Power distribution network low-carbon operation method considering demand response

Abstract

The invention relates to a power distribution network low-carbon operation method considering demand response. The method comprises the following steps: constructing a total objective function; constructing four constraint conditions; and forming a mixed integer linear programming model, and solving the mixed integer linear programming model to obtain an optimal operation strategy, namely a globally optimal solution, of the power distribution network under the condition of considering the demand response and the participation of the energy storage system. According to the invention, carbon emission minimization is taken as a target, and power generation scheduling and charge and discharge management of the energy storage system are optimized, so that the carbon emission can be effectively reduced in the operation process of the power distribution network; by adjusting the generated power, the charging and discharging state of the energy storage system and the like in real time, carbon emission is remarkably reduced on the premise that the system meets the power requirement, and the low-carbon and environment-friendly requirements are met; by optimizing the charging and discharging strategy of energy storage, necessary load support is provided at the peak of power demand, redundant power is effectively stored at the off-peak period, and the overall economic benefit of the energy storage system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a low-carbon operation method for a distribution network taking demand response into consideration. Background Art

[0002] Traditional distribution network dispatching usually takes economy as its main goal, focusing on power balance and cost minimization, and often ignoring the management of carbon emissions. This practice leads to a high reliance on high carbon emissions during the operation of the distribution network, especially during peak load periods, when the large-scale use of fossil fuel generators will significantly increase carbon emissions, making it difficult to meet the increasingly stringent low-carbon and environmental protection requirements of modern energy systems.

[0003] Demand response technology aims to smooth out peak loads by adjusting user electricity consumption behavior, but in existing technologies, demand response management is usually not fully utilized. The flexibility of user loads is not effectively used, resulting in a lack of flexible response measures for the power grid during peak loads, which not only aggravates the load pressure, but also further increases the system operating costs and carbon emissions. Therefore, in a power grid environment with frequent load fluctuations, the lack of an effective demand response mechanism will significantly affect the stability and operating efficiency of the power grid.

[0004] In addition, existing energy storage systems can often only participate in scheduling as simple auxiliary equipment, lacking scientific charging and discharging management strategies. The scheduling of energy storage systems is not closely integrated with load demand, power generation costs, and carbon emission optimization goals, resulting in limited role in peak load shaving and low-carbon operation. The lack of optimized energy storage management strategies often makes it difficult for energy storage systems to provide effective support during peak periods of electricity demand, and it is also difficult to achieve efficient storage of electricity during trough periods, ultimately leading to waste of resources and reduced economic benefits of the system. Summary of the invention

[0005] In order to solve the problems of the existing technology that it is difficult to meet low-carbon requirements, lack of effective demand response, and insufficient optimization of energy storage systems, the purpose of the present invention is to provide a low-carbon operation method of distribution networks that takes demand response into consideration, which improves the low-carbon level of distribution networks, meets environmental protection requirements, improves the utilization rate of demand response, improves the flexibility and stability of power grids, optimizes the scheduling of energy storage systems, reduces resource waste and reduces operating costs.

[0006] To achieve the above object, the present invention adopts the following technical solution: a method for low-carbon operation of a distribution network considering demand response, the method comprising the following steps in order:

[0007] (1) Construct the overall objective function E total ;

[0008] (2) To ensure the feasibility of the low-carbon operation strategy, four constraints are constructed;

[0009] (3) The overall objective function and the four constraints constitute a mixed integer linear programming model. By solving the mixed integer linear programming model, the optimal operation strategy of the distribution network considering demand response and the participation of the energy storage system, that is, the global optimal solution, is obtained.

