A low-carbon scheduling method for integrated energy systems based on dynamic carbon trading prices

Abstract

A low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices belongs to the technical field of energy system scheduling. The present invention considers the coupled operation of carbon capture power plant-power-to-gas (CCPP-P2G) units of a dynamic carbon trading mechanism and price-based demand response (PDR) to perform low-carbon economic scheduling on IES. The present invention establishes a dynamic carbon trading model for IES to flexibly capture the ebb and flow of supply and demand in the carbon trading market. In the presence of uncertainty in the system, a two-layer optimization model is constructed with the goal of minimizing operating costs. Verification results show that the dynamic carbon trading mechanism proposed in the present invention can effectively provide time-varying carbon trading price signals and fully reflect the supply and demand relationship of carbon emission quotas. The method proposed in the present invention is scientific and reasonable, has strong applicability, and good effect. It is quite effective in reducing imbalance problems such as wind curtailment, improving system efficiency, and reducing carbon emissions.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system scheduling, and in particular relates to a low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices. Background Art

[0002] Exploring low-carbon technologies and improving the operational mechanisms of carbon trading markets (CTMs) are crucial for achieving low-carbon development of integrated energy systems (IES). To explore these technologies, it is necessary to develop and utilize zero-carbon renewable energy sources (RESs), such as wind power (WP) and carbon capture technology (CCT), on the power generation side of IES. Due to the inherent randomness and volatility of RESs, large-scale RES consumption poses a significant challenge. The introduction of power-to-gas (P2G) can effectively improve power-gas coupling and enhance the stability and economic efficiency of system operation. Furthermore, CCT can capture and store CO2 from unit flue gas emissions, thereby reducing carbon emissions at the source. Since CCT can generate the CO2 required for the P2G process, the coordinated operation of CCT and P2G is feasible. Coupling P2G with CCT can effectively reduce system carbon emissions and improve renewable energy consumption.

[0003] In addition to the application of CCTs on the source side and the development and utilization of RES, demand response mechanisms on the load side can promote RES consumption and reduce carbon emissions. To reduce carbon emissions and ensure the sustainable development of the energy system, carbon trading is considered an effective solution, considering the cost and low-carbon nature of IES. Currently, there is no generalized energy storage scheduling method that can simultaneously reduce wind curtailment imbalances, improve system efficiency, reduce carbon emissions, and account for the time-shifted energy consumption characteristics of carbon capture power plants.

[0004] Therefore, a new technical solution is urgently needed in the existing technology to solve this problem. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices to solve the technical problem that there is currently no generalized energy storage scheduling method that can take into account the reduction of wind power imbalance, improve system efficiency, reduce carbon emissions, and consider the time-shift characteristics of energy consumption of carbon capture power plants.

[0006] A low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices includes the following steps, which are performed in sequence:

[0007] Step 1: Based on the energy flow relationship of the integrated energy system (IES), a coupling model of the carbon capture power plant-power-to-gas (CCPP)-P2G and a scheduling model for price-based demand response (PDR) are constructed.

[0008] 1) Energy flow relationship of the integrated energy system IES

[0009] In the integrated energy system (IES), electricity is provided by coal-fired power plants (TP), combined heat and power (CHP) units, wind turbines (WP), and gas turbines (GT). The required heat is provided by the CHP units, a power-to-heat system, and a heat storage system. Natural gas demand is balanced by gas sources and power-to-gas conversion. Because the TP and CHP units produce large amounts of CO2, a carbon capture device (CCD) and a power-to-gas (P2G) system are introduced. The CCD reduces CO2 emissions and supplies the captured CO2 to the P2G system. This creates a coupled model of a carbon capture plant, power-to-gas (CCPP), and P2G, enabling low-carbon operation of the IES.

[0010] 2) Based on the output limit of coal-fired power units and the energy loss of the carbon capture device and the energy consumption of power-to-gas in the CCPP-P2G coupling model, the net power output range of the CCPP-P2G is derived and obtained;

[0011] 3) Based on the electricity prices during peak, flat and valley periods, a price-based demand response model is constructed. The total electricity load changes during peak, flat and valley periods are obtained by using the expressions for the changes in total electricity load and electricity prices. The electricity load changes for each unit period in peak, flat and valley periods are obtained by using the expressions for the proportional distribution of the total electricity load changes in each period.

[0012] Step 2: The percentage of free carbon emission quotas varies at different stages of the carbon trading market. The dynamic carbon trading price is formulated based on the supply and demand constraints of carbon emission quotas in the carbon trading market. Based on the supply and demand constraints, a relationship model between carbon emission quotas and carbon trading price transaction amounts is constructed;

[0013] Step 3: Build an IES low-carbon scheduling model

[0014] The IES low-carbon scheduling model includes an upper model and a lower model. The objective function of the upper model is:

[0015]

[0016] Where, P t L,aft is the maximum fitting function of power load demand; P t w,max Power forecasting for wind power;

[0017] The lower model takes into account energy consumption costs and carbon trading costs, and its objective function is:

[0018]

[0019]

[0020] Where, are the total operating costs of coal-fired power units, carbon capture power plants, and cogeneration at time t, is the gas purchase cost, is the carbon trading cost, is the cost coefficient of coal-fired power unit i, is the start-up and shutdown cost of coal-fired power unit i, are the cost coefficients of carbon capture power plant i, is the start-up and shutdown cost of the carbon capture power plant, are the cost coefficients of cogeneration unit i, λ gas is the price of natural gas;

[0021] Step 4: Constrain the upper and lower models of the IES low-carbon scheduling model, solve the IES low-carbon scheduling model, and the optimal solution obtained is the low-carbon scheduling method of the integrated energy system based on dynamic carbon trading prices.

[0022] The CCPP-P2G coupling model in step 1) is defined as follows:

[0023]

[0024] Wherein, formula (1) represents the power balance of CCPP-P2G; formula (2) represents the energy loss of carbon capture device, including fixed energy loss P i CCS,B and operating energy loss P i CCS,OP ; Equation (3) represents the energy consumption of power-to-gas, including electrolytic cell EC and methanogenesis; where, P t TP represents the power generation of the coal-fired power unit at time t, P t N represents the net output power of the carbon capture power plant at time t, P t P2G represents the power consumption of the power-to-gas converter at time t, λ CCS represents the operating energy consumption coefficient, α CCS represents the capture rate, e g represents emission intensity, η EC Represents the energy conversion coefficient of the electrolytic cell, Ψ T is a set of time periods, P t EC Indicates the power consumption of the electrolytic cell, P t MET Indicates the power consumption of methane production, Indicates hydrogen production, η MET represents the methane production efficiency, Indicates the amount of methane produced.

