Energy storage real-time operation method and system considering power generation right transfer and storage medium

Abstract

The invention relates to an energy storage real-time operation method considering power generation right transfer. The method comprises the following steps: step 1, obtaining a day-ahead scheduling unit output plan curve, wind power prediction information, unit parameters and energy storage parameters; 2, constructing a benefit model of energy storage participating in real-time operation; step 3, constructing constraint conditions; 4, an energy storage charging and discharging strategy and a power generation right transaction volume solving process are carried out; 5, solving the peak regulation electric quantity at each moment; the method has the advantages that on the basis of the Bayesian equilibrium theory of the game theory, multi-time-scale coordinated optimization scheduling is adopted, and power generation right transfer is considered.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy storage operation strategies, and specifically relates to a real-time energy storage operation method, system, and storage medium taking into account the transfer of power generation rights. Background Art

[0002] As my country's power structure continues to optimize, the operation mechanism and balance mode of the power system have undergone profound changes. In line with this, the power market will also face major changes. The power trading ecosystem continues to expand. As the form and function of the energy Internet gradually improve, emerging entities such as virtual power plants, energy storage, demand-side response, and microgrids will widely participate in market interactions and further form multi-energy coupling and coordinated optimization such as electricity, heat, and gas. Market entities have diverse trading and service needs, and the power trading ecosystem is gradually formed and improved; this includes the demand for energy storage to participate in the power market. Taking new energy side energy storage as an example, as the penetration rate of new energy gradually increases, the scale of energy storage installed on the new energy side is also gradually expanding. As the problem of new energy consumption becomes more prominent, the new energy power consumption guarantee mechanism is continuously promoted and gradually improved. It is necessary to monitor, evaluate, and formally assess the responsible entities for new energy consumption weights. The new energy consumption guarantee mechanism with new energy consumption responsibility weights as the core aims to establish a long-term new energy consumption mechanism, promote new energy consumption, and improve new energy generation in the form of subsidies. The operating conditions of power companies and the weight of renewable energy consumption responsibilities have a certain impact on power generation rights trading. In future power generation rights market transactions, there will be greater room and potential for the replacement of renewable energy power generation with thermal power generation. In recent years, my country's renewable energy power generation technology has continued to improve, and photovoltaic power generation and wind power generation have reached a certain level of installed capacity. As important energy categories in power generation rights trading, solar energy and wind energy occupy a major position in the power generation rights trading market and play a key role in promoting the optimization of power supply structure. Under the influence of the continuous improvement of renewable energy consumption mechanisms, power generation rights replacement transactions such as "wind-thermal" and "solar-thermal" as well as bundled transmission forms such as "wind-thermal" and "solar-thermal" will have great room for development. The introduction of energy storage can enrich the content of power generation rights replacement transactions and further enhance the absorption capacity of renewable energy. Therefore, it is very necessary to provide a real-time energy storage operation method, system and storage medium based on the Bayesian equilibrium theory of game theory, adopting multi-time scale coordinated optimization scheduling, and considering the transfer of power generation rights. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology and to provide a real-time operation method, system and storage medium for energy storage based on the Bayesian equilibrium theory of game theory, adopting multi-time scale coordinated optimization scheduling and considering the transfer of power generation rights.

[0004] The object of the present invention is achieved by providing a real-time energy storage operation method considering the transfer of power generation rights, the method comprising the following steps:

[0005] Step 1: Obtain the day-ahead dispatch unit output plan curve, wind power forecast information, unit parameters, and energy storage parameters;

[0006] Step 2: Build a benefit model for energy storage participating in real-time operations;

[0007] Step 3: Construct constraints;

[0008] Step 4: Energy storage charging and discharging strategy and generation rights trading volume solution process;

[0009] Step 5: Calculate the peak power consumption at each moment.

[0010] The specific step 1 is as follows: the mathematical model constraints of energy storage participating in scheduling optimization mainly include charge and discharge constraints, power constraints and reserved backup constraints, wherein the charge and discharge constraints are: The power constraint is: Where, represents the charging and discharging power of energy storage s at time t; P s,max is the upper limit of energy storage power; Energy storage charge and discharge status bit 0-1 variable; E s,t is the energy storage capacity; are the charging and discharging efficiencies respectively; Δt represents the scheduling time interval; E s,max 、E s,min are the upper and lower limits of energy storage capacity; the reserve reserve constraint is:

