Peak-shaving and frequency-regulating method of clean energy system based on demand-side management and multi-energy complementarity

Abstract

The present invention belongs to the field of electric power technology, and particularly relates to a peak-shaving and frequency-regulating method for a clean energy system based on demand-side management and multi-energy complementarity; the present invention describes the uncertainty of wind, light output and load forecasting based on the opportunity constraint method, and converts the constraint conditions containing uncertain factors into deterministic equivalent classes; establishes a load demand response model according to the price elasticity coefficient to achieve the first peak-shaving; performs the second peak-shaving through energy storage; determines the primary frequency regulation safety constraint index of the hydropower unit, reserves the frequency regulation standby, establishes the frequency regulation constraint index by using the primary frequency regulation capacity of the hydropower unit, and accurately calculates the primary frequency regulation standby capacity; based on demand-side management and multi-energy complementarity, uses the above-mentioned peak-shaving method and frequency regulation method, and arranges the output of each energy source with the goal of minimizing net load fluctuation and minimizing abandoned energy; the present invention motivates users through demand-side management, reduces the load peak-to-valley difference and the difficulty of peak-shaving from the power supply side, and also avoids affecting the normal life of consumers during peak-shaving and frequency regulation.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and in particular relates to a peak-shaving and frequency-regulating method for a clean energy system based on demand-side management and multi-energy complementarity. Background Art

[0002] Tibet is rich in clean energy. With the prospect of new power systems with new energy as the main power source gradually replacing traditional power systems with fossil energy as the main power source, Tibet has great development potential. The entire Tibet region will plan to build three new energy bases, with the aim of creating a clean "zero-carbon" power system. The biggest difficulty it faces is how to ensure safe and reliable power supply from the power grid. The hydropower resources and solar energy resources in Tibet rank first in the country, forming a single power supply structure dominated by hydropower and photovoltaics, resulting in the prominent contradiction of "abundance and shortage" in Tibet's power supply, and the difficulty of peak and frequency regulation during power grid operation has increased. Due to the difficulty in predicting the output of new energy, the operation of the power grid faces many unexpected problems. In order to maintain the balance of power supply and demand, the Tibet power grid needs to export hydropower during the flood season and receive power from outside the region during the dry season. The randomness of the output of new energy and the large amount of power from outside the region further increase the difficulty of peak and frequency regulation of the power system.

[0003] In order to increase the peak-shaving capacity of the Tibet power grid, the current mainstream trend is to promote the installation of energy storage devices throughout the entire network and use multi-energy complementarity for peak-shaving. For the frequency stability problems caused by large-scale intermittent energy grid connection and power from outside the region, the Tibet power grid’s current main means of regulation is load shedding, which has seriously affected the normal lives of consumers. Summary of the invention

[0004] The present invention discloses a peak-shaving and frequency-regulating method for a clean energy system based on demand-side management and multi-energy complementarity, and is intended to solve the problem mentioned in the background technology that the current regulation method of load shedding seriously affects the normal life of consumers.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] A clean energy system peak load and frequency regulation method based on demand side management and multi-energy complementarity is characterized by comprising the following steps:

[0007] Step 1: Based on the chance constraint method, describe the uncertainty of wind, solar output and load forecasting, and transform the constraints containing uncertain factors into deterministic equivalent classes;

[0008] Step 2: Establish a load demand response model based on the price elasticity coefficient, and realize the first peak regulation based on the load demand response model; the load demand response model stimulates consumers to adjust their electricity consumption through reasonable electricity prices, optimizes the load curve, and achieves the purpose of the first peak regulation; this processing method can greatly reduce the impact of peak regulation on consumers' normal lives;

[0009] Step 3: Perform a second peak load regulation through an energy storage device, where the energy storage device is charged during the low load period and discharged during the peak load period; further shaving the peak load and filling the valley load, while improving the level of intermittent new energy consumption; the energy storage device includes an electrochemical energy storage device and a pumped energy storage device;

[0010] Step 4: Determine the primary frequency regulation safety constraint index of the hydropower unit, change the method of reserving a predetermined proportion of frequency regulation reserve, use the primary frequency regulation capacity of the hydropower unit to establish the frequency regulation constraint index, and accurately calculate the primary frequency regulation reserve capacity, thereby improving the stability of the power system frequency;

[0011] Step 5: Based on demand-side management and multi-energy complementarity, use the peak-shaving methods of steps 2 and 3, and the frequency regulation method described in step 4 to arrange the output of each energy source with the goal of minimizing the net load fluctuation of the system and minimizing the energy abandonment of the system.

