Energy storage capacity configuration method and device of'source-grid-load-storage 'power system

Abstract

The invention discloses an energy storage capacity configuration method of a'source-network-load-storage 'power system, which comprises the following steps of: quantifying a flexible bidirectional vacancy electric quantity value of the system into flexible vacancy economic equivalent cost, and constructing an energy storage capacity configuration model considering the balance of flexible supply and flexible demand of the system, the energy storage capacity configuration model takes the energy storage capacity configuration optimization of each region as a decision variable, takes the lowest investment operation cost and flexibility vacancy economic equivalent of the system as an objective function, and takes the power balance constraint of the region and the coupling constraint between the regions as constraint conditions; and solving the constructed energy storage capacity configuration model to obtain an energy storage optimal configuration scheme of each region. By adopting the method, the flexible adjustment capability of the power system is effectively improved, and the comprehensive economic benefit is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization, and in particular relates to a method for configuring energy storage capacity of a "source-grid-load-storage" power system. Background Art

[0002] As the proportion of renewable energy (such as wind and solar energy) in the power system continues to increase, the volatility and uncertainty of power supply have intensified, and the frequent start-up and shutdown of traditional thermal power units have led to a decline in the economy and stability of the system. At present, the power system is facing the challenge of the contradiction between supply and demand of flexibility. Especially in the context of the gradual enrichment of flexibility resources on the demand side, the traditional power dispatching and operation mode seems to be unable to cope with it. The introduction of energy storage technology has become a key means to solve this problem. It can effectively improve the system's flexible adjustment capabilities and alleviate the lack of flexibility.

[0003] The regulation capability of energy storage in the power system has gradually received attention, and the capacity configuration of energy storage has become an important issue to be solved under the development of a high proportion of new energy power systems. However, existing energy storage configuration technologies focus more on the investment economy of energy storage, ignoring the hidden economic costs caused by the lack of system flexibility. At the same time, only considering the operation regulation capability from the perspective of energy storage, ignoring the synergy between source, network, load and storage, thus limiting the effect of energy storage configuration. Summary of the invention

[0004] The purpose of the present invention is to solve the defects existing in the prior art, to provide a method for fully considering the economic cost of the flexibility deficiency of the power system under the synergy between the source, the network, the load and the storage, and to explore an efficient energy storage capacity configuration method to achieve a dual improvement in the economy and flexibility of the power system.

[0005] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0006] A method for configuring energy storage capacity of a "source-grid-load-storage" power system, wherein the power system has multiple regions, the source side of each region includes a thermal power unit and a new energy power generation system, the load side includes a conventional load and a transferable load, the storage side includes an existing energy storage power station and a new energy storage power station to be configured with capacity, and the grid side includes a connecting line for power interaction between the regions. The energy storage capacity configuration method of the present invention includes:

[0007] The system's bidirectional flexibility deficit electricity value is quantified as the flexibility deficit economic equivalent cost, and a storage capacity configuration model that considers the balance between the system's flexibility supply and flexibility demand is constructed. The energy storage capacity configuration model uses the optimization of the energy storage capacity configuration of each region as the decision variable, and takes the system's investment and operation cost and the minimum flexibility deficit economic equivalent as the objective function, and takes the regional power balance constraint and the inter-regional coupling constraint as the constraint conditions;

[0008] The constructed energy storage capacity configuration model is solved to obtain the optimal energy storage configuration plan for each region.

[0009] The present invention obtains the bidirectional shortfall value of system flexibility based on the flexibility quantification method, thereby establishing the flexibility shortfall economic equivalent, and fully considering the all-round flexibility requirements of "source-grid-load-storage" in each region, and constructing a multi-objective model for energy storage capacity configuration that considers the system's investment and operation costs and the flexibility shortfall economic equivalent, which effectively improves the system's flexible adjustment capabilities and improves the overall economic benefits.

[0010] Furthermore, the objective function of the above energy storage capacity configuration model is expressed as:

[0011]

[0012] Where: C is the objective function, C r,1 The economic equivalent cost of creating a flexibility deficit for region r, C r,2 is the investment and operation cost of region r, and R is the number of regions.

[0013] Furthermore, the expression of the economic equivalent cost of flexibility deficit is:

[0014]

[0015] Where: C r,1 is the economic equivalent cost of generating a two-way flexibility deficit in region r, π is the market electricity price, and c is the compensation electricity price. That is, when flexibility deficit occurs, it will be purchased from other regions at a price higher than the market electricity price π+c; and are the upward flexibility power shortage value and the downward flexibility power shortage value of the power grid at time t in region r, respectively; and They are and The corresponding weight coefficient is based on the typical daily operating cost of the system before configuring energy storage. Flexibility Upward Shortage Economic Equivalent Flexibility Downward Shortage Economic Equivalent get:

[0016]

[0017] Furthermore, the power balance constraint in each area needs to consider the power flowing in and out through the tie line, which is expressed as:

[0018]

[0019] Where: is the predicted value of the load in area r at time t, is the power of the transferable load in region r at time t, is the predicted value of renewable energy output in region r at time t, is the charging and discharging power of the energy storage power station in region r at time t, is the technical output power of thermal power units in region r;

[0020] Preferably, the expression of the inter-region coupling constraint is:

[0021] f r,r',t +f r',r,t =0 (4)

[0022] Where: f r,r',t is the power value transmitted from area r to area r' through the interconnection line at time t, f r',r,t It is the power value transmitted from area r' to area r through the interconnection line at time t.