[0010] The step (1) specifically comprises the following steps in order:

[0011] (1a) Add the carbon emissions and costs of each time period t to obtain the cumulative total carbon emission cost E for the entire scheduling period T total :

[0012]

[0013] In the formula, represents the total carbon emissions in time period t, represents the total operating cost in time period t; T represents the duration of the entire scheduling cycle, representing the total time period that the system needs to schedule; t represents the current time period, and the system will generate corresponding carbon emissions and operating costs in each time period t;

[0014] (1b) The formula is:

[0015]

[0016] Where P t is the load power in time period t; C t is the electricity price corresponding to time period t; is the charging power of the energy storage system in time period t; is the charging electricity price of the energy storage system in time period t; is the discharge power of the energy storage system in time period t; is the discharge electricity price of the energy storage system in time period t;

[0017] (1c) The formula is:

[0018]

[0019] In the formula, is the power generated by the i-th generator in time period t; β i is the unit electricity carbon emission coefficient of the i-th generator; is the power consumption of the jth load in time period t, γ j is the unit electricity carbon emission coefficient of the jth load, and N represents the total number of generators, specifically the total number of all units involved in power generation in the distribution grid.

[0020] In step (2), the four constraints include power balance constraint, power flow equation constraint, energy storage constraint and load constraint;

[0021] The power balance constraint requires the system to meet the supply and demand balance, that is, the total power generation plus the energy storage discharge power at any time must be equal to the load demand plus the energy storage charging power and line loss:

[0022]

[0023] In the formula, is the power generated by the i-th generator in time period t; is the discharge power of the energy storage system in time period t; is the charging power of the energy storage system in time period t; is the power consumption of the jth load in time period t; represents the power loss in time period t; N represents the total number of generators, specifically the total number of all units involved in power generation in the distribution network; M represents the total number of loads, specifically the number of loads that need to meet the power demand in the system; j represents the index of the load, which is used to distinguish the power demand of different loads in scheduling; t represents the current time period;

[0024] The power flow equation constraint is used to describe the power flow between nodes in the distribution network, including active power flow equation constraint and reactive power flow equation constraint. The active power flow equation constraint is:

[0025]

[0026] The reactive power flow equation constraint is:

[0027]

[0028] In the formula, represents the active power flow from the i-th to the j-th line, represents the reactive power flow from the i-th to the j-th line; and Respectively represent the voltage of the i-th node and the j-th node in time period t; G ij and B ij denote the real and imaginary parts of the admittance of line i to j respectively; Indicates voltage phase difference;

[0029] The energy storage constraint includes energy storage power constraint and energy storage quantity constraint. The energy storage power constraint is:

[0030]

[0031] In the formula, Indicates the maximum charging power limit of the energy storage system, Indicates the maximum discharge power limit of the energy storage system;

[0032] The formula for the energy storage capacity constraint is as follows:

[0033]

[0034] Among them, E t represents the energy state of the energy storage system in time period t, E t-1 represents the energy state of the energy storage system in time period t-1, represents the charging efficiency of the energy storage system in time period t, It represents the discharge efficiency of the energy storage system in time period t, E min Represents the minimum energy storage capacity limit of the energy storage system, E max Indicates the maximum energy storage capacity limit of the energy storage system;

[0035] The load constraints include load flexibility constraints and load regulation rate constraints. The load flexibility constraints are:

[0036]

[0037] in, represents the adjustment power of demand response in time period t, Indicates the total amount of load that can be dispatched by the system;

[0038] The load regulation rate constraint is:

[0039]

[0040] in, Indicates the load regulation rate change, R max Indicates the maximum rate of load regulation, It represents the adjustment power of demand response in time period t-1.

[0041] The step (3) specifically comprises the following steps in order:

[0042] (3a) Matrix the objective function and express the objective function and constraints using the standard matrix form of linear programming:

[0043] minc T x

[0044] Where c is the coefficient vector of the objective function, and x is the vector containing all decision variables, including the power generation power in each time period of the distribution network, the charging and discharging power of the energy storage system, and the demand response regulation power;

[0045] (3b) Linearize the constraints and express all the constraints in matrix form, i.e., linear matrix:

[0046] Ax≤b

[0047] Among them, A is the coefficient matrix of the constraint condition, and b is the boundary value of the constraint condition;