[0012] The present invention adopts chance constraints to describe the randomness of renewable energy output and load forecasting, and converts them into deterministic constraints, thereby reducing the difficulty of solving the problem. The present invention also incentivizes users through demand-side management, thereby reducing the load peak-to-valley difference to a certain extent and reducing the difficulty of peak load regulation from the power supply side. It also avoids affecting consumers' normal lives during peak load and frequency regulation.

[0013] Preferably, the step 1 comprises the following steps:

[0014] Step 1.1: Based on the theory of chance-constrained programming, the uncertainty of wind, solar output and load forecasts creates chance constraints for grid operation. That is, in order to cope with the uncertainty of wind, solar output and load forecasts, the power supply side will reserve spare capacity that meets the following constraints:

[0015]

[0016] Where: Pr{˙} represents the probability that the formula in brackets is true; N I Represents the total number of thermal power units; P i,t is the output of thermal power unit i at time t; u i,t is a 0-1 variable, indicating the start / stop status of thermal power unit i at time t; P i,max , P i,min are the upper and lower limits of the output of thermal power unit i; P L,cut,t is the load cut-out amount at time t; P wp,cut,tis the amount of wind and solar power abandoned at time t; ΔP pre,t is the total forecast error of wind, solar power output and grid load at time t; ω1 and ω2 are the confidence levels of upper and lower reserve constraints respectively;

[0017] Step 1.2: Determine the deterministic equivalent constraint of the opportunity constraint based on the opportunity constraint in step 1.1, that is, transform the opportunity constraint in step 1.1 into P r {f(x,λ)=λ-g(x)≤0}≥ω, if this formula holds, then there is only one g(x)≥min{Φ λ -1 (ω)};Φ λ (ω) represents the probability distribution function of the random variable λ;

[0018] Based on the above, the deterministic equivalent constraint of the opportunity constraint is derived as follows:

[0019]

[0020] Preferably, step 2 comprises the following steps:

[0021] Step 2.1: According to consumer psychology and economic principles, when adopting a time-of-use electricity price strategy, consumers will adjust their electricity consumption in different time periods according to changes in electricity prices in order to obtain economic benefits, appropriately reducing electricity consumption during peak periods and appropriately increasing electricity consumption during trough periods. Therefore, the price elasticity coefficient is determined based on the fact that consumers' electricity consumption varies with changes in electricity prices:

[0022] γ=(Δq·ρ) / (q·Δρ);

[0023] Where: γ is the price elasticity coefficient; Δρ is the electricity price ρ The amount of change per unit time; Δq is the amount of change in the amount of electricity q per unit time based on the change in electricity price;

[0024] Step 2.2: Obtain the load demand response model for period T based on the price elasticity coefficient:

[0025]

[0026] Where: γ mn is the elasticity coefficient, which is used to measure the scale of the change of user electricity consumption in time period m with the change of electricity price in time period n, m, n∈(1:T), T is the total scheduling period, m=n when γ mn is the elastic coefficient, when m≠n, γ mn is the cross elastic coefficient;

[0027] Step 2.3: In order to use demand-side management to load-shift without affecting the normal production and life of users within a scheduling cycle, it is necessary to meet the load elasticity constraint, load upper and lower limit constraints, and user satisfaction constraints, as follows:

[0028]

[0029] Where: α is the proportion of elastic load in the load; P L1,t is the system load at time t before demand response; P L2,t is the load at time t after demand response; P L1,max , P L1,min are the maximum and minimum values ​​of the original load respectively; β q The user's satisfaction with electricity usage; q min The user satisfaction with electricity usage is minimal.

