Double-layer scheduling method based on dynamic electricity price and carbon transaction mechanism

Abstract

The invention relates to the technical field of electric power dispatching of an electric power system, in particular to a double-layer dispatching method based on a dynamic electricity price and a carbon transaction mechanism. Under the background of the dynamic electricity price and the carbon transaction mechanism, a double-layer dispatching model is coordinated and optimized through an upper layer and a lower layer, the upper layer is mainly responsible for dispatching of an energy supply end, and the lower layer is mainly responsible for dispatching of an energy supply end; the operation cost and carbon emission conditions of various energy supply units are considered, and the operation cost minimization is used as a target function to formulate output plans of various energy supply units. And the lower layer mainly considers the demand response and energy management of the load side, and formulates an energy consumption plan of the user side such as an electric vehicle (EV) and a thermal load by taking the minimization of the energy consumption cost of the user as a target function, so that the dual goals of system economy and low carbon are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system power dispatching, and in particular to a double-layer dispatching method based on dynamic electricity price and carbon trading mechanism. Background Art

[0002] As the global climate change problem becomes increasingly serious, carbon emission control has become the focus of global attention. As a market-based means, the carbon trading mechanism encourages enterprises to reduce carbon emissions and improve energy efficiency by setting carbon emission quotas and trading prices. At the same time, with the advancement of power market reform, dynamic electricity prices, as a flexible electricity price mechanism, have gradually been applied in the power system by adjusting electricity prices in real time to reflect changes in electricity supply and demand and market competition.

[0003] The carbon trading mechanism consists of three parts: free carbon quotas, actual carbon emissions, and tiered carbon trading costs. Free carbon emission quotas are issued to the system. When the actual carbon emissions of the system are lower than the quota, the remaining quota can be sold on the carbon trading market; otherwise, additional quotas need to be purchased.

[0004] The dynamic electricity price mechanism is a pricing strategy based on the real-time changes in electricity supply and demand. It aims to optimize the allocation of electricity resources, balance electricity supply and demand, and promote the efficient use of energy and the stable operation of the power system by flexibly adjusting electricity prices. Specifically, the dynamic electricity price mechanism will dynamically adjust electricity prices based on the real-time supply and demand situation and power generation costs in the electricity market, thereby guiding users to increase electricity consumption during low electricity price periods and reduce electricity consumption during peak electricity price periods, achieving demand-side response and improving the economy and reliability of the power system.

[0005] The existing power dispatching methods of power systems do not fully consider the carbon trading mechanism and dynamic electricity price mechanism, resulting in the inability to achieve the dual goals of system economy and low carbon. Summary of the invention

[0006] In view of this, the purpose of the present invention is to propose a two-tier scheduling method based on dynamic electricity price and carbon trading mechanism to solve the problem that the existing methods cannot achieve the dual goals of system economy and low carbon.

[0007] Based on the above objectives, the present invention provides a two-tier scheduling method based on dynamic electricity price and carbon trading mechanism, including:

[0008] S1. The upper-level dispatch takes the minimization of the total cost on the energy side as the objective function. The total cost on the energy side includes the interaction cost and income with the power grid, carbon trading cost, and operation and maintenance cost;

[0009] S2, the lower-level scheduling takes the lowest electricity cost on the user side as the objective function;

[0010] S3. The upper layer updates the user-side cost according to the load power information fed back by the lower layer dispatcher, outputs the energy output in the form of achieving the minimum operating cost, and passes it to the lower layer. The lower layer outputs the load operation plan according to the output of each energy group.

[0011] Preferably, in step S1, the objective function for minimizing the total cost on the energy side is:

[0012] F1=min(M)

[0013]

[0014] Where M represents the total cost on the energy side, represents the carbon trading cost, M ope represents the operation and maintenance cost, M buy Represents the electricity purchase cost, M sell Represents the cost of electricity sales.

[0015] Preferably, the formulas for electricity purchase cost and electricity sales cost are:

[0016]

[0017] Where t represents the time, T represents the total time, represents the electricity purchase price at time t, represents the amount of electricity purchased at time t, is the electricity price at time t, represents the electricity sales at time t, represents the time-of-use electricity price of the λth iteration, represents the time-sharing heat price of the λth iteration, They represent the electrical and thermal equivalent loads during time period t respectively.

