Electric power and electric quantity balancing method considering new energy direct current delivery energy storage

Abstract

An electric power and electric quantity balancing method considering new energy direct current delivery energy storage comprises the steps that through analysis of historical meteorological data, hydrological data and load data, generating capacity and electric power requirements of wind energy, solar energy and hydropower are predicted respectively; based on real-time monitoring data of the energy storage system, state parameters of the energy storage system are evaluated; combining the prediction result and the energy storage state, establishing an electric power and electric quantity balance optimization scheduling model, and making a combined scheduling scheme of hydropower, wind power, photovoltaic power generation and energy storage by taking minimization of the operation cost and the direct current loss of the electric power system as a target; external operation data are obtained through a real-time monitoring system, a scheduling strategy is dynamically adjusted, and the rapid response capability of the system to sudden load fluctuation and new energy power generation uncertainty is guaranteed. According to the method disclosed by the invention, the power supply and demand balance of the power system in different time periods is realized by optimizing the regulation capability of water and electricity, dynamically managing the direct-current transmission energy storage and efficiently utilizing the new energy.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, in particular to the technical field of power system dispatching and control, and more particularly to a power and energy balance technology considering energy storage for DC transmission of new energy. Background Art

[0002] In the prior art, since the proportion of renewable energy such as wind power and photovoltaic power in the power system has been increasing year by year, and the new energy power generation has volatility and strong randomness, it has brought challenges to the stable operation of the power system. Especially in the DC transmission power system, the uncertainty of new energy power generation further exacerbates the problem of power and energy imbalance.

[0003] The patent document with the application publication number CN111987740A discloses a power and energy balance method for grid-connected operation of new energy. The operation status of wind turbines and photovoltaic generators is monitored and controlled in real time through a generator monitoring unit, which is convenient for distribution personnel to maintain them in time and reduce the impact on power and energy balance. "Research on Optimal Configuration of Energy Storage Capacity and Power Transmission Mode in a Wind-Solar-Fire-Energy Storage System" proposes a joint optimal configuration method for the optimal configuration of new energy and energy storage capacity and power transmission mode in a multi-energy complementary system, and analyzes the influence of the supporting energy storage scale and DC regulation performance on DC transmission. The above patent methods only focus on the power and energy balance on the power generation side of the power system involving new energy, lacking the consideration of the impact of the time-series fluctuation of new energy output on the scale of DC channel power transmission. And the above-mentioned paper mainly studies the configuration of energy storage and power transmission channel capacity, without considering the problem that it is difficult to maintain power and energy balance due to the large-scale access of new energy to the power grid.

[0004] Therefore, the applicant proposes a power and energy balance technology considering energy storage for DC transmission of new energy. Summary of the Invention

[0005] The purpose of the present invention is to solve the technical problem pointed out in the background art that the traditional transmission curve formulation is generally based on a deterministic single new energy output scenario, focusing on the energy balance under the reliable output levels of wind power and photovoltaic power, lacking the consideration of the impact of the time-series fluctuation of new energy output on the scale of DC channel power transmission, and being difficult to be applied to the analysis of large new energy base DC transmission systems mainly serving the transmission requirements of fluctuating energy such as wind power and photovoltaic power, thus resulting in the further exacerbation of the power and energy imbalance due to the uncertainty of new energy power generation in the DC transmission power system, and the present invention is proposed.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] A power and energy balance method considering energy storage for DC transmission of new energy, comprising the following steps:

[0008] Step 1: Collect the basic data of the power grid system and construct an initial database;

[0009] Step 2: Establish a power and energy balance model based on new - energy DC transmission. This model realizes the balance of power and energy in the power system, and aims to minimize the operation cost of the power system and DC losses;

[0010] Step 3: In the model established in Step 2, introduce the volatility constraints of new - energy power generation, DC transmission capacity constraints, and energy - storage charge - discharge constraints;

[0011] Step 4: Solve the power and energy balance model of new - energy DC transmission to obtain the optimal operation strategy of the system;

[0012] Step 5: According to the optimization results, adjust the operation mode of the power grid system to achieve power and energy balance.

[0013] In Step 1, it specifically includes the following steps:

[0014] Step 1 - 1: According to historical meteorological data, flow forecasts, etc., combined with meteorological models and hydrological models, predict the new - energy power generation in the next period (24 hours), including the power generation of wind energy, solar energy, and hydropower;

[0015] Step 1 - 2: Based on these predicted data, calculate the power output of new - energy and hydropower generation in each period;

[0016] Step 1 - 3: Based on historical load data, daily electricity - consumption patterns, seasonal changes, and weather factors, etc., use a load - forecasting model to predict the load demand in the next period (24 hours) of the power system. The load - demand prediction should consider the daily regularity, seasonality, and sudden - load changes of electricity consumption;

[0017] Step 1 - 4: Monitor parameters such as the remaining charge (SOC, State of Charge), health status (SOH, State of Health), charge - discharge efficiency, and maximum power of the energy - storage device.

[0018] In Step 2, for the established power and energy balance mathematical model based on new - energy DC transmission, the expression formula of its objective function is:

[0019]

[0020] In the formula, β is the loss coefficient; P dc (t) is the DC transmission power at time t; c ch is the energy - storage charging cost coefficient; c dis is the energy - storage discharging cost coefficient; P bat,in (t),P bat,out(t) is the charging power and discharging power of energy storage at time t; Δt is the time interval; α

[0021] is the weight coefficient of new energy utilization rate; P gen,i (t) is the power generation power of each new energy unit i at time t; is the maximum power generation power of each new energy unit i at time t.

[0022] Among them, the hydropower unit is used as a regulating power source, and the output function is:

[0023] P h (t) = k h ·Q(t)·H(t) (2)

[0024] In the formula, k h is the conversion coefficient; Q is the flow rate; H is the water head.

[0025] Among them, the wind power output model is:

[0026]

[0027] In the formula, ρ is the air density; A is the swept area of the wind turbine; C p is the power coefficient; v(t) is the wind speed.

[0028] Among them, the photovoltaic output model is:

[0029] P s (t) = η pv A pv I(t) (4)

[0030] In the formula, η pv is the photovoltaic module efficiency; A pv is the photovoltaic array area; I(t) is the solar radiation intensity.

