Novel method and model for influencing power grid operation scheduling by participation of energy storage in electric power spot market transaction

Abstract

The invention relates to the technical field of power grids, and discloses a novel method and model for influencing power grid operation scheduling by participation of energy storage in electric power spot market transactions, and the method comprises the steps: carrying out the real-time monitoring of the state of an energy storage system, the real-time state mainly comprises the charging and discharging state, the capacity and the efficiency, and also comprises the charge state, the charging and discharging rate and the temperature of a battery; through reasonable charging and discharging of the energy storage system, energy can be utilized more efficiently, energy loss can be reduced, finally, the influence of the energy storage system participating in electric power spot market transaction on power grid operation scheduling is comprehensively evaluated, it is guaranteed that whether the participating influence is a positive influence or a negative influence can be obtained in time, and therefore support is provided for subsequent decision making; after novel energy storage is introduced into the market, the flexibility and response speed of the novel energy storage system are fully utilized by optimizing and adjusting a power grid operation strategy, and the application capability of the novel energy storage system is improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and more specifically to a method and model for the impact of new energy storage participating in power spot market transactions on power grid operation and dispatching. Background Art

[0002] New energy storage refers to energy storage technology that mainly outputs electricity, in addition to pumped storage. It is an important supporting technology for building a new power system with new energy as the main body. The electricity spot market refers to a market where business entities that meet the access conditions conduct day-ahead, intraday and real-time electricity transactions. The electricity spot market forms market-clearing prices that reflect the value of time and space through competition, and carries out supporting transactions for auxiliary services such as frequency regulation and standby. In the operation of the market, new energy storage participates in the electricity market as an independent market entity, forms prices through market-based mechanisms, obtains reasonable returns, and promotes the healthy development of the energy storage industry.

[0003] Currently in the industry, many power grid operation and dispatching systems generally do not fully utilize the flexibility and response speed of new energy storage systems, resulting in the fact that after the new energy storage is introduced into the market, its application in the electricity spot market is often not ideal. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and model for the impact of new energy storage participating in electricity spot market transactions on power grid operation and dispatching, so as to solve the problem that the power grid operation and dispatching system existing in the above-mentioned background technology cannot fully utilize the flexibility and response speed of the new energy storage system.

[0005] The present invention provides the following technical solutions:

[0006] The method of how new energy storage participates in electricity spot market transactions to influence grid operation and dispatching is as follows:

[0007] S1: Real-time monitoring of the status of the energy storage system, which mainly includes: charging and discharging status, capacity and efficiency, as well as the battery's state of charge, charging and discharging rate and temperature;

[0008] S2: Acquire transaction information and historical data of the electricity spot market and analyze the transaction information. The transaction information mainly includes supply and demand status data and price fluctuation data;

[0009] S3: Based on the analysis of electricity spot market transaction information and the status of the energy storage system, an optimization algorithm is used to optimize the charging and discharging strategy;

[0010] S4: Based on the electricity spot market transaction information and the optimized charging and discharging strategy, an automated trading algorithm is used to make real-time market trading decisions;

[0011] S5: Adjust the grid operation and dispatch optimization strategy based on real-time market transaction decisions and optimized charging and discharging strategies;

[0012] S6: Conduct a comprehensive assessment of the impact of energy storage systems on the economics and reliability of the grid after participating in market transactions.

[0013] Preferably, in S2, the transaction information is analyzed, and the specific analysis steps are as follows:

[0014] The price fluctuations in the transaction information are analyzed by calculating the normalization of the price:

[0015]

[0016] In the formula, σ p is the standard deviation of price fluctuations, P t is the market price at time t, μ p is the average price, N is the number of samples, and i represents the sum index, which is used to traverse all sample points;

[0017] Predict the price trend by exponential smoothing method:

[0018] TE t =αJ t +(1-α)TE t-1 ;

[0019] Where α is the smoothing constant, J t is the current price, TE t is the predicted price at time t, TE t-1 is the price forecast value at the t-1th time point;

[0020] The supply and demand balance is predicted by using multiple linear regression:

[0021] BS t =β0+β1X 1t +β2X 2t +…+β N X Nt +∈ t ;

[0022] In the formula, BS t represents the balance of supply and demand at time t, X 1t +…+X Nt Represent the explanatory variables of the Nth sample that affect the balance of supply and demand, β0+…+β N Represent the corresponding regression coefficients, ∈ t is the error term.

[0023] Preferably, regarding the optimization algorithm used in S3 to optimize the charging and discharging strategy, the steps are as follows:

[0024] Minimize the total charging and discharging cost of the energy storage system:

[0025]

[0026] In the formula, Z t represents the total cost, c t represents the charge and discharge decision variable at time t, K t represents the spot price of electricity at time t, R t represents the charge and discharge rate at time t, and T represents the time period;

[0027] At the same time, the following constraints are met:

[0028] Constrain the battery's state of charge to be between minimum and maximum values:

[0029] SC min <SC(t)<SC max ;

[0030] Where SC(t) represents the state of charge of the battery at time t, SC min Represents the minimum state of charge of the battery, SC max Represents the maximum state of charge of the battery;

[0031] Constrain the battery's charge and discharge rate at any point in time:

[0032] R min <R t <R max ;

[0033] In the formula, R min Represents the minimum charge and discharge rate allowed by the battery, R max Represents the maximum charge and discharge rate allowed by the battery;

[0034] Constrain the energy balance of the battery:

[0035] E(t)=E(t-1)+R t ×Δt;

[0036] Where E(t) represents the battery energy storage at time t, E(t-1) represents the battery energy storage at time t-1, and Δt represents the time interval;

[0037] Constrain the energy conversion efficiency and energy loss of the battery during the charging and discharging process:

[0038] G(t)=G in (t)×R t -δ×G in (t);

[0039] Wherein, G(t) represents the energy output by the battery at time t, and G in (t) represents the energy input to the battery at time t, and δ represents the energy loss rate of the battery;

[0040] Constraints are imposed on the state of charge of the initial battery and the state of charge of the final battery:

[0041] SC(0) = SC in , SC(T) = SC fin ;

[0042] Wherein, SC(0) represents the state of charge of the battery at the start of the optimization period, SC(T) represents the state of charge of the battery at the end of the optimization period, SC in represents the value of the initial state of charge of the battery, and SC fin represents the value of the final state of charge of the battery.

[0043] Preferably, in S4, the specific steps for formulating real-time market trading decisions using an automated trading algorithm are as follows:

[0044] Real-time collection of the price K of the electricity spot market t , the electricity supply volume D t and the electricity demand volume X t ;

[0045] Evaluate whether the current energy storage level E(t) of the battery is between the minimum state of charge SC min of the battery and the maximum state of charge SC max of the battery:

[0046] The specific charging and discharging conditions are as follows:

[0047] Set the electricity price threshold as DJYZBB. If E(t) = SC min or E(t) = SC max , there is no need for immediate charging or discharging. If E(t) > SC min , charge when K t < DJYZBB. If E(t) < SC max , discharge when K t > DJYZBB;

[0048] If E(t) is not between SC min and SC max , the specific charging and discharging conditions are as follows:

[0049] If E(t) < SC min , it means the battery is in an over-discharged state, and look for time to charge;

[0050] If E(t) > SCmax , the battery may be in an overcharged state, looking for time to discharge;

[0051] Using historical data and current market information, the linear regression model is used to predict the market price P at t+1. t+1 :

[0052] Standardize historical data and current market information:

[0053]

[0054] In the formula, X is the original data, μ represents the mean of the original data, and σ represents the standard deviation of the original data;

[0055] Calculate the means of the independent and dependent variables:

[0056]

[0057] Where n is the number of observations, x n and n represent the independent variable and dependent variable of the nth observation, respectively. and represent the means of the independent and dependent variables respectively;

[0058] Calculate the covariance of the independent and dependent variables and the variance of the independent variables:

[0059]

[0060]

[0061] In the formula, f xy represents the covariance of the independent variable x and the dependent variable y, f xx represents the variance of the independent variable x;

[0062] Calculate the regression coefficient and the market price P at t+1 t+1 Make predictions:

[0063]

[0064] P t+1 =θ0+θ1x 1当前 +θ2x 2当前 ;

[0065] In the formula, θ1 represents the coefficient of the first independent variable x1, θ0 represents the intercept term, and x 1当前 and x 2当前 are the current supply and demand conditions and market prices, respectively, and θ2 represents the coefficient of the second independent variable x2;

[0066] For the market price P at time t t With the average price μ p Compare and combine the market price P at time t+1 t+1 Determine whether charging and discharging are required;

[0067] If P t >Average price μ p , and predict P t+1 will drop, then discharge;

[0068] If P t <Average price μ p , and predict P t+1 If it rises, charge it;

[0069] If P t = Average price μ p , then no charging or discharging is performed;

[0070] According to the current battery status and charge and discharge rate limit, the actual charge and discharge rate is calculated:

[0071] Charging rate R che =min(R max , S.C. max -E(t));

[0072] Discharge rate R dhr =max(-R min , E(t)-SC min );

[0073] Execute transactions based on decision logic and calculated charge and discharge rates:

[0074] If you decide to discharge, calculate the transaction volume Q deg =R dhr ×Δt, and sell it in the market;

[0075] If you decide to charge, calculate the transaction volume Q che =R che ×Δt, and buy in the market;

[0076] Update the battery's energy storage capacity E(t) in real time:

[0077] If discharging, E(t+1)=E(t)-Q deg ;

[0078] If charging, E(t+1)=E(t)-Q che ;

[0079] Where E(t+1) represents the energy storage of the battery at time t+1;

[0080] Set thresholds for market price fluctuations, implement real-time risk management strategies, and decide whether to trade:

[0081] The maximum acceptable market price volatility is set as V a ;

[0082] For price volatility V t Perform the calculation:

[0083] Where P t-1 represents the market price at time t-1;

[0084] If V t ≤V a , indicating that the market fluctuation is within an acceptable range and trading can be conducted;

[0085] If V t >V a , indicating that market volatility exceeds the risk threshold and trading is suspended.

