Energy storage optimal configuration method based on voltage treatment in power distribution area

Abstract

The invention relates to the technical field of power distribution of an electric power system, in particular to an energy storage optimal configuration method based on voltage treatment in a power distribution area, which comprises the following steps of: 1, an influence analysis step: analyzing the influence of an energy storage system on node voltage and network loss of a power distribution network; 2, a system modeling step: an energy storage system model is established, the energy storage system model is a digital model and provides key operation state characterization and security constraints for a subsequent optimization algorithm, and the core of the model comprises a state of charge (SOC) model used for characterizing an energy storage system and a power capability (SOP) model used for limiting an instantaneous power boundary of the energy storage system; step 3, constructing a double-layer collaborative optimization configuration model of light storage regulation capability and voltage optimization; the double-layer collaborative optimization configuration model comprises an upper-layer planning model and a lower-layer operation model, and full utilization of resources and effective treatment of voltage are ensured; and the economical efficiency and the safety are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution of power systems, and particularly relates to an energy storage optimization configuration method based on voltage management of distribution areas. BACKGROUND

[0002] With large-scale access of distributed power sources such as wind power and photovoltaic power to distribution networks, the randomness and volatility of the power output of the distributed power sources bring severe challenges to real-time power balance and safe operation of the power grid, which leads to problems such as voltage out-of-limit and voltage fluctuation in distribution areas, especially in urban and rural distribution areas, and seriously affects power supply quality.

[0003] As a flexible regulation resource, the energy storage system can realize time-space translation of electric energy through charging and discharging, effectively suppress fluctuations and manage voltage. However, the existing energy storage configuration scheme has the following shortcomings: The configuration process fails to fully consider the dynamic correlation characteristics of the area load and voltage, resulting in unreasonable configuration of the energy storage capacity and installation location, and poor voltage management effect; There is a lack of dynamic regulation mechanism linked with real-time operation state, and the charging and discharging strategy of the energy storage is rigid and cannot adapt to real-time changes in system state; The economic and technical targets cannot be effectively coordinated in the planning stage, resulting in high total life cycle cost of the energy storage system and restricting its large-scale application.

[0004] Therefore, there is an urgent need in the field for an energy storage optimization configuration method that can take into account voltage management effect, economy and operation adaptability. SUMMARY

[0005] To solve the above technical problems, the present application provides an energy storage optimization configuration method based on voltage management of distribution areas, which can take into account voltage management effect, economy and operation adaptability.

[0006] The energy storage optimization configuration method based on voltage management of distribution areas provided by the present application comprises the following steps: step one, influence analysis step: analyzing the influence of the energy storage system on node voltage and network loss of the distribution network; Step two, system modeling step: establishing an energy storage system model, which is a digital model and provides key operation state representation and safety constraints for subsequent optimization algorithms. The core of the model includes a state of charge (SOC) model for representing the energy storage system and a power capability (SOP) model for limiting the instantaneous power boundary of the energy storage system; Step three, constructing a double-layer collaborative optimization configuration model of light storage adjustment capacity and voltage optimization; the double-layer collaborative optimization configuration model includes an upper planning model and a lower running model, the upper planning model is responsible for setting global goals such as maximizing system revenue and grid stability, and the lower running model optimizes and adjusts according to specific environment and conditions to ensure full use of resources and effective voltage management; it realizes the transformation from "passive compensation" to "active optimization", has high governance accuracy, breaks through the bottleneck of "planning and operation disconnection", has stronger practicability, and balances the dual goals of "economy" and "technology", maximizes comprehensive benefits, constructs a "digital dynamic model", and has good system safety. Through the organic and coherent technical chain of "accurate analysis, modeling, and collaborative optimization", a set of systematic solution to the voltage problem of distribution area and the economic energy storage configuration method is provided, and the scheme has foresight, practicality, economy, and safety.

[0007] Preferably, the expression of the state of charge (SOC) model is: In the formula: is the rated capacity of the battery energy storage system, is the current power of the battery energy storage system. The calculation formula of the state of charge at any time is as follows: In the formula: is the state of charge at time t; is the charging power at time t; is the charging efficiency at time t; is the discharging power at time t; is the discharging efficiency at time t.

