Distributed shared energy storage optimal configuration method oriented to improvement of on-site absorption capability of new energy

Abstract

The invention discloses a distributed shared energy storage optimal configuration method oriented to improvement of local absorption capability of new energy. Firstly, adjustment requirements of a power supply side and a power grid side are considered, and a distributed shared energy storage operation mode oriented to source-network collaborative optimization is proposed; secondly, constructing a multi-time-scale distributed shared energy storage double-layer planning model, solving a long-time-scale configuration problem by taking the lowest annual average cost of a distributed shared energy storage system as an upper-layer target, and solving a long-time-scale configuration problem by taking the lowest daily comprehensive operation cost of a power distribution network-distributed new energy field station as a lower-layer target; solving a short time scale source-network collaborative optimization operation problem; thirdly, solving a distributed shared energy storage configuration condition and a power distribution network-distributed new energy station economic operation problem by adopting a double-layer iterative particle swarm algorithm combined with load flow calculation; the distributed shared energy storage operator realizes positive income, and the potential of investing and constructing the distributed shared energy storage power station is profitable.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and specifically relates to a distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy. Background Art

[0002] With the advancement of the electric energy substitution strategy, the penetration rate of new energy sources has gradually increased, and its volatility and uncertainty have brought major challenges to the safe and economical operation of the power system. In order to meet these challenges, the power system needs to develop new technical means to optimize resource allocation, improve the system's ability to absorb new energy locally, and ensure the smooth transition of the power system to a high-proportion new energy era. The distributed shared energy storage optimization configuration method proposed in the present invention came into being in this context, aiming to solve the absorption problem brought about by the access of new energy to the operation of the power system.

[0003] However, in actual use, there is a problem: the unplanned access to distributed shared energy storage is prone to wind and solar power abandonment, resulting in a large amount of regulation resources being unable to be effectively utilized. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy.

[0005] The technical solution adopted by the present invention is: a distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy, comprising the following steps:

[0006] S100: Proposes a distributed shared energy storage operation model for source-grid collaborative optimization;

[0007] S200: Construct a multi-time scale distributed shared energy storage two-level planning model with the lowest annual average cost of the distributed shared energy storage system as the upper level target and the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower level target;

[0008] S300: A two-layer iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and distribution network-distributed new energy station economic operation problems.

[0009] Specifically, in step S100, the distributed shared energy storage operation mode for source-network collaborative optimization is: the operation objectives of distributed shared energy storage operators, distributed new energy stations, and distribution networks.

[0010] In step S200, the multi-time scale distributed shared energy storage two-level planning model with the lowest annual average cost of the distributed shared energy storage system as the upper level target and the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower level target is:

[0011] (1) Upper model

[0012] 1) Objective function

[0013] The upper optimization goal is to minimize the average daily total cost of the distributed shared energy storage system, which can be expressed as:

[0014] minC 1 =C sto -C inc

[0015]

[0016] Where: C 1 is the average annual cost of the distributed shared energy storage system; C sto is the average annual investment and maintenance cost of distributed shared energy storage; C inc is the average annual income of the distributed shared energy storage system; T w is the number of days for each typical day; C new,w C is the electricity transaction fee of distributed shared energy storage and distributed new energy stations on a typical day; adn,w is the transaction fee of electricity between distributed shared energy storage and distribution network on a typical day; C ser,w is the typical day distributed shared energy storage capacity rental service fee; n is the number of energy storages; r is the discount rate; y is the life cycle of the energy storage equipment; are the unit power and unit capacity investment costs of energy storage respectively; P sto,i 、E sto,i are the rated power and rated capacity of energy storage i respectively; is the maintenance cost per unit power; T is 24 hours a day; N is the number of distributed new energy stations;. . is the unit electricity price of the distributed new energy station at time t; is the power sold by the new energy station j to the distributed shared energy storage system at time t on a typical day; is the electricity price per unit of distributed shared energy storage at time t; is the electricity price per unit of electricity sold by the distribution network at time t; The power sold by the distributed shared energy storage system to the distribution network at time t on a typical day; The power sold by the distribution network to the distributed shared energy storage system at time t on a typical day; s .The unit power service fee paid by the distribution network and distributed new energy stations to the distributed shared energy storage system;

[0017] (2) Lower-level model

[0018] 1) Objective function

[0019] The lower optimization goal is to minimize the sum of the annual comprehensive operating cost of the distribution network-distributed new energy station and the peak-valley difference penalty cost, which can be expressed as:

[0020]

