Full peer-to-peer power distribution system steady-state operation optimization method based on energy router

Abstract

The invention relates to the technical field of power grid operation control, and discloses a full peer-to-peer power distribution system steady-state operation optimization method based on energy routers, which comprises the following steps: configuring the energy routers on nodes of a full peer-to-peer power distribution system, introducing peer-to-peer units and configuring the peer-to-peer units on the energy routers, and connecting the C-port energy router with C-1 peer-to-peer units, the rest ports are connected with the all-peer power distribution system so as to realize steady-state operation optimization of the all-peer power distribution system, and the optimization process comprises the following steps: calculating power clearance values of peer-to-peer units, and establishing an all-peer power distribution system operation optimization model and a peer-to-peer unit operation optimization model; then, distributed decoupling compensation is carried out by adopting an alternating direction multiplier method, and the operation state optimization of the all-peer power distribution system is realized; meanwhile, the mutual balance degree between the peer-to-peer units and the utilization rate of the distributed power supply are calculated so as to verify the operation state of the optimized all-peer-to-peer power distribution system; according to the invention, steady-state operation control under power balance of the full-peer-to-peer power distribution system can be rapidly realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation control, and in particular to a method for optimizing the steady-state operation of a fully symmetrical power distribution system based on an energy router. Background Art

[0002] The core challenge in building a fully peer-to-peer distribution network lies in achieving flexible access and coordinated control of distributed resources. Traditional distribution networks employ a radial topology, making them difficult to adapt to the stochastic output characteristics of a high proportion of distributed power sources (such as photovoltaic and wind power). With the introduction of power electronics devices such as flexible soft switches and energy routers, fully peer-to-peer distribution networks are gradually evolving into a multi-node interconnected cellular architecture, supporting hybrid AC / DC operation and improving power supply reliability through closed-loop control. However, this highly electronic network structure significantly alters the system's dynamic characteristics. On the one hand, random fluctuations on both the source and load sides significantly increase the complexity of power balancing. On the other hand, the bidirectional power flow between peer units disrupts the unidirectional power flow characteristics of traditional distribution networks, posing new challenges to voltage regulation and frequency stability. Existing control strategies based on centralized regulation or fixed droop coefficients struggle to balance global system optimization with local autonomy requirements. This is particularly prone to power imbalances when responding to sudden load fluctuations or when distributed power sources go offline. Therefore, researching steady-state operation strategies for fully peer-to-peer distribution networks adapting to the access of distributed resources is crucial for ensuring their safe operation.

[0003] Currently, peer-to-peer power balancing modes are primarily divided into autonomous independent operation and interactive operation. Regarding independent operation, existing research on off-grid microgrid operation primarily focuses on the output characteristics of distributed power sources and energy storage, meeting the load demands of individual microgrids across time. However, with the development of microgrids, independent operation has poor anti-disturbance capabilities, and with increased load demands, the coordination of distributed power sources and energy storage is no longer sufficient for independent operation. Loads are often shelved to meet the power supply needs of critical loads, making autonomous independent operation difficult to maintain for long periods of time. Compared to autonomous independent operation, methods utilizing interactive operation of distribution lines have greater applicability in new multi-source distribution networks. However, existing methods use a centralized interactive approach to uniformly control all peer units, considering only the interactive operation of peer units and distribution lines, lacking research on power complementarity between peer units.

[0004] Therefore, the existing autonomous independent operation and centralized interactive operation modes have problems such as poor anti-disturbance ability, delayed control instructions, and the need to reconstruct the entire control system when adding new microgrids, which leads to unstable operation of the fully equivalent distribution network. Summary of the Invention

[0005] In response to the above-mentioned deficiencies in the prior art, the present invention provides a method for optimizing the steady-state operation of a fully peer-to-peer distribution system based on an energy router, which is used to solve the problems of poor anti-disturbance capability, control command lag, and the need to reconstruct the entire control system when adding new microgrids in the existing autonomous independent operation and centralized interactive operation modes, so as to achieve stable and safe operation of the fully peer-to-peer distribution network.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for optimizing the steady-state operation of a fully peer-to-peer distribution system based on an energy router is proposed. The energy router is configured at the node of the fully peer-to-peer distribution system. Peer units are introduced and configured on the energy router. That is, the C-port energy router is connected to C-1 peer units, and the remaining ports are connected to the fully peer-to-peer distribution system to achieve steady-state operation optimization of the fully peer-to-peer distribution system. The optimization process specifically includes: S1. Obtain line load data of the peer unit and the full peer-to-peer distribution system, distributed power generation data, and the installation location and capacity of the energy storage system and distributed power generation. Calculate the remaining active and reactive power of each peer unit in any time period and determine whether the power of the peer units is balanced. If so, terminate the program. Otherwise, execute step S2 to optimize the operating status of the full peer-to-peer distribution system. S2. Establish a multi-port coordination model for energy routers to generate power surplus and shortage values ​​for peer units; S3. Taking the minimization of the loss and comprehensive cost of the full-peer distribution system as the goal, set the constraints that take into account the operation and control of the full-peer distribution system and energy routers, and establish the full-peer distribution system operation optimization model; S4. Taking the minimum total operating cost of each peer unit as the goal, set the constraints of the peer unit operation and establish a peer unit operation optimization model; S5. Based on the power surplus or shortage values ​​of the peer units, the full peer-to-peer distribution system operation optimization model is used as the upper model, and the peer unit operation optimization model is used as the lower model. The alternating direction multiplier method is used to perform distributed decoupling compensation to obtain the optimal active power transmitted between peer units and the optimal active power transmitted between peer units and the full peer-to-peer distribution system, so as to achieve the operation state optimization of the full peer-to-peer distribution system. S6. Based on the optimal active power transmitted between peer units, the mutual balance between peer units and the utilization rate of distributed power sources within peer units are calculated respectively to verify the operating status of the optimized full peer distribution system.