[0023] Furthermore, the expression for the balance between the system flexibility supply and the system flexibility demand is:

[0024]

[0025] In the formula, are the upward flexibility supply and downward flexibility supply of region r at time t, and are the upward and downward flexibility supply of the transferable load in region r at time t, and They are the upward and downward flexibility supply of the energy storage power station in region r at time t, and They are the upward and downward flexibility supply of thermal power units in region r at time t, respectively; and are the upward flexibility margin and downward flexibility margin of region r, respectively; They are the upward and downward flexibility supply between regions r and r' at time t respectively.

[0026] Among them, upward flexibility shortage and downward flexibility shortfall It is generated when the margin is less than 0, and the expression is:

[0027]

[0028] Furthermore, the flexibility requirements of each side of the system "source-grid-load-storage" include the load flexibility requirements on the load side, the flexibility requirements of new energy generation on the source side, and the coordinated flexibility requirements of each side of "source-grid-load-storage", among which:

[0029] The load flexibility requirement is expressed and quantified as:

[0030]

[0031] Where: is the predicted value of load in area r at time t; is the maximum prediction error coefficient of load in region r; and are the upper and lower limits of load fluctuation in region r at time t, respectively; and are the upward flexibility demand and downward flexibility demand generated by the load in region r at time t, respectively;

[0032] The flexibility demand for renewable energy generation is expressed and quantified as:

[0033]

[0034] Where: is the predicted value of renewable energy output in region r at time t; is the maximum prediction error coefficient of new energy in region r; and They are the upper and lower limits of renewable energy output in region r at time t; and are the upward and downward flexibility demands generated by renewable energy in region r at time t, respectively;

[0035] The coordinated flexibility requirements of the “source-grid-load-storage” side are quantified as follows:

[0036]

[0037] Where: and They are the upward and downward flexibility demands generated by the net load in region r at time t, respectively; then, the flexibility supply is determined, including thermal power units and energy storage power stations as traditional flexibility resources on the power supply side. Conventional fixed loads can provide partial flexibility through load transfer, and the regulation capacity of inter-regional interconnection lines further enhances the flexibility supply of the entire system.

[0038] The flexibility supply on each side of the system "source-grid-load-storage" includes the flexibility supply of thermal power units on the source side, the flexibility supply of transferable loads on the load side, the flexibility supply of newly built energy storage power stations on the storage side, and the flexibility supply provided by the interconnection lines on the grid side, among which:

[0039] The flexibility supply of thermal power units is quantified as:

[0040]

[0041] Where: and They are the upward and downward flexibility supply of thermal power units in region r at time t, respectively; and are the upward and downward ramp rates of thermal power units in region r, respectively; τ is the time scale considered in the energy storage configuration planning problem, which is generally 1 hour; and are the technical outputs of thermal power units in region r The maximum and minimum values ​​of

[0042] The energy storage power station has a flexible operation mode and can share the peak load demand for thermal power units. Its flexibility supply is affected by the time scale and the amount of energy stored. Its flexibility supply can be expressed quantitatively as follows:

[0043]

[0044] Where: and They are the upward and downward flexibility supply of energy storage power stations in region r at time t; is the charging and discharging power of the energy storage power station in region r at time t; and are the maximum and minimum charging and discharging powers of the energy storage power station in region r, respectively; is the stored energy in region r at time t; and are the upper and lower limits of the storage energy capacity of region r, respectively;

[0045] The transferable load has a certain flexibility in the grid dispatching process. The power consumption plan can be appropriately changed according to the incentive measures to provide two-way flexibility supply. The flexibility supply is quantified as follows:

[0046]

[0047] Where: and are the upward and downward flexibility supplies of the transferable load in region r at time t, respectively; is the power of the transferable load in region r at time t; and are the upper and lower limits of the transferable load power in region r, respectively;

[0048] The tie line is the carrier for the coordination of inter-regional flexibility resources. The operating status and characteristics of the tie line will restrict the level of inter-regional flexibility mutual assistance. It is limited by the power adjustment rate, transmission power and maximum transmission capacity of the tie line. The flexibility supply it provides is quantified as:

[0049]

[0050] Where: f r,r',tis the power value on the tie line between regions r and r' at time t, which is positive when the power flows from region r to region r', otherwise it is negative; F r,r' , They are the maximum value, minimum value and maximum adjustment rate of transmission power on the regional tie line respectively; They are the upward and downward flexibility between regions r and r' at time t, respectively.

[0051] Furthermore, the above investment and operating cost C r,2 Including the investment cost of energy storage Unit operating cost Energy storage peak load cost and shiftable load compensation costs The above cost can be expressed as follows:

[0052]

[0053] Where: and They are the coal consumption cost of thermal power units in region r at time t, the energy storage peak regulation cost, and the transferable load compensation cost; is the daily cost of energy storage investment; T is the dispatch period; a, b and d are the cost coefficients of thermal power units; is the charging and discharging power of energy storage in region r at time t; is the peak-shaving cost of energy storage units; is the unit compensation cost of the shiftable load in region r participating in demand response; is the expected electricity demand of residents in area r at time t.

