A global optimization control method for source-network-load-storage without power supply expansion in buildings

Abstract

The present invention discloses a global optimization control method for source-network-load-storage for non-capacity-expansion power supply in buildings, the steps of which are as follows: the source-network-load-storage scheduling control unit obtains the status information of building loads, distributed power generation devices and energy storage systems in real time; it is judged whether the total power generation of the distributed power generation devices is greater than or equal to the building load. If so, the distributed power generation devices are connected to the building load, and the building load is completely powered by the distributed power generation devices. Otherwise, the distributed power generation devices, the energy storage system and the large power grid are respectively connected to the building load. The total power generation of the distributed power generation devices is all supplied to the building load, and the particle swarm optimization algorithm is run to determine the power supply of the large power grid and the discharge power of the energy storage system and control the large power grid and the energy storage system to supply power to the building load. The global optimization control method for source-network-load-storage for non-capacity-expansion power supply in buildings of the present invention reduces the dependence on the electric energy of the large power grid, reduces the peak-period electricity load, has a reasonable scheduling logic, and ensures the stable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of source-grid-load-storage control and optimization, and relates to a global optimization control method for source-grid-load-storage for power supply without expansion in buildings. Background Art

[0002] With the development of building electrification and the use of electric vehicles, the intensity of building power load has gradually increased. Especially in urban areas with limited space, it is difficult to expand the power system, which is a problem in the renovation of old communities / urban areas and urban renewal.

[0003] Distributed energy can alleviate the problem of power supply expansion in cities. However, due to the instability of distributed power generation, especially the influence of natural factors on solar photovoltaics, the output of distributed energy is intermittent and volatile, which will have a certain impact on the safe and stable operation of the entire power system when connected to the grid. How to dynamically dispatch the power supply of the grid to balance the decentralized distribution of power expansion and the efficient power supply of the power system (to meet the rigid electricity demand of residents).

[0004] Therefore, it is of great practical significance to develop a source-grid-load-storage system and its dispatching control method that can solve the problem of power supply expansion in buildings at low cost. Summary of the Invention

[0005] Due to the above-mentioned defects in the prior art, the present invention provides a source-grid-load-storage system and its dispatching control method that can solve the problem of power supply expansion in buildings at low cost, overcoming the defects of the existing methods that lack a reasonable method for dispatching the supply-demand relationship between distributed energy and the large power grid.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A global optimization control method for source-grid-load-storage for power supply without expansion in buildings, the method is used to optimize and control a source-grid-load-storage system, the source-grid-load-storage system includes a building microgrid and a large power grid that are interconnected, the building microgrid includes a building load, a distributed power generation device and a energy storage system arranged on the building, the building load, the distributed power generation device and the energy storage system are interconnected, and the building load, the distributed power generation device, the energy storage system and the large power grid are respectively connected to a source-grid-load-storage dispatching control unit;

[0008] The source-grid-load-storage dispatching control unit executes the following source-grid-load-storage global optimization control method:

[0009] (1) The source-grid-load-storage dispatching control unit obtains the status information of the building load, the distributed power generation device and the energy storage system in real time;

[0010] (2) Judge whether the total power generation power of the distributed power generation device is greater than or equal to the building load. If so, go to step (3), otherwise go to step (4);

[0011] (3) Connect the distributed generation device to the building load. When the building load is completely powered by the distributed generation device, determine whether the energy storage system has reached its maximum storage capacity. If so, reduce the distributed generation power through the control unit so that it is exactly equal to the building load, without charging the energy storage system, and return to step (1). Otherwise, connect the distributed generation device to the energy storage system, and the distributed generation device charges the energy storage system, then return to step (1);

[0012] (4) Connect the distributed generation device, the energy storage system, the large power grid and the building load respectively. The total power generation of the distributed generation device is all supplied to the building load. Run the particle swarm optimization algorithm to determine the power supply P n (t) of the large power grid and the discharge power P s - (t) of the energy storage system. Supply power to the building load by controlling the large power grid and the energy storage system according to the obtained power, and return to step (1). The optimization objective function of the particle swarm optimization algorithm is the independent variable setting value when the peak value W of the transformer load rate is the lowest under the predicted daily distribution curve C of building energy consumption, as shown in the following formula:

[0013] minW = F(P n (t), P s - (t), C).

