Static-mobile energy storage coordinated space-time flexible regulation and control method

Abstract

The invention relates to a static-mobile energy storage cooperative space-time flexible regulation and control method, and relates to an energy storage optimization scheduling method, the method comprises the following steps: (1) analyzing distributed new energy, load access characteristics and consumption demands of a regional integrated energy system, and constructing a renewable energy surplus model; (2) constructing a static energy storage charging and discharging scheduling model according to the source load characteristics of the regional integrated energy system; (3) proposing a space-time scheduling strategy of mobile energy storage according to a surplus model of renewable energy sources; (4) constructing a queuing model of a static-mobile energy storage charging and discharging strategy according to the static energy storage charging and discharging scheduling model and the mobile energy storage space-time scheduling strategy; and (5) according to the queuing model of the static-mobile energy storage charging and discharging strategy, realizing static-mobile energy storage collaborative dynamic scheduling based on improved Lyapunov optimization. The method provided by the invention provides support for cooperative consumption of distributed renewable energy sources among regional integrated energy systems.

Description

Technical Field

[0001] The present invention relates to an energy storage optimization scheduling method, and in particular to a static-mobile energy storage coordinated spatiotemporal flexible control method. Background Art

[0002] With the advancement of carbon peak and carbon neutrality goals, renewable energy will become the main energy source of future energy systems. However, renewable energy is affected by natural conditions, which introduces randomness and volatility. The application of energy storage systems can effectively promote the application of renewable energy and ensure energy supply. Traditional energy storage systems are mostly static and can only be used for peak shaving and valley filling in specific areas. However, due to the randomness of distributed energy generation and the mismatch between source and load capacity, wind and solar power abandonment or insufficient power supply in regional power grids are frequent. At the same time, it is difficult to achieve the coordinated consumption of distributed renewable energy between regional power grids due to the limited capacity of transmission channels. With the help of the capacity of urban transportation networks, mobile energy storage systems can realize the transfer of electricity in time and space scales, expand the spatial scope of energy optimization scheduling, and realize the coordinated consumption of distributed renewable energy between regional integrated energy systems.

[0003] In addition, the spatiotemporal coordinated scheduling of static-mobile energy storage systems requires consideration of complex decision-making issues involving the coordination of multiple types of equipment and multiple time scales. Considering the temporal coupling characteristics of static energy storage and mobile energy storage decisions, existing studies usually use Markov decision chains or dynamic optimization models to optimize their control strategies. These methods have high requirements for prior information of random processes and have high computational complexity. The Lyapunov optimization method can solve the spatiotemporal coupling problem in energy storage decisions and dynamically adjust system behavior to balance multi-objective optimization and system stability. Therefore, the introduction of Lyapunov optimization to achieve spatiotemporal optimization and control of the coordination of static energy storage and mobile energy storage is of great value for improving the operational efficiency and promotion of energy storage systems, as well as improving the coordinated absorption capacity of distributed renewable energy between regions. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for flexible spatiotemporal regulation of static-mobile energy storage coordination.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for flexible spatiotemporal control of static-mobile energy storage collaboration, the method comprising the following steps:

[0007] (1) Analyze the distributed renewable energy, load access characteristics and consumption demand of the regional integrated energy system, and build a renewable energy surplus model for the regional power grid;

[0008] (2) Based on the source-load characteristics of the regional integrated energy system, the battery energy storage and hydrogen energy storage control strategies in the static energy storage system are proposed, and a static energy storage charging and discharging scheduling model is constructed;

[0009] (3) Based on the surplus of renewable energy in the regional integrated energy system, a spatiotemporal scheduling strategy for mobile energy storage systems is proposed;

[0010] (4) Based on the static energy storage charging and discharging scheduling model and the spatiotemporal scheduling strategy of mobile energy storage, a queuing model of static-mobile energy storage charging and discharging strategy is constructed;

[0011] (5) According to the queuing model of static-mobile energy storage charging and discharging strategy, the coordinated dynamic scheduling of static-mobile energy storage is realized based on the improved Lyapunov optimization.

[0012] Step (1) is specifically as follows: by analyzing the distributed new energy, load access characteristics and consumption demand of the regional integrated energy system, the surplus calculation formula of the renewable energy of the regional integrated energy system is calculated, which can be specifically described as:

[0013]

[0014] Where τ is the duration of charge / discharge, is the renewable energy generation in region a at hour j, is the total electricity demand of region a at hour j, is the renewable energy capacity of region a.