[0086] Preferably, in S5, the specific steps of adjusting the power grid operation dispatch optimization strategy are as follows:

[0087] Minimize the total cost of grid operation:

[0088] ∑ t (G c ·F t +Z t ·(F c -F d ));

[0089] In the formula, G c represents the operating cost of the generator set, ∑ t Represents the summation symbol, F t represents the total power demand at time t, F c Represents the current charging power of the energy storage system, F d Represents the current discharge power of the energy storage system;

[0090] Constrain the balance between supply and demand to ensure that the power supply meets the demand at every moment:

[0091] S t =F t +F d -F c ;

[0092] In the formula, S t represents the total power supply at time t;

[0093] Constrain the operation of generator sets and energy storage systems to ensure they operate within a safe and efficient range:

[0094] F min ≤F t ≤F max , 0≤F t ≤F cmax , 0≤F d ≤F dmax ;

[0095] In the formula, F min and F max Represent the minimum and maximum operating power of the generator set, F cmax and F dmax They represent the maximum power charging and discharging limits of the energy storage system respectively;

[0096] Constrain the state of the energy storage system to ensure that the charging and discharging state of the energy storage system meets its capacity limit:

[0097]

[0098] In the formula, E t Represents the energy level of the energy storage system at time t, η c and η d Represent the charging and discharging efficiency of the energy storage system, E t+1 Represents the energy level of the energy storage system at time t+1;

[0099] Take the real-time market transaction results as input and adjust F t and G c :

[0100] G c =G c,bse +τ·(Pr t -Pr bse );

[0101] In the formula, G c,bse is the basic power generation cost, Pr t represents the electricity price, Pr bse represents the basic market electricity price, τ represents the adjustment coefficient;

[0102] F t =F t,bse -ξ·(Pr t -Pr bse );

[0103] In the formula, F t,bse represents the basic battery demand, ξ represents the response factor;

[0104] Adjust F according to the optimized charge and discharge strategy c and Fd ;

[0105] F c =max(0, F c，max -ψ c ·Pr t );

[0106] F d =max(0, F d，max -ψ d ·(E max -E t ));

[0107] In the formula, F c，max and F d，max Represent the maximum charging and discharging power of the energy storage system, E max represents the maximum energy capacity of the energy storage system, ψ c and ψ d They represent the parameters of the strategy respectively.

[0108] Preferably, in S6, a comprehensive assessment is conducted on the impact of the energy storage system on the economic performance and reliability of the power grid after participating in market transactions, and the specific steps are as follows:

[0109] Calculate the net economic benefits TB brought by the energy storage system in market transactions:

[0110] TB=M ie -O cs -T oy ;

[0111] Where M ie represents the market return, O cs represents the operating cost, T oy represents the initial investment;

[0112] Calculate the return on investment of energy storage system:

[0113]

[0114] In the formula, EI stands for economic index, which is used to reflect the return on investment;

[0115] Measuring the contribution of energy storage systems to grid reliability:

[0116]

[0117] In the formula, S AD Represents the average interruption duration of the system, S AF Represents the system average interruption frequency index, M pit Represents the maximum interruption time;

[0118] Comparing the improvement in grid reliability with and without energy storage systems:

[0119] RG=RM 有 -RM 无 ;

[0120] In the formula, RM 有 Represents the reliability index when there is an energy storage system, RM 无 Represents the reliability index when there is no energy storage system;

[0121] Evaluate the additional benefits that energy storage systems can bring through market trading strategies MTG:

[0122] MTG=P vd ×E SC ×N ot ;

[0123] Where P vd represents the peak-to-valley price difference, E SC Represents the energy storage capacity, N ot Represents the number of transactions;

[0124] Calculate the contribution of energy storage systems to reducing carbon emissions EBI:

[0125]

[0126] In the formula, C er Represents the reduction in carbon emissions, C efp Represents carbon emissions from traditional power generation;

[0127] By establishing a comprehensive evaluation model YBI, the overall benefits of the energy storage system are evaluated by comprehensively considering economic efficiency, reliability, market transaction income and environmental benefits:

[0128] YBI=w1×EI+w2×RG+w3×MTG+w4×EBI;

[0129] Among them, w1, w2, w3 and w4 represent the weights of EI, RG, MTG and EBI respectively;

[0130] If YBI ≤ 0, the market participation of the energy storage system is judged to be negative;

[0131] If YBI>0, the market participation of energy storage systems is judged to be positive.

[0132] Preferably, the model of the impact of new energy storage participating in electricity spot market transactions on grid operation and dispatching includes:

[0133] The monitoring module is used to monitor the status of the energy storage system in real time. The real-time status mainly includes: charging and discharging status, capacity and efficiency, as well as the battery's state of charge, charging and discharging rate and temperature;

[0134] The analysis module is used to obtain transaction information and historical data of the power spot market and analyze the transaction information. The transaction information mainly includes supply and demand status data and price fluctuation data;

[0135] The optimization module is used to optimize the charging and discharging strategy using an optimization algorithm based on the analysis of electricity spot market transaction information and the status of the energy storage system;

[0136] The trading module is used to make real-time market trading decisions based on the optimized charging and discharging strategies and electricity spot market trading information using automated trading algorithms;

[0137] The adjustment module adjusts the grid operation and dispatch optimization strategy based on real-time market transaction decisions and optimized charging and discharging strategies;

[0138] The evaluation module comprehensively evaluates the impact of energy storage systems on the economic performance and reliability of the power grid after participating in market transactions.