[0014] The present invention provides a source-network-load-storage system, which separates the building microgrid from the large power grid (external network). At the same time, the building microgrid is divided into three parts: distributed generation, building load, and energy storage system, and a multi-source load data architecture of the three is established to construct a platform-type system with multi-node distributed access based on the Internet of Things acquisition technology. By real-time monitoring the dynamic changes of the distributed generation power and the building load, charging the energy storage system when the distributed generation is excessive, discharging to the building load when the distributed generation is insufficient, and purchasing electricity from the large power grid when the internal electric energy of the microgrid cannot meet the building load, and using the particle swarm optimization method to regulate the charge-discharge relationship between systems. The addition of the particle swarm optimization algorithm can minimize the load rate of the transformer (that is, ensure that the user's power supply does not trip and does not cut off), and ensure the safe operation of the system. The method of the present invention aims to solve the problem that the building power load gradually increases while the power expansion is difficult, with the goal of reducing the peak power consumption load, globally optimizing and controlling the source-network-load-storage system of the building, achieving the purpose that the building power load in the area increases but there is no need for expansion, and is particularly suitable for the transformation of old communities and urban areas, with great application prospects.

[0015] As a preferred technical solution:

[0016] A global optimization control method for source-grid-load-storage for building power supply without capacity expansion as described above, wherein the distributed power generation device includes a solar photovoltaic device, an elevator energy recovery device, and a wind energy device;

[0017] The building load includes building flexible load and building rigid load;

[0018] The energy storage system includes an electricity storage device, a cold storage device, and a heat storage device. Of course, the protection scope of the present invention is not limited thereto. A feasible technical solution is given here, and those skilled in the art can select a reasonable form according to actual needs.

[0019] A global optimization control method for source-grid-load-storage for building power supply without capacity expansion as described above, wherein the P s - (t) is less than or equal to P s,max - , and the P s,max - is the maximum discharge power of the energy storage system.

[0020] A global optimization control method for source-grid-load-storage for building power supply without capacity expansion as described above. In step (4), before determining the power supply power P n (t) of the large power grid and the discharge power P s - (t) of the energy storage system, it is judged whether the energy of the energy storage system is greater than the minimum energy storage capacity of the energy storage system. If so, the particle swarm optimization algorithm is run to determine the power supply power of the large power grid and the discharge power of the energy storage system. Otherwise, P n (t) = L - P d , where L is the building load and P d is the total power generation power of the distributed power generation device.

[0021] A global optimization control method for source-grid-load-storage for building power supply without capacity expansion as described above. In step (3), when the energy storage system has not reached the maximum storage capacity, the following operations are performed:

[0022] On the premise that the total power generation power of the distributed power generation device is greater than the building load, the distributed power generation device is connected to the energy storage system, and the distributed power generation device charges the energy storage system.

[0023] A global optimization control method for source-grid-load-storage for building power supply without capacity expansion as described above, wherein the building load, the distributed power generation device, and the energy storage system are connected to the source-grid-load-storage dispatching control unit through a state acquisition device.

[0024] A global optimization control method for source-grid-load-storage for building power supply without capacity expansion as described above, wherein the large power grid is interconnected with the building microgrid, and the large power grid is powered by a public power supply network.

[0025] The above technical solutions are only one feasible technical solution of the present invention, and the protection scope of the present invention is not limited thereto. Those skilled in the art can reasonably adjust the specific design according to actual needs.