[0011] The benefit model for energy storage participating in real-time operation in step 2 is specifically constructed as follows: The incremental profit can be divided into the following four strategies: Among them, i represents the charging and discharging strategy that is affected by the moment i after moment t; They represent the T+I Alliance and choose one of the four strategies: selling power generation rights and energy storage charging, selling and discharging, buying and charging, and buying and discharging; They represent the incremental profit of the strategy selected at this time and the expected deviation power ΔP i The product of is used to express the alliance's willingness to choose this strategy, such as: Where p t represents the electricity price at time t in the time-of-use electricity price table; p c,t represents the carbon emission rights trading price per unit of electricity generation at time t, which can reflect the fluctuation of carbon price; P G,t represents the planned power generation of thermal power units at time t obtained based on the load forecast and wind power forecast; α represents the renewable energy quota rate; P g,tRepresents the price of the green certificate per unit of wind power generation; also records the energy storage capacity that is expected to be reserved at the time; also records the energy storage capacity that is expected to be reserved at time t+i Finally, with the goal of maximizing the wind-thermal power generation revenue at time t, the mathematical model is: Where, I G Represents the total revenue of conventional thermal power units; I W Represents the total revenue of wind turbines; They represent the strategy selection status bit, which is a 0-1 variable; δ represents the discount factor. The farther the time i is from the time t, the smaller its influence; C SC,t represents the energy storage capacity penalty; c CS E is the penalty amount per unit capacity of energy storage; S,t is the energy storage capacity; when energy storage does not participate in the wind and thermal power generation rights trading, the green certificate market and carbon market are used to stimulate wind and thermal power plants to trade power generation rights and promote wind power consumption. The model is as follows: Where: I G represents the total revenue of conventional thermal power units; T represents the total number of power generation rights trading periods in a day; I G,s,t represents the electricity sales revenue of the thermal power plant during period t; C fue,t represents the coal burning cost of thermal power units during period t; I cm,t represents the carbon emission rights trading income of the thermal power plant during period t. If it is greater than 0, it means selling carbon quotas, otherwise it means purchasing; C bp,t represents the emission reduction cost of thermal power units during period t; Among them, I W Represents the total revenue of wind turbines; I W,s,t represents the electricity sales revenue of the wind power plant during period t; I W,gc,t represents the green certificate income of the wind power plant in period t; and when the installed capacity of wind power gradually increases, the power generation right trading volume P at each moment trade,t In the absence of energy storage, negative values ​​often appear. That is, in order to meet the randomness and volatility of wind power output, thermal power plants sometimes need to generate more electricity to fill the planned power shortage of wind power plants. Adding energy storage on the wind power side can further promote wind power consumption while alleviating the peak load regulation pressure of thermal power. At this time, the profit function of the thermal power plant remains unchanged, while the profit of the wind power plant needs to increase the energy storage discharge profit I S,disc,t and energy storage discharge cost C S,disc,t , Among them, P S,disc,t represents the amount of energy storage discharged at time t; c disc Represents the unit discharge cost of energy storage at time t.

[0012] The construction constraints in step 3 include system power balance constraints, energy storage capacity constraints, unit output ramp constraints, and total energy storage reserved capacity constraints, where the system power balance constraints are: Among them, assuming P W,real,tis the actual wind power generation situation at each moment in the future t period; P W,pred,t represents the wind power forecast value at time t; P S,char,t Represents the energy storage charge at time t; U c,t 、U d,t Respectively represent the charge and discharge status bits, which are 0-1 variables; P B,t represents the load demand at time t; the total reserved capacity constraint of energy storage is:

[0013] The unit output and energy storage charge and discharge constraints are: Among them, P G,min 、P G,max Respectively represent the maximum and minimum output of thermal power units; P W,max Represents the maximum output of the wind turbine; P S,max Represents the maximum storage capacity of energy storage; h c 、h d They represent the time required to fully charge and discharge the energy storage respectively; the energy storage capacity and continuity constraints are: Among them, E S,t 、E S,t+1 Respectively represent the energy storage capacity at time t and t+1; η represents the energy storage charging and discharging efficiency; E S,1 、E S,T They represent the energy storage capacity at the beginning and end of the day, considering that the energy storage capacity at the beginning and end of each day should remain unchanged; the ramp constraint of the thermal power unit is: -Rate down Δt≤P G,t+1 -P G,t ≤Rate up Δt(10), where Rate up 、Rate down They represent the upward and downward ramp rates of the thermal power unit output respectively; Δt represents the time left for the unit to ramp from time t to t+1; when the installed capacity of wind power continues to increase, this constraint can be converted into a penalty term and written into the objective function.

[0014] The peak load calculation at each moment in step 5 is specifically as follows: the objective function and decision variables are as follows: Where M represents the total number of wind, fire and storage alliances; f m (X m ) represents the cost minus the benefit of the mth alliance, and the minimum is the maximum total profit; A m is the coefficient matrix; X m Represents the optimization goal: Among them, A m X m =b here represents the power balance constraint: That is, the real-time market thermal power increase P of each alliance at time t real,g,t, real-time market wind power generation capacity P rael,w,t and the real-time charge and discharge capacity of energy storage P real,s,t Equal to the actual increment P real,t , the overall f m (X m ) can be expressed as (thermal power fuel + thermal power carbon quota + thermal power emission reduction - thermal power electricity sales) + (wind power energy storage + wind power curtailment - wind power electricity sales), as follows: Where, P trade,m,t is the transaction volume between each group of thermal power and wind power; P dispatch,g,t P is the amount of electricity dispatched by thermal power plants on the day before; dispatch,w,t is the day-ahead dispatched electricity of wind power; a, b, c represent the cost characteristic parameters of thermal power units; P cm,ini represents the free carbon quota allocated to thermal power units; ω represents the carbon emissions per unit of thermal power generation; c bp represents the unit emission reduction cost; p c,t represents the carbon emission rights trading price per unit of electricity generation at time t, which can reflect the carbon price fluctuation; p s,t Charging cost for energy storage; p pen,t Punishment for abandoning wind power.

[0015] A real-time energy storage operation system considering the transfer of power generation rights includes a data acquisition module, a topology construction module, an energy storage capacity module, and an output calculation module. The real-time energy storage operation system at the source side considering the transfer of power generation rights is used to execute the real-time energy storage operation method considering the transfer of power generation rights described above.

