Multi-time-scale optimal scheduling method for regional power grid under wind power generation

Abstract

The invention relates to the technical field of power system dispatching, in particular to a regional power grid multi-time-scale optimal dispatching method under wind power generation, which comprises the following steps: acquiring wind power output data, user load data and characteristics of a sodium-sulfur battery energy storage system; with the lowest operation cost and the lowest wind curtailment cost of the power distribution network as targets, a day-ahead scheduling optimization model is established by performing short-term prediction on data acquired in the early stage; issuing a day-ahead scheduling plan according to historical user load data; the method comprises the following steps of: constructing an intra-day rolling optimization scheduling model according to an actual wind power output condition by taking the lowest operation cost, wind curtailment cost and correction scheduling cost of a power distribution network as a target, and performing rolling correction on a day-ahead scheduling plan; the wind power output is further adjusted, and the real-time scheduling model is optimized; and issuing the optimized scheduling plan to the wind power plant and the energy storage system for execution. According to the invention, the safety coefficient and the operation capability of the power system can be improved, and support is provided for efficient utilization of renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a multi-time scale optimization dispatching method for a regional power grid under wind power generation. Background Art

[0002] With the increasing attention paid to renewable energy around the world, wind power generation, as a clean and renewable energy form, has gradually improved its status in the power system. However, the volatility and intermittency of wind power have brought great challenges to the stable operation of the power system, especially in the peak load regulation of the power system. Therefore, it has become an important topic in the current power field to study effective energy storage technology and optimize scheduling strategies to improve the peak load regulation capacity and overall efficiency of wind power systems. Although traditional peak load regulation methods such as adjusting the power structure and building pumped storage power stations can alleviate the peak load regulation pressure to a certain extent, they are difficult to meet the needs of large-scale wind power grid connection. Therefore, it is necessary to explore new energy storage technologies and optimize scheduling strategies to improve the peak load regulation capacity and overall efficiency of the power system. As a new energy storage technology, sodium-sulfur batteries have the advantages of high energy storage density, high efficiency, and long life, and have broad application prospects in power system peak load regulation. Sodium-sulfur batteries convert electrical energy into chemical energy through chemical reactions for storage, and convert chemical energy into electrical energy for release through reverse reactions when needed. Its high energy density and rapid response capability enable sodium-sulfur batteries to absorb or release a large amount of electrical energy in a short period of time, thereby effectively smoothing the fluctuation of wind power. A lot of research has been done at home and abroad in the new power system peak shaving and wind power energy storage technology, and some results have been achieved. For example, China has achieved smoothing and peak shaving of wind farm output power through the reasonable configuration of battery energy storage systems and optimization of control strategies; foreign countries have achieved coordinated optimization of battery energy storage systems and wind power systems by using advanced control algorithms and prediction technologies. However, the application of sodium-sulfur batteries in power system peak shaving is still in its infancy, and further research and improvement of related technologies and scheduling strategies are needed.

[0003] In the future, with the continuous development of sodium-sulfur battery technology and the reduction of costs, its application in peak load optimization of wind power systems will become more extensive and in-depth. At the same time, it is necessary to further strengthen the research on related technologies and dispatching strategies to promote the widespread application and industrial development of sodium-sulfur battery energy storage technology in power systems. Summary of the invention

[0004] In view of this, the present invention provides a multi-time scale optimization scheduling method for regional power grids under wind power generation to solve the randomness, intermittency and volatility problems caused by large-scale access of renewable energy to the power grid.