[0026] The above invention has the following advantages or beneficial effects:

[0027] The present invention proposes a global optimization control method for building power supply without capacity expansion, considering the system architecture of the building microgrid, globally optimizing and controlling the distributed generation, energy storage system and building load in the building microgrid, starting from the relationship between distributed generation and load, and determining the power supply strategy of the load according to the power generation of the building microgrid itself, thereby reducing the dependence on the large power grid, achieving the purpose of reducing the peak power consumption load, and at the same time, its scheduling logic is reasonable, which can ensure the stable operation of the power grid (avoiding user power supply tripping and power outage), reducing the building power supply capacity expansion cost while meeting the rigid power consumption needs of residents, and having great application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, the present invention and its features, shape and advantages will become more obvious. The same reference numerals indicate the same parts in all the drawings. The drawings are not drawn to scale, and the focus is on showing the gist of the present invention.

[0029] Figure 1 is the architecture diagram of the source-network-load-storage system related to the present invention;

[0030] Figure 2 is the composition diagram of the source-network-load-storage system related to the present invention;

[0031] Figure 3 is the flow chart of the pure distributed generation power supply stage;

[0032] Figure 4 is the flow chart of the particle swarm optimization strategy. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The following further illustrates the structure in the present invention with reference to the drawings and specific embodiments, but it is not a limitation of the present invention.

[0034] Embodiment 1

[0035] A global optimization control method for building power supply without capacity expansion, which is used to optimize and control the source-network-load-storage system, and its source-network-load-storage system is as Figure 1 and 2As shown in the figure, it includes a building microgrid and a large power grid that are interconnected (the large power grid is independent of the building microgrid, and the large power grid is powered by a public power supply network). The building microgrid includes building loads (including building flexible loads and building rigid loads), distributed generation devices arranged on the building (including solar photovoltaic devices, elevator energy recovery devices, and wind energy devices), and an energy storage system (including battery energy storage devices, cold storage devices, and heat storage devices). The building loads, distributed generation devices, and energy storage system are interconnected. The large power grid is electrically connected to the source-network-load-storage dispatching and control unit, and the building loads, distributed generation devices, and energy storage system are connected to the source-network-load-storage dispatching and control unit through state acquisition devices;

[0036] The source-network-load-storage dispatching and control unit executes the following source-network-load-storage global optimization control method:

[0037] (1) The source-network-load-storage dispatching and control unit obtains the state information of the building loads, distributed generation devices, and energy storage system in real time;

[0038] (2) Determine whether the total power generation of the distributed generation devices is greater than or equal to the building load. If so, go to step (3); otherwise, go to step (5);

[0039] (3) Connect the distributed generation devices to the building load, and the building load is completely powered by the distributed generation devices. Determine whether the energy storage system has reached its maximum storage capacity. If so, reduce the distributed generation power through the control unit so that it is exactly equal to the building load, and there is no need to charge the energy storage system. Return to step (1); otherwise, go to step (4);

[0040] [[ID=!5]](4) On the premise that the total power generation of the distributed generation devices is greater than the building load, connect the distributed generation devices to the energy storage system, and the distributed generation devices charge the energy storage system. Return to step (1);

[0041] (5) Connect the distributed generation devices, energy storage system, and large power grid to the building load respectively. The total power generation of the distributed generation devices is all supplied to the building load. Determine whether the energy of the energy storage system is greater than the minimum energy storage capacity of the energy storage system. If so, go to step (6); otherwise, the large power grid supplies power to the building load according to P n (t), P n (t) = L - P d , where L is the building load and P d is the total power generation of the distributed generation devices. Return to step (1);

[0042] (6) Run the particle swarm optimization algorithm to determine the power supply power P n (t) of the large power grid and the discharge power P s -(t), control the large power grid and energy storage system to supply power to the building load according to the obtained power, and return to step (1). The optimization objective function of the particle swarm optimization algorithm is the independent variable setting value when the peak value W of the transformer load rate is the lowest under the predicted daily distribution curve C of building energy consumption, as shown in the following formula:

[0043] minW = F(P n (t), P s - (t), C).