[0008] represents the maximum power that the energy storage system can output or input under the current state, which is crucial for the safe operation and performance of the energy storage system. The expression is: In the formula: is the power under the current state; is the maximum power that the battery energy storage system can output or input.

[0009] Preferably, the expression of the upper planning model is: ​​​​​ wherein: is the annual comprehensive cost, unit: ten thousand yuan; is the total investment cost, unit: ten thousand yuan; is the operation income, unit: ten thousand yuan; is the initial purchase cost of energy storage, unit: ten thousand yuan; is the operation and maintenance cost of energy storage, unit: ten thousand yuan; is the decommissioning and disposal cost of energy storage, unit: ten thousand yuan; is the equivalent daily value coefficient; is the operation cost, unit: ten thousand yuan; is the operation cost after installing energy storage, unit: ten thousand yuan.

[0010] The total cost of energy storage equipment is converted to the equivalent daily value coefficient per day: wherein: is the discount rate; is the annual operation time of energy storage equipment, unit: d (day).

[0011] Preferably, the expression of the lower layer operation model is a day-ahead optimization scheduling model and an intra-day rolling optimization model, the day-ahead optimization scheduling model aims to minimize the scheduling cost of the light storage system, including the light abandonment cost, the energy storage operation cost and the purchase and sale electricity cost, the intra-day rolling optimization model ensures the safe operation of the distribution network by optimizing the voltage quality of the distribution network, the voltage quality can be analyzed by voltage stability and voltage deviation level, the static voltage stability is introduced as one of the indexes for evaluating the system stability and safe operation of the distribution network under power fluctuation; the user's satisfaction with power quality is improved.

[0012] Preferably, the expression of the day-ahead optimization scheduling model is: wherein: is the system scheduling cost, unit: ten thousand yuan; is the probability of scenario ; is the number of all scenarios; is the number of all scenarios; is the number of scheduling periods; is the unit light abandonment penalty cost, unit: yuan / MWh; is the light abandonment power at time under scenario , unit: MW; is the scheduling time interval, unit: min; is the unit charge and discharge power cost of energy storage, unit: yuan / MWh; is the light abandonment power at time under scenario Energy storage charging power at time t, unit: MW; For scenario Under Energy storage discharging power at time t, unit: MW; For Electricity price at time t, unit: yuan / MWh; For scenario Under Photovoltaic storage system selling power at time t, unit: MW; For scenario Under Photovoltaic storage system buying power at time t, unit: MW.

[0013] Preferably, the time granularity of the intra-day rolling optimization model is 1h, the total scheduling duration is 24h, the power supply characteristics and fast reading corresponding characteristics of the energy storage system are utilized to timely correct the distribution network voltage level and reduce the network loss, The expression of the intra-day rolling optimization model is: In the formula, Network loss function, unit: kWh; Voltage quality level; Voltage offset level weight; Voltage offset level, unit: pu; Static voltage stability; Static voltage stability weight.

[0014] In the formula, Scheduling period duration, unit: h; A set of all transmission lines in the distribution network; For Current value flowing through line in the distribution network at time t, unit: kA; Resistance value of line , unit: Ω.

[0015] In the formula, Number of nodes contained in the distribution network; For Node voltage per unit value at the th node at time t, unit: pu; Node voltage offset judgment reference value.

[0016] In the formula: for Timetable Static voltage stability; for Time of the first Net active power load of each node, in MW; for Time of the first Net reactive load value of each node, unit: MW; For the line The reactance value; For distribution network Time of the first Individual node voltage values, unit: pu; For all feeders The maximum value in kV.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: it realizes the transformation from "passive compensation" to "active optimization", achieves high precision in governance, breaks through the bottleneck of "disconnect between planning and operation", has stronger practicality, takes into account both "economic" and "technical" objectives, maximizes comprehensive benefits, constructs a "digital dynamic model", and has good system security. Through the organic and coherent technical chain of "precise analysis, modeling, and collaborative optimization", it provides a systematic solution to the voltage problem of distribution substations while ensuring economic efficiency in energy storage configuration. Its solution is forward-looking, practical, economical, and safe. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the power distribution network structure; Figure 3 It is the structure of a battery energy storage system. Detailed Implementation