[0005] The present invention provides a multi-time scale optimization dispatching method for a regional power grid under wind power generation, comprising:

[0006] S1: Obtain wind power output data, user load data and characteristics of the sodium-sulfur battery energy storage system;

[0007] S2: With the goal of minimizing the operating cost of the distribution network and the cost of wind curtailment, a day-ahead dispatch optimization model is established by making short-term predictions based on the data obtained in the early stage;

[0008] S3: Issue a day-ahead dispatch plan based on historical user load data;

[0009] S4: With the goal of minimizing the operation cost, wind curtailment cost and revised dispatching cost of the distribution network, a daily rolling optimization dispatching model is constructed according to the actual wind power output, and the day-ahead dispatching plan is revised in a rolling manner;

[0010] S5: Further adjust wind power output and optimize the real-time dispatch model;

[0011] S6: Send the optimized dispatch plan to the wind farm and energy storage system for execution.

[0012] Preferably, the day-ahead dispatch optimization model uses the grid load change, wind power and thermal power unit output data of the previous day to specify the day's dispatch plan, and calculates the difference between the day-ahead load and the sum of the wind power output and the thermal power unit output according to the power generation output of new energy, which is recorded as ΔP. ​​If the wind output is large and ΔP<0, the sodium-sulfur battery energy storage system is used to absorb new energy. At this time, the energy storage system is charged, and the particle swarm algorithm is used to calculate the charging power of the energy storage unit in the energy storage system. If it is full, electricity is sold to the superior power grid; if ΔP>0, it is at the peak load period, the wind and thermal power units are insufficient, the sodium-sulfur battery energy storage system is discharged, and the particle swarm algorithm is used to calculate the discharge rate of each unit. If the load demand is still not met, electricity is purchased from the next level.

[0013] Further preferably, the objective function of the day-ahead scheduling optimization model is as follows:

[0014]

[0015] Where: F cost is the system operating cost, f fire,t is the operating cost of conventional units and traditional generator sets, f ess,t is the operating cost of the sodium-sulfur battery energy storage power station, f dispatch,t is the management and scheduling cost, f abandon,t is the wind curtailment cost, f peak,t is the peak load cost, f essp,t is the peak load regulation benefit of energy storage, f gain,t To save fuel and environmental pollution costs;

[0016] Among them, the wind abandonment cost is added to the total cost as a penalty function:

[0017] minf abandon,t = k·max(P w,t -P plan,t ) (2)

[0018] Where: k is the unit wind curtailment cost, P w,t is the wind power generation power at time t, P plan,t Planned power for time t;

[0019] The operating cost of a sodium-sulfur battery energy storage unit is:

[0020] f ess,t =M i,t (P dc,t -P c,t )-V i,t (P dc,t +P c,t ) (3)

[0021] Where: M i,t is the energy storage operation cost, V i,t is the real-time electricity price of the power grid at time t, P dc,t , P c,t are the charging power at time t respectively.

[0022] Further preferably, the objective function of the intraday rolling optimization scheduling model is as follows:

[0023]

[0024] Where: f e ' ss,t Adjustment costs for sodium-sulfur battery energy storage units;

[0025] f e ' ss,t =(M i,t -V i,t )(P c ' h,t -P ch,t |-P c ' ,t -P c,t ) (5)

[0026] Where: P c ' h,t , P ch,t are the discharge power update value and the energy storage system discharge power, P c ' ,t Updates the value for charging power.

[0027] Further preferably, the constraints of the day-ahead scheduling optimization model and the intraday rolling optimization scheduling model are the same, and the constraints are as follows:

[0028] Power balance constraints:

[0029]

[0030] Where: P load,d,t P is the load part that does not change with the electricity price. f,d,t,j is the power generated by the traditional generator set at time t, P w,d,t is the power generation of the wind turbine at time t, is the discharge power of sodium-sulfur battery d at time t, P e c ss,d,t is the charging power of the sodium-sulfur battery at time t, P ls,d,t is the peak load shortage of the system at time t;

[0031] Conventional unit operation constraints:

[0032] Unit output constraints:

[0033] P fmin ≤P f,t ≤P fmax (7)

[0034] Where: P f,t is the power generation of the traditional diesel generator set at time t, P fmin and P fmax are the minimum power and maximum power of the diesel generator set respectively;

[0035] Unit climbing constraints:

[0036]