[0018] Preferably, the expressions of electrical and thermal equivalent loads are:

[0019]

[0020] Where P t GL , P t TSL , P t iL They are fixed load, time-shiftable load and interruptible load in period t, are the charging and discharging powers of the i-th electric vehicle charging station, and N is the number of electric vehicle charging stations.

[0021] Preferably, the dynamic electricity price rule of time-of-use electricity price is:

[0022]

[0023] in, is the valley electricity price, ω GT,t is the power generation cost coefficient, ω st.price,t Time-of-use electricity price, are the output power of micro gas turbine, low temperature waste heat power generation and the upper limit of power purchased by the power grid, E t is the expected output curve of wind turbine and photovoltaic power generation device, and the superscript (λ) represents the λth iteration.

[0024] Preferably, the dynamic heat price rules of time-sharing heat price are:

[0025]

[0026] Among them, ω WHB,t ,ω GB,t are the cost coefficients of the waste heat boiler and the gas boiler in period t, They are the upper limits of thermal power output of waste heat boiler and gas boiler respectively. They are the heating power of waste heat boiler and gas boiler respectively.

[0027] Preferably, the formula for operation and maintenance cost is:

[0028]

[0029] Among them, t represents the time, T represents the total time, S represents the device number, δ s represents the unit operating cost of the Sth device, P S,t Indicates the operating output of the Sth device.

[0030] Preferably, the formula for carbon trading cost is:

[0031]

[0032] Among them, ζ is the benchmark price of tiered carbon trading, v is the price growth rate, d is the length of the carbon emission interval, E p is the total carbon emissions generated by the operation of the system, E c The total amount of carbon quotas obtained by the system.

[0033] Preferably, in step S2, the objective function for minimizing the user-side electricity cost is:

[0034] F2=min(C buy VPP -C ZD )

[0035]

[0036] Among them, C buy VPP is the energy cost at the user side, CZD Compensation for user energy interruption, δ e,IL , δ h,IL It is the compensation benefit for unit electric load and thermal load interruption.

[0037] Beneficial effects of the present invention: Under the background of dynamic electricity prices and carbon trading mechanisms, the present invention adopts a two-layer scheduling model through coordinated optimization of the upper and lower layers. The upper layer is mainly responsible for scheduling the energy supply side, taking into account the operating costs and carbon emissions of various energy supply units, and taking the minimization of operating costs as the objective function to formulate output plans for various energy supply units. The lower layer mainly considers the demand response and energy management on the load side, takes the minimization of user energy costs as the objective function, and formulates energy consumption plans for users such as electric vehicles (EVs) and thermal loads to achieve the dual goals of system economy and low carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a microgrid structure according to an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a two-layer scheduling model framework according to an embodiment of the present invention;

[0041] Figure 3 This is a flowchart for solving a double-layer scheduling model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0043] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0044] like Figure 1 The structure of the microgrid is mainly composed of uncertain micro power sources such as photovoltaic cell power generation devices (PV), wind turbines (WT), and large power grids and loads including electric loads and thermal loads (supplied by WHB and EB). The electric loads include micro gas turbines (GT), low-temperature waste heat power generation (ORC), (electric vehicles) EV, WT and PV, and the thermal loads include waste heat boilers (WHB) and gas boilers (EB).

[0045] The equivalent expression of the controllable micro power unit is:

[0046]

[0047] Among them, M MT 、M FC are the fuel costs of micro gas turbine and fuel cell respectively; P MT (t), P FC (t) represents the output power of the micro gas turbine and the fuel cell at time t; m ng 、m fuel is the price of natural gas; c ng 、c fuel is the lower calorific value of natural gas; η MT , η FC It is the power generation efficiency of micro gas turbine and fuel cell.

[0048] In microgrids, there are often a large number of distributed photovoltaic power generation equipment. In view of the randomness and intermittency of wind power and photovoltaic power generation, the linearization and uncertainty analysis of wind and photovoltaic are carried out by using binary variables. The uncertainty model expression is:

[0049]

[0050] Where: P wtf , P pvf are the predicted output power of WT and PV respectively; ρ wtu (t),ρ wtl (t),ρ pvu (t),ρ pvl (t) WT and PV uncertainty fluctuation factors represent whether the wind turbine and photovoltaic unit have output fluctuations at time t; β is the prediction error; P wt,max , P pv,max are the upper limits of wind turbine and photovoltaic output power respectively; γ wt , γ pv is the introduced uncertainty variable, which takes an integer between 0 and T.