[0031] It is achieved by setting the upper and lower fluctuation range limits, and the constraint conditions are as follows:

[0032] Power balance constraint: The total power generation should meet the demand and the DC external transmission demand;

[0033]

[0034] In the formula, P load (t) is the load demand power of the system at time t;

[0035] Energy balance constraint: Ensure that within the entire scheduling period, the total electricity of power generation and energy storage is equal to the total electricity of load demand and losses;

[0036]

[0037] New energy power generation constraint: The new energy power generation shall not exceed its maximum available power generation:

[0038]

[0039] Energy storage dynamic constraint: The capacity change of the energy storage device is affected by the charge and discharge power;

[0040]

[0041] In the formula, E bat (t) is the remaining capacity of the energy storage device at time t; η ch and η dis are the charging and discharging efficiencies;

[0042] Energy storage capacity limit:

[0043] E bat,min (t) ≤ E bat (t) ≤ E bat,max (t) (9)

[0044] In the formula, E bat,min (t) is the minimum allowable capacity of the energy storage device at time t; E bat,max (t) is the maximum allowable capacity of the energy storage device at time t;

[0045] Energy storage charge and discharge constraint: The energy storage charge and discharge power is limited by the rated power of the device:

[0046]

[0047] In the formula, is the maximum charging power of the energy storage device at time t; is the maximum discharging power of the energy storage device at time t;

[0048] DC external transmission constraint: The DC external transmission power shall meet the line capacity limit;

[0049]

[0050] In the formula, is the capacity limit (maximum allowable transmission power) of the DC external transmission channel at time t.

[0051] In step 4, an optimization algorithm is used to solve the mathematical model to obtain the optimal operation strategy of the system, specifically:

[0052] (1) Optimize using the mixed integer linear programming (MILP) algorithm: Convert the power balance equation and the charge and discharge balance constraint into a linear form to form a linear optimization model;

[0053] The energy storage charge and discharge power P bat,in (t) and Pbat,out (t) is used as a decision variable, and integer variables are added to represent the switching state of the energy storage device;

[0054] Charge state:

[0055]

[0056] Discharge state:

[0057]

[0058] In the formula, u in (t) and u out (t) are binary variables indicating whether the energy storage system is charging or discharging in time period t. When the binary variable is 1, it means charging or discharging, and when it is 0, it means not charging or not discharging;

[0059] The MILP solution method is adopted, and CPLEX is used to determine the optimal power generation, energy storage, and DC power transmission scheduling scheme by solving the optimization model, minimizing the system operation cost while satisfying the power and energy balance;

[0060] (2) Robust optimization: Considering the new energy prediction error, construct the uncertainty set U and solve the optimal solution:

[0061]

[0062] In the formula, x is the optimization variable, usually representing the decision variable; μ is the variable in the uncertainty set, representing the new energy prediction error; U is the uncertainty set, representing the possible error range; C(x, μ) is the cost function under uncertain conditions; γ is the risk weight, used to balance the cost and risk; R(x, μ) is the risk measure brought by the uncertainty.

[0063] In step 5, according to the optimization results, adopt a hierarchical optimization strategy to adjust the operation mode of the power grid system to achieve power and energy balance.

[0064] The hierarchical optimization strategy includes upper-layer optimization and lower-layer optimization, specifically: (1) Upper-layer optimization: Use the genetic algorithm to coordinate new energy power generation, load demand, and energy storage scheduling to form a global optimization model, and the formula is:

[0065]

[0066] In the formula, F is the fitness function; λ is the weight coefficient for balancing new energy consumption and operation cost; C gen (t) is the power generation cost in time period t (such as the fuel cost and maintenance cost of the generator set); C storage (t) is the energy storage cost in time period t (such as the charging / discharging efficiency loss of the energy storage device and the cost of the battery management system);

[0067] (2) Lower - layer optimization: The particle swarm optimization algorithm is used for local adjustment, refining the charge - discharge strategy of the energy storage system, and adjusting the distribution of energy storage power in real - time. The formula is:

[0068]

[0069] In the formula, ν j (t) is the velocity of the j - th particle; ω is the inertia weight; c1 and c2 are learning factors; is the individual historical best position; x j (t) is the current position information of the j - th particle; g best is the global best position.

[0070] In addition, the traditional transmission curve formulation is generally based on a deterministic single new - energy output scenario, focusing on the power - quantity balance under the reliable output levels of wind power and photovoltaic power, lacking the consideration of the impact of the time - series fluctuations of new - energy output on the scale of DC - channel power transmission. It is difficult to be applicable to the analysis of large - scale new - energy base DC - out - of - area systems mainly serving the transmission needs of fluctuating energy such as wind power and photovoltaic power. As a result, in the DC - out - of - area power system, the uncertainty of new - energy power generation further exacerbates the imbalance between power and electricity;

[0071] To solve the above - mentioned technical problems, the present invention also proposes a DC - out - of - area energy storage system based on power - quantity balance. This system includes a data input module. The output end of the data input module is respectively connected to the input ends of a prediction module, an energy storage system evaluation module, and a real - time feedback module. The output ends of the prediction module, the energy storage system evaluation module, and the real - time feedback module are connected to the input end of an optimal scheduling module, and the optimal scheduling module outputs the power - quantity balance and scheduling strategy.

[0072] Among them, the data input module is used to input new - energy data, historical load data, real - time monitoring data of energy storage devices, and external monitoring data. After formatting them, the output results of the new - energy data and historical load data are output to the prediction module;

[0073] The prediction module receives the new - energy data and historical load data output by the data input module, uses the time - series analysis method to predict the future new - energy output and load demand, and outputs the predicted new - energy output and load - demand curves to the optimal scheduling module;

[0074] The energy storage system evaluation module receives the real - time monitoring data of energy storage devices output by the data input module, evaluates the current state of the energy storage system, judges the health state and availability of the energy storage devices, and outputs the charge - discharge capacity and efficiency of the energy storage system to the optimal scheduling module;

[0075] The real-time feedback module receives the external monitoring data output by the data input module, compares the deviation between the actual operation data and the predicted or optimized results, and adjusts the parameters of the scheduling optimization model according to the real-time monitoring and feedback, and outputs the corrected energy storage scheduling strategy and power distribution plan to the optimization scheduling module;

[0076] The optimization scheduling module receives the data output by the prediction module, the energy storage system evaluation module and the real-time feedback module, and based on the mixed integer linear programming algorithm, conducts the power and energy balance optimization scheduling to solve the optimal power distribution and energy storage scheduling strategy; finally, it outputs the power balance and scheduling strategy to provide scheduling reference for the actual operation of the system and store it as a historical record at the same time.