[0021] Where: C 2 is the annual comprehensive operating cost of the distribution network-distributed new energy station; C grid,w The cost of electricity purchased from the main grid by the distribution network on a typical day; C peak-valley,w is the penalty cost of the typical daily peak-to-valley difference; C grid,w The cost of purchasing electricity from the main grid for the distribution network; is the main grid electricity price at time t; is the power sold by the main grid to the distribution grid at time t on a typical day; peak-valley The penalty fee per unit power for the net load peak-to-valley difference is 0.65 yuan / kW; are the maximum and minimum values ​​of net load on a typical day, respectively; is the net load of the distribution network at time t on a typical day; is the load of node k at time t on a typical day; is the power sold by the new energy station j to the distribution network at time t on a typical day;

[0022] In the step S300, a double-layer iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and the distribution network-distributed new energy station economic operation problem, including:

[0023] The upper model is solved by particle swarm algorithm, where each particle consists of two parts: the rated power P of each energy storage sto,i 、Rated capacity E of each energy storage sto,i The lower model is solved by using a particle swarm algorithm combined with power flow calculation, where each particle also consists of two parts: the location x of each energy storage i and the charging and discharging power of each energy storage

[0024] Beneficial effects of the present invention: Firstly, the present invention takes into account the regulation requirements of both the power supply side and the power grid side, and proposes a distributed shared energy storage operation mode for source-grid collaborative optimization; secondly, a multi-time-scale distributed shared energy storage two-layer planning model is constructed, with the lowest annual average cost of the distributed shared energy storage system as the upper-level goal, to solve the long-time-scale configuration problem, and with the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower-level goal, to solve the short-time-scale source-grid collaborative optimization operation problem; thirdly, a two-layer iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and the distribution network-distributed new energy station economic operation problem; finally, through the comparative analysis of four scenario examples, it is verified that the configuration of distributed shared energy storage can effectively improve the new energy absorption rate, reduce the net load peak-valley difference, and improve the energy storage utilization rate. At the same time, the distributed shared energy storage operators have achieved positive returns, and investing in the construction of distributed shared energy storage power stations has the potential to make profits. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy provided by an exemplary embodiment of the present invention;

[0026] Figure 2 It is a flow chart of solving a two-level programming model for distributed shared energy storage optimization configuration provided by an exemplary embodiment of the present invention;

[0027] Figure 3 It is a structural diagram of a distribution network and a distributed new energy station provided by an exemplary embodiment of the present invention;

[0028] Figure 4 It is a power balance diagram of a distributed renewable energy station (typical day in spring) provided by an exemplary embodiment of the present invention;

[0029] Figure 5 is a power balance diagram of a distribution network (typical day in spring) provided by an exemplary embodiment of the present invention;

[0030] Figure 6 It is the distributed shared energy storage charging, discharging and charge state optimization result (typical spring day) provided by an exemplary embodiment of the present invention.

[0031] Figure 7 It is a structural schematic diagram of a distributed shared energy storage optimization configuration device for improving the local consumption capacity of new energy provided by an exemplary embodiment of the present invention.

[0032] Figure 8 is a structural diagram of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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, and are specifically described in combination with the embodiments below.

[0034] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0035] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0036] like Figure 1 As shown, a distributed shared energy storage optimization configuration method 100 for improving the local consumption capacity of new energy includes the following steps:

[0037] Step 101, proposing a distributed shared energy storage operation mode for source-grid collaborative optimization;

[0038] Step 102, constructing a multi-time scale distributed shared energy storage two-level planning model with the lowest annual average cost of the distributed shared energy storage system as the upper level target and the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower level target;

[0039] Step 103, using a double-layer iterative particle swarm algorithm combined with power flow calculation to solve the distributed shared energy storage configuration and the distribution network-distributed new energy station economic operation problem;

[0040] Specifically, the present invention provides a distributed shared energy storage optimization configuration method for improving the local absorption capacity of new energy. First, taking into account the regulation needs of the power supply side and the grid side, a distributed shared energy storage operation mode for source-grid collaborative optimization is proposed; secondly, a multi-time scale distributed shared energy storage two-level planning model is constructed, with the lowest annual average cost of the distributed shared energy storage system as the upper level goal, solving the long-time scale configuration problem, and with the lowest daily comprehensive operation cost of the distribution network-distributed new energy station as the lower level goal, solving the short-time scale source-grid collaborative optimization operation problem; thirdly, a two-level iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and the distribution network-distributed new energy station economic operation problem; finally, through the comparative analysis of four scenario examples, it is verified that the configuration of distributed shared energy storage can effectively improve the new energy absorption rate, reduce the net load peak-valley difference, and improve the energy storage utilization rate. At the same time, the distributed shared energy storage operator realizes positive returns, and the investment in the construction of distributed shared energy storage power stations has the potential to make a profit. The present invention solves the problem of wind and solar power abandonment that is prone to occur in the unplanned access of distributed shared energy storage in the prior art. The specific implementation steps are as follows:

[0041] Step S100: Proposing a distributed shared energy storage operation mode for source-grid collaborative optimization;

[0042] Step S200: constructing a multi-time scale distributed shared energy storage two-level planning model with the lowest annual average cost of the distributed shared energy storage system as the upper level target and the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower level target;

[0043] Step S300: using a double-layer iterative particle swarm algorithm combined with power flow calculation to solve the distributed shared energy storage configuration and the distribution network-distributed new energy station economic operation problem;

[0044] In a specific embodiment, the regulation requirements of the power supply side and the grid side are first taken into consideration, and a distributed shared energy storage operation mode for source-grid collaborative optimization is proposed.

[0045] The distributed shared energy storage in this study takes into account the regulation requirements of both the power supply side and the grid side, and provides a distributed shared energy storage operation mode for source-grid collaborative optimization. The operating objectives of the distributed shared energy storage operator, distributed new energy station, and distribution network are introduced as follows:

[0046] (1) Shared energy storage operators aim to provide charging and discharging services for distributed new energy power stations and distribution networks to achieve the lowest capacity configuration and operating cost of distributed shared energy storage systems. At the optimization configuration level, energy storage operators will aggregate the charging and discharging needs of distributed new energy power stations and active distribution networks, and centrally optimize the configuration of distributed shared energy storage system capacity. At the optimization operation level, shared energy storage operators provide charging and discharging services, and while charging service fees, they conduct electricity transactions through "low storage and high discharge" to achieve price arbitrage.

[0047] (2) Distributed new energy stations are designed to fully absorb distributed new energy, reduce the wind and solar power abandonment rate, and increase the absorption rate of new energy by utilizing the charging and discharging services of distributed shared energy storage power stations. New energy stations will give priority to supplying electricity to the distribution network to support its load. If the output of new energy exceeds the demand of the distribution network, the excess output of new energy will be sold to charge the distributed shared energy storage system.

[0048] (3) The distribution network aims to achieve the lowest net load peak-valley difference by utilizing the charging and discharging services of distributed shared energy storage power stations. The distribution network will give priority to consuming new energy output to meet load demand, and the power imbalance is the net load of the distribution network. During peak load periods, the distribution network will discharge through the distributed shared energy storage system or purchase electricity from the main grid to reduce the peak load. During low load periods, the distributed shared energy storage system will be charged to fill the valley, so as to further reduce the net load peak-valley difference.

[0049] Furthermore, step S200 includes: constructing a multi-time-scale distributed shared energy storage two-level planning model with the lowest annual average cost of the distributed shared energy storage system as the upper level target and the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower level target.

[0050] Furthermore, step S200 further includes:

[0051] Step S210: constructing an upper-level model with the goal of minimizing the average annual cost of the distributed shared energy storage system;

[0052] Step S220: constructing a lower-level model with the goal of minimizing the daily comprehensive operating cost of the distribution network-distributed new energy station;

[0053] Step S210: constructing an upper-level model with the goal of minimizing the average annual cost of the distributed shared energy storage system;

[0054] (1) Objective function

[0055] The upper optimization goal is to minimize the average daily total cost of the distributed shared energy storage system, which can be expressed as:

[0056] Formula 1:

[0057] Formula 2:

[0058] Formula 3:

[0059] Formula 4:

[0060] Formula 5:

[0061] Where: C 1 is the average annual cost of the distributed shared energy storage system; C sto is the average annual investment and maintenance cost of distributed shared energy storage; C inc is the average annual income of the distributed shared energy storage system; T w is the number of days for each typical day; C new,w C is the electricity transaction fee of distributed shared energy storage and distributed new energy stations on a typical day; adn,w is the transaction fee of electricity between distributed shared energy storage and distribution network on a typical day; C ser,w is the typical day distributed shared energy storage capacity rental service fee; n is the number of energy storages; r is the discount rate; y is the life cycle of the energy storage equipment; are the unit power and unit capacity investment costs of energy storage respectively; P sto,i 、E sto,i are the rated power and rated capacity of energy storage i respectively; is the maintenance cost per unit power; T is 24 hours a day; N is the number of distributed new energy stations; is the unit electricity price of the distributed new energy station at time t; The power sold by the new energy station j to the distributed shared energy storage system at time t on a typical day; is the electricity price per unit of distributed shared energy storage at time t; is the electricity price per unit of electricity in the distribution network at time t; The power sold by the distributed shared energy storage system to the distribution network at time t on a typical day; The power sold by the distribution network to the distributed shared energy storage system at time t on a typical day; δ s The unit power service fee paid by distribution networks and distributed new energy stations to distributed shared energy storage systems.

