Intelligent load management and distribution system and method for distributed photovoltaic-energy storage systems

Abstract

This disclosure relates to the field of distributed photovoltaic-energy storage system (PV-ESS) management technology in smart grids, specifically to an intelligent load management distribution system and method for distributed PV-ESS systems. In this distribution system, a medium-voltage distribution network sends control commands to the master nodes in each low-voltage distribution network based on the output power of each distribution transformer. Then, the master nodes and multiple non-master nodes in the low-voltage distribution network continuously iterate and update consistency variables based on a consensus algorithm, enabling the master nodes and multiple non-master nodes in the multiple low-voltage distribution networks to reach a system equilibrium state, thereby realizing intelligent load management of the distributed PV-ESS system. This disclosure achieves consistency between PV power generation fluctuations, the charging and discharging state of the energy storage system, and the grid connection requirements, and utilizes cooperative control among multiple distributed PV-ESS systems to avoid voltage over-limit and distribution transformer overload problems.

Description

Technical Field

[0001] This disclosure relates to the field of distributed photovoltaic-energy storage system management technology in smart grids, specifically to an intelligent load management and distribution system and method for distributed photovoltaic-energy storage systems. Background Technology

[0002] New energy is a crucial cornerstone for implementing the "dual-carbon" strategy and ensuring energy security. Currently, my country's installed capacity of new energy power generation exceeds 1.18 billion kilowatts, accounting for 38.4% of the total installed power generation capacity, making it the largest power source. However, distributed new energy power generation is highly random, intermittent, and volatile due to factors such as weather and uneven spatial and temporal distribution, leading to increased local imbalances in the distribution network and seriously threatening the safe and stable operation of the power grid. Voltage exceeding limits and distribution transformer overload are particularly prominent issues. For distributed photovoltaic energy storage systems, after grid connection, they may alter the flow direction of electricity in the grid, resulting in reverse flow; voltage exceeding limits can damage grid equipment, affect power quality, and cause power outages, disrupting socio-economic activities and residents' daily lives. Summary of the Invention

[0003] To address the problems in the related technologies, this disclosure provides an intelligent load management and distribution system and method for distributed photovoltaic-energy storage systems.

[0004] In a first aspect, this disclosure provides an intelligent load management and distribution system for a distributed photovoltaic-energy storage system. The distribution system includes: a medium-voltage distribution network and multiple distribution transformers connected to the medium-voltage distribution network. The distribution transformers are connected to one or more low-voltage distribution networks. The low-voltage distribution network includes multiple distributed photovoltaic-energy storage systems, where one distributed photovoltaic-energy storage system is a master node and the remaining distributed photovoltaic-energy storage systems are non-master nodes.

[0005] For each of the plurality of distribution transformers, the medium-voltage distribution network obtains the output power of the distribution transformer, calculates the active power regulation of each low-voltage distribution network according to the power limit of each low-voltage distribution network connected to the distribution transformer and the output power, and then sends control commands to the master nodes in each low-voltage distribution network according to the active power regulation of each low-voltage distribution network.

[0006] For each low-voltage distribution network, upon receiving the control command, the master node iteratively updates its consistency variables based on the active power adjustment of the low-voltage distribution network. Each of the multiple non-master nodes calculates the state transfer coefficient from its neighboring nodes to itself based on the network topology of the low-voltage distribution network. Based on the state transfer coefficient and the updated consistency variables of the neighboring nodes, iteratively updates its consistency variables. The master node and the multiple non-master nodes continuously iteratively update their corresponding consistency variables until the master node and all non-master nodes reach a system equilibrium state, wherein the neighboring nodes are the master node or other non-master nodes.

[0007] For the low-voltage distribution network connected to the multiple distribution transformers, a one-time variable continuous iterative update is performed on the main node and all non-main nodes in the low-voltage distribution network until the main node and all non-main nodes in the low-voltage distribution network reach a system balance state, thereby realizing intelligent load management of the distributed photovoltaic-energy storage system.

[0008] According to embodiments of this disclosure, calculating the active power regulation of each low-voltage distribution network based on the power limit of each low-voltage distribution network connected to the distribution transformer and the output power includes:

[0009] For each low-voltage distribution network, the medium-voltage distribution network calculates the active power adjustment amount of the low-voltage distribution network based on the maximum and minimum active power of the low-voltage distribution network and the output power of the distribution transformer connected to the low-voltage distribution network.

[0010] According to embodiments of this disclosure, calculating the active power regulation of the low-voltage distribution network based on the maximum and minimum active power of the low-voltage distribution network and the obtained output power of the distribution transformer connected to the low-voltage distribution network includes calculating the active power regulation using the following formula:

[0011]

[0012] Among them, the The active power regulation of the j-th low-voltage distribution network is... The output power of the distribution transformer connected to the j-th low-voltage distribution network is... The maximum active power of the j-th low-voltage distribution network is... Let be the minimum active power of the j-th low-voltage distribution network.

