An optimization method and system for a heterogeneous mobile energy storage coupled power system considering traffic delay

Abstract

The application discloses a kind of heterogeneous mobile energy storage coupling power system optimization method and system considering traffic delay, comprising: establishing the unified space-time coupling model of power system and traffic system;Establish heterogeneous mobile energy storage unit model, including initial condition constraint, electric quantity evolution equation, energy capacity limit constraint, the constraint that end electric quantity returns to initial state, heterogeneity characteristics;Establish power system operation model, including generator capacity constraint, climbing rate constraint, logic consistency and anti-simultaneity constraint, minimum start-up / shut-down time constraint, DC power flow constraint;Build joint optimization framework, with the minimum power system operation cost as objective function, obtain the optimal mobile path, stop plan and charge-discharge strategy of each heterogeneous mobile energy storage unit by mixed integer linear programming solution.Utilizing the application, the flexible adjustment capacity of mobile energy storage can be fully exerted, and the adaptability of power system to renewable energy fluctuation is improved.

Description

Technical Field

[0001] This invention relates to the field of power system optimization and dispatch, and in particular to an optimization method and system for heterogeneous mobile energy storage coupled power systems that takes into account traffic delays. Background Technology

[0002] The randomness and volatility of renewable energy output pose new challenges to the power system's supply and demand balance. Under conditions of high renewable energy penetration, the spatial distribution of power sources and load centers is uneven, and local power grids are prone to problems such as insufficient reserves, line congestion, and limited renewable energy absorption during peak load periods.

[0003] While traditional stationary energy storage devices offer rapid response capabilities, their fixed location limits their ability to be flexibly adjusted across nodes and regions. Mobile energy storage units, on the other hand, possess mobility and rechargeability, enabling cross-regional energy migration driven by transportation networks. This provides significant advantages in enhancing system flexibility and smoothing the output of new energy sources.

[0004] For example, Chinese patent document CN119891214A discloses a distribution network energy management method, which establishes a spatiotemporal state model of mobile energy storage to move electrical energy in space, providing support for achieving peak shaving and valley filling and solving the supply and demand balance under multiple spatiotemporal scales.

[0005] Chinese patent document CN119417077A discloses a method and system for intelligent optimization scheduling of power systems under energy transmission constraints. Based on meeting the load demand of electricity users, the method aims to minimize transportation costs and the amount of renewable energy wasted. It constructs an energy management objective function for the power supply side, and aims to minimize the amount of controllable load that needs to be cut to maintain supply and demand balance. This achieves the effect of maximizing the utilization rate of wind and solar power while ensuring the independent, safe and stable operation of renewable energy systems in areas with energy transmission constraints.

[0006] However, existing research on mobile energy storage dispatch has the following shortcomings: First, it generally ignores the heterogeneity caused by differences in capacity, power and speed among different mobile energy storage units; second, it treats the travel time between nodes of mobile energy storage units as a constant, failing to reflect the variable traffic delays caused by traffic conditions and vehicle performance; and third, it lacks an optimization framework that unifies the modeling of traffic movement and power system operation, making it difficult to minimize the overall system operating cost. Summary of the Invention

[0007] This invention provides an optimization method and system for heterogeneous mobile energy storage coupled power systems that takes into account traffic delays. It can fully leverage the flexible adjustment capabilities of mobile energy storage, enhance the power system's adaptability to renewable energy fluctuations, reduce system operating costs, and has good engineering application value.

[0008] An optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays includes the following steps: (1) Establish a unified spatiotemporal coupling model for the power system and the transportation system; wherein the power system consists of a set of nodes, a set of lines, a set of generators and a predicted load; the transportation system consists of a set of reachable nodes and their paths, and provides feasible mobile paths and travel times for mobile energy storage; (2) Establish a heterogeneous mobile energy storage unit model, including initial energy conditions constraints, energy evolution equations, energy capacity limits constraints, constraints on the return of end-point energy to the initial state, and heterogeneity characteristics; (3) Establish a power system operation model, including generator capacity constraints, ramp rate constraints, logical consistency and anti-simultaneity constraints, minimum start-up / stop time constraints, and DC power flow constraints; (4) Construct a joint optimization framework with multiple time periods and multiple nodes. With the minimum operating cost of the power system as the objective function, the optimal moving path, stopping plan and charging and discharging strategy of each heterogeneous mobile energy storage unit in the transportation system network are obtained by solving mixed integer linear programming.

