Double-layer optimization configuration method for rotary power flow controller and energy storage system

Abstract

The invention provides a double-layer optimal configuration method for a Rotation Power Flow Controller (RPFC) and an energy storage system, and belongs to the technical field of application of the Rotation Power Flow Controller. According to the method, a double-layer optimization configuration model is provided for solving the addressing and sizing problems of the RPFC and the energy storage system, a planning design layer model takes the system payback period under the RPFC and energy storage configuration in the whole life cycle as an optimization target, and optimal configuration of the RPFC and the energy storage position and capacity is achieved; the operation control layer model takes the annual operation cost of the power distribution network as an optimization target, and takes RPFC and energy storage as flexible adjustment resources to participate in operation optimization. According to the method, the model is solved through mixed optimization of a Pulp solver and a genetic algorithm, and finally the optimal RPFC and energy storage system locating and sizing scheme is obtained.

Description

Technical Field

[0001] The present application belongs to the technical field of application of rotating power flow controllers, and specifically relates to a double-layer optimization configuration method for a rotating power flow controller and an energy storage system. Background Art

[0002] In recent years, under the advocacy of the country's green, low-carbon and energy transformation, my country will form a "new power system with new energy as the main body", and distributed power sources with wind turbines and photovoltaics as the main body have developed rapidly. However, with the incorporation of a large number of distributed power sources into the distribution network, the problem of real-time dynamic balance between random new energy output and existing power generation and consumption has brought new challenges to the safe and economic operation of the power grid, making it more difficult for the power system to maintain the balance between supply and demand. Distributed generation will affect the load characteristics and power flow distribution in the distribution network, and some scenarios will cause overvoltage problems. Rotary power flow controller (RPFC) and energy storage, as flexible adjustment methods, play an important role in promoting photovoltaic consumption and ensuring the safe and stable operation of the power grid.

[0003] Reasonable site selection and sizing of RPFC and energy storage equipment can not only improve the safety and stability of the system, but also use RPFC and energy storage to achieve energy mutual assistance and peak shaving among different stations, thereby improving the consumption and economic benefits of new energy. Therefore, it is particularly important to study the site selection and sizing of RPFC and energy storage systems.

[0004] Most of the current research considers the coordinated operation optimization of energy storage systems and other dispatchable flexible resources in the power grid. Few studies comprehensively consider the joint site selection and sizing planning methods of rotating power flow controllers and energy storage, and ignore the flexibility of flexible interconnected devices RPFC as a control means. Summary of the invention

[0005] The present invention aims to propose a double-layer optimization configuration model for the site selection and sizing problem of RPFC and energy storage system, and proposes a double-layer optimization configuration method of rotating power flow controller and energy storage system with the shortest payback period and the minimum annual operating cost of the RPFC and energy storage combined system as the optimization objectives.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for double-layer optimization configuration of a rotating power flow controller and an energy storage system comprises the following steps:

[0008] Construct a two-layer optimization configuration model of active distribution network RPFC and energy storage system;

[0009] Based on the constructed two-layer optimization configuration model, a hybrid optimization of Pulp solver and genetic algorithm is used for solving;

[0010] Output the optimal location and capacity configuration scheme of RPFC and energy storage.

[0011] Furthermore, the two-layer optimization configuration model of RPFC and energy storage system includes a planning and design layer model and an operation and control layer model.

[0012] Furthermore, the planning and design model takes the shortest payback period of the RPFC and energy storage combined system as the objective function, and the specific expression is as follows:

[0013]

[0014]

[0015] Where: CF is the average annual net profit of the configured joint system; F inv is the original investment and installation cost of the system; λ is the discount rate, F OM is the fixed operation and maintenance cost of the system; F inv,ESS 、F inv,RPFC are the original investment costs of ESS and RPFC respectively; F p 、F e are the unit power price and unit capacity price of energy storage batteries respectively; P ess 、E ess F are the power and capacity configuration of energy storage batteries respectively; k,RPFC is the unit capacity investment cost of RPFC; E k,RPFC is the capacity of RPFC installed at location k; Ω k is the set of candidate locations for RPFC in the system; μ and β are the operation and maintenance cost coefficients of ESS and RPFC respectively.