[0013] According to embodiments of this disclosure:

[0014] In the At that time, the j-th low-voltage distribution network absorbs power relative to the medium-voltage distribution network;

[0015] In the At that time, the j-th low-voltage distribution network outputs power relative to the medium-voltage distribution network;

[0016] In the At that time, the j-th low-voltage distribution network is in a system balance state.

[0017] According to embodiments of this disclosure, sending control commands to the master nodes in each low-voltage distribution network based on the active power regulation of each low-voltage distribution network includes:

[0018] In the j-th low-voltage distribution network, or At that time, the medium-voltage distribution network sends a control command to the master node in the j-th low-voltage distribution network, wherein the control command includes an active power adjustment command.

[0019] According to embodiments of this disclosure, the master node iteratively updates its consistency variables based on the active power regulation of the low-voltage distribution network, including:

[0020] For any of the master node and / or non-master nodes, calculate the active power adjustment amount of the node in the k-th iteration update. When the active power adjustment amount in the k-th iteration update is 0 or less than a set threshold, the node reaches the system balance state. Wherein, when the node is a master node, k = k1, and when the node is a non-master node, k = k2.

[0021] According to an embodiment of this disclosure, the master node performs a k1-th iteration update on its consistency variables based on the consistency variables of the master node that have undergone k1-1 iteration updates and the active power regulation of the low-voltage distribution network. This includes performing the k1-th iteration update on the consistency variables of the master node according to the following formula:

[0022]

[0023] Among them, the The consistency variables that were updated for the master node in the k1th iteration, The consistency variable of the master node has been updated k1-1 times, where a is a convergence control parameter, 1≤k1≤K, and K is the preset maximum number of iterations.

[0024] According to embodiments of this disclosure, calculating the state transfer coefficient from adjacent nodes to the non-master nodes based on the network topology of the low-voltage distribution network includes:

[0025] The non-master node i+n calculates the state transfer coefficient from the neighboring node i to the non-master node i+n based on the total number of communication messages received by the non-master node i+n.

[0026] According to embodiments of this disclosure, the non-master node i+n calculates the state transfer coefficient from the neighboring node i to the non-master node i+n based on the total number of communication messages received by the non-master node i+n, including calculating the state transfer coefficient from the neighboring node i to the non-master node i+n according to the following formula:

[0027]

[0028] Wherein, the d i,i+n The state transfer coefficients from the adjacent node i to the non-master node i+n are... is the total number of communication messages received by the non-master node i+n, and b is the communication line in the low-voltage distribution network.

[0029] According to embodiments of this disclosure, the iterative update of the consistency variables of the non-master nodes based on the state transit coefficients and the updated consistency variables of the neighboring nodes includes performing the k2-th iterative update of the consistency variables of the non-master nodes according to the following formula:

[0030] u i+n,t (k2)=∑ i+n∈c d i,i+n u i,t (k2-1);

[0031] Wherein, the u i+n,t (k2) represents the consistency variable after the k2th iteration update of non-master node i+n, where u i,t (k2-1) is the consistency variable after k2-1 iterations of the neighboring node i, where c is the set of nodes in the low-voltage distribution network, 1≤k2≤K, and K is the preset maximum number of iterations.

[0032] According to embodiments of this disclosure, the master node and all non-master nodes have reached a system balance state, including:

[0033] For any of the master node and / or non-master nodes, calculate the active power adjustment amount of the node in the k-th iteration update. When the active power adjustment amount in the k-th iteration update is 0 or less than a set threshold, the node reaches the system balance state. Wherein, when the node is a master node, k = k1, and when the node is a non-master node, k = k2.

[0034] According to embodiments of this disclosure, the consistency variables of the master node and / or non-master node are related to the charging and discharging states of the corresponding master node and / or non-master node, the output power of photovoltaic power generation, and changes in grid power demand.

[0035] According to embodiments of this disclosure, the system further includes: a power measurement device;

[0036] The power measuring device is installed between the low-voltage distribution network and the distribution transformer to measure the output power of the distribution transformer.

[0037] According to an embodiment of this disclosure, the master node is the distributed photovoltaic-energy storage system in the low-voltage distribution network that is closest to the distribution transformer connected to the low-voltage distribution network.

[0038] Secondly, this disclosure provides an intelligent load management method for a distributed photovoltaic-energy storage system. The distributed photovoltaic-energy storage system is installed in a power distribution system, which includes a medium-voltage power distribution network and multiple distribution transformers connected to the medium-voltage power distribution network. The distribution transformers are connected to one or more low-voltage power distribution networks, which include multiple distributed photovoltaic-energy storage systems. One distributed photovoltaic-energy storage system in the low-voltage power distribution network is a master node, and the remaining distributed photovoltaic-energy storage systems are non-master nodes. The method includes:

[0039] For each of the plurality of distribution transformers, the medium-voltage distribution network obtains the output power of the distribution transformer, calculates the active power regulation of each low-voltage distribution network according to the power limit of each low-voltage distribution network connected to the distribution transformer and the output power, and then sends control commands to the master nodes in each low-voltage distribution network according to the active power regulation of each low-voltage distribution network.