[0139] Technical effects and advantages of the present invention:

[0140] By real-time monitoring of the status of the energy storage system and market information, the grid dispatch can respond more flexibly to changes in supply and demand, and under the optimization algorithm, it can fully optimize the charging and discharging strategy. Under the automated trading algorithm, the energy storage system can participate in market transactions to maximize cost-effectiveness, and can quickly respond to market changes, improve the efficiency and response speed of market transactions, and the optimized charging and discharging strategy can be adjusted when the grid load is at peak or trough to ensure stable operation of the grid. At the same time, the participation of the energy storage system can better integrate different renewable resources and improve the grid's adaptability to new energy. By optimizing the charging and discharging strategy, it can reduce dependence on expensive peak power, thereby reducing the grid operation cost. Through the reasonable charging and discharging of the energy storage system, energy can be used more efficiently and energy loss can be reduced. Finally, a comprehensive assessment of the impact of the energy storage system's participation in electricity spot market transactions on grid operation and dispatching is made to ensure that the impact of participation is positive or negative in a timely manner, thereby providing support for subsequent decision-making, ensuring that after the new energy storage is introduced into the market, by optimizing and adjusting the grid operation strategy, the flexibility and response speed of the new energy storage system are fully utilized to improve the application capability of the new energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] Figure 1 It is a flow chart of the method for influencing the operation and dispatching of power grid by the new type of energy storage participating in the electricity spot market transaction of the present invention;

[0142] Figure 2 This is a modular structure diagram of the model of the impact of the new energy storage participating in electricity spot market transactions on power grid operation and dispatching. DETAILED DESCRIPTION

[0143] The present invention is further described below in conjunction with specific embodiments. However, people familiar with the art should understand that the detailed description given here in conjunction with the drawings is for better explanation, and the structure of the present invention necessarily exceeds these limited embodiments. For some equivalent replacement schemes or common means, they are no longer described in detail herein, but still belong to the scope of protection of the present application.

[0144] Figures 1-2 The best embodiment of the present invention is shown below in conjunction with the attached Figures 1-2 The present invention is further described.

[0145] The method of how new energy storage participates in electricity spot market transactions to influence grid operation and dispatching is as follows:

[0146] S1: Real-time monitoring of the status of the energy storage system, which mainly includes: charging and discharging status, capacity and efficiency, as well as the battery's state of charge, charging and discharging rate and temperature;

[0147] S2: Acquire transaction information and historical data of the electricity spot market and analyze the transaction information. The transaction information mainly includes supply and demand status data and price fluctuation data;

[0148] S3: Based on the analysis of electricity spot market transaction information and the status of the energy storage system, an optimization algorithm is used to optimize the charging and discharging strategy;

[0149] S4: Based on the electricity spot market transaction information and the optimized charging and discharging strategy, an automated trading algorithm is used to make real-time market trading decisions;

[0150] S5: Adjust the grid operation and dispatch optimization strategy based on real-time market transaction decisions and optimized charging and discharging strategies;

[0151] S6: Conduct a comprehensive assessment of the impact of energy storage systems on the economics and reliability of the grid after participating in market transactions.

[0152] As a further step of the present invention, regarding S2, the specific analysis steps for the transaction information are as follows:

[0153] The price fluctuations in the transaction information are analyzed by calculating the normalization of the price:

[0154]

[0155] In the formula, σ p is the standard deviation of price fluctuations, P t is the market price at time t, μ pis the average price, N is the number of samples, and i represents the sum index, which is used to traverse all sample points;

[0156] Predict the price trend by exponential smoothing method:

[0157] TE t =αJ t +(1-α)TE t-1 ;

[0158] Where α is the smoothing constant, J t is the current price, TE t is the predicted price at time t, TE t-1 is the price forecast value at the t-1th time point;

[0159] The supply and demand balance is predicted by using multiple linear regression:

[0160] BS t =β0+β1X 1t +β2X 2t +…+β N X Nt +∈ t ;

[0161] In the formula, BS t represents the balance of supply and demand at time t, X 1t +…+X Nt Represent the explanatory variables of the Nth sample that affect the balance of supply and demand, β0+…+β N Represent the corresponding regression coefficients, ∈ t is the error term.

[0162] As a further step of the present invention, regarding the optimization algorithm used in S3 to optimize the charging and discharging strategy, the steps are as follows:

[0163] Minimize the total charging and discharging cost of the energy storage system:

[0164]

[0165] In the formula, Z t represents the total cost, c t represents the charge and discharge decision variable at time t, K t represents the spot price of electricity at time t, R t represents the charge and discharge rate at time t, and T represents the time period;

[0166] At the same time, the following constraints are met:

[0167] Constrain the battery's state of charge to be between minimum and maximum values:

[0168] SC min <SC(t)<SC max ;

[0169] Where SC(t) represents the state of charge of the battery at time t, SC min Represents the minimum state of charge of the battery, SC max Represents the maximum state of charge of the battery;

[0170] Constrain the battery's charge and discharge rate at any point in time:

[0171] R min <R t <R max ;

[0172] In the formula, R min Represents the minimum charge and discharge rate allowed by the battery, R max Represents the maximum charge and discharge rate allowed by the battery;

[0173] Constrain the energy balance of the battery:

[0174] E(t)=E(t-1)+R t ×Δt;

[0175] Where E(t) represents the battery energy storage at time t, E(t-1) represents the battery energy storage at time t-1, and Δt represents the time interval;

[0176] Constrain the energy conversion efficiency and energy loss of the battery during the charging and discharging process:

[0177] G(t)=G in (t)×R t -δ×G in (t);

[0178] In the formula, G(t) represents the energy output by the battery at time t, G in (t) represents the energy input to the battery at time t, and δ represents the energy loss rate of the battery;

[0179] Constrain the initial battery state of charge and the final battery state of charge:

[0180] SC(0)=SC in , SC(T)=SC fin ;

[0181] Where SC(0) represents the battery state of charge at the beginning of the optimization cycle, SC(T) represents the battery state of charge at the end of the optimization cycle, and SC in The value representing the initial battery state of charge, SC fin A value representing the final battery state of charge.