[0025] The output limiting formula of the coal-fired power generation unit in step 12) is:

[0026]

[0027] Where, P i TP Indicates the output of coal-fired power units, P i min Indicates the lower limit of coal-fired power generation unit output, P max Indicates the upper limit of coal-fired power unit output.

[0028] The net power output range of CCPP-P2G in step 1) is shown in formula (5):

[0029]

[0030] Where, P i TP,N Indicates the net power output of CCPP-P2G, Indicates the lower limit of coal-fired power unit output, Indicates the upper limit of coal-fired power generation unit output, Indicates the maximum output of P2G. Indicates the minimum output of P2G, Ψ TP Assemble for thermal power units;

[0031] The price-based demand response model in step 1 (3) is:

[0032]

[0033] Where E represents the elasticity matrix, E FF represents the peak-to-peak elastic modulus, E FP Indicates the peak-to-flat elastic coefficient, E FG Indicates the peak-to-valley elastic coefficient, E PF represents the flat-peak elastic coefficient, E PP represents the flat-flat elastic coefficient, E PG represents the flat-valley elastic coefficient, E GF Represents the valley-peak elastic coefficient, E GP Denotes the valley-flat elastic coefficient, E GG represents the valley-to-valley elastic coefficient, Δp F Indicates the change in power load during each peak period, Δp P Indicates the change in power load during each normal period, Δp G Indicates the change of power load in each valley period, P F Represents the total power load during each peak period, P P Indicates the total power load in each normal period, PG Indicates the total power load during each valley period, Δq F Indicates the change in electricity price during each peak period, Δq P Indicates the change in electricity price in each normal period, Δq G Indicates the change of electricity price in each valley period, represents the electricity price before price-based demand response;

[0034] The expression of the change of the total electric load and electricity price is:

[0035]

[0036] Where,

[0037] The expression for the proportional distribution of the total power load change in each period is:

[0038]

[0039] in, Indicates the change in power load during peak period at time t, P t F represents the electricity load at time t during the peak period, Indicates the change in power load at time t during normal periods, P t P represents the power load at time t during normal times, Indicates the change in power load during the valley period at time t, P t G It represents the electricity load during the valley period at time t.

[0040] The relationship model formula between carbon emission quota and carbon trading price transaction amount in step 1 (3) is:

[0041]

[0042] Where, is the carbon quota trading volume at time t, is the lower limit of carbon trading price, is the upper limit of carbon trading price, is the average carbon trading price, is the maximum trading volume of carbon emission quotas in IES, which is expressed by formula (11):

[0043]

[0044] In formula (11): free Indicates the percentage of free carbon emission allowances; represents the carbon emission quota allowed in IES, which is expressed by formula (9):

[0045]

[0046] In formula (9), and Represent the demand for electricity, heat and natural gas respectively, κ e , κ h and κ g They represent the carbon emission quota coefficients of electric load, thermal load and natural gas load respectively, β represents the heat-to-electricity ratio, and χ represents the mass of CO2 produced per cubic meter of natural gas combustion.

[0047] The constraints of the upper model in step 4 are:

[0048] (1) Electric load balance constraints

[0049] Before and after the demand response, the total power demand remains unchanged, and the amount of transferable load in each period is limited to equations (15) to (17):

[0050] Δp F +Δp P +Δp G =0 (15)

[0051]

[0052] Where, P t L is the power demand before demand response at time t, ε is the load transfer ratio at each moment, and δ is the load transfer ratio at each time period;

[0053] (2) Electricity price constraints

[0054] The electricity prices during peak, flat and off-peak periods are subject to the following constraints:

[0055]

[0056] Where ζ1 and ζ2 are the electricity price ratios of peak and valley periods respectively, Q G , Q P , Q F These are the electricity prices during valley, flat and peak periods respectively;

[0057] (3) User satisfaction constraints

[0058] In order to ensure that demand response can satisfy customers, customer satisfaction with electricity charges and customer satisfaction with electricity usage methods are introduced as evaluation indicators to constrain user satisfaction, as shown in formula (19):

[0059]

[0060] Where S 1,minis the minimum value of customer satisfaction with electricity charges, S 2,min is the minimum value of customer satisfaction with electricity usage, Δp t is the load change at time t, P t L is the load at time t before demand response, P t L,aft is the load at time t after demand response, is the electricity price at time t after demand response, is the electricity price at time t before demand response.

[0061] The constraints of the lower model in step 4 are:

[0062] (1) Power balance constraints

[0063]

[0064] Where: is the power generated by thermal power unit i at time t, is the power generation of CCPP i at time t, is the power generation capacity of cogeneration unit i at time t, is the power generation of gas generator set i at time t, is the power generated by wind turbine i at time t, P t P2G is the power consumption of P2G at time t, P t PDR is the demand response change, C p 、 T t S 、T t R are the specific heat capacity, mass flow rate, inlet temperature and outlet temperature of the fluid respectively, TP is the set of thermal power units, Ψ CCPP is the set of CCPP units, Ψ CHP is the set of cogeneration units, Ψ GT is the collection of gas units, Ψ W is the set of wind turbines, P t P2H is the energy loss from converting electricity to heat, is the heat output of cogeneration unit i at time t, is the heat generated by electric heating at time t, represents the heat release of the heat storage system at time t, represents the heat capacity of the heat storage system at time t, They represent the heat released and stored in the heat storage tank at time t respectively;

[0065] (2) Carbon emission quota balance constraints

[0066]

[0067] Where β is the ratio of electrical power to thermal power, represents the amount of CO2 captured at time t, is the carbon emission intensity of thermal power units, is the carbon emission intensity of the cogeneration unit, is the carbon emission intensity of the gas-fired unit, is the amount of carbon captured, Free carbon emission quotas for carbon emission sources in IES, Offsets for nationally certified emission reductions from renewable energy generators;