[0015] Based on the thermal power and electric power balance constraints, the model is built with the goal of minimizing the operating cost of the integrated energy system, specifically:

[0016] (101) Electric boilers generate heat energy by consuming electricity. When heat load is required, the waste heat is recovered and released to achieve sustainable utilization of energy. The specific model of electric boilers is as follows:

[0017] G EB (t) = P EB (t)*η EB

[0018] 0≤G EB (t)≤G EB.max (t)

[0019] G ice (t) = G EB (t)*η ice

[0020] In the formula, G EB (t) and P EB (t) represent the thermal power and power consumption of the electric boiler, η EBis the efficiency of the electric boiler, G EB.max (t) is the maximum calorific value of the electric boiler, G ice (t) is the recovered waste heat, η ice is the waste heat recovery efficiency of the system;

[0021] (102) Gas boilers provide heat by consuming gas. The specific model of a gas boiler is as follows:

[0022] G Q (t) = Q(t)*η Q

[0023] 0≤G Q (t)≤G Qmax (t)

[0024] G(t)+G EB (t)+G Q (t) = G Load (t)

[0025] In the formula, G Q (t) is the waste heat recovery efficiency of the gas boiler, Q(t) is the gas volume at time t, η Q is the gas heating efficiency, G Qmax (t) is the maximum heat generation, G Load (t) is the caloric demand at time t;

[0026] (103) The power balance constraints of the integrated energy system are:

[0027]

[0028] In the formula, is the photovoltaic power generation, is the amount of wind power generated, It is the demand load of the integrated energy system. The electric boiler and hydrogen storage system are coupling devices between the power system and the thermal system.

[0029] (104) The operating costs of the integrated energy system are described as follows:

[0030]

[0031] In the formula, f1(t) is the cost of hydrogen energy trading, f2(t) is the cost of natural gas trading, f3(t) is the cost of electricity trading, and f4(t) is the total cost of reducing wind power and photovoltaic power generation.

[0032] Step (2) is specifically as follows: based on the battery energy storage and hydrogen energy storage control strategies in static energy storage, a static energy storage charging and discharging scheduling model is constructed to achieve peak shaving and valley filling of electricity.

[0033] (201) The specific model of battery energy storage system charging and discharging can be described as:

[0034] E B (t) = E B (t-1)(1-σ)+P t ch -P t dis

[0035]

[0036] P t ch =η B.C P B.C (t)Δt

[0037] In the formula, E B (t) is the capacity of the battery energy storage system at time t, σ is the capacity decay rate, P B.D (t) and P B.C (t) Discharge and charge power at time t, η B.D and η B.C is the discharge and charge efficiency.

[0038] (202) The scheduling model of hydrogen energy storage in static energy storage system is as follows:

[0039] The electrolyzer device produces hydrogen by consuming electricity and water. The hydrogen produced by the electrolyzer device is specifically:

[0040]

[0041] In the formula, is the amount of hydrogen produced by the electrolyzer at time t, η EL is the hydrogen production efficiency, is the power of the electrolyzer, and Respectively represent the maximum power and minimum power of the electrolyzer;

[0042] The amount of hydrogen produced by the hydrogen storage system is:

[0043]

[0044] In the formula, HS m (t) is the amount of hydrogen at time t, represents the amount of hydrogen consumed at time t, and Represents the trading volume of hydrogen.

[0045] Step (3) is as follows: The mobile energy storage comprehensively evaluates the level of renewable energy in the departure and destination areas and proposes a spatiotemporal scheduling strategy for the mobile energy storage. The key part of this strategy is to determine the charging and discharging power, which is determined by the following four conditions:

[0046] (301) Both regions have a surplus of renewable energy. The mobile energy storage system is charged in the current area. The charging capacity of the mobile energy storage system can be specifically described as:

[0047]

[0048] The first term in the formula is the remaining energy storage capacity of the mobile energy storage system based on the remaining renewable energy production, E Mtravel is the amount of electricity used to move mobile energy storage from one area to another, C M is the capacity of the mobile energy storage system, c is the battery capacity of the mobile energy storage system, It is the charge state of the mobile energy storage system when it reaches its current location.