[0051] The equivalent load expression is:

[0052]

[0053] in are the electrical and thermal equivalent loads in time period t, P t GL , P t TSL , P t iL They are fixed load, time-shiftable load and interruptible load in period t, are the charging and discharging powers of the i-th EV charging station, and is the number of EVs.

[0054] The dynamic electricity price mechanism established according to the equivalent load model includes thermal load as well as electrical load.

[0055] The dynamic electricity price pricing mechanism is as follows:

[0056]

[0057] in, is the valley electricity price, ω GT,t is the power generation cost coefficient, ω st.price,t Time-of-use electricity price, is the time-of-use electricity price of λ iterations, is the output power of GT and ORG and the upper limit of power purchased from the power grid, E t It is the expected output curve of WT and PV.

[0058] The dynamic heat price rules are:

[0059]

[0060] in is the dynamic heat price in the λth iteration, ωWHB,t ,ω GB,t are the WHB cost coefficient and GB cost coefficient in period t respectively. They are the upper limits of WHB and GB output thermal power, are the heating powers of WHB and GB respectively.

[0061] The ladder-type carbon trading mechanism is as follows:

[0062]

[0063] in is the carbon trading cost of the system; ζ is the benchmark price of tiered carbon trading, which is 100 yuan / t in this paper; v is the price growth rate, which is 35%; d is the length of the carbon emission interval.

[0064] The two-layer dispatch model achieves the dual goals of system economy and low carbon through coordinated optimization of the upper and lower layers. The upper layer is mainly responsible for the dispatch of the energy supply side, taking into account the operating costs and carbon emissions of various energy supply units, and formulating output plans for various energy supply units with the minimization of operating costs as the objective function. The lower layer mainly considers the demand response and energy management on the load side, and formulates energy consumption plans for electric vehicles (EVs), thermal loads, etc. with the minimization of user energy consumption costs as the objective function. Its structure is as follows: Figure 2 shown.

[0065] The upper-level dispatch model takes the minimum total energy cost as the objective function. The objective function comprehensively considers the interaction cost and benefits with the power grid, carbon trading costs, and operation and maintenance costs. The total cost formula is:

[0066]

[0067] in is the electricity purchase price at time t, is the electricity purchase amount at time t, is the electricity selling price at time t, and is the electricity selling amount at time t; δ s is the unit operating cost of the Sth device, P S,t Contribute to the operation of the Sth device.

[0068] The constraints include power balance constraints, unit climbing constraints, and unit output constraints.

[0069] The power constraint expression is:

[0070]

[0071] Where V GT,t 、V GB,t is the gas consumption of GT and GB at time t.

[0072] The climbing expression of the unit is:

[0073]

[0074] Among them, Pt s is the upper and lower limits of the output of the Sth unit in period t, ΔP S is the upper limit of the climbing power of the Sth unit.

[0075] Unit output upper and lower limit constraints:

[0076]

[0077] in The upper and lower limits of the output of the Sth unit.

[0078] The lower-level scheduling model takes into account the user-side energy consumption cost of electric vehicles and takes the minimum user charging cost as the objective function. Its expression is:

[0079] The objective function includes the energy cost on the user side and the compensation for energy interruption on the user side. Its expression is as follows:

[0080] F2=min(C buy VPP -C ZD )

[0081]

[0082] Among them C buy VPP is the energy cost at the user side, C ZD Compensation for user energy interruption, δ e,IL , δ h,IL It is the compensation benefit for unit electric load and thermal load interruption.

[0083] The lower-level constraints mainly include constraints related to electric and thermal loads and constraints on electric vehicles themselves.

[0084] The heat load demand response expression is:

[0085]

[0086] in, are the upper and lower limits of the time-shifted power of the heat load in period t respectively; σ h is the interruptible heat load ratio.

[0087] The electric load demand response expression is:

[0088]

[0089] in, is the initial electric load demand in period t.

[0090] Electric vehicle constraints include its SOC constraints, charge and discharge constraints, and power constraints.

[0091] The SOC constraint expression is:

[0092]

[0093] Where s is the EV’s full-day driving distance, S ar is the initial SOC, S le The SOC at the end of EV charging, W s,100 is the amount of electricity required for EV to travel 100km, and B is the EV battery capacity.

[0094] The charge and discharge constraint expression is:

[0095]

[0096] Where t′ is the planned charging and discharging period of the i-th EV; t ar ,t le are the time when the i-th EV starts charging and leaves the charging station respectively; Indicates rounding down; This constraint ensures that the EV charging and discharging plan is arranged within the time period when the EV connects to the charging pile and leaves the charging station.