[0030] Preferably, step 3 comprises the following steps:

[0031] Step 3.1: Establish a charging and discharging model for chemical energy storage devices:

[0032]

[0033] Where: E s,t E is the remaining power of the energy storage battery at time t; s,t-1 is the remaining power of the energy storage battery at time t-1; μ is the energy storage energy loss rate; u t c is a 0-1 variable representing the energy storage charging state, u t c When it is 1, it indicates energy storage charging; u t d is a 0-1 variable representing the energy storage discharge state, u t d When it is 1, it indicates energy storage discharge; P s1,t , P s2,t Respectively represent the energy storage charging and discharging power at time t; η s1 , η s2 are the energy storage charging and discharging efficiency respectively;

[0034] Step 3.2: Introduce the state of charge that represents the ratio of the remaining power of the energy storage to the rated capacity, and establish the output model of the energy storage based on SOC:

[0035]

[0036] Where: S oc,t E is the energy storage charge state at time t; s Indicates the capacity of the energy storage battery;

[0037] Step 3.3: Establish the upper and lower limit constraints of the state of charge of electrochemical energy storage, the single charge and discharge constraints of energy storage, and the working state conversion constraints, as follows:

[0038]

[0039] Where: S oc,min is the minimum value of energy storage charge state; S oc,t S is the energy storage charge state at time t; oc,max is the maximum value of the energy storage charge state; P s1,min is the minimum charging power of energy storage; P s1,max is the maximum charging power of energy storage; P s1,t is the charging power of the energy storage at time t; P s2,min is the minimum discharge power of energy storage; P s2,max is the maximum discharge power of energy storage; P s2,t is the discharge power of the energy storage at time t;

[0040] Step 3.4: Establish the upper and lower power constraints, storage capacity constraints and working state transition constraints of the pumped storage unit during operation, as follows:

[0041]

[0042] Where: P ph1,t , P ph2,t are the pumping power and generating power of the pumped storage unit at time t respectively; P ph1,max is the maximum pumping power of the pumped storage unit; P ph2,max is the maximum power generation capacity of the pumped storage unit; V ph,t is the reservoir capacity at time t; V ph,t-1 is the reservoir capacity at time t-1; V ph,min is the minimum storage capacity of the reservoir; V ph,max is the maximum storage capacity of the reservoir; Q g,t , Q p,t are the generating flow and pumping flow of the unit respectively; η g , η p are the water-to-electricity conversion efficiencies of the pumped storage unit when generating electricity and pumping water; u g,t and u p,t are 0-1 variables indicating that the unit is operating in power generation state and pumped storage state respectively;

[0043] Step 3.5: Establish the power balance constraints of the pumped storage unit, as follows:

[0044]

[0045] Where: η phis the pumping / power generation conversion efficiency; N PH is the total number of pumped storage units;

[0046] The above power balance constraint is transformed into the following formula:

[0047]

[0048] Where: χ represents the margin left for the pumping / power generation conversion efficiency.

[0049] Preferably, step 4 comprises the following steps:

[0050] Step 4.1: Use the allowable frequency deviation and turbine regulation coefficient to evaluate the ability of the hydropower unit to adjust the frequency upward:

[0051]

[0052] Where: P h,up,t P is the ability of the hydropower unit h to adjust frequency upward at time t; h,max is the upper limit of the output of hydropower unit h; P h,t is the output of hydropower unit h at time t; Δf * The per-unit value of the frequency deviation allowed by the system; is the regulation coefficient of the turbine unit; Indicates the rated power of the hydropower unit;

[0053] Step 4.2: Use the method in step 4.1 to evaluate the ability of the hydropower unit to adjust frequency to the next time:

[0054]

[0055] Where: P h,down,t is the next frequency regulation capability of hydropower unit h at time t; P h,min is the lower limit of the output of hydropower unit h;

[0056] Step 4.3: Establish a frequency regulation capacity constraint:

[0057]

[0058] Where: N H is the total number of hydropower units; ΔP L,up,t , ΔP L,down,t They are load power adjustment upward and downward respectively; ΔP up,t , ΔP down,t They are the maximum excess power and maximum power shortage in the system respectively.