[0007] The present invention has the following beneficial effects: 1. The proposed method for optimizing the steady-state operation of a fully peer-to-peer power distribution system based on an energy router utilizes energy routers to control power transfer and grid-connected interaction between multiple peer units, enabling safe and stable distribution and optimization of power flows. 2. Calculate the surplus and shortage values ​​of each peer unit to design a hierarchical model for the operation optimization of the full peer distribution system and peer units. This achieves a complete steady-state operation optimization of the full peer distribution system and has engineering and practical value for the economic operation and expansion construction of the full peer distribution system. 3. Collect the actual full-peer distribution system line load data, distributed power generation data, energy storage system and distributed power installation location and capacity, and set up peer units. The power balance status of the peer units can be directly judged. By performing accurate distributed decoupling compensation between peer units, not only the solution speed is improved, but also the distributed power efficiency is improved, while the autonomy of the peer units is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of the method for optimizing the steady-state operation of a fully symmetrical power distribution system based on an energy router proposed in the present invention; Figure 2 Schematic diagram of the topology of the power distribution network in the embodiment; Figure 3 Schematic diagram of source-load power data of the distribution network in the embodiment; Figure 4 Schematic diagram of the internal resource scheduling result of the full-peer power distribution system in the embodiment; Figure 5 Schematic diagram of the transmission power of four peer units connected to the energy router in the embodiment. DETAILED DESCRIPTION

[0009] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0010] like Figure 1 As shown, a method for optimizing the steady-state operation of a fully peer-to-peer distribution system based on an energy router is characterized in that the energy router is configured on a node of the fully peer-to-peer distribution system, peer units are introduced and configured on the energy router, that is, the C-port energy router is connected to C-1 peer units, and the remaining ports are connected to the fully peer-to-peer distribution system to achieve steady-state operation optimization of the fully peer-to-peer distribution system.

[0011] In this embodiment, multiple peer units are connected to nodes in a fully peer-to-peer power distribution system, and energy routers are configured between the fully peer-to-peer power distribution system and the peer units. For example, a 5-port energy router is connected to 4 peer units, and a 4-port energy router is connected to 3 peer units. The remaining ports of the energy routers are then connected to the fully peer-to-peer power distribution system. When a peer unit has surplus power, the excess power is transmitted via the energy router to the peer unit with power shortage, thus flexibly allocating and controlling power. Each peer unit integrates multiple devices, including wind turbines and photovoltaic generators, and interacts with other peer units.

[0012] The optimization process specifically includes steps S1-S6: S1. Obtain line load data of the peer unit and the full peer distribution system, distributed power generation data, installation location and capacity of the energy storage system and distributed power supply, calculate the remaining active and reactive power of each peer unit in any time period, and determine whether the power of the peer unit is balanced. If so, terminate the program; otherwise, execute step S2 to optimize the operating status of the full peer distribution system.

[0013] Specifically, the formula for calculating the remaining active and reactive power of each peer unit in any period is:

[0014]

[0015] in, Represents a peer unit, 、 Respectively represent peer units exist Residual active power and residual reactive power of the time period, Represents a peer unit Internal wind turbine exist The active power transmitted during the period, Represents a peer unit Internal photovoltaic generator exist The active power transmitted during the period, 、 Respectively represent peer units Internal energy storage system Charging power and discharging power of the time period, Indicates load, 、 Respectively represent peer units Internal load exist Active power and reactive power of the time period.

[0016] Specifically, the process of determining whether the power of the peer units is balanced is to determine whether the remaining active and reactive powers of each peer unit in any time period are both 0. If so, the power of each peer unit is in a balanced state; otherwise, the power of each peer unit is unbalanced.

[0017] In this embodiment, if 、 If both are 0, the power is determined to be balanced. Timely termination of the program can avoid unnecessary waste of computing resources and improve the efficiency of system operation. When the power is unbalanced, subsequent optimization steps are triggered to ensure that system problems are handled in a timely manner and guarantee the stable and economic operation of the fully symmetrical power distribution system.

