Energy storage configuration method and system considering wind power plant access and peak regulation requirements and medium

Abstract

The invention relates to an energy storage configuration method and system considering wind power plant access and peak regulation requirements, and a medium. The method comprises the following steps: obtaining operation data related to energy storage in a power system accessed to a wind power plant; based on the operation data, a double-layer optimization model considering wind power consumption and peak regulation requirements is constructed, the double-layer optimization model comprises an outer-layer optimization sub-model and an inner-layer optimization sub-model, the outer-layer optimization sub-model takes the maximum energy storage net income and the optimal wind power consumption effect as optimization targets, and the inner-layer optimization sub-model takes the maximum energy storage net income and the optimal wind power consumption effect as optimization targets; the inner-layer optimization sub-model takes the lowest peak regulation total cost as an optimization target; the double-layer optimization model is solved, an energy storage configuration scheme is output, and the energy storage configuration process is completed. Compared with the prior art, the method has the advantages of improving the peak regulation capability and the operation economy of the power system and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to a method, system and medium for energy storage configuration considering the access of wind farms and peak shaving requirements. Background Art

[0002] In the power system, wind power, as an important renewable energy source, is of great significance for optimizing the energy structure and environmental protection. However, the randomness and volatility of wind power output pose challenges to the stable operation and peak shaving of the power system. With the increase in the grid-connected capacity of wind power, the power system requires higher peak shaving flexibility to cope with the uncertainty of wind power. In this context, the energy storage system, as an effective peak shaving resource, is widely regarded as one of the key technologies for improving the peak shaving capacity of the power system due to its fast response speed, safe and controllable operation, and flexible installation location.

[0003] The main disadvantages of the current technology are that when thermal power units perform deep peak shaving, they not only generate additional oil injection costs and high unit loss costs, which affect the safety and economy of the units, but also lack the ability to adapt to the volatility of wind power and cannot flexibly respond to the rapid changes in wind power output. In addition, the existing application of energy storage systems fails to fully utilize their peak shaving potential, resulting in limited wind power accommodation capacity, serious wind curtailment phenomena, poor system operation economy, and imperfect policies and market mechanisms, which affect the enthusiasm of power generation enterprises to participate in peak shaving.

[0004] Reasonable configuration of energy storage in the power grid can effectively alleviate the operation pressure brought by the grid connection of a high proportion of new energy, reduce the peak shaving deficit of the system, and improve the economy of system operation. The peak shaving capacity of an energy storage power station is positively correlated with its capacity, but the high investment cost limits the capacity of the energy storage system from being infinitely large. Therefore, seeking the balance point between the peak shaving demand of the power grid and the benefits of the energy storage power station is the key to the optimal configuration of the energy storage system capacity.

[0005] In summary, the existing power system faces a series of technical challenges under the background of the increase in the grid-connected capacity of wind power, including:

[0006] ① High peak shaving cost: The existing technology mainly relies on thermal power units for peak shaving. Especially during the period of high wind power generation, thermal power units need to perform deep peak shaving to adapt to the volatility of wind power, which results in additional oil injection costs and unit loss costs, significantly increasing the peak shaving cost of the power system.

[0007] ② Insufficient peak shaving flexibility: Due to the slow peak shaving response speed of thermal power units, they cannot quickly adapt to the rapid changes in wind power output, resulting in the lack of sufficient flexibility of the power system in the face of wind power fluctuations.

[0008] ③Limited wind power consumption capacity: The existing technology fails to fully utilize the peak shaving potential of the energy storage system, resulting in the inability to fully consume wind power during periods of high wind power generation and the phenomenon of wind curtailment.

[0009] ④Poor system operation economy: Due to the failure to fully utilize the advantages of the energy storage system, the existing technology leads to poor operation economy of the power system.

[0010] ⑤Environmental impact issues: The oil injection operation during the deep peak shaving of thermal power units will increase pollutant emissions and have a negative impact on the environment.

[0011] ⑥Incomplete policies and market mechanisms: The progress of the power market reform is slow, and the specific sources and distribution mechanisms of peak shaving compensation fees have not been clarified, affecting the enthusiasm of power generation enterprises to participate in peak shaving. Summary of the Invention

[0012] The purpose of the present invention is to provide an energy storage configuration method, system and medium that consider the access of wind farms and peak shaving requirements and achieve a balance between peak shaving requirements and the benefits of energy storage power stations.

[0013] The purpose of the present invention can be achieved through the following technical solutions:

[0014] An energy storage configuration method that considers the access of wind farms and peak shaving requirements includes the following steps:

[0015] Obtain the operation data related to energy storage in the power system accessing the wind farm;

[0016] Based on the operation data, construct a two-layer optimization model considering wind power consumption and peak shaving requirements, where the two-layer optimization model includes an outer-layer optimization sub-model and an inner-layer optimization sub-model. The outer-layer optimization sub-model takes the maximum net income of the energy storage and the best wind power consumption effect as the optimization objectives, and the inner-layer optimization sub-model takes the lowest total peak shaving cost as the optimization objective;

[0017] Solve the two-layer optimization model and output the energy storage configuration plan to complete the energy storage configuration process.

[0018] Furthermore, the operation data includes the operation data of the wind farm, the operation data of the thermal power unit and the operation data of the energy storage power station.

[0019] Furthermore, the outer-layer optimization sub-model includes an objective function and corresponding constraint conditions. The objective function includes a sub-objective function with the maximum net income of the energy storage and a sub-objective function with the best wind power consumption effect. The constraint conditions include:

[0020] ①Power flow constraint:

[0021]

[0022] ② Node voltage constraint:

[0023]

[0024] ③ Energy storage capacity constraint:

[0025]

[0026] ④ Energy storage charge and discharge power constraint:

[0027]

[0028] ⑤ Energy storage state of charge constraint:

[0029]

[0030] S oc,start = S oc,end

[0031] In the formula, P Gi and Q Gi are the active and reactive power outputs of the thermal power unit at node i respectively, P si is the active power output of the wind power at node i, P Ei and Q Ei are the active and reactive power outputs of the energy storage at node i respectively, P li and Q li are the active and reactive power of the load at node i respectively, P loss,i is the power loss at node i, G ij and B ij are the real and imaginary parts of the i-th row and j-th column of the branch node admittance matrix, θ ij is the phase angle difference between nodes i and j, U i and U j are the voltages of nodes i and j, are the minimum and maximum voltage amplitudes respectively, are the minimum and maximum configured capacities of the energy storage power station respectively, E E is the capacity of the energy storage, P ch,t , P dis,t are the charging and discharging powers of the energy storage at time t respectively, P max is the maximum charging and discharging power of the energy storage, S oc,t is the charge of the energy storage at time t, δ is the self-discharge power of the energy storage, S oc,max , S oc,min are the upper and lower limits of the energy storage state of charge, η C , η D are the charge and discharge efficiencies of the energy storage, and Δt is the sampling time step.