[0048] (3c) Mixed integer linear programming solution: The optimization solver is used to solve the linear matrix Ax≤b through an iterative algorithm. The iteration starts from the initial solution in the initialization phase. The optimization solver generates an initial value for each decision variable. The condition for the end of the iteration is that the objective function change is less than the threshold and all constraints are satisfied. After the optimization process is completed, the iterative algorithm outputs the optimal decision for each time period in the entire scheduling cycle. The optimal decision includes power generation allocation, energy storage charging and discharging strategy, demand response adjustment power, and the corresponding total carbon emissions and operating costs. Based on the optimal decision, the optimal solution of the optimal power generation allocation of the generator, the optimal charging and discharging strategy of the energy storage system, and the optimal power of the demand response adjustment is finally generated. After the solution is completed and the iterative convergence is achieved, the iterative algorithm will output the power scheduling plan, the optimal values ​​of carbon emissions and operating costs, and the optimization strategy of scheduling and energy storage. The mixed integer linear programming algorithm is used to gradually adjust the decision variables according to the constraints to finally find the global optimal solution.

[0049] It can be seen from the above technical scheme that the beneficial effects of the present invention are as follows: First, the present invention improves the low-carbon level of the distribution network and meets environmental protection requirements: by taking carbon emission minimization as the goal, optimizing power generation scheduling and charging and discharging management of the energy storage system, the distribution network can effectively reduce carbon emissions during operation; traditional distribution network scheduling focuses on cost and load balance, and often ignores the control of carbon emissions. In response to this problem, the present invention adjusts the power generation power, the charging and discharging state of the energy storage system in real time, etc., so that the system can significantly reduce carbon emissions while meeting the power demand, and conforms to the demand for low-carbon and environmental protection; second, the utilization rate of demand response is improved, and the flexibility and stability of the power grid are improved: the present invention introduces demand response technology, makes full use of the flexibility of user loads, and timely reduces load demand during peak hours, thereby shaving peaks and filling valleys, This not only reduces the load pressure on the power grid during peak periods, but also can flexibly respond to load fluctuations and improve the power grid's supply and demand balancing capabilities; at the same time, the participation of demand response reduces operating costs, helps to improve the overall economy and efficiency of the power system, and provides users with the enthusiasm to participate in scheduling; third, it optimizes the scheduling of the energy storage system, reduces resource waste and reduces operating costs: the existing distribution network energy storage system management is relatively simple, and the present invention optimizes the charging and discharging strategy of energy storage to provide necessary load support during peak power demand and effectively store excess electricity during off-peak periods. The optimized management of energy storage can not only balance the supply and demand relationship of the power system, but also to a certain extent reduce the dependence on traditional generators during peak periods, thereby reducing carbon emissions. The optimization of the charging and discharging strategy not only reduces resource waste, but also improves the overall economic benefits of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0051] like Figure 1 As shown, a method for low-carbon operation of a distribution network considering demand response comprises the following steps in sequence:

[0052] (1) Construct the overall objective function E total ;

[0053] (2) To ensure the feasibility of the low-carbon operation strategy, four constraints are constructed;

[0054] (3) The overall objective function and the four constraints constitute a mixed integer linear programming model. By solving the mixed integer linear programming model, the optimal operation strategy of the distribution network considering demand response and the participation of the energy storage system, that is, the global optimal solution, is obtained.