[0016] Furthermore, the constraints satisfied by the planning and design layer model include: energy storage configuration constraints and RPFC planning constraints.

[0017] Furthermore, the energy storage configuration constraint adopts the following formula:

[0018]

[0019] Where: They are the minimum installable power and the maximum installable power of energy storage respectively; They are the minimum installable capacity and the maximum installable capacity of energy storage respectively.

[0020] The RPFC planning constraints are based on the following formula:

[0021]

[0022] Where: is the maximum permissible installation capacity of RPFC at location k; nk is the number of RPFCs installed at location k; E is the unit installation capacity of RPFC.

[0023] Furthermore, the operation control layer model takes the minimum annual operation total cost as the objective function, and the specific expression is as follows:

[0024] f c =C grid +C DG +C waste +C loss

[0025]

[0026] Where: F C is the total operating cost; is the cost of purchasing electricity from the main grid; for photovoltaic and wind power generation costs; Penalty costs for curtailing wind and solar power; Cost of power loss; is the electricity price of the power grid during period t; is the active power injected from the feeder into the distribution network in the tth period; Δt is the time interval, 1h; P t PV , P t WP is the photovoltaic and wind power generation power in the t period; c PV 、c WT are the electricity costs of photovoltaic power and wind power respectively; P t PV* , P t WT* are the expected power generation of photovoltaic and wind power in period t respectively; are the penalty coefficients for abandoned solar power and abandoned wind respectively; η is the annual power supply loss cost coefficient; N is the number of system nodes; P i,t is the sum of active power injected into node i in period t; ω is the annual operating loss coefficient of RPFC, is the active power flowing through node i at time t.

[0027] Furthermore, the constraints satisfied by the operation control layer model include: power balance constraints, RPFC operation constraints and energy storage system operation constraints.

[0028] Furthermore, the power balance constraint adopts the following formula:

[0029] P t buy +P t WP +P t PV -P tch +P t dis -P t loss =P t load

[0030] Where: P t ch , P t dis are the charging and discharging power of the energy storage device; P t loss is the system active power loss; P t load is the total system load in period t.

[0031] The RPFC operation constraints are as follows:

[0032]

[0033] Where: are the active and reactive powers flowing through nodes i and j at time t respectively; μ is the absolute value of the sine of the power factor angle; is the RPFC equivalent resistance; is the RPFC transmission loss; is the RPFC capacity between nodes i and j.

[0034] The energy storage system operation constraints are as follows:

[0035]

[0036] Where: E ESS is the rated capacity of the energy storage device; are the upper and lower limits of the state of charge of the energy storage device respectively; η ch , η dis are the charging and discharging efficiency of the energy storage device; P t ch,max , P t dis,max are the maximum values ​​of charging and discharging power of the energy storage device respectively; and is a 0-1 variable, where is 1, 0 means charging, otherwise it means discharging.

[0037] In summary, the present invention proposes a two-layer optimization configuration model for the site selection and sizing problems of RPFC and energy storage systems. The planning and design layer model takes the system payback period under the RPFC and energy storage configuration within the entire life cycle as the optimization target, and realizes the optimal configuration of the location and capacity of RPFC and energy storage; the operation control layer model takes the annual operating cost of the distribution network as the optimization target, and uses RPFC and energy storage as flexible regulation resources to participate in operation optimization. This method uses the Pulp solver and genetic algorithm hybrid optimization to solve the model, and finally obtains the optimal site selection and sizing solution for RPFC and energy storage systems. After simulation verification by example, the above technical solution conceived by the present invention can improve the new energy absorption rate, system flexibility and economy compared with the existing solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a solution process of a rotating power flow controller and a double-layer optimization configuration method of an energy storage system provided by an embodiment of the present invention.

[0040] Figure 2 A schematic diagram of a power distribution network with distributed power sources, a rotating power flow controller and a double-layer optimization configuration method for an energy storage system provided by an embodiment of the present invention.

[0041] Figure 3 An 18th node operating output optimization result of a rotating power flow controller and an energy storage system double-layer optimization configuration method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The following is a detailed description of a rotating power flow controller and a double-layer optimization configuration method for an energy storage system of the present invention in conjunction with the accompanying drawings and embodiments.