[0062] (2) Constraints

[0063] 1) Energy rate constraint

[0064] Formula 6: .E sto,i =βP sto,i .

[0065] Where: β is the energy rate of the energy storage battery.

[0066] 2) Distributed shared energy storage power constraints

[0067] Formula 7: P sto,i,min ≤P sto,i ≤P sto,i,max

[0068] Where: P sto,i,min , P sto,i,max They are the minimum power and maximum power of distributed shared energy storage installed at each node.

[0069] 3) Distributed shared energy storage charging and discharging power constraints

[0070] Formula 8:

[0071] Where: are the charging and discharging power of energy storage i at time t respectively; They are respectively the charge and discharge flags of energy storage i at time t.

[0072] 4) State of charge constraints of distributed shared energy storage systems

[0073] Formula 9:

[0074] Where: is the state of charge of energy storage i at time t on the wth typical day; η sto,c , η sto,d are the energy storage charging and discharging efficiency respectively.

[0075] Step S220: constructing a lower-level model with the goal of minimizing the daily comprehensive operating cost of the distribution network-distributed new energy station;

[0076] (1) Objective function

[0077] The lower optimization goal is to minimize the sum of the annual comprehensive operating cost of the distribution network-distributed new energy station and the peak-valley difference penalty cost, which can be expressed as:

[0078] Formula 10:

[0079] Formula 11:

[0080] Formula 12:

[0081] Where: C 2 is the annual comprehensive operating cost of the distribution network-distributed new energy station; C grid,w The cost of electricity purchased from the main grid by the distribution network on a typical day; C peak-valley,w is the penalty cost of the typical daily peak-to-valley difference; C grid,w The cost of purchasing electricity from the main grid for the distribution network; is the main grid electricity price at time t; is the power sold by the main grid to the distribution grid at time t on a typical day; peak-valley The penalty fee per unit power for the net load peak-to-valley difference is 0.65 yuan / kW; are the maximum and minimum values ​​of net load on a typical day, respectively; is the net load of the distribution network at time t on a typical day; is the load of node k at time t on a typical day; is the power sold by the new energy station j to the distribution network at time t on a typical day.

[0082] 2) Constraints

[0083] 1) Output constraints of distributed renewable energy stations

[0084] Formula 13:

[0085] Where: is the actual output of renewable energy station j at time t on a typical day; It is the ideal output for a typical day's new energy station.

[0086] 2) Distributed new energy stations and distributed shared energy storage power purchase and sales constraints

[0087] Formula 14:

[0088] Where: It is the maximum interactive power between new energy sites and distributed shared energy storage.

[0089] 3) Power balance constraints of distributed new energy stations

[0090] Formula 15:

[0091] 4) Constraints on power purchase and sale between distribution network and distributed shared energy storage

[0092] Formula 16:

[0093] Where: It is a flag for the interaction between the distribution network and the distributed shared energy storage power; It is the maximum interaction power between the distribution network and distributed shared energy storage.

[0094] 5) Power balance constraints of distribution network

[0095] Formula 17:

[0096] Where: P loss,t,w is the distribution network loss at time t on a typical day.

[0097] 6) Distribution network flow constraints

[0098] Formula 18:

[0099] Where: P i t , are the active and reactive power injected into node i at time t respectively; are the voltage amplitudes of nodes i and j at time t; G ij , B ij are the conductance and susceptance between nodes i and j respectively; θ ij is the phase angle difference between nodes i and j.

[0100] 7) Node i voltage constraint

[0101] Formula 19:

[0102] Where: U i,min , U i,max are the minimum and maximum voltage amplitudes at node i, respectively.

[0103] 8) Branch capacity constraints

[0104] Formula 20:

[0105] Where: is the transmission power between nodes i and j at time t; S ij,max is the maximum value of the power that can be transmitted between nodes i and j.

