Low-carbon economic optimization scheduling method suitable for intelligent power distribution network system

Abstract

The invention relates to a carbon emission control method in the operation process of an intelligent power distribution network, in particular to a low-carbon economic optimization scheduling method suitable for an intelligent power distribution network system, which comprises the following steps of: firstly, proposing a seasonal carbon transaction model, and secondly, proposing a demand response model considering an industrial load process; the demand response potential and the low-carbon operation potential of the load side of the system are fully excavated; thirdly, building a network model of the system; and finally, factors such as electricity purchase cost, operation cost, demand response excitation cost, carbon transaction cost and carbon emission are comprehensively considered. By comprehensively considering and balancing the factors, a solution with the best cost benefit is found, and effective support is provided for low-carbon economic dispatching of the intelligent power distribution network. According to the method, the consumption amount of source-side wind-solar new energy output can be obviously increased, the output of a low-carbon emission unit is improved, the output of a high-carbon emission unit is inhibited, the annual carbon emission of the system is obviously reduced, and the load-side demand response amount of the system is improved.

Description

Technical Field

[0001] The present invention relates to a carbon emission control method during the operation of a smart distribution network, and in particular to a low-carbon economic optimization scheduling method suitable for a smart distribution network system. Background Art

[0002] Nowadays, in the smart distribution network system, due to the inability to fully absorb the output of new energy and the high carbon emission defects of traditional power generation units, a series of environmental problems have been caused, making the low-carbon operation of the smart distribution network particularly urgent. In addition, due to the imperfection of the existing energy trading market mechanism and the defects in the relevant low-carbon operation technologies, the exploration of the low-carbon potential of the source and load sides of the smart distribution network is still insufficient. It is urgent to propose a low-carbon economic dispatching method suitable for the smart distribution network system to explore the low-carbon potential of the source-load side and realize the low-carbon economic operation of the smart distribution system. Summary of the invention

[0003] Aiming at the problems of insufficient absorption of high-proportion wind and solar power output from new energy sources and excessive carbon emissions of the system during the operation of the smart distribution network, the present invention provides a low-carbon economic optimization scheduling method suitable for a smart distribution network system.

[0004] The present invention is implemented by adopting the following technical solution: A low-carbon economic optimization dispatching method suitable for a smart distribution network system comprises the following steps: First, a seasonal carbon trading model is designed.

[0005] Where D year is the annual carbon quota of the system, P net,d is the net load of the system on a certain day, P L,j,t is the load of node j at time t, P WS,i,t is the wind and solar power output of node i at time t, r s is the carbon quota rate for the four seasons, For each season's carbon quota, Ω W&S For wind and solar power generation units, select typical days in four seasons and calculate the net load of the system on a certain day P according to the seasonal carbon trading model. net,d and carbon quota for each season Secondly, set the objective function as: System cost C total Including the system's cost of purchasing electricity from the transmission grid Gas turbine generator operating costs Carbon trading costs on the power generation side and demand response costs in

[0006]

[0007] Where α is the unit price of electricity, P Gen,i,t is the active power output of the ith generator at time t, c i is the operation and maintenance cost coefficient of generator set i, is the actual carbon emission coefficient of generator set i in season s, T is one quarter, C car is the carbon trading cost coefficient, e is the electricity price, Ω 1 is the grid-connected generator set, Ω 2,3 is the gas turbine generator set, Ω E For grid-connected generator sets and gas turbine generator sets, δ DR is the demand response subsidy price, P after,j,t is the load of node j after demand response at time t; the seasonal carbon trading model is optimized using the objective function.

[0008] The above-mentioned low-carbon economic optimization dispatching method suitable for smart distribution network system designs a refined demand response model P after,j,t =P fore,j,t +P delta,j,t ,t∈(1,24),j∈Ω EL , -θ t P fore,j,t ≤P delta,j,t ≤θ t P fore,j,t , Where P fore,j,t is the load of node j before demand response at time t, P delta,j,t is the load of node j’s demand response at time t, P after,j,t is the load of node j after demand response at time t, θ t represents the corresponding ratio of demand response at time t, Ω EL is the set of loads, and P is calculated according to the refined demand response model. delta,j,t ; Then use the objective function to debug the refined demand response model.

[0009] Based on the seasonal carbon trading method and the refined demand response model considering the industrial load process, the present invention can significantly increase the consumption of wind and solar energy output on the source side, improve the output of low-carbon emission units, suppress the output of high-carbon emission units, significantly reduce the system's annual carbon emissions, and improve the system's load-side demand response. The method of the present invention realizes the low-carbon and economic operation of the smart distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is the factory load curve.