[0044] A two-layer optimization configuration model of active distribution network RPFC and energy storage system is constructed, including a planning and design layer model and an operation and control layer model.

[0045] The planning and design model takes the shortest payback period of the RPFC and energy storage combined system as the objective function, which is expressed as follows:

[0046]

[0047]

[0048] Where: CF is the average annual net profit of the configured joint system; F inv is the original investment and installation cost of the system; λ is the discount rate, F OM is the fixed operation and maintenance cost of the system; F inv,ESS 、F inv,RPFC are the original investment costs of ESS and RPFC respectively; F p 、F e are the unit power price and unit capacity price of energy storage batteries respectively; P ess 、E ess F are the power and capacity configuration of energy storage batteries respectively; k,RPFC is the unit capacity investment cost of RPFC; E k,RPFC is the capacity of RPFC installed at location k; Ω k is the set of candidate locations for RPFC in the system; μ and β are the operation and maintenance cost coefficients of ESS and RPFC respectively.

[0049] The constraints satisfied by the planning and design layer model include: energy storage configuration constraints and RPFC planning constraints.

[0050] The energy storage configuration constraint adopts the following formula:

[0051]

[0052] Where: They are the minimum installable power and the maximum installable power of energy storage respectively; They are the minimum installable capacity and the maximum installable capacity of energy storage respectively.

[0053] The RPFC planning constraints are based on the following formula:

[0054]

[0055] Where: is the maximum permissible installation capacity of RPFC at location k; n k is the number of RPFCs installed at location k; E is the unit installation capacity of RPFC.

[0056] The operation control layer model takes the minimum annual operation cost as the objective function, and the specific expression is as follows:

[0057] f c =C grid +C DG +C waste +C loss

[0058]

[0059] Where: F C is the total operating cost; is the cost of purchasing electricity from the main grid; for photovoltaic and wind power generation costs; Penalty costs for curtailing wind and solar power; Cost of power loss; is the electricity price of the power grid during period t; is the active power injected from the feeder into the distribution network in the tth period; Δt is the time interval, 1h; P t PV , P t WP is the photovoltaic and wind power generation power in the t period; c PV 、c WT are the electricity costs of photovoltaic power and wind power respectively; P t PV* , P t WT* are the expected power generation of photovoltaic and wind power in period t respectively; are the penalty coefficients for abandoned solar power and abandoned wind respectively; η is the annual power supply loss cost coefficient; N is the number of system nodes; P i,t is the sum of active power injected into node i in period t; ω is the annual operating loss coefficient of RPFC, is the active power flowing through node i at time t.

[0060] The constraints satisfied by the operation control layer model include: power balance constraints, RPFC operation constraints and energy storage system operation constraints.

[0061] The power balance constraint adopts the following formula:

[0062] P t buy +P t WP +P t PV -P t ch +P t dis -P t loss =Pt load

[0063] Where: P t ch , P t dis are the charging and discharging power of the energy storage device; P t loss is the system active power loss; P t load is the total system load in period t.

[0064] The RPFC operation constraints are as follows:

[0065]

[0066] Where: are the active and reactive powers flowing through nodes i and j at time t respectively; μ is the absolute value of the sine of the power factor angle; is the RPFC equivalent resistance; is the RPFC transmission loss; is the RPFC capacity between nodes i and j.

[0067] The energy storage system operation constraints are as follows:

[0068]

[0069] Where: E ESS is the rated capacity of the energy storage device; are the upper and lower limits of the state of charge of the energy storage device respectively; η ch , η dis are the charging and discharging efficiency of the energy storage device; P t ch,max , P t dis,max are the maximum values ​​of charging and discharging power of the energy storage device respectively; and is a 0-1 variable, where is 1, 0 means charging, otherwise it means discharging.

[0070] Considering that the constructed two-level optimization configuration model of RPFC and energy storage combined system belongs to a large-scale nonlinear programming problem, in order to reduce the calculation scale and shorten the solution time, it is decomposed into two non-independent single-objective optimization problems, and a hybrid optimization method combining genetic algorithm and Pulp solver is used to solve it, and the RPFC and energy storage location and capacity configuration are optimized. The specific principle and process are as follows Figure 1 shown.

