Dispatching method and device for distributed energy storage under uncertain requirements

Abstract

The invention relates to a distributed energy storage scheduling method and device under an uncertain demand, and the method comprises the steps: constructing a distributed energy storage planning model under the uncertain demand; the charge state and the energy storage capacity of the distributed energy storage power station are considered, the constraint condition of the charge and discharge quantity of the distributed energy storage planning model is rewritten, and the distributed energy storage planning model is rewritten based on the number of scenes, so that each scene covers the requirements of all nodes; and solving the rewritten distributed energy storage planning model by adopting a high-order sound field algorithm to obtain an optimal scheduling scheme, and scheduling distributed energy storage based on the optimal scheduling scheme. The overall operation performance of the power system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a method and device for dispatching distributed energy storage under uncertain demand. Background Art

[0002] With the development of global energy transformation, increasing the proportion of renewable energy in electricity supply has become a key goal of energy development. However, renewable energy (such as solar energy and wind energy) has significant intermittency and volatility, which poses severe challenges to the stable operation of the power system. Distributed energy storage (DES) has emerged as an effective solution. It can store electrical energy close to the energy generation end or the user end, realize the space-time transfer of energy, thereby improving the absorption capacity of renewable energy and enhancing the flexibility and stability of the power system.

[0003] In the current operation of the power system, demand uncertainty is a factor that cannot be ignored. Load changes on the demand side are affected by many factors, including weather conditions, user behavior patterns, and the level of economic activity. This uncertainty makes it difficult for traditional power dispatching methods to accurately meet real-time demand, which can easily lead to an imbalance between supply and demand, and thus affect the stability of the power grid and the quality of power supply. In addition, energy storage technology itself also faces many technical and economic challenges in practical applications. On the one hand, the investment cost of energy storage equipment is relatively high, including the cost of equipment purchase, installation and commissioning, and later maintenance, which to a certain extent limits its large-scale promotion and application; on the other hand, the optimization of technical parameters such as energy conversion efficiency, charge and discharge life, and capacity configuration of the energy storage system is also an important factor affecting its performance and economy.

[0004] In the field of academic research and engineering practice, many scholars and research institutions have conducted extensive and in-depth research on the optimal configuration and scheduling of distributed energy resources (including distributed generation and distributed energy storage). For example, some studies analyze the optimal layout of distributed generation in the distribution network by establishing a static load model to reduce network losses; many studies use intelligent optimization algorithms (such as particle swarm optimization algorithm, genetic algorithm, etc.) to optimize the parameters of the energy storage system to improve system performance. However, despite the fruitful results achieved by predecessors in related fields, there are still many areas that need further improvement and perfection in the face of the actual complex and changeable power system operating environment. In particular, how to more accurately consider demand uncertainty, how to more effectively coordinate energy storage optimization decisions at different time scales, and how to comprehensively consider multiple economic and technical factors for system optimization, etc., the existing research still needs to be further explored. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and device for dispatching distributed energy storage under uncertain demand, so as to improve the overall operating performance of the power system.

[0006] The technical solution adopted by the present invention to solve the technical problem is: to provide a method for dispatching distributed energy storage under uncertain demand, comprising the following steps:

[0007] Construct a distributed energy storage planning model under demand uncertainty, which includes an upper-level site selection planning model and a lower-level operation optimization model; the site selection planning model minimizes construction costs while satisfying site selection, capacity, and connection constraints; the operation optimization model maximizes operational benefits while taking into account electricity price fluctuations and equipment health;

[0008] Considering the state of charge of the distributed energy storage power station and its energy storage capacity, the constraints of the charge and discharge capacity of the distributed energy storage planning model are restated, and the distributed energy storage planning model is rewritten based on the number of scenarios so that each scenario covers the needs of all nodes;

[0009] A high-order acoustic field algorithm is used to solve the rewritten distributed energy storage planning model to obtain the optimal scheduling plan, and the distributed energy storage is scheduled based on the optimal scheduling plan.

[0010] The site selection planning model is constructed with the goal of minimizing construction cost. The objective function of the site selection planning model is expressed as: Among them, f 1 is the objective function of the site planning model, represents the fixed cost of distributed energy storage power station construction, represents the variable cost of distributed energy storage power station construction, represents the cost of the wires connecting the power grid and the distributed energy storage power station; where p f represents the fixed power of distributed energy storage power station construction, y i represents the fixed cost value per kilowatt-hour of power, including site preparation and transformation costs, building installation costs, transmission and distribution infrastructure construction costs, etc. N represents the relevant cost items for the construction of distributed energy storage power stations, N′ represents the relevant cost items for connecting the power grid and the distributed energy storage power station, and p v represents the variable power of distributed energy storage power station construction, k i represents the capacity of the distributed energy storage power station constructed at the i-th network node, p l represents the power of the wires connecting the grid and the distributed energy storage power station, x i,j represents the connection status between the distributed energy storage power station constructed at the i-th network node and the j-th grid node, l i,j The length of the power line between the distributed energy storage power station built for the i-th network node and the j-th grid node.