[0009] This invention first establishes a spatiotemporal modeling framework for power-transport coupling. Within this framework, a spatiotemporal state model is constructed based on parameters such as the capacity, charging / discharging power, travel speed, and accessibility of different mobile energy storage units. This model describes the movement and access process of energy storage units in the transportation network. A variable traffic delay model is also established, incorporating travel time between nodes, to characterize the differences in departure and arrival times of energy storage units at different times and nodes. Furthermore, a mobile energy storage unit model is established, uniformly modeling the energy evolution, power constraints, and heterogeneity characteristics of the energy storage units. Simultaneously, a power system operation model is established, incorporating operational constraints such as unit start-up and shutdown, power flow / balance, and reserve demand into the scheduling description. Based on this, a multi-time-period, multi-node joint optimization framework is constructed. With the goal of minimizing system operating costs, the optimal movement path and charging / discharging strategy for each heterogeneous mobile energy storage unit are obtained through mixed-integer linear programming.

[0010] In step (1), when establishing a unified spatiotemporal coupling model of the power system and the transportation system, the following constraints are included: power injection location consistency constraint, unique state constraint, flow continuity constraint with delay, dwell state continuity constraint, node balance constraint with MESU injection, spinning reserve constraint considering the contribution of mobile energy storage units, and arc feasibility constraint.

[0011] Power injection location consistency constraints ensure that the charging and discharging power injection of mobile energy storage units is consistent with their residence location; unique state constraints ensure that mobile energy storage units can only be in a unique state at any given time period; flow continuity constraints with delays explicitly embed traffic delays into the state evolution process of mobile energy storage units; residence state continuity constraints constrain the continuity of residence states between adjacent time periods; node balance constraints with MESU injection explicitly include the power injection of mobile energy storage units in the node active power balance; and spinning reserve constraints considering the contribution of mobile energy storage units constrain the coupling system during time periods. The rotating reserve requirement; the arc feasibility constraint is used to limit the migration of mobile energy storage units to only occur on feasible arcs allowed by the transportation network.

[0012] In step (2), the initial power condition constraint is used to give the initial power value at the start of the scheduling cycle; the power evolution equation is used to describe the temporal evolution of the power of the energy storage unit with the charging and discharging behavior; the energy capacity limit constraint is used to constrain the energy storage power to always be within the rated capacity range; the constraint that the power at the end of the cycle returns to the initial state requires that the power at the end of the cycle is within the allowable deviation from the initial value, so as to avoid the model from gaining a short-term cost advantage by overdrawing the power at the end; the heterogeneity feature is used to describe that different mobile energy storage units are allowed to have different energy capacities, different charging and discharging capabilities and different moving speeds, and thus form different traffic delay parameters.

[0013] In step (3), the generator capacity constraint is used to limit the unit output within the technically feasible range; the ramp rate constraint is used to limit the rate of change of the unit output in adjacent time periods; the logical consistency and anti-simultaneity constraint is used to ensure the logical self-consistency between the unit state variables and start-up / shutdown variables; the minimum start-up / shutdown time constraint is used to characterize the continuous operation requirements after the unit starts up and stops; and the DC power flow constraint is used to describe the network power flow and line capacity limitations.

[0014] In step (4), the objective function is to minimize the operating cost of the power system, specifically: ; in, Indicates the unit The fuel cost function; the start / stop cost term is determined by the start / stop decision variables. and Triggered entry; Indicates mobile energy storage unit Is it in Time period from node Migrate to node ; Indicates the unit At any moment The output power; and They represent the generating units. Start-up and shutdown costs; Indicates mobile energy storage unit Self-node Move to node The transportation cost coefficient, and its correlation with the corresponding migration decision variables. The product of these costs forms the transportation cost item.