[0018] S2. Establish a multi-port coordination model of the energy router and generate the power surplus or shortage value of the peer unit.

[0019] Specifically, the multi-port coordination model of the energy router is:

[0020] in, 、 Respectively expressed in Time-slot energy router Port The active power and reactive power of Indicates Time-slot energy router Port The active power loss, 、 Respectively expressed in Time-slot energy router Port The upper and lower limits of active power, Energy Router Port The loss coefficient, Energy Router Port capacity limit.

[0021] In this embodiment, the calculation formula for the power surplus or shortage value of the peer unit is: 、 Therefore, based on this formula, it is possible to determine whether the power of each peer unit is redundant, thereby optimizing the power interaction in subsequent steps to achieve the optimization of the operating status of the full peer-to-peer distribution system.

[0022] S3. With the goal of minimizing the loss and comprehensive cost of the fully symmetrical distribution system, constraints are set considering the operation and control of the fully symmetrical distribution system and energy routers, and an operation optimization model of the fully symmetrical distribution system is established.

[0023] In this embodiment, the reduction of system losses can reduce energy waste and improve the utilization efficiency of distributed power sources. The optimization of comprehensive costs covers various expenses such as equipment operation and maintenance, which helps to improve the economic benefits of the system. Combining the constraints of the full-peer distribution system and the operation and control of the energy router, the constructed model is more in line with the actual operation scenario, ensuring the feasibility and safety of the optimization scheme. These constraints not only limit the boundaries of system operation, avoiding problems such as exceeding the equipment carrying capacity and affecting system stability, but also define a reasonable range for the values ​​of the optimization variables. The complex system optimization problem is converted into a computable and solvable mathematical problem to ensure the efficient and economic operation of the full-peer distribution system.

[0024] Specifically, the constraints for the operation and control of the full-peer distribution system and the energy router include the first power balance constraint, the node voltage constraint, the energy storage system constraint, and the first peer unit grid connection constraint. The corresponding formulas are:

[0025] in, represents the first power balance constraint, Indicates the upper power grid with higher voltage level to which the full-equivalent distribution system is connected. 、 Represents the full-peer distribution system and the upper power grid respectively exist Active power and reactive power transmitted during the time period, Distributed power supply The total number of 、 Respectively represent Distributed Power Generation exist Active power and reactive power transmitted during the time period, Energy storage system The total number of Indicates the Energy storage systems exist The active power transmitted during the period, Energy Router The total number of 、 Respectively represent the use of the first Energy Routers exist Active power and reactive power transmitted during the time period, Indicates the number of nodes in the full peer-to-peer distribution system, 、 Represents the full peer distribution system nodes exist Active power and reactive power of load in a certain period of time.

[0026] In this embodiment, the first power balance constraint is for a fully peer-to-peer distribution system. This constraint includes the power of the higher-voltage upper grid connected to the fully peer-to-peer distribution system, the power of the distributed generation (DG) power, the power of the energy storage system, and the power transmitted between the peer units and the fully peer-to-peer distribution system using energy routers. When power is interrupted by the upper grid, a sudden failure of the DG power supply, or abnormal power exchange between peer units occurs, these constraints can quickly identify power shortfalls or surpluses, providing a basis for scheduling strategies (such as emergency charging and discharging of energy storage and adjustments to peer-to-peer interactions), thereby improving the system's ability to withstand disturbances.

[0027]

[0028] in, represents the node voltage constraint, Represents a fully peer-to-peer distribution system node exist The voltage amplitude of the time period, Represents a fully peer-to-peer distribution system node exist The voltage amplitude of the time period, 、 、 、 、 Represents the full peer distribution system nodes To Node The resistance value, active power, inductive reactance, reactive power, and current amplitude of the circuit. 、 Represents the full peer distribution system nodes exist The upper and lower limits of the voltage amplitude in the time period, 、 Represents the full peer distribution system nodes exist The upper and lower limits of the voltage amplitude during the time period.

[0029] In this embodiment, the node voltage constraint involves calculating the line node voltage. This constraint is solved using known line power, line impedance, and the initial node voltage. These constraints allow the calculation of each node voltage to prevent voltage violations that could damage equipment or degrade power quality.

[0030]

[0031] in, represents the energy storage system constraint, express The discharge state of the energy storage system during the time period, express The charging status of the time-slot energy storage system, 、 Respectively The charging power and discharging power of the time-slot energy storage system, represents the maximum power of the energy storage system, 、 Respectively Time period, The state of charge of the time-slot energy storage system, 、 Respectively represent the upper and lower limits of the state of charge of the energy storage system, 、 Respectively represent the charging efficiency and discharging efficiency of the energy storage system, Indicates the time interval, represents the self-loss coefficient of the energy storage system, Indicates the total capacity of the energy storage system.

