Network construction / network following type energy storage optimization configuration method and system considering frequency support and wind power consumption, medium and equipment

Abstract

The invention relates to the field of energy storage grid connection and operation control, and discloses a network construction / network following type energy storage optimization configuration method and system considering frequency support and wind power consumption, a medium and equipment, and the method comprises the steps: inputting wind power output data and microgrid related parameters into a constructed network construction / network following type energy storage configuration model; solving the model, optimizing the output of each device by taking the minimum daily average cost in a planning period as an optimization target, and obtaining a typical daily operation result of each device; taking random fluctuation of fan operation as disturbance power of frequency safety to check whether the micro-grid meets the frequency safety constraint or not, and according to a check result of the frequency safety constraint, updating a grid constructing type energy storage configuration scheme and a grid following type energy storage configuration scheme; whether the relative difference between the current optimal feasible solution and the optimal solution reaches a set value or not is judged, and if yes, the optimal configuration scheme and the corresponding costs are output; and if not, recording the optimal configuration scheme and the corresponding operation result of the network construction type energy storage and the network following type energy storage, and continuing to update the energy storage configuration scheme.

Description

Technical Field

[0001] The present invention relates to the field of energy storage grid connection and operation control technology, and in particular to a method, system, medium and equipment for optimizing grid-building / grid-following energy storage configuration that takes frequency support and wind power consumption into consideration. Background Art

[0002] In order to reduce carbon emissions and power generation costs, wind power generation systems are connected to offshore oil and gas field power grids in large numbers, resulting in a "high proportion of renewable energy and high proportion of power electronic equipment" form. Due to the intermittent and random characteristics of wind power output, large-scale offshore wind power grid connection is difficult to absorb, and the system inertia and damping level will further decrease. The stability problem is becoming increasingly prominent and needs to be solved urgently. The battery energy storage system (BESS) has flexible charging and discharging characteristics, can participate in inertia response and primary frequency modulation, and effectively improve the frequency stability of the power system.

[0003] Existing energy storage configurations are designed to promote the consumption of renewable energy, and generally consider distributed energy in distribution networks, and there is no relevant research on offshore oil and gas field microgrids. The energy storage configuration plan takes economy as the optimization goal, considers system operation and equipment constraints, builds a model, and completes the solution based on an appropriate intelligent algorithm or solver. The battery energy storage planning method that considers dynamic frequency support disclosed in existing literature takes the system's short-term frequency safety requirements as constraints, guides energy storage site selection by evaluating the system's inertia distribution, and plans energy storage capacity with the goal of minimizing investment and system operating costs. The energy storage planning method for battery energy storage participating in frequency response adopts a process such as Figure 1 As shown. The upper planning layer model calculates the system node inertia based on the unit parameters to guide the energy storage site selection, and the lower layer solves the planned capacity and typical daily operation results of the energy storage. Finally, through the upper and lower layers of the cycle iteration, the optimal planning result is obtained. The two-layer model process is as follows Figure 1 However, this technology has the following problems:

[0004] 1) The configured energy storage type is single: only grid-type energy storage is configured for frequency support, and grid-following energy storage is not configured to improve wind power consumption; 2) The economic efficiency is limited due to the configuration of only grid-type energy storage: the configuration cost of grid-type energy storage is high, and the configuration cost of grid-following energy storage is relatively low. The configuration of a single energy storage limits the economic potential of the energy storage system configuration; 3) The disturbance power of this method is fixed, resulting in low applicability of the method to the volatility of wind power output; 4) The frequency-constrained linearization method used in this method has many coefficients, and the coefficients need to be refitted as the data changes, resulting in a large computational overhead.

[0005] However, there are still the following difficulties in solving these problems: when configuring energy storage with multiple control types (grid-building control / grid-following control), it is necessary to analyze the characteristics of different types of energy storage and study the coordinated optimization operation between different energy storage; in order to achieve the best economy, the different costs of different energy storage operation control methods should be fully considered, so as to achieve further improvement in new energy consumption and system economy; in order to fully consider the volatility of wind power output, it is necessary to study the impact of the disturbance power generated by the day-ahead wind turbine prediction error on the grid frequency, so as to get closer to the real scene and improve the adaptability of the proposed method to the volatility of wind power output; considering the multi-variable and high computational overhead characteristics of the existing frequency constraint linearization, it is necessary to study a simple and universal linearization method. Summary of the invention

[0006] In view of the above problems, the purpose of the present invention is to provide a method, system, medium and equipment for optimizing the configuration of grid-building / grid-following type energy storage taking into account frequency support and wind power absorption, which is particularly aimed at offshore oil and gas field microgrids with a high proportion of offshore wind power grid-connected, and adopts a hybrid configuration of grid-building type and grid-following type battery energy storage systems (BESS) to achieve the optimal balance between cost-effectiveness and system stability.

[0007] To achieve the above-mentioned purpose, in the first aspect, the technical solution adopted by the present invention is: a method for optimizing the configuration of grid-building / grid-following energy storage considering frequency support and wind power consumption, which comprises: setting an offshore oil and gas field microgrid architecture and related parameters, inputting wind turbine output data and microgrid related parameters into the constructed grid-building / grid-following energy storage configuration model; solving the grid-building / grid-following energy storage configuration model, optimizing the output of each device with the minimum daily average cost during the planning period as the optimization goal, and obtaining the typical daily operation results of each device; using the random fluctuations of wind turbine operation as the disturbance power of frequency safety to check whether the microgrid meets the frequency safety constraints, and updating the grid-building and grid-following energy storage configuration schemes according to the inspection results of the frequency safety constraints; judging whether the relative gap between the current best feasible solution and the optimal solution reaches the set value, and if so, outputting the optimal configuration scheme and the corresponding costs; if not, recording the optimal configuration scheme and the corresponding operation results of the grid-building and grid-following energy storage, and continuing to update the energy storage configuration scheme.