[0019] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0020] Example This invention provides an energy storage optimization configuration method based on distribution area voltage management. Step 1: Impact Analysis Step: Analyze the impact of the energy storage system on the voltage of distribution network nodes and network losses; Analysis of the influence of energy storage system on the voltage of distribution network node The energy storage system realizes flexible adjustment of active power through the charging and discharging process, and thus has a significant impact on the voltage of the distribution network node. When the energy storage system is charging, it absorbs active power from the distribution network, which is equivalent to adding a load. This will increase the current in the distribution network. According to Ohm's law (where is the voltage drop, is the current, is the line resistance), the voltage drop on the line also increases, resulting in a decrease in the node voltage. When the energy storage system is discharging, it injects active power into the distribution network, which is equivalent to adding a power source. This will reduce the current in the distribution network, and the voltage drop on the line will also decrease, resulting in an increase in the node voltage.

[0021] When the distribution network is short of reactive power, the load needs to absorb more reactive power from the grid, which will increase the current and increase the voltage drop on the line, resulting in a decrease in the node voltage. The energy storage system can output reactive power to the grid by controlling the converter, which is equivalent to increasing the supply of reactive power, reducing the reactive power absorbed by the load from the grid, thereby reducing the line current and voltage drop, and achieving the purpose of increasing the node voltage. When the distribution network has excess reactive power, the energy storage system can absorb the excess reactive power to prevent the node voltage from being too high. Through this flexible reactive power adjustment, the energy storage system can adjust the output or absorption of reactive power in real time according to the actual demand of the distribution network, keep the grid voltage within a reasonable range, and improve the stability and reliability of the distribution network operation. (2) Analysis of the influence of energy storage system on the network loss of distribution network Active power loss is an important indicator of the stability and economic operation level of the distribution network, and the smaller the value, the more reliable and more economical the network operation.

[0022] In the formula: and are the active power and reactive power on the line , respectively; is the voltage amplitude of the node .

[0023] It can be seen from the formula that the active power loss of the distribution network is related to the active power, reactive power and voltage. When the output of the distributed photovoltaic power is at its peak in the middle of the day, the power flow in the distribution network tends to reverse from the normal direction of "head-end to tail-end" to the reverse direction of "tail-end to head-end". The reverse power flow will increase the transmission power of the line and may cause the overload of the transformer, resulting in additional loss. The energy storage system can smooth the output fluctuation of the photovoltaic power and avoid the reverse loss of the power flow by means of "charging during the peak output of the photovoltaic power and discharging during the trough output of the photovoltaic power". Meanwhile, the expansion of the peak-valley difference of the load of the distribution network is the key factor leading to the increase of the network loss. Taking the residential distribution network as an example, the load peak is concentrated in the period from 18:00 to 22:00, and the power can reach 3-4 times of that in the trough period. At this time, the current of the line increases sharply, and the loss increases in a square level. The energy storage system can reduce the transmission power of the line during the peak period by means of the peak load shaving and valley load filling strategy of "charging during the trough period and discharging during the peak period".

[0024] Step two, system modeling step: establishing an energy storage system model, which is a digital model, providing key operating state representations and safety constraints for subsequent optimization algorithms. The core of the model includes a state of charge (SOC) model for representing the energy storage system and a power capability (SOP) model for limiting the instantaneous power boundary of the energy storage system; Step three, building a double-layer collaborative optimization configuration model of photovoltaic storage regulation capacity and voltage optimization; the double-layer collaborative optimization configuration model includes an upper planning model and a lower running model. The upper planning model is responsible for setting global targets such as maximizing system revenue and grid stability. The lower running model optimizes and adjusts according to specific environments and conditions to ensure full utilization of resources and effective voltage management; The expression of the state of charge (SOC) model is: In the formula, is the rated capacity of the battery energy storage system, is the current power of the battery energy storage system. The calculation formula of the state of charge at any time is as follows: In the formula, is the state of charge at time t, is the charging power at time t, is the charging efficiency at time t, is the discharging power at time t, is the discharging efficiency at time t.