[0016] The data acquisition module is used to obtain grid network parameters, wind power forecast output and update output;

[0017] The topology construction module is used to construct and calculate the energy storage reserve capacity model based on the grid network parameters output by the data acquisition module and the wind power output prediction model;

[0018] The energy storage capacity module is used to calculate the energy storage reserved capacity at the next moment based on wind power forecast data, update data, and the time-of-use electricity price curve of the current month;

[0019] The output calculation module pre-stores the cost and carbon emission level of each unit, and is used to calculate the output change of each unit at the current moment based on the reserved capacity output by the energy storage capacity module at the previous moment.

[0020] A storage medium includes a computer-readable storage medium and a computer program / instruction, wherein the computer-readable storage medium is used to store the computer program / instruction; the computer program / instruction is used to control the energy storage real-time operation system considering the transfer of power generation rights as described above to realize data retrieval and revenue record comparison.

[0021] The computer-readable storage medium is any available medium that can be stored by a computing device or a data center data storage device containing one or more available media.

[0022] The usable medium is a magnetic medium, an optical medium or a semiconductor medium.

[0023] The beneficial effects of the present invention are as follows: the present invention provides a real-time operation method, system and storage medium for energy storage considering the transfer of power generation rights. In view of the problem that traditional energy storage generates multi-time operation strategies at one time according to day-ahead clearing, ignores the real-time load and fluctuations in new energy output, resulting in a decrease in the profit margin of energy storage, a real-time operation method for source-side energy storage considering the transfer of power generation rights is proposed, so as to obtain a solution for new energy to participate in the real-time market to obtain higher profits, thereby increasing the consumption of new energy, reducing the carbon emissions of thermal power units and increasing the benefits of both parties. In use, firstly, based on the theory of Bayesian equilibrium of game theory, multi-time scale coordinated optimization scheduling is adopted to propose a model that considers the latest wind power forecast information for a subsequent period of time, obtains the current reserved capacity of energy storage, and then guides the current operation strategy of energy storage according to real-time market information; then, its calculation amount is simplified so that energy storage can participate in the real-time market wind-fire-storage joint operation simulation, and further enables thermal power units and new energy units to participate in peak regulation; the present invention has the advantages of being based on the Bayesian equilibrium theory of game theory, adopting multi-time scale coordinated optimization scheduling, and considering the transfer of power generation rights. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Flowchart of the present invention.

[0025] Figure 2 This is the topology diagram of the IEEE 30-node test system of the present invention.

[0026] Figure 3 This is a comparison chart of the charging and discharging results of the improved wind turbine energy storage real-time operation strategy and the day-ahead scheduling strategy of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described below with reference to the accompanying drawings.

[0028] Example 1

[0029] like Figure 1-3 As shown, a real-time operation method of energy storage considering the transfer of power generation rights includes the following steps:

[0030] Step 1: Obtain the day-ahead dispatch unit output plan curve, wind power forecast information, unit parameters, and energy storage parameters;

[0031] In this embodiment, current research on wind energy storage mainly includes energy storage capacity configuration, robust optimization scheduling considering uncertainty, and multi-time scale collaborative optimization for participation in the day-ahead electricity energy market and the reserve auxiliary market. The mathematical model constraints of energy storage in scheduling optimization mainly include charge and discharge constraints, power constraints, and reserved reserve constraints. The charge and discharge constraints are: The power constraint is: Where, represents the charging and discharging power of energy storage s at time t; P s,max is the upper limit of energy storage power; Energy storage charge and discharge status bit 0-1 variable; E s,t is the energy storage capacity; are the charging and discharging efficiencies respectively; Δt represents the scheduling time interval; E s,max 、E s,min The reserve capacity is the upper and lower limits of the energy storage capacity; the reserve reserve constraint is: The mathematical formula of the reserve is relatively independent, and it is the reserve capacity added by the intraday auxiliary service market. Generally divided into those calculated by day-ahead scheduling, those modified over time, or fixed constants:

[0032] Step 2: Build a benefit model for energy storage participating in real-time operations;

[0033] In this embodiment, under ideal day-ahead energy storage conditions, the charge and discharge capacity at the previous moment is affected by the later moment, and energy storage's acceptance of excess wind power at the current moment competes with wind-thermal power generation rights. This means that thermal power companies want to share profits from wind power and profit from selling carbon emission rights by trading more power generation rights, while also hoping that energy storage can compensate for low wind power generation to avoid reducing their own profits. Therefore, to find a better strategy, the alliance profit margins under different scenarios are designed to facilitate energy storage charging and discharging strategy decisions, as shown in Table 1 below.