[0055] The step (1) specifically comprises the following steps in order:

[0056] (1a) Add the carbon emissions and costs of each time period t to obtain the cumulative total carbon emission cost E for the entire scheduling period T total :

[0057]

[0058] In the formula, represents the total carbon emissions in time period t, represents the total operating cost in time period t; T represents the duration of the entire scheduling cycle, representing the total time period that the system needs to schedule; t represents the current time period, and the system will generate corresponding carbon emissions and operating costs in each time period t;

[0059] (1b) The formula is:

[0060]

[0061] Where P t is the load power in time period t; C t is the electricity price corresponding to time period t; is the charging power of the energy storage system in time period t; is the charging electricity price of the energy storage system in time period t; is the discharge power of the energy storage system in time period t; is the discharge electricity price of the energy storage system in time period t;

[0062] (1c) The formula is:

[0063]

[0064] In the formula, is the power generated by the i-th generator in time period t; β i is the unit electricity carbon emission coefficient of the i-th generator; is the power consumption of the jth load in time period t, γ j is the unit electricity carbon emission coefficient of the jth load, and N represents the total number of generators, specifically the total number of all units involved in power generation in the distribution grid.

[0065] In step (2), the four constraints include power balance constraint, power flow equation constraint, energy storage constraint and load constraint;

[0066] The power balance constraint requires the system to meet the supply and demand balance, that is, the total power generation plus the energy storage discharge power at any time must be equal to the load demand plus the energy storage charging power and line loss:

[0067]

[0068] In the formula, is the power generated by the i-th generator in time period t; is the discharge power of the energy storage system in time period t; is the charging power of the energy storage system in time period t; is the power consumption of the jth load in time period t; represents the power loss in time period t; N represents the total number of generators, specifically the total number of all units involved in power generation in the distribution network; M represents the total number of loads, specifically the number of loads that need to meet the power demand in the system; j represents the index of the load, which is used to distinguish the power demand of different loads in scheduling; t represents the current time period;

[0069] The power flow equation constraint is used to describe the power flow between nodes in the distribution network, including active power flow equation constraint and reactive power flow equation constraint. The active power flow equation constraint is:

[0070]

[0071] The reactive power flow equation constraint is:

[0072]

[0073] In the formula, represents the active power flow from the i-th to the j-th line, represents the reactive power flow from the i-th to the j-th line; and Respectively represent the voltage of the i-th node and the j-th node in time period t; G ij and B ij denote the real and imaginary parts of the admittance of line i to j respectively; Indicates voltage phase difference;

[0074] The energy storage constraint includes energy storage power constraint and energy storage quantity constraint. The energy storage power constraint is:

[0075]

[0076] In the formula, Indicates the maximum charging power limit of the energy storage system, Indicates the maximum discharge power limit of the energy storage system;

[0077] The formula for the energy storage capacity constraint is as follows:

[0078]

[0079] Among them, E t represents the energy state of the energy storage system in time period t, Et-1 represents the energy state of the energy storage system in time period t-1, represents the charging efficiency of the energy storage system in time period t, It represents the discharge efficiency of the energy storage system in time period t, E min Represents the minimum energy storage capacity limit of the energy storage system, E max Indicates the maximum energy storage capacity limit of the energy storage system;

[0080] The load constraints include load flexibility constraints and load regulation rate constraints. The load flexibility constraints are:

[0081]

[0082] in, represents the adjustment power of demand response in time period t, Indicates the total amount of load that can be dispatched by the system;

[0083] The load regulation rate constraint is:

[0084]

[0085] in, Indicates the load regulation rate change, R max Indicates the maximum rate of load regulation, It represents the adjustment power of demand response in time period t-1.

[0086] The step (3) specifically comprises the following steps in order:

[0087] (3a) Matrix the objective function and express the objective function and constraints using the standard matrix form of linear programming:

[0088] minc T x

[0089] Where c is the coefficient vector of the objective function, and x is the vector containing all decision variables, including the power generation power in each time period of the distribution network, the charging and discharging power of the energy storage system, and the demand response regulation power;

[0090] (3b) Linearize the constraints and express all the constraints in matrix form, i.e., linear matrix:

[0091] Ax≤b

[0092] Among them, A is the coefficient matrix of the constraint condition, and b is the boundary value of the constraint condition;