[0071] The application of the above structure and method is further explained below in conjunction with a specific simulation example.

[0072] According to the time-of-use electricity price according to the electricity purchase policy, the time periods are divided into the off-peak period from 23:00 to 7:00, 0.2951 yuan / kWh; the normal period from 7:00 to 9:00, 12:00 to 16:00, and 21:00 to 23:00, 0.6415 yuan / kWh; the peak period from 9:00 to 12:00 and 16:00 to 21:00, 0.9622 yuan / kWh.

[0073] In order to fully consider the impact of wind turbines and photovoltaic units on the system, 10 photovoltaic units and 5 wind turbines were connected in the example to fully simulate the impact of distributed power sources. The configuration parameters are shown in Table 1.

[0074] Table 1 Distributed power configuration parameters

[0075]

[0076] The IEEE33 node is used for case analysis. The RPFC replaces the traditional tie switch. That is, the RPFC is sited at five locations, S1 to S5. The distribution of distributed power sources and the selected locations of RPFC are as follows: Figure 2 shown.

[0077] The energy storage system is configured so that there is no need to replace equipment and batteries during its life cycle. The widely used lithium iron phosphate energy storage battery is selected. The technology and basic parameters of the energy storage system are shown in Table 2.

[0078] Table 2 Energy storage configuration parameters

[0079]

[0080] Below, this embodiment sets 4 scenarios to analyze the effectiveness of the present invention:

[0081] Scenario 1: No energy storage and RPFC;

[0082] Scenario 2: No energy storage system is configured, and only RPFC is configured in the distribution network;

[0083] Scenario 3: Configuring an energy storage system in a distribution network without RPFC;

[0084] Scenario 4: Configure a RPFC and energy storage system.

[0085] According to the established two-layer optimization configuration model of RPFC and energy storage combined system, the system configuration schemes obtained for scenarios 3 and 4 are shown in Tables 3 and 4.

[0086] Table 3 System configuration results for scenario 3

[0087]

[0088] Table 4 System configuration results for scenario 4

[0089]

[0090] Comparing the energy storage configuration costs of each scenario, the energy storage configuration solution with 2 access points has the lowest cost compared to the solutions with 1 and 3 energy storage access points. When energy storage is connected alone at the feeder end node 18, due to the large line loss generated during the power regulation process, in order to ensure that the voltage at each point in the system is at a qualified level, a large-power energy storage system needs to be configured at the 18 nodes; when energy storage is connected to 2 points, the system voltage problem can be well solved. When energy storage is connected to 3 points, the total cost is higher due to the increase in fixed investment costs.

[0091] By comparing the system configuration results of scenarios 3 and 4, it can be seen that the coordinated optimization configuration of RPFC and energy storage can effectively reduce the configuration power and capacity of energy storage, reduce the energy storage configuration cost, and thus reduce the total system cost.

[0092] Taking S2 connected to nodes 18 and 33 as an example, the power balance curves of each scenario after optimization scheduling are randomly selected for display. The daily operation output optimization results of node 18 (including photovoltaic) are as follows: Figure 3 shown.

[0093] The configuration of RPFC and energy storage can improve the economic efficiency of optimized scheduling during the day. Charging is carried out during the low-peak period at night and during the period of large photovoltaic and wind power generation, and discharging is carried out during the peak period of electricity prices and large load demand, so as to achieve energy mutual assistance between different feeders of RPFC and peak-valley arbitrage of energy storage, reduce the system's electricity cost and transmission loss, and achieve better economic efficiency.

[0094] The use of RPFC and energy storage to optimize the configuration and achieve the reasonable distribution of power flow provides a new idea for promoting the consumption of new energy in the new distribution network with high penetration rate, which is conducive to the safe and stable operation of the distribution network. Considering the above economic benefits and actual engineering value, the significance of the coordinated optimization configuration of RPFC and energy storage will be further enhanced.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. 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 double-layer optimization configuration method for a rotating power flow controller and an energy storage system, characterized in that: The following steps are involved: Construct a two-layer optimization configuration model of active distribution network rotary power flow controller (RPFC) and energy storage system; Based on the constructed two-layer optimization configuration model, a hybrid optimization of Pulp solver and genetic algorithm is used for solving; Output the optimal location and capacity configuration scheme of RPFC and energy storage.