[0059] Preferably, step 5 comprises the following steps:

[0060] Step 5.1: Establish the objective function with the goal of minimizing the system net load fluctuation and the system energy abandonment:

[0061]

[0062] Where: P net,t is the net load of the system at time t; P net,av is the average value of the system net load within a scheduling cycle;

[0063] The system net load P net,t and its average value P net,av The expression is as follows:

[0064]

[0065] Where: P L,t represents the load of the system at time t; P w,t represents the output of the wind turbine at time t; P p,t represents the output of the photovoltaic unit at time t; P s,t represents the output of chemical energy storage at time t; when the energy storage is charged, P s,t <0, when the energy storage is discharged, P s,t >0;

[0066] Step 5.2: Establish the power balance constraint of the multi-energy complementary system:

[0067]

[0068] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0069] 1. The present invention adopts chance constraints to describe the randomness of renewable energy output and load forecasting, and converts them into deterministic constraints, thereby reducing the difficulty of solving the problem. The present invention also incentivizes users through demand-side management, thereby reducing the load peak-to-valley difference to a certain extent and reducing the difficulty of peak load regulation from the power supply side. It also avoids affecting consumers' normal lives during peak load and frequency regulation.

[0070] 2. The present invention makes full use of the complementary characteristics of wind, solar, water, fire and storage in the new power system, enhances the peak-shaving capacity of the power grid, and reduces the abandonment rate of solar and wind power.

[0071] 3. The present invention additionally considers the primary frequency regulation capability of the hydropower unit and adds the transient frequency constraint of the system, thereby accurately reserving the primary frequency regulation standby capacity of the hydropower unit. While enhancing the frequency regulation capability of the system, it also increases the wind and solar power absorption power, thereby improving the safety and reliability of the system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0073] Figure 1 is a modeling flow chart of the present invention;

[0074] Figure 2 A comparison diagram of load curves before and after demand response of the present invention;

[0075] Figure 3 This is a schematic diagram of the output of various energy sources when the peak-shaving and frequency-regulating method of the present invention is not used;

[0076] Figure 4 This is a schematic diagram of the output of various energy sources after using the peak-shaving and frequency-modulating method of the present invention; DETAILED DESCRIPTION

[0077] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0078] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings;

[0079] See also Figure 1 As shown, the clean energy system peak-shaving and frequency-regulating method based on demand-side management and multi-energy complementarity of the present invention comprises the following steps:

[0080] Step 1: Use the chance constraint method to describe the uncertainty of wind, solar output and load forecasts, and obtain the reserve capacity constraint to deal with the uncertainty:

[0081]

[0082] Where: P r {˙} represents the probability that the formula in brackets is true; N I Represents the total number of thermal power units; P i,t is the output of thermal power unit i at time t; u i,t is a 0-1 variable, indicating the start / stop status of thermal power unit i at time t; P i,max , P i,min are the upper and lower limits of the output of thermal power unit i; PL,cut,t is the load cut-out amount at time t; P wp,cut,t is the amount of wind and solar power abandoned at time t; ΔP pre,t is the total forecast error of wind, solar power output and grid load at time t; ω1 and ω2 are the confidence levels of upper and lower reserve constraints respectively;

[0083] Convert this to a deterministic equivalence constraint:

[0084]

[0085] Where: ω1 and ω2 are the confidence levels of upper and lower backup constraints respectively, and both are 0.95 in the examples adopted by the present invention;

[0086] represents the inverse function of the probability distribution function; the total prediction error ΔP of wind, solar output and grid load at time t pre,t The mean is 0 and the variance is The normal distribution of is:

[0087]

[0088] Where: P w,pre,t , P p,pre,t and P L,pre,t are the predicted values ​​of wind, solar output and load at time t, as shown in Table 1; r w 、r p 、r L , and is the correlation coefficient, which is 0.1, 0.1, 0.02, 0.02, and 0.02 respectively; and are the installed capacities of wind power and photovoltaic power, respectively. In the example adopted by the present invention, the installed capacity of wind power is 200MW, the installed capacity of photovoltaic power is 1015MW, and the predicted values ​​of load, wind turbine output and photovoltaic output are shown in Table 1;

[0089] Table 1 Predicted values ​​of load, wind turbine output and photovoltaic output in each period

[0090]

[0091]