[0032] In this embodiment, energy storage system constraints are a series of constraints that the energy storage system must comply with during operation, including power limits, capacity limits, and State of Charge (SOC) limits. These constraints ensure that the battery energy storage system is effectively managed in terms of safety and efficiency.

[0033]

[0034] in, Indicates the first peer unit grid connection constraint, 、 、 、 Respectively represent the use of the first Energy Routers exist The maximum active power, minimum active power, maximum reactive power, and minimum reactive power transmitted during the time period.

[0035] In this embodiment, in order to maintain reliable power supply, the peer unit uses the energy router Transmission with a fully peer-to-peer distribution system must satisfy the above-mentioned first peer-to-peer unit grid connection constraints.

[0036] Specifically, the full-peer distribution system operation optimization model is:

[0037]

[0038] in, represents the target value of the full-equivalent distribution system operation optimization model, Indicates taking the minimum value, 、 They represent the normalization coefficients corresponding to the minimum loss target and the minimum comprehensive cost target of the fully equitable distribution system, which are the inverse of the historical average values. represents the scheduling period, Indicates that the full peer distribution system is Network loss power during the time period, express The time-period full-equivalent distribution system is connected to the upper grid with a higher voltage level. The cost of purchasing electricity, express Equipment maintenance costs within the full-time equivalent distribution system, express Time-of-day peer units and full-peer distribution systems using energy routers The power interaction cost, Indicates to satisfy it.

[0039] S4. With the goal of minimizing the total operating cost of each peer unit, set constraints that take into account the operation of the peer units and establish a peer unit operation optimization model.

[0040] In this embodiment, with the goal of minimizing the total operating cost of each peer unit, a peer unit operation optimization model is established in combination with four types of constraints (taking into account the constraints on the operation of peer units). This can accurately balance the power within the unit, ensure stable supply and demand, clarify the upper and lower power limits and grid connection requirements, avoid equipment overload or illegal grid connection, standardize the interactive power between units, reduce conflicts, and reduce energy consumption and operation and maintenance expenses through the coordination of cost optimization goals and constraints, improve the economy and reliability of the system, and provide technical support for efficient collaboration of peer units.

[0041] Specifically, the constraints for the operation of the peer units include the second power balance constraint, the peer unit power constraint, the second peer unit grid connection constraint, and the interaction power constraint between peer units. The corresponding formulas are:

[0042] in, represents the second power balance constraint, Represents the interaction between peer units, 、 Respectively represent peer units With other peer units Active power and reactive power transmitted during the time period.

[0043] In this embodiment, the second power balance constraint is the constraint of the interaction power between the peer unit and the full peer distribution system, the distributed power supply within the peer unit, the energy storage power, and the load, so as to achieve steady-state operation of the peer unit.

[0044]

[0045] in, represents the peer unit power constraint, 、 Respectively represent peer units Internal photovoltaic generator exist The upper and lower limits of the active power transmitted during the time period, 、 Respectively represent peer units Internal wind turbine exist The upper and lower limits of the active power transmitted during the time period.

[0046] In this embodiment, since the micro-sources in the peer unit take wind power and photovoltaic power into consideration, they are called according to actual load requirements, thereby establishing the power constraints of the peer unit.

[0047]

[0048] in, Indicates the grid connection constraint of the second peer unit.

[0049] In this embodiment, in order to maintain reliable power supply, the peer unit uses the energy router Transmission with a fully peer-to-peer distribution system must satisfy the grid connection constraints of the second peer unit.

[0050]

[0051] in, represents the interaction transmission power constraint between peer units, 、 、 、 Respectively represent peer units Use energy routers with other peer units exist The maximum active power, minimum active power, maximum reactive power, and minimum reactive power transmitted during the time period.

[0052] In this embodiment, in order to maintain steady-state operation between peer units, a peer unit utilizing the energy router ER to transmit with other peer units must meet the interaction power constraint between the peer units.

[0053] Specifically, the peer unit operation optimization model is:

[0054]

[0055] in, represents the target value of the optimization model run by the peer unit, Indicates the number of peer units, express Time period peer unit maintenance costs, express Time period peer unit Depreciation expense, express Time period peer unit The cost of interacting with other peers, express Time period peer unit and full peer distribution system utilize the Energy Routers The power interaction cost.

[0056] S5. Based on the power surplus or shortage value of the peer unit, the full peer distribution system operation optimization model is used as the upper model, and the peer unit operation optimization model is used as the lower model. The alternating direction multiplier method is used for distributed decoupling compensation to obtain the optimal active power transmitted between peer units and the optimal active power transmitted between peer units and the full peer distribution system, so as to achieve the optimization of the operating status of the full peer distribution system.

[0057] In this embodiment, a layered model architecture and distributed decoupling approach precisely utilize power surpluses and shortages to optimize power interactions. The upper layer coordinates the overall system balance, while the lower layer focuses on unit costs and constraints. Alternating iterative solutions efficiently obtain the optimal power interaction solution, ensuring economical system operation while improving the collaborative efficiency and responsiveness of peer units, enhancing the operational flexibility and reliability of the entire system.