[0093] (3c) Mixed integer linear programming solution: The linear matrix Ax≤b is solved by an optimization solver through an iterative algorithm. The iteration starts from the initial solution in the initialization stage. The optimization solver will generate an initial value for each decision variable. The condition for the end of the iteration is that the change of the objective function is less than the threshold and all constraints are satisfied. After the optimization process is completed, the iterative algorithm outputs the optimal decision for each time period in the entire scheduling cycle. The optimal decision includes power generation allocation, energy storage charging and discharging strategy, demand response adjustment power, and the corresponding total carbon emissions and operating costs. Based on the optimal decision, the optimal power generation allocation of the generator, the optimal charging and discharging strategy of the energy storage system, and the optimal power of the demand response adjustment are finally generated. After the solution is completed and the iterative convergence is achieved, the iterative algorithm will output the power scheduling plan, the optimal values ​​of carbon emissions and operating costs, and the optimization strategy of scheduling and energy storage. The mixed integer linear programming algorithm, namely the MILP algorithm, is used to gradually adjust the decision variables according to the constraints and finally find the global optimal solution.

[0094] In summary, the present invention optimizes the management of power generation scheduling and energy storage system charging and discharging by taking carbon emission minimization as the goal, so that the distribution network can effectively reduce carbon emissions during operation; by adjusting the power generation power, energy storage system charging and discharging state, etc. in real time, the system can significantly reduce carbon emissions while meeting the power demand, which complies with the demand for low-carbon environmental protection; the present invention introduces demand response technology to make full use of the flexibility of user loads, timely reduce load demand during peak power consumption periods, thereby reducing peaks and filling valleys, which not only reduces the load pressure of the power grid during peak periods, but also can flexibly respond to load fluctuations and improve the supply and demand balancing ability of the power grid; at the same time, the participation of demand response reduces operating costs, contributes to the improvement of the overall economy and efficiency of the power system, and provides users with the enthusiasm to participate in scheduling; the present invention optimizes the charging and discharging strategy of energy storage, provides necessary load support during peak power demand, and effectively stores excess power during valley periods. The optimized management of energy storage can not only balance the supply and demand relationship of the power system, but also reduce the dependence on traditional generators during peak periods to a certain extent, thereby reducing carbon emissions. The optimization of the charging and discharging strategy not only reduces resource waste, but also improves the overall economic benefits of the energy storage system.

Claims

1. A method for low-carbon operation of a distribution network considering demand response, characterized in that: The method comprises the following steps in order: (1) Construct the overall objective function E total ; (2) To ensure the feasibility of the low-carbon operation strategy, four constraints are constructed; (3) The overall objective function and the four constraints constitute a mixed integer linear programming model. By solving the mixed integer linear programming model, the optimal operation strategy of the distribution network considering demand response and the participation of the energy storage system, that is, the global optimal solution, is obtained.

2. The method for low-carbon operation of a distribution network considering demand response according to claim 1, characterized in that: The step (1) specifically comprises the following steps in order: (1a) Add the carbon emissions and costs of each time period t to obtain the cumulative total carbon emission cost E for the entire scheduling period T total : In the formula, represents the total carbon emissions in time period t, represents the total operating cost in time period t; T represents the duration of the entire scheduling cycle, representing the total time period that the system needs to schedule; t represents the current time period, and the system will generate corresponding carbon emissions and operating costs in each time period t; (1b) The formula is: Where P t is the load power in time period t; C t is the electricity price corresponding to time period t; is the charging power of the energy storage system in time period t; is the charging electricity price of the energy storage system in time period t; is the discharge power of the energy storage system in time period t; is the discharge electricity price of the energy storage system in time period t; (1c) The formula is: In the formula, is the power generated by the i-th generator in time period t; β i is the unit electricity carbon emission coefficient of the i-th generator; is the power consumption of the jth load in time period t, γ j is the unit electricity carbon emission coefficient of the jth load, and N represents the total number of generators, specifically the total number of all units involved in power generation in the distribution grid.