[0040] For each low-voltage distribution network, upon receiving the control command, the master node iteratively updates its consistency variables based on the active power adjustment of the low-voltage distribution network. Each of the plurality of non-master nodes calculates the state transfer coefficient from its neighboring nodes to itself based on the network topology of the low-voltage distribution network. Based on the state transfer coefficient and the updated consistency variables of the neighboring nodes, iteratively updates its consistency variables. The master node and the plurality of non-master nodes continuously iteratively update their corresponding consistency variables until the master node and all non-master nodes reach a system equilibrium state, wherein the neighboring nodes are the master node or other non-master nodes.

[0041] For the multiple distribution transformers, repeat the above steps until the main nodes and all non-main nodes in the low-voltage distribution network connected to the multiple distribution transformers reach a system balance state, thereby realizing intelligent load management of the distributed photovoltaic-energy storage system.

[0042] Thirdly, embodiments of this disclosure provide a computer-readable storage medium having computer instructions stored thereon that, when executed by a processor, implement the method described in the second aspect.

[0043] Fourthly, embodiments of this disclosure provide a computer program product including computer instructions that, when executed by a processor, implement the method as described in the second aspect.

[0044] This disclosure achieves intelligent load management of a distributed photovoltaic-energy storage system (PV-energy storage system) by utilizing a medium-voltage distribution network and multiple distribution transformers connected to the medium-voltage distribution network, and one or more low-voltage distribution networks connected to the distribution transformers. The medium-voltage distribution network outputs control commands to the master nodes in the low-voltage distribution network based on the output power of the distribution transformers. The master nodes and each non-master node continuously iterate and update consistency variables until all master nodes and multiple non-master nodes in the low-voltage distribution network reach a system equilibrium state, thereby realizing intelligent load management of the distributed PV-energy storage system. This disclosure achieves consistency between PV power generation fluctuations, the charging and discharging state of the energy storage system, and the grid connection requirements, and utilizes cooperative control among multiple distributed PV-energy storage systems to avoid voltage over-limit and distribution transformer overload problems.

[0045] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0046] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments, taken in conjunction with the accompanying drawings. In the drawings:

[0047] Figure 1 A schematic diagram of the structure of an intelligent load management and distribution system for a distributed photovoltaic-energy storage system according to an embodiment of the present disclosure is shown.

[0048] Figure 2 A schematic diagram of the topology of a master node in a low-voltage distribution network according to an embodiment of the present disclosure is shown.

[0049] Figure 3 A flowchart is shown for an intelligent load management method for a distributed photovoltaic-energy storage system according to an embodiment of the present disclosure. Detailed Implementation

[0050] In the following, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings to enable those skilled in the art to readily implement them. Furthermore, for clarity, portions unrelated to the description of exemplary embodiments have been omitted from the drawings.

[0051] In this disclosure, it should be understood that terms such as “comprising” or “having” are intended to indicate the presence of features, figures, steps, behaviors, components, parts or combinations thereof disclosed in this specification, and are not intended to exclude the possibility of the presence or addition of one or more other features, figures, steps, behaviors, components, parts or combinations thereof.

[0052] It should also be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0053] As mentioned earlier, distributed renewable energy generation is highly random, intermittent, and volatile due to factors such as weather and uneven spatial and temporal distribution. This exacerbates local imbalances in the distribution network, seriously threatening the safe and stable operation of the power grid. Among these issues, voltage exceeding limits and distribution transformer overload are particularly prominent. Specifically, after grid connection, distributed photovoltaic systems may alter the flow direction of electrical energy in the grid, resulting in reverse flow. Voltage exceeding limits can damage grid equipment, affect power quality, and even lead to power outages.

[0054] With the grid connection of distributed photovoltaic-energy storage systems, while bringing flexibility and power supply reliability, some significant drawbacks have also been introduced. For example, besides being greatly affected by weather and time factors, the limitations of energy storage technology (such as limited storage capacity, low efficiency, and limited lifespan of storage media) restrict the application and development of distributed photovoltaic-energy storage systems. The connection of distributed photovoltaic-energy storage systems can also impact grid operation and management, such as changing power flow direction and increasing the difficulty of reactive power balancing. The inventors have found that to ensure the safe and stable operation of the power grid, it is necessary to strengthen research and modification of grid adaptability.

[0055] This disclosure provides an intelligent load management and distribution system for a distributed photovoltaic-energy storage system. The distribution system includes a medium-voltage distribution network and multiple distribution transformers connected to the medium-voltage distribution network. The distribution transformers are connected to one or more low-voltage distribution networks. The low-voltage distribution network includes multiple distributed photovoltaic-energy storage systems, where one distributed photovoltaic-energy storage system is a master node and the remaining distributed photovoltaic-energy storage systems are non-master nodes.