[0077] When the above system is used, it includes the following steps:

[0078] Step 1: Collect the basic data of the power grid system and construct an initial database;

[0079] Step 2: Establish a power and energy balance model based on new energy DC transmission, which realizes the balance of power and energy in the power system and aims to minimize the operation cost and DC loss of the power system;

[0080] Step 3: In the model established in Step 2, introduce the volatility constraint of new energy power generation, the DC transmission capacity constraint and the energy storage charge and discharge constraint;

[0081] Step 4: Solve the power and energy balance model of new energy DC transmission to obtain the optimal operation strategy of the system;

[0082] Step 5: According to the optimization results, adjust the operation mode of the power grid system to achieve power and energy balance.

[0083] In Step 1, it specifically includes the following steps:

[0084] Step 1-1: According to the historical meteorological data, flow forecast, combined with the meteorological model and hydrological model, predict the new energy power generation in the future period, including the power generation of wind energy, solar energy and hydropower;

[0085] Step 1-2: On the basis of these prediction data, calculate the new energy and hydropower power output in each period;

[0086] Step 1-3: Based on the historical load data, daily electricity consumption patterns, seasonal changes and weather factors, use the load prediction model to predict the load demand in the future period of the power system. The load demand prediction should consider the daily regularity, seasonality and sudden load changes of electricity consumption;

[0087] Step 1-4: Monitor the remaining power, health status, charge and discharge efficiency and maximum power of the energy storage device.

[0088] The established mathematical model of power and energy balance based on new - energy DC power transmission has the following expression formula for the objective function:

[0089]

[0090] In the formula, β is the loss coefficient; P dc (t) is the DC power transmission at time t; c ch is the energy - storage charging cost coefficient; c dis is the energy - storage discharging cost coefficient; P bat,in (t), P bat,out (t) are the charging power and discharging power of the energy storage at time t; Δt is the time interval; α is the weight coefficient of new - energy utilization rate; P gen,i (t) is the power generation of each new - energy unit i at time t; is the maximum power generation of each new - energy unit i at time t.

[0091] Among them, the hydropower output function is:

[0092] P h (t)=k h ·Q(t)·H(t)

[0093] In the formula, k h is the conversion coefficient; Q is the flow rate; H is the head.

[0094] Among them, the wind - power output function is:

[0095]

[0096] In the formula, ρ is the air density; A is the swept area of the wind turbine; C p is the power coefficient; v(t) is the wind speed.

[0097] Among them, the PV - power output function is:

[0098] P s (t)=η pv A pv I(t)

[0099] In the formula, η pv is the PV - module efficiency; A pv is the PV - array area; I(t) is the solar radiation intensity.

[0100] It is achieved by setting the upper and lower fluctuation - range limits, and the constraint conditions are as follows:

[0101] Power - balance constraint: The total power generation should meet the demand and the DC power - transmission demand;

[0102]

[0103] In the formula, P load (t) is the load demand power of the system at time t;

[0104] Power balance constraint: Ensure that within the entire scheduling period, the total power generation and energy storage is equal to the total power of the load demand and losses;

[0105]

[0106] New energy power generation constraint: The new energy power generation shall not exceed its maximum power generation capacity:

[0107]

[0108] Energy storage dynamic constraint: The capacity change of the energy storage device is affected by the charging and discharging power;

[0109]

[0110] In the formula, E bat (t) is the remaining capacity of the energy storage device at time t; η ch and η dis are the charging and discharging efficiencies;

[0111] Energy storage capacity limit:

[0112] E bat,min (t) ≤ E bat (t) ≤ E bat,max (t)

[0113] In the formula, E bat,min (t) is the minimum allowable capacity of the energy storage device at time t; E bat,max (t) is the maximum allowable capacity of the energy storage device at time t;

[0114] Energy storage charging and discharging constraint: The energy storage charging and discharging power is limited by the rated power of the device:

[0115]

[0116] In the formula, is the maximum charging power of the energy storage device at time t; is the maximum discharging power of the energy storage device at time t;

[0117] DC external transmission constraint: The DC external transmission power should meet the line capacity limit;

[0118]

[0119] In the formula, is the capacity limit of the DC external transmission channel at time t.

[0120] In step 4, an optimization algorithm is used to solve the mathematical model to obtain the optimal operation strategy of the system, specifically:

[0121] (1) Optimization using the Mixed Integer Linear Programming (MILP) algorithm: Convert the power balance equation and the power quantity balance constraint into linear forms to form a linear optimization model;

[0122] Take the charge and discharge power P bat,in (t) and P bat,out (t) as decision variables, and add integer variables to represent the switch state of the energy storage device;

[0123] Charge state:

[0124]

[0125] Discharge state:

[0126]

[0127] In the formula, u in (t) and u out (t) are binary variables indicating whether the energy storage system is charging or discharging at time period t. When the binary variable is 1, it means charging or discharging, and when it is 0, it means not charging or not discharging;

[0128] Adopt the MILP solution method, use CPLEX to determine the optimal power generation, energy storage, and DC external transmission power scheduling scheme by solving the optimization model, and minimize the system operation cost while meeting the power and energy balance;

[0129] (2) Robust optimization: Considering the new energy prediction error, construct the uncertainty set U and solve the optimal solution:

[0130]

[0131] In the formula, x is the optimization variable, usually representing the decision variable; μ is the variable in the uncertainty set, representing the new energy prediction error; U is the uncertainty set, representing the possible error range; C(x, μ) is the cost function under uncertain conditions; γ is the risk weight, used to balance the cost and risk; R(x, μ) is the risk measure brought by the uncertainty.

[0132] In step 5, according to the optimization results, adopt a hierarchical optimization strategy to adjust the operation mode of the power grid system to achieve power and energy balance;

[0133] The hierarchical optimization strategy includes upper-layer optimization and lower-layer optimization, specifically:

[0134] (1) Upper - layer optimization: The genetic algorithm is adopted to coordinate new - energy power generation, load demand, and energy - storage scheduling, forming a global optimization model. The formula is as follows:

[0135]

[0136] In the formula, \(F\) is the fitness function; \(\lambda\) is the weight coefficient for balancing new - energy consumption and operation cost; \(C\) gen (t) is the power - generation cost at time period \(t\); \(C\) storage (t) is the energy - storage cost at time period \(t\).