[0182] As a further aspect of the present invention, regarding step S4, the specific steps for formulating real-time market trading decisions using an automated trading algorithm are as follows:

[0183] Collect the real-time price K of the electricity spot market t , the electricity supply quantity D t and the electricity demand quantity X t ;

[0184] Evaluate whether the current energy storage level E(t) of the battery is between the minimum state of charge SC min and the maximum state of charge SC max of the battery:

[0185] The specific charging and discharging conditions are as follows:

[0186] Set the electricity price threshold as DJYZBB. If E(t) = SC min or E(t) = SC max , there is no need to charge or discharge immediately. If E(t) > SC min , charge when K t < DJYZBB. If E(t) < SC max , discharge when K t > DJYZBB;

[0187] If E(t) is not between SC min and SC max , the specific charging and discharging conditions are as follows:

[0188] If E(t) < SC min , it means the battery is in an over-discharged state, and look for time to charge;

[0189] If E(t) > SC max , the battery may be in an over-charged state, and look for time to discharge;

[0190] Use historical data and current market information to predict the market price P at time t + 1 through a linear regression model t+1 :

[0191] Standardize the historical data and current market information:

[0192]

[0193] In the formula, X is the original data, μ represents the mean of the original data, and σ represents the standard deviation of the original data;

[0194] Calculate the means of the independent variable and the dependent variable:

[0195]

[0196] Where n is the number of observations, x n and n represent the independent variable and dependent variable of the nth observation, respectively. and represent the means of the independent and dependent variables respectively;

[0197] Calculate the covariance of the independent and dependent variables and the variance of the independent variables:

[0198]

[0199] In the formula, f xy represents the covariance of the independent variable x and the dependent variable y, f xx represents the variance of the independent variable x;

[0200] Calculate the regression coefficient and the market price P at t+1 t+1 Make predictions:

[0201]

[0202] P t+1 =θ0+θ1x 1当前 +θ2x 2当前 ;

[0203] In the formula, θ1 represents the coefficient of the first independent variable x1, θ0 represents the intercept term, and x 1当前 and x 2当前 are the current supply and demand conditions and market prices, respectively, and θ2 represents the coefficient of the second independent variable x2;

[0204] For the market price P at time t t With the average price μ p Compare and combine the market price P at time t+1 t+1 Determine whether charging and discharging are required;

[0205] If P t >Average price μ p , and predict P t+1 will drop, then discharge;

[0206] If P t <Average price μ p , and predict P t+1 If it rises, charge it;

[0207] If P t = Average price μ p , then no charging or discharging is performed;

[0208] According to the current battery status and charge and discharge rate limit, the actual charge and discharge rate is calculated:

[0209] Charging rate R che =min(R max , S.C. max -E(t));

[0210] Discharge rate R dhr =max(-R min , E(t)-SC min );

[0211] Execute transactions based on decision logic and calculated charge and discharge rates:

[0212] If you decide to discharge, calculate the transaction volume Q deg =R dhr ×Δt, and sell it in the market;

[0213] If you decide to charge, calculate the transaction volume Q che =R che ×Δt, and buy in the market;

[0214] Update the battery's energy storage capacity E(t) in real time:

[0215] If discharging, E(t+1)=E(t)-Q deg ;

[0216] If charging, E(t+1)=E(t)-Q che ;

[0217] Where E(t+1) represents the energy storage of the battery at time t+1;

[0218] Set thresholds for market price fluctuations, implement real-time risk management strategies, and decide whether to trade:

[0219] The maximum acceptable market price volatility is set as V a ;

[0220] For price volatility V t Perform the calculation:

[0221] Where P t-1 represents the market price at time t-1;

[0222] If V t ≤V a , indicating that the market fluctuation is within an acceptable range and trading can be conducted;

[0223] If V t >V a, indicating that market volatility exceeds the risk threshold and trading is suspended.

[0224] As a further step of the present invention, in S5, the specific steps of adjusting the power grid operation dispatch optimization strategy are as follows:

[0225] Minimize the total cost of grid operation:

[0226] Σ t (G c ·F t +Z t ·(F c -F d ));

[0227] In the formula, G c represents the operating cost of the generator set, ∑ t Represents the summation symbol, F t represents the total power demand at time t, F c Represents the current charging power of the energy storage system, F d Represents the current discharge power of the energy storage system;

[0228] Constrain the balance between supply and demand to ensure that the power supply meets the demand at every moment:

[0229] S t =F t +F d -F c ;

[0230] In the formula, S t represents the total power supply at time t;

[0231] Constrain the operation of generator sets and energy storage systems to ensure they operate within a safe and efficient range:

[0232] F min ≤F t ≤F max , 0≤F t ≤F cmax , 0≤F d ≤F dmax ;

[0233] In the formula, F min and F max Represent the minimum and maximum operating power of the generator set, F cmax and F dmax They represent the maximum power charging and discharging limits of the energy storage system respectively;

[0234] Constrain the state of the energy storage system to ensure that the charging and discharging state of the energy storage system meets its capacity limit:

[0235]

[0236] In the formula, E t Represents the energy level of the energy storage system at time t, η c and η d Represent the charging and discharging efficiency of the energy storage system, E t+1 Represents the energy level of the energy storage system at time t+1;

[0237] Take the real-time market transaction results as input and adjust F t and G c :

[0238] G c =G c,bse +τ·(Pr t -Pr bse );

[0239] In the formula, G c,bse is the basic power generation cost, Pr t represents the electricity price, Pr bse represents the basic market electricity price, τ represents the adjustment coefficient;

[0240] F t =F t,bse -ξ·(Pr t -Pr bse );

[0241] In the formula, F t,bse represents the basic battery demand, ξ represents the response factor;

[0242] Adjust F according to the optimized charge and discharge strategy c and F d ;

[0243] F c =max(0, F c，max -ψ c ·Pr t );

[0244] F d =max(0, F d，max -ψ d ·(E max -E t ));

[0245] In the formula, F c，max and F d，max Represent the maximum charging and discharging power of the energy storage system, E max represents the maximum energy capacity of the energy storage system, ψ c and ψ d They represent the parameters of the strategy respectively.