[0037] Where: is the maximum value of the unit power rise within a unit time scale, It is the maximum value of the power drop of the unit within a unit time scale;

[0038] Fan output constraints:

[0039]

[0040] Where: are the minimum and maximum values ​​of wind power generation, P w,d,t is the actual output of the wind turbine generator set at time t on day d;

[0041] Sodium-sulfur battery charging and discharging constraints:

[0042]

[0043] Where: are the discharge power and charging power of the sodium-sulfur battery, are the maximum values ​​of discharge power and charge power of sodium-sulfur battery, P ess,t is the output power of the sodium-sulfur battery, are the minimum and maximum output power of sodium-sulfur batteries, respectively;

[0044] Charge constraints of sodium-sulfur batteries:

[0045] soc min ≤soc t ≤soc max (11)

[0046] Where: soc t is the state of charge of the sodium-sulfur battery energy storage station, soc max 、soc min They are respectively the upper and lower limits of the state of charge of the sodium-sulfur battery energy storage power station.

[0047] The present invention provides a multi-time scale optimization and dispatching method for regional power grids under wind power generation, which can significantly improve the safety factor and economic operation capacity of the power system, and provide strong support for the large-scale access and efficient utilization of renewable energy. By utilizing the high efficiency, long life and large-scale energy storage capacity of the sodium-sulfur battery energy storage system, the balance and coordination of the power system at different time scales can be achieved through multi-time scale collaborative optimization and dispatching. The sodium-sulfur battery energy storage system can quickly respond to changes in the output of renewable energy, provide the necessary regulation capabilities and backup capacity, and thus enhance the flexibility and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the framework of the multi-time scale optimization dispatching method for regional power grids under wind power generation provided by the present invention;

[0049] Figure 2 A flow chart for solving a mathematical model of a multi-time-scale optimization dispatching method for a regional power grid under wind power generation provided by the present invention;

[0050] Figure 3 Schematic diagram of day-ahead scheduling and intraday scheduling strategies of the multi-time-scale optimization scheduling method for regional power grid under wind power generation provided by the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that in order to avoid blurring the present invention due to unnecessary details, only the processing steps closely related to the scheme of the present invention are shown in the accompanying drawings, and other details that are not closely related to the present invention are omitted.

[0052] like Figure 1 As shown, the present invention provides a multi-time scale optimization scheduling method for regional power grid under wind power generation, comprising the following steps:

[0053] S1: Obtain wind power output data, user load data and characteristics of the sodium-sulfur battery energy storage system;

[0054] S2: With the goal of minimizing the operating cost of the distribution network and the cost of wind curtailment, a day-ahead dispatch optimization model is established by making short-term predictions based on the data obtained in the early stage;

[0055] S3: Issue a day-ahead dispatch plan based on historical user load data;

[0056] S4: With the goal of minimizing the operation cost, wind curtailment cost and revised dispatching cost of the distribution network, a daily rolling optimization dispatching model is constructed according to the actual wind power output, and the day-ahead dispatching plan is revised in a rolling manner;

[0057] S5: Further adjust wind power output and optimize the real-time dispatch model;

[0058] S6: Send the optimized dispatch plan to the wind farm and energy storage system for execution.

[0059] As an improvement of the technical solution, Figure 2 As shown in the figure, the day-ahead dispatch optimization model uses the previous day's grid load change, wind power and thermal power unit output data to specify the day's dispatch plan. According to the power generation output of new energy, the difference between the day-ahead load and the sum of wind power output and thermal power unit output is calculated, recorded as ΔP. ​​If the wind output is large and ΔP<0, the sodium-sulfur battery energy storage system is used to absorb new energy. At this time, the energy storage system is charged, and the particle swarm algorithm is used to calculate the charging power of the energy storage unit in the energy storage system. If it is full, electricity is sold to the upper-level power grid; if ΔP>0, it is at the peak load period, the wind and thermal power units are insufficient, the sodium-sulfur battery energy storage system is discharged, and the particle swarm algorithm is used to calculate the discharge rate of each unit. If it still does not meet the load demand, electricity is purchased from the upper level.