[0056] For each of the plurality of distribution transformers, the medium-voltage distribution network acquires the output power of the distribution transformer. Based on the power limits of each of the plurality of low-voltage distribution networks connected to the distribution transformer and the output power, it calculates the active power regulation of each low-voltage distribution network. Then, based on the active power regulation of each low-voltage distribution network, it sends a control command to the master node in each low-voltage distribution network. For each low-voltage distribution network, upon receiving the control command, the master node iteratively updates its consistency variables based on the active power regulation of the low-voltage distribution network. Each of the plurality of non-master nodes calculates its adjacent nodes based on the network topology of the low-voltage distribution network. The state propagation coefficient of the non-master node is used as the basis for iteratively updating the consistency variables of the non-master node based on the state propagation coefficient and the updated consistency variables of the adjacent nodes. The master node and the multiple non-master nodes continuously iteratively update their corresponding consistency variables until the master node and all non-master nodes reach a system equilibrium state, wherein the adjacent nodes are the master node or other non-master nodes. For the low-voltage distribution network connected to the multiple distribution transformers, a one-time variable of the master node and all non-master nodes in the low-voltage distribution network is continuously iteratively updated until the master node and all non-master nodes in the low-voltage distribution network reach a system equilibrium state, thereby realizing intelligent load management of the distributed photovoltaic-energy storage system.

[0057] This disclosure achieves consistency between photovoltaic power generation fluctuations, the charging and discharging status of energy storage systems, and the grid connection requirements of the power grid. It also utilizes the collaborative control between multiple distributed photovoltaic-energy storage systems to avoid voltage overruns and distribution transformer overloads, thereby improving the stability of the power grid, realizing intelligent management and control of the power grid, and promoting the construction of a new type of power system.

[0058] Figure 1 A schematic diagram of a smart load management and distribution system for a distributed photovoltaic-energy storage system is shown according to an embodiment of the present disclosure.

[0059] The power distribution system includes: a medium-voltage power distribution network and multiple distribution transformers connected to the medium-voltage power distribution network. The distribution transformers are connected to one or more low-voltage power distribution networks. The low-voltage power distribution network includes multiple distributed photovoltaic-energy storage systems. One of the distributed photovoltaic-energy storage systems in the low-voltage power distribution network is a master node, and the remaining distributed photovoltaic-energy storage systems are non-master nodes.

[0060] In this disclosure, the master node is the distributed photovoltaic-energy storage system closest to the distribution transformer connected to the low-voltage distribution network. The node closest to the distribution transformer (the distributed photovoltaic-energy storage system) is chosen as the master node because this node facilitates communication, making grid monitoring and control more convenient.

[0061] like Figure 1 As shown, assuming the medium-voltage distribution network is connected to m distribution transformers, and each distribution transformer is connected to j low-voltage distribution networks, where m > 1 and j ≥ 1; then, when j = 1, the distribution transformer is connected to one low-voltage distribution network, and when j > 1, the distribution transformer is connected to multiple low-voltage distribution networks. The number of low-voltage distribution networks connected to each distribution transformer can be the same or different. For example, for the first distribution transformer, j = 10, for the second distribution transformer, j = 20, and so on. Those skilled in the art will understand that the values ​​of m and j are not intended to limit the scope of this disclosure.

[0062] According to embodiments of this disclosure, for each of the plurality of distribution transformers, the medium-voltage distribution network acquires the output power of the distribution transformer, calculates the active power regulation of each low-voltage distribution network based on the power limit of each low-voltage distribution network connected to the distribution transformer and the output power, and then sends control commands to the master nodes in each low-voltage distribution network based on the active power regulation of each low-voltage distribution network.

[0063] The medium-voltage distribution network sends control commands only to the distributed photovoltaic-energy storage system (PV-energy storage system), which acts as the master node, based on the active power regulation required by each low-voltage distribution network. The master node then updates its own consistency variables according to these commands and distributes the control commands (regulation schemes) to other nodes (distributed PV-energy storage systems) through a consensus algorithm. This control method reduces reliance on communication, simplifies intelligent load management, and improves processing speed.

[0064] Specifically, for each low-voltage distribution network, the medium-voltage distribution network calculates the active power regulation of the low-voltage distribution network based on the maximum and minimum active power of the low-voltage distribution network and the output power of the distribution transformer connected to the low-voltage distribution network. Specifically, the active power regulation is calculated using the following formula:

[0065]

[0066] Among them, the The active power regulation of the j-th low-voltage distribution network is... The output power of the distribution transformer connected to the j-th low-voltage distribution network is... The maximum active power of the j-th low-voltage distribution network is... Let be the minimum active power of the j-th low-voltage distribution network.

[0067] From the above formula, we can see that:

[0068] In the At that time, the j-th low-voltage distribution network absorbs power relative to the medium-voltage distribution network;

[0069] In the At that time, the j-th low-voltage distribution network outputs power relative to the medium-voltage distribution network;

[0070] In the At that time, the j-th low-voltage distribution network is in a system balance state.

[0071] According to embodiments of this disclosure, in the j-th low-voltage distribution network, or At that time, the medium-voltage distribution network sends a control command to the master node in the j-th low-voltage distribution network, wherein the control command includes an active power adjustment command.