[0032] Furthermore, the net energy storage revenue takes into account the investment and construction costs, operation and maintenance costs, peak shaving cost sharing, deep peak shaving revenue, and arbitrage revenue of the energy storage power station. The expression of the sub-objective function with the maximum net energy storage revenue is as follows:

[0033]

[0034] Where:

[0035]

[0036]

[0037] In the formula, f1 is the net revenue of the energy storage power station, S is the number of typical output scenarios, ρ s is the probability of the occurrence of the typical output scenario s, I 1,s , I 2,s are the arbitrage revenue and peak shaving compensation revenue of the energy storage power station on the s typical day, C 3,s , C 4,s are the operation and maintenance costs and peak shaving cost sharing of the energy storage power station on the s typical day, C inv is the "daily" cost of the investment and construction of the energy storage power station after conversion, β is the capital recovery factor, r and y ess are the discount rate and the service life of the energy storage respectively, C P , C E are the unit price of capacity configuration and the unit price of power configuration, P E , E E are the planned power and capacity of the energy storage power station, T is the total duration of the sampling time, are the charge and discharge powers of the energy storage power station at time t on the s typical day respectively, η C , η D are the charge and discharge efficiencies of the energy storage, P p,t is the real-time peak-valley electricity price of the power grid at time t, Δt is the sampling time step, P com is the peak shaving compensation unit price, C OM is the unit price of the operation and maintenance cost of the energy storage power station, is the total peak shaving compensation expenditure at time t; is the grid-connected power and grid-connected capacity of the conventional thermal power unit i at time t.

[0038] Furthermore, the increased wind power acceptance capacity after configuring the energy storage in the power system is used to represent the wind power consumption effect. The expression of the sub-objective function with the optimal wind power consumption effect is as follows:

[0039] max f2 = Q wind

[0040]

[0041] wherein, f2 is the sub-objective function with the optimal wind power consumption effect, and Q wind is the increased wind power acceptance capacity of the power system after configuring the energy storage, T is the total duration of the sampling time, and P wind,t is the wind power acceptance power of the power system without energy storage at time t.

[0042] Furthermore, the inner-layer optimization sub-model includes an objective function and corresponding constraint conditions, where the objective function is expressed as:

[0043]

[0044] where:

[0045]

[0046] wherein, F s is the objective function, S is the number of typical output scenarios, ρ s is the probability of the occurrence of the typical output scenario s, C G,s is the operating cost and deep peak shaving cost of the thermal power unit, C wloss,s is the penalty cost for wind abandonment, C ess,s is the peak shaving cost paid to the energy storage power station, and numerically, the peak shaving cost C ess,s paid to the energy storage power station is equal to the peak shaving compensation income I 2,s obtained by the energy storage power station, i G,s is the peak shaving compensation income of the thermal power unit, T is the total duration of the sampling time, c w is the unit penalty cost for wind abandonment, is the predicted output of the wind turbine at time t under the typical day s, is the wind power grid connection power at time t under the typical day s, γ p is the unit peak shaving power compensation cost of the thermal power unit, is the deep peak shaving power of the unit, and Δt is the sampling time step;

[0047] The constraint conditions include:

[0048] ① Power balance constraint:

[0049]

[0050] ② Operating constraint of the thermal power unit:

[0051]

[0052] ③ Wind power constraint:

[0053]

[0054] ④ Energy storage capacity constraint:

[0055]

[0056] ⑤ Energy storage charge and discharge power constraint:

[0057]

[0058] ⑥ Energy storage state of charge constraint:

[0059]

[0060] S oc,start = S oc,end

[0061] In the formula, N G is the number of thermal power units, P l,t,s is the load forecast value at time t under scenario s, is the actual output power of the energy storage power station at time t under the typical day s, is the output of the thermal power unit, is the output power of the wind farm at time t under the typical day s, are the upper and lower limits of the output of the i-th thermal power unit, is the output of the i-th thermal power unit, are the maximum downward ramp rate and the maximum upward ramp rate of the i-th thermal power unit respectively, u i,t is the start-stop state of unit i in the t period, 1 means "start", 0 means "stop", is the minimum continuous start-up and shutdown time of unit i, are the actual output and the predicted output of the wind power respectively; are the minimum configured capacity and the maximum configured capacity of the energy storage power station respectively, E E is the capacity of the energy storage, P ch,t 、P dis,t are the charge and discharge powers of the energy storage at time t respectively, P max is the maximum charge and discharge power of the energy storage, S oc,t is the charge of the energy storage at time t, δ is the self-discharge power of the energy storage, S oc,max 、S oc,min are the upper and lower limits of the energy storage state of charge, η C 、η D are the charge and discharge efficiencies of the energy storage, and Δt is the sampling time step.

[0062] Furthermore, the operating cost and deep peak shaving cost C of the thermal power unit G,s include the operating cost of the thermal power unit in the conventional peak shaving stage, and the deep peak shaving costs in the non-oil injection deep peak shaving stage and the oil injection deep peak shaving stage. The operating cost and deep peak shaving cost C of the thermal power unit G,s are expressed as:

[0063]

[0064] Wherein:

[0065]

[0066] In the formula, C G,s,1 is the operating cost of the thermal power unit, C Gloss,s is the unit loss cost when the thermal power unit enters the deep peak shaving stage, C oil,s is the additional oil injection cost generated when the thermal power unit enters the deep peak shaving stage, is the minimum output value of the thermal power unit for conventional peak shaving, is the maximum output value of the thermal power unit, is the minimum output value of the thermal power unit for non-oil-injected deep peak shaving, is the minimum output value of the thermal power unit for oil-injected deep peak shaving, represents the output of the thermal power unit, a i , b i and c i respectively represent the consumption coefficient of the i-th thermal power unit, γ is the actual operation loss coefficient of the thermal power plant, S u,i is the purchase cost of the i-th thermal power unit, N G is the number of thermal power units, T is the total sampling time duration, N f,i,t is the number of rotor crack initiation cycles of the i-th thermal power unit at time t, r oil is the oil price in the current season, is the oil injection volume of the i-th thermal power unit at time t during the oil-injected deep peak shaving stage.