[0058] Specifically, step S5 includes S51-S53: S51. Based on the power surplus or shortage value of the peer unit, the full peer distribution system operation optimization model is used as the upper model, and the peer unit operation optimization model is used as the lower model. The alternating direction multiplier method is used to perform distributed decoupling compensation to generate decomposition sub-problems, namely:

[0059]

[0060] in, Represents a peer unit The decomposition subproblem of solving the objective function, Represents a peer unit All the equation constraints contained in it, Represents a peer unit All inequality constraints included, 、 、 、 Respectively represent peer units With peer units , peer unit With peer units , peer unit With peer units , peer unit With peer units Active power interacting between them.

[0061] In this embodiment, Represents a peer unit All the equation constraints included include ; Represents a peer unit All inequality constraints included, including .

[0062] S52. Iteratively optimize the decomposed subproblems to obtain the active power transmitted between peer units and the active power transmitted between the peer units and the full peer distribution system in each iteration, that is:

[0063]

[0064] in, represents the number of iterations, Indicates the Peer unit Active power transferred between other peer units, Indicates the Peer unit With full peer distribution system using the Energy Routers The active power transmitted, Indicates the Peer unit With full peer distribution system using the Energy Routers The active power transmitted, represents the independent variable when the function is minimized, represents the Lagrange multiplier, Indicates the At the iteration The dual variable of the period.

[0065] In this embodiment, the optimal solution is gradually approached through multiple rounds of iterations, and the active power transmission values ​​between peer units and between peer units and the full peer distribution system are dynamically adjusted to achieve efficient and rational power flow, enhance the stability and economy of the entire system operation, and provide a reliable dynamic basis for optimization decision-making.

[0066] S53. Determine whether the active power transmitted between peer units and the active power transmitted between peer units and the full peer distribution system in each iteration meets the convergence conditions. If so, obtain the optimal active power transmitted between peer units and the optimal active power transmitted between peer units and the full peer distribution system. Otherwise, continue to execute step S52.

[0067] The convergence condition is:

[0068]

[0069] in, 、 They represent the first convergence accuracy and the second convergence accuracy respectively.

[0070] In this embodiment, the termination node of the iterative optimization is determined by the convergence condition to ensure the accuracy and reliability of the power transmission results. When the convergence condition is met, the optimal power transmission value obtained can accurately match the system supply and demand balance and economic goals, avoiding the solution deviation caused by insufficient iterations; when it is not met, continuous adjustment can correct the power interaction deviation and gradually eliminate the sub-problem coupling conflict. Finally, through strict convergence verification, it is ensured that the power transmission scheme achieves a balance between global optimality and local constraint satisfaction. Among them, The value is 0.0001. The value is 0.0001.

[0071] S6. Based on the optimal active power transmitted between peer units, the mutual balance between peer units and the utilization rate of distributed power sources within peer units are calculated respectively to verify the operating status of the optimized full peer distribution system.

[0072] In this embodiment, the optimization effect can be intuitively quantified by calculating mutual balance and distributed power utilization. Mutual balance reflects the degree of coordination of power interactions between peer units; higher values ​​indicate more efficient unit collaboration; distributed power utilization reflects the level of clean energy utilization. Combining these two measures verifies whether the system achieves precise matching of power supply and demand and efficient resource utilization, providing data support for the effectiveness of the optimization solution.

[0073] Specifically, step S6 includes S61-S62: S61. Based on the optimal active power transmitted between the peer units and the active power of the load within the peer units, the mutual balance degree between the peer units is calculated, that is:

[0074] in, Represents a peer unit With other peer units throughout the scheduling cycle T Internal balance.

[0075] In this embodiment, mutual balance is used to verify system optimization performance. That is, mutual balance can reflect the compensation ability of other peer units under the same energy router management area to a certain peer unit, and the larger the value, the stronger the complementary ability of each peer unit in the energy router management area.

[0076] S62. Based on the optimal active power transmitted between the peer units, obtain the active power transmitted by the photovoltaic generator in the peer unit, the charging power and the discharging power of the energy storage system in the peer unit during the time period corresponding to the optimal active power, and calculate the distributed power utilization rate in the peer unit, that is:

[0077] in, Indicates the utilization of distributed power within the peer unit. In this embodiment, the utilization rate of the distributed power supply of the peer unit is calculated to reflect the utilization degree of the distributed power supply by the system optimization scheduling.