[0054] The daily cost of energy storage investment is The calculation is based on:

[0055]

[0056] C r,EBATs and C r,EBATp are the energy storage configuration capacity and energy storage configuration power cost in region r respectively;

[0057] L l is the annual capital recovery factor;

[0058] and are the maximum rated capacity and maximum rated charge and discharge power of the battery in region r respectively;

[0059] c bs is the unit capacity battery investment cost, the battery discharge depth is 90%; c bpis the unit power investment cost of the converter, the converter overload factor is 1.00; b is the discount rate; l is the equivalent total cycle life of the energy storage battery, which is 10 years.

[0060] Weight coefficient of the above flexibility economic equivalent cost and Obtained by the following formula:

[0061]

[0062] Where: They represent the upward flexibility deficit and downward flexibility deficit of the power system on a typical day, respectively, and then multiply them by the price (π+c) to obtain They are respectively the upward flexibility shortfall and the downward flexibility shortfall of the power system on a typical day before energy storage configuration; The operating and maintenance costs of the power system described above for energy storage configuration on a typical day.

[0063] Furthermore, the constructed energy storage capacity configuration model is solved, and the model includes multiple coupled regional problems. The coupling constraints are relaxed by the target cascade method, and the problem is decomposed into sub-problems for iterative solution. The following steps are included:

[0064] Step 1: Initialize all parameters and set coupling variables, including the initial value of the inter-regional interaction power, the initial value of the penalty function multiplier p, the initial value of the iteration rate β, and the number of iterations k;

[0065] Step 2: Introduce Lagrange multipliers Relax the inter-regional tie line power constraint f r,r',t +f r',r,t = 0, and penalize the slack term The optimization configuration problem is transformed into an augmented Lagrangian relaxation problem, which can be expressed as follows:

[0066]

[0067] Satisfy the operating constraints and power balance constraints of each device in the system:

[0068]

[0069] In the formula, is the predicted value of the load in area r at time t, is the power of the transferable load in region r at time t, is the predicted value of renewable energy output in region r at time t, is the charging and discharging power of the energy storage power station in region r at time t, is the technical output power of thermal power units in region r;

[0070] The equipment includes thermal power units, energy storage power stations and transferable loads;

[0071] Step 3: Decompose the augmented Lagrangian relaxation problem into regional sub-problems and obtain the k-th iterative regional optimization sub-problem, which can be expressed as follows:

[0072]

[0073] Satisfy the operating constraints and power balance constraints of each device in the system:

[0074]

[0075] Step 4: Solve the optimization problem for each region r to obtain the coupling variables

[0076] Step 5: Determine the iterative process through the following convergence formula:

[0077]

[0078] In the formula, is the dual value of the subproblem in region r of the kth iteration; ε1 and ε2 are the set convergence condition judgment values ​​respectively;

[0079] If the above two convergence conditions are met at the same time, the current iteration process is stopped and the optimal result is output; otherwise, the parameter modification subroutine is entered;

[0080] The parameter modification subroutine is: let k = k + 1, and use the latest coupling variable value to update the Lagrange multiplier The expression is as follows:

[0081]

[0082] In the formula, p k is the current penalty factor, p k+1 is the new penalty factor; k is the previous iteration rate, >=1; C k-1 The target value for the previous iteration optimization, C k The current optimization target value;

[0083] Step 6: Move to the next sub-problem and repeat Step 4 and Step 5 to solve the k+1th iteration regional sub-problem until convergence or the maximum number of iterations is reached. After the solution is completed, the energy storage configuration and operation results of each region are obtained.

[0084] The present invention also provides a device using the above energy storage capacity configuration method, including a model building module and a model solving module, wherein:

[0085] A model building module, used to quantify the bidirectional power shortage value of the above-mentioned system into the flexibility shortage economic equivalent cost, and to build an energy storage capacity configuration model that considers the coordination of the flexibility demand and flexibility supply on each side of the "source-grid-load-storage" of the system and balances the system flexibility demand and system flexibility supply. The energy storage capacity configuration model uses the optimization of energy storage capacity configuration in each region as a decision variable, and takes the lowest investment and operation cost of the system and the economic equivalent of the flexibility shortage as the objective function, and takes the power balance constraint of the region and the coupling constraint between regions as constraints;

[0086] The model solving module is used to solve the constructed energy storage capacity configuration model and obtain the energy storage optimization configuration plan.

[0087] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method are implemented.

[0088] Compared with the prior art, the present invention has the following advantages:

[0089] The present invention takes into account the flexibility requirements of interconnected multi-region energy storage configuration, takes the flexibility deficit economic equivalent and the lowest investment and operation cost as the objective function, and establishes a "source-grid-load-storage" collaborative energy storage capacity configuration model. And adopts an improved target cascade algorithm, decomposes the augmented Lagrangian relaxation problem into regional sub-problems, and performs optimization solutions for each region, thereby obtaining a multi-region energy storage optimization configuration plan, simplifying the complexity of the optimization problem.