[0008] Furthermore, the construction of the grid-building / grid-following energy storage configuration model includes: key equipment modeling and energy storage planning model;

[0009] Key equipment modeling includes BESS modeling, synchronous unit modeling and wind turbine modeling;

[0010] BESS modeling needs to consider charging and discharging power limits, working state constraints, and capacity update restrictions;

[0011] Synchronous unit modeling includes unit output upper and lower limit constraints, unit start and stop time constraints, and unit climbing constraints;

[0012] Wind turbine modeling: Wind turbine output is affected by wind speed, temperature, and appropriate meteorological conditions. The maximum output in a certain period of time is the value of maximum power tracking. Considering the improvement of wind power consumption, the upper and lower limits of wind turbine output are set.

[0013] Furthermore, the upper and lower limits of fan output are:

[0014]

[0015] In the formula, is the actual power of wind turbine w in period t; is the maximum power tracking value of D wind turbine w in period t; γ is the maximum wind abandonment rate; W is the number of installed wind turbines in the microgrid.

[0016] Furthermore, the energy storage planning model includes optimization objectives and system safety operation constraints;

[0017] The optimization goal is to minimize the average daily cost during the planning period, including the average daily investment and construction cost, operation and maintenance cost, fuel cost, synchronous machine start-up and shutdown cost, wind curtailment penalty cost and carbon emission cost;

[0018] System safety operation constraints include system frequency safety constraints and node power balance constraints involving meshed BESS.

[0019] Furthermore, the system frequency security constraints of the grid-type BESS are:

[0020] FR(t)=PFR(t)+FFR(t)

[0021]

[0022] Where FR is the total frequency response reserve in the system, which is composed of PFR provided by the synchronous machine and FFR provided by the grid-type BESS; is the PFR power of unit g; is the FFR power of the grid-type BESSe; G and E represent the number of generators and the number of grid-type energy storage, respectively; T PFR and T FFR are the complete response times of PFR and FFR, respectively, and define

[0023] RoCoF constraint: Limit the maximum RoCoF (t=0) to the system safety limit RoCoF max the following;

[0024] Quasi-steady-state constraint: The frequency is stable in the quasi-steady state. Assuming that RoCoF is 0 in the quasi-steady state, FFR and PFR have fully responded, and the quasi-steady-state constraint is obtained;

[0025] Frequency minimum point constraint: ensure that the maximum frequency deviation is less than the maximum allowable frequency deviation Δf max ; The lowest point will occur at the moment when RoCoF becomes zero nadir ; Frequency minimum point t nadir ∈[T FFR ,T PFR ), solve the FR function for the corresponding period t nadir The value of is used to obtain the frequency minimum point constraint.

[0026] Furthermore, the frequency minimum point constraint is:

[0027]

[0028] Where H is the total inertia of the system; ΔP e is the power loss; f0 is the standard frequency of the system; T PFR and T FFR are the full response times of PFR and FFR respectively; Δf max is the maximum allowable frequency deviation.

[0029] Furthermore, when solving the grid-building / grid-following energy storage configuration model, the frequency minimum point constraint is linearized, including:

[0030] For the nonlinear quadratic term (ΔP e -R E ) 2 , the convex hull relaxation method of quadratic function is used to construct a convex hull area using the relationship of the original variables and linearize the nonlinear terms;

[0031] The nonlinear term on the left side of the frequency minimum point constraint formula includes the product of two continuous variables H·R G and R G ·R E , according to the properties of the inequality, we finally get the constraint formula of the lowest frequency point.

[0032] In the second aspect, the technical solution adopted by the present invention is: a grid-building / grid-following type energy storage optimization configuration system considering frequency support and wind power consumption, which includes: a parameter setting module, which sets the offshore oil and gas field microgrid architecture and related parameters, and inputs the wind turbine output data and microgrid related parameters into the constructed grid-building / grid-following type energy storage configuration model; a model solving module, which solves the grid-building / grid-following type energy storage configuration model, takes the minimum daily average cost during the planning period as the optimization goal, optimizes the output of each device, and obtains the typical daily operation results of each device; a microgrid frequency detection module, which uses the random fluctuations of wind turbine operation as the disturbance power of frequency safety to check whether the microgrid meets the frequency safety constraints, and updates the grid-building and grid-following type energy storage configuration schemes according to the inspection results of the frequency safety constraints; an optimization configuration module, which determines whether the relative gap between the current best feasible solution and the optimal solution reaches the set value, and if it reaches the set value, outputs the optimal configuration scheme and the corresponding costs; if it does not reach the set value, records the optimal configuration scheme and the corresponding operation results of the grid-building and grid-following type energy storage, and continues to update the energy storage configuration scheme.

[0033] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0034] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0035] The present invention adopts the above technical solution, which has the following advantages:

[0036] The present invention adopts a hybrid energy storage configuration scheme for offshore oil and gas field microgrids with a high proportion of wind power access, combines the advantages of grid-building and grid-following BESS, and utilizes the lower construction and operation costs of the latter to improve the economy of the overall system. For an actual offshore oil and gas field microgrid in a certain place, examples with different wind power penetration rates and wind power disturbance sizes are designed. Comparative verification shows that the configuration methods proposed in the present invention are all optimal solutions. The hybrid configuration scheme minimizes costs and maximizes wind energy consumption by optimizing configuration while ensuring the stability of the grid frequency. Experimental data show that compared with a single configuration, the total cost of the hybrid configuration scheme is reduced by 1.5%, and the wind power utilization rate is increased by 0.5%. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Solve the flow chart for existing technology energy storage planning;