[0092] Step 2: According to consumer psychology and economic principles, when adopting a time-of-use electricity price strategy, consumers will adjust their electricity consumption in different time periods according to changes in electricity prices in order to obtain economic benefits, appropriately reducing electricity consumption during peak periods and appropriately increasing electricity consumption during trough periods. Therefore, the price elasticity coefficient is determined based on the fact that consumers' electricity consumption varies with changes in electricity prices:

[0093] γ=(Δq·ρ) / (q·Δρ);

[0094] Where: γ is the price elasticity coefficient; Δρ is the electricity price ρ The amount of change per unit time; Δq is the amount of change in the amount of electricity q per unit time based on the change in electricity price;

[0095] According to the price elasticity coefficient γ, the load demand response model for period T is established:

[0096]

[0097] In the formula: In the embodiment of the present invention, T is 24; γ mn is the elasticity coefficient, which is used to measure the scale of the change of user electricity consumption in time period m with the change of electricity price in time period n, m, n∈(1:T), when m=n mn is the elastic coefficient, taking -0.02, when m≠n mn is the cross elasticity coefficient. The cross elasticity coefficient for the same electricity price period is 0.001, the cross elasticity coefficient for the peak-normal period and the normal-valley period is 0.002, and the cross elasticity coefficient for the peak-valley period is 0.0033.

[0098] In order not to affect the normal production and life of users, it is also necessary to meet the load elasticity constraints, load upper and lower limit constraints, and user satisfaction constraints:

[0099]

[0100] Where: α is the proportion of elastic load in the load, which is 5%; P L1,t is the system load at time t before demand response; P L2,t is the load at time t after demand response; P L1,max , P L1,min are the maximum and minimum values ​​of the original load respectively; β q The user's satisfaction with electricity usage; q min The minimum level of user satisfaction with electricity usage is 0.9.

[0101] The load curve before and after demand response is as follows Figure 2 As shown, it can be seen that: after managing the demand side, the load in the two trough periods of the curve has increased significantly, the load change in the morning peak period is not obvious, but the load in the evening peak period is significantly reduced, and the load peak-to-valley difference is also significantly reduced, indicating that the elastic load of the evening peak load is transferred to the load trough period through demand response, and the role of demand response in "peak shaving and valley filling" is very significant.

[0102] Step 3: Establish an energy storage charging and discharging model based on the remaining power of the chemical energy storage device at the last moment and its own properties:

[0103]

[0104] Among them, E s,t E is the remaining power of the energy storage battery at time t; s,t-1 is the remaining power of the energy storage battery at time t-1; μ is the energy storage energy loss rate, which is 0.5% in the example adopted by the present invention; u t c is a 0-1 variable representing the energy storage charging state at time t, u t c When it is 1, it indicates energy storage charging; u t d is a 0-1 variable representing the energy storage discharge state at time t, u t d When it is 1, it indicates energy storage discharge; P s1,t , P s2,t Respectively represent the energy storage charging and discharging power at time t; η s1 , η s2 are respectively the energy storage charging and discharging efficiency, and both are taken as 95% in the examples adopted by the present invention;

[0105] The state of charge (SOC) is introduced to describe the output model of energy storage:

[0106]

[0107] Among them, S oc,t is the energy storage charge state at time t; the energy storage battery capacity E s Take 300MW;

[0108] Electrochemical energy storage also needs to meet the upper and lower limit constraints of the state of charge, the single charge and discharge constraints of energy storage, and the working state conversion constraints:

[0109]

[0110] Where: S oc,min is the minimum value of energy storage charge state; S oc,t S is the energy storage charge state at time t; oc,max is the maximum value of the energy storage charge state; P s1,min is the minimum charging power of energy storage; P s1,max is the maximum charging power of energy storage; P s1,t is the charging power of the energy storage at time t; P s2,min is the minimum discharge power of energy storage; P s2,max is the maximum discharge power of energy storage; P s2,t is the discharge power of the energy storage at time t;

[0111] When the pumped storage unit is in operation, it must also meet the power upper and lower limit constraints, storage capacity constraints, and working state conversion constraints:

[0112]