[0090] The method of the present invention improves the flexibility and balance ability of each area, improves the comprehensive benefits of energy storage configuration, improves the convergence speed, and has good convergence performance. The proposed improved target cascade method algorithm is scalable on the basis of ensuring the rapid convergence of the problem, provides guarantees for the future multiple development forms of power grids and other complex optimization problems of power systems, has good engineering practical value, and is expected to bring significant economic benefits to the optimal configuration of new energy and energy storage in power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a flow chart of the energy storage capacity configuration method of the "source-grid-load-storage" power system of the present invention;

[0092] Figure 2 A topological structure diagram of a regional power system in Embodiment 1 of the present invention;

[0093] Figure 3 This is a framework diagram of an algorithm for solving the energy storage capacity configuration model in Example 11 of the present invention. DETAILED DESCRIPTION

[0094] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0095] Example 1

[0096] The power system of the present invention has multiple regions. The source side of each region includes thermal power units and new energy power generation systems, the load side includes conventional loads and transferable loads, the storage side includes existing energy storage power stations and new energy storage power stations that need to be configured, and the grid side includes interconnection lines for power exchange between regions. Figure 2 The figure shows the topological structure of the power system, in which area 1 contains thermal power units and photovoltaic power generation units to meet the load of nodes 4 and 5, and energy storage is planned to be configured at node 2; area 2 contains thermal power units and photovoltaic power generation units to meet the power load demand of node 2, and energy storage is planned to be configured at node 3; area 3 contains thermal power units and wind power generation units to meet the power load demand of nodes 4 and 5, and energy storage is planned to be configured at node 2; the three areas are interconnected through interconnection lines l1, l2 and l3 to realize power transmission.

[0097] like Figure 1 As shown, the energy storage capacity configuration method of the present invention includes:

[0098] The system's bidirectional flexibility deficit electricity value is quantified as the flexibility deficit economic equivalent cost, and a storage capacity configuration model that considers the balance between the system's flexibility supply and flexibility demand is constructed. The energy storage capacity configuration model uses the optimization of the energy storage capacity configuration of each region as the decision variable, and takes the system's investment and operation cost and the minimum flexibility deficit economic equivalent as the objective function, and takes the regional power balance constraint and the inter-regional coupling constraint as the constraint conditions;

[0099] The constructed energy storage capacity configuration model is solved to obtain the optimal energy storage configuration plan for each region.

[0100] Example 2

[0101] This embodiment is further designed on the basis of the embodiment 1 in that the expression of the objective function of the energy storage capacity configuration model in this embodiment is: The expression of the objective function is:

[0102]

[0103] Where: C is the objective function, C r,1 The economic equivalent cost of creating a flexibility deficit for region r, C r,2 is the investment and operation cost of region r, and R is the number of regions.

[0104] Example 3

[0105] This embodiment is further designed on the basis of the embodiment 2 in that the expression of the economic equivalent cost of the flexibility deficit is:

[0106]

[0107] Where: C r,1 is the economic equivalent cost of generating a two-way flexibility deficit in region r, π is the market electricity price, and c is the compensation electricity price. That is, when flexibility deficit occurs, it will be purchased from other regions at a price higher than the market electricity price π+c; and are the upward flexibility power shortage value and the downward flexibility power shortage value of the power grid at time t in region r, respectively; and They are and The corresponding weight coefficient is based on the typical daily operating cost of the system before configuring energy storage. Flexibility Upward Shortage Economic Equivalent Flexibility Downward Shortage Economic Equivalent get:

[0108]

[0109] Example 4

[0110] This embodiment is further designed on the basis of the embodiment 3 in that the power balance constraint in each area in this embodiment needs to consider the power flowing in and out through the tie line, and its expression is:

[0111]

[0112] Where: is the predicted value of the load in area r at time t, is the power of the transferable load in region r at time t, is the predicted value of renewable energy output in region r at time t, is the charging and discharging power of the energy storage power station in region r at time t, is the technical output power of thermal power units in region r;

[0113] Example 5

[0114] This embodiment is further designed on the basis of Embodiment 4 in that the expression of the inter-region coupling constraint in this embodiment is:

[0115] f r,r',t +f r',r,t =0 (4)

[0116] Where: f r,r',t is the power value transmitted from area r to area r' through the interconnection line at time t, f r',r,t It is the power value transmitted from area r' to area r through the interconnection line at time t.

[0117] Example 6

[0118] This embodiment is further designed on the basis of Embodiment 5 in that the expression for balancing the system flexibility supply and the system flexibility demand in this embodiment is:

[0119]

[0120] In the formula, are the upward flexibility supply and downward flexibility supply of region r at time t, and are the upward and downward flexibility supply of the transferable load in region r at time t, and They are the upward and downward flexibility supply of the energy storage power station in region r at time t, and They are the upward and downward flexibility supply of thermal power units in region r at time t, respectively; and are the upward flexibility margin and downward flexibility margin of region r, respectively; They are the upward and downward flexibility supply between regions r and r' at time t respectively.