[0246] As a further step of the present invention, in S6, a comprehensive evaluation is conducted on the impact of the energy storage system on the economic performance and reliability of the power grid after participating in market transactions. The specific steps are as follows:

[0247] Calculate the net economic benefits TB brought by the energy storage system in market transactions:

[0248] TB=M ie -O cs -T oy ;

[0249] Where M ie represents the market return, O cs represents the operating cost, T oy represents the initial investment;

[0250] Calculate the return on investment of energy storage system:

[0251]

[0252] In the formula, EI stands for economic index, which is used to reflect the return on investment;

[0253] Measuring the contribution of energy storage systems to grid reliability:

[0254]

[0255] In the formula, S AD Represents the average interruption duration of the system, S AF Represents the system average interruption frequency index, M pit Represents the maximum interruption time;

[0256] Comparing the improvement in grid reliability with and without energy storage systems:

[0257] RG=RM 有 -RM 无 ;

[0258] In the formula, RM 有 Represents the reliability index when there is an energy storage system, RM 无 Represents the reliability index when there is no energy storage system;

[0259] Evaluate the additional benefits that energy storage systems can bring through market trading strategies MTG:

[0260] MTG=P vd ×E SC ×N ot ;

[0261] Where P vd represents the peak-to-valley price difference, E SCRepresents the energy storage capacity, N ot Represents the number of transactions;

[0262] Calculate the contribution of energy storage systems to reducing carbon emissions EBI:

[0263]

[0264] In the formula, C er Represents the reduction in carbon emissions, C efp Represents carbon emissions from traditional power generation;

[0265] By establishing a comprehensive evaluation model YBI, the overall benefits of the energy storage system are evaluated by comprehensively considering economic efficiency, reliability, market transaction income and environmental benefits:

[0266] YBI=w1×EI+w2×RG+w3×MTG+w4×EBI;

[0267] Among them, w1, w2, w3 and w4 represent the weights of EI, RG, MTG and EBI respectively;

[0268] If YBI ≤ 0, the market participation of the energy storage system is judged to be negative;

[0269] If YBI>0, the market participation of energy storage systems is judged to be positive.

[0270] As a further aspect of the present invention, a model for the impact of new energy storage participating in electricity spot market transactions on grid operation and dispatching includes:

[0271] The monitoring module is used to monitor the status of the energy storage system in real time. The real-time status mainly includes: charging and discharging status, capacity and efficiency, as well as the battery's state of charge, charging and discharging rate and temperature;

[0272] The analysis module is used to obtain transaction information and historical data of the power spot market and analyze the transaction information. The transaction information mainly includes supply and demand status data and price fluctuation data;

[0273] The optimization module is used to optimize the charging and discharging strategy using an optimization algorithm based on the analysis of electricity spot market transaction information and the status of the energy storage system;

[0274] The trading module is used to make real-time market trading decisions based on the optimized charging and discharging strategies and electricity spot market trading information using automated trading algorithms;

[0275] The adjustment module adjusts the grid operation and dispatch optimization strategy based on real-time market transaction decisions and optimized charging and discharging strategies;

[0276] The evaluation module comprehensively evaluates the impact of energy storage systems on the economic performance and reliability of the power grid after participating in market transactions.

[0277] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for influencing power grid operation and dispatching by new energy storage participating in power spot market transactions, characterized in that: The specific steps are as follows: S1: Real-time monitoring of the status of the energy storage system, which mainly includes: charging and discharging status, capacity and efficiency, as well as the battery's state of charge, charging and discharging rate and temperature; S2: Acquire transaction information and historical data of the electricity spot market and analyze the transaction information. The transaction information mainly includes supply and demand status data and price fluctuation data; S3: Based on the analysis of electricity spot market transaction information and the status of the energy storage system, an optimization algorithm is used to optimize the charging and discharging strategy; S4: Based on the electricity spot market transaction information and the optimized charging and discharging strategy, an automated trading algorithm is used to make real-time market trading decisions; S5: Adjust the grid operation and dispatch optimization strategy based on real-time market transaction decisions and optimized charging and discharging strategies; S6: Conduct a comprehensive assessment of the impact of energy storage systems on the economics and reliability of the grid after participating in market transactions.