[0106] Furthermore, step S300 includes:

[0107] A two-layer iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and the economic operation problem of distribution network-distributed new energy stations;

[0108] Specifically, step S300 is as follows: Figure 2 As shown in the figure, the upper model is solved by particle swarm algorithm, where each particle consists of two parts: the rated power P of each energy storage sto,i 、Rated capacity E of each energy storage sto,i The lower model is solved by using a particle swarm algorithm combined with power flow calculation, where each particle also consists of two parts: the location x of each energy storage i and the charging and discharging power of each energy storage

[0109] At this point, a distributed shared energy storage optimization configuration model for improving the local consumption capacity of new energy has been established and can be applied to actual examples.

[0110] In order to verify the effectiveness of the proposed method, the following example is set:

[0111] The wind, solar and load data of Lankao County, Henan Province in 2022 are used to verify the above-mentioned optimization configuration model. Figure 3 In the distribution network system structure shown in the figure, 1200kW photovoltaic is connected at node 9, and 1200kW wind power is connected at node 20 as distributed new energy stations. For the distributed shared energy storage system, the nodes allowed to be connected are 2-33, with a maximum of 6 energy storage connections. The minimum rated power of the energy storage connection is 100kW, the maximum rated power is 500kW, the discount rate of the energy storage is 0.05, the service life is 8 years, the unit power investment cost is 1173 yuan / kW, the unit capacity investment cost is 1650 yuan / (kW·h), the unit power maintenance cost is 97 yuan / (year·kW), the unit power service fee of the energy storage is 0.05 yuan / (kW·h), and the energy storage charging and discharging efficiency is 0.9.

[0112] In order to analyze the rationality of distributed shared energy storage configuration, four scenarios are set up for comparative analysis.

[0113] Scenario 1: Without energy storage, the excess power of distributed renewable energy stations is directly abandoned, and the power imbalance of the distribution network is directly purchased from the main grid.

[0114] Scenario 2: The distribution network, new energy site 1 (the photovoltaic power station connected to node 9), and new energy site 2 (the wind power station connected to node 20) invest in the construction of energy storage on their own. The energy storage of Scenario 2 is configured based on the local consumption level of new energy and the peak-to-valley difference obtained in Scenario 4 as constraints, achieving peak shaving and valley filling and improving the consumption rate of new energy.

[0115] Scenario 3: Configure centralized shared energy storage and compare the differences between centralized shared energy storage and distributed shared energy storage in terms of local consumption level of new energy, peak-to-valley difference and economic efficiency.

[0116] Scenario 4: Configure distributed shared energy storage, use distributed shared energy storage to smooth out peak loads and fill valleys, and increase the consumption rate of new energy.

[0117] Figure 4 The power balance of distributed renewable energy stations on a typical spring day in scenarios 1 and 4. Figure 4The positive power represents the output of each new energy station, the negative power represents the power sold by each new energy station to the distribution network and the distributed shared energy storage system, and the ideal output of the distributed new energy station represents the sum of the maximum power available to all new energy stations during this time period. Analysis of the power balance diagram of the distributed new energy station in the typical spring day of scenario 1 shows that the distribution network cannot absorb all the new energy output from 11 to 15 hours. At this time, there is a phenomenon of power abandonment in the distributed new energy station, and the amount of wind and solar power abandoned is 1501 kWh. Scenario 4 is equipped with distributed shared energy storage. When the distribution network cannot absorb all the new energy output, the distributed new energy station sells the excess power to the distributed shared energy storage to improve the local consumption level of new energy. The local consumption level of new energy in the scenario is more than 95%.

[0118] Table 1 shows the local consumption level of new energy at different times on typical days in spring and autumn for scenario 1 and scenario 4. It can be seen that compared with scenario 1, the local consumption level of new energy in scenario 4 is greatly improved, and the consumption can reach more than 95% at the moments when wind and solar power abandonment is relatively serious in scenario 1.

[0119] Table 2 shows the economic benefits of scenario 1 and scenario 4. Scenario 1 does not configure energy storage, the average annual total cost of the distributed shared energy storage system is 0 yuan, and the annual comprehensive operating cost of the distribution network-distributed new energy station is 4.34 million yuan. Scenario 1 has the phenomenon of abandoned wind and solar. Scenario 4 configures distributed shared energy storage, and the average annual cost of the distributed shared energy storage system is -9552 yuan. The energy storage has achieved profitability, and the daily comprehensive operating cost of the distribution network-distributed new energy station has been reduced by 220,000 yuan compared with scenario 1. The level of local consumption of new energy is more than 95%. Through the comparative analysis of scenario 1 and scenario 4, it is verified that the configuration of distributed shared energy storage can effectively improve the level of local consumption of new energy.