[0025] ​​​​​The maximum power that the energy storage system can output or input in the current state is crucial to the safe operation and performance of the energy storage system. The expression is: In the formula: P is the power in the current state; Pmax is the maximum power that the battery energy storage system can output or input.

[0026] The expression of the upper planning model is: In the formula: C is the annual comprehensive cost, unit: ten thousand yuan; I is the total investment cost, unit: ten thousand yuan; R is the operating income, unit: ten thousand yuan; C0 is the initial purchase cost of energy storage, unit: ten thousand yuan; C1 is the operation and maintenance cost of energy storage, unit: ten thousand yuan; C2 is the retirement disposal cost of energy storage, unit: ten thousand yuan; β is the daily equivalent value coefficient; C is the operating cost, unit: ten thousand yuan; C is the operating cost after installing energy storage, unit: ten thousand yuan.

[0027] The total cost of energy storage equipment is converted to the daily equivalent value coefficient per day: In the formula: r is the discount rate; T is the annual operation time of energy storage equipment, unit: d (day).

[0028] The expression of the lower operation model includes a day-ahead optimization scheduling model and an intra-day rolling optimization model. The day-ahead optimization scheduling model aims to minimize the scheduling cost of the light storage system, including the cost of abandoned light, the operation cost of energy storage, and the cost of purchasing and selling electricity. The intra-day rolling optimization model ensures the safe operation of the distribution network by optimizing the voltage quality of the distribution network. Voltage quality can be analyzed through voltage stability and voltage deviation level. Static voltage stability is introduced as one of the indicators to evaluate the stability and safe operation of the distribution network under power fluctuation, and to improve the user's satisfaction with power quality.

[0029] The expression of the day-ahead optimization scheduling model is: In the formula: C is the system scheduling cost, unit: ten thousand yuan; For the scene The probability of; The number of all scenarios; Number of scheduling periods; The cost of curtailment penalty per unit of solar power, expressed in yuan / MWh; For the scene Down Light curtailment power at any given moment, in MW; The scheduling time interval is expressed in minutes. Cost of energy storage unit charge / discharge power, unit: yuan / MWh; For the scene Down Real-time energy storage and charging power, unit: MW; For the scene Down Energy storage and discharge power at any given time, in MW; for Electricity price at any time, unit: yuan / MWh; For the scene Down Power output of the photovoltaic-storage system at any given time, in MW; For the scene Down Power purchased by the photovoltaic-storage system at any given time, in MW.

[0030] The intraday rolling optimization model has a time granularity of 1 hour and a total scheduling duration of 24 hours. It utilizes the power supply characteristics and fast response characteristics of the energy storage system to promptly correct the voltage level of the distribution network and reduce network losses. The expression for the intraday rolling optimization model is: In the formula: Network loss function, unit: kWh; Voltage quality level; As a voltage offset level weight; Voltage offset level, unit: pu; Static voltage stability; This represents the weighting for static voltage stability.

[0031] In the formula: The duration of the scheduling cycle is expressed in hours (h). It is the collection of all transmission lines in the power distribution network; for The lines flowing through the distribution network at all times The current value on the line, unit: kA; The resistance value of the line, unit: Ω. The resistance value of the line, unit: Ω.

[0032] In the formula: The number of nodes contained in the power distribution network; The node voltage value at the i-th node at the moment t, unit: pu. The node voltage value at the i-th node at the moment t, unit: pu. The node voltage value at the i-th node at the moment t, unit: pu. The node voltage offset judgment reference value.

[0033] In the formula: The static voltage stability of the line at the moment t; The static voltage stability of the line at the moment t; The active net load value of the i-th node at the moment t, unit: MW. The active net load value of the i-th node at the moment t, unit: MW. The active net load value of the i-th node at the moment t, unit: MW. The reactance value of the line; The voltage value of the i-th node in the power distribution network at the moment t, unit: pu. The voltage value of the i-th node in the power distribution network at the moment t, unit: pu. The voltage value of the i-th node in the power distribution network at the moment t, unit: pu. The maximum value in all feeder lines, unit: kV. The maximum value in all feeder lines, unit: kV. The maximum value in all feeder lines, unit: kV. The maximum value in all feeder lines, unit: kV. The maximum value in all feeder lines, unit: kV. The maximum value in all feeder lines, unit: kV. The maximum value in all feeder lines, unit: kV.