[0121] Among them, upward flexibility shortage and downward flexibility shortfall It is generated when the margin is less than 0, and the expression is:

[0122]

[0123] Example 7

[0124] This embodiment is further designed on the basis of Embodiment 6 in that the flexibility requirements of each side of the system "source-grid-load-storage" in this embodiment include the load flexibility requirements of the load side, the new energy power generation flexibility requirements of the source side and the coordinated flexibility requirements of each side of "source-grid-load-storage", wherein:

[0125] The load flexibility requirement is expressed and quantified as:

[0126]

[0127] Where: is the predicted value of load in area r at time t; is the maximum prediction error coefficient of load in region r; and are the upper and lower limits of load fluctuation in region r at time t, respectively; and are the upward flexibility demand and downward flexibility demand generated by the load in region r at time t, respectively;

[0128] The flexibility demand for renewable energy generation is expressed and quantified as:

[0129]

[0130] Where: is the predicted value of renewable energy output in region r at time t; is the maximum prediction error coefficient of new energy in region r; and They are the upper and lower limits of renewable energy output in region r at time t; and are the upward and downward flexibility demands generated by renewable energy in region r at time t, respectively;

[0131] The coordinated flexibility requirements of the “source-grid-load-storage” side are quantified as follows:

[0132]

[0133] Where: and They are the upward and downward flexibility demands generated by the net load in region r at time t, respectively; then, the flexibility supply is determined, including thermal power units and energy storage power stations as traditional flexibility resources on the power supply side. Conventional fixed loads can provide partial flexibility through load transfer, and the regulation capacity of inter-regional interconnection lines further enhances the flexibility supply of the entire system.

[0134] Example 8

[0135] This embodiment is further designed on the basis of Embodiment 7 in that the flexibility supply of each side of the system "source-grid-load-storage" in this embodiment includes the flexibility supply of thermal power units on the source side, the flexibility supply of transferable loads on the load side, the flexibility supply of newly built energy storage power stations on the storage side, and the flexibility supply provided by the interconnection line on the grid side, wherein:

[0136] The flexibility supply of thermal power units is quantified as:

[0137]

[0138] Where: and They are the upward and downward flexibility supply of thermal power units in region r at time t, respectively; and are the upward and downward ramp rates of thermal power units in region r, respectively; τ is the time scale considered in the energy storage configuration planning problem, which is generally 1 hour; and are the technical outputs of thermal power units in region r The maximum and minimum values ​​of

[0139] The energy storage power station has a flexible operation mode and can share the peak load demand for thermal power units. Its flexibility supply is affected by the time scale and the amount of energy stored. Its flexibility supply can be expressed quantitatively as follows:

[0140]

[0141] Where: and They are the upward and downward flexibility supply of energy storage power stations in region r at time t; is the charging and discharging power of the energy storage power station in region r at time t; and are the maximum and minimum charging and discharging powers of the energy storage power station in region r, respectively; is the stored energy in region r at time t; and are the upper and lower limits of the storage energy capacity of region r, respectively;

[0142] The transferable load has a certain flexibility in the grid dispatching process. The power consumption plan can be appropriately changed according to the incentive measures to provide two-way flexibility supply. The flexibility supply is quantified as follows:

[0143]

[0144] Where: and are the upward and downward flexibility supplies of the transferable load in region r at time t, respectively; is the power of the transferable load in region r at time t; and are the upper and lower limits of the transferable load power in region r, respectively;

[0145] The tie line is the carrier for the coordination of inter-regional flexibility resources. The operating status and characteristics of the tie line will restrict the level of inter-regional flexibility mutual assistance. It is limited by the power adjustment rate, transmission power and maximum transmission capacity of the tie line. The flexibility supply it provides is quantified as:

[0146]

[0147] Where: f r,r',t is the power value on the tie line between regions r and r' at time t, which is positive when the power flows from region r to region r', otherwise it is negative; F r,r' , They are the maximum value, minimum value and maximum adjustment rate of transmission power on the regional tie line respectively; They are the upward and downward flexibility between regions r and r' at time t, respectively.

[0148] Example 9

[0149] This embodiment is further designed on the basis of the embodiment 8 in that the investment and operation cost C in this embodiment is r,2 Including the investment cost of energy storage Unit operating cost Energy storage peak load cost and transferable load compensation costs The above cost can be expressed as follows:

[0150]

[0151] Where: and They are the coal consumption cost of thermal power units in region r at time t, the energy storage peak regulation cost, and the transferable load compensation cost; is the daily cost of energy storage investment; T is the dispatch period; a, b and d are the cost coefficients of thermal power units; is the charging and discharging power of energy storage in region r at time t; is the peak-shaving cost of energy storage units; is the unit compensation cost of the shiftable load in region r participating in demand response; is the expected electricity demand of residents in area r at time t.

[0152] The daily cost of energy storage investment is The calculation is based on:

[0153]

[0154] C r,EBATs and C r,EBATp are the energy storage configuration capacity and energy storage configuration power cost in region r respectively;

[0155] L l is the annual capital recovery factor;

[0156] and are the maximum rated capacity and maximum rated charge and discharge power of the battery in region r respectively;

[0157] c bs is the unit capacity battery investment cost, the battery discharge depth is 90%; c bp is the unit power investment cost of the converter, the converter overload factor is 1.00; b is the discount rate; l is the equivalent total cycle life of the energy storage battery, which is 10 years.

[0158] Example 10

[0159] This embodiment is further designed on the basis of the embodiment 9 in that the weight coefficient of the flexibility economic equivalent cost in this embodiment is and Obtained by the following formula:

[0160]

[0161] Where: They represent the upward flexibility deficit and downward flexibility deficit of the power system on a typical day, respectively, and then multiply them by the price (π+c) to obtain They are respectively the upward flexibility shortfall and the downward flexibility shortfall of the power system on a typical day before energy storage configuration; The operating and maintenance costs of the power system described above for energy storage configuration on a typical day.

