CG algorithm to solve, we can get the target value ψ and the worst scenario of new energy output. If ψ>0, we can obtain Add new variables iter+1 , v iter+1 , generate the C&CG cutting plane as follows:

[0150]

[0151] Step C2: Inner C&CG iteration, use v(inner) (inner represents the number of inner C&CG iterations, v (inner) Represents the initial assignment of 0 / 1 variables in the subproblem) to replace the 0 / 1 variables in the subproblem, and then obtain the equivalent dual model of the subproblem, where the vector of dual variables is η. As shown below:

[0152]

[0153] Among them, i inner is the number of inner layer iterations; η μ is the inner dual variable; v μ It is an inner 0 / 1 variable.

[0154] Solving the above equation, we get the inner optimal solution κ (safety violation in the worst case scenario of the inner subproblem) and Update UB = min{UB,κ}. Based on the obtained Substitute the above formula to get the optimal value of v. Update LB = max{LB,f T s}.

[0155]

[0156] If (UB-LB) / UB<ε(ε=0.01), ψ and Return the outer C&CG iteration; otherwise, set i inner =i inner +1, and move to outer C&CG iteration.

[0157] After the above-mentioned solving process, the optimal solution of the objective function is finally solved, including the energy storage investment and construction plan and the energy storage operation plan. The investment and construction plan includes the investment and construction quantity of energy storage, the power upper limit of energy storage, and the capacity upper limit of energy storage; the operation plan includes the charging state and discharging state of each energy storage in each period of a typical day, as well as the charging power and discharging power of each energy storage.

[0158] In this embodiment, by maximizing the utilization rate of the DC transmission channel of the new energy base, that is, minimizing the remaining capacity of the DC transmission channel, and minimizing the total cost of energy storage construction cost and grid operation cost, a robust model considering the uncertainty of new energy is constructed, and the energy storage investment and construction plan and operation plan are solved. Through reasonable planning of energy storage construction and operation, support is provided for the safe and stable operation of the power grid system, and at the same time, the utilization rate of the base's new energy is further improved.

[0159] By utilizing the strategic interactions in game theory to guide the iterative process of the C&CG algorithm, in each iteration, participants (sub-problem solvers in the algorithm) can adjust their strategies according to the other party's behavior (constraints and variable updates in the main problem) to achieve better solutions, thereby improving the efficiency and effectiveness of the solution.

[0160] Method Example 2

[0161] Another specific embodiment of the present invention discloses simulation calculation results of a robust configuration method for electrochemical energy storage to support the transmission of new energy bases.

[0162] Specifically, the “Shagohuang” new energy base in a certain northwestern region is taken as an example for analysis, and the wind and solar data and load forecast data of typical days in twelve months are selected for calculation.

[0163] Table 1 shows the parameters of thermal power units:

[0164] Table 1 Thermal power unit parameters

[0165]

[0166] Furthermore, six scenarios are set up for comparative analysis, as shown in Table 2.

[0167] Table 2 Example comparison settings

[0168]

[0169] Figure 2 It is the initial DC power transmission curve after configuring the new energy capacity and thermal power installed capacity.

[0170] Furthermore, other parameters are set as follows: the prediction error of new energy is 10%, and the penalty for abandoning wind and solar power is 100 yuan / MWh. The installed cost of wind power is 4 million yuan / MW, and the installed cost of photovoltaic power is 3.5 million yuan / MW; the unit cost of carbon emissions is 30 yuan / ton; the energy storage charging time is 2 hours, the charging efficiency is 0.95, and the discharge efficiency is 0.95; the investment and construction cost of energy storage is power cost 15 (10,000 yuan / MW) / construction cost 7.5 (10,000 yuan / MWh), and the life span is 15 years; the energy storage capacity needs to ensure continuous discharge for 2 hours; the life span of the energy storage unit is set to 20 years, and the discount rate is 0.8.

[0171] Furthermore, for 6 examples, the optimal configuration of energy storage under different coal-fired power peak-shaving depths and the optimal configuration of energy storage under different new energy uncertainties are analyzed respectively.