[0011] The operation optimization model is constructed with the goal of maximizing the operation benefit. The objective function of the operation optimization model is expressed as:

[0012]

[0013] Among them, f 2 is the objective function of the operation optimization model, o i,t ·p subsidy Subsidy income for the government, i,t ·p t To meet the charging demand, a i,t ·p t The cost of purchasing electricity from the grid. Carbon emission costs generated by charging and discharging distributed energy storage stations and demand shifting, (ξ i,t -o i,t )·p penalty is the penalty cost for not meeting charging demand, where o i,t represents the electricity sales of the distributed energy storage power station built at the i-th network node during period t, p subsidy represents the power corresponding to the income from government subsidies, p t represents the power corresponding to the income required to meet the charging demand, a i,t represents the amount of electricity purchased by the distributed energy storage power station built at the i-th network node during period t, p m Indicates the power corresponding to the cost of purchasing electricity from the grid, m i,j,t represents the demand transfer state between the distributed energy storage power station built at the i-th road network node and the distributed energy storage power stations built at other road nodes during period t, p carbon represents the power corresponding to the carbon emission cost generated by the charging and discharging of distributed energy storage equipment and the movement of demand, ξ i,t represents the demand for distributed energy storage power stations built at the i-th network node in period t, p penalty The power corresponding to the penalty cost of unmet demand.

[0014] Considering the state of charge and energy storage capacity of the distributed energy storage power station, the constraints of the charge and discharge capacity of the distributed energy storage planning model are restated as follows:

[0015] Initial state of charge constraint: The initial state of charge of the energy storage station is equal to the energy storage capacity of the energy storage station;

[0016] State of charge update constraint: The current state of charge of the energy storage power station is equal to the residual value of the state of charge in the previous stage after considering the decay rate, plus the current amount of electricity purchased from the grid multiplied by the charging efficiency, minus the current discharge amount divided by the discharge efficiency;

[0017] State of charge range constraint: the state of charge of the energy storage power station is greater than or equal to the product of the energy storage capacity of the energy storage power station and the minimum state of charge, and less than or equal to the product of the energy storage capacity of the energy storage power station and the maximum state of charge;

[0018] Constraints on the relationship between charging capacity and state of charge: There is a quadratic function relationship between the charging efficiency of the energy storage power station and the state of charge of the energy storage power station, and there is a quadratic function relationship between the discharge efficiency of the energy storage power station and the state of charge of the energy storage power station;

[0019] Constraints on the relationship between discharge capacity and demand: The current discharge capacity of the energy storage power station is less than or equal to the current demand;

[0020] Non-negative constraint condition for charge and discharge: the charge and discharge capacity of the energy storage power station is greater than or equal to zero;

[0021] Demand transfer variable constraint: the demand transfer between different energy storage power stations is greater than or equal to zero;

[0022] Constraints associated with demand transfer and charge and discharge capacity: The demand after transfer is less than or equal to the original demand forecast minus the demand transferred from other nodes, plus the demand transferred to other nodes.

[0023] The technical solution adopted by the present invention to solve the technical problem is: to provide a dispatching device for distributed energy storage under uncertain demand, comprising:

[0024] A construction module is used to construct a distributed energy storage planning model under demand uncertainty, wherein the distributed energy storage planning model includes an upper-level site selection planning model and a lower-level operation optimization model; the site selection planning model minimizes construction costs while satisfying site selection, capacity, and connection constraints; the operation optimization model maximizes operation benefits while taking into account electricity price fluctuations and equipment health;

[0025] A restatement and rewrite module is used to consider the state of charge of the distributed energy storage power station and its energy storage capacity, restate the constraints of the charge and discharge capacity of the distributed energy storage planning model, and rewrite the distributed energy storage planning model based on the number of scenarios, so that each scenario covers the needs of all nodes;

[0026] The solution scheduling module is used to solve the rewritten distributed energy storage planning model using a high-order acoustic field algorithm to obtain the optimal scheduling plan, and schedule the distributed energy storage based on the optimal scheduling plan.

[0027] The construction module constructs the site selection planning model with the goal of minimizing construction cost. The objective function of the site selection planning model is expressed as: Among them, f 1 is the objective function of the site planning model, represents the fixed cost of distributed energy storage power station construction, represents the variable cost of distributed energy storage power station construction, represents the cost of the wires connecting the power grid and the distributed energy storage power station; where p f represents the fixed power of distributed energy storage power station construction, y i represents the fixed cost value per kilowatt-hour of power, including site preparation and transformation costs, building installation costs, transmission and distribution infrastructure construction costs, etc. N represents the relevant cost items for the construction of distributed energy storage power stations, N′ represents the relevant cost items for connecting the power grid and the distributed energy storage power station, and p v represents the variable power of distributed energy storage power station construction, k i represents the capacity of the distributed energy storage power station constructed at the i-th network node, p l represents the power of the wires connecting the grid and the distributed energy storage power station, x i,j represents the connection status between the distributed energy storage power station constructed at the i-th network node and the j-th grid node, l i,j The length of the power line between the distributed energy storage power station built for the i-th network node and the j-th grid node.

[0028] The construction module constructs the operation optimization model with the goal of maximizing the operation benefit. The objective function of the operation optimization model is expressed as:

[0029]

[0030] Among them, f 2 is the objective function of the operation optimization model, o i,t ·p subsidy Subsidy income for the government, i,t ·p t To meet the charging demand, a i,t ·p t The cost of purchasing electricity from the grid. Carbon emission costs generated by charging and discharging distributed energy storage stations and demand shifting, (ξ i,t -o i,t )·p penalty is the penalty cost for not meeting charging demand, where o i,t represents the electricity sales of the distributed energy storage power station built at the i-th network node during period t, p subsidy represents the power corresponding to the income from government subsidies, p t represents the power corresponding to the income required to meet the charging demand, a i,t represents the amount of electricity purchased by the distributed energy storage power station built at the i-th network node during period t, p m Indicates the power corresponding to the cost of purchasing electricity from the grid, m i,j,trepresents the demand transfer state between the distributed energy storage power station built at the i-th road network node and the distributed energy storage power stations built at other road nodes during period t, p carbon represents the power corresponding to the carbon emission cost generated by the charging and discharging of distributed energy storage equipment and the movement of demand, ξ i,t represents the demand for distributed energy storage power stations built at the i-th network node in period t, p penalty The power corresponding to the penalty cost of unmet demand.