[0034] Table 1

[0035]

[0036] Among them, incremental profits can be divided into the following four strategies: Among them, i represents the charging and discharging strategy that is affected by the moment i after moment t; They represent the T+I Alliance and choose one of the four strategies: selling power generation rights and energy storage charging, selling and discharging, buying and charging, and buying and discharging; They represent the incremental profit of the strategy selected at this time and the expected deviation power ΔP i The product of is used to express the alliance's willingness to choose this strategy, such as: Where p t represents the electricity price at time t in the time-of-use electricity price table; pc,t represents the carbon emission rights trading price per unit of electricity generation at time t, which can reflect the fluctuation of carbon price; P G,t represents the planned power generation of thermal power units at time t obtained based on the load forecast and wind power forecast; α represents the renewable energy quota rate; P g,t Represents the price of the green certificate per unit of wind power generation; also records the energy storage capacity that is expected to be reserved at the time; also records the energy storage capacity that is expected to be reserved at time t+i Finally, with the goal of maximizing the wind-thermal power generation revenue at time t, the mathematical model is: Where, I G Represents the total revenue of conventional thermal power units; I W Represents the total revenue of wind turbines; They represent the strategy selection status bit, which is a 0-1 variable; δ represents the discount factor. The farther the time i is from the time t, the smaller its influence; C SC,t Represents the energy storage capacity penalty. The more energy stored, the better. It should be used in a timely manner to avoid overly conservative strategy selection. CS E is the penalty amount per unit capacity of energy storage; S,t is the energy storage capacity; when energy storage does not participate in the wind and thermal power generation rights trading, the green certificate market and carbon market are used to stimulate wind and thermal power plants to trade power generation rights and promote wind power consumption. The model is as follows: Where: I G Represents the total revenue of conventional thermal power units; T represents the total number of power generation rights trading periods in a day. If the transaction is once every hour, T is 24; I G,s,t represents the electricity sales revenue of the thermal power plant during period t; C fue,t represents the coal burning cost of thermal power units during period t; I cm,t represents the carbon emission rights trading income of the thermal power plant during period t. If it is greater than 0, it means selling carbon quotas, otherwise it means purchasing; C bp,t represents the emission reduction cost of thermal power units during period t; I G,s,t =(P G,t -P trade,t )p t (20), C fue,t =a(P G,t -P trade,t ) 2 +b(P G,t -P trade,t )+c(21),I cm,t =[P cm,ini -(P G,t -P trade,t )]×p c,t (22), C bp,t =ωc bp (P G,t -P trade,t )(23), where PG,t represents the planned power generation of thermal power units at time t obtained based on the load forecast and wind power forecast; P trade,t represents the transaction volume of power generation rights transferred from thermal power generation units to wind power generation units during period t; p t represents the electricity price at time t in the time-of-use electricity price table; a, b, and c represent the cost characteristic parameters of the thermal power unit; P cm,ini Represents the free carbon quotas allocated to thermal power units; p c,t represents the carbon emission rights trading price per unit of electricity generation at time t, which can reflect the fluctuation of carbon price; ω represents the carbon emissions per unit of thermal power generation; c bp represents the unit emission reduction cost; Among them, I W Represents the total revenue of wind turbines; I W,s,t represents the electricity sales revenue of the wind power plant during period t; I W,gc,t Represents the green certificate income of the wind power plant in period t; Among them, P W,t represents the planned power generation of wind turbines; α represents the renewable energy quota rate; p g,t The price of green certificates representing unit wind power generation;

[0037] When the installed capacity of wind power gradually increases, the power generation rights trading volume P at each moment trade,t Without energy storage, negative values ​​often occur. This means that thermal power plants sometimes need to generate more electricity to meet the randomness and volatility of wind power output. Adding energy storage on the wind power side can further promote wind power consumption while alleviating the peak load regulation pressure of thermal power.

[0038] At this time, the profit function of the thermal power plant remains unchanged, while the profit of the wind power plant needs to increase the energy storage discharge profit I S,disc,t and energy storage discharge cost C S,disc,t , Among them, P S,disc,t represents the amount of energy storage discharged at time t; c disc Represents the unit discharge cost of energy storage at time t.

[0039] Step 3: Construct constraints;

[0040] In this embodiment, the constraints include system power balance constraint, energy storage capacity constraint, unit output ramp constraint, and total energy storage reserved capacity constraint. ① System power balance constraint is: Among them, assuming P W,real,t is the actual wind power generation situation at each moment in the future t period; P W,pred,t represents the wind power forecast value at time t; P S,char,t Represents the energy storage charge at time t; U c,t 、U d,t Respectively represent the charge and discharge status bits, which are 0-1 variables; PB,t represents the load demand at time t;

[0041] ② The unit output and energy storage charge and discharge constraints are: Among them, P G,min 、P G,max Respectively represent the maximum and minimum output of thermal power units; P W,max Represents the maximum output of the wind turbine; P S,max Represents the maximum storage capacity of energy storage; h c 、h d Respectively represent the time required to fully charge and discharge the energy storage;

[0042] ③ The energy storage capacity and continuity constraints are: Among them, E S,t 、E S,t+1 Respectively represent the energy storage capacity at time t and t+1; η represents the energy storage charging and discharging efficiency; E S,1 、E S,T They represent the energy storage capacity at the beginning and end of the day, respectively, considering that the energy storage capacity at the beginning and end of each day should remain unchanged;

[0043] ④ The climbing constraint of thermal power unit is: -Rate down Δt≤P G,t+1 -P G,t ≤Rate up Δt(10), where Rate up 、Rate down Represent the up and down ramp rates of the thermal power unit output respectively; Δt represents the time left for the unit to ramp from time t to time t+1; as the installed capacity of wind power continues to increase, this constraint can be converted into a penalty term and written into the objective function;

[0044] ⑤ The total reserved capacity constraint of energy storage is: Its function is to avoid overly conservative energy storage and to always select a charging strategy to store wind power for subsequent moments.