[0067] Furthermore, the double-layer optimization model is solved by an alternating iteration method of an inner-layer optimization sub-model and an outer-layer optimization sub-model, wherein the improved multi-objective continuous particle swarm optimization algorithm is used to solve the outer-layer optimization sub-model, and the CPLEX solver is used to solve the inner-layer optimization sub-model.

[0068] The present invention also provides an energy storage configuration system considering the access of a wind farm and peak shaving requirements, including:

[0069] Data acquisition module: used to acquire the operation data related to energy storage in the power system accessing the wind farm;

[0070] Optimization model construction module: used to construct a double-layer optimization model considering wind power consumption and peak shaving requirements based on the operation data, wherein the double-layer optimization model includes an outer-layer optimization sub-model and an inner-layer optimization sub-model, the outer-layer optimization sub-model takes the maximum net income of energy storage and the best wind power consumption effect as optimization objectives, and the inner-layer optimization sub-model takes the lowest total peak shaving cost as the optimization objective;

[0071] Solving module: used to solve the double-layer optimization model, output the energy storage configuration plan, and complete the energy storage configuration process.

[0072] The present invention also provides a computer-readable storage medium, including one or more programs executed by one or more processors of an electronic device, and the one or more programs include instructions for executing the energy storage configuration method considering the access of a wind farm and peak shaving requirements as described above.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] (1) The present invention comprehensively considers the access of a wind farm and the system peak shaving requirements, constructs a double-layer optimization model of energy storage. In this double-layer optimization model, the outer-layer optimization sub-model takes the maximum net income of energy storage and the best wind power consumption effect as optimization objectives, which can improve the wind power consumption capacity of the power system and reduce the wind curtailment rate under the condition of the maximum net income of energy storage. The inner-layer optimization sub-model takes the lowest total peak shaving cost as the optimization objective, which can reduce the peak shaving cost under the condition of meeting the peak shaving requirements, thus realizing the balance between peak shaving requirements and the income of the energy storage power station.

[0075] (2) The uncertainty of new energy output and load is considered in the outer-layer optimization sub-model of the present invention. By using typical output scenarios for optimization and combining the regulation characteristics of energy storage itself, it can promote wind power consumption, increase the acceptance space of wind power, and alleviate the problem of large wind curtailment during the low-load period.

[0076] (3) In the inner-layer optimization sub-model of the present invention, the total peak shaving cost considers multiple aspects of costs such as the operating cost and deep peak shaving cost of thermal power units, wind curtailment penalty cost, peak shaving cost paid to the energy storage power station, and peak shaving compensation income of thermal power units, which is beneficial to improving the calculation accuracy of the total peak shaving cost. And most of the peak shaving tasks are borne by thermal power units, and the peak shaving process of thermal power units is divided into three stages: conventional peak shaving, oil-free deep peak shaving, and oil-injected deep peak shaving. Coordinate and optimize peak shaving with energy storage to optimize the output of energy storage and thermal power units, achieve the effect of peak shaving and valley filling, effectively reduce the wind curtailment power during the low-load period, and alleviate the peak shaving difficulty. Description of the Drawings

[0077] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0078] Figure 2 It is a schematic diagram of the peak shaving of thermal power units of the present invention;

[0079] Figure 3 It is the IEEE30 node system of the present invention;

[0080] Figure 4 It is the output situation of the peak shaving units in Scenarios 1 and 2 of the present invention;

[0081] Figure 5 Wind power accommodation for Scenarios 1 and 2 of the present invention;

[0082] Figure 6 Optimization scheduling result of the power system when the energy storage of the present invention is optimally configured;

[0083] Figure 7 Optimization result of the wind curtailment rate under different wind power installed capacities of the present invention;

[0084] Figure 8 Flow chart for solving the present invention. Detailed implementation manners

[0085] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives detailed implementation manners and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0086] Embodiment 1

[0087] This embodiment provides an energy storage configuration method considering the access of wind farms and peak shaving requirements. This method establishes a two-layer optimization configuration model that takes into account wind power accommodation and system peak shaving requirements and balances the benefits of energy storage power stations (energy storage systems) and system peak shaving costs. In the outer optimization sub-model, on the basis of the traditional model of energy storage power stations, the peak shaving compensation income and sharing costs of energy storage are considered, and the economy of the energy storage system and the wind power accommodation level are comprehensively considered; secondly, in order to better meet the future peak shaving requirements of the power system, the inner optimization sub-model is a system optimization scheduling model, which comprehensively considers the participation of the energy storage system in peak shaving and the deep peak shaving of thermal power units, and takes the minimum total system peak shaving cost as the goal to optimize the output of energy storage and thermal power units. First, clarify the principle of deep peak shaving optimization of the power system with energy storage participation:

[0088] 1) Analysis of the deep peak shaving characteristics of thermal power units

[0089] As Figure 2As shown in the figure, according to the output of thermal power units, the peak shaving process of thermal power units is divided into conventional peak shaving, deep peak shaving without oil injection, and deep peak shaving with oil injection. Because the output reduction in deep peak shaving exceeds the specified limit of conventional free peak shaving, it is also known as paid peak shaving. When the output of a thermal power unit is between Pmax and Pmin, it provides conventional peak shaving auxiliary services for the system. During the low load period at night when the wind power output is large, in order to improve the consumption of wind power, the thermal power units required to perform peak shaving tasks reduce their output. At this time, the output drops below Pmin and is in the stage of deep peak shaving without oil injection, generally about 50% of the unit capacity. If the unit output further decreases to less than 40% of the unit capacity, in order to maintain the stable operation of the peak shaving unit, oil injection measures need to be taken, and the unit is in the state of deep peak shaving with oil injection. During the deep peak shaving process of thermal power units, with the decrease of the load rate, additional oil injection costs and higher unit loss costs will be generated, affecting the safety and economy of the unit.

[0090] Therefore, in order to improve the enthusiasm of units to participate in deep peak shaving, peak shaving auxiliary service mechanisms have been successively implemented in various regions, and thermal power units are stimulated to actively participate in grid peak shaving through reasonable peak shaving compensation.

[0091] 2) Analysis of energy storage participating in system peak shaving

[0092] Energy storage facilities store electricity during low load periods and discharge electricity during peak load periods to alleviate the situation of power shortage and surplus, thereby reducing the peak-to-valley difference of the system load, improving the utilization efficiency of system power sources, and also being able to smooth the volatility of renewable energy such as wind power.

[0093] Combining the deep peak shaving means of thermal power units with the peak shaving and valley filling function of the energy storage system can effectively reduce the wind power abandonment during low load periods and alleviate the system peak shaving difficulty.