[0038] Figure 2 This is a flow chart of the energy storage optimization configuration method in an embodiment of the present invention;

[0039] Figure 3 A flow chart of solving the energy storage configuration model in an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the grid topology of an offshore oil and gas field in an embodiment of the present invention;

[0041] Figure 5 A typical daily curve diagram of wind power and load in an embodiment of the present invention;

[0042] Figure 6 It is the output curve diagram of each unit in the embodiment of the present invention;

[0043] Figure 7 This is a graph showing the output curve of a grid-type BESS in an embodiment of the present invention;

[0044] Figure 8 This is a graph showing the output curve of a grid-following BESS in an embodiment of the present invention;

[0045] Fig. 9 FIG. 4 is a diagram showing the full response of FFR in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to overcome the shortcomings of offshore oil and gas field power grid energy storage configuration in the prior art, the present invention proposes a grid-building / grid-following type energy storage optimization configuration method, system, medium and equipment that considers frequency support and wind power consumption. The present invention adopts a hybrid energy storage configuration scheme, a hybrid energy storage site selection and capacity planning model, wind power day-ahead prediction error processing and nonlinear term relaxation technology to solve the following technical problems:

[0047] (1) A storage configuration method is proposed that can not only meet the frequency stability requirements of offshore oil and gas field power grids, but also improve wind energy absorption capacity and have optimal economic benefits.

[0048] (2) Consider the impact of wind power fluctuations on grid stability within the forecast time interval of seconds to minutes in day-ahead planning.

[0049] (3) For the nonlinear terms in the frequency constraints, a linearization method is adopted that can be solved quickly and can adapt to a variety of scenarios and requirements.

[0050] The hybrid energy storage configuration scheme of the present invention is as follows: 1) Grid-following BESS: used to reduce peak loads and fill valleys, transfer excess power when the wind turbine is reversed, and use it in periods when the wind turbine output is low to improve wind power consumption. 2) Grid-building BESS: provides virtual inertia support and fast frequency response (FFR) to replace some synchronous machines, effectively improve the initial inertia and frequency response speed of the power grid, and at the same time has the wind power consumption capacity of the grid-following BESS.

[0051] The hybrid energy storage site selection and capacity planning model of the present invention is: the model includes the modeling of grid-following type and grid-forming type BESS, wind turbines and synchronous machines, as well as system operation safety constraints.

[0052] The wind power day-ahead prediction error processing of the present invention is: treating the wind power day-ahead prediction error as the disturbance power of the power grid. This strategy helps to consider the uncertainty of wind power in power grid planning and operation, thereby improving the adaptability and reliability of the system.

[0053] The nonlinear term relaxation technology of the present invention is: using the convex hull relaxation method of quadratic function and the arithmetic mean-geometric mean inequality to effectively process the nonlinear term in the frequency safety constraint.

[0054] In addition, the present invention ensures system frequency safety through system frequency safety constraints: the first-order swing equation is used to mathematically describe the dynamic frequency response after the disturbance power occurs, and RoCoF constraints, quasi-steady-state constraints and frequency minimum point constraints are set. And the configuration scheme optimization goal: to minimize the energy storage investment cost and the total system operation cost, and seek the best balance between economy and system stability.

[0055] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0056] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0057] In one embodiment of the present invention, a method for optimizing the configuration of grid-connected / grid-following energy storage is provided, which takes frequency support and wind power consumption into consideration. Figure 2 , Figure 3 As shown, the method comprises the following steps:

[0058] 1) Set the offshore oil and gas field microgrid architecture and related parameters, and input wind power output data and microgrid related parameters into the constructed grid-building / grid-following energy storage configuration model;

[0059] 2) Solve the grid-building / grid-following energy storage configuration model, take the minimum average daily cost during the planning period as the optimization goal, optimize the output of each device, and obtain the typical daily operation results of each device;

[0060] 3) The random fluctuations of wind turbine operation are used as the disturbance power of frequency safety to check whether the microgrid meets the frequency safety constraints. According to the inspection results of the frequency safety constraints, the grid-building type and grid-following type energy storage configuration schemes are updated;

[0061] 4) Determine whether the relative gap between the current best feasible solution and the optimal solution reaches the set value. If so, output the optimal configuration plan and the corresponding costs. If not, record the optimal configuration plan and the corresponding operation results of the grid-building type and grid-following type energy storage, and continue to update the energy storage configuration plan.

[0062] In the above step 1), the construction of the grid-building / grid-following energy storage configuration model includes: key equipment modeling and energy storage planning model;

[0063] 1.1) Key equipment modeling includes BESS modeling, synchronous unit modeling and wind turbine modeling;

[0064] 1.1.1) BESS modeling should consider the charging and discharging power limits such as equations (1-2), (9-10), working state constraints such as equations (3) and (11); capacity update constraints such as equations (4-8) and (12-19);

[0065] a) Grid-based BESS modeling: The mathematical model is shown in equations (1) to (8):

[0066]

[0067] Where: B is the number of nodes configured in the network-based BESS; and are the charging and discharging power of the ith BESS node in period t, respectively; is the maximum charge and discharge power of the i-th BESS node in period t; and are 0-1 variables for the charging and discharging working states of BESS respectively; is the actual output power of the i-th BESS node in period t; and are the minimum and maximum capacity limits of the ith BESS node, respectively; is the BESS remaining power of the i-th BESS node in period t; η i,t is the charging and discharging efficiency of the i-th BESS node in period t, η c and η d charging and discharging efficiency of BESS; is the remaining power at the end of a scheduling cycle; Formula (8) indicates that the remaining power at the end of a scheduling cycle should be equal to the initial power at the beginning of the next scheduling cycle, so as to keep the initial value consistent.