[0031] The restatement and rewriting module restates the constraint conditions of the charge and discharge capacity of the distributed energy storage planning model as follows:

[0032] Initial state of charge constraint: The initial state of charge of the energy storage station is equal to the energy storage capacity of the energy storage station;

[0033] State of charge update constraint: The current state of charge of the energy storage power station is equal to the residual value of the state of charge in the previous stage after considering the decay rate, plus the current amount of electricity purchased from the grid multiplied by the charging efficiency, minus the current discharge amount divided by the discharge efficiency;

[0034] State of charge range constraint: The state of charge of the energy storage power station is greater than or equal to the product of the energy storage capacity of the energy storage power station and the minimum state of charge, and less than or equal to the product of the energy storage capacity of the energy storage power station and the maximum state of charge;

[0035] Constraints on the relationship between charging capacity and state of charge: There is a quadratic function relationship between the charging efficiency of the energy storage power station and the state of charge of the energy storage power station, and there is a quadratic function relationship between the discharge efficiency of the energy storage power station and the state of charge of the energy storage power station;

[0036] Constraints on the relationship between discharge capacity and demand: The current discharge capacity of the energy storage power station is less than or equal to the current demand;

[0037] Non-negative constraint condition for charge and discharge: the charge and discharge capacity of the energy storage power station is greater than or equal to zero;

[0038] Demand transfer variable constraint: the demand transfer between different energy storage power stations is greater than or equal to zero;

[0039] Constraints associated with demand transfer and charge and discharge capacity: The demand after transfer is less than or equal to the original demand forecast minus the demand transferred from other nodes, plus the demand transferred to other nodes.

[0040] The restatement and rewriting module rewrites the distributed energy storage planning model based on the number of scenarios so that each scenario covers the needs of all nodes. Specifically, a distributed energy storage planning model scenario tree is constructed. The distributed energy storage planning model scenario tree is a complete binary tree structure, wherein the needs of the nodes in the first layer are demand two and demand three; the needs of the nodes in the second layer are demand three and demand four, and the needs of the nodes in the third layer are demand two and demand four; the nodes in the fourth layer correspond to 8 different scenarios, respectively, and each scenario covers the needs of all nodes.

[0041] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for dispatching distributed energy storage under uncertain demand are implemented.

[0042] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method for dispatching distributed energy storage under uncertain demand are implemented.

[0043] Beneficial Effects

[0044] Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the prior art: The present invention constructs a two-stage optimization model for distributed energy storage planning under demand uncertainty, which is composed of an upper-level site selection planning model and a lower-level operation optimization model. Through the coordination of these two layers, the optimal configuration of the distributed energy storage system is achieved, and the overall operation performance of the power system is improved. Among them, the upper-level model minimizes construction costs while satisfying site selection, capacity and connection constraints. The upper-level model minimizes development costs by determining the optimal location and capacity of the DESPS within the road node, as well as the connection with the grid node. The lower-level model takes into account factors such as electricity price fluctuations and equipment health, and maximizes operational benefits by optimizing the charging and discharging and power status of the energy storage station. The focus of the operation stage of the lower-level model is to optimize the charging and discharging plan to balance supply and demand and maximize operational benefits from subsidies, sales and reduced procurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of a method for dispatching distributed energy storage under uncertain demand according to a first embodiment of the present invention;

[0046] Figure 2 is a rewriting model scene tree diagram in the first embodiment of the present invention;

[0047] Figure 3It is a schematic diagram of the change of the energy storage power station status of the node over time in different scenarios in the embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0049] The first embodiment of the present invention relates to a method for dispatching distributed energy storage under uncertain demand, such as Figure 1 As shown, the following steps are included:

[0050] Step 1: construct a distributed energy storage planning model under demand uncertainty, wherein the distributed energy storage planning model includes an upper-level site selection planning model and a lower-level operation optimization model; the site selection planning model minimizes construction costs while satisfying site selection, capacity and connection constraints; the operation optimization model maximizes operational benefits while considering electricity price fluctuations and equipment health.

[0051] In this step, a distributed energy storage planning model under demand uncertainty is constructed to target the impact of load forecasting errors on system balance. The model architecture consists of an upper-level site selection planning model and a lower-level operation optimization model. Through the coordination of these two layers, the optimal configuration of the distributed energy storage system is achieved. The overall objective function f of the distributed energy storage planning model is expressed as:

[0052] maxf=f 2 -f 1

[0053] Among them, f 1 represents the site selection and capacity determination cost during the planning period, f 2 In order to maximize the benefits of the distributed energy storage planning model in order to achieve the operating benefits during the operation period, it is necessary to build a site selection planning model with the goal of minimizing the site selection and capacity determination costs during the planning period, and to build an operation optimization model with the goal of maximizing the operating benefits during the operation period.