[0068] in, As shown in formula (10):

[0069]

[0070] As shown in formula (12):

[0071]

[0072] in, Represents the national certified emission reduction deduction coefficient, P t w represents wind power;

[0073] (3) Constraints on coal-fired power units

[0074]

[0075] Where, represents the start and stop status of coal-fired power unit i at time t, They represent the upward climbing rate and downward climbing rate of coal-fired power unit i, are the minimum downtime and minimum start-up time of coal-fired power unit i respectively; and are the start and stop states of the unit in period k and period t, The start time of the thermal power unit, The downtime of thermal power units;

[0076] (4) CCPP unit constraints

[0077] Since CCPP is derived from coal-fired power units, CCPP units and coal-fired power units have the same constraints. The operating constraints of CCPP units are shown in formula (2);

[0078] (5) Cogeneration unit constraints

[0079] The convex combination of the extreme points of the feasible region of cogeneration is used to formulate the thermal power and power generation capacity of the extracted cogeneration unit:

[0080]

[0081] Where, They represent the downward climbing speed and upward climbing speed of the cogeneration unit i, is the heat output of C cogeneration unit i at time t, is the electrical output of the vertex of the feasible region of the thermal power unit, is the convex combination binary variable of the cogeneration unit, H i CHP,k is the thermal output of the vertex of the feasible region of the thermal power unit;

[0082] (6) Gas turbine unit constraints

[0083]

[0084] Where η GT Indicates the thermal efficiency of the steam turbine, RD GT , RU GT They represent the downward ramp rate and upward ramp rate of the steam turbine respectively; is the natural gas consumption, is the minimum output of the gas unit, The maximum output of the gas unit;

[0085] (7) Wind power output constraints

[0086]

[0087] is the maximum output of the wind turbine;

[0088] (8) Power-to-gas constraints

[0089]

[0090] Where η EC represents the energy conversion coefficient of the electric EC, Respectively represent the minimum output power and maximum output power of EC, RD EC , RU EC Indicates the EC's downward climbing power and upward climbing power, Indicates the minimum output power and maximum output power of MET. is the reaction coefficient of the methane production equipment, The amount of carbon captured by the carbon capture equipment, Carbon consumption of methane production equipment;

[0091] (9) Electricity-to-heat constraints

[0092]

[0093] Where, is the heat generated by electric heating at time t, η P2H is the electric heating coefficient, P t P2H Electric heating power consumption, The rated power of electric heating;

[0094] (10) Thermal storage system constraints

[0095]

[0096] Where, are the discharge state and charge state of the heat storage system at time t, They represent the minimum heat charging rate and minimum heat releasing rate of the heat storage system respectively. Indicates the maximum heat charging rate and maximum heat releasing rate of the heat storage system. represents the heat release of the heat storage system at time t, represents the heat storage capacity of the heat storage system at time t, β d , β c are the storage efficiency and release efficiency of the heat storage system, They are the minimum and maximum heat storage capacity of the heat storage tank respectively;

[0097] (11) Regional heating network transmission constraints

[0098]

[0099] Where, represents the outlet fluid temperature of pipe j at time t, is the time at t-τ j The fluid temperature at the pipe inlet at the moment, represents the inlet fluid temperature of pipe k at time t, T n,t represents the temperature of node n at time period t, Ψ H,pipe is a set of pipelines in DHS, T n,min 、T n,max Respectively represent the lowest temperature and the highest temperature of node n, m j,t 、m k,t are the mass flow rates of pipe j and pipe k at time t, l j is the length of pipe j, λ represents the heat transfer coefficient of the pipe, T t am is the ambient temperature, is the set of pipes where the working fluid flows into n nodes, The set of pipes where the working medium flows out of n nodes, is the outlet fluid temperature of pipe k at time t;

[0100] (12) Grid transmission constraints

[0101]

[0102] Where, is the maximum transmission capacity, θ i,t ,θ j,t They represent the voltage angles of nodes i and j at time t, respectively, and x ij ,Ψ E,bus is the set of busbar nodes of the transmission network;

[0103] (13) Natural gas network transmission constraints

[0104]

[0105]

[0106] Where, All are natural gas pipeline parameters, f t mn is the natural gas mass flow rate in pipeline mn, Ψ G,node is the node set in NGN, is the average pressure of mn gas in the pipeline, is the air pressure at node m, Indicates the gas storage capacity of the pipeline in mn, Indicates the gas supply at time t, Average natural gas mass flow rate in pipeline mn, is the pipeline gas flow direction, are P2G gas production, gas load and gas consumption of gas generator sets, respectively, t mk Net natural gas inflow at node m, are the minimum and maximum limits of natural gas pipeline mass flow, Ψ G,node Natural gas pipeline node collection;

[0107] (14) Spinning reserve constraints

[0108]

[0109] Where, represents the power generation of wind turbine i at time t, P t L represents the power demand before demand response at time t, is the climbing power of the thermal power unit, The gas unit is in start and stop state. is the climbing power of the gas generator set, are the fuzzy expressions of wind power and electric load forecasting errors, respectively.

[0110] The spinning reserve constraint is the fuzzy expression of power load change, specifically:

[0111]

[0112] Where k 2L is the fuzzy parameter of power load, is the power load change rate error.

[0113] Through the above design scheme, the present invention can bring the following beneficial effects:

[0114] This paper presents an IES low-carbon economic dispatch method for coupled carbon capture power plant-power-to-gas (CCPP-P2G) units and price-based demand response (PDR) that considers a dynamic carbon trading mechanism. This method first considers both the source-side CCPP-P2G and the load-side PDR, achieving coordinated low-carbon operation through synchronized operation of CCPP-P2G and PDR, and elucidates the underlying mechanism. Subsequently, a dynamic carbon trading model for the IES is established to flexibly capture the fluctuations in supply and demand within the carbon trading market. Under system uncertainty, a two-level optimization model is constructed with the goal of minimizing operating costs. Finally, the effectiveness of the proposed method is verified on an IES consisting of an IEEE-30-node power system, a 6-node natural gas system, and a 6-node district heating system. The results demonstrate that the proposed dynamic carbon trading mechanism effectively provides time-varying carbon trading price signals that fully reflect the supply and demand relationship of carbon emission quotas. Test cases demonstrate the scientific rationality, strong applicability, and excellent results of the proposed method, demonstrating its considerable effectiveness in reducing imbalances such as wind curtailment, improving system efficiency, and reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0116] Figure 1 This is an IES structure diagram of a low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices in the present invention;