[0045] Step 4: Energy storage charging and discharging strategy and generation rights trading volume solution process;

[0046] In this embodiment, the optimization results of the energy storage charging and discharging strategies at each moment, obtained using the day-ahead forecast data, are clearly overly ideal without considering the subsequent deviation in electricity settlement. This is because the energy storage capacity at a single moment is related to the wind power output at each moment, and the uncertainty of wind power output will lead to errors between the actual output of wind turbines at subsequent moments and the predicted output. Therefore, the energy storage charge and discharge capacity at each moment cannot be calculated by the optimal model due to the lack of the actual output of subsequent wind power.

[0047] Assuming that the actual wind power data for the day is known before the energy storage system is used, the planned power generation of wind and thermal power units is obtained through forecast data, and the ideal profit I is calculated using the optimization model. When the actual wind power data is unknown, the real-time energy storage charging and discharging strategy and power generation rights trading volume data are obtained using the game model, and the actual profit is finally calculated for comparison: I = max(I G +I W )(12).

[0048] Step 5: Calculate the peak power consumption at each moment.

[0049] In this embodiment, the objective function and decision variables are as follows: Where M represents the total number of wind, fire and storage alliances; f m (X m ) represents the cost minus the benefit of the mth alliance, and the minimum is the maximum total profit; A m is the coefficient matrix; X m Represents the optimization goal: Among them, A m X m =b here represents the power balance constraint: That is, the real-time market thermal power increase P of each alliance at time t real,g,t , real-time market wind power generation capacity P rael,w,t and the real-time charge and discharge capacity of energy storage P real,s,t Equal to the actual increment P real,t ;

[0050] Overall f m (X m ) can be expressed as:

[0051] (Thermal power fuel + thermal power carbon quota + thermal power emission reduction - thermal power electricity sales) + (wind power energy storage + wind power curtailment - wind power electricity sales), as follows: Where, P trade,m,t is the transaction volume between each group of thermal power and wind power; P dispatch,g,t P is the amount of electricity dispatched by thermal power plants on the day before; dispatch,w,t is the day-ahead dispatched electricity of wind power; a, b, c represent the cost characteristic parameters of thermal power units; P cm,ini represents the free carbon quota allocated to thermal power units; ω represents the carbon emissions per unit of thermal power generation; c bp represents the unit emission reduction cost; p c,t represents the carbon emission rights trading price per unit of electricity generation at time t, which can reflect the carbon price fluctuation; p st Charging cost for energy storage; p pen,t is the penalty for wind curtailment; taking the optimization target of the first group of thermal power and wind power at the first moment as an example: introduce the Lagrangian multiplier and rewrite it into the augmented Lagrangian function: Among them, ρ is the penalty coefficient; the iterative solution of the first group at the first moment is expressed as: Until all alliances converge at that moment, the data calculated in advance for thermal power, wind power, and energy storage at each moment can provide a computer-readable storage medium for storing computer programs or instructions. When the computer programs or instructions are executed by the processing equipment, the above-mentioned data calls and revenue record comparisons are realized.

[0052] Example analysis: In order to provide a more specific explanation of how the method of the present invention can effectively improve the overall benefits of new energy and thermal power units participating in the transfer of power generation rights while ensuring the level of new energy consumption, the method of the present invention is used to test the power transmission scheduling optimization of the test system. The IEEE 30-node test system contains a total of 6 generators and 41 transmission lines. The topology diagram can be referred to Figure 2 Detailed data of each unit and line can be found in Table 2 and Table 3, and the load forecast value of each node can be found in Table 4. The unit at node 23 is changed to a wind turbine unit and equipped with energy storage, forming an alliance with the thermal power unit at node 22.

[0053] In this embodiment, a storage reserve capacity model is constructed using the grid network parameters and the wind power output forecast model. The reserved capacity is used as the lower limit of the storage capacity at that moment. On this basis, the ADMM algorithm is used to iteratively solve the output, charge and discharge changes of each thermal power, wind power and storage at that moment.

[0054] Specifically, obtaining the grid network parameters and the wind power output forecast model to construct the energy storage reserve capacity model includes: ① calculating the DC linear power flow between the lines based on the grid network parameters, and updating the constructed energy storage reserve capacity model based on the DC linear power flow and the wind power forecast output; ② the grid network parameters include at least the line impedance, line length, line conductance and admittance, unit and energy storage parameters, and the wind power output includes at least the historical wind speed fitting parameters and the forecast output curve for the day and the updated output at each moment;

[0055] Table 5 shows the changes in wind turbine revenue under different energy storage operation strategies when both systems participate in scheduling. The optimization results of the energy storage charging and discharging strategies at each moment, obtained using the day-ahead forecast data, are clearly overly ideal without considering the subsequent deviation electricity settlement. This is because the energy storage capacity at a single moment is related to the wind power output at each moment, and the uncertainty of wind power output will lead to errors between the actual output of wind turbines at subsequent moments and the predicted output. Therefore, the energy storage charge and discharge capacity at each moment cannot be calculated by the optimal model due to the lack of the actual output of subsequent wind power.