[0054] The upper-level site planning model in this implementation method can minimize the construction cost while satisfying the site selection, capacity and connection constraints. It can minimize the development cost by determining the optimal location and capacity of DESPS within the road node and the connection with the grid node. Therefore, the objective function of the site planning model can be expressed as:

[0055]

[0056] in, represents the fixed cost of distributed energy storage power station construction, represents the variable cost of distributed energy storage power station construction, represents the cost of the wires connecting the power grid and the distributed energy storage power station; where p f represents the fixed power of distributed energy storage power station construction, y i represents the fixed cost value per kilowatt-hour of power, including site preparation and transformation costs, building installation costs, transmission and distribution infrastructure construction costs, etc. N represents the relevant cost items for the construction of distributed energy storage power stations, N′ represents the relevant cost items for connecting the power grid and the distributed energy storage power station, and p v represents the variable power of distributed energy storage power station construction, k i represents the capacity of the distributed energy storage power station constructed at the i-th network node, p l represents the power of the wires connecting the grid and the distributed energy storage power station, x i,j represents the connection status between the distributed energy storage power station constructed at the i-th network node and the j-th grid node, l i,j The length of the power line between the distributed energy storage power station built for the i-th network node and the j-th grid node.

[0057] The lower-level operation optimization model in this implementation takes into account factors such as electricity price fluctuations and equipment health, and maximizes operational benefits by optimizing the charging and discharging and power status of the energy storage station. The focus of the lower-level model operation phase is to optimize the charging and discharging plan to balance supply and demand and maximize operational benefits from subsidies, sales, and reduced procurement. Therefore, the objective function of the operation optimization model can be expressed as:

[0058]

[0059] Among them, i,t ·p subsidy Subsidy income for the government, i,t ·p t To meet the charging demand, a i,t ·p t The cost of purchasing electricity from the grid. Carbon emission costs generated by charging and discharging distributed energy storage stations and demand shifting, (ξ i,t -o i,t )·p penalty is the penalty cost for not meeting charging demand, where o i,t represents the electricity sales of the distributed energy storage power station built at the i-th network node during period t, p subsidy represents the power corresponding to the income from government subsidies, p t represents the power corresponding to the income required to meet the charging demand, a i,t represents the amount of electricity purchased by the distributed energy storage power station built at the i-th network node during period t, pm Indicates the power corresponding to the cost of purchasing electricity from the grid, m i,j,t represents the demand transfer state between the distributed energy storage power station built at the i-th road network node and the distributed energy storage power stations built at other road nodes during period t, p carbon represents the power corresponding to the carbon emission cost generated by the charging and discharging of distributed energy storage equipment and the movement of demand, ξ i,t represents the demand for distributed energy storage power stations built at the i-th network node in period t, p penalty The power corresponding to the penalty cost of unmet demand.

[0060] Step 2, considering the state of charge of the distributed energy storage power station and its energy storage capacity, restate the constraints on the charge and discharge capacity of the distributed energy storage planning model, and rewrite the distributed energy storage planning model based on the number of scenarios so that each scenario covers the needs of all nodes.

[0061] In this step, the state of charge and energy storage capacity of the distributed energy storage power station are considered, and the constraints of the charge and discharge capacity of the distributed energy storage planning model are restated, specifically as follows:

[0062] Initial state constraint: In the model, the formula SOC i,1 =k i , In the initial stage (t = 1), the state of charge (SOC i,1 ) is equal to its energy storage capacity k i ,This constraint determines the initial energy reserve of the power station and provides a ,starting state basis for subsequent charging and discharging operations.

[0063] Constraints for the state of charge update in the subsequent phase: Formula Describes the subsequent stage (t∈T, t<t max -1), the update method of the state of charge of the distributed energy storage power station. It is equal to the state of charge SOC of the previous stage i,1 Considering the attenuation rate η decay The remaining value after the completion of the experiment plus the amount of electricity purchased from the power grid in this stage i,t Multiply by the charging efficiency η in,i,t , minus the discharge amount of this stage Divided by the discharge efficiency η out,i,t .

[0064] State of charge range constraint: Formula k i ·SOC min ≤SOC i,t ≤k i ·SOC max , t∈T limits the state of charge range of the distributed energy storage power station during the entire operation process to ensure that it is always in a healthy operating range. i,t Must be greater than or equal to its energy storage capacity k i and minimum state of charge SOC min The product of i and maximum state of charge SOC max The product of .

[0065] Constraints on the relationship between charge capacity and state of charge: defines the charging efficiency η in,i,t and discharge efficiency η out,i,t State of charge SOC i,t-1 The quadratic function relationship of charging efficiency Discharge efficiency This nonlinear relationship indicates that as the state of charge changes, the charging and discharging efficiency will also change accordingly, which indirectly affects the actual effect of charging and discharging and becomes an important factor constraining the charging and discharging amount.

[0066] Discharge and demand relationship constraints: Formula o i,t ≤D i,t , t∈T directly constrains the discharge amount o in each stage i.t , it is stipulated that it must be less than or equal to the demand D of this stage i,t , ensuring that the discharge operation does not exceed the demand range and avoiding adverse effects of excessive discharge on the system.

[0067] Non-negative constraint on charge and discharge capacity: Formula o i,t ≥0,a i,t ≥0, t∈T respectively for the charge amount o i,t ≥0 and discharge amount a i,t ≥0 is non-negatively constrained to ensure that the charging and discharging operations of the distributed energy storage power station are reasonable and feasible at any time.

[0068] Demand transfer and charge / discharge capacity relationship constraints, demand transfer variable constraints: Formula m i,j,t ≥0, m in t∈T i,j,t ≥0 represents the demand transfer between different distributed energy storage nodes, which must satisfy the non-negativity constraint. This constraint ensures that the transfer of demand between nodes is reasonable and in line with the actual situation, and provides a basis for accurately describing the operation of distributed energy storage systems under uncertain demand.

[0069] Demand transfer and charge and discharge capacity association constraints: Formula The demand shift is closely linked to the charging and discharging capacity of distributed energy storage power stations and demand forecasts. It represents the demand after the shift D i,t Must be less than or equal to the original demand forecast ξ i,t Subtract the demand transferred from other nodes Plus the need to transfer to other nodes This constraint requires that when considering the charging and discharging amount, the transfer of demand between different nodes must be fully considered to ensure that supply and demand can be balanced under various demand scenarios and optimize the overall operation of the distributed energy storage system.