[0049] The charging state of the target area can be described as:

[0050]

[0051] In the formula, It is the minimum limit for battery charging in mobile energy storage systems.

[0052] (302) The current region has excess renewable energy production, but the next region does not have excess renewable energy production, i.e. The mobile energy storage system is charged in the current area. The charging capacity of the mobile energy storage system can be specifically described as:

[0053]

[0054] Wherein, the first term evaluates the power required to travel from region a to region b, and the state of charge of the target region is estimated by the formula above (301).

[0055] (303) The next region has excess renewable energy production, but the current region does not have excess renewable energy production, i.e. The mobile energy storage system is charged in the next area, which means that the mobile energy storage system can be discharged in the current area. The discharge capacity of the mobile energy storage system can be specifically described as:

[0056]

[0057] The first term of the formula evaluates the amount of electricity that needs to be transported from location b to location a, C Miis the initial amount of electricity stored by the mobile energy storage system when it arrives at the current area, and the target charge state at the current location, which can be calculated by the following formula:

[0058]

[0059] (304) There is no excess renewable energy production in either region, i.e. The mobile energy storage system discharges at the current location. The discharge capacity of the mobile energy storage system can be specifically described as:

[0060]

[0061] In the formula, the first term indicates that the available energy storage in the mobile energy storage system will be allocated in proportion to the lack of renewable energy generation, and the target charge state of the current area is calculated by the formula in (303).

[0062] The constraints of the mobile energy storage system can be described as follows:

[0063]

[0064] e min ≤e M,n ≤e max

[0065]

[0066] In the formula, e ev,n , e min and e max Represent the maximum and minimum charge and discharge power respectively.

[0067] Step (4) is specifically as follows: construct a queuing model for the static-mobile energy storage charging and discharging strategy, and for the time coupling constraint, establish an energy storage virtual queuing model to represent the energy storage state.

[0068] The queuing model of the static-mobile energy storage charging and discharging strategy can be specifically expressed as:

[0069]

[0070] Z(t)=E(t)-ν

[0071]

[0072] Where A(t), Z(t) and V(t) represent the energy changes of the energy storage device. ν and υ are often used to ensure HS m (t), E(t), and is a non-negative constant.

[0073] In step (5), the Lyapunov function L(t) is defined to describe the congestion level of the queue, which can be specifically described as:

[0074]

[0075] Based on the L(t) function, Δ(t) is established to represent the drift of the queue. When L(t) is small, the queue stability is better. In order to stabilize the queue model, the deviation Δ(t) must be minimized:

[0076]

[0077] Therefore, J is defined to represent a trade-off between the two objectives. A larger value of J means that minimizing cost takes precedence over minimizing drift, and a smaller value of J means that minimizing cost takes precedence over minimizing drift.

[0078] In the case of Lyapunov variables for battery energy storage systems, hydrogen energy storage systems, and mobile energy storage systems, capacity constraints are considered, specifically:

[0079]

[0080] In the formula, when 0≤J≤J max When the energy storage system is fully utilized, the capacity constraints of the battery energy storage system, hydrogen energy storage system and mobile energy storage system will always be met.

[0081] Based on the improved Lyapunov optimization, the dynamic scheduling of static-mobile energy storage coordination is realized. The static energy storage system and the mobile energy storage system are reasonably allocated and scheduled in each time period to maximize the energy utilization rate. The objective function can be equivalent to a random network optimization problem:

[0082]

[0083] Where C1(t) represents the total cost, including the cost of natural gas trading, electricity trading, and wind and photovoltaic power generation reduction.

[0084] Compared with the prior art, the present invention has the following advantages: according to the control strategies of mobile energy storage and static energy storage under different conditions, a control strategy model for static-mobile energy storage coordination is established; based on the improved Lyapunov optimization method, the static energy storage system and the mobile energy storage system are reasonably allocated and scheduled in each time period to maximize the utilization rate of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 It is a flow chart of a method for flexible spatiotemporal regulation of static-mobile energy storage collaboration in the present invention;

[0086] Figure 2 This is the power difference of renewable energy generation and load in the three regions under the test scenario.