[0137] (2) Lower - layer optimization: The particle - swarm optimization algorithm is used for local adjustment, refining the charge - discharge strategy of the energy - storage system and adjusting the distribution of energy - storage power in real - time. The formula is as follows:

[0138]

[0139] x j (t + 1)=x j (t)+ν j (t + 1)

[0140] In the formula, ν j (t) is the velocity of the \(j\) - th particle; \(\omega\) is the inertia weight; \(c_1\), \(c_2\) are learning factors; is the individual historical best position; \(x\) j (t) is the current position information of the \(j\) - th particle; \(g\) best is the global best position.

[0141] Compared with the prior art, the present invention has the following technical effects:

[0142] 1) By introducing a DC - out energy - storage system, that is, the energy - storage system sends electric energy into the power grid in DC form through a dedicated DC - transmission interface, the flexibility and reliability of power dispatching are increased. The DC - out energy - storage system has the following advantages: reducing energy loss: DC transmission has less loss during long - distance transmission, especially suitable for large - scale new - energy grid - connection systems; improving energy - consumption capacity: the DC - out energy - storage can store energy when new - energy power generation is excessive and transmit it to areas with large demand through DC, avoiding the limitations of traditional AC power grids;

[0143] 2) The present invention proposes a more intelligent energy - storage charge - discharge control method. By real - time monitoring of power demand and new - energy power generation and based on prediction data (such as load prediction, power - generation prediction, etc.), dynamic optimization dispatching is carried out. This method uses a real - time optimization algorithm, and through intelligent control, it ensures that the energy - storage system maximizes the economy of the system while ensuring the stability of the power grid. This method not only improves the accuracy of power dispatching but also can more efficiently consume new energy;

[0144] 3) The present invention proposes a comprehensive power and electricity balance method, which integrates the real-time scheduling and coordination of new energy power generation, energy storage systems (including DC external power transmission energy storage) and grid loads to form a closed-loop optimization system. Through this method, the volatility of new energy and the changes in load demand are precisely adjusted, and the joint optimization of the energy storage system and the DC external power transmission energy storage system can effectively ensure the electricity balance of the power system. Especially when the supply of new energy is excessive, the energy storage system can store the excess electric energy and send it to the long-distance demand center through DC external power transmission, reducing power losses;

[0145] 4) The present invention innovatively proposes an independent scheduling model for the power of DC external power transmission energy storage. Considering the characteristics of the DC power grid, it optimizes the charging and discharging process of the energy storage system and the energy exchange method with the power grid. By establishing an electricity balance equation and integrating it with the charging and discharging of the energy storage and the grid load constraints, precise coordinated scheduling of the DC external power transmission energy storage and the power system is achieved. Especially in the case of efficient consumption of new energy, the DC external power transmission energy storage system can quickly respond and optimize the energy distribution;

[0146] 5) The present invention proposes a multi-level grid stability control strategy, which not only relies on the charging and discharging regulation of the energy storage system, but also enhances the stability of the grid through the flexibility of the DC external power transmission energy storage. Based on real-time prediction, scheduling algorithms and the state feedback of the energy storage system, when the new energy power generation fluctuates greatly, by adjusting the power output of the energy storage system and the DC external power transmission energy storage system, it can quickly respond to the changes in grid load and frequency deviation, thereby ensuring the stability of the grid;

[0147] 6) The present invention can well solve the impact of the uncertainty of the output of wind power, photovoltaic, etc. in new energy bases on power external transmission. By comprehensively considering the volatility of new energy power generation, the characteristics of DC external power transmission and the regulation ability of the energy storage system, real-time balance of power and electricity is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0148] The present invention will be further described below in conjunction with the drawings and embodiments:

[0149] Figure 1 is the flowchart of the method of the present invention;

[0150] Figure 2 is the schematic diagram of the DC channel of the present invention;

[0151] Figure 3 is the structural block diagram of the system in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0152] As Figure 1 shown, a power and electricity balance method considering new energy DC external power transmission energy storage includes the following steps:

[0153] Step 1: Collect the basic data of the power grid system and construct an initial database;

[0154] Step 2: Establish a power and energy balance model based on new - energy DC external power transmission. This model realizes the balance of power and energy in the power system, and aims to minimize the operation cost of the power system and DC losses;

[0155] Step 3: In the model established in Step 2, introduce the volatility constraints of new - energy power generation, DC external - power - transmission capacity constraints, and energy - storage charge - and - discharge constraints;

[0156] Step 4: Solve the power and energy balance model of new - energy DC external power transmission to obtain the optimal operation strategy of the system;

[0157] Step 5: According to the optimization results, adjust the operation mode of the power grid system to achieve power and energy balance.

[0158] In Step 1, it specifically includes the following steps:

[0159] Step 1 - 1: According to historical meteorological data, flow forecasts, etc., combined with meteorological models and hydrological models, predict the new - energy power generation in the next period (24 hours), including the power generation of wind energy, solar energy, and hydropower;

[0160] Step 1 - 2: Based on these prediction data, calculate the new - energy and hydropower power output in each period;

[0161] Step 1 - 3: Based on historical load data, daily electricity - consumption patterns, seasonal changes, and weather factors, etc., use a load - forecasting model to predict the load demand in the next period (24 hours) of the power system. The load - demand forecast should consider the daily regularity, seasonality, and sudden - load changes of electricity consumption;

[0162] Step 1 - 4: Monitor parameters such as the remaining charge (SOC, State of Charge), health status (SOH, State of Health), charge - and - discharge efficiency, and maximum power of the energy - storage device.

[0163] In Step 2, for the established power and energy balance mathematical model based on new - energy DC external power transmission, the expression formula of its objective function is:

[0164]

[0165] In the formula, β is the loss coefficient; P dc (t) is the DC external - power - transmission power at time t; c ch is the energy - storage charging cost coefficient; c dis is the energy - storage discharging cost coefficient; P bat,in (t), P bat,out(t) is the charging power and discharging power of energy storage at time t; Δt is the time interval; α is the weight coefficient of new energy utilization rate; P gen,i (t) is the power generation of each new energy unit i at time t; is the maximum power generation of each new energy unit i at time t.

[0166] Among them, the hydropower output function is:

[0167] P h (t) = k h ·Q(t)·H(t) (2)

[0168] In the formula, k h is the conversion coefficient; Q is the flow rate; H is the head.

[0169] Among them, the wind power output function is:

[0170]

[0171] In the formula, ρ is the air density; A is the swept area of the wind turbine; C p is the power coefficient; v(t) is the wind speed.