[0117] Figure 2 WP and energy demand forecast data diagram in an embodiment of the present invention;

[0118] Figure 3 1 is a diagram showing the scheduling results of Example 1 in an embodiment of the present invention;

[0119] Figure 4is the CEA balance diagram in Example 1 of the embodiment of the present invention;

[0120] Figure 5 This is the CTP and CEA diagram of buy-back resale in Example 1 of the embodiment of the present invention;

[0121] Figure 6 is a scheduling result diagram of Example 2 in an embodiment of the present invention;

[0122] Figure 7 This is a thermal power balance diagram of Example 2 in an embodiment of the present invention;

[0123] Figure 8 is the embodiment of the present invention Example 1 and Example 2 Comparison chart with CEA;

[0124] Figure 9 is a graph showing changes in electricity demand and price after PDR in an embodiment of the present invention;

[0125] Figure 10 is a scheduling result diagram of Example 3 in an embodiment of the present invention;

[0126] Figure 11 is a CEA graph allowed by different calculation examples in the embodiment of the present invention;

[0127] Figure 12 is a net power output diagram of the TP unit in an embodiment of the present invention;

[0128] Figure 13 1 is a CTP and CEA diagram of Example 4 in an embodiment of the present invention;

[0129] Figure 14 is a scheduling result diagram of Example 5 in an embodiment of the present invention;

[0130] Figure 15 1 is a diagram of the absorption rate of WP under different calculation examples in the embodiment of the present invention. DETAILED DESCRIPTION

[0131] The following further illustrates a low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices using the accompanying drawings and examples.

[0132] A low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices was developed to improve energy efficiency and achieve multi-energy coordination. An integrated IES (Energy-Energy System) was constructed to integrate electricity, heat, and gas. Electricity is provided by coal-fired power plants (TP), combined heat and power (CHP), wind power (WP), and gas turbines (GT). Required heat is provided by CHP, power-to-heat, and thermal storage systems. Natural gas demand is balanced by natural gas sources and power-to-gas. Since the coal-fired CHP plants generate significant amounts of CO2, carbon capture technology was introduced to reduce CO2 emissions. The captured CO2 is then fed into the power-to-gas system, enabling low-carbon operation of the IES.

[0133] CCPP-P2G coupling model and low-carbon mechanism: Introducing carbon capture technology and power-to-gas in thermal power plants to achieve CCPP-P2G integrated operation can significantly reduce CO2 emissions and improve wind power absorption capacity.

[0134] CCPP-P2G low-carbon mechanism: The CCPP-P2G integrated operation mode has a lower output limit due to carbon capture technology and power-to-gas power consumption. Therefore, with the same reserve capacity, the system can absorb additional wind energy. The reduction in IES carbon emissions under the CCPP-P2G coupled operation mode is caused by three factors: 1) The CO2 produced by the coal combustion of the cogeneration unit is captured. 2) The increase in wind power consumption based on the reduction in CCPP power output is equivalent to replacing the power output of coal-fired power units with zero-carbon wind power. 3) Power-to-gas converts excess wind energy into natural gas, reducing the output of the gas source, which is equivalent to using zero-carbon wind energy to supply the consumed natural gas load.

[0135] Regarding the dispatch model for price-based demand response: Based on the principles of consumer psychology, power grid companies set reasonable electricity prices to encourage residential and commercial consumers to change their electricity usage patterns, helping to smooth the load curve. Based on current pilot studies of electricity pricing during peak, flat, and off-peak periods in my country, a price-based demand response model has been constructed.

[0136] A low-carbon mechanism based on price-based demand response: By setting electricity prices for peak, off-peak, and off-peak periods, some of the electricity load during peak periods can be shifted to off-peak or off-peak periods, achieving peak-to-valley shifting. This can increase wind power consumption during off-peak periods and reduce the output of high-carbon units caused by insufficient net CCPP output during peak periods. This is equivalent to using zero-carbon wind power during off-peak periods to replace high-carbon units during peak periods, thereby reducing system carbon emissions.

[0137] Regarding the operating mechanism of the carbon trading market: Carbon trading is essentially the buying and selling of greenhouse gas emission rights. There are two main types of commodities in the carbon trading market (CTM): one is mandatory carbon emission allowances (CEAs), and the other is incentive-based carbon offsets (NCERs). The former is a government or regulatory body that controls total carbon emissions. First, a cap on the total carbon emission allowances in the carbon trading market is determined, followed by an initial allocation of allowances to carbon emission sources. Carbon emission allowances can be traded between emission sources, generating revenue or covering costs based on the prevailing carbon trading price. The latter represents NCER projects, such as renewable energy and forestry carbon sinks. The carbon trading market provides a certain percentage of NCERs in lieu of carbon emission allowances, which emitters can use to offset their carbon emission allowance settlements.

[0138] Carbon trading mechanisms use market-based mechanisms to limit total CO2 emissions, encouraging carbon emission sources to proactively reduce their emissions. In the energy sector, carbon emission allowances were generally allocated free of charge in the early stages of carbon trading markets. This proportion of free allowances decreases significantly in later stages of carbon trading markets. The initial allocation of carbon emission allowances is related to the load of the energy system, and excess or insufficient carbon emission allowances can be traded in the carbon trading market. However, most current carbon trading markets operate on an annual timescale, and carbon trading prices are determined through auctions or government approval. Annual carbon emission limits cannot be integrated with scheduling timescales such as hourly or 15-minute intervals, and cannot reflect the supply and demand relationship between carbon emission sources and carbon emission allowances. Therefore, to achieve the integration of carbon trading markets and future IES scheduling, this study assumes that the total allowable carbon emission allowances of IES are allocated in advance based on the seasonality and diurnal periodicity of their production, with different carbon emission allowances allocated for different time periods.