[0078] In addition, in order to verify the effectiveness of the proposed method for optimizing the steady-state operation of a fully symmetrical power distribution system based on energy routers, the following experiments were conducted: A full peer-to-peer distribution system including energy routers and peer units is selected, and its distribution network topology is as follows: Figure 2 As shown, the energy storage system is installed at node 17, the distributed power source is installed at nodes 15 and 19 of the full peer-to-peer distribution system, the peer units MG1~MG4 are controlled by the energy router ER1 to access the full peer-to-peer distribution system node 11 (that is, the energy router ER1 is a five-port energy router, which controls the transmission between the peer units MG1~MG4 and the transmission between each peer unit and the full peer-to-peer distribution system), and the peer units MG5~MG7 are controlled by the energy router ER2 to access the full peer-to-peer distribution system node 29 (that is, the energy router ER2 is a four-port energy router, which controls the transmission between the peer units MG5~MG7 and the transmission between each peer unit and the full peer-to-peer distribution system). In addition, each peer unit is connected to a variety of distributed power sources, energy storage systems and power loads. The distribution network system parameters are shown in Table 1, and its peer parameters are shown in Table 2: Table 1 Distribution network system simulation parameters

[0079] Table 2: Peer unit simulation parameters

[0080] In addition, the source and load power data of the distribution network are as follows: Figure 3 As shown. Then set the simulation time of the program to 24 hours and the simulation step to 1 hour. The method proposed in the present invention is tested and verified by simulation data; First, the operation is performed according to step S1 of the method proposed in the present invention, that is, initial data of sources, loads and energy storage in the full peer-to-peer power distribution system is collected, and the power balance status of each peer unit is determined.

[0081] In this simulation step, the initial data collected is Figure 3 The power balance of a peer unit can be determined by determining the power data for the daytime source and load. The difference between the load curve within the peer unit and the maximum load value and the wind and solar power generation curve can be used to determine the power balance of the peer unit. Furthermore, the peer unit experiences peak load periods between 10:00 AM and 2:00 PM and 8:00 PM and 10:00 PM. Wind and solar power generation within the unit alone cannot operate autonomously and independently, so the unit is in an interactive operation phase, establishing a power transmission relationship with the outside world.

[0082] Secondly, the method according to the present invention is operated in steps S2-S5, that is, the surplus and shortage values ​​of each peer unit are calculated, and an optimization model is established for the full peer distribution system and the peer units; In this simulation, the five-port energy router ER1 and the four-port energy router ER2 are used as base stations to connect peer units and peer units with the full peer distribution system. Then, a hierarchical optimization is performed. The hierarchical optimization results are shown in Table 3: Table 3 Comparison of simulation results

[0083] From Table 3, we can see that under the energy optimization method of the energy router, the autonomous ability of the distribution network is improved; and the internal resource scheduling results of the full-peer distribution system are as follows: Figure 4 As shown in the figure, the power of the distribution network comes from distributed power sources and the upper power grid, which meets the load demand and the power demand of the energy router through scheduling. At the same time, when the energy router has power surplus, it will also be transmitted to the distribution network. Figure 4 It can also be seen that the energy router has excess power from 10:00 to 12:00. During this period, the photovoltaic microgrid under the energy router meets the internal power demand and has excess power, which is transmitted to the distribution network through the port control of the energy router, optimizing the support capacity of the peer unit layer to the distribution network layer. Figure 4It can also be seen that the hierarchical optimization model has a better power allocation capability and can better absorb and store a variety of distributed power sources over time.

[0084] At the same time, the alternating direction multiplier method is used for distributed decoupling. In this simulation, the five-port energy router ER1 is taken as an example. Each port is connected to a peer unit. During the scheduling period, the transmission power of each port of the energy router is as follows: Figure 5 As shown, the four peer units utilize the port characteristics of the energy router to transfer power, where negative values ​​indicate outbound power and positive values ​​indicate inbound power. Peer units MG1 connected to port 1 and MG4 connected to port 4 have excess power between 10:00 AM and 3:00 PM, which can be exported to peer units MG2 connected to port 2 and MG3 connected to port 3. This fully utilizes the energy management of the energy router. Furthermore, power purchases from the distribution network occur during the morning and evening hours, when purchase costs are low, while power sales to the distribution network occur between 10:00 AM and 12:00 PM, when sales revenue is high. This demonstrates the economic superiority of the energy router-based energy optimization approach.

[0085] Finally, the operation is performed according to step S6 of the method proposed in the present invention, that is, the mutual balance and the utilization rate of the distributed power supply are used to verify the optimization performance.

[0086] In this simulation, different schemes are set for the interactive connection of each peer unit. Scheme 1: the existing method of connecting multiple peer units using switch devices, and Scheme 2: the energy router proposed in the present invention is used to connect and control multiple peer units. The indicator results of Scheme 1 and Scheme 2 are shown in Table 4: Table 4 Comparison of indicators of different schemes

[0087] As shown in Table 4, because the unified management of energy routers reduces redundant lines between multiple peer units and increases the degree of correlation between levels, the transmission cost of Scheme 2 is lower than that of Scheme 1, and both the mutual balance and resource utilization are higher than those of Scheme 1. Therefore, the optimization method proposed in this invention uses energy routers to promote the flow of power between multiple peer units and the complementary balance of power storage and transmission during peak loads. In summary, the method proposed in this invention can reduce costs and improve the utilization of distributed power sources.