[0097] The charge and discharge power constraint expression is:

[0098]

[0099] in, They are the minimum and maximum values ​​of EV charging and discharging power, respectively.

[0100] WT and PV are the main renewable energy sources. Their output power is affected by a variety of environmental factors and has significant randomness. In order to characterize the impact of this randomness on system operation, we introduce chance constrained programming. The solution process is as follows: Figure 3 As shown in the figure: Based on the cumulative probability distribution of WT and PV output power, the corresponding probabilistic sequence is constructed, and the probabilistic sequence of the combined output of the two is obtained through efficient convolution calculation technology. This process realizes the transformation of the originally complex chance constraints into easy-to-handle deterministic constraints, which is easy to solve. This processing method not only enhances the adaptability of the model to actual complex situations, but also provides strong mathematical support for the efficient and stable operation of microgrids under uncertain conditions.

[0101] Those skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A two-tier dispatching method based on dynamic electricity price and carbon trading mechanism, characterized in that: The method comprises: S1. The upper-level dispatch takes the minimization of the total cost on the energy side as the objective function. The total cost on the energy side includes the interaction cost and income with the power grid, carbon trading cost, and operation and maintenance cost; S2, the lower-level scheduling takes the lowest electricity cost on the user side as the objective function; S3. The upper layer updates the user-side cost according to the load power information fed back by the lower layer dispatcher, outputs the energy output in the form of achieving the minimum operating cost, and passes it to the lower layer. The lower layer outputs the load operation plan according to the output of each energy group.

2. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 1 is characterized in that: In step S1, the objective function for minimizing the total cost on the energy side is: F1=min(M) Where M represents the total cost on the energy side, represents the carbon trading cost, M ope represents the operation and maintenance cost, M buy Represents the electricity purchase cost, M sell Represents the cost of electricity sales.

3. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 2 is characterized in that: The formulas for electricity purchase cost and electricity sales cost are: Where t represents the time, T represents the total time, represents the electricity purchase price at time t, represents the amount of electricity purchased at time t, is the electricity price at time t, represents the electricity sales at time t, represents the time-of-use electricity price of the λth iteration, represents the time-sharing heat price of the λth iteration, They represent the electrical and thermal equivalent loads during time period t respectively.

4. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 3 is characterized in that: The expressions of the electrical and thermal equivalent loads are: Where P t GL , P t TSL , P t iL They are fixed load, time-shiftable load and interruptible load in period t, are the charging and discharging powers of the i-th electric vehicle charging station, and N is the number of electric vehicle charging stations.

5. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 4 is characterized in that: The dynamic electricity price rules of the time-of-use electricity price are: in, is the valley electricity price, ω GT,t is the power generation cost coefficient, ω st.price,t Time-of-use electricity price, are the output power of micro gas turbine, low temperature waste heat power generation and the upper limit of power purchased by the power grid, E t is the expected output curve of wind turbine and photovoltaic power generation device, and the superscript (λ) represents the λth iteration.

6. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 4 is characterized in that: The dynamic heat price rules of the time-sharing heat price are: Among them, ω WHB,t ,ω GB,t are the cost coefficients of the waste heat boiler and the gas boiler in period t, They are the upper limits of thermal power output of waste heat boiler and gas boiler respectively. They are the heating power of waste heat boiler and gas boiler respectively.

7. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 2 is characterized in that: The formula for the operation and maintenance cost is: Among them, t represents the time, T represents the total time, S represents the device number, δ s represents the unit operating cost of the Sth device, P S,t Indicates the operating output of the Sth device.

8. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 2 is characterized in that: The formula for carbon trading cost is: Among them, ζ is the benchmark price of tiered carbon trading, v is the price growth rate, d is the length of the carbon emission interval, E p is the total carbon emissions generated by the operation of the system, E c The total amount of carbon quotas obtained by the system.

9. The dual-tier dispatching method based on dynamic electricity price and carbon trading mechanism according to claim 1 is characterized in that: In step S2, the objective function for minimizing the user-side electricity cost is: F2=min(C buy VPP -C ZD ) Among them, C buy VPP is the energy cost at the user side, C ZD Compensation for user energy interruption, δ e,IL , δ h,IL It is the compensation benefit for unit electric load and thermal load interruption.

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