2. The method for influencing power grid operation and dispatching by participating in power spot market transactions of the new energy storage according to claim 1 is characterized in that: Regarding S2, the transaction information is analyzed. The specific analysis steps are as follows: The price fluctuations in the transaction information are analyzed by calculating the normalization of the price: In the formula, σ p is the standard deviation of price fluctuations, P t is the market price at time t, μ p is the average price, N is the number of samples, and i represents the sum index, which is used to traverse all sample points; Predict the price trend by exponential smoothing method: TE t =αJ t +(1-α)TE t-1 ; Where α is the smoothing constant, J t is the current price, TE t is the predicted price at time t, TE t-1 is the price forecast value at the t-1th time point; The supply and demand balance is predicted by using multiple linear regression: BS t =β0+β1X 1t +β2X 2t +…+b N X Nt +∈ t ; In the formula, BS t represents the balance of supply and demand at time t, X 1t +…+X Nt Represent the explanatory variables of the Nth sample that affect the balance of supply and demand, β0+…+β N Represent the corresponding regression coefficients, ∈ t is the error term.

3. The method for influencing power grid operation and dispatching by participating in power spot market transactions of the new energy storage according to claim 1 is characterized in that: Regarding the optimization algorithm used in S3 to optimize the charging and discharging strategy, the steps are as follows: Minimize the total charging and discharging cost of the energy storage system: In the formula, Z t represents the total cost, c t represents the charge and discharge decision variable at time t, K t represents the spot price of electricity at time t, R t represents the charge and discharge rate at time t, and T represents the time period; At the same time, the following constraints are met: Constrain the battery's state of charge to be between minimum and maximum values: SC min <SC(t)<SC max ; Where SC(t) represents the state of charge of the battery at time t, SC min Represents the minimum state of charge of the battery, SC max Represents the maximum state of charge of the battery; Constrain the battery's charge and discharge rate at any point in time: R min <R t <R max ; In the formula, R min Represents the minimum charge and discharge rate allowed by the battery, R max Represents the maximum charge and discharge rate allowed by the battery; Constrain the energy balance of the battery: E(t)=E(t-1)+R t ×Δt; Where E(t) represents the battery energy storage at time t, E(t-1) represents the battery energy storage at time t-1, and Δt represents the time interval; Constrain the energy conversion efficiency and energy loss of the battery during the charging and discharging process: G(t)=G in (t)×R t -δ×G in (t); In the formula, G(t) represents the energy output by the battery at time t, G in (t) represents the energy input to the battery at time t, and δ represents the energy loss rate of the battery; Constrain the initial battery state of charge and the final battery state of charge: SC(0)=SC in ,SC(T)=SC fin ; Where SC(0) represents the battery state of charge at the beginning of the optimization cycle, SC(T) represents the battery state of charge at the end of the optimization cycle, and SC in Represents the value of the initial battery state of charge, SC fin A value representing the final battery state of charge.

4. The method for influencing power grid operation and dispatching by participating in power spot market transactions of the new energy storage according to claim 3 is characterized in that: The specific steps for using automated trading algorithms to make real-time market trading decisions in S4 are as follows: Collect the price K of the electricity spot market in real time t , power supply D t and power demand X t ; Evaluate whether the current energy storage capacity of the battery E(t) is in the minimum state of charge SC of the battery min and the maximum state of charge SC of the battery max between: The specific charging and discharging conditions are as follows: Set the electricity price threshold as DJYZBB. If E(t) = SC min or E(t) = SC max , no immediate charge or discharge is required. If E(t) > SC min , charge when K t < DJYZBB. If E(t) < SC max , discharge when K t > DJYZBB; If E(t) is not in SC min and SC max The specific charging and discharging conditions are as follows: If E(t) <SC min , indicating that the battery is in an over-discharged state and is looking for time to charge; If E(t)>SC max , the battery may be in an overcharged state, looking for time to discharge; Using historical data and current market information, the linear regression model is used to predict the market price P at t+1. t+1 : Standardize historical data and current market information: In the formula, X is the original data, μ represents the mean of the original data, and σ represents the standard deviation of the original data; Calculate the means of the independent and dependent variables: Where n is the number of observations, x n and n represent the independent variable and dependent variable of the nth observation, respectively. and represent the means of the independent and dependent variables respectively; Calculate the covariance of the independent and dependent variables and the variance of the independent variables: In the formula, f xy represents the covariance of the independent variable x and the dependent variable y, f xx represents the variance of the independent variable x; Calculate the regression coefficient and the market price P at t+1 t+1 Make predictions: P t+1 =θ0+θ1x 1当前 +θ2x 2当前 ; In the formula, θ1 represents the coefficient of the first independent variable x1, θ0 represents the intercept term, and x 1当前 and x 2当前 are the current supply and demand conditions and market prices, respectively, and θ2 represents the coefficient of the second independent variable x2; For the market price P at time t t With the average price μ p Compare and combine the market price P at time t+1 t+1 Determine whether charging and discharging are required; If P t >Average price μ p , and predict P t+1 will drop, then discharge; If P t <Average price μ p , and predict P t+1 If it rises, charge it; If P t = Average price μ p , then no charging or discharging is performed; According to the current battery status and charge and discharge rate limit, the actual charge and discharge rate is calculated: Charging rate R che =min(R max , S.C. max -E(t)); Discharge rate R dhr =max(-R min , E(t)-SC min ); Execute transactions based on decision logic and calculated charge and discharge rates: If you decide to discharge, calculate the transaction volume Q deg =R dhr ×Δt, and sell it in the market; If you decide to charge, calculate the transaction volume Q che =R che ×Δt, and buy in the market; Update the battery's energy storage capacity E(t) in real time: If discharging, E(t+1)=E(t)-Q deg ; If charging, E(t+1)=E(t)-Q che ; Where E(t+1) represents the energy storage of the battery at time t+1; Set thresholds for market price fluctuations, implement real-time risk management strategies, and decide whether to trade: The maximum acceptable market price volatility is set as V a ; For price volatility V t Perform the calculation: Where P t-1 represents the market price at time t-1; If V t ≤V a , indicating that the market fluctuation is within an acceptable range and trading can be conducted; If V t >V a , indicating that market volatility exceeds the risk threshold and trading is suspended.