[0070] In this step, the distributed energy storage planning model is rewritten based on the number of scenarios, so that each scenario covers the needs of all nodes. Taking 4 needs as an example, the specific steps are as follows: Construct a distributed energy storage planning model scenario tree (see Figure 2 ), the distributed energy storage planning model scenario tree is a complete binary tree structure, wherein the demands of the nodes in the first layer are demands 2 and 3; the demands of the nodes in the second layer are demands 3 and 4, and the demands of the nodes in the third layer are demands 2 and 4; the nodes in the fourth layer correspond to 8 different scenarios, and each scenario covers the demands of all nodes. The scenario tree adopts a complete binary tree structure, and the branches of each layer represent different possibilities for demand values. Starting from the root node, there are 2 possible demands (2 or 3) in the first layer, 2 possibilities (3 or 4) in each branch of the second layer, and 2 possibilities (2 or 4) in each branch of the third layer. Through this hierarchical demand setting, according to the multiplication principle, the total number of scenarios is the product of the number of possible values ​​in each layer, that is, 2×2×2=8 scenarios. In these 8 scenarios, since the demand values ​​of each location have different combinations in different scenarios, various demand changes can be fully considered, thereby ensuring that each scenario covers the demands of all nodes. For example, when the first position is 2, combined with 3 or 4 in the second position and 2 or 4 in the third position, a series of different demand combination scenarios can be obtained; when the first position is 3, the same combination with different values ​​in other positions will produce new scenarios. These scenarios together constitute a complete demand coverage.

[0071] Step 3: Use a high-order acoustic field algorithm to solve the rewritten distributed energy storage planning model to obtain the optimal scheduling plan, and schedule the distributed energy storage based on the optimal scheduling plan.

[0072] Since the distributed energy storage planning model constructed above is nonlinear, it cannot be solved using a standard linear programming model. This step uses the high-order acoustic field (HOA) algorithm to find the global optimal solution by introducing overall iteration and random step size. The HOA algorithm can explore more solution spaces through the diversity within the population, thereby increasing the possibility of finding the global optimal solution. During the algorithm iteration process, by continuously updating the position and fitness values ​​of the individuals in the population, the optimal configuration and scheduling plan are gradually approached, and the optimal scheduling plan can be quickly found through the HOA algorithm.

[0073] The results of the distributed energy storage planning model constructed in this implementation method are as follows: the optimal value of the function is 426.0897 yuan, of which the construction period cost is 4911 yuan and the operating period profit is 5337.0897 yuan. Figure 3 The change of the state of the energy storage power station at node 7 over time in different scenarios is given. It can be clearly seen that there are significant differences in the state of charge curves in different scenarios, which reflects the different adjustments of the energy storage power station to the charging and discharging strategies under different power demand conditions. It is worth noting that in scenario 3, the SOC is at a low level in the 5th to 8th cycle. This phenomenon occurs mainly due to the low power demand near node 7 during this period. In this way, the power demand in the system can be met by the energy storage power stations of other nodes, thereby reducing the charging and discharging demand of node 7. Since the demand can be flexibly transmitted between different nodes, the power consumption of some nodes is reduced, which is manifested as maintaining a low SOC level. In addition, due to the high electricity prices from the 2nd to the 5th period, the SOC of all schemes has dropped significantly. In order to reduce the overall operating cost, the energy storage system chooses to reduce discharge operation during the period of high electricity prices, and even reduces the purchase of electricity from the power grid, resulting in a general decrease in the SOC level. Through such adjustments, the energy storage system tries to avoid purchasing a large amount of electricity during the peak period of electricity prices, thereby saving operating costs.

[0074] It is not difficult to find that through accurate demand forecasting and dynamic scheduling strategies, the energy storage system can adjust the charging and discharging status in time according to real-time demand changes. During the low demand period, the energy storage system gives priority to charging and storing excess electricity; during the peak demand period, reasonable discharge meets the load demand. For example, through demand analysis of different nodes and different time periods, energy is transferred from nodes with lower demand to nodes with higher demand, achieving the spatiotemporal optimization of energy allocation, avoiding the idleness of energy storage equipment and energy waste, thereby significantly improving the overall utilization of the energy storage system.

[0075] In the planning stage, this implementation method avoids unnecessary construction investment by optimizing site selection and capacity configuration, taking into account factors such as fixed costs, variable costs, and connection costs. For example, according to the load demand characteristics of the node, the distance from the power grid, and land costs, the most suitable node is selected to build an energy storage power station, and a reasonable capacity scale is determined to minimize the construction cost. In the operation stage, full use is made of electricity price fluctuations for arbitrage operations. When the electricity price is low, a large amount of charging is carried out, and when the electricity price is high, electricity is discharged and sold. At the same time, combined with the demand transfer strategy, the need to purchase electricity from the power grid at a high price is reduced. In addition, by optimizing the charging and discharging strategy, the loss of energy storage equipment is reduced, the service life of the equipment is extended, and the cost of equipment replacement and maintenance is indirectly reduced.