[0068] b) Modeling of grid-type BESS: The difference between grid-type and grid-following type modeling lies in the power limitation and capacity change of BESS when providing FFR. The specific model is as follows:

[0069]

[0070] Where: E is the number of nodes configured in the meshed BESS; Equations (13) and (17) are the power limit and power change of FFR provided by BESS respectively; ΔE e,t The power variation when providing FFR to BES, where Δt1, Δt2, and Δt3 are 5, 25, and 300 s, respectively.

[0071] 1.1.2) Synchronous unit modeling includes unit output upper and lower limit constraints (20), unit start and stop time constraints (21-22), and unit ramp constraints (23-24);

[0072]

[0073] [X on,g,(t-1) -T on,g ]·[I g,(t-1) -I g,t ]≥0 (21)

[0074] [X off,g,(t-1) -T off,g ]·[I g,t -I g,(t-1) ]≥0 (22)

[0075]

[0076] Where: I g,t It is a 0-1 variable to identify the operation of the synchronous unit; and is the upper and lower limits of the synchronous unit output; X on,g,t and X off,g,t T is the time of continuous startup and shutdown of the unit; on,g and T off,g The minimum startup and minimum downtime constraints; URg and DR g It is the up and down climbing limit of the unit.

[0077] 1.1.3) Wind turbine modeling: Wind turbine output is affected by wind speed, temperature and appropriate meteorological conditions. The maximum output in a certain period of time is the value of maximum power tracking. Considering the improvement of wind power consumption, the upper and lower limits of wind turbine output are set.

[0078] In the above embodiment, the upper and lower limits of the fan output are:

[0079]

[0080] In the formula, is the actual power of wind turbine w in period t; is the maximum power tracking value of D wind turbine w in period t; γ is the maximum wind abandonment rate; W is the number of installed wind turbines in the microgrid.

[0081] 1.2) The energy storage planning model includes optimization objectives and system safety operation constraints;

[0082] 1.2.1) The optimization goal is to minimize the average daily cost during the planning period, including the average daily investment and construction cost, operation and maintenance cost, fuel cost, synchronous machine start-up and shutdown cost, wind curtailment penalty cost and carbon emission cost;

[0083] The objective function can be written as:

[0084] f=min(C plan )=min(C inv +C main +C G +C SS +C W +C E )(26)

[0085] Where: C plan , C inv , C main , C G , C SS , C W , C E They are the average daily investment and construction cost, operation and maintenance cost, fuel cost, synchronous machine start-up and shutdown cost, wind curtailment penalty cost and carbon emission cost.

[0086]

[0087] Where: r ESS is the discount rate of ESS; ESS is the service life of ESS; They are the unit capacity investment cost and unit power investment cost of the grid-building BESS respectively; are the grid-connected storage capacity and power capacity of the grid-building BESS respectively; They are the unit capacity investment cost and unit power investment cost of the grid-connected BESS respectively; They are the grid-connected storage capacity and power capacity of the grid-following BESS respectively.

[0088]

[0089] Where: They are the unit charging and discharging power operation and maintenance costs of grid-following type and grid-building type BESS respectively.

[0090]

[0091] Where: c i is the unit fuel cost of the synchronous unit; G is the number of synchronous units; Output for synchronous units.

[0092]

[0093] Where: and is the start-up and shutdown cost coefficient of the unit; α g,t and β g,t It is a 0-1 variable to identify the start and stop of the synchronous machine.

[0094]

[0095] Where: c W and c e is the wind curtailment and carbon emission cost coefficient; is the maximum power tracking value of the wind turbine; is the actual output of the fan; car is the carbon emission correlation coefficient of synchronous machine output.

[0096] 1.2.2) System safety operation constraints include system frequency safety constraints and node power balance constraints in which the meshed BESS participates.

[0097] In the above embodiment, the FFR provided by the grid-type BESS can provide faster power injection within one second after the power imbalance begins. In this embodiment, PFR and FFR are considered to be approximately linear functions, which has been proven to be accurate.

[0098] The system frequency security constraints of the networked BESS are:

[0099] FR(t)=PFR(t)+FFR(t) (33)

[0100]

[0101] Where FR is the total frequency response reserve in the system, which is composed of PFR provided by the synchronous machine and FFR provided by the grid-type BESS; is the PFR power of unit g; is the FFR power of the grid-type BESSe; G and E represent the number of generators and the number of grid-type energy storage, respectively; T PFR and T FFR are the complete response times of PFR and FFR, respectively, and define

[0102] The frequency dynamics after the disturbance is mathematically described by the first-order swing equation, as shown in equation (36):

[0103]

[0104] Where: Δf(t) is the frequency deviation at time t; H is the total amount of the system; f0 is the standard frequency of the system; D is the load damping coefficient; P D is the system load size; ΔP e The power loss is caused by the unbalanced power of the system and the uncertainty of wind turbine output.

[0105] The effect of load damping on frequency change can be ignored. Formula (36) is simplified as shown in formula (37):

[0106]

[0107] RoCoF constraint: The maximum RoCoF occurs at t = 0, when the system unbalanced power is the largest because the frequency response power is in the dead zone and the contribution is zero. Therefore, by limiting the highest RoCoF (t = 0) to the system safety limit RoCoF max The following requirements are required, and the system RoCoF constraint is shown in formula (38);

[0108]

[0109] The total system inertia H is provided by the inertia of the synchronous machine and the virtual inertia of the networked BESS. That is:

[0110]

[0111] Where: H g is the inertia constant of the unit g; is the rated capacity of unit g; u g is the operating status of unit g; h e is the inertia constant of the grid-type BESS e; is the rated power of the grid-type BESS e.