[0072] That is, the medium-voltage distribution network sends control commands to the master node in the j-th low-voltage distribution network only when the j-th low-voltage distribution network is absorbing or outputting power relative to the medium-voltage distribution network. When the j-th low-voltage distribution network is in a system balance state, no control commands are sent. In other words, at this time, it is not necessary to regulate the active power of the master node in the low-voltage distribution network.

[0073] According to embodiments of this disclosure, the system further includes a power measuring device; the power measuring device is disposed between the medium-voltage distribution network and the distribution transformer, thereby measuring the output power of the distribution transformer. The power measuring device may be a power meter, a power quality analyzer, etc.

[0074] According to embodiments of this disclosure, for each low-voltage distribution network, after receiving the control command, the master node iteratively updates its consistency variables based on the active power adjustment of the low-voltage distribution network. The master node continuously iterates and updates its consistency variables until it reaches a system equilibrium state.

[0075] The master node iteratively updates its consistency variables based on the active power regulation of the low-voltage distribution network, including:

[0076] The master node performs the k1-th iteration update of its consistency variables based on the consistency variables of the master node that have undergone k1-1 iteration updates and the active power regulation of the low-voltage distribution network. Specifically, the k1-th iteration update of the master node's consistency variables is performed according to the following formula:

[0077]

[0078] Among them, the The consistency variables that were updated for the master node in the k1th iteration, The consistency variable of the master node has been updated k1-1 times. a is a convergence control parameter, 1≤k1≤K, where K is the preset maximum number of iterations, and t represents the dynamic change of the consistency variable's iterative update over time.

[0079] Furthermore, the convergence control parameters can be set according to the response characteristics of the distributed photovoltaic-energy storage system, the accuracy requirements of the control target, and the communication cost of the power grid.

[0080] According to embodiments of this disclosure, for each low-voltage distribution network, after receiving the control command, each of the plurality of non-master nodes calculates the state transfer coefficient from the adjacent node to the non-master node based on the network topology of the low-voltage distribution network. Based on the state transfer coefficient and the updated consistency variables of the adjacent nodes, the consistency variables of the non-master nodes are iteratively updated. The plurality of non-master nodes continuously iteratively update the corresponding consistency variables until all non-master nodes reach a system equilibrium state. The adjacent nodes are the master node or other non-master nodes.

[0081] Specifically, the non-master node continuously iterates and updates its consistency variables according to the control instructions assigned by the master node based on the consensus algorithm.

[0082] The general expression for the consensus algorithm is: x i (k+1)=∑ j∈v d i,j x j (k); where x j (k) represents the state variable of node i; k is the iteration number, predetermined by the experiment; d i,j Let be the state transfer coefficient from node i to node j.

[0083] The step of calculating the state transfer coefficient from the adjacent node to the non-master node based on the network topology of the low-voltage distribution network includes: the non-master node i+n calculates the state transfer coefficient from the adjacent node i to the non-master node i+n based on the total number of communication messages received by the non-master node i+n. Specifically, the state transfer coefficient from the adjacent node i to the non-master node i+n is calculated according to the following formula:

[0084]

[0085] Wherein, the d i,i+n The state transfer coefficients from the adjacent node i to the non-master node i+n are... is the total number of communication messages received by the non-master node i+n, and b is the communication line in the low-voltage distribution network.

[0086] Furthermore, the adjacent node i is a node directly connected to the non-master node i+n in the communication network, and the distance of the adjacent node i to the master node is closer than the distance of the non-master node i+n to the master node. When n=0, the adjacent node i is the master node; since the total number of communication messages received by different nodes in the communication network is different, the state transfer coefficients between different non-master nodes i+n and adjacent nodes i are different.

[0087] The total amount of communication information includes power regulation commands, voltage levels, frequency, or other signals related to grid operation. By calculating the state transfer coefficient between adjacent nodes based on the total amount of communication information, each node can coordinate its behavior to respond to the global needs of the grid without centralized control.

[0088] Figure 2 A schematic diagram of the topology of a master node in a low-voltage distribution network according to an embodiment of the present disclosure is shown.

[0089] like Figure 2 As shown, if the non-master node i+1 and non-master node i+2 have the same neighbor node i, then when calculating the state transfer coefficient from neighbor node i to non-master node i+1, it is necessary to calculate based on the total number of communication messages received by non-master node i+1. Similarly, when calculating the state transfer coefficient from neighbor node i to non-master node i+2, it is necessary to calculate based on the total number of communication messages received by non-master node i+2.

[0090] Furthermore, the iterative update of the consistency variables of the non-master nodes based on the state transit coefficients and the updated consistency variables of the neighboring nodes includes performing the k2-th iteration update of the consistency variables of the non-master nodes according to the following formula:

[0091] u i+n,t (k2)=∑ i+n∈c d i,i+n u i,t (k2-1);

[0092] Wherein, the u i+n,t (k2) represents the consistency variable after the k2th iteration update of non-master node i+n, where u i,t (k2-1) is the consistency variable after k2-1 iterations of the neighboring node i, where c is the set of nodes in the low-voltage distribution network, 1≤k2≤K, and K is the preset maximum number of iterations.