2. The method for double-layer optimization configuration of a rotating power flow controller and an energy storage system according to claim 1, characterized in that: The RPFC and energy storage system two-layer optimization configuration model includes a planning and design layer model and an operation and control layer model.

3. The method for double-layer optimization configuration of a rotating power flow controller and an energy storage system according to claim 2, characterized in that: The planning and design model takes the shortest payback period of the RPFC and energy storage combined system as the objective function, and the specific expression is as follows: Where: CF is the average annual net profit of the configured joint system; F inv is the original investment and installation cost of the system; λ is the discount rate, F OM is the fixed operation and maintenance cost of the system; F inv,ESS 、F inv,RPFC are the original investment costs of ESS and RPFC respectively; F p 、F e They are the unit power price and unit capacity price of energy storage batteries respectively; P ess 、E ess F are the power and capacity configuration of energy storage batteries respectively; k,RPFC is the unit capacity investment cost of RPFC; E k,RPFC is the capacity of RPFC installed at location k; Ω k is the set of candidate locations for RPFC in the system; μ and β are the operation and maintenance cost coefficients of ESS and RPFC respectively.

4. The method for double-layer optimization configuration of a rotating power flow controller and an energy storage system according to claim 2, characterized in that: The constraints satisfied by the planning and design layer model include: energy storage configuration constraints and RPFC planning constraints.

5. The method for double-layer optimization configuration of a rotating power flow controller and an energy storage system according to claim 4, characterized in that: The energy storage configuration constraint adopts the following formula: Where: They are the minimum installable power and the maximum installable power of energy storage respectively; They are the minimum installable capacity and the maximum installable capacity of energy storage respectively; The RPFC planning constraint adopts the following formula: Where: is the maximum permissible installation capacity of RPFC at location k; n k is the number of RPFCs installed at location k; E is the unit installation capacity of RPFC.

6. The method for double-layer optimization configuration of a rotating power flow controller and an energy storage system according to claim 2, characterized in that: The operation control layer model takes the minimum annual operation total cost as the objective function, and the specific expression is as follows: minF c =C grid +C DG +C waste +C loss Where: F C is the total operating cost; is the cost of purchasing electricity from the main grid; for photovoltaic and wind power generation costs; Penalty costs for curtailing wind and solar power; Cost of power loss; is the electricity price of the power grid during period t; is the active power injected from the feeder into the distribution network in the tth period; Δt is the time interval, 1h; is the photovoltaic and wind power generation power in the t period; c PV 、c WT The cost per kilowatt-hour of photovoltaic power and wind power respectively; are the expected power generation of photovoltaic and wind power in period t respectively; are the penalty coefficients for abandoned solar power and abandoned wind respectively; η is the annual power supply loss cost coefficient; N is the number of system nodes; P i,t is the sum of active power injected into node i in period t; ω is the annual operating loss coefficient of RPFC, is the active power flowing through node i at time t.

7. The method for double-layer optimization configuration of a rotating power flow controller and an energy storage system according to claim 2, characterized in that: The constraints satisfied by the operation control layer model include: power balance constraints, RPFC operation constraints and energy storage system operation constraints.

8. The method for double-layer optimization configuration of a rotating power flow controller and an energy storage system according to claim 7, characterized in that: The power balance constraint adopts the following formula: Where: are the charging and discharging power of the energy storage device respectively; is the system active power loss; is the total system load in period t; The RPFC operation constraint adopts the following formula: Where: are the active and reactive powers flowing through nodes i and j at time t respectively; μ is the absolute value of the sine of the power factor angle; is the RPFC equivalent resistance; is the RPFC transmission loss; is the RPFC capacity connected between nodes i and j; The energy storage system operation constraints are as follows: Where: E ESS is the rated capacity of the energy storage device; They are the upper and lower limits of the state of charge of the energy storage device, respectively; η ch , η dis are the charging and discharging efficiency of the energy storage device, respectively; are the maximum values ​​of charging and discharging power of the energy storage device respectively; and is a 0-1 variable, where is 1, 0 means charging, otherwise it means discharging.

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