[0076] A second embodiment of the present invention relates to a dispatching device for distributed energy storage under uncertain demand, comprising:

[0077] A construction module is used to construct a distributed energy storage planning model under demand uncertainty, wherein the distributed energy storage planning model includes an upper-level site selection planning model and a lower-level operation optimization model; the site selection planning model minimizes construction costs while satisfying site selection, capacity, and connection constraints; the operation optimization model maximizes operation benefits while taking into account electricity price fluctuations and equipment health;

[0078] A restatement and rewrite module is used to consider the state of charge of the distributed energy storage power station and its energy storage capacity, restate the constraints of the charge and discharge capacity of the distributed energy storage planning model, and rewrite the distributed energy storage planning model based on the number of scenarios, so that each scenario covers the needs of all nodes;

[0079] The solution scheduling module is used to solve the rewritten distributed energy storage planning model using a high-order acoustic field algorithm to obtain the optimal scheduling plan, and schedule the distributed energy storage based on the optimal scheduling plan.

[0080] The construction module constructs the site selection planning model with the goal of minimizing construction cost. The objective function of the site selection planning model is expressed as: Among them, f 1 is the objective function of the site planning model, represents the fixed cost of distributed energy storage power station construction, represents the variable cost of distributed energy storage power station construction, represents the cost of the wires connecting the power grid and the distributed energy storage power station; where p f represents the fixed power of distributed energy storage power station construction, y irepresents the fixed cost value per kWh of power, including site preparation and transformation costs, building installation costs, transmission and distribution infrastructure construction costs, etc. N represents the relevant cost items for the construction of distributed energy storage power stations, N′ represents the relevant cost items for connecting the power grid and distributed energy storage power stations, and p v represents the variable power of distributed energy storage power station construction, k i represents the capacity of the distributed energy storage power station constructed at the i-th network node, p l represents the power of the wires connecting the grid and the distributed energy storage power station, x i,j represents the connection status between the distributed energy storage power station constructed at the i-th network node and the j-th grid node, l i,j The length of the power line between the distributed energy storage power station constructed at the i-th network node and the j-th grid node.

[0081] The construction module constructs the operation optimization model with the goal of maximizing the operation benefit. The objective function of the operation optimization model is expressed as:

[0082]

[0083] Among them, f 2 is the objective function of the operation optimization model, o i,t ·p subsidy Subsidy income for the government, i,t ·p t To meet the charging demand, a i,t ·p t The cost of purchasing electricity from the grid. Carbon emission costs generated by charging and discharging distributed energy storage stations and demand shifting, (ξ i,t -o i,t )·p penalty is the penalty cost for not meeting charging demand, where o i,t represents the electricity sales of the distributed energy storage power station built at the i-th network node during period t, p subsidy represents the power corresponding to the income from government subsidies, p t represents the power corresponding to the income required to meet the charging demand, a i,t represents the amount of electricity purchased by the distributed energy storage power station built at the i-th network node during period t, p m Indicates the power corresponding to the cost of purchasing electricity from the grid, m i,j,t represents the demand transfer state between the distributed energy storage power station built at the i-th road network node and the distributed energy storage power stations built at other road nodes during period t, p carbon represents the power corresponding to the carbon emission cost generated by the charging and discharging of distributed energy storage equipment and the movement of demand, ξ i,t represents the demand for distributed energy storage power stations built at the i-th network node in period t, p penaltyThe power corresponding to the penalty cost of unmet demand.

[0084] The restatement and rewriting module restates the constraint conditions of the charge and discharge capacity of the distributed energy storage planning model as follows:

[0085] Initial state of charge constraint: The initial state of charge of the energy storage station is equal to the energy storage capacity of the energy storage station;

[0086] State of charge update constraint: The current state of charge of the energy storage power station is equal to the residual value of the state of charge in the previous stage after considering the decay rate, plus the current amount of electricity purchased from the grid multiplied by the charging efficiency, minus the current discharge amount divided by the discharge efficiency;

[0087] State of charge range constraint: The state of charge of the energy storage power station is greater than or equal to the product of the energy storage capacity of the energy storage power station and the minimum state of charge, and less than or equal to the product of the energy storage capacity of the energy storage power station and the maximum state of charge;

[0088] Constraints on the relationship between charging capacity and state of charge: There is a quadratic function relationship between the charging efficiency of the energy storage power station and the state of charge of the energy storage power station, and there is a quadratic function relationship between the discharge efficiency of the energy storage power station and the state of charge of the energy storage power station;

[0089] Constraints on the relationship between discharge capacity and demand: The current discharge capacity of the energy storage power station is less than or equal to the current demand;

[0090] Non-negative constraint condition for charge and discharge: the charge and discharge capacity of the energy storage power station is greater than or equal to zero;

[0091] Demand transfer variable constraint: the demand transfer between different energy storage power stations is greater than or equal to zero;

[0092] Constraints associated with demand transfer and charge and discharge capacity: The demand after transfer is less than or equal to the original demand forecast minus the demand transferred from other nodes, plus the demand transferred to other nodes.

[0093] The restatement and rewriting module rewrites the distributed energy storage planning model based on the number of scenarios so that each scenario covers the needs of all nodes. Specifically, a distributed energy storage planning model scenario tree is constructed. The distributed energy storage planning model scenario tree is a complete binary tree structure, wherein the needs of the nodes in the first layer are demand two and demand three; the needs of the nodes in the second layer are demand three and demand four, and the needs of the nodes in the third layer are demand two and demand four; the nodes in the fourth layer correspond to 8 different scenarios, respectively, and each scenario covers the needs of all nodes.

[0094] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for dispatching distributed energy storage under uncertain demand of the first embodiment when executing the computer program.