[0060] As an improvement of the technical solution, the day-ahead dispatch optimization model aims to minimize the operating cost of the distribution network and the cost of wind power abandonment. The dispatch optimization model is established by short-term forecasting of the power generation power of new energy sources and user loads, and then the day-ahead dispatch instructions are issued according to the load call volume; the day-ahead dispatch plan has a 24-hour execution cycle and an hour as the time scale; in order to improve the grid's ability to absorb new energy and improve the grid's power supply reliability in emergency mode, the objective function of the day-ahead dispatch model should be optimized on the basis of minimizing the total operating cost of the system; the optimization includes converting the wind abandonment into a penalty cost and including it in the system operating cost, and considering the operating cost of sodium-sulfur battery energy storage; in this way, the economy of the system can be improved, the absorption capacity of new energy can be enhanced, and the reliability of power supply in emergency mode can be ensured. The objective function of the day-ahead dispatch optimization model is as follows:

[0061]

[0062] Where: F cost is the system operating cost, f fire,t is the operating cost of conventional units and traditional generator sets, f ess,t is the operating cost of the sodium-sulfur battery energy storage power station, f dispatch,t is the management and scheduling cost, f abandon,t is the wind curtailment cost, f peak,t is the peak load cost, f essp,t is the peak load regulation benefit of energy storage, f gain,t To save fuel and environmental pollution costs;

[0063] Among them, the wind abandonment cost is added to the total cost as a penalty function:

[0064] minf abandon,t = k·max(P w,t -P plan,t ) (2)

[0065] Where: k is the unit wind curtailment cost, P w,t is the wind power generation power at time t, P plan,t Planned power for time t;

[0066] The operating cost of a sodium-sulfur battery energy storage unit is:

[0067] f ess,t =M i,t (P dc,t -P c,t )-V i,t (P dc,t +P c,t ) (3)

[0068] Where: M i,t is the energy storage operation cost, Vi,t is the real-time electricity price of the power grid at time t, P dc,t , P c,t are the charging power at time t respectively.

[0069] As an improvement to the technical solution, the intraday rolling method is adopted in the intraday dispatching link. In this way, the day-ahead dispatching results of the distribution network on that day are monitored in real time, and then the most appropriate control sequence is calculated by combining the wind load forecast data with a time scale of 15 minutes in the next six hours. The goal of the intraday rolling optimization dispatching model is still to minimize the system operation cost, but compared with the day-ahead dispatching optimization model, the intraday rolling optimization dispatching model takes into account the economy and power volatility of the power grid operation cost, and constructs an optimization model for the power grid from the perspective of intraday correction dispatching cost. By following the day-ahead dispatching with distributed energy storage devices, the uncertainty of distribution network dispatching can be reduced. The objective function of the intraday rolling optimization dispatching model is as follows:

[0070]

[0071] Where: f e ' ss,t Adjustment costs for sodium-sulfur battery energy storage units;

[0072] f e ' ss,t =(M i,t -V i,t )(P c ' h,t -P ch,t |-P c ' ,t -P c,t ) (5)

[0073] Where: P c ' h,t , P ch,t are the discharge power update value and the energy storage system discharge power, P c ' ,t Updates the value for charging power.

[0074] As an improvement of the technical solution, Figure 3 As shown in the figure, the power balance constraint and the power quality constraint need to be met during the day-ahead dispatch and intraday dispatch. At the same time, attention should be paid to the impact of output fluctuations to ensure that the distribution network can generate enough electricity to meet the user-side load and loss requirements in each dispatch period. The day-ahead dispatch optimization model has the same constraints as the intraday rolling optimization dispatch model, which are as follows:

[0075] Power balance constraints:

[0076]