[0015] In step (4), the solution also yields the optimal output allocation and start-up / shutdown plan for each conventional unit in the power system, and provides the optimal solution for the operating cost of the power system.

[0016] An optimization system for a heterogeneous mobile energy storage coupled power system that takes into account traffic delays includes a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, they implement the aforementioned optimization method for a heterogeneous mobile energy storage coupled power system.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Significantly reduce the overall operating cost of the power-transportation coupled system. By jointly optimizing the power system operation decision and the movement path and charging / discharging strategy of heterogeneous mobile energy storage units under a unified spatiotemporal coupling framework, the cross-spatial regulation capability of mobile energy storage is fully explored. Under the premise of meeting traffic delays and grid operation constraints, the start-up and shutdown of high-cost units and the demand for power generation during high-price periods are effectively reduced, thereby reducing the overall system operating cost.

[0018] 2. Effectively smooths the output curve of conventional units, alleviating the operating pressure of the units. By introducing mobile energy storage to participate in node power balancing and spinning reserve configuration, mobile energy storage can be flexibly transferred in time and space and undertake some peak shaving, frequency regulation and reserve functions, realizing peak shaving and valley filling of the output of conventional units, reducing the frequency and amplitude of unit ramping, thereby reducing the operating pressure of the units and extending the service life of the equipment.

[0019] 3. Accurately characterize the impact of traffic delays on mobile energy storage scheduling, enhancing the feasibility of the model engineering. By incorporating delay-based flow continuity constraints and arc feasibility constraints, the traffic network structure and travel time between nodes are explicitly embedded into the mobile energy storage state evolution process, avoiding the neglect of unrealistic scheduling results caused by traffic delays and improving the feasibility of optimization results in actual engineering scenarios. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of an optimization method for a heterogeneous mobile energy storage coupled power system that takes into account traffic delays, according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that, unless otherwise specified, the features in the following embodiments and implementation methods can be combined with each other.

[0024] like Figure 1 As shown, an optimization method for a heterogeneous mobile energy storage coupled power system considering traffic delay mainly includes: the establishment of a coupled power-transportation system model, the establishment of a heterogeneous mobile energy storage unit model, the establishment of a power system operation model, and a solution step based on mixed integer linear programming.

[0025] S1: First, establish a unified spatiotemporal coupling model for the power network and the transportation network. The power system consists of a set of nodes, a set of lines, a set of generating units, and a predicted load; the transportation system consists of a set of reachable nodes and their paths, and provides feasible movement paths and travel times for mobile energy storage.

[0026] To fully characterize the mobility of mobile energy storage units within the power grid, we define spatiotemporal variables on the traffic map: Let Indicates mobile energy storage unit Is it in Time period from node Migrate to node ( (Time indicates residence). Arc set Each directed arc Related migration time Network topology With the migration matrix All are user-defined, supporting flexible adaptation to different infrastructures. The specific modeling of this coupled model is detailed in the subsequent constraints section. (a) Power injection location consistency constraints, as detailed below: ; This formula is used to ensure that the charging and discharging power injection of the mobile energy storage unit is consistent with its residence location. In the formula, Indicates mobile energy storage unit During the period Residing at the node , Indicates its position in the node Power injection is allowed only when the cell resides at the node; if the cell is in a migration state, the power injection of the corresponding node is forced to 0. Indicates mobile energy storage unit The upper limit of charging and discharging power. This constraint excludes non-physical cases of "in-transit grid-connected injection" from the perspective of spatial consistency, ensuring that the energy storage injection included in the power balance of subsequent nodes has a clear spatial landing point.