[0094] Combined with Figure 1 and the above deep peak shaving optimization principle, this method includes the following steps:

[0095] S1. Obtain the operation data related to energy storage in the power system connected to the wind farm.

[0096] In this embodiment, the power system containing energy storage is also connected to a wind farm. First, collect the historical and real-time data of the power system, including the operation data of the wind farm, the operation data of thermal power units, and the operation data of energy storage power stations, specifically including: wind power output, load demand, and thermal power unit operation parameters, etc.

[0097] S2. Construct a two-layer optimization model considering wind power consumption and peak shaving sharing strategy.

[0098] 1) Outer layer optimization sub-model

[0099] The outer optimization sub-model optimizes the energy storage system configuration with the goals of maximizing the net income of the energy storage system and achieving the best wind power consumption effect. Among them, the net income model considers the investment and construction costs, operation and maintenance costs, peak shaving cost sharing, deep peak shaving income, and arbitrage income of the energy storage power station, aiming to maximize the income. For the convenience of calculation and analysis, the established energy storage income model takes "day" as the cycle. Considering the uncertainty of new energy output and load, the historical data of new energy output and load are clustered to generate multiple typical output scenarios, and the specific objective function is as follows:

[0100]

[0101] In the formula: f1 is the net income of the energy storage power station; S is the number of typical output scenarios, which are generated from the historical operation data of the power system through the clustering analysis method, and ρ s is the probability of the occurrence of the typical output scenario s; I 1,s 、I 2,s are the arbitrage income and peak shaving compensation income of the energy storage system on the s typical day; C 3,s 、C 4,s are the operation and maintenance costs and peak shaving cost sharing of the energy storage system on the s typical day; C inv is the "day" cost of the investment and construction of the energy storage system after conversion.

[0102] ① Annual value of the investment and construction cost of the energy storage system

[0103]

[0104] In the formula: β is the capital recovery factor; r and y ess are the discount rate and the service life of the energy storage, with values of 0.1 and 20; C P 、C E are the unit price of capacity configuration and the unit price of power configuration; P E 、E E are the power and capacity of the planned energy storage system.

[0105] ② Arbitrage income of the energy storage system

[0106]

[0107] In the formula: T is the total duration of the sampling time, which is 24 hours a day; are the charge and discharge powers of the energy storage system at time t on the s typical day respectively; η C 、η D are the charge and discharge efficiencies of the energy storage; P p,t is the real-time peak-valley electricity price of the power grid at time t; Δt is the sampling time step.

[0108] ③ Peak shaving compensation income of the energy storage system

[0109]

[0110] In the formula: P com is the peaking compensation unit price.

[0111] ④ Operation and maintenance cost of energy storage system

[0112]

[0113] In the formula: C OM is the operation and maintenance cost unit price of the energy storage system.

[0114] ⑤ Peaking cost sharing cost of energy storage system

[0115]

[0116] In the formula: is the total peaking compensation expenditure at time t; is the grid-connected capacity of the on-grid power of conventional thermal power unit i at time t.

[0117] After the energy storage is configured in the system, the energy storage promotes the accommodation of wind power through its own regulation characteristics, increases the accommodation space of wind power, and alleviates the problem of large amounts of wind power abandonment during the low load period. Therefore, the accommodation effect of wind power is reflected by the newly increased wind power accommodation volume after the system configures the energy storage, and the objective function is as follows:

[0118] max f2 = Q wind (8)

[0119]

[0120] In the formula: Q wind is the newly increased wind power accommodation volume of the system after configuring the energy storage; is the wind power grid-connected power at time t after configuring the energy storage; P wind,t is the wind power accommodation power at time t without the energy storage system.

[0121] The constraint conditions of the outer optimization sub-model include: system power flow constraint, node voltage constraint, energy storage system capacity constraint, energy storage charge and discharge power constraint, and energy storage state of charge constraint, which are specifically as follows:

[0122] ① System power flow constraint:

[0123]

[0124] In the formula: P Gi and Q Gi are the active and reactive powers output by the thermal power unit at node i respectively; P si is the active power output of wind power at node i; P Ei and Q Eiare the active and reactive power output by the energy storage at node i; P li and Q li are the active and reactive power of the load at node i; P loss,i is the power loss at node i; G ij and B ij are the real and imaginary parts of the admittance matrix of the branch node in the i-th row and j-th column; θ ij is the phase angle difference between nodes i and j; U i and U j are the voltages of nodes i and j.

[0125] ② Node voltage constraint:

[0126]

[0127] In the formula: are the minimum and maximum values of the voltage amplitude respectively.

[0128] ③ Energy storage capacity constraint:

[0129]

[0130] In the formula: are the minimum configured capacity and the maximum configured capacity of the energy storage system respectively.

[0131] ④ Energy storage charge and discharge power constraint:

[0132]

[0133] In the formula: P ch,t 、P dis,t are the charge and discharge powers of the energy storage at time t respectively.

[0134] ⑤ Energy storage state of charge constraint:

[0135]

[0136] In the formula: S oc,t is the state of charge of the energy storage at time t; δ is the self-discharge power of the energy storage; E E is the capacity of the energy storage; S oc,max 、S oc,min are the upper and lower limits of the state of charge of the energy storage. The formula for calculating the state of charge of the energy storage is as follows:

[0137] Considering the sustainability of the energy storage operation, the energy storage needs to return to the initial state after one day of operation, that is:

[0138] S oc,start =S oc,end (15)

[0139] In the formula: S oc,startis the state of charge of the energy storage system at the initial moment; S oc,end is the state of charge of the energy storage system at the end moment.

[0140] 2) Inner layer optimization sub-model

[0141] The inner layer optimization sub-model comprehensively considers the operating cost and deep peak shaving cost of thermal power units, the penalty cost of wind curtailment, the peak shaving cost paid by the system to the energy storage power station, and the peak shaving compensation income of thermal power units. With the lowest total system peak shaving cost as the goal, the objective function is as follows:

[0142]

[0143] In the formula: C G,s is the operating cost and deep peak shaving cost of thermal power units; C wloss,s is the penalty cost of wind curtailment; C ess,s is the peak shaving cost paid by the system to the energy storage power station. Numerically, the peak shaving cost C ess,s paid by the system to the energy storage power station is equal to the peak shaving compensation income I 2,s obtained by the energy storage power station; I G,s is the peak shaving compensation income of thermal power units.