[0044] Among them, when the building load is completely supplied by distributed generation devices, it is the pure distributed generation power supply stage;

[0045] The definition of the pure distributed generation power supply stage is that within the time period T1, the total power generation of distributed generation is greater than or equal to the building load, and its mathematical expression is as follows:

[0046] P d ≥L

[0047] In the formula: P d is the electric power of distributed generation, and L is the building electricity load;

[0048] In the pure distributed generation power supply stage, the building electricity load is all supplied by distributed generation devices, and there is no need to purchase electric energy from the large power grid. Moreover, the excess electric energy of the distributed generation devices is stored in the energy storage system for use during peak electricity consumption periods. The charging power of the energy storage system is:

[0049] P s + = P d -L

[0050] 0 ≤ P s + ≤ P s,max +

[0051] In the formula: P s + is the charging power of the energy storage system, and P s,max + is the upper limit of the charging power of the energy storage system.

[0052] In the pure distributed generation power supply stage, the stored energy of the energy storage system is:

[0053]

[0054] E′ s ≤ E s,max

[0055] In the formula: E sThe energy stored in the energy storage system, E0 is the initial energy of the energy storage system, E s,max is the maximum storage capacity of the energy storage system.

[0056] When the energy storage system is fully charged, it will no longer be charged. The flowchart of the pure distributed generation power supply stage is as Figure 3 shown;

[0057] The building load is jointly powered by the distributed generation device, the energy storage system, and the large power grid, which is the joint power supply stage;

[0058] [[ID=`13]]The definition of the joint power supply stage is that within the time period T2, the total power generation of the distributed generation device is less than the building load, and its mathematical expression is as follows:

[0059] P d <L

[0060] During the joint power supply stage, the building electrical load is jointly borne by the distributed generation device, the energy storage system, and the large power grid, and the electric energy of the distributed generation device is preferentially used. The discharge power of the energy storage system and the power purchased from the large power grid satisfy the following formula:

[0061] P s - +P n =L - P d

[0062] In the formula: P n is the power purchased from the large power grid, P s - is the discharge power of the energy storage system;

[0063] When using the energy storage system for power supply, the energy stored in the energy storage system should be greater than the minimum energy storage capacity:

[0064] E0 > E s,min

[0065] The limit of the discharge power of the energy storage system is:

[0066] 0 ≤ P s - ≤ P s,max -

[0067] In the formula: E s,min is the minimum energy storage capacity of the energy storage system, P s,max - is the upper limit of the discharge power of the energy storage system;

[0068] When the energy storage system supplies power, the stored energy of the energy storage system is:

[0069]

[0070] How to allocate the electric power P purchased by the large power grid? n and the discharge power P of the energy storage system s - You can use Figure 4 The particle swarm optimization algorithm shown.

[0071] Taking into account the need for the source-grid-load-storage system to develop in parallel with the energy service business and future load aggregator business, the construction of the above platform architecture needs to consider: the source-grid-load-storage system will be constructed in a step-by-step manner. The construction of the source-grid-load-storage system will not affect the normal operation of existing business module products. The platform will use mature, stable technical products and technical solutions that have been widely used as much as possible to ensure that the platform architecture can support the gradual expansion and increase of new integrated energy service businesses. High availability design should be carried out in accordance with the requirements of large-scale Internet applications to ensure uninterrupted service to business channels and operation management of the system, and to ensure a smooth and reliable end-user experience. Support smooth gradual linear expansion capabilities to cope with the expansion of business scope and increase in business volume during the system evolution process.

[0072] Those skilled in the art should understand that they can implement variations by combining the prior art with the above embodiments, which will not be described in detail here. Such variations do not affect the essence of the present invention and will not be described in detail here.

[0073] The above describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above-mentioned specific embodiments, and the devices and structures that are not described in detail should be understood to be implemented in a common manner in the art; any technician familiar with the art can use the above-mentioned disclosed methods and technical contents to make many possible changes and modifications to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, or modify them into equivalent embodiments of equivalent changes, which does not affect the essential content of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention that do not depart from the content of the technical solutions of the present invention are still within the scope of protection of the technical solutions of the present invention.