[0172] Among them, the photovoltaic output function is:

[0173] P s (t) = η pv A pv I(t) (4)

[0174] In the formula, η pv is the photovoltaic module efficiency; A pv is the photovoltaic array area; I(t) is the solar radiation intensity.

[0175] It is achieved by setting the upper and lower fluctuation range limits, and the constraint conditions are as follows:

[0176] Power balance constraint: The total power generation should meet the demand and the DC transmission demand;

[0177]

[0178] In the formula, P load (t) is the load demand power of the system at time t;

[0179] Energy balance constraint: Ensure that within the entire scheduling period, the total electricity of power generation and energy storage is equal to the total electricity of load demand and losses;

[0180]

[0181] New energy power generation constraint: The new energy power generation shall not exceed its maximum available power generation:

[0182]

[0183] Energy storage dynamic constraint: The capacity change of the energy storage device is affected by the charge and discharge power;

[0184]

[0185] In the formula, E bat (t) is the remaining capacity of the energy storage device at time t; η ch and η dis are the charging and discharging efficiencies;

[0186] Energy storage capacity limit:

[0187] E bat,min (t) ≤ E bat (t) ≤ E bat,max (t) (9)

[0188] In the formula, E bat,min (t) is the minimum allowable capacity of the energy storage device at time t; E bat,max (t) is the maximum allowable capacity of the energy storage device at time t;

[0189] Energy storage charge and discharge constraint: The energy storage charge and discharge power is limited by the rated power of the device:

[0190]

[0191] In the formula, is the maximum charging power of the energy storage device at time t; is the maximum discharging power of the energy storage device at time t;

[0192] DC transmission constraint: The DC transmission power should meet the line capacity limit;

[0193]

[0194] In the formula, is the capacity limit (maximum allowable transmission power) of the DC transmission channel at time t.

[0195] In step 4, an optimization algorithm is used to solve the mathematical model to obtain the optimal operation strategy of the system, specifically:

[0196] (1) Optimization using the mixed-integer linear programming (MILP) algorithm: Convert the power balance equation and the charge balance constraint into linear forms to form a linear optimization model;

[0197] Take the energy storage charge and discharge powers P bat,in (t) and P bat,out (t) as decision variables, and add integer variables to represent the switching state of the energy storage device;

[0198] Charging state:

[0199]

[0200] Discharging state:

[0201]

[0202] where u in (t) and u out (t) are binary variables indicating whether the energy storage system is charging or discharging at time period t. When the binary variable is 1, it indicates charging or discharging, and when it is 0, it indicates not charging or not discharging;

[0203] Using the MILP solution method, CPLEX is used to determine the optimal power generation, energy storage, and DC power transmission scheduling plan by solving the optimization model, minimizing the system operation cost while satisfying the power and energy balance;

[0204] (2) Robust optimization: Considering the new energy prediction error, construct the uncertainty set U and solve the optimal solution:

[0205]

[0206] where x is the optimization variable, usually representing the decision variable; μ is the variable in the uncertainty set, representing the new energy prediction error; U is the uncertainty set, representing the possible error range; C(x, μ) is the cost function under uncertain conditions; γ is the risk weight, used to balance cost and risk; R(x, μ) is the risk measure brought by uncertainty.

[0207] In step 5, according to the optimization results, adopt a hierarchical optimization strategy to adjust the operation mode of the power grid system to achieve power and energy balance.

[0208] The hierarchical optimization strategy includes upper-layer optimization and lower-layer optimization, specifically: (1) Upper-layer optimization: Use the genetic algorithm to coordinate new energy power generation, load demand, and energy storage scheduling to form a global optimization model, and the formula is:

[0209]

[0210] where F is the fitness function; λ is the weight coefficient for balancing new energy consumption and operation cost; C gen (t) is the power generation cost at time period t (fuel cost, maintenance cost, etc. of the generator set); C storage (t) is the energy storage cost at time period t (charging / discharging efficiency loss of the energy storage device, cost of the battery management system, etc.);

[0211] (2) Lower layer optimization: The particle swarm optimization algorithm is adopted for local adjustment to refine the charge and discharge strategy of the energy storage system and adjust the distribution of energy storage power in real time. The formula is as follows:

[0212]

[0213] In the formula, ν j (t) is the velocity of the j-th particle; ω is the inertia weight; c1 and c2 are learning factors; is the individual historical best position; x j (t) is the current position information of the j-th particle; g best is the global best position.

[0214] The traditional transmission curve formulation is generally based on a deterministic single new energy output scenario, focusing on the power balance under the reliable output levels of wind power and photovoltaic power, lacking consideration of the impact of the time-series fluctuations of new energy output on the scale of DC channel power transmission, and being difficult to be applied to the analysis of large-scale new energy base DC transmission systems mainly serving the transmission demands of fluctuating energy such as wind power and photovoltaic power. As a result, in the DC transmission power system, the uncertainty of new energy generation further exacerbates the power and energy imbalance;

[0215] To solve the above technical problems, the present invention also proposes a DC transmission energy storage system based on power and energy balance. The system includes a data input module, and the output end of the data input module is respectively connected to the input ends of a prediction module, an energy storage system evaluation module, and a real-time feedback module. The output ends of the prediction module, the energy storage system evaluation module, and the real-time feedback module are connected to the input end of an optimization scheduling module, and the optimization scheduling module outputs power balance and scheduling strategies.

[0216] Among them, the data input module is used to input new energy data, historical load data, real-time monitoring data of energy storage devices, and external monitoring data. After formatting them, the output results of the new energy data and historical load data are output to the prediction module;

[0217] The prediction module receives the new energy data and historical load data output by the data input module, uses the time series analysis method to predict the future new energy output and load demand, and outputs the predicted new energy output and load demand curves to the optimization scheduling module;

[0218] The energy storage system evaluation module receives the real-time monitoring data of the energy storage device output by the data input module, evaluates the current state of the energy storage system and judges the health state and availability of the energy storage device, and outputs the charge and discharge capacity and efficiency of the energy storage system to the optimization scheduling module;

[0219] The real-time feedback module receives the external monitoring data output by the data input module, compares the deviation between the actual operation data and the predicted or optimized results, and adjusts the parameters of the scheduling optimization model according to the real-time monitoring and feedback, and outputs the corrected energy storage scheduling strategy and power distribution plan to the optimal scheduling module;

[0220] The optimal scheduling module receives the data output by the prediction module, the energy storage system evaluation module and the real-time feedback module, and performs optimal scheduling of power and electricity balance based on the mixed integer linear programming algorithm to solve the optimal power distribution and energy storage scheduling strategy; finally, it outputs the power and electricity balance and scheduling strategy to provide scheduling reference for the actual operation of the system, and at the same time stores it as a historical record.