[0144] ① Operating cost and deep peak shaving cost of thermal power units

[0145] Thermal power units undertake most of the system peak shaving tasks. The peak shaving process is divided into 3 stages, namely conventional peak shaving, oil-free deep peak shaving, and oil-injected deep peak shaving, and the costs in each stage are different. In the conventional peak shaving stage, the operating cost of thermal power units is mainly the coal consumption cost:

[0146]

[0147] In the formula: C G,s,1 represents the operating coal consumption cost of thermal power units; a i , b i and c i respectively represent the consumption coefficient of thermal power unit i; represents the output of thermal power units.

[0148] The unit loss cost when thermal power units enter the deep peak shaving stage is as follows:

[0149]

[0150] In the formula: γ is the actual operating loss coefficient of the thermal power plant; S u,i is the purchase cost of the i-th thermal power unit; N f,i,t is the number of rotor crack initiation cycles of the i-th thermal power unit at time t, and this value is related to the unit output.

[0151] At this time, the thermal power unit continues to perform deep peak shaving. When the output level of the thermal power unit is lower than a certain value, additional oil injection costs will also be incurred:

[0152]

[0153] In the formula: r oil is the oil price in the current season; is the oil injection volume of the i-th thermal power unit at time t during the oil injection deep peak shaving stage.

[0154] To sum up, the specific hierarchical energy consumption cost function during the peak shaving of thermal power units is as follows:

[0155]

[0156] In the formula: is the maximum output value of the thermal power unit; is the minimum output value of the thermal power unit during conventional peak shaving; is the minimum output value of non-oil-injected deep peak shaving; is the minimum output value of oil-injected deep peak shaving.

[0157] ② Penalty cost for wind abandonment

[0158]

[0159] In the formula: c w is the unit penalty cost for wind abandonment; is the predicted output of the wind turbine at time t on the typical day s; is the wind power grid-connected power at time t on the typical day s.

[0160] ③ Compensation income for peak shaving of thermal power units

[0161]

[0162] In the formula: γ p is the compensation cost per unit of peak shaving power of the thermal power unit; is the deep peak shaving power of the unit.

[0163] The constraint conditions of the inner-layer optimization sub-model include:

[0164] ① Power balance constraint:

[0165]

[0166] In the formula: P l,t,s is the load prediction value at time t in scenario s; is the actual output power of the energy storage system at time t on the typical day s; is the output power of the wind farm at time t on the typical day s.

[0167] ②Operating constraints of thermal power units:

[0168] The operating constraints of thermal power units mainly include power constraints, ramp rate constraints, and start-stop constraints of thermal power units, which are specifically as follows:

[0169]

[0170] In the formula: are the upper and lower limits of the output of the i-th thermal power unit; is the output of the i-th thermal power unit; are the maximum downward ramp and maximum upward ramp of the i-th thermal power unit respectively; u i,t is the start-stop state of unit i at time t (1 represents "start", 0 represents "stop"); is the minimum continuous start-up and shutdown time of unit i.

[0171] ③Wind power constraints:

[0172]

[0173] In the formula: are the actual output and predicted output of wind power respectively.

[0174] The operating constraints of the energy storage system are the same as those of the outer-layer optimization sub-model, which will not be elaborated here.

[0175] S3. Solve the double-layer optimization model to obtain the energy storage configuration plan and complete the energy storage configuration process.

[0176] ①Solution method

[0177] As Figure 8 shown in the solution process, the decision variables of the outer-layer optimization sub-model are the location selection and capacity configuration of the energy storage system. Among them, the location selection is an integer variable, and the capacity configuration of the energy storage system is a continuous variable. When introducing an algorithm to optimize the energy storage configuration, an integer operation is performed on the location parameter. The particle swarm algorithm has certain advantages relying on the population advantage. In this paper, the solution of the outer-layer optimization sub-model is based on the optimal power flow, and an improved multi-objective continuous particle swarm method is used to realize the site selection and capacity determination of the energy storage.

[0178] In the inner-layer energy storage-assisted thermal power unit peak shaving optimization scheduling model, the CPLEX solver in Matlab is used for solution, so as to obtain the energy storage charge and discharge power and the newly added wind power acceptance under different energy storage system configuration plans.

[0179] ②Solution process

[0180] The double-layer optimization model first assigns an initial configuration plan to the outer-layer optimization sub-model. Based on this, the inner-layer optimization sub-model conducts optimization, and the obtained result is returned to the outer-layer optimization sub-model. The inner and outer layers iterate alternately until the optimization result is obtained, completing the energy storage configuration process.

[0181] In this embodiment, an improved IEEE 30-node system is used for case analysis.

[0182] (1) System parameter settings

[0183] As Figure 3 shown. The system includes six thermal power units and a wind farm. The corresponding situation between the thermal power units and the nodes is shown in Table 1, and the installed capacity of the wind farm is 420 MW. Select a 200 MW thermal power unit for deep peak shaving, and other units only perform conventional peak shaving. The minimum load rate during the conventional peak shaving stage of the unit is 50%, the minimum load rate during the non-oil deep peak shaving stage is 40%, and the minimum load rate during the oil-injected deep peak shaving stage is 30%. γ is the actual operation loss coefficient of the thermal power unit, and γ = 1.2 is set during the deep peak shaving stage. S u,i is the purchase cost of the thermal power unit, and the unit construction cost is 3,464 yuan / kW; the fuel consumption of the unit during the oil-injected deep peak shaving stage is 4.8 t / h, and the oil price is 6,130 yuan / t. Set the compensation price for the non-oil deep peak shaving stage of the thermal power unit to 200 yuan / MWh, and the compensation price for the oil-injected stage to 500 yuan / MWh.

[0184] The basic parameters of the energy storage are shown in Table 2. The arbitrage income of the energy storage system is realized according to the peak-valley electricity price. The time-of-use electricity price is set as follows: during the high electricity price period, from 10:00 to 14:00 and from 19:00 to 21:00, the electricity price is 920 yuan / MW·h; during the low electricity price period, from 00:00 to 09:00, from 15:00 to 18:00, and from 22:00 to 24:00, the electricity price is set at 498 yuan / MW·h. The penalty coefficient for wind power curtailment is 500 yuan / MW.

[0185] Table 1 Output unit parameters

[0186]

[0187] Table 2 Energy storage device parameters

[0188]

[0189] (2) Implementation process

[0190] Through the execution process of the above method for implementation and solution, the optimal energy storage configuration plan is obtained, and the following scenarios are set for comparative analysis:

[0191] Scenario 1: Without considering energy storage, the thermal power units perform conventional peak shaving;

[0192] Scenario 2: Without considering energy storage, the thermal power unit performs deep peak shaving;

[0193] Scenario 3: Considering the economy of energy storage, without considering the sharing cost of energy storage and the effect of wind power accommodation, the thermal power unit performs deep peak shaving;

[0194] Scenario 4: Considering the economy of energy storage and the effect of wind power accommodation, without considering the sharing cost of energy storage, the thermal power unit performs deep peak shaving;

[0195] Scenario 5: Considering the economy of energy storage and the effect of wind power accommodation, taking into account the sharing cost of energy storage, the thermal power unit performs deep peak shaving.