[0088] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0089] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the steady-state operation of a fully symmetrical power distribution system based on an energy router, characterized in that: Energy routers are configured on nodes of the fully peer-to-peer distribution system. Peer units are introduced and configured on the energy routers. That is, the C-port energy router is connected to C-1 peer units, and the remaining ports are connected to the fully peer-to-peer distribution system to achieve steady-state operation optimization of the fully peer-to-peer distribution system. The optimization process specifically includes: S1. Obtain line load data of the peer unit and the full peer-to-peer distribution system, distributed power generation data, and the installation location and capacity of the energy storage system and distributed power generation. Calculate the remaining active and reactive power of each peer unit in any time period and determine whether the power of the peer units is balanced. If so, terminate the program. Otherwise, execute step S2 to optimize the operating status of the full peer-to-peer distribution system. S2. Establish a multi-port coordination model for energy routers to generate power surplus and shortage values ​​for peer units; S3. Taking the minimization of the loss and comprehensive cost of the full-peer distribution system as the goal, set the constraints that take into account the operation and control of the full-peer distribution system and energy routers, and establish the full-peer distribution system operation optimization model; S4. Taking the minimum total operating cost of each peer unit as the goal, set the constraints of the peer unit operation and establish a peer unit operation optimization model; S5. Based on the power surplus or shortage values ​​of the peer units, the full peer-to-peer distribution system operation optimization model is used as the upper model, and the peer unit operation optimization model is used as the lower model. The alternating direction multiplier method is used to perform distributed decoupling compensation to obtain the optimal active power transmitted between peer units and the optimal active power transmitted between peer units and the full peer-to-peer distribution system, so as to achieve the operation state optimization of the full peer-to-peer distribution system. S6. Based on the optimal active power transmitted between peer units, the mutual balance between peer units and the utilization rate of distributed power sources within peer units are calculated respectively to verify the operating status of the optimized full peer distribution system.

2. The method for optimizing the steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 1 is characterized in that: The formula for calculating the remaining active and reactive power of each peer unit in any period of time is: in, Represents a peer unit, 、 Respectively represent peer units exist Residual active power and residual reactive power of the time period, Represents a peer unit Internal wind turbine exist The active power transmitted during the time period, Represents a peer unit Internal photovoltaic generator exist The active power transmitted during the period, 、 Respectively represent peer units Internal energy storage system Charging power and discharging power of the time period, Indicates load, 、 Respectively represent peer units Internal load exist Active power and reactive power of the time period.

3. The method for optimizing the steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 2 is characterized in that: The process of determining whether the power of the peer units is balanced is to determine whether the residual active power and reactive power of each peer unit in any time period are both 0. If so, the power of each peer unit is in a balanced state; otherwise, the power of each peer unit is unbalanced.

4. The method for optimizing steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 3 is characterized in that: The multi-port coordination model of the energy router is: in, 、 Respectively expressed in Time-slot energy router Port The active power and reactive power of Indicates Time-slot energy router Port The active power loss, 、 Respectively expressed in Time-slot energy router Port The upper and lower limits of active power, Energy Router Port The loss coefficient, Energy Router Port capacity limit.

5. The method for optimizing steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 4 is characterized in that: The constraints for the operation and control of the full-peer distribution system and energy routers include the first power balance constraint, node voltage constraint, energy storage system constraint, and the first peer unit grid connection constraint. The corresponding formulas are: in, represents the first power balance constraint, Indicates the upper power grid with higher voltage level to which the full-equivalent distribution system is connected. 、 Represents the full-peer distribution system and the upper power grid respectively exist Active power and reactive power transmitted during the time period, Distributed power supply The total number of 、 Respectively represent Distributed Power Generation exist Active power and reactive power transmitted during the time period, Energy storage system The total number of Indicates the Energy storage systems exist The active power transmitted during the time period, Energy Router The total number of 、 Respectively represent the use of the first Energy Routers exist Active power and reactive power transmitted during the time period, Indicates the number of nodes in the full peer-to-peer distribution system, 、 Represents the full peer distribution system nodes exist Active power and reactive power of load during the time period; in, represents the node voltage constraint, Represents a fully peer-to-peer distribution system node exist The voltage amplitude of the time period, Represents a fully peer-to-peer distribution system node exist The voltage amplitude of the time period, 、 、 、 、 Represents the full peer distribution system nodes To Node The resistance value, active power, inductive reactance, reactive power, and current amplitude of the circuit. 、 Represents the full peer distribution system nodes exist The upper and lower limits of the voltage amplitude during the time period, 、 Represents the full peer distribution system nodes exist The upper and lower limits of the voltage amplitude during the time period; in, represents the energy storage system constraint, express The discharge state of the energy storage system during the time period, express The charging status of the time-slot energy storage system, 、 Respectively The charging power and discharging power of the time-slot energy storage system, represents the maximum power of the energy storage system, 、 Respectively Time period, The state of charge of the time-slot energy storage system, 、 Respectively represent the upper and lower limits of the state of charge of the energy storage system, 、 Respectively represent the charging efficiency and discharging efficiency of the energy storage system, Indicates the time interval, represents the self-loss coefficient of the energy storage system, Indicates the total capacity of the energy storage system; in, Indicates the first peer unit grid connection constraint, 、 、 、 Respectively represent the use of the first Energy Routers exist The maximum active power, minimum active power, maximum reactive power, and minimum reactive power transmitted during the time period.