[0077] Where: P load,d,t P is the load part that does not change with the electricity price. f,d,t,j is the power generated by the traditional generator set at time t, P w,d,t is the power generation of the wind turbine at time t, is the discharge power of sodium-sulfur battery d at time t, is the charging power of the sodium-sulfur battery at time t, P ls,d,t is the peak load shortage of the system at time t;

[0078] Conventional unit operation constraints:

[0079] Unit output constraints:

[0080] P fmin ≤P f,t ≤P fmax (7)

[0081] Where: P f,t is the power generation of the traditional diesel generator set at time t, P fmin and P fmax are the minimum power and maximum power of the diesel generator set respectively;

[0082] The unit climbing rate is used to measure the ability of the unit to increase or decrease the load. It is an important parameter in the normal operation of the unit. When the slope of the load curve is greater than the limit of the unit climbing rate, the unit's power output will not be able to maintain the balance of the grid, and the power quality and operational safety will be threatened. Therefore, the unit climbing constraint conditions are required:

[0083]

[0084] Where: is the maximum value of the unit power rise within a unit time scale, It is the maximum value of the power drop of the unit within a unit time scale;

[0085] Fan output constraints:

[0086]

[0087] Where: are the minimum and maximum values ​​of wind power generation, P w,d,t is the actual output of the wind turbine generator set at time t on day d;

[0088] Sodium-sulfur battery charging and discharging constraints:

[0089]

[0090] Where: are the discharge power and charging power of the sodium-sulfur battery, are the maximum values ​​of discharge power and charge power of sodium-sulfur battery, P ess,t is the output power of the sodium-sulfur battery, are the minimum and maximum output power of sodium-sulfur batteries, respectively;

[0091] Charge constraints of sodium-sulfur batteries:

[0092]

[0093] Where: soc t is the state of charge of the sodium-sulfur battery energy storage station, soc max 、soc min They are respectively the upper and lower limits of the state of charge of the sodium-sulfur battery energy storage power station.

[0094] The multi-time scale optimization scheduling method for regional power grids under wind power generation provided by the present invention studies and analyzes the output characteristics of the sodium-sulfur battery energy storage power station, and formulates a day-ahead scheduling plan according to the multi-time scale characteristics of the load-side demand response resources. In addition, the uncertainty in new energy forecasting and load forecasting is reduced by intra-day rolling optimization and real-time correction. The multi-time scale can fully utilize the rapid adjustment capabilities of the sodium-sulfur battery energy storage power station and the load-side demand response resources, so that the system can predict data more accurately.

[0095] It should be noted that the purpose of publishing the embodiments is to help further understand the present invention, but those skilled in the art can understand that various substitutions and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the contents disclosed in the embodiments, and the scope of protection claimed by the present invention shall be subject to the scope defined in the claims.

Claims

1. A multi-time scale optimization scheduling method for regional power grid under wind power generation, characterized in that: include: Obtain wind power output data, user load data, and characteristics of sodium-sulfur battery energy storage systems; With the goal of minimizing the operating cost of the distribution network and the cost of wind curtailment, a day-ahead dispatch optimization model is established by making short-term predictions based on the data obtained in the early stage. Issue day-ahead dispatch plans based on historical user load data; With the goal of minimizing the operating cost, wind curtailment cost and revised dispatching cost of the distribution network, a daily rolling optimization dispatching model is constructed according to the actual wind power output, and the day-ahead dispatching plan is revised in a rolling manner; Further adjust wind power output and optimize the real-time dispatch model; The optimized dispatch plan is sent to the wind farm and energy storage system for execution.