[0027] (b) Unique state constraints, as detailed below: ; This formula is used to constrain mobile energy storage units to be in only one state at any given time. In the formula, Representation unit During the period Migration / residency decisions ( For the purpose of staying, For migration), then for fixed The decision variables for all feasible node pairs satisfy the uniqueness requirement of "only one can be activated" during the time period, meaning that the unit either resides at a certain node or migrates along a certain arc in each time period. This constraint is used to ensure the traceability of the spatiotemporal path and the non-conflict of states, providing a consistent state basis for subsequent continuous modeling with delays.

[0028] (c) Flow continuity constraints with delay, as detailed below: ; This formula is used to explicitly embed traffic delay into the state evolution process of mobile energy storage units. In the formula, if... and Representation unit During the period From node Depart for the node , Indicates the corresponding migration duration, then the unit will enter a continuous phase. The migration period; during the migration period, the unit does not have the conditions for grid connection and its charging and discharging power injection is prohibited until it reaches the node at the designated time. The system then resumes its resident operation. The overall purpose of this constraint is to characterize the time consistency of "departure-on-the-arrival" and to avoid unreasonable instantaneous jumps across nodes or participation in node injection before arrival in the model.

[0029] (d) Continuity constraints of dwell state, as follows: ; This formula is used to constrain the continuity of dwell states in adjacent time periods. In the formula, if... Representation unit During the period Residing at the node Then in the next time period Cells can only remain at nodes Continue to stay (still meets the requirements) ), or from the node Initiate migration (there is a certain make This constraint is used to exclude non-physical state transitions such as "direct node swapping without a migration process," ensuring that the movement path is coherent and interpretable on the timeline.

[0030] (e) Node balance constraints including MESU injection, as detailed below: ; This formula is used to explicitly account for the power injection of mobile energy storage units in the nodal active power balance. In the formula, Representation unit At the node Time period Power injection when the cell resides at the node The injection value is set to its charge / discharge value when the value is in the specified condition; otherwise, the injection value is 0, thereby directly correcting the net injection power of the node. Representing nodes respectively A set of routes with a starting point and an ending point. Represents a node The total power transmitted through the lines. Indicates the inflow node The total power of the lines. This constraint forces the realization of hourly active power balance at each node, ensuring that the coupling relationship of "energy storage participating in power balance and settlement wherever it moves" is implemented in the power grid equations.

[0031] (f) Considering the spinning reserve constraint contributed by mobile energy storage units, as detailed below: With the grid connection of mobile energy storage units, the spinning reserve constraint needs to be revised—as it can provide support for system backup. However, mobile energy storage units that are only stationary and have sufficient remaining capacity... Only then can it be considered a valid backup provider.

[0032] ; This formula is used to constrain the system during a time period. The rotational backup requirement. Where, This indicates the system's spin-off backup requirement. and They represent the generating units. During the period The unit's output and capacity limit, and the backup power it can provide are its remaining adjustable output. Give; Indicates mobile energy storage unit During the period The available capacity is reserved, and the reserve contribution is limited to when the unit is stationed and connected to the grid, as specified by the stationary state variable. The overall meaning of this formula is that the sum of the unit's remaining capacity and the reserve contribution of mobile energy storage that meets the grid connection conditions should not be less than [amount missing]. This allows for the achievement of system safety margin constraints while taking into account traffic migration status.

[0033] (g) Arc feasibility constraints, as detailed below: ; This formula limits the migration of mobile energy storage units to feasible arcs permitted by the transportation network. In the formula, if node pairs... If a variable does not belong to the traffic arc set (inaccessible or not permitted), then the corresponding migration decision variable is forced to be selected. The constraint is set to 0 in all time periods, thus prohibiting cells from migrating along invalid node pairs. This constraint is used to hard-code the traffic network topology and reachability boundaries into the optimization model, preventing the solver from selecting cross-node paths that do not exist in reality.

[0034] The above formulas collectively embed the migration process of mobile energy storage units into the power system model, realizing a collaborative mechanism of spatial perception and delay coordination.

[0035] S2: Establishment of a heterogeneous mobile energy storage unit model.