[0093] According to embodiments of this disclosure, the consistency variables of the master node and / or non-master node are related to the charging and discharging states of the corresponding master node and / or non-master node, the output power of photovoltaic power generation, and changes in grid power demand.

[0094] For each of the primary and / or non-primary nodes:

[0095] The charge / discharge state is represented by the ratio of the node's energy storage power to its installed capacity, specifically... Among them, P PCC,i Let S be the energy storage power of node i. PV,i Let be the installed capacity of node i.

[0096] The output power of photovoltaic power generation is affected not only by the charging and discharging state of the energy storage system, but also by factors such as the installation angle of the photovoltaic system, its topological location in the power system, and the intensity of solar irradiance. The output power of photovoltaic power generation under the influence of these multiple factors is also a significant and undeniable factor affecting the consistency variables of the master node and / or non-master nodes.

[0097] The change in grid power demand is defined as the ratio of the power exchanged between a node and the grid to the node's rated power within the time interval between two adjacent iterations. Among them, P ESS,i Let S be the grid-connected power of node i, Δt be the time interval between two adjacent iterations, and S be the grid-connected power of node i. ESS,i Let be the rated power of node i.

[0098] According to embodiments of this disclosure, the master node and all non-master nodes have reached a system balance state, including:

[0099] For any of the master node and / or non-master nodes, calculate the active power adjustment amount of the node in the k-th iteration update. When the active power adjustment amount in the k-th iteration update is 0 or less than a set threshold, the node reaches the system balance state. Wherein, when the node is a master node, k = k1, and when the node is a non-master node, k = k2.

[0100] Specifically, for any of the master node and / or non-master nodes, the active power adjustment of any node i in the k-th iteration is calculated according to the following formula:

[0101]

[0102] Among them, P ESS,i,t (k) represents the active power adjustment of any node i in k iterations, u i,t (k) is the consistency variable updated by any node i in the k-th iteration, S ESS,i S is the rated power of any node i. PV,i Let p be the installed capacity of any node i. PV,i Let S be the photovoltaic power of any node i. PV,i Let be the installed capacity of any node i.

[0103] According to embodiments of this disclosure, for a low-voltage distribution network connected to the plurality of distribution transformers, a one-time variable of the main node and all non-main nodes in the low-voltage distribution network is continuously iterated and updated until the main node and all non-main nodes in the low-voltage distribution network reach a system balance state, thereby realizing intelligent load management of the distributed photovoltaic-energy storage system.

[0104] This disclosure achieves consistency between photovoltaic power generation fluctuations, the charging and discharging status of energy storage systems, and the grid connection requirements of the power grid, thereby maximizing the energy utilization efficiency of the entire power distribution system. Furthermore, the coordinated control between distributed photovoltaic and energy storage systems can more effectively balance power demand and supply, thereby reducing voltage fluctuations, improving grid stability, avoiding voltage over-limit and distribution transformer overload problems, and ultimately realizing intelligent management and control of the power grid, promoting the rapid development of smart grids, and also providing a reference for the access and grid connection of other new energy sources (such as wind and thermal energy).

[0105] Figure 3 A flowchart illustrating an intelligent load management method for a distributed photovoltaic-energy storage system according to an embodiment of the present disclosure is shown. The distributed photovoltaic-energy storage system is installed in a power distribution system, which includes a medium-voltage power distribution network and multiple distribution transformers connected to the medium-voltage power distribution network. The distribution transformers are connected to one or more low-voltage power distribution networks, which include multiple distributed photovoltaic-energy storage systems. One distributed photovoltaic-energy storage system in the low-voltage power distribution network is a master node, and the remaining distributed photovoltaic-energy storage systems are non-master nodes.

[0106] like Figure 3 As shown, the method includes the following steps S301 to S303:

[0107] In step S301, for each of the plurality of distribution transformers, the medium-voltage distribution network obtains the output power of the distribution transformer, calculates the active power regulation of each low-voltage distribution network based on the power limit of each low-voltage distribution network connected to the distribution transformer and the output power, and then sends control commands to the master nodes in each low-voltage distribution network based on the active power regulation of each low-voltage distribution network.

[0108] In step S302, for each low-voltage distribution network, after receiving the control command, the master node iteratively updates its consistency variables based on the active power adjustment of the low-voltage distribution network; each of the plurality of non-master nodes calculates the state transfer coefficient from the adjacent node to the non-master node according to the network topology of the low-voltage distribution network, and iteratively updates the consistency variables of the non-master node based on the state transfer coefficient and the updated consistency variables of the adjacent node; the master node and the plurality of non-master nodes continuously iteratively update their corresponding consistency variables until the master node and all non-master nodes reach a system equilibrium state, wherein the adjacent node is the master node or other non-master nodes.

[0109] In step S303, the above steps are repeated for the plurality of distribution transformers until the main nodes and all non-main nodes in the low-voltage distribution network connected to the plurality of distribution transformers reach a system balance state, thereby realizing intelligent load management of the distributed photovoltaic-energy storage system.