2. The method for multi-time scale optimization scheduling of regional power grid under wind power generation according to claim 1 is characterized by: The day-ahead dispatch optimization model uses the previous day's grid load change, wind power and thermal power unit output data to specify the day's dispatch plan, and calculates the difference between the day-ahead load and the sum of the wind power output and the thermal power unit output according to the power generation output of new energy, recorded as ΔP. ​​If the wind output is large and ΔP<0, the sodium-sulfur battery energy storage system is used to absorb new energy. At this time, the energy storage system is charged, and the particle swarm algorithm is used to calculate the charging power of the energy storage unit in the energy storage system. If it is full, electricity is sold to the superior power grid; if ΔP>0, it is at the peak load period, the wind and thermal power units are insufficient, the sodium-sulfur battery energy storage system is discharged, and the particle swarm algorithm is used to calculate the discharge rate of each unit. If it still does not meet the load demand, electricity is purchased from the next level.

3. The method for multi-time scale optimization scheduling of regional power grid under wind power generation according to claim 1 is characterized by: The objective function of the day-ahead scheduling optimization model is as follows: Where: F cost is the system operating cost, f fire,t is the operating cost of conventional units and traditional generator sets, f ess,t is the operating cost of the sodium-sulfur battery energy storage power station, f dispatch,t is the management and scheduling cost, f abandon,t is the wind curtailment cost, f peak,t is the peak load cost, f essp,t is the peak load regulation benefit of energy storage, f gain,t is the fuel saved and environmental pollution cost; among them, the wind abandonment cost is added to the total cost as a penalty function: minf abandon,t =k·max(P w,t -P plan,t ) (2) Where: k is the unit wind curtailment cost, P w,t is the wind power generation power at time t, P plan,t is the planned power at time t; the operating cost of the sodium-sulfur battery energy storage unit is: f ess,t =M i,t (P dc,t -P c,t )-V i,t (P dc,t +P c,t ) (3) Where: M i,t is the energy storage operation cost, V i,t is the real-time electricity price of the power grid at time t, P dc,t , P c,t are the charging power at time t respectively.

4. The method for multi-time scale optimization scheduling of regional power grid under wind power generation according to claim 1 is characterized by: The objective function of the intraday rolling optimization scheduling model is as follows: Where: f e ' ss,t Adjustment costs for sodium-sulfur battery energy storage units; f e ′ ss,t =(M i,t -V i,t )(P c ′ h,t -P ch,t |-P c ′ ,t -P c,t ) (5) Where: P c ' h,t , P ch,t are the discharge power update value and the energy storage system discharge power, P c ' ,t Updates the value for charging power.

5. The method for multi-time-scale optimization scheduling of regional power grids under wind power generation according to claim 1 is characterized by: The constraints of the day-ahead scheduling optimization model and the intraday rolling optimization scheduling model are the same, and the constraints are as follows: Power balance constraints: Where: P load,d,t P is the load part that does not change with the electricity price. f,d,t,j is the power generated by the traditional generator set at time t, P w,d,t is the power generation of the wind turbine at time t, is the discharge power of sodium-sulfur battery d at time t, is the charging power of the sodium-sulfur battery at time t, P ls,d,t is the shortage of peak load regulation of the system at time t; the operating constraints of conventional units are: Unit output constraints: P fmin ≤P f,t ≤P fmax (7) Where: P f,t is the power generation of the traditional diesel generator set at time t, P fmin and P fmax are the minimum power and maximum power of the diesel generator set respectively; Unit climbing constraints: Where: is the maximum value of the unit power rise within a unit time scale, It is the maximum value of the power drop of the unit within a unit time scale; Fan output constraints: Where: are the minimum and maximum values ​​of wind power generation, P w,d,t is the actual output of the wind turbine generator set at time t on day d; Sodium-sulfur battery charging and discharging constraints: Where: are the discharge power and charging power of the sodium-sulfur battery, are the maximum values ​​of discharge power and charge power of sodium-sulfur battery, P ess,t is the output power of the sodium-sulfur battery, are the minimum and maximum output power of sodium-sulfur batteries, respectively; Charge constraints of sodium-sulfur batteries: soc min ≤soc t ≤soc max (11) Where: soc t is the state of charge of the sodium-sulfur battery energy storage station, soc max 、soc min They are respectively the upper and lower limits of the state of charge of the sodium-sulfur battery energy storage power station.

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