[0112] The quasi-steady-state constraint states that before the SFR restores the frequency to the standard frequency of 50 Hz, the frequency is stable in the quasi-steady state (qss). Assuming that RoCoF is 0 in the quasi-steady state, at this time, FFR and PFR have fully responded, and the quasi-steady-state constraint is obtained;

[0113] R G +R E ≥ΔP e (1)

[0114] This means that the total amount of FR must be at least equal to the power deficit ΔP e .

[0115] Frequency minimum point constraint: In order to meet the frequency minimum point constraint and avoid the load shedding relay being triggered by too low frequency, the maximum frequency deviation must be less than the maximum allowable frequency deviation Δf max ; The lowest point will occur at the moment when RoCoF becomes zero nadir ; Frequency minimum point t nadir ∈[T FFR ,T PFR ), solve the FR function for the corresponding period t nadir The value of is used to obtain the frequency minimum point constraint.

[0116]

[0117] Then set RoCoF to zero and substitute the corresponding period FR function into equation (37) to solve for t nadir Value

[0118]

[0119] Finally, by substituting equation (42) into equation (41), the frequency minimum point constraint is obtained as follows:

[0120]

[0121] b) Node power balance constraints.

[0122]

[0123] Where: N is the number of all nodes; P Load,a,t is the node load; P L,a,t Transmit power to the line; and is the active power output by the grid-following and grid-forming BESSs at node a.

[0124] In the above step 2), since the constraint relationship of the lowest frequency point such as formula (43) is a nonlinear constraint, and considering that complex nonlinear problems will increase the amount of calculation and reduce the solution speed in the process of solving the model, it is necessary to convert the lowest frequency point constraint formula (43) into a linear constraint.

[0125] When solving the grid-building / grid-following energy storage configuration model, the frequency minimum point constraint is linearized, including:

[0126] For the nonlinear quadratic term (ΔP e -R E ) 2 , the convex hull relaxation method of the quadratic function is used to construct a convex hull region using the relationship between the original variables, and the nonlinear terms are linearized, as shown in formula (45):

[0127]

[0128] In the formula, α i and b i Used to represent the linear boundary within the convex hull, K represents the total number of convex hull vertices or segmented areas.

[0129] The nonlinear term on the left side of the frequency minimum point constraint formula includes the product of two continuous variables H·R G and R G ·R E According to the properties of the inequality, we can get equation (46), and finally get the frequency minimum point constraint equation (47).

[0130]

[0131] In this implementation example, a certain offshore oil and gas field power grid is used as an example to verify the feasibility and effectiveness of the proposed method. Figure 4 As shown in the figure. An offshore oil and gas field contains 3 central platforms (CEP) and 8 wellhead platforms (WHP). The blue connecting line in the figure is a 35kV submarine cable, and the green connecting line between CEP and WHP is a few dozen meters of onshore trestle cable. Each CEP is equipped with a synchronous machine and a wind turbine.

[0132] (1) Example parameters

[0133] The unit reactance of the power grid line of an offshore oil and gas field in a certain place is 0.02Ω / Km, the reference power is 100MVA, and the reference reactance is 12.25Ω. Each CEP is equipped with a 30MW wind turbine generator, and the wind turbine penetration rate is 95%. The wind turbine daily maximum power tracking curve and the oil and gas field power grid load curve are shown in Figure 5As shown. The synchronous machines of the offshore oil and gas field power grid are distributed on CEP 1, CEP 2 and CEP 3, with installed capacities of 5×13MW, 4×13MW and 5×13MW respectively. Both the grid-building and grid-following BESS use a single 2h system unit with a capacity of 1MW·h / 0.5MW. A single node grid-building and grid-following BESS is set to connect up to 100 BESS units each, and the initial charge state is set to 50%. Assume that the disturbance power, i.e. the short-term output shortage of the wind turbine, is e 10% of the actual output of the fan, synchronous machine H g The H of the grid-type BESS is 8s. e is 4s, the reference frequency f o =50Hz,RoCoF max =1Hz / s, maximum allowable frequency deviation Δf max =0.5Hz. Other parameters are shown in Table 1.

[0134] Table 1 Price parameters of each equipment

[0135]

[0136] In order to verify the effectiveness of the method, 8 scenarios are set up and the specific scenario conditions are shown in Table 2.

[0137] Table 2 Different comparison scenarios

[0138]

[0139] The 8 scenarios set include: ①95% penetration rate, 10% disturbance, scenario 1, BESS access is not considered; scenario 2, configure meshing type BESS, do not consider meshing type BESS; scenario 3, consider hybrid BESS access. ②70% penetration rate, 10% disturbance, scenario 4, do not consider BESS access; scenario 5, configure meshing type BESS, do not consider meshing type BESS; scenario 6, consider hybrid BESS access. ③95% penetration rate, 5% disturbance, consider hybrid BESS access, scenario 7, do not consider BESS access; scenario 8, configure meshing type BESS, do not consider meshing type BESS; scenario 9, consider hybrid BESS access. ④95% penetration rate, 15% disturbance, scenario 10, consider hybrid BESS access.

[0140] (2) Analysis of BESS configuration results

[0141] The BESS capacity configuration and optimization results for scenarios 1-3 are summarized in Table 3.