[0221] When the above system is used, it includes the following steps:

[0222] Step 1: Collect the basic data of the power grid system and construct an initial database;

[0223] Step 2: Establish a power and electricity balance model based on new energy DC transmission, which realizes the balance of power and electricity in the power system, and at the same time aims to minimize the operation cost and DC loss of the power system;

[0224] Step 3: In the model established in Step 2, introduce the volatility constraint of new energy power generation, the DC transmission capacity constraint and the energy storage charge and discharge constraint;

[0225] Step 4: Solve the power and electricity balance model of new energy DC transmission to obtain the optimal operation strategy of the system;

[0226] Step 5: Adjust the operation mode of the power grid system according to the optimization results to achieve power and electricity balance.

[0227] In Step 1, it specifically includes the following steps:

[0228] Step 1-1: According to the historical meteorological data, flow forecast, combined with the meteorological model and hydrological model, predict the new energy power generation in the future period, including the power generation of wind energy, solar energy and hydropower;

[0229] Step 1-2: Based on these prediction data, calculate the new energy and hydropower power output in each period;

[0230] Step 1-3: Based on the historical load data, daily power consumption patterns, seasonal changes and weather factors, use the load prediction model to predict the load demand in the future period of the power system. The load demand prediction should consider the daily regularity, seasonality and sudden load changes of power consumption;

[0231] Step 1-4: Monitor the remaining power, health status, charge and discharge efficiency and maximum power of the energy storage device.

[0232] In step 2, for the established mathematical model of power and energy balance based on new energy DC power transmission, the expression formula of its objective function is as follows:

[0233]

[0234] In the formula, β is the loss coefficient; P dc (t) is the DC power transmission at time t; c ch is the energy storage charging cost coefficient; c dis is the energy storage discharging cost coefficient; P bat,in (t), P bat,out (t) are the charging power and discharging power of the energy storage at time t; Δt is the time interval; α is the weight coefficient of new energy utilization rate; P gen,i (t) is the power generation of each new energy unit i at time t; is the maximum power generation of each new energy unit i at time t.

[0235] Among them, the hydropower output function is:

[0236] P h (t) = k h ·Q(t)·H(t)

[0237] In the formula, k h is the conversion coefficient; Q is the flow rate; H is the head.

[0238] Among them, the wind power output function is:

[0239]

[0240] In the formula, ρ is the air density; A is the swept area of the wind turbine rotor; C p is the power coefficient; v(t) is the wind speed.

[0241] Among them, the photovoltaic output function is:

[0242] P s (t) = η pv A pv I(t)

[0243] In the formula, η pv is the photovoltaic module efficiency; A pv is the photovoltaic array area; I(t) is the solar radiation intensity.

[0244] It is achieved by setting the upper and lower fluctuation range limits, and the constraint conditions are as follows:

[0245] Power balance constraint: The total power generation should meet the demand and the DC power transmission demand;

[0246]

[0247] In the formula, P load (t) is the load demand power of the system at time t;

[0248] Power balance constraint: Ensure that within the entire scheduling period, the total power generation and energy storage is equal to the total power of the load demand and losses;

[0249]

[0250] New energy power generation constraint: The new energy power generation shall not exceed its maximum power generation capacity:

[0251]

[0252] Energy storage dynamic constraint: The capacity change of the energy storage device is affected by the charging and discharging power;

[0253]

[0254] In the formula, E bat (t) is the remaining capacity of the energy storage device at time t; η ch and η dis are the charging and discharging efficiencies;

[0255] Energy storage capacity limit:

[0256] E bat,min (t) ≤ E bat (t) ≤ E bat,max (t)

[0257] In the formula, E bat,min (t) is the minimum allowable capacity of the energy storage device at time t; E bat,max (t) is the maximum allowable capacity of the energy storage device at time t;

[0258] Energy storage charging and discharging constraint: The charging and discharging power of the energy storage is limited by the rated power of the device:

[0259]

[0260] In the formula, is the maximum charging power of the energy storage device at time t; is the maximum discharging power of the energy storage device at time t;

[0261] DC external transmission constraint: The DC external transmission power shall meet the line capacity limit;

[0262]

[0263] In the formula, is the capacity limit of the DC external transmission channel at time t.

[0264] In step 4, an optimization algorithm is used to solve the mathematical model to obtain the optimal operation strategy of the system, specifically:

[0265] (1) Optimize using the Mixed Integer Linear Programming (MILP) algorithm: Convert the power balance equation and the power quantity balance constraint into linear forms to form a linear optimization model;

[0266] Take the charge and discharge power P bat,in (t) and P bat,out (t) as decision variables, and add integer variables to represent the switch state of the energy storage device;

[0267] Charge state:

[0268]

[0269] Discharge state:

[0270]

[0271] In the formula, u in (t) and u out (t) are binary variables indicating whether the energy storage system is charging or discharging at time period t. When the binary variable is 1, it means charging or discharging, and when it is 0, it means not charging or not discharging;

[0272] Adopt the MILP solution method, and use CPLEX to determine the optimal power generation, energy storage, and DC external transmission power dispatch plan by solving the optimization model, minimizing the system operation cost while meeting the power and energy balance;

[0273] (2) Robust optimization: Considering the new energy prediction error, construct the uncertainty set U and solve the optimal solution:

[0274]

[0275] In the formula, x is the optimization variable, usually representing the decision variable; μ is the variable in the uncertainty set, representing the new energy prediction error; U is the uncertainty set, representing the possible error range; C(x, μ) is the cost function under uncertain conditions; γ is the risk weight, used to balance the cost and risk; R(x, μ) is the risk measure brought by the uncertainty.

[0276] In step 5, according to the optimization result, adopt a hierarchical optimization strategy to adjust the operation mode of the power grid system to achieve power and energy balance;

[0277] The hierarchical optimization strategy includes upper-layer optimization and lower-layer optimization, specifically:

[0278] (1) Upper - layer optimization: The genetic algorithm is adopted to coordinate new - energy power generation, load demand, and energy - storage scheduling, forming a global optimization model. The formula is as follows:

[0279]

[0280] In the formula, F is the fitness function; λ is the weight coefficient for balancing new - energy consumption and operation cost; C gen (t) is the power - generation cost at time period t; C storage (t) is the energy - storage cost at time period t.