[0120] time 11 12 13 14 15 Scenario 1 90％ 87％ 82％ 69％ 86％ Scene 4 More than 95% More than 95% More than 95% More than 95% More than 95%

[0121] Table 1: Local consumption of new energy at different times in scenario 1 and scenario 4 (typical days in spring and autumn)

[0122]

[0123] Table 2: Economic benefits of scenario 1 and scenario 4

[0124] Figure 5 The power balance of the distribution network in a typical spring day in scenario 1 and scenario 4 is shown in Figure 2. Figure 5The positive power represents the power provided by the outside world to the distribution network, and the negative power represents the network loss in the distribution network and the power consumed by all electrical loads. The difference between the maximum and minimum values ​​of the net load curve is the peak-to-valley difference. Analysis of the power balance diagram of the distribution network on a typical spring day in scenario 1 shows that the distribution network gives priority to the power provided by distributed renewable energy sites. When the power provided by distributed renewable energy sites is insufficient, the distribution network directly purchases electricity from the main grid to meet the power demand of the load. Since the load has peak-to-valley characteristics, but the output of renewable energy has anti-peak characteristics, Figure 5 It can be seen that during the 10-15h period, the load is low but the output of new energy is large, resulting in a net load curve close to 0 at this time. However, during the peak periods of 7-9h and 19-22h, the load increases but the output of new energy decreases, and the distribution network can only purchase a large amount of electricity from the main grid. Analysis of the power balance diagram of the distribution network on a typical spring and autumn day in scenario 4 shows that the distribution network prioritizes the consumption of electricity provided by distributed energy sites. During the 10-15h load valley period, the distribution network fills the valley by selling electricity to distributed shared energy storage. During the 7-9h and 19-22h load peak period, the distribution network purchases electricity from distributed shared energy storage to reduce the peak-to-valley difference of the net load of the distribution network.

[0125] Table 3 shows the economic benefits of scenario 1 and scenario 4. The comparative analysis of the average annual cost of the distributed shared energy storage system and the annual comprehensive operating cost of the distribution network-distributed new energy station in the two scenarios is consistent with the above, but the average net load peak-to-valley difference in scenario 4 is reduced by 1397kW compared with scenario 1. The comparative analysis of scenario 1 and scenario 4 verifies that the configuration of distributed shared energy storage can effectively reduce the peak-to-valley difference.

[0126]

[0127] Table 3 Economic benefits of scenario 1 and scenario 4

[0128] Table 4 shows the optimization configuration results of energy storage in scenario 2 and scenario 4. The energy storage of the distribution network in scenario 2 is configured based on the peak-to-valley difference obtained in scenario 4. It can be seen that the total configuration capacity in scenario 2 is 4372kW·h, and the total configuration capacity of distributed shared energy storage in scenario 4 is 2848kW·h, which is 35% less than the total capacity configured in scenario 2. It can be seen that by reasonably sharing distributed energy storage, time-sharing reuse of energy storage is achieved, and the utilization rate of energy storage resources is improved, so that energy storage with smaller power and capacity can meet the energy storage usage needs of distributed new energy sites and distribution networks.

[0129]

[0130] Table 4 Energy storage optimization configuration results for scenario 2 and scenario 4

[0131] Figure 6The optimization results of energy storage charging and discharging behavior and state of charge in a typical spring day in scenario 4 are shown in Figure 4. The positive power represents energy storage charging, and the negative power represents energy storage discharging. Figure 6 It can be seen that distributed shared energy storage 1 and 2 both reached the maximum charging power during the off-peak period and the maximum discharging power during the peak load period, that is, the distributed shared energy storage has full charging and full discharging behaviors. In addition, distributed shared energy storage 1 reached the maximum state of charge of 0.9 in 15 hours and the minimum state of charge of 0.1 in 9 hours. Distributed shared energy storage 2 reached the maximum state of charge of 0.9 in 15 hours and the minimum state of charge of 0.2 in 24 hours, indicating that the power of distributed shared energy storage has reached the upper or lower limit of capacity. Distributed shared energy storage aggregates the energy demand of the distribution network and distributed new energy stations, and reasonably allocates the charging and discharging of each energy storage, so that the power of each energy storage reaches the upper and lower limits of capacity, and fully utilizes the energy storage capacity resources.