[0162] Embodiment 11

[0163] This embodiment is further designed on the basis of the embodiment 10 in that the energy storage capacity configuration model constructed in this embodiment is solved. The model includes multiple coupled regional problems, and the coupling constraints are relaxed by the target cascade method, which is decomposed into sub-problems and solved iteratively. The following steps are included:

[0164] Step 1: Initialize all parameters and set coupling variables, including the initial value of the inter-regional interaction power is set to 0, the initial value of the penalty function multiplier p is set to 0.01, the initial value of the iteration rate β is set to 1.05, and the number of iterations k is set to 100 times;

[0165] Step 2: Introduce Lagrange multipliers Relax the inter-regional tie line power constraint f r,r',t +f r',r,t = 0, and penalize the slack term The optimization configuration problem is transformed into an augmented Lagrangian relaxation problem, which can be expressed as follows:

[0166]

[0167] Satisfy the operating constraints and power balance constraints of each device in the system:

[0168]

[0169] In the formula, is the predicted value of the load in area r at time t, is the power of the transferable load in region r at time t, is the predicted value of renewable energy output in region r at time t, is the charging and discharging power of the energy storage power station in region r at time t, is the technical output power of thermal power units in region r;

[0170] The equipment includes thermal power units, energy storage power stations and transferable loads;

[0171] Step 3: Decompose the augmented Lagrangian relaxation problem into regional sub-problems and obtain the k-th iterative regional optimization sub-problem, which can be expressed as follows:

[0172]

[0173] Satisfy the operating constraints and power balance constraints of each device in the system:

[0174]

[0175] Step 4: Solve the optimization problem for each region r to obtain the coupling variables

[0176] Step 5: Determine the iterative process through the following convergence formula:

[0177]

[0178] In the formula, is the dual value of the subproblem in region r of the kth iteration; ε1 and ε2 are the set convergence condition judgment values ​​respectively;

[0179] If the above two convergence conditions are met at the same time, the current iteration process is stopped and the optimal result is output; otherwise, the parameter modification subroutine is entered;

[0180] The parameter modification subroutine is: let k = k + 1, and use the latest coupling variable value to update the Lagrange multiplier The expression is as follows:

[0181]

[0182] In the formula, p k is the current penalty factor, p k+1 is the new penalty factor; k is the previous iteration rate, >=1; C k-1 The target value for the previous iteration optimization, C k The current optimization target value;

[0183] Step 6: Move to the next sub-problem and repeat Step 4 and Step 5 to solve the k+1th iteration regional sub-problem until convergence or the maximum number of iterations is reached. After the solution is completed, the energy storage configuration and operation results of each region are obtained.

[0184] Example 12

[0185] This embodiment adopts the energy storage capacity configuration method of embodiment 11 to optimize the energy storage configuration of each region, wherein the installed capacity of thermal power units in regions 1, 2 and 3 is 2800kW, 2500kW and 3000kW respectively, the minimum technical output level is 100kW, the ramp rate of thermal power units is 300kW / h, and the cost coefficients a, b and d of thermal power units are 0.003 yuan / MW respectively. 2 , 100 yuan / MW, 7000 yuan. The installed capacity of photovoltaic machine is 3000kW, and the installed capacity of wind turbine is 2500kW. According to the historical load data, historical photovoltaic power generation and historical wind power generation of each area of ​​the power grid, the typical daily output curves of load, wind and light hourly resolution of each area are obtained as the predicted values ​​of load and new energy output of each area of ​​the power grid during the energy storage capacity planning period. The unit cost of energy storage investment is 2000 yuan / kWh.

[0186] According to the load and new energy forecast output, take the maximum fluctuation coefficient ε of load, wind power and photovoltaic L , ε W , ε PV They are 0.05, 0.10 and 0.08 respectively, and the regional flexibility supply and demand of the power grid system are calculated.

[0187] Among them, when calculating the economic equivalent cost of flexibility shortage according to formula (2) of embodiment 3, π is the market electricity price, which is 350 yuan / MWh, and c is the compensation electricity price, which is 50 yuan / MWh. That is, when flexibility shortage occurs, the system will consider purchasing from other regions at a price higher than the market electricity price of 400 yuan / MWh. and is the weight coefficient of the economic equivalent cost of flexibility, based on the typical daily operating cost of the system before configuring the energy storage power station. Flexibility Upward Shortage Economic Equivalent Flexibility Downward Shortage Economic Equivalent get:

[0188] The energy storage capacity optimization configuration model was solved using the method of Example 11. After the solution was completed, the energy storage configuration and operation results of the three regions were obtained, as shown in Table 1.

[0189] Table 1 Regional energy storage configuration and operation results

[0190]

[0191] When the three regions are independently operated and configured with energy storage, the average daily total investment and operation cost of the three regions is 70,680 yuan, and the system generates a total flexibility shortage economic cost of 2,584.78 yuan. After considering the mutual assistance of the regional interconnection lines, the operating costs of the three regions are reduced to 67,936 yuan, and the flexibility shortage economic equivalent cost is reduced to 608.71 yuan. It can be seen that the interconnection lines have significantly improved the economy of the regional energy storage configuration and the flexibility shortage. The optimization problem is solved by the proposed improved target cascade method, and compared with the traditional target cascade method and the alternating direction multiplier method, as shown in Table 2. It can be seen that the use of the improved dynamic iteration rate based on the decrease ratio of the two optimization target values ​​can make the sub-problems converge faster in the direction of the target cost reduction.