[0281] (2) Lower - layer optimization: The particle - swarm optimization algorithm is used for local adjustment, refining the charge - discharge strategy of the energy - storage system, and real - time adjusting the distribution of energy - storage power. The formula is as follows:

[0282]

[0283] x j (t + 1)=x j (t)+ν j (t + 1)

[0284] In the formula, ν j (t) is the velocity of the j - th particle; ω is the inertia weight; c1, c2 are learning factors; is the individual historical best position; x j (t) is the current position information of the j - th particle; g best is the global best position.

[0285] Using the above - mentioned system, compared with the existing technology: By introducing a DC - out energy - storage system based on power and energy balance, that is, the energy - storage system sends electric energy into the power grid in DC form through a special DC - transmission interface, the flexibility and reliability of power dispatch are increased. The DC - out energy - storage system has the following advantages: Reducing energy loss: DC transmission has less loss during long - distance transmission, especially suitable for large - scale new - energy grid - connection systems; Improving energy - consumption capacity: The DC - out energy - storage can store energy when new - energy power generation is excessive and transmit it to areas with large demand through DC, avoiding the limitations of traditional AC power grids.

[0286] Embodiment:

[0287] This invention takes a new energy DC external transmission energy storage system in the southwestern region as an example for simulation analysis. The simulation is based on the typical daily scenario of this region with a time step of 1 hour. During the simulation process, the installed capacities of wind power and photovoltaic in the system are set to 300 MW and 200 MW respectively, the total installed capacity is 500 MW, the capacity of the energy storage system is set to 400 MWh, and the maximum charge-discharge power is 100 MW. The system transmits electricity through a ±500 kV HVDC transmission line, and the power range of the external transmission channel is set to 250 MW to 350 MW. At the same time, to ensure the stability of the external transmission power, the power adjustment range is limited within ±50 MW, the maximum number of adjustable times per day is 6 times, and the minimum stable operation time is 2 hours. The system's curtailment cost is set to 0.3 yuan / kWh, the load non-satisfaction cost is 0.5 yuan / kWh, and the energy storage loss cost is 0.1 yuan / kWh. The test is executed on a computer equipped with an Intel Core i7 processor and 32 GB of memory.

[0288] The main performance detection results of the new energy DC external transmission energy storage system simulation are shown, including key indicators such as the new energy curtailment rate, load non-satisfaction rate, and total system operation cost. It can be seen that the system can achieve a good power and energy balance effect under different operation scenarios. However, through comparison, it can be found that when adopting the dynamic optimization scheduling strategy, the curtailment rate and load non-satisfaction rate of the system are significantly reduced, and the total system operation cost is more optimal. Therefore, in the following simulation tests, the curtailment rate, load non-satisfaction rate, and total system operation cost are selected as the core detection indicators to comprehensively evaluate the economy and operation stability of the system. Among them, the curtailment rate (CR) represents the proportion of electricity curtailed due to new energy generation exceeding the load demand or external transmission capacity, the load non-satisfaction rate (LD) represents the proportion of power shortage in the system during the simulation period due to insufficient power generation or untimely scheduling, and the total system operation cost (TC) comprehensively considers the economic costs brought by curtailment losses, load non-satisfaction penalties, and energy storage losses. The specific formulas for the detection indicators are as follows:

[0289]

[0290] In the formula, CR is the curtailment rate, and E curtailed represents the electricity curtailed due to excessive new energy generation in the system, and E total represents the total new energy generation.

[0291]

[0292] In the formula, LD is the load non-satisfaction rate, and E deficit is the load non-satisfaction electricity in the system due to insufficient power generation or untimely scheduling, and E demand is the total system load demand.

[0293] TC = C curtail+C deficit +C storage

[0294] Wherein, TC is the total operating cost of the system, C curtail is the economic loss caused by curtailment of electricity, C deficit is the economic loss caused by unmet load, C storage is the economic cost generated by energy loss during the operation of the energy storage system.

[0295] Table 1 Test results of the basic operation strategy

[0296]

[0297] The assumption condition of Table 1 is that new energy power generation gives priority to meeting the DC power transmission demand, and the energy storage system only participates in regulation when there is an obvious surplus or deficit, resulting in low scheduling flexibility. The results show that the curtailment rate and the unmet load rate are relatively high, and the total operating cost of the system is large.

[0298] Table 2 Test results of the dynamic optimization scheduling strategy

[0299]

[0300]

[0301] The assumption condition of Table 2 is to introduce a dynamic optimization scheduling method to adjust the charging and discharging power of the energy storage system in real time, and combine the flexible regulation ability of the power transmission power to minimize the curtailment of electricity and the unmet load situation as much as possible. The results show that the curtailment rate and the unmet load rate are significantly reduced, and the total operating cost of the system is reduced.

[0302] Table 3 Test results of the combined optimization strategy (MILP+PSO)

[0303]

[0304] The assumption condition of Table 3 is to adopt a method combining mixed integer linear programming (MILP) and particle swarm optimization (PSO) to globally optimize the scheduling scheme of new energy power generation, energy storage charging and discharging, and DC power transmission power. The results show that the curtailment rate and the unmet load rate reach the lowest level, and the total operating cost of the system is further significantly reduced.

[0305] It can be seen from the comparison of the three sets of data that the curtailment rate of the basic operation strategy is 12.00%, the load non-satisfaction rate is 10.50%, and the total system operation cost is 735,000 yuan. Through optimized scheduling, the curtailment rate of the dynamic optimization scheduling strategy is reduced to 6.00%, the load non-satisfaction rate is reduced to 5.50%, and the total system operation cost is reduced to 355,000 yuan. After adopting the joint optimization strategy, the curtailment rate is further reduced to 3.00%, the load non-satisfaction rate is reduced to 2.00%, and the total system operation cost is significantly reduced to 175,000 yuan. Therefore, the joint optimization strategy has obvious advantages in reducing the curtailment rate, reducing the load non-satisfaction rate, and reducing the total system operation cost, and can effectively improve the economy and operation stability of the system.