[0172] (1) Analysis of optimal energy storage configuration under different coal-fired power peak regulation depths

[0173] Specifically, in order to analyze the impact of different coal-fired power peak-shaving depths on the configured energy storage capacity, the peak-shaving depth of coal-fired power is changed to analyze the configured energy storage capacity in scenarios with and without coal-fired power peak-shaving depth. Table 3 shows the capacity of the newly configured energy storage under different coal-fired power peak-shaving depths, including Examples 1 to 4.

[0174] Table 3 Energy storage configuration capacity under different coal-fired power peak regulation depths

[0175]

[0176]

[0177] Attached Figure 3 The typical daily operation results of different examples are shown in the attached figure. Figure 3 It can be seen that:

[0178] ① By comparing Example 1 and Example 2, it can be found that when no energy storage is configured, wind power will be abandoned at night, solar power will be abandoned at noon, and power shortages will occur during the evening peak; after energy storage is configured, wind power at night and photovoltaic power at noon are basically absorbed, the utilization rate of new energy is thereby improved, and sufficient supply is ensured during the evening peak period, the system's peak supply capacity is improved, and carbon emissions are reduced.

[0179] ② By comparing Example 2 and Example 4, it can be found that after coal-fired power has the ability of deep regulation, the scale of energy storage configuration is reduced, but from the results, the amount of wind and solar power abandoned by coal-fired power with 20% deep regulation is roughly the same as that of coal-fired power with 30% deep regulation. Therefore, after coal-fired power has the ability of deep regulation, the scale of energy storage required can be reduced and the utilization rate of energy storage can be improved.

[0180] (2) Analysis of energy storage configuration capacity under different new energy uncertainties

[0181] Table 4 gives the energy storage configuration results under different wind-solar ratios:

[0182] Table 4 Energy storage optimization configuration results under different new energy prediction errors

[0183]

[0184] It can be seen from Table 3 and Table 4 that: comparing the results of Example 2, Example 5 and Example 6, with the increase of new energy prediction error, that is, the probability of the emergence of unfavorable new energy scenarios, in order to meet a certain new energy consumption rate and electricity share as well as peak supply capacity, the energy storage scale required by the channel increases. The robust configuration method proposed in this application can find the most unfavorable new energy processing scenario for new energy consumption and system supply peak.

[0185] Attached Figure 4 The output of each energy source under different prediction errors of new energy sources is shown. By comparing the three pictures, it can be seen that as the prediction error of new energy sources increases, the amount of wind and solar power abandoned at noon increases, and thus the required energy storage scale also increases. Therefore, it can be concluded that the energy storage scale is mainly determined by the high output period of new energy sources at noon in spring and autumn.

[0186] Method Example 3

[0187] Another specific embodiment of the present invention discloses simulation calculation results of a robust configuration method for electrochemical energy storage to support the transmission of new energy bases.

[0188] Specifically, in order to analyze the impact of energy storage configuration scale on the ratio of coal-fired power generation and new energy installed capacity, as well as the operating economy, safety and new energy utilization rate, examples considering different factors are set up in Table 5 for comparative analysis.

[0189] Table 5 Example comparison settings

[0190]

[0191] Table 6 Ratio of thermal power generation units and renewable energy generation capacity and system cost results

[0192]

[0193] Specifically, the consumption of new energy under different energy storage configuration capacities is shown in the attached Figure 5 As shown:

[0194] Case 1 is equipped with 4 million kW of coal-fired power and no energy storage. The results show that wind and solar power abandonment is serious, the new energy consumption rate is low, about 90%, and there is a shortage of electricity.

[0195] Example 2 is equipped with 1 million kW of energy storage. Compared with the results of Example 1, it can be seen that the total installed capacity of new energy is reduced, the phenomenon of wind and solar power abandonment is significantly reduced, the utilization rate of new energy is roughly around 96.5%, the phenomenon of power shortage is reduced, and the total investment cost and operating cost are slightly reduced;

[0196] Example 3 is equipped with 2 million kW of energy storage, which is slightly lower than the installed capacity of new energy in Example 2. Almost all wind power and photovoltaic power are absorbed, roughly around 99.1%, and the power shortage is also roughly zero, further improving the safety of the system and the power supply.

[0197] Through comparative analysis, it is concluded that as the scale of energy storage increases, the scale of new energy required to meet the proportion of new energy electricity in the channel decreases, and the reasonable ratio of thermal power units and new energy increases accordingly, with a maximum of about 0.42.