Claims

1. A global optimization control method for power supply without capacity expansion in buildings, characterized in that: The method is used to optimize the control of the source-network-load-storage system, which includes an interconnected building microgrid and a large power grid. The building microgrid includes building loads, as well as distributed generation devices and energy storage systems arranged on the building. The building loads, distributed generation devices, and energy storage systems are interconnected, and the building loads, distributed generation devices, energy storage systems, and large power grid are respectively connected to the source-network-load-storage dispatching and control unit; The source-network-load-storage dispatching and control unit executes the following source-network-load-storage global optimization control method: (1) The source-network-load-storage dispatching and control unit obtains the status information of the building loads, distributed generation devices, and energy storage systems in real time; (2) Judge whether the total power generation of the distributed generation device is greater than or equal to the building load. If so, go to step (3); otherwise, go to step (4); (3) Connect the distributed generation device to the building load, and the building load is completely powered by the distributed generation device. Judge whether the energy storage system has reached its maximum storage capacity. If so, reduce the distributed generation power through the control unit so that it is exactly equal to the building load and there is no need to charge the energy storage system, and return to step (1). Otherwise, connect the distributed generation device to the energy storage system, and the distributed generation device charges the energy storage system, and return to step (1); (4) Connect the distributed generation device, energy storage system, large power grid and building load respectively. The total power generation of the distributed generation device is all supplied to the building load. Run the particle swarm optimization algorithm to determine the power supply P n (t) of the large power grid and the discharge power P s - (t) of the energy storage system. Supply power to the building load from the large power grid and the energy storage system according to the obtained power, and return to step (1). The optimization objective function of the particle swarm optimization algorithm is the independent variable setting value when the peak value W of the transformer load rate is the lowest under the predicted daily distribution curve C of building energy consumption, as shown in the following formula: minW = F(P n (t), P s - (t), C).

2. The global optimization control method for source-network-load-storage for power supply without expansion in buildings according to claim 1, characterized in that, The distributed generation device includes a solar photovoltaic device, an elevator energy recovery device, and a wind energy device; The building load includes building flexible loads and building rigid loads; The energy storage system includes an electricity storage device, a cold storage device, and a heat storage device.

3. A global optimization control method for source-network-load-storage for building power supply without capacity expansion according to claim 1, characterized in that, The described P s - (t) is less than or equal to P s,max - , the described P s,max - is the maximum discharge power of the energy storage system.

4. A global optimization control method for source-network-load-storage for building power supply without capacity expansion according to claim 1, characterized in that, In step (4), before determining the power supply P n (t) of the large power grid and the discharge power P s - (t) of the energy storage system, it is judged whether the energy of the energy storage system is greater than the minimum energy storage capacity of the energy storage system. If so, the particle swarm optimization algorithm is run to determine the power supply of the large power grid and the discharge power of the energy storage system. Otherwise, P n (t) = L - P d , where L is the building load and P d is the total power generation of the distributed generation device.

5. The global optimization control method for source-network-load-storage for non-expansion power supply in buildings according to claim 1, characterized in that, In step (3), when the energy storage system has not reached its maximum storage capacity, the following operations are performed: On the premise that the total power generation of the distributed generation device is greater than the building load, connect the distributed generation device to the energy storage system, and the distributed generation device charges the energy storage system.

6. The global optimization control method for source-network-load-storage for building power supply without capacity expansion according to claim 1, wherein The building loads, distributed generation devices, and energy storage systems are connected to the source-network-load-storage dispatching and control unit through a status acquisition device.

7. A global optimization control method for source-network-load-storage for building power supply without capacity expansion according to claim 1, characterized in that, The large power grid is interconnected with the building microgrid, and the large power grid is powered by a public power supply network.

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