[0139] In the carbon trading market, free carbon emission allowances are allocated to IES carbon emission sources. When there is a surplus of carbon emission allowances, they can be sold to other carbon market participants. When the supply of carbon emission allowances is low, they must be purchased from the government. However, renewable energy generators do not produce carbon emissions while generating electricity, and the nationally certified emission reductions they generate can be sold to other carbon emission entities in the carbon trading market.

[0140] Therefore, this invention considers dynamic carbon trading pricing. Dynamic carbon trading pricing is determined based on the supply and demand tensions of carbon emission allowances in the carbon trading market. According to basic economic principles, the relationship between the commodity in the carbon market and its price satisfies a certain relationship, which can generally be described as a piecewise linear function.

[0141] As demand for carbon emission allowances increases, the carbon trading price continues to rise. When the demand for carbon emission allowances exceeds the total amount of paid carbon emission allowances available for purchase, the carbon trading price becomes a constant upper limit. As the sales volume of carbon emission allowances increases, the carbon trading price tends to decline. When the sales volume of carbon emission allowances or nationally certified emission reductions exceeds a certain limit, the carbon trading price becomes a constant upper limit. In the present invention, a piecewise function is used to construct the relationship between carbon emission allowances and the carbon trading price transaction amount.

[0142] The present invention constructs an IES low-carbon scheduling model, which is a two-layer optimization model, including an upper model and a lower model. The objective function of the upper model is:

[0143]

[0144] Where, P t L,aft is the maximum fitting function of power load demand; P t w,max Power forecasting for wind power;

[0145] The lower model takes into account energy consumption costs and carbon trading costs, and its objective function is:

[0146]

[0147] Where, are the total operating costs of coal-fired power units, carbon capture power plants, and cogeneration at time t, is the gas purchase cost, is the carbon trading cost, is the cost coefficient of coal-fired power unit i, is the start-up and shutdown cost of coal-fired power unit i, are the cost coefficients of carbon capture power plant i, is the start-up and shutdown cost of the carbon capture power plant, are the cost coefficients of cogeneration unit i, λ gas is the price of natural gas;

[0148] The upper and lower models are constrained by constraints. The constraints on the lower model include spinning reserve constraints, which involve fuzzy expressions of power load changes.

[0149] The specific method of obtaining is:

[0150] Considering that the information acquisition is insufficient or there is no large amount of field data in the actual system, the triangular membership function is used to represent the wind power and power load forecast errors and the uncertainty of power load changes to set the spinning reserve capacity of IES.

[0151] (1) Fuzzy expression of wind power prediction error

[0152]

[0153] Where k 1w 、k 2w is the fuzzy parameter of wind power generation.

[0154] (2) Fuzzy expression of power load forecast error

[0155]

[0156] Where k 1L 、k 2L is the fuzzy parameter of power load.

[0157] (3) Fuzzy expression of power load changes

[0158] Fuzzy Expression of Power Load Change Including power load forecast error and power load change rate error

[0159]

[0160] Maximum error of electric load change rate δ t Affected by electricity prices, expressed as:

[0161]

[0162] Among them, k1 and k2 represent the proportional coefficients before and after price dominance respectively.

[0163] After fuzzy deduction, the fuzzy expression of electric load change is finally defined as:

[0164]

[0165] The proposed IES low-carbon scheduling model was programmed using MATLAB 2016a and solved using Gurobi 9.5. The nonlinear models in Equations (27) and (47) were piecewise linearized. Details can be found in [ 1 ]. Furthermore, based on the theory of fuzzy chance-constrained programming, the uncertain constraints in Equations (54) and (55) can be transformed into deterministic constraints.

[0166] The following examples are used for simulation analysis:

[0167] A modified IEEE-30-node power system, a 6-node natural gas system, and a 6-node district heating system are introduced to verify the effectiveness of the proposed low-carbon dispatch strategy.

[0168] The system structure adopts an improved IEEE-30 node power system, a 6-node natural gas system and a 6-node central heating system to verify the effectiveness of the proposed low-carbon scheduling strategy. Figure 1 As shown, the CHP unit is connected to bus 2 of the power system. The GT unit is connected to bus 8 of the power system and node 1 of the natural gas system. If CCS and P2G are introduced, the TP in bus 1 will be transformed into a CCPP-P2G unit, and the natural gas generated by it will be injected into node 5 of the natural gas system. The wind power output forecast and energy demand data are provided in the supplementary file. Figure 2 The parameters of the TP and CHP units are listed in Tables A1 to A7 in the supplementary documents. Coal and natural gas prices were $171.43 / t and $0.34 / Nm³, respectively. The average CTP was $20 / t, with a fluctuation of 40%.

[0169] This paper designs five examples to verify that the proposed method can achieve low-carbon operation of IES. The specific classification is shown in Table 1.

[0170] Table 1 Scene settings

[0171]

[0172] Scheduling result analysis:

[0173] Example 1: Without considering AHS, PDR, CCPP and P2G, the power dispatch results are as follows: Figure 3 As shown. Figure 3 As shown, the IES WP decreased significantly between 1:00 AM and 8:00 AM and 11:00 PM and 12:00 AM. This decrease is due to two factors: first, the TP units provide backup capacity to ensure safe and stable system operation, limiting their ability to adjust downward. Second, the "heat-to-power" operating constraints of the cogeneration units limit power generation.

[0174] The CEA balance in Example 1 is as follows: Figure 4 As shown, Figure 5 The CTP and CEA of buying and selling in each period are shown. Figure 4 and 5 As shown, IES sells CEA between 10:00 PM and 12:00 PM and buys CEA at other times. The supply and demand relationship for CEA follows a similar trend to that observed in the CTP. Between 2:00 AM and 4:00 AM and 11:00 AM and 6:00 PM, the amount of CEA IES needs to purchase exceeds the total available CEA, and the CTP reaches its price ceiling.

[0175] Example 2: AHS can decouple the "heat-to-power" operation constraint of thermal power units and improve the WP absorption capacity of the IES system. The scheduling results and thermal power balance are shown in the following figure. Figure 6 and 7 During the periods of 01:00-08:00 and 23:00-24:00, P2H and TSS contribute to the heat energy supply, reducing the thermal power output of CHP, thereby reducing the electrical power output of CHP and expanding the WP's capacity.