[0095] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for dispatching distributed energy storage under uncertain demand of the first embodiment.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0100] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for dispatching distributed energy storage under uncertain demand, characterized in that: The following steps are involved: Construct a distributed energy storage planning model under demand uncertainty, which includes an upper-level site selection planning model and a lower-level operation optimization model; the site selection planning model minimizes construction costs while satisfying site selection, capacity, and connection constraints; the operation optimization model maximizes operational benefits while taking into account electricity price fluctuations and equipment health; Considering the state of charge of the distributed energy storage power station and its energy storage capacity, the constraints of the charge and discharge capacity of the distributed energy storage planning model are restated, and the distributed energy storage planning model is rewritten based on the number of scenarios so that each scenario covers the needs of all nodes; A high-order acoustic field algorithm is used to solve the rewritten distributed energy storage planning model to obtain the optimal scheduling plan, and the distributed energy storage is scheduled based on the optimal scheduling plan.

2. The method for dispatching distributed energy storage under uncertain demand according to claim 1, characterized in that: The site selection planning model is constructed with the goal of minimizing construction cost. The objective function of the site selection planning model is expressed as: Among them, f1 is the objective function of the site selection planning model, represents the fixed cost of distributed energy storage power station construction, represents the variable cost of distributed energy storage power station construction, represents the cost of the wires connecting the power grid and the distributed energy storage power station; where p f represents the fixed power of distributed energy storage power station construction, y i represents the fixed cost value per kWh of power, including site preparation and transformation costs, building installation costs, transmission and distribution infrastructure construction costs, etc. N represents the relevant cost items for the construction of distributed energy storage power stations, N′ represents the relevant cost items for connecting the power grid and the distributed energy storage power station, and p v represents the variable power of distributed energy storage power station construction, k i represents the capacity of the distributed energy storage power station constructed at the i-th network node, p l represents the power of the wires connecting the grid and the distributed energy storage power station, x i,j represents the connection status between the distributed energy storage power station constructed at the i-th network node and the j-th grid node, l i,j The length of the power line between the distributed energy storage power station built for the i-th network node and the j-th grid node.

3. The method for dispatching distributed energy storage under uncertain demand according to claim 1, characterized in that: The operation optimization model is constructed with the goal of maximizing the operation benefit. The objective function of the operation optimization model is expressed as: Among them, f2 is the objective function of the operation optimization model, o i,t ·p subsidy Subsidy income for the government, i,t ·p t To meet the charging demand, a i,t ·p t The cost of purchasing electricity from the grid. Carbon emission costs generated by charging and discharging distributed energy storage stations and demand shifting, (ξ i,t -o i,t )·p penalty is the penalty cost for not meeting charging demand, where o i,t represents the electricity sales of the distributed energy storage power station built at the i-th network node during period t, p subsidy represents the power corresponding to the income from government subsidies, p t represents the power corresponding to the income required to meet the charging demand, a i,t represents the amount of electricity purchased by the distributed energy storage power station built at the i-th network node during period t, p m Indicates the power corresponding to the cost of purchasing electricity from the grid, m i,j,t represents the demand transfer state between the distributed energy storage power station built at the i-th road network node and the distributed energy storage power stations built at other road nodes during period t, p carbon represents the power corresponding to the carbon emission cost generated by the charging and discharging of distributed energy storage equipment and the movement of demand, ξ i,t represents the demand for distributed energy storage power stations built at the i-th network node in period t, p penalty The power corresponding to the penalty cost of unmet demand.

4. The method for dispatching distributed energy storage under uncertain demand according to claim 1, characterized in that: The constraints of the charge and discharge capacity of the distributed energy storage planning model are restated as follows: Initial state of charge constraint: The initial state of charge of the energy storage station is equal to the energy storage capacity of the energy storage station; State of charge update constraint: The current state of charge of the energy storage power station is equal to the residual value of the state of charge in the previous stage after considering the decay rate, plus the current amount of electricity purchased from the grid multiplied by the charging efficiency, minus the current discharge amount divided by the discharge efficiency; State of charge range constraint: the state of charge of the energy storage power station is greater than or equal to the product of the energy storage capacity of the energy storage power station and the minimum state of charge, and less than or equal to the product of the energy storage capacity of the energy storage power station and the maximum state of charge; Constraints on the relationship between charging capacity and state of charge: There is a quadratic function relationship between the charging efficiency of the energy storage power station and the state of charge of the energy storage power station, and there is a quadratic function relationship between the discharge efficiency of the energy storage power station and the state of charge of the energy storage power station; Constraints on the relationship between discharge capacity and demand: The current discharge capacity of the energy storage power station is less than or equal to the current demand; Non-negative constraint condition for charge and discharge: the charge and discharge capacity of the energy storage power station is greater than or equal to zero; Demand transfer variable constraint: the demand transfer between different energy storage power stations is greater than or equal to zero; Constraints associated with demand transfer and charge and discharge capacity: The demand after transfer is less than or equal to the original demand forecast minus the demand transferred from other nodes, plus the demand transferred to other nodes.

5. The method for dispatching distributed energy storage under uncertain demand according to claim 1, characterized in that: The distributed energy storage planning model is rewritten based on the number of scenarios so that each scenario covers the needs of all nodes. Specifically, a distributed energy storage planning model scenario tree is constructed, and the distributed energy storage planning model scenario tree is a complete binary tree structure, wherein the needs of the nodes in the first layer are demand two and demand three; the needs of the nodes in the second layer are demand three and demand four, and the needs of the nodes in the third layer are demand two and demand four; the nodes in the fourth layer correspond to 8 different scenarios, respectively, and each scenario covers the needs of all nodes.