Claims

1. A method for power and energy balance considering energy storage for DC transmission of new energy, characterized in that, It includes the following steps: Step 1: Collect the basic data of the power grid system and construct an initial database; Step 2: Establish a power and energy balance model based on new energy DC external transmission. This model realizes the balance of power and energy in the power system, and aims to minimize the operation cost of the power system and DC losses; Step 3: In the model established in Step 2, introduce the volatility constraint of new energy power generation, the DC external transmission capacity constraint, and the energy storage charge and discharge constraint; Step 4: Solve the power and energy balance model of new energy DC external transmission to obtain the optimal operation strategy of the system; Step 5: According to the optimization results, adjust the operation mode of the power grid system to achieve power and energy balance.

2. The method according to claim 1, characterized in that, In Step 1, it specifically includes the following steps: Step 1-1: According to historical meteorological data, flow forecasts, combined with meteorological models and hydrological models, predict the new energy power generation in the future for a period of time, including the power generation of wind energy, solar energy, and hydropower; Step 1-2: Based on these prediction data, calculate the new energy and hydropower power output in each period; Step 1-3: Based on historical load data, daily electricity consumption patterns, seasonal changes, and weather factors, use a load forecasting model to predict the load demand in the future period of the power system. The load demand prediction should consider the daily regularity, seasonality, and sudden load changes of electricity consumption; Step 1-4: Monitor the remaining power, health status, charge and discharge efficiency, and maximum power of the energy storage device.

3. The method according to claim 2, wherein In Step 2, for the established power and energy balance mathematical model based on new energy DC external transmission, the expression formula of its objective function is: where β is the loss coefficient; Pdc(t) is the DC power transmitted externally at time t; c ch is the energy storage charging cost coefficient; c dis is the energy storage discharging cost coefficient; P bat,in (t), P bat,out (t) are the charging power and discharging power of the energy storage at time t; Δt is the time interval; α is the weight coefficient of the new energy utilization rate; P gen,i (t) is the power generation of each new energy unit i at time t; is the maximum power generation of each new energy unit i at time t.

4. The method according to claim 3, characterized in that, Where, The hydropower output function is: P h P(t) = k h ·Q(t)·H(t) (2) where k h is the conversion coefficient; Q is the flow rate; H is the head.

5. The method according to claim 3, wherein Where, The wind power output function is: Where ρ is the air density; A is the swept area of the wind turbine rotor; C p is the power coefficient; v(t) is the wind speed.

6. The method according to claim 3, wherein Where, The photovoltaic output function is: P s (t) = η pv A pv I(t) (4) where η pv is the efficiency of the photovoltaic module; A pv is the area of the photovoltaic array; and I(t) is the solar radiation intensity.

7. The method according to claim 1, wherein It is achieved by setting the upper and lower fluctuation range limits, and the constraint conditions are as follows: Power balance constraint: The total power generation should meet the load demand and DC external transmission demand; Wherein, P load (t) is the load demand power of the system at time t; Energy balance constraint: Ensure that within the entire scheduling period, the total energy of power generation and energy storage is equal to the total energy of load demand and losses; New energy power generation constraint: The new energy power generation should not exceed its maximum available power generation; Energy storage dynamic constraint: The capacity change of the energy storage device is affected by the charge and discharge power; where Ebat(t) is the remaining capacity of the energy storage device at time t; η ch and η dis are the charging and discharging efficiencies; Energy storage capacity limit: E bat,min ψ(t) ≤ E bat φ(t) ≤ E bat,max ω(t) (9) where E bat,min (t) is the minimum allowable capacity of the energy storage device at time t; E bat,max (t) is the maximum allowable capacity of the energy storage device at time t; Energy storage charge and discharge constraint: The energy storage charge and discharge power is limited by the rated power of the device; Wherein, is the maximum charging power of the energy storage device at time t; is the maximum discharging power of the energy storage device at time t; DC external transmission constraint: The DC external transmission power should meet the line capacity limit; Wherein, is the capacity limit of the DC external transmission channel at time t.

8. The method according to claim 1, wherein In Step 4, an optimization algorithm is used to solve the mathematical model to obtain the optimal operation strategy of the system, specifically: (1) Optimize using the mixed integer linear programming MILP algorithm: Transform the power balance equation and energy balance constraint into linear forms to form a linear optimization model; Take the energy storage charge and discharge power P bat,in (t) and P bat,out (t) as decision variables, and add integer variables to represent the switching state of the energy storage device; Charge state: Discharge state: where u in (t) and u out (t) are binary variables indicating whether the energy storage system is charging or discharging at time period t. When the binary variable is 1, it represents charging or discharging, and when it is 0, it represents not charging or not discharging; Use the MILP solution method, and use CPLEX to determine the optimal power generation, energy storage, and DC external transmission power scheduling plan by solving the optimization model, which minimizes the system operation cost while meeting the power and energy balance; (2) Robust optimization: Considering the new energy prediction error, construct an uncertainty set U and solve the optimal solution: Where x is the optimization variable, usually representing the decision variable; μ is the variable in the uncertainty set, representing the error of new energy prediction; U is the uncertainty set, representing the possible error range; C(x, μ) is the cost function under uncertain conditions; γ is the risk weight, used to balance cost and risk; R(x, μ) is the risk measure brought by uncertainty.

9. The method according to claim 1, wherein In step 5, according to the optimization results, a hierarchical optimization strategy is adopted to adjust the operation mode of the power grid system to achieve power and energy balance. The hierarchical optimization strategy includes upper-layer optimization and lower-layer optimization, specifically: (1) Upper-layer optimization: The genetic algorithm is used to coordinate new energy generation, load demand, and energy storage scheduling to form a global optimization model, and the formula is: In the formula, F is the fitness function; λ is the weight coefficient for balancing new energy consumption and operation cost; C gen (t) is the power generation cost at time period t; C storage (t) is the energy storage cost at time period t; (2) Lower-layer optimization: The particle swarm optimization algorithm is used for local adjustment, refining the charge and discharge strategy of the energy storage system, and real-time adjusting the distribution of energy storage power, and the formula is: where ν j (t) is the velocity of the j-th particle; ω is the inertia weight; c1 and c2 are learning factors; is the historical best position of the individual; xj(t) is the current position information of the j-th particle; g best is the global best position.

10. A DC external power transmission energy storage system based on power and electricity balance, characterized in that, It includes a data input module. The output end of the data input module is respectively connected to the input ends of the prediction module, the energy storage system evaluation module, and the real-time feedback module. The output ends of the prediction module, the energy storage system evaluation module, and the real-time feedback module are connected to the input end of the optimal scheduling module, and the optimal scheduling module outputs the power balance and scheduling strategy.

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