[0087] Figure 3 The charging and discharging power of electrochemical energy storage and mobile energy storage in three areas under the test scenario. DETAILED DESCRIPTION

[0088] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0089] Implementation example Figure 1 As shown, a method for spatiotemporal flexible control of static-mobile energy storage collaboration includes the following steps:

[0090] Step 1: Analyze the access type and power generation characteristics of regional renewable energy, collect renewable energy power generation data, and also collect and analyze relevant data on load access characteristics and consumption demand.

[0091] Step 2: By analyzing the regional new energy and load related data, the calculation formula for the surplus of renewable energy in the regional power grid can be described as follows:

[0092]

[0093] Where τ is the duration of charge / discharge, is the renewable energy generation in region a at hour j, is the total electricity demand of region a at hour j, is the renewable energy capacity of region a.

[0094] Step 3: Based on the thermal power and electrical power balance constraints, modeling is carried out with the goal of minimizing the operating cost of the integrated energy system, specifically:

[0095] Electric boilers generate heat by consuming electricity. When heat load is required, the waste heat is recovered and released to achieve sustainable use of energy. The specific model of electric boilers is as follows:

[0096] G EB (t) = P EB (t)*η EB

[0097] 0≤G EB (t)≤G EB.max (t)

[0098] G ice (t) = G EB (t)*η ice

[0099] In the formula, G EB (t) and P EB (t) represent the thermal power and power consumption of the electric boiler, η EBis the efficiency of the electric boiler, G EB.max (t) is the maximum calorific value of the electric boiler, G ice (t) is the recovered waste heat, η ice is the waste heat recovery efficiency of the system.

[0100] Gas boilers provide heat by consuming gas. The specific model of gas boilers is:

[0101] G Q (t) = Q(t)*η Q

[0102] 0≤G Q (t)≤G Qmax (t)

[0103] G(t)+G EB (t)+G Q (t) = G Load (t)

[0104] In the formula, G Q (t) is the waste heat recovery efficiency of the gas boiler, Q(t) is the gas volume at time t, η Q is the gas heating efficiency, G Qmax (t) is the maximum heat generation, G Load (t) is the caloric demand at time t;

[0105] The electric power balance constraints of the integrated energy system are as follows:

[0106]

[0107] In the formula, is the photovoltaic power generation, is the amount of wind power generated, It is the demand load of the integrated energy system. The electric boiler and hydrogen storage system are coupling devices between the power system and the thermal system.

[0108] The operating costs of the integrated energy system are described as:

[0109]

[0110] In the formula, f1(t) is the cost of hydrogen energy trading, f2(t) is the cost of natural gas trading, f3(t) is the cost of electricity trading, and f4(t) is the total cost of reducing wind power and photovoltaic power generation.

[0111] Step 4: The battery energy storage system in the static energy storage system is designed to meet the power demand. The specific model of the battery energy storage system can be described as:

[0112] E B (t) = EB (t-1)(1-σ)+P t ch -P t dis

[0113]

[0114] P t ch =η B.C P B.C (t)Δt

[0115] 0≤[P B.C (t),P B.D (t)]≤P B (t)·[I B.C (t),I B.D (t)]

[0116] C B (1-D)≤E B (t)≤C B

[0117] [I B.C (t),I B.D (t)]∈{0,1}

[0118] In the formula, E B (t) is the capacity of the battery energy storage system at time t, σ is the capacity decay rate, P B.D (t) and P B.C (t) Discharge and charge power at time t, η B.D and η B.C is the discharge and charge efficiency. B (t) is the rated power, I B,C (t) and I B,D (t) is a binary variable for charging and discharging. It cannot be charged and discharged at the same time. B is the maximum capacity of the battery energy storage system and D is the maximum discharge depth.

[0119] The hydrogen energy storage system model in the static energy storage system can be specifically described as:

[0120]

[0121] HS min ≤HS m (t)≤HS max

[0122]

[0123] In the formula, HS m (t) is the amount of hydrogen at time t, represents the amount of hydrogen consumed at time t, and Represents the trading volume of hydrogen, HS min and HS max are the minimum and maximum hydrogen storage capacities, is the amount of hydrogen produced by the electrolyzer at time t, and are the maximum and minimum consumption rates of hydrogen, and is the maximum trading value of hydrogen, and It is a binary variable and cannot be charged and discharged at the same time.