[0176] Examples 1 and 2 Comparison with CTP can be seen in Figure 8 In Example 2, the amount of NCER convertible into WP increases as WP consumption increases. The offsetting CEAs increase in IES, thereby reducing the paid CEAs and CTPs.

[0177] Example 3: This example considers PDR. The electricity price and electricity demand changes after PDR are as follows: Figure 9 As shown in Figure 2. Based on the electricity price signal, the electricity load is transferred from other time periods to 01:00-08:00, which can enhance the consumption of WP. The power scheduling results of Example 3 are shown in Figure 2. Figure 10 shown.

[0178] The total free CEA of IES for cases 1, 2 and 3 is as follows Figure 11 Total free CEA is the sum of free CEA released by carbon emission sources and the NCER reimbursement from RES. Compared to Example 1, in Example 2, total free CEA increases with the increase in WP usage. In Example 3, the consideration of PDR, load variation, and increased WP regulation lead to changes in total free CEA.

[0179] Example 4: Based on Example 3, CCT is integrated into TP to construct CCPP. The sum of the net power output of the TP units in Examples 3 and 4 is as follows: Figure 12 As shown. Figure 12 As shown in Figure 3, during 01:00–08:00 and 23:00–24:00, the TP net power output decreases due to the energy consumption caused by the CCT capture process, which enhances the WP absorption of IES. Figure 13 The supply and demand of CTP and CEAs for Case 4 in each period are shown. Figure 13 As shown, the actual carbon emissions of IES are lower than those of free CEA, resulting in a CTP below $20 / t and reaching the price floor in some periods.

[0180] Example 5: Considering CCT and P2G, CCPP-P2G joint operation is realized. Excess wind energy is converted into hydrogen, which reacts with captured CO2 to generate CH4, which is injected into the natural gas pipeline network to achieve comprehensive energy utilization. The power dispatch results of Example 5 are as follows: Figure 14 shown.

[0181] WP absorption and cost comparison:

[0182] 1) WP consumption

[0183] The WP consumption rate in each period under the five situations is as follows: Figure 15 The WP absorption rate represents the ratio of WP grid-connected power to forecasted output. Due to the backup provided by TP units, the "heat-based power" operation constraints of CHP units, and the anti-peak shaving characteristics of WP, the WP absorption rate in Example 1 is less than 80% during the 01:00-08:00 and 23:00-24:00 periods. In Example 2, the AHS can convert excess wind energy into heat, facilitating wind power absorption, thereby improving the wind power absorption rate during these periods. In Example 3, some peak load is shifted to off-peak periods, increasing wind energy absorption, but its absorption capacity is limited. Figure 15 This is also demonstrated by the introduction of CCPP in Case 4, which lowers the lower limit of TP unit output and provides space for WP integration. However, in Case 5, when AHS, PDR, and CCPP-P2G are introduced, WP consumption is almost full, which means that the proposed low-carbon dispatch strategy helps promote the utilization of wind energy resources in IES.

[0184] 2) Cost comparison

[0185] Table 2 lists the specific values ​​of TP operating costs, CHP operating costs, natural gas consumption costs, carbon trading costs, wind curtailment, and carbon emissions for the five scenarios. Case 1 exhibits the highest wind curtailment, carbon emissions, and total costs. Case 2 uses P2H and TSS as additional heat sources, effectively improving the flexible scheduling capability of the CHP unit. Compared with Case 1, wind curtailment, total operating costs, and carbon emissions are reduced by 556 MWh, $24,879, and 420 tons, respectively. In Case 3, PDR shifts some electricity demand from peak periods to off-peak periods. Compared with Case 2, total costs and carbon emissions are reduced by $6,616 and 103 tons, respectively, while wind power consumption is increased by 109 MWh; however, the scheduling capability is limited. In Cases 4 and 5, CCT captures CO2, enabling IES to benefit from CTM. Compared to Example 3, the total costs of Examples 4 and 5 were reduced by $46,493 and $55,665, respectively. Furthermore, the introduction of the CCPP-P2G converged operation model in Example 5 reduced the total cost by $9,172 compared to Example 4.

[0186] Table 2 System parameters of different examples

[0187]

[0188] To sum up, the integrated flexible carbon capture power plant can make more accurate dispatch plans while effectively reducing net output to absorb wind power and achieve equivalent substitution of electricity, and increase net output to make up for load shortfalls and address imbalance problems by leveraging a wider net output adjustment range in the pre-dispatching stage and a faster adjustment rate and more flexible and controllable low-carbon dispatch characteristics in the real-time dispatching stage.