[0034] The main functions realized by the present application are: 1. The transformation from "passive compensation" to "active optimization" is realized, and the treatment accuracy is high.

[0035] The traditional energy storage configuration is often based on experience or local compensation, and the present application first accurately identifies the key nodes and sensitivity of the district voltage and network loss problem through the systematic influence analysis of step one, so that the subsequent energy storage configuration can be "aimed". This ensures that the optimization process is based on the actual physical characteristics of the power grid, and improves the pertinence and accuracy of the treatment scheme from the source.

[0036] 2. The bottleneck of "planning and operation disconnection" is broken, and the scheme is more practical.

[0037] The application creatively integrates long-term economic planning (upper planning model) and short-term technical operation (lower operation model) by the "planning-operation" double-layer collaborative optimization model constructed by step three. The upper planning has foreseen the operation strategy and benefits of the energy storage system in the whole life cycle at the time of decision-making, thereby avoiding the disjointed problem that the configuration scheme is economically unfeasible or technically unexecutable. This makes the finally derived configuration scheme (capacity, location) and operation strategy (charging and discharging plan) highly collaborative and feasible.

[0038] 3. Both "economic" and "technical" objectives are considered, and the comprehensive benefit is maximized.

[0039] The double-layer model clearly takes "economic indicators" and "technical indicators" as the optimization objectives of the upper and lower layers respectively, and solves them collaboratively through algorithms, achieving the technical requirements of voltage control, network loss reduction, and power supply quality improvement while ensuring economic returns on investment. This solves the industry problem that single-target optimization cannot balance comprehensive benefits, and provides a cost and benefit balanced solution for the large-scale promotion of energy storage.

[0040] 4. A "digital dynamic model" is constructed, and the system safety is good.

[0041] In step two, by introducing an accurate SOC model and a dynamic SoP model as core constraints, it is ensured that the operation state of the energy storage system is always limited within a safe and healthy range during the entire optimization process. This avoids damaging the equipment life or causing safety risks in pursuit of extreme performance, so that the optimization result is not only efficient, but also safe and reliable.

[0042] The energy storage optimization configuration method based on distribution area voltage control of the application has common mechanical installation, connection or setting methods, and can be implemented as long as it can achieve the beneficial effects. The SOC model is defined by a discrete time recursive formula and meets the preset SOC operation interval constraint. The SOP model is defined by a charging and discharging power inequality constraint, and the power limit value is a dynamic value. An iterative algorithm is used to solve the double-layer collaborative optimization configuration model. The iterative algorithm includes: passing the configuration scheme output by the upper planning model to the lower operation model for simulation, and feeding back the operation cost and control effect output by the lower operation model to the upper planning model, and cyclically iterating until convergence.

[0043] The above is only the preferred embodiment of the application, and it should be noted that for those skilled in the art, without departing from the technical principles of the application, several improvements and modifications can be made, and these improvements and modifications should be considered as the protection scope of the application.

Claims

1. An energy storage optimization configuration method based on distribution substation voltage management, characterized in that, The method comprises the following steps: Step one, impact analysis step: analyze the impact of energy storage system on the node voltage and network loss of distribution network; Step two, system modeling step: establish a digital operation model of the energy storage system, which includes a state of charge (SOC) model and a power capability (SOP) model; Step three, optimal configuration step: construct and solve a double-layer collaborative optimization configuration model to obtain the configuration scheme and operation strategy of the energy storage system; the double-layer collaborative optimization configuration model includes an upper planning model and a lower operation model, the upper planning model is responsible for setting global targets such as maximizing system revenue and grid stability, and the lower operation model optimizes and adjusts according to specific environment and conditions to ensure full use of resources and effective voltage management.