5. The method for influencing power grid operation and dispatching by participating in power spot market transactions of the new energy storage according to claim 1 is characterized in that: The specific steps for adjusting the grid operation dispatch optimization strategy in S5 are as follows: Minimize the total cost of grid operation: ∑ t (G c ·F t +Z t ·(F c -F d )); In the formula, G c represents the operating cost of the generator set, ∑ t Represents the summation symbol, F t represents the total power demand at time t, F c Represents the current charging power of the energy storage system, F d Represents the current discharge power of the energy storage system; Constrain the balance between supply and demand to ensure that the power supply meets the demand at every moment: S t =F t +F d -F c ; In the formula, S t represents the total power supply at time t; Constrain the operation of generator sets and energy storage systems to ensure they operate within a safe and efficient range: F min ≤F t ≤F max ,0≤F t ≤F cmax ,0≤F d ≤F dmax ; In the formula, F min and F max Represent the minimum and maximum operating power of the generator set, F cmax and F dmax They represent the maximum power charging and discharging limits of the energy storage system respectively; Constrain the state of the energy storage system to ensure that the charging and discharging state of the energy storage system meets its capacity limit: In the formula, E t Represents the energy level of the energy storage system at time t, η c and η d Represent the charging and discharging efficiency of the energy storage system, E t+1 Represents the energy level of the energy storage system at time t+1; Take the real-time market transaction results as input and adjust F t and G c : G c =G c,bse +τ·(Pr t -Pr bse , In the formula, G c,bse is the basic power generation cost, Pr t represents the electricity price, Pr bse represents the basic market electricity price, τ represents the adjustment coefficient; F t =F t,bse -ξ·(Pr t -Pr bse , In the formula, F t,bse represents the basic battery demand, ξ represents the response factor; Adjust F according to the optimized charge and discharge strategy c and F d ; F c =max(0,F c，max -ψ c ·Pr t , F d =max(0,F d，max -ψ d ·(E max -E t )); In the formula, F c，max and F d，max Represent the maximum charging and discharging power of the energy storage system, E max represents the maximum energy capacity of the energy storage system, ψ c and ψ d They represent the parameters of the strategy respectively.

6. The method for influencing power grid operation and dispatching by participating in power spot market transactions of the new energy storage according to claim 1 is characterized in that: Regarding S6, a comprehensive assessment is conducted on the impact of energy storage systems on the economics and reliability of the grid after participating in market transactions. The specific steps are as follows: Calculate the net economic benefits TB brought by the energy storage system in market transactions: TB=M ie -O cs -T oy ; Where M ie represents the market return, O cs represents the operating cost, T oy represents the initial investment; Calculate the return on investment of energy storage system: In the formula, EI stands for economic index, which is used to reflect the return on investment; Measuring the contribution of energy storage systems to grid reliability: In the formula, S AD Represents the average interruption duration of the system, S AF Represents the system average interruption frequency index, M pit Represents the maximum interruption time; Comparing the improvement in grid reliability with and without energy storage systems: RG=RM 有 -RM 无 ; In the formula, RM 有 Represents the reliability index when there is an energy storage system, RM 无 Represents the reliability index when there is no energy storage system; Evaluate the additional benefits that energy storage systems can bring through market trading strategies MTG: MTG=P vd ×E SC ×N ot ; Where P vd represents the peak-to-valley price difference, E SC Represents the energy storage capacity, N ot Represents the number of transactions; Calculate the contribution of energy storage systems to reducing carbon emissions EBI: In the formula, C er Represents the reduction in carbon emissions, C efp Represents carbon emissions from traditional power generation; By establishing a comprehensive evaluation model YBI, the overall benefits of the energy storage system are evaluated by comprehensively considering economic efficiency, reliability, market transaction income and environmental benefits: YBI=w1×EI+w2×RG+w3×MTG+w4×EBI; Among them, w1, w2, w3 and w4 represent the weights of EI, RG, MTG and EBI respectively; If YBI ≤ 0, the market participation of the energy storage system is judged to be negative; If YBI>0, the market participation of energy storage systems is judged to be positive.

7. The model of the impact of the new energy storage participating in the electricity spot market transaction on the grid operation and dispatching according to claim 1 is characterized in that: include: The monitoring module is used to monitor the status of the energy storage system in real time. The real-time status mainly includes: charging and discharging status, capacity and efficiency, as well as the battery's state of charge, charging and discharging rate and temperature; The analysis module is used to obtain transaction information and historical data of the power spot market and analyze the transaction information. The transaction information mainly includes supply and demand status data and price fluctuation data; The optimization module is used to optimize the charging and discharging strategy using an optimization algorithm based on the analysis of electricity spot market transaction information and the status of the energy storage system; The trading module is used to make real-time market trading decisions based on the optimized charging and discharging strategies and electricity spot market trading information using automated trading algorithms; The adjustment module adjusts the grid operation and dispatch optimization strategy based on real-time market transaction decisions and optimized charging and discharging strategies; The evaluation module comprehensively evaluates the impact of energy storage systems on the economic performance and reliability of the power grid after participating in market transactions.

Images