[0189] Obviously, the above embodiments are merely examples for clear explanation and are not limitations on the implementation methods. For ordinary technicians in the field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here, and the obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices, characterized by: The process includes the following steps, which are performed in sequence: Step 1: Based on the energy flow relationship of the integrated energy system (IES), a coupling model of the carbon capture power plant-power-to-gas (CCPP)-P2G and a scheduling model for price-based demand response (PDR) are constructed. 1) Energy flow relationship of the integrated energy system IES In the integrated energy system (IES), electricity is provided by coal-fired power plants (TP), combined heat and power (CHP) units, wind turbines (WP), and gas turbines (GT). The required heat is provided by the CHP units, a power-to-heat system, and a heat storage system. Natural gas demand is balanced by gas sources and power-to-gas conversion. Because the TP and CHP units produce large amounts of CO2, a carbon capture device (CCD) and a power-to-gas (P2G) system are introduced. The CCD reduces CO2 emissions and supplies the captured CO2 to the P2G system. This creates a coupled model of a carbon capture plant, power-to-gas (CCPP), and P2G, enabling low-carbon operation of the IES. The CCPP-P2G coupling model is defined as follows: Wherein, formula (1) represents the power balance of CCPP-P2G; formula (2) represents the energy loss of carbon capture device, including fixed energy loss P i CCS,B and operating energy loss P i CCS,OP ; Equation (3) represents the energy consumption of power-to-gas, including electrolytic cell EC and methanogenesis; where, P t TP represents the power generation of the coal-fired power unit at time t, P t N represents the net output power of the carbon capture power plant at time t, P t P2G represents the power consumption of the power-to-gas converter at time t, λ CCS represents the operating energy consumption coefficient, α CCS represents the capture rate, e g represents emission intensity, η EC Represents the energy conversion coefficient of the electrolytic cell, Ψ T is a set of time periods, P t EC Indicates the power consumption of the electrolytic cell, P t MET Indicates the power consumption of methane production, Indicates hydrogen production, η MET represents the methane production efficiency, Indicates the amount of methane produced; 2) Based on the output limit of coal-fired power units and the energy loss of the carbon capture device and the energy consumption of power-to-gas in the CCPP-P2G coupling model, the net power output range of the CCPP-P2G is derived and obtained; 3) Based on the electricity prices during peak, flat and valley periods, a price-based demand response model is constructed. The total electricity load changes during peak, flat and valley periods are obtained by using the expressions for the changes in total electricity load and electricity prices. The electricity load changes for each unit period in peak, flat and valley periods are obtained by using the expressions for the proportional distribution of the total electricity load changes in each period. Step 2: The percentage of free carbon emission quotas varies at different stages of the carbon trading market. The dynamic carbon trading price is formulated based on the supply and demand constraints of carbon emission quotas in the carbon trading market. Based on the supply and demand constraints, a relationship model between carbon emission quotas and carbon trading price transaction amounts is constructed; Step 3: Build an IES low-carbon scheduling model The IES low-carbon scheduling model includes an upper model and a lower model. The objective function of the upper model is: Where, P t L,aft is the maximum fitting function of power load demand; P t w,max Power forecasting for wind power; The lower model takes into account energy consumption costs and carbon trading costs, and its objective function is: Where, are the total operating costs of coal-fired power units, carbon capture power plants, and cogeneration at time t, is the gas purchase cost, is the carbon trading cost, is the cost coefficient of coal-fired power unit i, is the start-up and shutdown cost of coal-fired power unit i, are the cost coefficients of carbon capture power plant i, is the start-up and shutdown cost of the carbon capture power plant, are the cost coefficients of cogeneration unit i, λ gas is the price of natural gas; Step 4: Constrain the upper and lower models of the IES low-carbon scheduling model, solve the IES low-carbon scheduling model, and the optimal solution obtained is the low-carbon scheduling method of the integrated energy system based on dynamic carbon trading prices.

2. A low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices according to claim 1, characterized in that: The output limiting formula of the coal-fired power generation unit in step 12) is: Where, P i TP Indicates the output of coal-fired power units, P i min Indicates the lower limit of coal-fired power unit output, P max Indicates the upper limit of coal-fired power unit output.

3. The low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices according to claim 1 is characterized by: The net power output range of CCPP-P2G in step 1) is shown in formula (5): Where, P i TP,N Indicates the net power output of CCPP-P2G, Indicates the lower limit of coal-fired power unit output, Indicates the upper limit of coal-fired power generation unit output, Indicates the maximum output of P2G. Indicates the minimum output of P2G, Ψ TP A collection of thermal power units.

4. The low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices according to claim 1 is characterized by: The price-based demand response model in step 1 (3) is: Where E represents the elasticity matrix, E FF represents the peak-to-peak elastic modulus, E FP Indicates the peak-to-flat elastic coefficient, E FG Indicates the peak-to-valley elastic coefficient, E PF represents the flat-peak elastic coefficient, E PP represents the flat-flat elastic coefficient, E PG represents the flat-valley elastic coefficient, E GF Represents the valley-peak elastic coefficient, E GP Denotes the valley-flat elastic coefficient, E GG represents the valley-to-valley elastic coefficient, Δp F Indicates the change in power load during each peak period, Δp P Indicates the change in power load during each normal period, Δp G Indicates the change of power load in each valley period, P F Represents the total power load during each peak period, P P Indicates the total power load in each normal period, P G Indicates the total power load during each valley period, Δq F Indicates the change in electricity price during each peak period, Δq P Indicates the change in electricity price in each normal period, Δq G Indicates the change of electricity price in each valley period, represents the electricity price before price-based demand response; The expression of the change of the total electric load and electricity price is: Where, The expression for the proportional distribution of the total power load change in each period is: in, Indicates the change in power load during peak period at time t, P t F represents the electricity load at time t during the peak period, Indicates the change in power load at time t during normal periods, P t P represents the power load at time t during normal times, Indicates the change in power load during the valley period at time t, P t G It represents the electricity load during the valley period at time t.

5. The low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices according to claim 1 is characterized by: The relationship model formula between carbon emission quota and carbon trading price transaction amount in step 1 (3) is: Where, is the carbon quota trading volume at time t, is the lower limit of carbon trading price, is the upper limit of carbon trading price, is the average carbon trading price, is the maximum trading volume of carbon emission quotas in IES, which is expressed by formula (11): In formula (11): free Indicates the percentage of free carbon emission allowances; represents the carbon emission quota allowed in IES, which is expressed by formula (9): In formula (9), P t e 、 and Represent the demand for electricity, heat and natural gas respectively, κ e , κ h and κ g They represent the carbon emission quota coefficients of electric load, thermal load and natural gas load respectively, β represents the heat-to-electricity ratio, and χ represents the mass of CO2 produced per cubic meter of natural gas combustion.

6. The low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices according to claim 1 is characterized by: The constraints of the upper model in step 4 are: (1) Electric load balance constraints Before and after the demand response, the total power demand remains unchanged, and the amount of transferable load in each period is limited to equations (15) to (17): Δp F +Δp P +Δp G =0 (15) Where, P t L is the power demand before demand response at time t, ε is the load transfer ratio at each moment, and δ is the load transfer ratio at each time period; (2) Electricity price constraints The electricity prices during peak, flat and off-peak periods are subject to the following constraints: Where ζ1 and ζ2 are the electricity price ratios of peak and valley periods respectively, Q G , Q P , Q F These are the electricity prices during valley, flat and peak periods respectively; (3) User satisfaction constraints In order to ensure that demand response can satisfy customers, customer satisfaction with electricity charges and customer satisfaction with electricity usage methods are introduced as evaluation indicators to constrain user satisfaction, as shown in formula (19): Where S 1,min is the minimum value of customer satisfaction with electricity charges, S 2,min is the minimum value of customer satisfaction with electricity usage, Δp t is the load change at time t, P t L is the load at time t before demand response, P t L,aft is the load at time t after demand response, is the electricity price at time t after demand response, is the electricity price at time t before demand response.