[0196] Scenarios 1 and 2 are scenarios without energy storage configuration. Then, the inner and outer layer problems are transformed into an inner layer single-objective optimization problem, considering the differences between the conventional peak shaving and deep peak shaving of the thermal power unit without energy storage configuration. The optimization results of Scenarios 1 and 2 are shown in Table 3.

[0197] Table 3 Results of each system operation index for Scenarios 1 and 2

[0198]

[0199] As shown in Table 3, without energy storage configuration, the total system peak shaving cost is relatively high. The thermal power unit undertakes most of the peak shaving tasks, resulting in a relatively high unit operation cost. In Scenario 1, the thermal power unit only performs conventional peak shaving. The limited output of the unit causes the system's wind curtailment rate to reach 9.49%, and a wind curtailment penalty cost as high as 332,800 yuan is generated. The system faces great peak shaving pressure. In Scenario 2, the thermal power unit performs deep peak shaving. The reduction in the output of the thermal power unit provides space for the grid connection of wind power, and the wind curtailment rate is reduced by 5.05%, reducing the wind curtailment penalty cost by 177,200 yuan. Although the unit loss cost and oil injection cost increase when the thermal power unit performs deep peak shaving, making the operation cost of the thermal power unit increase by 46,500 yuan compared with Scenario 1, the unit obtains a peak shaving compensation income of 141,100 yuan. And benefiting from the significant reduction in the wind curtailment penalty cost, the total system peak shaving cost in Scenario 2 is reduced by 271,800 yuan.

[0200] Such as Figure 4 、 5 are respectively the peak shaving unit output situation and wind power accommodation situation for Scenarios 1 and 2. It can be seen from Figure 6 that in Scenario 2, a 200MW thermal power unit participates in the system's deep peak shaving and is in the oil injection stage. Therefore, a part of the oil injection cost is generated, increasing the unit operation cost. Figure 5It can also be seen from Comparative Scenarios 1 and 2 that deep peak shaving of thermal power units significantly promotes the accommodation of wind power, and the wind curtailment volume is significantly reduced. To sum up, under the premise of ensuring the minimum system operation cost, deep peak shaving of thermal power units provides more grid connection space for wind power, improves the flexibility of the system, and alleviates the peak shaving pressure of the system.

[0201] After the power grid is configured with energy storage, the optimized results of the energy storage capacity configuration for Scenarios 3-5 are shown in Table 4.

[0202] Through the analysis of the simulation results of Scenarios 3 and 4 in Table 4, without considering the cost sharing of energy storage, in Scenario 4, the effect of wind power accommodation is considered in the objective function. Compared with Scenario 3, the net income of energy storage only decreases by 0.45 million yuan, but the wind power accommodation volume increases by 78.1 MW·h. Therefore, if only the system operation cost is considered, there are limitations for further wind power accommodation. Comparing the simulation results of Scenarios 4 and 5, it can be seen that without considering the cost sharing of energy storage, the configured energy storage capacity of the system is relatively small, and at this time the system still faces the problem of large peak shaving pressure. Scenario 5 considers the effect of wind power accommodation and the cost sharing of energy storage, and when the configured energy storage capacity is 55 MW / 180 MW·h, a good income of 4.09 million yuan is obtained. At this time, not only a large amount of wind power is accommodated, but also the peak shaving demand of the system can be better met, and the peak shaving pressure can be alleviated.

[0203] Table 4 Location and Capacity Determination Results of Energy Storage under Different Scenarios

[0204]

[0205] Table 5 shows the system peak shaving operation indexes under different scenarios. Compared with when no energy storage is configured, although a part of the income is increased after the system is configured with energy storage, the access of the energy storage system significantly reduces the operation cost of thermal power units, greatly reduces the wind curtailment rate, and also greatly reduces the total system peak shaving cost. Compared with Scenario 3, Scenario 4 considers the effect of wind power accommodation, the wind curtailment rate is reduced by 1.12%, the wind curtailment penalty cost is reduced by 3.91 million yuan, and therefore the total system peak shaving cost is also reduced by 4.56 million yuan. After the energy storage is accessed in Scenarios 3 and 4, the peak-valley difference of the load is relatively reduced. Limited by the capacity of the energy storage system, its peak shaving and valley filling ability is limited. In Scenario 5, the cost sharing of energy storage is considered in the objective function, and the obtained energy storage configuration result can better meet the system requirements, and the promotion effect on the accommodation of wind and light is more obvious. The wind curtailment rate is only 0.71%, and the total system peak shaving cost is also reduced by 2.56 million yuan compared with Scenario 4. To sum up, when considering the effect of risk accommodation and the cost sharing of energy storage, the energy storage power station not only obtains good income, but also the energy storage configuration scheme at this time can better meet the system requirements, effectively reducing the wind curtailment volume and the system peak shaving cost.

[0206] Table 5 System Peak Shaving Operation Indexes under Different Scenarios

[0207]

[0208] As Figure 6 Shown in Figure 6 is the optimal power system operation scheduling result under the optimal energy storage configuration. It can be seen that during the load valley, the energy storage is charged, and during the load peak, the energy storage is discharged, reducing the peak-valley difference of the grid load and making the power generation and load demand tend to be balanced. Therefore, the access of energy storage effectively alleviates the problem of insufficient peak regulation of the system. While the energy storage power station obtains good benefits, it also reduces the system peak regulation cost.

[0209] To further analyze the promotion effect of the method proposed in the present invention on the system's wind power accommodation, the influence of the installed capacity of wind and light on the system optimization result in different scenarios is studied, the accommodation of different wind power installed capacities in each scenario is analyzed, and the effectiveness of the proposed method in improving wind power accommodation is verified.

[0210] As Figure 7 Shown in Figure 7 are the curtailment rate optimization results of different wind power installed capacities in different scenarios. It can be seen from the figure that as the wind power installed capacity increases, the curtailment rates of different scenarios all increase. However, at the same installed capacity, the curtailment rates from high to low are Scenario 1, Scenario 2, Scenario 3, Scenario 4, and Scenario 5, indicating that deep peak regulation of thermal power units can improve the wind power accommodation ratio, and reasonable configuration of energy storage in the system can effectively reduce the curtailment volume. It can be seen from this that the method proposed in the present invention can effectively improve the wind power accommodation level.