[0036] This invention models the energy evolution, power limitation, and heterogeneity characteristics of mobile energy storage units together, with the following specific constraints: (a) Initial constraints on electrical charge, as follows: Initial energy capacity of energy storage unit proportional to capacity set up: ; This formula is used for the initial power value at the start of a given scheduling cycle. In the formula, Indicates energy storage unit The amount of electricity in the initial period, Indicates its rated energy capacity, The coefficient is set for the initial energy quantity according to the capacity ratio; the overall meaning of this formula is to fix the initial energy state of each energy storage unit to the "capacity ratio given value", thereby ensuring that the subsequent energy quantity recursion equation has a clear initial boundary.

[0037] (b) The equation for the evolution of electrical charge is as follows: The electrical capacity of mobile energy storage evolves with charging and discharging behavior, and its mathematical form is: ; This formula describes the time-series evolution of the energy storage unit's charge and discharge behavior. In the formula, and These represent the battery status in adjacent time periods. Indicates time period The charging and discharging power. The overall meaning of this formula is to embed the physical process of "charging increases the amount of power and discharging decreases the amount of power" into the optimization model in a discrete recursive form, providing state equation support for SOC constraints and power decisions.

[0038] (c) Energy capacity constraints, as detailed below: The energy storage capacity must be within its rated capacity: ; This formula is used to ensure that the energy storage capacity remains within the rated capacity range. In the formula, For energy storage units During the period Battery status, 0 and These represent the minimum and maximum allowable power boundaries, respectively. The overall meaning of this formula is to prevent power from exceeding the limits and ensure that the charging and discharging scheduling results meet the energy capacity and safe operating range of the energy storage equipment.

[0039] (d) Constraints for the terminal charge to return to the initial state are as follows: To ensure repeatability between scheduling cycles, the power level at the end of the cycle must meet the following requirements: ; in For mobile energy storage units The battery level at the end of the time. For mobile energy storage units The initial charge, The allowable deviation for the power consumption is defined by the formula, which requires that the power consumption at the end of the cycle be within the allowable deviation from the initial value. This prevents the model from gaining a short-term cost advantage by overdrawing the power consumption at the end of the cycle, thereby improving the engineering feasibility of the solution.

[0040] (e) Heterogeneity Mobile energy storage fleets are heterogeneous, with different energy storage devices allowing for different energy capacities. Different charge and discharge capabilities And different movement speeds, which correspond to different traffic delay parameters. The overall meaning of this part is to simultaneously characterize the mixed fleet structure of "large-capacity low-speed equipment" and "small-capacity high-speed equipment" under the same optimization framework, thereby improving the model's ability to express the differences in real resource endowments.

[0041] S3: Establishment of power system operation model.

[0042] This step presents a modeling scheme for the power system in the coupled system. The index includes generators. busbar ,line and discrete time periods ( ).

[0043] (a) Generator capacity constraints, as detailed below: ; This formula is used to limit the unit output to within a technically feasible range. In the formula, For the unit During the period Power contribution (MW). This indicates whether the generator unit is powered on. and These represent the lower and upper limits of the unit's technical output, respectively; the overall meaning of this formula is: the unit's output is constrained at startup. and Internally, when the unit is shut down, its output is forced to zero, thereby ensuring that the scheduling results meet the unit capacity boundary.

[0044] (b) Climbing rate constraint, as detailed below: ; ; This formula is used to limit the rate of change in unit output between adjacent time periods. In the formula, and These are the unit's upper and lower ramp limits, respectively. and These represent the time intervals of the generator set. The start-up and shutdown status indication (1 indicates start-up / shutdown); the overall meaning of this formula is: under the premise of considering start-up and shutdown behavior, the increase or decrease of unit output between adjacent time periods should not exceed its ramp-up capability, thereby avoiding unrealistic rapid adjustments.