7. The low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices according to claim 1 is characterized by: The constraints of the lower model in step 4 are: (1) Power balance constraints Where: is the power generated by thermal power unit i at time t, is the power generation of CCPP i at time t, is the power generation capacity of cogeneration unit i at time t, is the power generation of gas generator set i at time t, is the power generated by wind turbine i at time t, P t P2G is the power consumption of P2G at time t, P t PDR is the demand response change, C P 、 T t S 、T t R are the specific heat capacity, mass flow rate, inlet temperature and outlet temperature of the fluid respectively, TP is the set of thermal power units, Ψ CCPP is the set of CCPP units, Ψ CHP is the set of cogeneration units, Ψ GT is the collection of gas units, Ψ W is the set of wind turbines, P t P2H is the energy loss from converting electricity to heat, is the heat output of cogeneration unit i at time t, is the heat generated by electric heating at time t, represents the heat release of the heat storage system at time t, represents the heat storage system's charge at time t, They represent the heat released and stored in the heat storage tank at time t respectively; (2) Carbon emission quota balance constraints Where β is the ratio of electrical power to thermal power, represents the amount of CO2 captured at time t, is the carbon emission intensity of thermal power units, is the carbon emission intensity of the cogeneration unit, is the carbon emission intensity of the gas-fired unit, is the amount of carbon captured, Free carbon emission quotas for carbon emission sources in IES, Offsets for nationally certified emission reductions from renewable energy generators; in, As shown in formula (10): As shown in formula (12): in, Represents the national certified emission reduction deduction coefficient, P t w represents wind power; (3) Constraints on coal-fired power units Where, represents the start and stop status of coal-fired power unit i at time t, They represent the upward climbing rate and downward climbing rate of coal-fired power unit i, are the minimum downtime and minimum start-up time of coal-fired power unit i, and are the start and stop states of the unit in period k and period t, is the start-up time of the thermal power unit, The downtime of thermal power units; (4) CCPP unit constraints Since CCPP is derived from coal-fired power units, CCPP units and coal-fired power units have the same constraints. The operating constraints of CCPP units are shown in formula (2); (5) Cogeneration unit constraints The convex combination of the extreme points of the feasible region of cogeneration is used to formulate the thermal power and power generation capacity of the extracted cogeneration unit: Where, They represent the downward climbing speed and upward climbing speed of the cogeneration unit i, is the heat output of C cogeneration unit i at time t, is the electrical output of the vertex of the feasible region of the thermal power unit, is a convex combination binary variable of the cogeneration unit, is the thermal output of the vertex of the feasible region of the thermal power unit; (6) Gas turbine unit constraints Where η GT Indicates the thermal efficiency of the steam turbine, RD GT , RU GT They represent the downward ramp rate and upward ramp rate of the steam turbine respectively; is the natural gas consumption, is the minimum output of the gas unit, The maximum output of the gas unit; (7) Wind power output constraints is the maximum output of the wind turbine; (8) Power-to-gas constraints Where η EC represents the energy conversion coefficient of the electric EC, Respectively represent the minimum output power and maximum output power of EC, RD EC , RU EC Indicates the EC's downward climbing power and upward climbing power, Indicates the minimum output power and maximum output power of MET. is the reaction coefficient of the methane production equipment, The amount of carbon captured by the carbon capture equipment, Carbon consumption of methane production equipment; (9) Electricity-to-heat constraints Where, is the heat generated by electric heating at time t, η P2H is the electric heating coefficient, P t P2H Electric heating power consumption, The rated power of electric heating; (10) Thermal storage system constraints Where, are the discharge state and charge state of the heat storage system at time t, They represent the minimum heat charging rate and minimum heat releasing rate of the heat storage system respectively. Indicates the maximum heat charging rate and maximum heat releasing rate of the heat storage system. represents the heat release of the heat storage system at time t, represents the heat storage capacity of the heat storage system at time t, β d , β c are the storage efficiency and release efficiency of the heat storage system, They are the minimum and maximum heat storage capacity of the heat storage tank respectively; (11) Regional heating network transmission constraints Where, represents the outlet fluid temperature of pipe j at time t, is at t-τ j The fluid temperature at the pipe inlet at the moment, represents the inlet fluid temperature of pipe k at time t, T n,t represents the temperature of node n at time period t, Ψ H,pipe is a set of pipelines in DHS, T n,min 、T n,max Respectively represent the lowest temperature and the highest temperature of node n, m j,t 、m k,t are the mass flow rates of pipe j and pipe k at time t, l j is the length of pipe j, λ represents the heat transfer coefficient of the pipe, T t am is the ambient temperature, is the set of pipes where the working fluid flows into n nodes, The set of pipes where the working medium flows out of n nodes, is the outlet fluid temperature of pipe k at time t; (12) Grid transmission constraints Where, is the maximum transmission capacity, θ i,t ,θ j,t They represent the voltage angles of nodes i and j at time t, respectively, and x ij ,Ψ E,bus is the set of busbar nodes of the transmission network; (13) Natural gas network transmission constraints Where, All are natural gas pipeline parameters, f t mn is the natural gas mass flow rate in pipeline mn, Ψ G,node is the node set in NGN, is the average pressure of mn gas in the pipeline, is the air pressure at node m, Indicates the gas storage capacity of the pipeline in mn, Indicates the gas supply at time t, Average natural gas mass flow rate in pipeline mn, is the pipeline gas flow direction, are P2G gas production, gas load and gas consumption of gas generator sets, respectively, t mk Net natural gas inflow at node m, are the minimum and maximum limits of natural gas pipeline mass flow, Ψ G,node Natural gas pipeline node collection; (14) Spinning reserve constraints Where, represents the power generation of wind turbine i at time t, P t L represents the power demand before demand response at time t, is the climbing power of the thermal power unit, The gas unit is in start and stop state. is the climbing power of the gas generator set, are the fuzzy expressions of wind power and electric load forecasting errors, respectively.

8. The low-carbon scheduling method for an integrated energy system based on dynamic carbon trading prices according to claim 7 is characterized by: The spinning reserve constraint is the fuzzy expression of power load change, specifically: Where k 2L is the fuzzy parameter of power load, is the power load change rate error.

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