[0132] Table 5 shows the economic benefits of Scenario 2 and Scenario 4. It can be seen that the distributed shared energy storage system in Scenario 4 has achieved profitability, with an average annual total cost of -9552 yuan. However, the energy storage cost of Scenario 2 is 81851 yuan, because the energy storage capacity configured in Scenario 2 is 35% more than that in Scenario 1, and Scenario 2 is self-equipped energy storage, so there is no profit in energy storage, so the comprehensive cost is much greater than Scenario 1. The annual comprehensive operating cost of the distribution network-distributed new energy station in Scenario 4 is 330,000 yuan less than that in Scenario 2. Through the comparative analysis of Scenario 2 and Scenario 4, it is verified that the configuration of distributed shared energy storage can reduce the operating costs of the distribution network-distributed new energy station while taking into account the economic efficiency of shared energy storage investors, achieving a win-win situation for all parties.

[0133]

[0134] Table 5 Economic benefits of scenario 2 and scenario 4

[0135] The comparison of the distributed shared energy storage optimization configuration of scenario 3 and scenario 4 is shown in Table 6. The economic benefits of scenario 3 and scenario 4 are shown in Table 7. By comparing the economic benefits of the two scenarios, it can be found that the distributed shared energy storage system of scenario 4 has an additional profit of 5021 yuan compared with scenario 3, and the net load peak-to-valley difference is also significantly lower than that of scenario 3, and there is a phenomenon of wind and solar abandonment in scenario 3. Therefore, the effect of configuring centralized energy storage under the constraint of energy storage capacity is not as good as distributed energy storage.

[0136]

[0137] Table 6 Energy storage optimization configuration results for scenario 3 and scenario 4

[0138]

[0139] Table 7 Economic benefits of scenario 3 and scenario 4

[0140] Therefore, the present invention firstly takes into account the regulation requirements of both the power supply side and the power grid side, and proposes a distributed shared energy storage operation mode for source-grid collaborative optimization; secondly, a multi-time-scale distributed shared energy storage two-layer planning model is constructed, with the lowest annual average cost of the distributed shared energy storage system as the upper-level goal, to solve the long-time-scale configuration problem, and with the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower-level goal, to solve the short-time-scale source-grid collaborative optimization operation problem; thirdly, a two-layer iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and the distribution network-distributed new energy station economic operation problem; finally, through the comparative analysis of four scenario examples, it is verified that the configuration of distributed shared energy storage can effectively improve the new energy absorption rate, reduce the net load peak-valley difference, and improve the energy storage utilization rate. At the same time, the distributed shared energy storage operators have achieved positive returns, and investing in the construction of distributed shared energy storage power stations has the potential to make profits.

[0141] After adopting the technical solution of the present invention, the following technical effects can be achieved:

[0142] (1) By configuring distributed shared energy storage, the distribution network and distributed new energy sites can obtain energy storage charging and discharging services at a relatively low cost, raising the local consumption level of new energy to more than 95% and reducing the peak-to-valley difference by 72%;

[0143] (2) By reasonably sharing distributed energy storage, time-sharing reuse of energy storage can be achieved, and the utilization rate of energy storage resources can be improved, so that energy storage with a smaller capacity can meet the energy storage needs of distributed new energy stations and distribution networks. Compared with independent energy storage scenarios, distributed shared energy storage can reduce the configuration capacity by 35%;

[0144] (3) Through the distributed shared energy storage system service and the reasonable configuration of energy storage quantity, the distribution network and distributed new energy stations can reduce their own operating costs. At the same time, the distributed shared energy storage operators can achieve positive returns, which is conducive to increasing their investment enthusiasm and promoting the development of distributed shared energy storage;

[0145] (4) The proposed distributed shared energy storage optimization configuration method can be used to optimize the site selection and capacity of distributed shared energy storage, which helps to provide a strong basis for the configuration of energy storage systems to promote the local consumption of new energy.

[0146] Figure 7 Schematic diagram of a distributed shared energy storage optimization configuration device for improving the local consumption capacity of new energy provided by an exemplary embodiment of the present invention. Figure 7 As shown, the apparatus 700 includes:

[0147] Establishing module 710, for establishing a single-unit operation model of distributed energy storage resources and using convex polytopes to characterize its feasible domain space, to obtain a single-unit power feasible domain space of multiple devices of distributed energy storage resources;

[0148] Aggregation module 720, used for performing inner approximation and Minkowski summation on the power feasible domain space of multiple devices based on Chino polyhedron, and determining the power aggregation feasible domain of distributed energy storage resources;

[0149] A solution module 730 is used to establish and solve a distribution network optimization dispatching model containing aggregated distributed energy storage resources according to the power aggregation feasible domain, and obtain a distribution network optimization dispatching result involving distributed energy storage resources;

[0150] The acquisition module 740 is used to obtain the power control instructions of the distributed energy storage cluster according to the distribution network optimization scheduling result containing the aggregated distributed energy storage resources, and use the water injection algorithm to decompose the control instructions inside the distributed energy storage cluster to determine the charging power and discharging power of each energy storage in the distributed energy storage resources.