[0045] (c) Logical consistency and "anti-simultaneity" constraints, as detailed below: ; ; This formula ensures logical consistency between unit state variables and start / stop variables. In the formula, unit state changes are characterized by both start and stop variables; the first equation forces the unit's start / stop state changes to be consistent with the start / stop variables, while the second inequality prohibits simultaneous start-up and shutdown within the same time period. The overall meaning of this formula is to eliminate logical conflicts in unit start / stop decisions and ensure that the unit combination results are executable.

[0046] (d) Minimum power-on / power-off time constraints, as detailed below: ; ; This formula is used to describe the continuous operation requirements of the unit after start-up and shutdown. In the formula, This indicates whether the generator unit is powered on. and These represent the minimum start-up time and minimum shutdown time (h) of the unit, respectively; the overall meaning of this formula is: once the unit starts up, it must run continuously for no less than [time value missing]. Once the machine stops, it must remain shut down for no less than [number] consecutive days. This reflects the physical and operational constraints of the unit and avoids infeasible solutions caused by frequent start-ups and shutdowns.

[0047] (e) DC power flow constraints, as detailed below: ; ; This formula is used to describe network power flow and line capacity limitations. In the formula, busbar During the period Voltage phase angle (rad), line Connecting bus and , For series reactors of the line, The active power flow (MW) of the line. This represents the upper limit of line thermal stability. The overall meaning of this formula is that the power flow of the line is determined by the phase angle difference between the two ends and the reactance, and is constrained by the upper limit of thermal stability, thereby ensuring that the network transmission capacity and safety boundary are included in the scheduling.

[0048] S4: Unified optimization solution based on mixed integer linear programming.

[0049] Based on S1–S3 above, this invention constructs a unified mixed integer linear programming (MILP) model with the goal of minimizing the operating cost of the power system.

[0050] (1) Objective function Total operating costs include fuel costs for thermal power units, start-up and shutdown costs, and transportation costs for mobile energy storage: ; As shown in the formula above, the total operating cost consists of the fuel cost of the thermal power unit, the start-up and shutdown cost of the unit, and the transportation cost of moving mobile energy storage across nodes. Where, Indicates the unit The fuel cost function; the start / stop cost term is determined by the start / stop decision variables ( and Triggered and entered; Indicates mobile energy storage unit Is it in Time period from node Migrate to node ; Indicates the unit At any moment The output power; and They represent the generating units. Start-up and shutdown costs; Indicates mobile energy storage unit Self-node Move to node The transportation cost coefficient, and its correlation with the corresponding migration decision variables. Multiplying these terms creates the transportation cost term. The overall meaning of the objective function is: under constraints such as unit operation, power flow balance, reserve, and movement delay, to comprehensively weigh the "power-side dispatching cost" and the "transportation-side movement cost" to obtain the optimal coordinated dispatching scheme across all nodes and throughout the entire lifecycle. (2) Solution results Solving this model simultaneously yields the optimal movement path and stopping plan of the mobile energy storage unit in the transportation network, along with the matching charging and discharging timing and power evolution process. On the power system side, the optimal output allocation and start-up / shutdown plans for each conventional unit can be further obtained, along with the corresponding optimal solution for system operating costs. By jointly optimizing the spatiotemporal movement decisions of mobile energy storage with power system dispatching decisions within a unified framework, this invention achieves coordinated operation of the power system and the transportation system, improving the flexibility of the power system, reducing operating costs, and enhancing the supply-demand balance capability under conditions of high proportion of renewable energy.

[0051] The embodiments described above provide a detailed explanation of the technical solutions and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays, characterized in that, Includes the following steps: (1) Establish a unified spatiotemporal coupling model for the power system and the transportation system; wherein the power system consists of a set of nodes, a set of lines, a set of generators and a predicted load; the transportation system consists of a set of reachable nodes and their paths, and provides feasible mobile paths and travel times for mobile energy storage; (2) Establish a heterogeneous mobile energy storage unit model, including initial energy conditions constraints, energy evolution equations, energy capacity limits constraints, constraints on the return of end-point energy to the initial state, and heterogeneity characteristics; (3) Establish a power system operation model, including generator capacity constraints, ramp rate constraints, logical consistency and anti-simultaneity constraints, minimum start-up / stop time constraints, and DC power flow constraints; (4) Construct a joint optimization framework with multiple time periods and multiple nodes. With the minimum operating cost of the power system as the objective function, the optimal moving path, stopping plan and charging and discharging strategy of each heterogeneous mobile energy storage unit in the transportation system network are obtained by solving mixed integer linear programming.