[0056] Table 6 shows the process of calculating the current energy storage reserved capacity in a period, using the subsequent 6 moments as a reference to provide guidance for the current energy storage operation; Figure 3As shown, the day-ahead high-charge, low-discharge energy storage operation strategy and the real-time strategy have roughly the same discharge period. However, because the real-time energy storage acquires more recent data and a shorter data period, coupled with the transfer of power generation rights by thermal power units, the energy storage operation is relatively stable.

[0057] In summary, the method of the present invention can effectively increase the income of new energy participating in the real-time market and reduce the operating cost of energy storage.

[0058] Table 2

[0059] unit node <![CDATA[P G,max (MW)]]> <![CDATA[P G,min (MW)]]> <![CDATA[a($ / MWh 2 )]]> <![CDATA[b($ / MWh 2 )]]> <![CDATA[c($ / MWh 2 )]]> 1 1 80 0 0.02 2 0 2 2 80 0 0.0175 1.75 0 3 22 50 0 0.0625 1 0 4 27 55 0 0.0083 3.25 0 5 23 30 0 0 0 0 6 13 40 0 0.025 3 0

[0060] Table 3

[0061]

[0062]

[0063] Table 4

[0064]

[0065]

[0066] Table 5

[0067] Day-ahead operation strategy Running strategies in real time Expected Returns 22217.2 22242.3 Total power generation 287.9 287.9 Imbalance Fees 161.2 216.0 Unbalanced power 1.792 2.419 Power generation rights transfer 10.8 11.1 Wind curtailment 0 0 Energy storage costs 55 45

[0068] Table 6

[0069]

[0070] The present invention provides a real-time operation method, system and storage medium for energy storage that takes into account the transfer of power generation rights. This method addresses the problem that traditional energy storage generates multiple-time operation strategies at one time based on day-ahead clearing, ignoring real-time load and fluctuations in renewable energy output, which leads to a decrease in energy storage profit margins. A real-time operation method for source-side energy storage that takes into account the transfer of power generation rights is proposed, so as to obtain a solution for new energy to participate in the real-time market and obtain higher profits, thereby increasing the absorption of new energy, reducing carbon emissions from thermal power units and increasing the benefits of both parties. In use, firstly, based on the theory of Bayesian equilibrium in game theory and adopting multi-time scale coordinated optimization scheduling, a model is proposed that considers the latest wind power forecast information for a subsequent period of time, obtains the current reserved capacity of energy storage, and then guides the current operation strategy of energy storage according to real-time market information. Then, the computational complexity is simplified so that energy storage can participate in the real-time market wind-fire-storage joint operation simulation, further allowing thermal power units and renewable energy units to participate in peak regulation. The present invention has the advantages of being based on the Bayesian equilibrium theory in game theory, adopting multi-time scale coordinated optimization scheduling, and taking into account the transfer of power generation rights.

[0071] Example 2

[0072] like Figure 1-3As shown, the energy storage real-time operation system considering the transfer of power generation rights includes a data acquisition module, a topology construction module, an energy storage capacity module, and an output calculation module. The source-side energy storage real-time operation system considering the transfer of power generation rights is used to execute the above-mentioned energy storage real-time operation method considering the transfer of power generation rights;

[0073] The data acquisition module is used to obtain grid network parameters, wind power forecast output and update output;

[0074] The topology construction module is used to construct and calculate the energy storage reserve capacity model based on the grid network parameters output by the data acquisition module and the wind power output prediction model;

[0075] The energy storage capacity module is used to calculate the energy storage reserved capacity at the next moment based on wind power forecast data, update data, and the time-of-use electricity price curve of the current month;

[0076] The output calculation module pre-stores the cost and carbon emission level of each unit, and is used to calculate the output change of each unit at the current moment based on the reserved capacity output by the energy storage capacity module at the previous moment.

[0077] In this embodiment, the data acquisition module is connected to the topology construction module, the energy storage capacity module is connected to the data acquisition module and the topology construction module respectively, and the output calculation module is connected to the data acquisition module, the topology construction module and the energy storage capacity module respectively.

[0078] The present invention provides a real-time operation method, system and storage medium for energy storage that takes into account the transfer of power generation rights. This method addresses the problem that traditional energy storage generates multiple-time operation strategies at one time based on day-ahead clearing, ignoring real-time load and fluctuations in renewable energy output, which leads to a decrease in energy storage profit margins. A real-time operation method for source-side energy storage that takes into account the transfer of power generation rights is proposed, so as to obtain a solution for new energy to participate in the real-time market and obtain higher profits, thereby increasing the absorption of new energy, reducing carbon emissions from thermal power units and increasing the benefits of both parties. In use, firstly, based on the theory of Bayesian equilibrium in game theory and adopting multi-time scale coordinated optimization scheduling, a model is proposed that considers the latest wind power forecast information for a subsequent period of time, obtains the current reserved capacity of energy storage, and then guides the current operation strategy of energy storage according to real-time market information. Then, the computational complexity is simplified so that energy storage can participate in the real-time market wind-fire-storage joint operation simulation, further allowing thermal power units and renewable energy units to participate in peak regulation. The present invention has the advantages of being based on the Bayesian equilibrium theory in game theory, adopting multi-time scale coordinated optimization scheduling, and taking into account the transfer of power generation rights.

[0079] Example 3

[0080] like Figure 1-3As shown, a storage medium includes a computer-readable storage medium and a computer program / instruction, wherein the computer-readable storage medium is used to store the computer program / instruction; the computer program / instruction is used to control the energy storage real-time operation system considering the transfer of power generation rights as mentioned above to realize data call and income record comparison.