[0110] This disclosure enables the optimization of power allocation and scheduling among distributed photovoltaic-energy storage systems through collaborative calculation of consistency variables, thereby more effectively balancing power demand and supply, reducing voltage fluctuations, improving grid stability, and ultimately realizing intelligent management and control of the grid.

[0111] In particular, according to embodiments of this disclosure, the methods described above can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for performing the methods described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium.

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0113] The units or modules described in the embodiments of this disclosure can be implemented in software or programmable hardware. The described units or modules can also be located in a processor, and the names of these units or modules do not necessarily constitute a limitation on the unit or module itself.

[0114] In another aspect, this disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system described above; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to perform the methods described in this disclosure.

[0115] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. An intelligent load management and distribution system for distributed photovoltaic-energy storage systems, characterized in that, The power distribution system comprises a medium-voltage power distribution network and a plurality of power distribution transformers connected to the medium-voltage power distribution network, the power distribution transformers being connected to a low-voltage power distribution network, the low-voltage power distribution network comprising a plurality of distributed photovoltaic- energy storage systems, one of the distributed photovoltaic- energy storage systems in the low-voltage power distribution network being a master node and the remaining distributed photovoltaic- energy storage systems being non-master nodes; For each of the plurality of power distribution transformers, the medium-voltage power distribution network obtains an output power of the power distribution transformer, calculates an active power adjustment amount of the low-voltage power distribution network according to a power limit of the low-voltage power distribution network connected to the power distribution transformer and the output power, and sends a control instruction to the master node in the low-voltage power distribution network according to the active power adjustment amount of the low-voltage power distribution network, the power limit of the low-voltage power distribution network being a maximum active power and a minimum active power of the low-voltage power distribution network; For the low-voltage power distribution network, after receiving the control instruction, the master node iteratively updates a consistency variable of the master node according to the active power adjustment amount of the low-voltage power distribution network; each of the plurality of non-master nodes calculates a state transfer coefficient of adjacent nodes to the non-master node according to a network topology of the low-voltage power distribution network, and iteratively updates a consistency variable of the non-master node based on the state transfer coefficient and the consistency variable of the adjacent nodes after the update; the master node and the plurality of non-master nodes continuously iteratively update the corresponding consistency variables until the master node and all non-master nodes reach a system balance state, wherein the adjacent nodes are the master node or other non-master nodes, the consistency variable of the master node or non-master node is related to the charge-discharge state of the corresponding master node or non-master node, the output power of photovoltaic power generation, and the change in power demand of the power grid, the charge-discharge state is the ratio of the energy storage power to the installed capacity of the corresponding node, and the change in power demand of the power grid is the ratio of the exchanged power between the corresponding node and the power grid to the rated power within the time interval of adjacent two iterative updates; For the low-voltage power distribution network connected to the plurality of power distribution transformers, the consistency variables of the master node and all non-master nodes in the low-voltage power distribution network are continuously iteratively updated until the master node and all non-master nodes in the low-voltage power distribution network reach a system balance state, thereby realizing intelligent load control of the distributed photovoltaic- energy storage systems.

2. The system of claim 1, wherein, According to the maximum active power and the minimum active power of the low-voltage power distribution network and the obtained output power of the power distribution transformer connected to the low-voltage power distribution network, the active power adjustment amount of the low-voltage power distribution network is calculated, including calculating the active power adjustment amount by using the following formula: ； Wherein, the is the active power regulation amount of the jth low-voltage distribution network, and the is the output power of the distribution transformer connected with the jth low-voltage distribution network, and the is the maximum active power of the jth low-voltage distribution network, and the is the minimum active power of the jth low-voltage distribution network.

3. The system of claim 2, wherein: In the At that time, the j-th low-voltage distribution network absorbs power relative to the medium-voltage distribution network; In the At that time, the j-th low-voltage distribution network outputs power relative to the medium-voltage distribution network; In the case that the jth low-voltage power distribution network is in a system balance state, the system balance state of the jth low-voltage power distribution network is determined according to the following formula: when the jth low-voltage power distribution network is 4. The system of claim 3, wherein, The sending of the control instruction to the master node in the low-voltage power distribution network according to the active power adjustment amount of the low-voltage power distribution network comprises: in the jth low-voltage power distribution network, or when the jth low-voltage power distribution network is in an abnormal state, the medium-voltage power distribution network sends a control instruction to a master node in the jth low-voltage power distribution network, wherein the control instruction comprises an active power adjustment instruction.

5. The system of claim 2, wherein, The iterative update of the consistency variable of the master node according to the active power adjustment amount of the low-voltage power distribution network comprises: The master node performs the k1th iteration update on the consensus variable of the master node according to the consensus variable of the master node after the k1-1th iteration update and the active power adjustment of the low-voltage distribution network.