[0192] Table 3 Impact of solution algorithm on energy storage configuration optimization solution

[0193]

[0194] Embodiment 13

[0195] The present invention provides a distribution network energy storage optimization configuration system, in which a plurality of electric vehicles are arranged in the distribution network area, and includes a model building module and a model solving module, wherein:

[0196] A model building module is used to aggregate electric vehicles as flexible resources to form virtual energy storage units in the distribution network, and to build an energy storage optimization configuration model that considers the flexibility aggregation of electric vehicles. The energy storage optimization configuration model uses conventional energy storage configuration capacity as a decision variable, and takes the lowest total cost over the entire life cycle of energy storage as the objective function, and takes electric vehicle power constraints that take into account the aggregation capacity of electric vehicles, system power balance constraints, transformer reverse load rate constraints, and energy storage charging and discharging constraints as constraints;

[0197] The model solving module is used to solve the constructed energy storage optimization configuration model and obtain the energy storage optimization configuration solution.

[0198] Embodiment 14

[0199] A computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0200] The above description is only a specific implementation 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 a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for configuring energy storage capacity of a "source-grid-load-storage" power system, wherein the power system has multiple regions, the source side of each region includes a thermal power unit and a new energy power generation system, the load side includes conventional loads and transferable loads, the storage side includes existing energy storage power stations and new energy storage power stations that need to be configured, and the grid side includes a connecting line for power exchange between regions, characterized in that: The energy storage capacity configuration method comprises: Quantify the bidirectional flexibility deficit electricity value of the system as the flexibility deficit economic equivalent cost, and construct an energy storage capacity configuration model that considers the balance between the flexibility supply and flexibility demand of the system. The energy storage capacity configuration model uses the optimization of energy storage capacity configuration in each region as a decision variable, and takes the investment and operation cost of the system and the minimum flexibility deficit economic equivalent as the objective function, and takes the power balance constraint of the region and the coupling constraint between regions as constraints; The constructed energy storage capacity configuration model is solved to obtain the optimal energy storage configuration plan for each region.

2. The energy storage capacity configuration method according to claim 1, characterized in that: The expression of the objective function of the energy storage capacity configuration model is: Where: C is the objective function, C r,1 The economic equivalent cost of creating a flexibility deficit for region r, C r,2 is the investment and operation cost of region r, and R is the number of regions.

3. The energy storage capacity configuration method according to claim 2, characterized in that: The expression of the economic equivalent cost of the flexibility deficit is: Where: C r,1 is the economic equivalent cost of generating a two-way flexibility deficit in region r, π is the market electricity price, c is the compensation electricity price, and are the upward flexibility shortage value and downward flexibility shortage value of the power grid at time t in region r, and They are and The corresponding weight coefficient.

4. The energy storage capacity configuration method according to claim 2, characterized in that: The expression of the power balance constraint of the area is: Where: is the predicted value of the load in area r at time t, is the power of the transferable load in region r at time t, is the predicted value of renewable energy output in region r at time t, is the charging and discharging power of the energy storage power station in region r at time t, is the technical output power of thermal power units in region r; The expression of the inter-region coupling constraint is: f r,r',t +f r',r,t =0 (4) Where: f r,r',t is the power value transmitted from area r to area r' through the interconnection line at time t, f r',r,t It is the power value transmitted from area r' to area r through the interconnection line at time t.

5. The energy storage capacity configuration method according to claim 4, characterized in that: The expression for balancing the system flexibility supply and system flexibility demand is: In the formula, are the upward flexibility supply and downward flexibility supply of region r at time t, and are the upward and downward flexibility supply of the transferable load in region r at time t, and They are the upward and downward flexibility supply of the energy storage power station in region r at time t, and They are the upward and downward flexibility supply of thermal power units in region r at time t, respectively; They are the upward and downward flexibility supply between regions r and r' at time t, respectively; and They are the upward flexibility margin and downward flexibility margin of region r respectively. The upward flexibility shortfall and downward flexibility shortfall It is generated when the margin is less than 0, and the expression is:

6. The energy storage capacity configuration method according to claim 5, characterized in that: The flexibility requirements of the system "source-grid-load-storage" on each side include the load flexibility requirements on the load side, the new energy power generation flexibility requirements on the source side, and the coordinated flexibility requirements of the "source-grid-load-storage" side, where: The load flexibility requirement is expressed and quantified as: Where: is the predicted value of load in area r at time t; is the maximum prediction error coefficient of load in region r; and are the upper and lower limits of load fluctuation in region r at time t, respectively; and are the upward flexibility demand and downward flexibility demand generated by the load in region r at time t, respectively; The flexibility demand for renewable energy generation is expressed and quantified as: Where: is the predicted value of renewable energy output in region r at time t; is the maximum prediction error coefficient of new energy in region r; and They are the upper and lower limits of renewable energy output in region r at time t; and are the upward and downward flexibility demands generated by renewable energy in region r at time t, respectively; The collaborative flexibility requirements of the "source-grid-load-storage" sides are quantified as follows: Where: and are the upward and downward flexibility demands generated by the net load in region r at time t, respectively; The flexibility supply of each side of the "source-grid-load-storage" system includes the supply flexibility of thermal power units on the source side, the supply flexibility of transferable loads on the load side, the supply flexibility of newly built energy storage power stations on the storage side, and the supply flexibility provided by the interconnection lines on the grid side, among which: The flexibility supply of thermal power units is quantified as: Where: and They are the upward and downward flexibility supply of thermal power units in region r at time t, respectively; and are the upward and downward ramp rates of thermal power units in region r, respectively, τ is the time scale considered in the energy storage configuration planning problem, and are the technical outputs of thermal power units in region r The maximum and minimum values ​​of The flexibility supply of the energy storage power station is quantified as: Where: and They are the upward and downward flexibility supply of energy storage power stations in region r at time t; is the charging and discharging power of the energy storage power station in region r at time t; and are the maximum and minimum charging and discharging powers of the energy storage power station in region r, respectively; is the stored energy in region r at time t; and are the upper and lower limits of the storage energy capacity of region r, respectively; The transferable load flexibility supply is quantified as: Where: and are the upward and downward flexibility supplies of the transferable load in region r at time t, respectively; is the power of the transferable load in region r at time t; and are the upper and lower limits of the transferable load power in region r, respectively; The flexibility provided by the tie line is quantified as: Where: f r,r',t is the power value on the tie line between regions r and r' at time t, which is positive when the power flows from region r to region r', otherwise it is negative; F r,r' , They are the maximum value, minimum value and maximum adjustment rate of transmission power on the regional tie line respectively; They are the upward and downward flexibility between regions r and r' at time t, respectively.

7. The energy storage capacity configuration method according to claim 3, characterized in that: The investment and operating cost C r,2 Including the investment cost of energy storage Unit operating cost Energy storage peak load cost and transferable load compensation costs The above cost can be expressed as follows: Where: T is the dispatch period; a, b and d are the cost coefficients of thermal power units; is the charging and discharging power of energy storage in region r at time t; is the peak-shaving cost of energy storage units; is the unit compensation cost of the shiftable load in region r participating in demand response; is the expected electricity demand of residents in area r at time t; The weight coefficient of the flexibility economic equivalent cost and Obtained by the following formula: Where: They represent the upward flexibility deficit and downward flexibility deficit of the power system on a typical day before the energy storage power station is configured, and then multiply them by the price (π+c) to obtain They are respectively the upward flexibility shortfall and the downward flexibility shortfall of the power system on a typical day before energy storage configuration; The operating and maintenance costs of the power system described above for energy storage configuration on a typical day.

8. The energy storage capacity configuration method according to claim 1, characterized in that: The method for solving the constructed energy storage capacity configuration model to obtain the energy storage optimization configuration scheme for each region includes: Step 1: Initialize various parameters, including setting the initial value of the inter-region interaction power, the initial value of the penalty function multiplier p, the initial value of the iteration rate β, and the number of iterations k; Step 2: Introduce Lagrange multipliers Relax the inter-regional tie line power constraint f r,r',t +f r',r,t = 0, and penalize the slack term The optimization configuration problem is transformed into an augmented Lagrangian relaxation problem, which can be expressed as follows: Satisfy the operating constraints and power balance constraints of each device in the system: In the formula, is the predicted value of the load in area r at time t, is the power of the transferable load in region r at time t, is the predicted value of renewable energy output in region r at time t, is the charging and discharging power of the energy storage power station in region r at time t, is the technical output power of thermal power units in region r; The equipment includes thermal power units, energy storage power stations and transferable loads; Step 3: Decompose the augmented Lagrangian relaxation problem into regional sub-problems and obtain the k-th iterative regional optimization sub-problem, which can be expressed as follows: Satisfy the operating constraints and power balance constraints of each device in the system: Step 4: Solve the optimization problem for each region r to obtain the coupling variables Step 5: Determine the iterative process through the following convergence formula: In the formula, is the dual value of the subproblem in region r of the kth iteration; ε1 and ε2 are the set convergence condition judgment values ​​respectively; If the above two convergence conditions are met at the same time, the current iteration process is stopped and the optimal result is output; otherwise, the parameter modification subroutine is entered; The parameter modification subroutine is: let k = k + 1, and use the latest coupling variable value to update the Lagrange multiplier The expression is as follows: In the formula, p k is the current penalty factor, p k+1 is the new penalty factor; k is the previous iteration rate, >=1; C k-1 The target value for the previous iteration optimization, C k The current optimization target value; Step 6: Move to the next sub-problem and repeat Step 4 and Step 5 to solve the k+1th iteration regional sub-problem until convergence or the maximum number of iterations is reached. After the solution is completed, the energy storage configuration and operation results of each region are obtained.

9. A device using the energy storage capacity configuration method according to claim 1, characterized in that: It includes a model building module and a model solving module, among which, The model building module is used to quantify the bidirectional power shortage value of the system as the flexibility shortage economic equivalent cost, and to build an energy storage capacity configuration model that considers the coordination of the flexibility demand and flexibility supply of each side of the "source-grid-load-storage" of the system and balances the system flexibility demand and system flexibility supply. The energy storage capacity configuration model uses the energy storage capacity configuration optimization of each region as a decision variable, and takes the investment and operation cost of the system and the minimum flexibility shortage economic equivalent as the objective function, and takes the power balance constraint of the region and the coupling constraint between regions as constraints; The model solving module is used to solve the constructed energy storage capacity configuration model to obtain an energy storage optimization configuration solution.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 8 are implemented.

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