2. The energy storage optimization configuration method based on power distribution substation voltage regulation according to claim 1, wherein, The expression of the state of charge (SOC) model is: In the formula: is the rated capacity of the battery energy storage system, is the current charge of the battery energy storage system; The calculation formula of the state of charge at any time is as follows: In the formula: is the state of charge at the moment; is the charging power at the moment; is the charging efficiency at the moment; is the discharging power at the moment; is the discharging efficiency at the moment; represents the maximum power that the energy storage system can output or input under the current state, which is crucial for the safe operation and performance of the energy storage system; The expression is: In the formula: Pcurrentis the power in the current state; Pmaxis the maximum power that the battery energy storage system can output or input.

3. The energy storage optimization configuration method based on power distribution substation voltage regulation according to claim 1, wherein, The expression of the upper planning model is: In the formula: is the annual comprehensive cost, unit: ten thousand yuan; is the total investment cost, unit: ten thousand yuan; is the operating income, unit: ten thousand yuan; is the initial purchase cost of energy storage, unit: ten thousand yuan; is the operation and maintenance cost of energy storage, unit: ten thousand yuan; is the retirement disposal cost of energy storage, unit: ten thousand yuan; is the equal day value coefficient; is the operation cost, unit: ten thousand yuan; is the operation cost after installing energy storage, unit: ten thousand yuan; The total cost of the energy storage device is converted into the equivalent daily value coefficient per day: In the formula: is the discount rate; is the time of operation of the energy storage device per year, in units of d (days).

4. The energy storage optimal configuration method based on power distribution substation voltage regulation according to claim 1, wherein, The expression of the lower operation model includes a day-ahead optimal scheduling model and an intra-day rolling optimization model, the day-ahead optimal scheduling model takes minimizing the scheduling cost of the light storage system as the target, including the cost of abandoned light, the operation cost of the energy storage system, and the cost of purchasing and selling electricity, the intra-day rolling optimization model optimizes the voltage quality of the distribution network to ensure the safe operation of the distribution network, the voltage quality can be analyzed through the voltage stability and voltage deviation level, and the static voltage stability is introduced as one of the indicators for evaluating the system stability and safe operation of the distribution network under power fluctuation.

5. The energy storage optimization configuration method based on power distribution substation voltage regulation according to claim 4, characterized in that, The expression of the day-ahead optimal scheduling model is: In the formula: is the system scheduling cost, unit: ten thousand yuan; is the probability of scenario ; is the number of all scenarios; is the number of scheduling periods; is the unit penalty cost of light rejection, unit: yuan / MWh; is the light rejection power of scenario at time, unit: MW; is the scheduling time interval, unit: min; is the unit charging and discharging power cost of energy storage, unit: yuan / MWh; is the charging power of energy storage at scenario at time, unit: MW; is the discharging power of energy storage at scenario at time, unit: MW; is the electricity price at time, unit: yuan / MWh; is the selling power of the light and energy storage system at scenario at time, unit: MW; is the buying power of the light and energy storage system at scenario at time, unit: MW.

6. The energy storage optimal configuration method based on power distribution substation voltage regulation according to claim 4, wherein, The time granularity of the intra-day rolling optimization model is 1h, and the total scheduling time is 24h, the power supply characteristics and fast reading characteristics of the energy storage system are used to timely correct the voltage level of the distribution network and reduce the network loss, The expression of the intra-day rolling optimization model is: wherein: is the network loss function, unit: kWh; is the voltage quality level; is the voltage excursion level weight; is the voltage excursion level, unit: pu; is the static voltage stability; is the static voltage stability weight; In the formula: is the scheduling period length, unit: h; is the set of all transmission lines in the power distribution network; is the set of all transmission lines in the power distribution network; is the current value flowing through the line in the power distribution network at time t, unit: kA; is the resistance value of the line , unit: Ω; In the formula: is the number of nodes contained in the power distribution network; is is the node voltage at the is the node voltage at the is the node voltage offset judgment reference value; In the formula: for Timetable Static voltage stability; for Time of the first Net active power load of each node, in MW; for Time of the first Net reactive load value of each node, unit: MW; For the line The reactance value; For distribution network Time of the first Individual node voltage values, unit: pu; For all feeders The maximum value in kV.

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