[0142] Table 3 Comparison of energy storage configuration schemes and typical daily costs under various scenarios

[0143]

[0144] Compared with not configuring BESS, the investment and operating costs of configuring BESS can be fully compensated by reducing the start-up and shutdown costs, fuel costs, carbon emission costs, and wind abandonment costs of synchronous machines, and obtain a generous return of at least 49%. Comparing scenarios 1 and 2, it is found that the frequency disturbance caused by the short-term power shortage of wind turbines in scenario 1 can only be met by the synchronous machine through dynamic response to meet the system frequency safety constraints. Since the disturbance power is proportional to the actual output of the wind turbine, if the frequency safety constraints are to be met, it can only be achieved by reducing the disturbance or increasing the number of synchronous machines. Therefore, even in the case of a high penetration rate, multiple synchronous units are still turned on, and a large amount of wind abandonment is generated. In scenario 2, the method of configuring a grid-type BESS is adopted to solve the problems of poor economic efficiency and weak new energy absorption capacity of offshore oil and gas field power grids. The virtual inertia provided by the grid-type BESS can replace the synchronous machine, effectively improving the overall inertia of the power grid; and through the fast response of FFR, it can meet the frequency minimum point constraint and quasi-steady-state constraint under large disturbances, greatly reducing the number of synchronous machines in operation and improving the utilization rate of wind energy, and finally achieving a 90% reduction in the number of synchronous machines in operation and an 11% increase in wind power utilization. Comparing scenarios 2 and 3, additionally configuring a grid-type BESS can transfer the excess power of the wind turbine during reverse load at a lower price by peak shaving and valley filling, and use it for periods when the wind turbine has less output. The total cost was reduced by 1.5%, and the utilization rate of wind power was increased by 0.5%, which further improved the economy and greenness of the power grid. Through scenario comparison and analysis, it is confirmed that scenario 3, i.e., the solution proposed by the present invention, is the optimal configuration solution.

[0145] Further analysis of scenario 3 shows that the output of each unit in the load curve is as follows: Figure 6 . Through the optimal operation planning, BESS and wind turbines operate in coordination. When the value of the load curve (red) is greater than the value of the wind turbine maximum tracking curve (dark blue), the power required by the load is supplemented by the output of the synchronous machine; when the maximum tracking power of the wind turbine is greater than the system load, the grid-forming BESS and the grid-following BESS transfer the load that cannot be absorbed at this time to the time when the wind power output is insufficient. During the period of 7:00-24:00, the discharge of the synchronous machine has always been maintained above the minimum output power. This is because under the initial RoCoF constraint, the power grid needs to start a part of the synchronous machine to provide inertia. During the period of 0:00-6:00, the difference between the wind turbine output and the load at this time is too large. If the BESS is fully charged and stored at this time, it will cause the stored energy to be unable to be fully consumed in other periods. Therefore, the utilization rate of wind power is only increased to 96.94% in the end, and it cannot be fully absorbed.

[0146] Figure 7 and Figure 8The output curves of the grid-building BESS and the grid-following BESS show that the charging time of the BESS is 0:00-6:00, 13:00, 17:00, and 24:00 when the maximum tracking power of wind power is greater than the system load, thus achieving the role of assisting wind power consumption and improving economic efficiency. Fig. 9 FFR full response power of meshed BESS. It can be found that the FFR output of meshed BESS is all below the rated power. It can be inferred from formula (13) that since a large number of meshed BESS are configured to improve inertia, FFR has a large frequency modulation space, and meshed BESS can meet the frequency modulation requirements without charging.

[0147] (3) Analysis of power fluctuation and penetration rate in BESS configuration

[0148] Table 4 shows the example data when the wind power penetration rate is 70% and the volatility is 10%. Scenarios 4-6 still correspond to no BESS, only grid-type BESS, and hybrid BESS.

[0149] Table 4 Comparison of energy storage configuration schemes and typical daily costs for wind power penetration rate of 70% and volatility of 10%

[0150]

[0151] As shown in Table 4, under low penetration rates, the wind power utilization rates of the three schemes all reached a high level. Since the wind turbine output is relatively low in this scenario, the synchronous machine needs to output a certain amount of power to meet the load demand while providing inertial support. Therefore, when the wind power utilization rate is high enough, the effect of configuring energy storage to reduce the total cost is not significant enough, and it is reduced by about 10%. Comparing scenarios 4 and 5, it is obvious that the result of the mixed configuration of grid-building and grid-following energy storage is still the optimal strategy, achieving 100% absorption of wind power while also slightly reducing the total cost.

[0152] Table 5 shows the example data under different volatility when the wind power penetration rate is 95%. Scenarios 7-9 still correspond to no BESS, only grid-type BESS and hybrid BESS. Scenario 3 shows the example data with a wind power penetration rate of 95% and a volatility of 10%. Scenario 10 shows the example data with a wind power penetration rate of 95% and a volatility of 15%. They are compared and analyzed with scenarios 3 and 9.

[0153] Table 5 Comparison of energy storage configuration schemes and typical daily costs under different wind power fluctuation rates

[0154]

[0155] After analysis, it is found that the configuration of hybrid BESS in scenario 9 is still the optimal solution. The total cost is reduced by 2% compared with only configuring the grid-type, and the wind energy utilization rate is increased by 0.5%, which further confirms the superiority of the method of the present invention. Compared with scenario 3, it is found that for the calculation example with higher volatility, the corresponding wind power utilization rate is higher. The reasons are analyzed as follows: the greater disturbance power leads to a greater demand for the original inertia of the system, and the corresponding number of grid-type BESS is required to be more, and the cost of only dispatching the grid-type BESS for wind energy consumption is lower than the cost of cutting off excess wind power and using synchronous machine power supply when wind power is insufficient, and it is also lower than the cost of building a grid-type BESS and participating in the dispatching and consumption. Therefore, if the economic cost is used as the objective function, without changing the start and stop status of the synchronous machine, the more grid-type BESS is configured, the higher the corresponding wind energy utilization rate. Data with a fluctuation of 15% are given in scenario 10 to provide evidence.