[0081] The computer-readable storage medium is any available medium that can be stored by a computing device or a data center data storage device containing one or more available media.

[0082] The available medium is a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state drive).

[0083] The present invention provides a real-time operation method, system and storage medium for energy storage that takes into account the transfer of power generation rights. This method addresses the problem that traditional energy storage generates multiple-time operation strategies at one time based on day-ahead clearing, ignoring real-time load and fluctuations in renewable energy output, which leads to a decrease in energy storage profit margins. A real-time operation method for source-side energy storage that takes into account the transfer of power generation rights is proposed, so as to obtain a solution for new energy to participate in the real-time market and obtain higher profits, thereby increasing the absorption of new energy, reducing carbon emissions from thermal power units and increasing the benefits of both parties. In use, firstly, based on the theory of Bayesian equilibrium in game theory and adopting multi-time scale coordinated optimization scheduling, a model is proposed that considers the latest wind power forecast information for a subsequent period of time, obtains the current reserved capacity of energy storage, and then guides the current operation strategy of energy storage according to real-time market information. Then, the computational complexity is simplified so that energy storage can participate in the real-time market wind-fire-storage joint operation simulation, further allowing thermal power units and renewable energy units to participate in peak regulation. The present invention has the advantages of being based on the Bayesian equilibrium theory in game theory, adopting multi-time scale coordinated optimization scheduling, and taking into account the transfer of power generation rights.

Claims

1. A real-time energy storage operation method considering the transfer of power generation rights, characterized by: The method comprises the following steps: Step 1: Obtain the day-ahead dispatch unit output plan curve, wind power forecast information, unit parameters, and energy storage parameters; Step 2: Build a benefit model for energy storage participating in real-time operations; Step 3: Construct constraints; Step 4: Energy storage charging and discharging strategy and generation rights trading volume solution process; Step 5: Calculate the peak power consumption at each moment.

2. The method for real-time operation of energy storage considering power generation rights transfer according to claim 1, characterized in that: The specific step 1 is as follows: the mathematical model constraints of energy storage participating in scheduling optimization mainly include charge and discharge constraints, power constraints and reserved backup constraints, wherein the charge and discharge constraints are: The power constraint is: Where, represents the charging and discharging power of energy storage s at time t; P s,max is the upper limit of energy storage power; Energy storage charge and discharge status bit 0-1 variable; E s,t is the energy storage capacity; are charge and discharge efficiency respectively; Δt represents the scheduling time interval; E s,max 、E s,min are the upper and lower limits of energy storage capacity; the reserve reserve constraint is:

3. The method for real-time operation of energy storage considering power generation rights transfer according to claim 1, characterized in that: The benefit model for energy storage participating in real-time operation in step 2 is specifically constructed as follows: The incremental profit can be divided into the following four strategies: Among them, i represents the charging and discharging strategy that is affected by the moment i after moment t; They represent the T+I Alliance and choose one of the four strategies: selling power generation rights and energy storage charging, selling and discharging, buying and charging, and buying and discharging; They represent the incremental profit of the strategy selected at this time and the expected deviation power ΔP i The product of is used to express the alliance's willingness to choose this strategy, such as: Where p t represents the electricity price at time t in the time-of-use electricity price table; p c,t represents the carbon emission rights trading price per unit of electricity generation at time t, which can reflect the fluctuation of carbon price; P G,t represents the planned power generation of thermal power units at time t obtained based on the load forecast and wind power forecast; α represents the renewable energy quota rate; P g,t Represents the price of the green certificate per unit of wind power generation; records the energy storage capacity that is expected to be reserved at the time; records the energy storage capacity E that is expected to be reserved at time t+i i S Finally, with the goal of maximizing the wind-thermal power generation revenue at time t, the mathematical model is: Where, I G Represents the total revenue of conventional thermal power units; I W Represents the total revenue of wind turbines; They represent the strategy selection status bit, which is a 0-1 variable; δ represents the discount factor. The farther the time i is from the time t, the smaller its influence; C SC,t represents the energy storage capacity penalty; c CS E is the penalty amount per unit capacity of energy storage; S,t is the energy storage capacity; when energy storage does not participate in the wind and thermal power generation rights trading, the green certificate market and carbon market are used to stimulate wind and thermal power plants to trade power generation rights and promote wind power consumption. The model is as follows: Where: I G represents the total revenue of conventional thermal power units; T represents the total number of power generation rights trading periods in a day; I G,s,t represents the electricity sales revenue of the thermal power plant during period t; C fue,t represents the coal burning cost of thermal power units during period t; I cm,t represents the carbon emission rights trading income of the thermal power plant during period t. If it is greater than 0, it means selling carbon quotas, otherwise it means purchasing; C bp,t represents the emission reduction cost of thermal power units during period t; Among them, I W Represents the total revenue of wind turbines; I W,s,t represents the electricity sales revenue of the wind power plant during period t; I W,gc,t represents the green certificate income of the wind power plant in period t; and when the installed capacity of wind power gradually increases, the power generation right trading volume P at each moment trade,t In the absence of energy storage, negative values ​​often appear. That is, in order to meet the randomness and volatility of wind power output, thermal power plants sometimes need to generate more electricity to fill the planned power shortage of wind power plants. Adding energy storage on the wind power side can further promote wind power consumption while alleviating the peak load regulation pressure of thermal power. At this time, the profit function of the thermal power plant remains unchanged, while the profit of the wind power plant needs to increase the energy storage discharge profit I S,disc,t and energy storage discharge costs Among them, P S,disc,t represents the amount of energy storage discharged at time t; c disc Represents the unit discharge cost of energy storage at time t.