[0211] On the premise of obtaining the optimal energy storage configuration, that is, under Scenario 5, the influence of different wind power installed capacities on the system economy is explored, and the results are shown in Table 6. It can be seen that as the wind power installed capacity increases, the curtailment penalty cost also increases significantly. When the wind power installed capacity is 510 MW, the curtailment penalty cost has reached as high as 461,900 yuan, causing a large amount of resource waste. At this time, in order to accommodate wind power as much as possible, the thermal power units carry out deep peak regulation by injecting oil. Therefore, the peak regulation income of thermal power increases continuously. However, the thermal power units are in the oil injection stage for a long time, which not only generates high oil injection costs but also causes great losses to the units, reducing the life of the thermal power units and seriously affecting the stable operation of the system. To sum up, the installed capacity of wind power in the system is not necessarily the larger the better, and various factors should be comprehensively considered to select an appropriate installed capacity.

[0212] Table 6 System peak regulation operation indexes for different new energy installed capacities in Scenario 5

[0213]

[0214] In summary, by comprehensively considering the access of wind farms and the system peak shaving requirements and optimizing the configuration of the energy storage system, the present invention can not only improve the peak shaving capacity and operation economy of the power system, but also reduce environmental pollution, promote the market-oriented reform of the power industry, support the sustainable development of new energy, and provide a more efficient, economic and environmentally friendly peak shaving solution for the power system. By optimizing the energy storage configuration, the present invention reduces the dependence on deep peak shaving of thermal power units to lower the overall peak shaving cost; by virtue of the fast response characteristic of the energy storage power station, the present invention improves the adaptability of the power system to wind power fluctuations and enhances the peak shaving flexibility; by reasonably configuring the energy storage, the present invention improves the wind power accommodation capacity of the power system and reduces the wind curtailment rate; by the coordinated optimal scheduling of the energy storage power station and the thermal power units, the present invention improves the operation economy of the power system; by reducing the oil injection deep peak shaving of thermal power units, the present invention reduces environmental pollution; by establishing a peak shaving compensation income and cost sharing model for the energy storage power station, the present invention provides a reference for policy making, promotes the market-oriented reform of the power industry, and improves the enthusiasm of power generation enterprises to participate in peak shaving.

[0215] Embodiment 2

[0216] This embodiment provides an energy storage configuration system considering the access of wind farms and peak shaving requirements, including:

[0217] A data acquisition module: configured to acquire the operation data related to energy storage in the power system accessing the wind farm;

[0218] An optimization model construction module: configured to construct a two-layer optimization model considering wind power accommodation and peak shaving requirements based on the operation data, where the two-layer optimization model includes an outer-layer optimization sub-model and an inner-layer optimization sub-model, the outer-layer optimization sub-model takes the maximum net income of the energy storage and the best wind power accommodation effect as optimization objectives, and the inner-layer optimization sub-model takes the lowest total peak shaving cost as the optimization objective;

[0219] A solution module: configured to solve the two-layer optimization model and output an energy storage configuration plan to complete the energy storage configuration process.

[0220] The rest is the same as in Embodiment 1.

[0221] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0222] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention can be implemented using various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0223] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0224] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in this computer-readable memory generate a manufactured article including an instruction device, and this instruction device realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0225] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 one process or a plurality of processes and / or boxes Figure 1 steps for the functions specified in one box or a plurality of boxes.

[0226] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0227] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for energy storage configuration considering the access of wind farms and peak shaving requirements, characterized in that, It includes the following steps: Obtain the operation data related to energy storage in the power system accessing the wind farm; Based on the operation data, construct a two-layer optimization model considering wind power consumption and peak shaving requirements, where the two-layer optimization model includes an outer-layer optimization sub-model and an inner-layer optimization sub-model. The outer-layer optimization sub-model aims to maximize the net income of energy storage and optimize the wind power consumption effect, and the inner-layer optimization sub-model aims to minimize the total peak shaving cost; Solve the two-layer optimization model and output the energy storage configuration plan to complete the energy storage configuration process.

2. The energy storage configuration method considering the access of wind farms and peak shaving requirements according to claim 1, wherein, The operation data includes the operation data of the wind farm, the operation data of thermal power units, and the operation data of the energy storage power station.

3. The energy storage configuration method considering the access of wind farms and peak shaving requirements according to claim 1, characterized in that The outer-layer optimization sub-model includes an objective function and corresponding constraint conditions. The objective function includes a sub-objective function for maximizing the net income of energy storage and a sub-objective function for optimizing the wind power consumption effect. The constraint conditions include: ① Power flow constraint: ② Node voltage constraint: ③ Energy storage capacity constraint: ④ Energy storage charge and discharge power constraint: ⑤ Energy storage state of charge constraint: S oc,start = S oc,end Wherein, P Gi and Q Gi are the active and reactive power outputs of the thermal power unit at node i respectively, P si is the active power output of the wind power at node i, P Ei and Q Ei are the active and reactive power outputs of the energy storage at node i respectively, P li and Q li are the active and reactive power of the load at node i respectively, P loss,i is the power loss at node i, G ij and B ij are the real and imaginary parts of the admittance matrix of the branch node at the i-th row and j-th column, θ ij is the phase angle difference between nodes i and j, U i and U j are the voltages of nodes i and j, are the minimum and maximum values of the voltage amplitude respectively, are the minimum configuration capacity and the maximum configuration capacity of the energy storage power station respectively, E E is the capacity of the energy storage, P ch,t 、P dis,t are the charging and discharging powers of the energy storage at time t respectively, P max is the maximum charging and discharging power of the energy storage, S oc,t is the state of charge of the energy storage at time t, δ is the self-discharge power of the energy storage, S oc,max 、S oc,min are the upper and lower limits of the state of charge of the energy storage, η C 、η D are the charging and discharging efficiencies of the energy storage, and Δt is the sampling time step.