[0156] In one embodiment of the present invention, a grid-building / grid-following type energy storage optimization configuration system considering frequency support and wind power consumption is provided, which includes:

[0157] The parameter setting module sets the offshore oil and gas field microgrid architecture and related parameters, and inputs wind power output data and microgrid related parameters into the constructed grid-building / grid-following energy storage configuration model;

[0158] The model solving module solves the grid-building / grid-following energy storage configuration model, takes the minimum average daily cost during the planning period as the optimization goal, optimizes the output of each device, and obtains the typical daily operation results of each device;

[0159] The microgrid frequency detection module uses the random fluctuations of wind turbine operation as the disturbance power of frequency safety to check whether the microgrid meets the frequency safety constraints. According to the inspection results of the frequency safety constraints, the grid-building type and grid-following type energy storage configuration schemes are updated;

[0160] The configuration optimization module determines whether the relative gap between the current best feasible solution and the optimal solution reaches the set value. If so, the optimal configuration plan and the corresponding costs are output; if not, the optimal configuration plan and the corresponding operating results of the grid-building type and grid-following type energy storage are recorded, and the energy storage configuration plan continues to be updated.

[0161] In the above embodiment, the construction of the grid-building / grid-following type energy storage configuration model includes: key equipment modeling and energy storage planning model;

[0162] Key equipment modeling includes BESS modeling, synchronous unit modeling and wind turbine modeling;

[0163] BESS modeling needs to consider charging and discharging power limits, working state constraints, and capacity update restrictions;

[0164] Synchronous unit modeling includes unit output upper and lower limit constraints, unit start and stop time constraints, and unit climbing constraints;

[0165] Wind turbine modeling: Wind turbine output is affected by wind speed, temperature, and appropriate meteorological conditions. The maximum output in a certain period of time is the value of maximum power tracking. Considering the improvement of wind power consumption, the upper and lower limits of wind turbine output are set.

[0166] In the above embodiment, the upper and lower limits of the fan output are:

[0167]

[0168] In the formula, is the actual power of wind turbine w in period t; is the maximum power tracking value of D wind turbine w in period t; γ is the maximum wind abandonment rate; W is the number of installed wind turbines in the microgrid.

[0169] In the above embodiment, the energy storage planning model includes optimization objectives and system safety operation constraints;

[0170] The optimization goal is to minimize the average daily cost during the planning period, including the average daily investment and construction cost, operation and maintenance cost, fuel cost, synchronous machine start-up and shutdown cost, wind curtailment penalty cost and carbon emission cost;

[0171] System safety operation constraints include system frequency safety constraints and node power balance constraints involving meshed BESS.

[0172] In the above embodiment, the system frequency security constraints in which the meshed BESS participates are:

[0173] FR(t)=PFR(t)+FFR(t)

[0174]

[0175] Where FR is the total frequency response reserve in the system, which is composed of PFR provided by the synchronous machine and FFR provided by the grid-type BESS; is the PFR power of unit g; is the FFR power of the grid-type BESSe; G and E represent the number of generators and the number of grid-type energy storage, respectively; T PFR and T FFR are the complete response times of PFR and FFR, respectively, and define

[0176] RoCoF constraint: Limit the maximum RoCoF (t=0) to the system safety limit RoCoF max the following;

[0177] Quasi-steady-state constraint: The frequency is stable in the quasi-steady state. Assuming that RoCoF is 0 in the quasi-steady state, FFR and PFR have fully responded, and the quasi-steady-state constraint is obtained;

[0178] Frequency minimum point constraint: ensure that the maximum frequency deviation is less than the maximum allowable frequency deviation Δf max ; The lowest point will occur at the moment when RoCoF becomes zero nadir ; Frequency minimum point t nadir ∈[T FFR ,T PFR ), solve the FR function for the corresponding period t nadir The value of is used to obtain the frequency minimum point constraint.

[0179] In the above embodiment, the frequency minimum point constraint is:

[0180]

[0181] Where H is the total inertia of the system; ΔP e is the power loss; f0 is the standard frequency of the system; T PFR and T FFR are the full response times of PFR and FFR respectively; Δf max is the maximum allowable frequency deviation.

[0182] In the above embodiment, when solving the grid-building / grid-following energy storage configuration model, the frequency minimum point constraint is linearized, including:

[0183] For the nonlinear quadratic term (ΔP e -R E ) 2 , the convex hull relaxation method of quadratic function is used to construct a convex hull region using the relationship of the original variables and linearize the nonlinear terms;

[0184] The nonlinear term on the left side of the frequency minimum point constraint formula includes the product of two continuous variables H·R G and R G ·R E , according to the properties of the inequality, we finally get the constraint formula of the lowest frequency point.

[0185] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0186] In one embodiment of the present invention, a computing device structure is provided. The computing device may be a terminal, which may include: a processor, a communication interface, a memory, a display screen and an input device. The processor, the communication interface and the memory communicate with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. When the computer program is executed by the processor, the method in each of the above embodiments is implemented; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface is used to communicate with an external terminal in a wired or wireless manner. The wireless manner may be implemented through WIFI, a management network, NFC (near field communication) or other technologies. The display screen may be a liquid crystal display screen or an electronic ink display screen. The input device may be a touch layer covered on the display screen, or a key, a trackball or a touchpad provided on the housing of the computing device, or an external keyboard, touchpad or mouse. The processor may call the logic instructions in the memory.

[0187] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0188] In one embodiment of the present invention, a computer program product is provided, wherein the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0189] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores server instructions, and the computer instructions enable a computer to execute the methods provided in the above embodiments.