[0124] Step 5: Mobile energy storage By comprehensively evaluating the level of renewable energy in the departure and destination areas, a spatiotemporal scheduling strategy for mobile energy storage is proposed. The key part of this strategy is to determine the charging and discharging power, which is determined by the following four conditions:

[0125] Both regions have a surplus of renewable energy. The mobile energy storage system is charged in the current area. The charging capacity of the mobile energy storage system can be specifically described as:

[0126]

[0127] The first term in the formula is the remaining energy storage capacity of the mobile energy storage system based on the remaining renewable energy production, E Mtravel is the amount of electricity used to move mobile energy storage from one area to another, C M is the capacity of the mobile energy storage system, c is the battery capacity of the mobile energy storage system, It is the charge state of the mobile energy storage system when it reaches its current location.

[0128] The charging state of the target area can be described as:

[0129]

[0130] In the formula, It is the minimum limit for battery charging in mobile energy storage systems.

[0131] The current region has excess renewable energy production, but the next region does not have excess renewable energy production, i.e. The mobile energy storage system is charged in the current area. The charging capacity of the mobile energy storage system can be specifically described as:

[0132]

[0133] Where the first term evaluates the power required to travel from area a to area b, and the state of charge of the target area is estimated from the first case above.

[0134] The next region has excess renewable energy production, but the current region does not have excess renewable energy production, i.e. The mobile energy storage system is charged in the next area, which means that the mobile energy storage system can be discharged in the current area. The discharge capacity of the mobile energy storage system can be specifically described as:

[0135]

[0136] The first term of the formula evaluates the amount of electricity that needs to be transported from location b to location a, C Mi is the initial amount of electricity stored by the mobile energy storage system when it arrives at the current area, and the target charge state at the current location, which can be calculated by the following formula:

[0137]

[0138] There is no excess renewable energy production in either region, i.e. The mobile energy storage system discharges at the current location. The discharge capacity of the mobile energy storage system can be specifically described as:

[0139]

[0140] The first term indicates that the available energy storage in the mobile energy storage system will be allocated in proportion to the lack of renewable energy generation, and the target state of charge in the current area is calculated by the formula in the third area.

[0141] Step 6: The queuing model of the static-mobile energy storage charging and discharging strategy can be specifically expressed as:

[0142]

[0143] Z(t)=E(t)-ν

[0144]

[0145] Where A(t), Z(t) and V(t) represent the energy changes of the energy storage device. ν and υ are often used to ensure HS m (t), E(t), and is a non-negative constant.

[0146] Step 7: Define the Lyapunov function L(t) to describe the congestion of the queue, which can be described as:

[0147]

[0148] Based on the L(t) function, Δ(t) is established to represent the drift of the queue. When L(t) is small, the queue stability is better. In order to stabilize the queue model, the deviation Δ(t) must be minimized:

[0149]

[0150] Therefore, J is defined to represent a trade-off between the two objectives. A larger value of J means that minimizing cost takes precedence over minimizing drift, and a smaller value of J means that minimizing cost takes precedence over minimizing drift.

[0151] Step 8: Consider the capacity constraints for the Lyapunov variables of the battery energy storage system, hydrogen energy storage system and mobile energy storage system, specifically:

[0152]

[0153] In the formula, when 0≤J≤J max When the energy storage system is fully utilized, the capacity constraints of the battery energy storage system, hydrogen energy storage system and mobile energy storage system will always be met.

[0154] Based on the improved Lyapunov optimization, the dynamic scheduling of static-mobile energy storage coordination is realized. The static energy storage system and the mobile energy storage system are reasonably allocated and scheduled in each time period to maximize the energy utilization rate. The objective function can be equivalent to a random network optimization problem:

[0155]

[0156] Where C1(t) represents the total cost, including the cost of natural gas trading, electricity trading, and wind and photovoltaic power generation reduction.

[0157] This embodiment is based on the power generation statistics of Shanghai in 2022, the power generation of renewable energy wind power and photovoltaic power, and the difference between renewable energy power generation and demand. It uses three areas under the integrated energy system, which are completely self-generated and self-used through wind and solar power generation. Area 1 is characterized by high wind power generation capacity; Area 2 is characterized by high load demand; Area 3 is characterized by more balanced power generation and use, with higher load demand only during specific peak hours. There are 3 mobile energy storage systems in the integrated energy system. Each mobile energy storage system has a rated power of 20kW and a rated capacity of 60kWh. The mobile energy storage system consumes 1kWh when it moves from one area to another, and the initial state of charge of the mobile energy storage system is 0.2. The method of the present invention can quantify the flexibility of the spatiotemporal regulation of static-mobile energy storage coordination, the power difference between new energy power generation and load, such as Figure 2 As shown; the charging and discharging power of electrochemical energy storage and mobile energy storage in the three regions, such as Figure 3 shown.