[0198] Example 4 is equipped with 2 million kW of coal-fired power and no energy storage. Compared with the typical summer day in Example 1, in order to meet the power demand, the installed capacity of new energy sources needs to increase by 20%. The power shortage is more serious, and there is a certain amount of abandoned light, which leads to a decrease in the utilization rate of new energy and a significant increase in investment and operating costs.

[0199] Example 5 is configured with 1 million kW. Compared with Example 4, the installed capacity of new energy is increased by about 9%, the power shortage phenomenon is reduced, and the phenomenon of abandoned light is also reduced. Due to the reduction of thermal power output, the operating cost and carbon emissions are reduced, but the total cost is increased;

[0200] In Example 6, the energy storage capacity is 2 million kW. Compared with Example 5, the total installed capacity of new energy remains roughly the same, and there is still power shortage in winter. Although the utilization rate of new energy has increased, the total investment cost has increased. Compared with Example 3, the total investment cost has increased by about 29%, the power shortage is serious, and the utilization rate of new energy is low. In order to ensure supply, the proportion of new energy electricity has increased by 21.2%.

[0201] Comparative analysis shows that thermal power is indispensable for ensuring the power supply of the channel; under different energy storage scales, in order to meet the requirements of new energy utilization, the maximum reasonable ratio of thermal power units to new energy is about 0.15, and the role of energy storage in peak shaving and valley filling and power peak is more obvious.

[0202] System Example

[0203] A robust configuration system for electrochemical energy storage supporting the delivery of new energy bases, characterized by comprising a model management module, an objective function construction module and a model solving module:

[0204] Model management module, used to build energy storage construction cost model based on energy storage power upper limit and energy storage capacity upper limit; build power grid operation cost model based on thermal power unit output, thermal power unit carbon emissions, new energy uncertain output and actual new energy output; build DC transmission channel model based on thermal power unit output, actual new energy output, energy storage discharge power and energy storage charging power;

[0205] An objective function construction module is used to construct an objective function with the goal of minimizing the total cost of energy storage construction cost and grid operation cost and minimizing the remaining capacity of the DC transmission channel;

[0206] The model solving module is used to find the optimal solution to the objective function based on the flexibility constraints of the DC transmission channel, node balance constraints, thermal power operation constraints and energy storage operation constraints, and obtain the energy storage investment and construction plan and operation plan.

[0207] Compared with the prior art, the beneficial effects of the robust configuration system for electrochemical energy storage supporting the transmission of new energy bases provided in this embodiment are basically the same as the beneficial effects provided in method embodiment 1, and are not elaborated here.

[0208] It should be noted that the above embodiments are based on the same inventive concept and parts not described repeatedly can be used as reference for each other.

[0209] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A robust configuration method for electrochemical energy storage supporting the delivery of new energy bases, characterized in that: The steps include: The energy storage construction cost model is constructed based on the energy storage power upper limit and energy storage capacity upper limit; the grid operation cost model is constructed based on the output of thermal power units, carbon emissions of thermal power units, uncertain output of new energy and actual output of new energy; the DC transmission channel model is constructed based on the output of thermal power units, actual output of new energy, energy storage discharge power and energy storage charging power; Based on the energy storage construction cost model, the grid operation cost model and the DC transmission channel model, an energy storage robust optimization configuration model is constructed. The objective function of the energy storage robust optimization configuration model is constructed with the goal of minimizing the total cost of energy storage construction cost and grid operation cost and minimizing the remaining capacity of the DC transmission channel. Based on the flexibility constraints of DC transmission channels, node balance constraints, thermal power operation constraints, energy storage planning constraints and energy storage operation constraints, the optimal solution of the objective function is sought to obtain the energy storage investment and construction plan and operation plan.

2. The configuration method according to claim 1, characterized in that: The flexibility constraints of the DC transmission channel include upper and lower limit constraints on the transmission power of the DC transmission channel, power constraints of the DC transmission channel, power flexible adjustment rate constraints of the DC transmission channel, flexible adjustment times of the DC transmission channel throughout the day, transmission power operation time constraints and adjacent time period adjustment constraints.