3. The method for low-carbon operation of a distribution network considering demand response according to claim 1, characterized in that: In step (2), the four constraints include power balance constraint, power flow equation constraint, energy storage constraint and load constraint; The power balance constraint requires the system to meet the supply and demand balance, that is, the total power generation plus the energy storage discharge power at any time must be equal to the load demand plus the energy storage charging power and line loss: In the formula, is the power generated by the i-th generator in time period t; is the discharge power of the energy storage system in time period t; is the charging power of the energy storage system in time period t; is the power consumption of the jth load in time period t; represents the power loss in time period t; N represents the total number of generators, specifically the total number of all units involved in power generation in the distribution network; M represents the total number of loads, specifically the number of loads that need to meet the power demand in the system; j represents the index of the load, which is used to distinguish the power demand of different loads in scheduling; t represents the current time period; The power flow equation constraint is used to describe the power flow between nodes in the distribution network, including active power flow equation constraint and reactive power flow equation constraint. The active power flow equation constraint is: The reactive power flow equation constraint is: In the formula, represents the active power flow from the i-th to the j-th line, represents the reactive power flow from the i-th to the j-th line; V i t and Respectively represent the voltage of the i-th node and the j-th node in time period t; G ij and B ij denote the real and imaginary parts of the admittance of line i to j respectively; Indicates voltage phase difference; The energy storage constraint includes energy storage power constraint and energy storage quantity constraint. The energy storage power constraint is: In the formula, Indicates the maximum charging power limit of the energy storage system, Indicates the maximum discharge power limit of the energy storage system; The formula for the energy storage capacity constraint is as follows: Among them, E t represents the energy state of the energy storage system in time period t, E t-1 represents the energy state of the energy storage system in time period t-1, represents the charging efficiency of the energy storage system in time period t, It represents the discharge efficiency of the energy storage system in time period t, E min Represents the minimum energy storage capacity limit of the energy storage system, E max Indicates the maximum energy storage capacity limit of the energy storage system; The load constraints include load flexibility constraints and load regulation rate constraints. The load flexibility constraints are: in, represents the adjustment power of demand response in time period t, Indicates the total amount of load that can be dispatched by the system; The load regulation rate constraint is: in, Indicates the load regulation rate change, R max Indicates the maximum rate of load regulation, It represents the adjustment power of demand response in time period t-1.

4. The method for low-carbon operation of a distribution network considering demand response according to claim 1, characterized in that: The step (3) specifically comprises the following steps in order: (3a) Matrix the objective function and express the objective function and constraints using the standard matrix form of linear programming: minc T x Where c is the coefficient vector of the objective function, and x is the vector containing all decision variables, including the power generation power in each time period of the distribution network, the charging and discharging power of the energy storage system, and the demand response regulation power; (3b) Linearize the constraints and express all the constraints in matrix form, i.e., linear matrix: Ax≤b Among them, A is the coefficient matrix of the constraint condition, and b is the boundary value of the constraint condition; (3c) Mixed integer linear programming solution: The optimization solver is used to solve the linear matrix Ax≤b through an iterative algorithm. The iteration starts from the initial solution in the initialization phase. The optimization solver generates an initial value for each decision variable. The condition for the end of the iteration is that the objective function change is less than the threshold and all constraints are satisfied. After the optimization process is completed, the iterative algorithm outputs the optimal decision for each time period in the entire scheduling cycle. The optimal decision includes power generation allocation, energy storage charging and discharging strategy, demand response adjustment power, and the corresponding total carbon emissions and operating costs. Based on the optimal decision, the optimal solution of the optimal power generation allocation of the generator, the optimal charging and discharging strategy of the energy storage system, and the optimal power of the demand response adjustment is finally generated. After the solution is completed and the iterative convergence is achieved, the iterative algorithm will output the power scheduling plan, the optimal values ​​of carbon emissions and operating costs, and the optimization strategy of scheduling and energy storage. The mixed integer linear programming algorithm is used to gradually adjust the decision variables according to the constraints to finally find the global optimal solution.

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