4. A method for energy storage configuration considering the access of wind farms and peak shaving requirements according to claim 3, characterized in that The net income of energy storage takes into account the investment and construction cost, operation and maintenance cost, peak shaving sharing cost, deep peak shaving income, and arbitrage income of the energy storage power station. The expression of the sub-objective function for maximizing the net income of energy storage is: Where: Wherein, f1 is the net income of the energy storage power station, S is the number of typical output scenarios, and ρ s is the probability of occurrence of the typical output scenario s, and I 1,s , I 2,s are the arbitrage income and peak shaving compensation income of the energy storage power station on the typical day s, and C 3,s , C 4,s are the operation and maintenance cost and peak shaving sharing cost of the energy storage power station on the typical day s, and C inv is the converted investment and construction "daily" cost of the energy storage power station, β is the capital recovery factor, r and y ess are the discount rate and the service life of the energy storage respectively, and C P , C E are the unit price of capacity configuration and the unit price of power configuration, P E , E E are the power and capacity of the planned energy storage power station, T is the total duration of the sampling time, are the charge and discharge powers of the energy storage power station at the t-th moment on the typical day s respectively, and η C , η D are the charge and discharge efficiencies of the energy storage, P p,t is the real-time peak-valley electricity price of the power grid at the t-th moment, Δt is the sampling time step, and P com is the peak shaving compensation unit price, and C OM is the unit price of the operation and maintenance cost of the energy storage power station, is the total peak shaving compensation expenditure at the t-th moment; is the grid-connected power and grid-connected capacity of the conventional thermal power unit i at the t-th moment.

5. The energy storage configuration method considering the access of wind farms and peak shaving requirements according to claim 3, wherein The increased wind power acceptance after configuring energy storage in the power system is used to represent the wind power consumption effect. The expression of the sub-objective function for optimizing the wind power consumption effect is: maxf2 = Q wind In the formula, f2 is the sub-objective function with the optimal wind power consumption effect, Q wind is the additional wind power acceptance capacity of the power system after configuring energy storage, T is the total duration of the sampling time, P wind,t is the wind power acceptance power of the power system without energy storage at time t.

6. A method for energy storage configuration considering the access of wind farms and peak shaving requirements according to claim 1, characterized in that, The inner-layer optimization sub-model includes an objective function and corresponding constraint conditions. The objective function is expressed as: Where: In the formula, F s is the objective function, S is the number of typical output scenarios, and ρ s is the probability of the occurrence of the typical output scenario s. C G,s is the operating cost and deep peak shaving cost of thermal power units. C wloss,s is the curtailment penalty cost. C ess,s is the peak shaving cost paid to the energy storage power station. Numerically, the peak shaving cost C ess,s paid to the energy storage power station is equal to the peak shaving compensation income I 2,s obtained by the energy storage power station. I G,s is the peak shaving compensation income of thermal power units, T is the total duration of the sampling time, and c w is the unit curtailment penalty cost. is the predicted output of the wind turbine at time t under the typical day s. is the wind power grid-connected power at time t under the typical day s. γ p is the unit peak shaving power compensation cost of the thermal power unit. is the deep peak shaving power of the unit, and Δt is the sampling time step. The constraint conditions include: ① Power balance constraint: ② Thermal power unit operation constraint: ③ Wind power constraint: ④ Energy storage capacity constraint: ⑤ Energy storage charge and discharge power constraint: ⑥ Energy storage state of charge constraint: S oc,start = S oc,end Where N G is the number of thermal power units, P l,t,s is the load prediction value at time t under scenario s, is the actual output power of the energy storage system power station at time t under the typical day s, is the output of the thermal power unit, is the output power of the wind farm at time t under the typical day s, are the upper and lower limits of the output of the i-th thermal power unit, is the output of the i-th thermal power unit, are the maximum downward ramp rate and the maximum upward ramp rate of the i-th thermal power unit respectively, u i,t is the start-stop state of unit i in the t period, 1 means "start", 0 means "stop", is the minimum continuous start-up and shut-down time of unit i, are the actual output and the predicted output of the wind power respectively; are the minimum configured capacity and the maximum configured capacity of the energy storage power station respectively, E E is the capacity of the energy storage, P ch,t 、P dis,t are the charging and discharging powers of the energy storage at time t respectively, P max is the maximum charging and discharging power of the energy storage, S oc,t is the state of charge of the energy storage at time t, δ is the self-discharge power of the energy storage, S oc,max 、S oc,min are the upper and lower limits of the state of charge of the energy storage, η C 、η D are the charging and discharging efficiencies of the energy storage, and Δt is the sampling time step.

7. A method for energy storage configuration considering the access of wind farms and peak shaving requirements according to claim 6, characterized in that The operating cost and deep peak shaving cost C of the thermal power unit G,s include the operating cost of the thermal power unit in the conventional peak shaving stage, and the deep peak shaving costs in the oil-free deep peak shaving stage and the oil-injected deep peak shaving stage. The operating cost and deep peak shaving cost C of the thermal power unit G,s are expressed as: Where: Where, C G,s,1 is the operating cost of the thermal power unit, C Gloss,s is the unit loss cost when the thermal power unit enters the deep peak shaving stage, C oil,s is the additional oil injection cost generated when the thermal power unit enters the deep peak shaving stage, is the minimum output value of the thermal power unit for conventional peak shaving, is the maximum output value of the thermal power unit, is the minimum output value of oil - free deep peak shaving, is the minimum output value of oil - injection deep peak shaving, represents the output of the thermal power unit, a i , b i and c i respectively represent the consumption coefficients of the thermal power unit i, γ is the actual operation loss coefficient of the thermal power plant, S u,i is the purchase cost of the i - th thermal power unit, N G is the number of thermal power units, T is the total duration of the sampling time, N f,i,t is the number of rotor crack - causing cycles of the i - th thermal power unit at time t, r oil is the oil price in the current season, is the oil injection volume of the i - th thermal power unit at time t during the oil - injection deep peak shaving stage.

8. A method for energy storage configuration considering the access of wind farms and peak shaving requirements according to claim 1, characterized in that The two-layer optimization model is solved by the method of alternating iteration between the inner-layer optimization sub-model and the outer-layer optimization sub-model. The outer-layer optimization sub-model is solved by using an improved multi-objective continuous particle swarm algorithm, and the inner-layer optimization sub-model is solved by using a CPLEX solver.

9. A energy storage configuration system considering the access of wind farms and peak shaving requirements, characterized in that, It includes: Data acquisition module: used to obtain the operation data related to energy storage in the power system accessing the wind farm; Optimization model construction module: used to construct a two-layer optimization model considering wind power consumption and peak shaving requirements based on the operation data. The two-layer optimization model includes an outer-layer optimization sub-model and an inner-layer optimization sub-model. The outer-layer optimization sub-model aims to maximize the net income of energy storage and optimize the wind power consumption effect, and the inner-layer optimization sub-model aims to minimize the total peak shaving cost; Solution module: used to solve the two-layer optimization model and output the energy storage configuration plan to complete the energy storage configuration process.

10. A computer-readable storage medium, characterized in that, It includes one or more programs executed by one or more processors of the power supply device. The one or more programs include instructions for executing the energy storage configuration method considering wind farm access and peak shaving requirements as described in any one of claims 1-8.

Images