3. The configuration method according to claim 2, characterized in that: The power constraints of the DC transmission channel include limiting the annual utilization hours of the DC transmission channel based on the DC transmission power and the upper limit of the DC transmission power, which can be expressed as: Among them, t represents the time period, and T is the total number of time periods in the whole year; is the DC transmission power in period t; represents the upper limit of DC transmission power in period t; γ represents the minimum utilization hours of the transmission channel throughout the year.

4. The configuration method according to claim 2, characterized in that: The power flexible adjustment rate constraints of the DC transmission channel include: Where t represents the time period, is the DC transmission power in period t; and Both are 0 / 1 variables, representing the upward and downward power regulation states of DC transmission in period t; Δv max The maximum value of the adjustment rate.

5. The configuration method according to claim 2, characterized in that: The external power operation time constraint is expressed as: Where t and τ represent time periods, T is the total number of time periods throughout the year, and H represents the stable duration required for DC transmission power; and Both are 0 / 1 variables, representing the upward and downward power regulation states of DC transmission in period t.

6. The configuration method according to claim 2, characterized in that: The adjacent time period adjustment constraint includes indicating that the adjacent time periods cannot be adjusted in the reverse direction, which is expressed as: in, and Both are 0 / 1 variables, representing the upward and downward power regulation states of DC transmission in period t.

7. The configuration method according to any one of claims 1 to 6, characterized in that: The objective function is expressed as: Among them, C total is the total cost; C Inv is the energy storage construction cost; C Ope is the operating cost of the power grid system; L rate is the remaining capacity ratio of the outbound channel; π r is the proportion of typical days r in a year; t represents the time period; is the upper limit of DC transmission power during period t; is the DC transmission power in period t; Among them, r, t, g, n, and b are the subscripts of typical day, time period, thermal power unit, new energy power station, and energy storage respectively; c g is the operating cost coefficient of thermal power unit; c e is the carbon emission cost coefficient; c cur P is the penalty coefficient for wind and solar power abandonment; i,t,r is the output of the i-th thermal power unit; E i,t,t is the carbon emission of the i-th thermal power unit; and P n,t,t are the uncertain output of renewable energy and the actual output of renewable energy on a typical day, respectively; U is the set of renewable energy outputs in uncertain scenarios; v∈Ξ is a 0 / 1 variable; p∈Ψ is a continuous variable; C P and C E are the cost coefficients of power and capacity of energy storage, respectively; and are the upper power limit and capacity limit of energy storage respectively; T life is the useful life of energy storage; s is the investment discount rate; and are the discharge power and charging power of energy storage respectively.

8. The configuration method according to claim 7, characterized in that: When solving the objective function, a typical daily operation simulation model of new energy uncertainty is established, and the new energy uncertainty set is expressed as: Among them, U is the set of new energy outputs in uncertain scenarios; Contribute to the uncertainty of new energy; Forecast new energy output for a typical day; Forecast deviation for new energy output; and As an auxiliary variable, it describes whether the output of new energy reaches the boundary of the uncertain set; Γ T Unsure budget for time.

9. The configuration method according to claim 8, characterized in that: Based on the bargaining game model and the improved column and constraint generation algorithm BC&CG, the objective function is solved to obtain the energy storage investment and construction plan and the operation plan. The investment and construction plan includes the investment and construction quantity of energy storage, the power upper limit of energy storage, and the capacity upper limit of energy storage; the operation plan includes the charging state and discharging state of each energy storage in each period of a typical day and the charging power and discharging power of each energy storage.

10. A robust configuration system for electrochemical energy storage supporting the delivery of new energy bases, characterized in that: include: Model management module, used to build energy storage construction cost model based on energy storage power upper limit and energy storage capacity upper limit; A grid operation cost model is constructed based on the output of thermal power units, carbon emissions of thermal power units, uncertain output of new energy, and actual output of new energy. A DC transmission channel model is constructed based on the output of thermal power units, actual output of new energy, energy storage discharge power, and energy storage charging power. An objective function construction module is used to construct an objective function with the goal of minimizing the total cost of energy storage construction cost and grid operation cost and minimizing the remaining capacity of the DC transmission channel; The model solving module is used to find the optimal solution to the objective function based on the flexibility constraints of the DC transmission channel, node balance constraints, thermal power operation constraints and energy storage operation constraints, and obtain the energy storage investment and construction plan and operation plan.

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