2. The optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays according to claim 1, characterized in that, In step (1), when establishing a unified spatiotemporal coupling model of the power system and the transportation system, the following constraints are included: power injection location consistency constraint, unique state constraint, flow continuity constraint with delay, dwell state continuity constraint, node balance constraint with MESU injection, spinning reserve constraint considering the contribution of mobile energy storage units, and arc feasibility constraint.

3. The optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays according to claim 2, characterized in that, Power injection location consistency constraints ensure that the charging and discharging power injection of mobile energy storage units is consistent with their residence location; unique state constraints ensure that mobile energy storage units can only be in a unique state at any given time period; flow continuity constraints with delays explicitly embed traffic delays into the state evolution process of mobile energy storage units; residence state continuity constraints constrain the continuity of residence states in adjacent time periods; node balance constraints with MESU injection explicitly include the power injection of mobile energy storage units in the node active power balance; spinning reserve constraints constrain coupling in time periods. The rotating reserve requirement; the arc feasibility constraint is used to limit the migration of mobile energy storage units to only occur on feasible arcs allowed by the transportation network.

4. The optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays according to claim 1, characterized in that, In step (2), the initial power condition constraint is used to give the initial power value at the start of the scheduling cycle; the power evolution equation is used to describe the temporal evolution of the power of the energy storage unit with the charging and discharging behavior; the energy capacity limit constraint is used to constrain the energy storage power to always be within the rated capacity range; the constraint that the power at the end of the cycle returns to the initial state requires that the power at the end of the cycle is within the allowable deviation from the initial value, so as to avoid the model from gaining a short-term cost advantage by overdrawing the power at the end of the cycle. Heterogeneity is used to describe how different mobile energy storage units can have different energy capacities, different charging and discharging capabilities, and different moving speeds, which in turn result in different traffic delay parameters.

5. The optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays according to claim 1, characterized in that, In step (3), the generator capacity constraint is used to limit the unit output within the technically feasible range; the ramp rate constraint is used to limit the rate of change of the unit output in adjacent time periods; the logical consistency and anti-simultaneity constraint is used to ensure the logical self-consistency between the unit state variables and start-up / shutdown variables; the minimum start-up / shutdown time constraint is used to characterize the continuous operation requirements after the unit starts up and stops; and the DC power flow constraint is used to describe the network power flow and line capacity limitations.

6. The optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays according to claim 1, characterized in that, In step (4), the objective function is to minimize the operating cost of the power system, specifically: ； in, Indicates the unit The fuel cost function; the start / stop cost term is determined by the start / stop decision variables. and Triggered entry; Indicates mobile energy storage unit Is it in Time period from node Migrate to node ; Indicates the unit At any moment The output power; and They represent the generating units. Start-up and shutdown costs; Indicates mobile energy storage unit Self-node Move to node The transportation cost coefficient, and its correlation with the corresponding migration decision variables. The product of these costs forms the transportation cost item.

7. The optimization method for heterogeneous mobile energy storage coupled power systems considering traffic delays according to claim 1, characterized in that, In step (4), the solution also yields the optimal output allocation and start-up / shutdown plan for each conventional unit in the power system, and provides the optimal solution for the operating cost of the power system.

8. An optimization system for a heterogeneous mobile energy storage coupled power system considering traffic delay, characterized in that, The system includes a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to implement the heterogeneous mobile energy storage coupled power system optimization method according to any one of claims 1-7.

Images