6. The system of claim 5, wherein, The master node performs the k1th iteration update on the consensus variable of the master node according to the consensus variable of the master node after the k1-1th iteration update and the active power adjustment of the low-voltage distribution network, including performing the k1th iteration update on the consensus variable of the master node according to the following formula: ； The consistency variable updated by the master node for the k1th iteration The consistency variable updated by the master node for the k1th iteration The consistency variable updated by the master node for the k1th iteration The convergence control parameter, 1≤k1≤K, wherein K is a preset maximum iteration number.

7. The system of claim 1, wherein, The calculation of the state transmission coefficient of the adjacent node to the non-master node according to the network topology of the low-voltage distribution network includes: The non-master node i+n calculates the state transmission coefficient of the adjacent node i to the non-master node i+n according to the total number of communication information received by the non-master node i+n.

8. The system of claim 7, wherein, The non-master node i+n calculates the state transmission coefficient of the adjacent node i to the non-master node i+n according to the total number of communication information received by the non-master node i+n, including calculating the state transmission coefficient of the adjacent node i to the non-master node i+n according to the following formula: ； Wherein, the is the state transition coefficient from the adjacent node i to the non-master node i+n, the is the total number of communication information received by the non-master node i+n, and the b is a communication line in the low-voltage power distribution network.

9. The system of claim 8, wherein, The iteration update of the consensus variable of the non-master node based on the state transmission coefficient and the consensus variable of the adjacent node after the update includes the k2th iteration update of the consensus variable of the non-master node according to the following formula: ； Wherein, the The consistency variable after the k2th iteration update of the non-master node i+n, the consistency variable after the k2-1th iteration update of the adjacent node i, and the c are the node set in the low-voltage power distribution network, 1≤k2≤K, and K is a preset maximum iteration number. The consistency variable after the k2th iteration update of the non-master node i+n, the consistency variable after the k2-1th iteration update of the adjacent node i, and the c are the node set in the low-voltage power distribution network, 1≤k2≤K, and K is a preset maximum iteration number.

10. The system of claim 9, wherein, The master node and all non-master nodes reach the system balance state, including: For any one of the master node and / or non-master node, the active power adjustment of the any one node in the kth iteration update is calculated, and when the active power adjustment in the kth iteration update is 0 or less than a set threshold, the any one node reaches the system balance state, wherein when the any one node is a master node, k=k1, and when the any one node is a non-master node, k=k2.

11. The system of claim 1, wherein, The system further includes a power measuring device; The power measuring device is arranged between the low-voltage distribution network and the distribution transformer, so as to measure the output power of the distribution transformer.

12. The system of claim 1, wherein, The master node is the distributed photovoltaic- energy storage system closest to the distribution transformer connected to the low-voltage distribution network in the low-voltage distribution network.

13. An intelligent load management method for a distributed photovoltaic- energy storage system, characterized in that, The distributed photovoltaic- energy storage system is arranged in a power distribution system, the power distribution system includes a medium-voltage distribution network and a plurality of distribution transformers connected to the medium-voltage distribution network, the distribution transformers are connected to a low-voltage distribution network, the low-voltage distribution network includes a plurality of distributed photovoltaic- energy storage systems, one of the distributed photovoltaic- energy storage systems in the low-voltage distribution network is a master node, and the remaining distributed photovoltaic- energy storage systems are non-master nodes; the method includes: The medium-voltage power distribution network obtains output power of each of the plurality of power distribution transformers, calculates active power adjustment amount of the low-voltage power distribution network according to the power limit of the low-voltage power distribution network connected with the power distribution transformer and the output power, and sends a control instruction to a master node in the low-voltage power distribution network according to the active power adjustment amount of the low-voltage power distribution network, wherein the power limit of the low-voltage power distribution network is the maximum active power and the minimum active power of the low-voltage power distribution network; After receiving the control instruction, the master node of the low-voltage power distribution network iteratively updates the consistency variable of the master node according to the active power adjustment amount of the low-voltage power distribution network; each of a plurality of non-master nodes calculates a state transfer coefficient of adjacent nodes to the non-master node according to the network topology of the low-voltage power distribution network, and iteratively updates the consistency variable of the non-master node based on the state transfer coefficient and the consistency variable of the adjacent nodes after the update; the master node and the plurality of non-master nodes continuously iteratively update the corresponding consistency variables until the master node and all non-master nodes reach a system balance state, wherein the adjacent nodes are the master node or other non-master nodes, the consistency variable of the master node or non-master node is related to the charge-discharge state of the corresponding master node or non-master node, the output power of photovoltaic power generation and the change of power demand of the power grid, the charge-discharge state is the ratio of the energy storage power to the installed capacity of the corresponding node, and the change of power demand of the power grid is the ratio of the exchanged power between the corresponding node and the power grid to the rated power within the time interval of adjacent two iterative updates; The above steps are repeated for the plurality of power distribution transformers until the master node and all non-master nodes in the low-voltage power distribution network connected with the plurality of power distribution transformers reach a system balance state, thereby realizing intelligent load control of the distributed photovoltaic- energy storage system.

14. A computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are executed by the processor to implement the method of claim 13.

15. A computer program product, characterised in that, The computer instructions are executed by the processor to implement the method of claim 13.

Images