6. The method for optimizing steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 5 is characterized in that: The operation optimization model of the fully symmetrical distribution system is: in, represents the target value of the full-equivalent distribution system operation optimization model, Indicates taking the minimum value, 、 They represent the normalized coefficients corresponding to the minimum loss target and the minimum comprehensive cost target of the fully equitable distribution system, represents the scheduling period, Indicates that the full peer distribution system is Network loss power during the time period, express The time-period full-equivalent distribution system is connected to the upper grid with a higher voltage level. The cost of purchasing electricity, express Equipment maintenance costs within the full-time equivalent distribution system, express Time-of-day peer units and full-peer distribution systems using energy routers The power interaction cost, Indicates to satisfy it.

7. The method for optimizing steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 6 is characterized in that: The constraints for peer unit operation include the second power balance constraint, the peer unit power constraint, the second peer unit grid connection constraint, and the interaction power constraint between peer units. The corresponding formulas are: in, represents the second power balance constraint, Represents the interaction between peer units, 、 Respectively represent peer units With other peer units Active power and reactive power transmitted during the time period; in, represents the peer unit power constraint, 、 Respectively represent peer units Internal photovoltaic generator exist The upper and lower limits of the active power transmitted during the time period, 、 Respectively represent peer units Internal wind turbine exist Upper and lower limits of active power transmitted during a time period; in, Indicates the grid connection constraint of the second peer unit. in, represents the interaction transmission power constraint between peer units, 、 、 、 Respectively represent peer units Use energy routers with other peer units exist The maximum active power, minimum active power, maximum reactive power, and minimum reactive power transmitted during the time period.

8. The method for optimizing steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 7 is characterized in that: The peer unit runs the optimization model as follows: in, represents the target value of the optimization model run by the peer unit, Indicates the number of peer units, express Time period peer unit maintenance costs, express Time period peer unit Depreciation expense, express Time period peer unit The cost of interacting with other peers, express Time period peer unit and full peer distribution system utilize the Energy Routers The power interaction cost.

9. The method for optimizing steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 8, characterized in that: Step S5 specifically includes: S51. Based on the power surplus or shortage value of the peer unit, the full peer distribution system operation optimization model is used as the upper model, and the peer unit operation optimization model is used as the lower model. The alternating direction multiplier method is used to perform distributed decoupling compensation to generate decomposition sub-problems, namely: in, Represents a peer unit The decomposition subproblem of solving the objective function, Represents a peer unit All the equation constraints contained in it, Represents a peer unit All inequality constraints included, 、 、 、 Respectively represent peer units With peer units , peer unit With peer units , peer unit With peer units , peer unit With peer units Active power of interaction between S52. Iteratively optimize the decomposed subproblems to obtain the active power transmitted between peer units and the active power transmitted between the peer units and the full peer distribution system in each iteration, that is: in, represents the number of iterations, Indicates the Peer unit Active power transferred between other peer units, Indicates the Peer unit With full peer distribution system using the Energy Routers The active power transmitted, Indicates the Peer unit With full peer distribution system using the Energy Routers The active power transmitted, represents the independent variable when the function is minimized, represents the Lagrange multiplier, Indicates the At the iteration The dual variable of the period; S53. Determine whether the active power transmitted between peer units and the active power transmitted between the peer units and the full peer-to-peer power distribution system in each iteration meets the convergence condition. If so, obtain the optimal active power transmitted between peer units and the optimal active power transmitted between the peer units and the full peer-to-peer power distribution system. Otherwise, continue to step S52. The convergence condition is: in, 、 They represent the first convergence accuracy and the second convergence accuracy respectively.

10. The method for optimizing steady-state operation of a fully symmetrical power distribution system based on an energy router according to claim 9, characterized in that: Step S6 specifically includes: S61. Based on the optimal active power transmitted between the peer units and the active power of the load within the peer units, the mutual balance degree between the peer units is calculated, that is: in, Represents a peer unit With other peer units throughout the scheduling cycle T Internal balance; S62. Based on the optimal active power transmitted between the peer units, obtain the active power transmitted by the photovoltaic generator in the peer unit, the charging power and the discharging power of the energy storage system in the peer unit during the time period corresponding to the optimal active power, and calculate the distributed power utilization rate in the peer unit, that is: in, Indicates the utilization of distributed power within the peer unit.

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