[0190] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0191] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0192] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0193] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the configuration of grid-connected / grid-following energy storage considering frequency support and wind power consumption, characterized in that: include: Set the offshore oil and gas field microgrid architecture and related parameters, and input wind turbine output data and microgrid related parameters into the constructed grid-building / grid-following energy storage configuration model; Solve the grid-building / grid-following energy storage configuration model, take the minimum average daily cost during the planning period as the optimization goal, optimize the output of each device, and obtain the typical daily operation results of each device; The random fluctuations of wind turbine operation are used as disturbance power of frequency safety to check whether the microgrid meets the frequency safety constraint. According to the inspection results of the frequency safety constraint, the grid-building type and grid-following type energy storage configuration schemes are updated; Determine whether the relative gap between the current best feasible solution and the optimal solution reaches the set value. If so, output the optimal configuration plan and the corresponding costs. If not, record the optimal configuration plan and the corresponding operating results of the grid-building and grid-following energy storage, and continue to update the energy storage configuration plan.

2. The method for optimizing grid-building / grid-following energy storage configuration considering frequency support and wind power consumption as claimed in claim 1, characterized in that: The construction of the grid-building / grid-following energy storage configuration model includes: key equipment modeling and energy storage planning model; Key equipment modeling includes BESS modeling, synchronous unit modeling and wind turbine modeling; BESS modeling needs to consider charging and discharging power limits, working state constraints, and capacity update restrictions; Synchronous unit modeling includes unit output upper and lower limit constraints, unit start and stop time constraints, and unit climbing constraints; Wind turbine modeling: Wind turbine output is affected by wind speed, temperature, and appropriate meteorological conditions. The maximum output in a certain period of time is the value of maximum power tracking. Considering the improvement of wind power consumption, the upper and lower limits of wind turbine output are set.

3. The method for optimizing grid-building / grid-following energy storage configuration considering frequency support and wind power consumption as claimed in claim 2, characterized in that: The upper and lower limits of fan output are: In the formula, is the actual power of wind turbine w in period t; is the maximum power tracking value of D wind turbine w in period t; γ is the maximum wind abandonment rate; W is the number of installed wind turbines in the microgrid.

4. The method for optimizing grid-building / grid-following energy storage configuration considering frequency support and wind power consumption as claimed in claim 2, characterized in that: The energy storage planning model includes optimization objectives and system safety operation constraints; The optimization goal is to minimize the average daily cost during the planning period, including the average daily investment and construction cost, operation and maintenance cost, fuel cost, synchronous machine start-up and shutdown cost, wind abandonment penalty cost and carbon emission cost; System safety operation constraints include system frequency safety constraints and node power balance constraints involving meshed BESS.

5. The method for optimizing the configuration of grid-building / grid-following energy storage considering frequency support and wind power consumption as claimed in claim 4, characterized in that: The system frequency security constraints of the networked BESS are: FR(t)=PFR(t)+FFR(t) Where FR is the total frequency response reserve in the system, which is composed of PFR provided by the synchronous machine and FFR provided by the grid-type BESS; is the PFR power of unit g; is the FFR power of the grid-type BESSe; G and E represent the number of generators and the number of grid-type energy storage, respectively; T PFR and T FFR are the complete response times of PFR and FFR, respectively, and define RoCoF constraint: Limit the maximum RoCoF (t=0) to the system safety limit RoCoF max the following; Quasi-steady-state constraint: The frequency is stable in the quasi-steady state. Assuming that RoCoF is 0 in the quasi-steady state, FFR and PFR have fully responded, and the quasi-steady-state constraint is obtained; Frequency minimum point constraint: ensure that the maximum frequency deviation is less than the maximum allowable frequency deviation Δf max ; The lowest point will occur at the moment when RoCoF becomes zero t nadir ; Frequency minimum point t nadir ∈[T FFR ,T PFR ), solve the FR function for the corresponding period t nadir The value of is used to obtain the frequency minimum point constraint.

6. The method for optimizing the configuration of grid-connected / grid-following energy storage considering frequency support and wind power consumption as claimed in claim 5, characterized in that: The frequency minimum point constraint is: Where H is the total inertia of the system; ΔP e is the power loss; f0 is the standard frequency of the system; T PFR and T FFR are the full response times of PFR and FFR respectively; Δf max is the maximum allowable frequency deviation.

7. The method for optimizing grid-building / grid-following energy storage configuration considering frequency support and wind power consumption as claimed in claim 6, characterized in that: When solving the grid-building / grid-following energy storage configuration model, the frequency minimum point constraint is linearized, including: For the nonlinear quadratic term (ΔP e -R E ) 2 , the convex hull relaxation method of quadratic function is used to construct a convex hull area using the relationship of the original variables and linearize the nonlinear terms; The nonlinear term on the left side of the frequency minimum point constraint formula includes the product of two continuous variables H·R G and R G ·R E , according to the properties of the inequality, we finally get the constraint formula of the lowest frequency point.

8. A grid-building / grid-following energy storage optimization configuration system considering frequency support and wind power consumption, characterized in that: include: The parameter setting module sets the offshore oil and gas field microgrid architecture and related parameters, and inputs wind turbine output data and microgrid related parameters into the constructed grid-building / grid-following energy storage configuration model; The model solving module solves the grid-building / grid-following energy storage configuration model, takes the minimum average daily cost during the planning period as the optimization goal, optimizes the output of each device, and obtains the typical daily operation results of each device; The microgrid frequency detection module uses the random fluctuations of wind turbine operation as the disturbance power of frequency safety to check whether the microgrid meets the frequency safety constraints. According to the inspection results of the frequency safety constraints, the grid-building type and grid-following type energy storage configuration schemes are updated; The configuration optimization module determines whether the relative gap between the current best feasible solution and the optimal solution reaches the set value. If so, the optimal configuration plan and the corresponding costs are output; if not, the optimal configuration plan and the corresponding operating results of the grid-building type and grid-following type energy storage are recorded, and the energy storage configuration plan continues to be updated.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7.

10. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods described in claims 1 to 7.

Images