[0151] Figure 8 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 8 As shown, the electronic device 80 includes one or more processors 81 and a memory 82 .

[0152] The processor 81 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0153] The memory 82 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 81 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 83 and an output device 84, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0154] In addition, the input device 83 may also include, for example, a keyboard, a mouse, etc.

[0155] The output device 84 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0156] Of course, to simplify, Figure 8 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0157] An embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, causes the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0158] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0159] The embodiments of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0160] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and scope of the elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy, characterized in that: The steps include: S100: Proposes a distributed shared energy storage operation model for source-grid collaborative optimization; S200: Construct a multi-time scale distributed shared energy storage two-level planning model with the lowest annual average cost of the distributed shared energy storage system as the upper level target and the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower level target; S300: A two-layer iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and distribution network-distributed new energy station economic operation problems.

2. A distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy according to claim 1, characterized in that: In the step S100, the distributed shared energy storage operation mode for source-network collaborative optimization is: the operation objectives of the distributed shared energy storage operator, the distributed new energy station, and the distribution network.

3. A distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy according to claim 1, characterized in that: In step S200, the multi-time scale distributed shared energy storage two-level planning model with the lowest annual average cost of the distributed shared energy storage system as the upper level target and the lowest daily comprehensive operating cost of the distribution network-distributed new energy station as the lower level target is: (1) Upper model 1) Objective function The upper optimization goal is to minimize the average daily total cost of the distributed shared energy storage system, which can be expressed as: minC1=C sto -C inc Where: C1 is the average annual cost of the distributed shared energy storage system; C sto is the average annual investment and maintenance cost of distributed shared energy storage; C inc is the average annual income of the distributed shared energy storage system; T w is the number of days for each typical day; C new,w C is the electricity transaction fee of distributed shared energy storage and distributed new energy stations on a typical day; adn,w is the transaction fee of electricity between distributed shared energy storage and distribution network on a typical day; C ser,w is the typical day distributed shared energy storage capacity rental service fee; n is the number of energy storages; r is the discount rate; y is the life cycle of the energy storage equipment; They are the investment cost per unit power and per unit capacity of energy storage respectively; P sto,i 、E sto,i are the rated power and rated capacity of energy storage i respectively; is the maintenance cost per unit power; T is 24 hours a day; N is the number of distributed new energy stations; . is the unit electricity price of the distributed new energy station at time t; is the power sold by the new energy station j to the distributed shared energy storage system at time t on a typical day; is the electricity price per unit of distributed shared energy storage at time t; is the electricity price per unit of electricity in the distribution network at time t; The power sold by the distributed shared energy storage system to the distribution network at time t on a typical day; The power sold by the distribution network to the distributed shared energy storage system at time t on a typical day; s .The unit power service fee paid by the distribution network and distributed new energy stations to the distributed shared energy storage system; (2) Lower-level model 1) Objective function The lower optimization goal is to minimize the sum of the annual comprehensive operating cost of the distribution network-distributed new energy station and the peak-valley difference penalty cost, which can be expressed as: Where: C2 is the annual comprehensive operating cost of the distribution network-distributed new energy station; C grid,w The cost of electricity purchased from the main grid by the distribution network on a typical day; C peak-valley,w is the penalty cost of the typical daily peak-to-valley difference; C grid,w The cost of purchasing electricity from the main grid for the distribution network; is the main grid electricity price at time t; is the power sold by the main grid to the distribution grid at time t on a typical day; peak-valley The penalty fee per unit power for the net load peak-to-valley difference is 0.65 yuan / kW; are the maximum and minimum values ​​of net load on a typical day, respectively; is the net load of the distribution network at time t on a typical day; is the load of node k at time t on a typical day; is the power sold by the new energy station j to the distribution network at time t on a typical day.

4. A distributed shared energy storage optimization configuration method for improving the local consumption capacity of new energy according to claim 1, characterized in that: In the step S300, a double-layer iterative particle swarm algorithm combined with power flow calculation is used to solve the distributed shared energy storage configuration and the distribution network-distributed new energy station economic operation problem, including: The upper model is solved by particle swarm algorithm, where each particle consists of two parts: the rated power P of each energy storage sto,i 、Rated capacity E of each energy storage sto,i The lower model is solved by a particle swarm algorithm combined with power flow calculation, where each particle also consists of two parts: the location x of each energy storage i and the charging and discharging power of each energy storage

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