[0011] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0012] This paper is dedicated to improving the carbon emission trading mechanism and building a refined model for demand response to address the problems of insufficient absorption of high-proportion wind and solar power output and excessive carbon emissions in the operation of smart distribution networks. In order to establish the model and perform optimization, the research is carried out based on the following constraints and assumptions.

[0013] First, in response to the existing carbon trading methods, a carbon trading method that takes into account the seasonal net load differences of the system is proposed, breaking the traditional carbon trading model based on an annual scale. By comprehensively considering the differences in wind and solar power output in the four seasons of spring, summer, autumn and winter and the differences in user loads, the trading model is transformed into a quarterly one, aiming to further reduce carbon emissions on the load side of the system.

[0014] Secondly, based on the existing demand response model, combined with the advantages of higher flexibility and greater adjustable capacity of industrial load, a demand response model considering industrial load processes is proposed to fully tap the demand response potential and low-carbon operation potential on the load side of the system.

[0015] Furthermore, the operation of the system must meet the requirements of power balance and supply-demand matching to ensure the stable operation of the power grid. Therefore, the optimization plan needs to consider the balance between power generation and load demand as well as network losses. On this basis, the seasonal output of wind and solar energy, as well as distributed energy such as energy storage devices, are considered to complete the construction of the system network model.

[0016] Finally, in the optimization plan, it is necessary to comprehensively consider factors such as power purchase cost, operating cost, demand response incentive cost, carbon trading cost and carbon emissions. By comprehensively considering and weighing these factors, the most cost-effective solution can be found to provide effective support for the low-carbon economic dispatch of smart distribution networks.

[0017] A. Demand response model considering industrial load processes

[0018] The present invention considers four types of industrial loads, including composite material factories, chemical fiber factories, food manufacturing factories and ceramic factories. Taking spring as an example, the daily load curves of the four industrial factories are as follows: Figure 1As shown. The demand response cycles and response rates of four types of factories are set, and the response rates are evenly distributed in each hour during the peak period according to the overall demand response rate. The traditional demand response model does not take into account the different load characteristics of peak and off-peak periods within a day. The demand response model of the present invention takes into account the response capabilities of each factory in different periods. In addition, compared with the traditional model, the model applies a higher demand response rate during both peak and off-peak periods, and the response rates are the same for the two periods. For example, the demand response period of a food manufacturing plant is 15:00-19:00. The ceramic factory is 01:00-09:00 and 12:00-19:00, the composite material factory is 11:00-15:00, and the chemical plant is 14:00-17:00.

[0019] The refined demand response model established by the present invention takes into account that industrial processes can be transferred at different times. The demand response model is as follows. Constraints (1)-(3) represent the demand response model considering industrial load processes. Figure 1 shown.

[0020] P after,j,t =P fore,j,t +P delta,j,t ,t∈(1,24),j∈Ω EL (1)

[0021] -θ t P fore,j,t ≤P delta,j,t ≤θ t P fore,j,t (2)

[0022]

[0023] P fore,j,t is the load of node j before demand response at time t, P delta,j,t is the load of node j’s demand response at time t, P after,j,t is the load of node j after demand response at time t, θ t represents the corresponding ratio of demand response at time t, Ω EL The set where the load is located.

[0024] B. Seasonal Carbon Trading Model

[0025] 1. Carbon quota pre-allocation model, seasonal carbon trading model

[0026] The current carbon quota allocation method is mainly based on annual allocation, which is not suitable for short-term and medium-term transactions in the carbon trading market. Under this trading model, the carbon quota remains unchanged throughout the year. This method does not take into account the changes in renewable energy production and the energy consumption patterns of users in different seasons, thereby limiting the ability to fully utilize the low-carbon potential of the system. According to the Chinese government's carbon quota policy for 2021-2022, the present invention innovates the carbon quota pre-allocation method and performs seasonal allocation of annual carbon quotas according to the net load of the system.

[0027] 1) Carbon quota pre-allocation model

[0028] Q E =PB E F 1 F r F f (4)

[0029] Where Q E is the total carbon quota of the smart distribution network, P is the energy output of the unit, and B E The power supply reference value for the generator set, F 1 is the unit cooling method correction factor, F r is the unit heat supply correction factor, F f It is the unit load factor correction factor.

[0030] 2) Seasonal carbon trading model

[0031] In the present invention, the carbon allocation for each season is proportional to the net load of that season, that is, the total load minus the total wind and solar output. This method takes into account both the seasonal variation of wind and solar power generation and the seasonal fluctuations of the system. This method avoids the problem of excessive carbon emissions in a specific season and helps to achieve low-carbon optimization of the system.