4. The method for real-time operation of energy storage considering power generation rights transfer according to claim 1, characterized in that: The constraints constructed in step 3 include system power balance constraints, unit output and energy storage charging and discharging constraints, energy storage capacity and continuity constraints, thermal power unit ramping constraints, and total energy storage reserved capacity constraints, where the system power balance constraints are: Among them, assuming P W,real,t is the actual wind power generation situation at each moment in the future t period; P W,pred,t represents the wind power forecast value at time t; P S,char,t Represents the energy storage charge at time t; U c,t 、U d,t Respectively represent the charge and discharge status bits, which are 0-1 variables; P B,t represents the load demand at time t; the total reserved capacity constraint of energy storage is:

5. The method for real-time operation of energy storage considering the transfer of power generation rights according to claim 4, characterized in that: The unit output and energy storage charge and discharge constraints are: Among them, P G,min 、P G,max Respectively represent the maximum and minimum output of thermal power units; P W,max Represents the maximum output of the wind turbine; P S,max Represents the maximum storage capacity of energy storage; h c 、h d They represent the time required to fully charge and discharge the energy storage respectively; the energy storage capacity and continuity constraints are: Among them, E S,t 、E S,t+1 Respectively represent the energy storage capacity at time t and t+1; η represents the energy storage charging and discharging efficiency; E S,1 、E S,T They represent the energy storage capacity at the beginning and end of the day, considering that the energy storage capacity at the beginning and end of each day should remain unchanged; the ramp constraint of the thermal power unit is: -Rate down Δt≤P G,t+1 -P G,t ≤Rate up Δt(10), where Rate up 、Rate down They represent the upward and downward ramp rates of the thermal power unit output respectively; Δt represents the time left for the unit to ramp from time t to t+1; when the installed capacity of wind power continues to increase, this constraint can be converted into a penalty term and written into the objective function.

6. The method for real-time operation of energy storage considering power generation rights transfer according to claim 1, characterized in that: The peak load calculation at each moment in step 5 is specifically as follows: the objective function and decision variables are as follows: Where M represents the total number of wind, fire and storage alliances; f m (X m ) represents the cost minus the benefit of the mth alliance, and the minimum is the maximum total profit; A m is the coefficient matrix; X m Represents the optimization goal: Among them, A m X m =b here represents the power balance constraint: That is, the real-time market thermal power increase P of each alliance at time t real,g,t , real-time market wind power generation capacity P rael,w,t and the real-time charge and discharge capacity of energy storage P real,s,t Equal to the actual increment P real,t , the overall f m (X m ) can be expressed as (thermal power fuel + thermal power carbon quota + thermal power emission reduction - thermal power electricity sales) + (wind power energy storage + wind power curtailment - wind power electricity sales), as follows: Where, P trade,m,t is the transaction volume between each group of thermal power and wind power; P dispatch,g,t P is the amount of electricity dispatched by thermal power plants on the day before; dispatch,w,t is the day-ahead dispatched electricity of wind power; a, b, c represent the cost characteristic parameters of thermal power units; P cm,ini represents the free carbon quota allocated to thermal power units; ω represents the carbon emissions per unit of thermal power generation; c bp represents the unit emission reduction cost; p c,t represents the carbon emission rights trading price per unit of electricity generation at time t, which can reflect the carbon price fluctuation; p s,t Charging cost for energy storage; p pen,t Punishment for abandoning wind power.

7. A real-time energy storage operation system considering power generation rights transfer includes a data acquisition module, a topology construction module, an energy storage capacity module, and an output calculation module, characterized by: The source-side energy storage real-time operation system considering the transfer of power generation rights is used to execute the energy storage real-time operation method considering the transfer of power generation rights according to any one of claims 1 to 6; The data acquisition module is used to obtain grid network parameters, wind power forecast output and update output; The topology construction module is used to construct and calculate the energy storage reserve capacity model based on the grid network parameters output by the data acquisition module and the wind power output prediction model; The energy storage capacity module is used to calculate the energy storage reserved capacity at the next moment based on wind power forecast data, update data, and the time-of-use electricity price curve of the current month; The output calculation module pre-stores the cost and carbon emission level of each unit, and is used to calculate the output change of each unit at the current moment based on the reserved capacity output by the energy storage capacity module at the previous moment.

8. A storage medium, characterized in that: It includes a computer-readable storage medium and a computer program / instruction, wherein the computer-readable storage medium is used to store the computer program / instruction; the computer program / instruction is used to control the energy storage real-time operation system considering the transfer of power generation rights as described in any one of claims 1 to 7 to realize data calling and revenue record comparison.

9. A storage medium according to claim 8, characterized in that: The computer-readable storage medium is any available medium that can be stored by a computing device or a data center data storage device containing one or more available media.

10. A storage medium according to claim 9, characterized in that: The usable medium is a magnetic medium, an optical medium or a semiconductor medium.

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