[0158] The application scenarios of this embodiment include the following: providing technical support for complex systems of regional energy of static-mobile energy storage systems, maximizing energy utilization based on improved Lyapunov optimization; providing technical support for achieving spatiotemporal power balance of regional energy, and promoting the use of renewable energy.

Claims

1. A method for flexible spatiotemporal control of static-mobile energy storage collaboration, characterized in that: The method comprises the following steps: (1) Analyze the distributed renewable energy, load access characteristics and consumption demand of the regional integrated energy system, and build a surplus calculation model for renewable energy in the regional power grid; (2) Based on the source-load characteristics of the regional integrated energy system, the battery energy storage and hydrogen energy storage control strategies in the static energy storage system are proposed, and a static energy storage charging and discharging scheduling model is constructed; (3) Based on the surplus of renewable energy in the regional integrated energy system, a spatiotemporal scheduling strategy for the mobile energy storage system is proposed; (4) Based on the static energy storage charging and discharging scheduling model and the spatiotemporal scheduling strategy of mobile energy storage, a queuing model of static-mobile energy storage charging and discharging strategy is constructed; (5) According to the queuing model of static-mobile energy storage charging and discharging strategy, the coordinated dynamic scheduling of static-mobile energy storage is realized based on the improved Lyapunov optimization.

2. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 1, characterized in that: In step (1): by analyzing the distributed new energy, load access characteristics and consumption demand of the regional comprehensive energy system, the surplus calculation formula of renewable energy in the regional power grid is calculated, which is specifically: Where τ is the duration of charge / discharge, is the renewable energy generation in region a at hour j, is the total electricity demand of region a at hour j, is the renewable energy capacity of region a.

3. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 2, characterized in that: In step (1), based on the thermal power and electric power balance constraints, the model is built with the goal of minimizing the operating cost of the integrated energy system, specifically: (101) Electric boilers generate heat energy by consuming electricity. When heat load is required, the waste heat is recovered and released to achieve sustainable utilization of energy. The specific model of electric boilers is as follows: G EB (t)=P EB (t)*η EB 0≤G EB (t)≤G EB.max (t) G ice (t)=G EB (t)*η ice In the formula, G EB (t) and P EB (t) represent the thermal power and power consumption of the electric boiler, η EB is the efficiency of the electric boiler, G EB.max (t) is the maximum calorific value of the electric boiler, G ice (t) is the recovered waste heat, η ice is the waste heat recovery efficiency of the system; (102) Gas boilers provide heat by consuming gas. The specific model of a gas boiler is as follows: G Q (t)=Q(t)*η Q 0≤G Q (t)≤G Qmax (t) G(t)+G EB (t)+G Q (t)=G Load (t) In the formula, G Q (t) is the waste heat recovery efficiency of the gas boiler, Q(t) is the gas volume at time t, η Q is the gas heating efficiency, G Qmax (t) is the maximum heat generation, G Load (t) is the caloric demand at time t; (103) The power balance constraints of the integrated energy system are: In the formula, is the photovoltaic power generation, is the amount of wind power generated, It is the demand load of the integrated energy system. The electric boiler and hydrogen storage system are coupling devices between the power system and the thermal system. (104) The operating costs of the integrated energy system are described as follows: In the formula, f1(t) is the cost of hydrogen energy trading, f2(t) is the cost of natural gas trading, f3(t) is the cost of electricity trading, and f4(t) is the total cost of reducing wind power and photovoltaic power generation.

4. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 3, characterized in that: In step (2), the scheduling model of battery energy storage in the static energy storage system is specifically: AND B (t)=E B (t-1)(1-σ)+P t ch -P t dis P t ch =η B.C P B.C (t)Δt In the formula, E B (t) is the capacity of the battery energy storage system at time t, σ is the capacity decay rate, P B.D (t) and P B.C (t) Discharge and charge power at time t, η B.D and η B.C is the discharge and charge efficiency.

5. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 4, characterized in that: In step (2), the scheduling model of hydrogen energy storage in the static energy storage system is specifically: The electrolyzer device produces hydrogen by consuming electricity and water. The hydrogen produced by the electrolyzer device is specifically: In the formula, is the amount of hydrogen produced by the electrolyzer at time t, η EL is the hydrogen production efficiency, is the power of the electrolyzer, and Respectively represent the maximum power and minimum power of the electrolyzer; The amount of hydrogen produced by the hydrogen storage system is: In the formula, HS m (t) is the amount of hydrogen at time t, represents the amount of hydrogen consumed at time t, and Represents the trading volume of hydrogen.

6. A method for spatiotemporal flexible control of static-mobile energy storage collaboration according to claim 5, characterized in that: Step (3) is specifically as follows: (301) If both regions have excess renewable energy, that is, The mobile energy storage system is charged in the current area. The charging capacity of the mobile energy storage system is specifically described as: The first term in the formula is the remaining energy storage capacity of the mobile energy storage system based on the remaining renewable energy production, E Mtravel is the amount of electricity used to move mobile energy storage from one area to another, C M is the capacity of the mobile energy storage system, c is the battery capacity of the mobile energy storage system, It is the state of charge of the mobile energy storage system when it reaches the current location; The charge state of the target area is described as: In the formula, It is the minimum limit for battery charging in mobile energy storage systems; (302) If the current region has excess renewable energy production, but the next region does not have excess renewable energy production, that is, The mobile energy storage system is charged in the current area. The charging capacity of the mobile energy storage system is specifically described as: Wherein, the first term evaluates the power required to travel from region a to region b, and the state of charge of the target region is estimated by the formula in step (301); (303) If the next region has excess renewable energy production, but the current region does not have excess renewable energy production, that is, The mobile energy storage system is charged in the next area, which means that the mobile energy storage system can discharge in the current area. The discharge capacity of the mobile energy storage system is specifically described as: The first term of the formula evaluates the amount of electricity that needs to be transported from location b to location a, C Mi is the initial amount of electricity stored by the mobile energy storage system when it arrives at the current area, and the target charge state at the current location is calculated by the following formula: (304) If there is no excess renewable energy production in both regions, The mobile energy storage system discharges at the current location. The discharge capacity of the mobile energy storage system is specifically described as: Wherein, the first term indicates that the available energy storage in the mobile energy storage system will be allocated in proportion to the lack of renewable energy generation, and the target state of charge of the current area is calculated by the formula in step (303); The constraints of the mobile energy storage system are specifically described as follows: and min ≤e M,n ≤e max In the formula, e ev,n , e min and e max Represent the maximum and minimum charge and discharge power respectively.

7. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 6, characterized in that: In step (4), the queuing model of the static-mobile energy storage charging and discharging strategy is specifically: Z(t)=E(t)-ν Where A(t), Z(t) and V(t) represent the energy changes of the energy storage device. ν and υ are often used to ensure HS m (t), E(t), and is a non-negative constant.

8. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 7, characterized in that: In step (5), the Lyapunov function L(t) is defined to describe the degree of congestion of the queue, which is specifically described as: Based on the L(t) function, Δ(t) is established to represent the drift of the queue and minimize the deviation Δ(t): J is defined to represent the trade-off between the two objectives. A larger J value means that minimizing cost has a higher priority than minimizing drift, and a smaller J value means that minimizing cost has a lower priority than minimizing drift.

9. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 8, characterized in that: In step (5), under the Lyapunov variable conditions of the battery energy storage system, hydrogen energy storage system and mobile energy storage system, the capacity constraints are considered, specifically: In the formula, when 0≤J≤J max When the energy storage system is fully utilized, the capacity constraints of the battery energy storage system, hydrogen energy storage system and mobile energy storage system will always be met.

10. A method for flexible spatiotemporal control of static-mobile energy storage collaboration according to claim 9, characterized in that: In step (5), based on the improved Lyapunov optimization, the static-mobile energy storage coordinated dynamic scheduling is realized, and the static energy storage system and the mobile energy storage system are reasonably allocated and scheduled in each time period to maximize the energy utilization rate, specifically: This objective function is equivalent to a random network optimization problem: Where C1(t) represents the total cost, including the electricity trading cost and the cost of reducing wind and photovoltaic power generation.

Images