[0032]

[0033]

[0034] Where D year is the annual carbon quota of the system, P net,d is the net load of the system on a certain day, P L,j,t is the load of node j at time t, P WS,i,t is the wind and solar power output of node i at time t, r s is the carbon quota rate for the four seasons, For each season's carbon quota, Ω W&S A collection of wind and solar power generators.

[0035] C. Low-carbon optimization model and constraints of smart distribution network

[0036] 1. Objective function and constraints

[0037] The present invention proposes a low-carbon economic optimization model of a seasonal carbon trading method considering industrial process demand response to reduce the carbon emissions and costs of the system.

[0038] 1) Objective function

[0039] The cost includes the cost of purchasing electricity from the transmission grid Gas turbine generator operating costs Carbon trading costs on the power generation side and demand response costs

[0040]

[0041] Where α is the unit price of electricity, P Gen,i,t is the active power output of the ith generator at time t, c i is the operation and maintenance cost coefficient of generator set i, is the actual carbon emission coefficient of generator set i in season s, T is a quarter, which has 90 days, C car is the carbon trading cost coefficient, e is the electricity price, Ω 1 is the grid-connected generator set, Ω 2,3 is the gas turbine generator set, Ω E For grid-connected generator sets and gas turbine generator sets, δ DR Subsidize prices for demand response.

[0042] 2) Constraints

[0043] A simplified DC network Dist-Flow model is used, including real-time active and reactive power balance, line voltage constraints, maximum and minimum generator output constraints, and maximum and minimum bus voltage constraints:

[0044]

[0045] Q m,n -∑Q n =-Q n (15)

[0046] v m -v n =r m,n P m,n +x m,n Q m,n (16)

[0047]

[0048] V min ≤v m ≤V max,(19)

[0049] |P Gen,i,t -P Gen,i,t-1 |≤δ Gen (20)

[0050] P m,n and Q m,n Represent the active power and reactive power of the system network transmission line from node m to node n, P n and Q n Represent the active power and reactive power of node n, v m and v n Represent the voltage magnitudes of nodes m and n, respectively, r m,n and x m,n Represent the resistance and reactance of the line respectively, and Respectively represent the upper and lower limits of active output of all generating units, and Respectively represent the upper and lower limits of reactive power output of all generator sets, V max and V min Represent the upper and lower limits of the system voltage, δ Gen Represents the unit climbing constraint, Represents a collection of system network nodes.

[0051] D. Model solving process

[0052] First, typical days in four seasons are selected, and then the carbon quota for each season is calculated based on renewable energy output and system load data. Secondly, we build a smart distribution network system and set up common constraints. Then we use the objective function to compare and optimize seasonal carbon trading and traditional carbon trading methods, and to compare and debug traditional demand response and refined demand response. Figure 2 shown.

Claims

1. A low-carbon economic optimization dispatching method applicable to a smart distribution network system, characterized in that: The following steps are involved: First, design a seasonal carbon trading model Where D year is the annual carbon quota of the system, P net,d is the net load of the system on a certain day, P L,j,t is the load of node j at time t, P WS,i,t is the wind and solar power output of node i at time t, r s is the carbon quota rate for the four seasons, For each season's carbon quota, Ω W&S For wind and solar power generation units, select typical days in four seasons and calculate the net load of the system on a certain day P according to the seasonal carbon trading model. net,d and carbon quota for each season Secondly, set the objective function as: System cost C total Including the system's cost of purchasing electricity from the transmission grid Gas turbine generator operating costs Carbon trading costs on the power generation side and demand response costs in Where α is the unit price of electricity, P Gen,i,t is the active power output of the ith generator at time t, c i is the operation and maintenance cost coefficient of generator set i, is the actual carbon emission coefficient of generator set i in season s, T is one quarter, C car is the carbon trading cost coefficient, e is the electricity price, θ1 is the grid-connected generator, Ω 2,3 is the gas turbine generator set, Ω E For grid-connected generator sets and gas turbine generator sets, δ DR is the demand response subsidy price, P after,j,t is the load of node j after demand response at time t; the seasonal carbon trading model is optimized using the objective function.

2. A low-carbon economic optimization dispatching method applicable to a smart distribution network system according to claim 1, characterized in that: Designing a refined demand response model after,j,t =P fore,j,t +P delta,j,t ,t∈(1,24),j∈Ω EL , -θ t P fore,j,t ≤P delta,j,t ≤θ t P fore,j,t , Where P fore,j,t is the load of node j before demand response at time t, P delta,j,t is the load of node j’s demand response at time t, P after,j,t is the load of node j after demand response at time t, θ t represents the corresponding ratio of demand response at time t, Ω EL is the set of loads, and P is calculated according to the refined demand response model. delta,j,t ; Then use the objective function to debug the refined demand response model.

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