6. A dispatching device for distributed energy storage under uncertain demand, characterized in that: include: A construction module, used to construct a distributed energy storage planning model under demand uncertainty, wherein the distributed energy storage planning model includes an upper-layer site selection planning model and a lower-layer operation optimization model; The site planning model minimizes construction costs while satisfying site selection, capacity and connection constraints; the operation optimization model maximizes operation benefits while taking into account electricity price fluctuations and equipment health; A restatement and rewrite module is used to consider the state of charge of the distributed energy storage power station and its energy storage capacity, restate the constraints of the charge and discharge capacity of the distributed energy storage planning model, and rewrite the distributed energy storage planning model based on the number of scenarios, so that each scenario covers the needs of all nodes; The solution scheduling module is used to solve the rewritten distributed energy storage planning model using a high-order acoustic field algorithm to obtain the optimal scheduling plan, and schedule the distributed energy storage based on the optimal scheduling plan.

7. The dispatching device for distributed energy storage under uncertain demand according to claim 6, characterized in that: The construction module constructs the site selection planning model with the goal of minimizing construction cost. The objective function of the site selection planning model is expressed as: Among them, f1 is the objective function of the site selection planning model, represents the fixed cost of distributed energy storage power station construction, represents the variable cost of distributed energy storage power station construction, represents the cost of the wires connecting the power grid and the distributed energy storage power station; where p f represents the fixed power of distributed energy storage power station construction, y i represents the fixed cost value per kWh of power, including site preparation and transformation costs, building installation costs, transmission and distribution infrastructure construction costs, etc. N represents the relevant cost items for the construction of distributed energy storage power stations, N′ represents the relevant cost items for connecting the power grid and the distributed energy storage power station, and p v represents the variable power of distributed energy storage power station construction, k i represents the capacity of the distributed energy storage power station constructed at the i-th network node, p l represents the power of the wires connecting the grid and the distributed energy storage power station, x i,j represents the connection status between the distributed energy storage power station constructed at the i-th network node and the j-th grid node, l i,j The length of the power line between the distributed energy storage power station built for the i-th network node and the j-th grid node.

8. The dispatching device for distributed energy storage under uncertain demand according to claim 6, characterized in that: The construction module constructs the operation optimization model with the goal of maximizing the operation benefit. The objective function of the operation optimization model is expressed as: Among them, f2 is the objective function of the operation optimization model, o i,t ·p subsidy Subsidy income for the government, i,t ·p t To meet the charging demand, a i,t ·p t The cost of purchasing electricity from the grid. Carbon emission costs generated by charging and discharging distributed energy storage stations and demand shifting, (ξ i,t -o i,t )·p penalty is the penalty cost for not meeting charging demand, where o i,t represents the electricity sales of the distributed energy storage power station built at the i-th network node during period t, p subsidy represents the power corresponding to the income from government subsidies, p t represents the power corresponding to the income required to meet the charging demand, a i,t represents the amount of electricity purchased by the distributed energy storage power station built at the i-th network node during period t, p m Indicates the power corresponding to the cost of purchasing electricity from the grid, m i,j,t represents the demand transfer state between the distributed energy storage power station built at the i-th road network node and the distributed energy storage power stations built at other road nodes during period t, p carbon represents the power corresponding to the carbon emission cost generated by the charging and discharging of distributed energy storage equipment and the movement of demand, ξ i,t represents the demand for distributed energy storage power stations built at the i-th network node in period t, p penalty The power corresponding to the penalty cost of unmet demand.

9. The dispatching device for distributed energy storage under uncertain demand according to claim 6, characterized in that: The restatement and rewriting module restates the constraint conditions of the charge and discharge capacity of the distributed energy storage planning model as follows: Initial state of charge constraint: The initial state of charge of the energy storage station is equal to the energy storage capacity of the energy storage station; State of charge update constraint: The current state of charge of the energy storage power station is equal to the residual value of the state of charge in the previous stage after considering the decay rate, plus the current amount of electricity purchased from the grid multiplied by the charging efficiency, minus the current discharge amount divided by the discharge efficiency; State of charge range constraint: the state of charge of the energy storage power station is greater than or equal to the product of the energy storage capacity of the energy storage power station and the minimum state of charge, and less than or equal to the product of the energy storage capacity of the energy storage power station and the maximum state of charge; Constraints on the relationship between charging capacity and state of charge: There is a quadratic function relationship between the charging efficiency of the energy storage power station and the state of charge of the energy storage power station, and there is a quadratic function relationship between the discharge efficiency of the energy storage power station and the state of charge of the energy storage power station; Constraints on the relationship between discharge capacity and demand: The current discharge capacity of the energy storage power station is less than or equal to the current demand; Non-negative constraint condition for charge and discharge: the charge and discharge capacity of the energy storage power station is greater than or equal to zero; Demand transfer variable constraint: the demand transfer between different energy storage power stations is greater than or equal to zero; Constraints associated with demand transfer and charge and discharge capacity: The demand after transfer is less than or equal to the original demand forecast minus the demand transferred from other nodes, plus the demand transferred to other nodes.

10. The dispatching device for distributed energy storage under uncertain demand according to claim 6, characterized in that: The restatement and rewriting module rewrites the distributed energy storage planning model based on the number of scenarios so that each scenario covers the needs of all nodes. Specifically, a distributed energy storage planning model scenario tree is constructed. The distributed energy storage planning model scenario tree is a complete binary tree structure, wherein the needs of the nodes in the first layer are demand two and demand three; the needs of the nodes in the second layer are demand three and demand four, and the needs of the nodes in the third layer are demand two and demand four; the nodes in the fourth layer correspond to 8 different scenarios, respectively, and each scenario covers the needs of all nodes.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for dispatching distributed energy storage under uncertain demand as described in any one of claims 1-5 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for dispatching distributed energy storage under uncertain demand as described in any one of claims 1 to 5 are implemented.

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