[0113] Where: P ph1,t , P ph2,t are the pumping power and generating power of the pumped storage unit at time t respectively; P ph1,max is the maximum pumping power of the pumped storage unit; P ph2,max is the maximum power generation capacity of the pumped storage unit; V ph,t is the reservoir capacity at time t; V ph,t-1 is the reservoir capacity at time t-1; V ph,min is the minimum storage capacity of the reservoir; V ph,max is the maximum storage capacity of the reservoir; Q g,t , Q p,t are the generating flow and pumping flow of the unit respectively; η g , η p are the water-to-electricity conversion efficiencies of the pumped-storage unit when generating electricity and pumping water, which are 99.8 and 74.9 respectively; u g,t and u p,t They are 0-1 variables indicating that the unit is operating in power generation state and pumped storage state respectively.

[0114] The pumped storage unit must meet the power balance constraint within a regulation cycle to ensure that the reservoir water level remains basically unchanged:

[0115]

[0116] Where: Pumping / power generation conversion efficiency η ph is η p / η g ; N PH is the total number of pumped storage units;

[0117] The pumping / power generation conversion efficiency is left with a certain margin of χ = 10%, and the above formula is converted into:

[0118]

[0119] Step 4: Use the allowable frequency deviation and turbine regulation coefficient to evaluate the ability of the hydropower unit to adjust the frequency upward:

[0120]

[0121] Where: P h,up,t is the upward frequency regulation capability of hydropower unit h at time t; P h,max is the upper limit of the output of hydropower unit h; P h,t is the output of hydropower unit h at time t; Δf * The per-unit value of the system allowable frequency deviation Δf is 0.2Hz, then Δf * 0.2 / 50; is the adjustment coefficient of the turbine unit, which is 4%; Indicates the rated power of the hydropower unit.

[0122] The same method is used to evaluate the ability of the hydropower unit to adjust frequency for the next time:

[0123]

[0124] Where: P h,down,t is the next frequency regulation capability of hydropower unit h at time t; P h,min is the lower limit of the output of hydropower unit h;

[0125] In order to ensure the stability of system frequency, the system's primary frequency regulation response must be greater than the system's primary frequency regulation demand, so a primary frequency regulation capacity constraint is established:

[0126]

[0127] Where: N H is the total number of hydropower units; ΔP L,up,t , ΔP L,down,t They are load power adjustment upward and downward respectively; ΔP up,t , ΔP down,t They are the maximum excess power and maximum power shortage in the system respectively.

[0128] Step 5: Based on demand-side management and multi-energy complementarity, use the above-mentioned peak-shaving and frequency-regulating methods to reasonably arrange the output of each energy source with the goal of minimizing the system net load fluctuation and the system energy abandonment.

[0129] The objective function is as follows:

[0130]

[0131] Where: P net,t is the net load of the system at time t; P net,av is the average value of the system net load within a scheduling cycle;

[0132] The system net load P net,t and its average value P net,av The expression is as follows:

[0133]

[0134] Where: P L,t represents the load of the system at time t; Pw,t represents the output of the wind turbine at time t; P p,t represents the output of the photovoltaic unit at time t; P s,t represents the output of chemical energy storage at time t; when the energy storage is charged, P s,t <0, when the energy storage is discharged, P s,t >0;

[0135] The multi-energy complementary system should meet the power balance constraint:

[0136]

[0137] In the example of the present invention, the installed capacities of wind turbines, photovoltaics, hydropower units, thermal power units, chemical energy storage units and pumped storage units are 200MW, 1015MW, 1508MW, 500MW, 300MW and 112.5MW respectively. Before and after using the model of the present invention, the schematic diagrams of the output of each energy source are as follows: Figure 3 and Figure 4 As shown, other results are shown in Table 2;

[0138] Table 2

[0139] Before peak and frequency regulation After peak and frequency regulation Net load fluctuation 275.00 143.75 Wind and solar power curtailment 844.15 0

[0140] Depend on Figure 3 It can be seen that when the peak-shaving and frequency regulation model proposed in the present invention is not adopted, in order to ensure the stability of the system frequency, the hydropower units need to reserve a certain percentage of frequency regulation standby. Therefore, during the period of maximum wind and solar power output, measures need to be taken to abandon wind and solar power.

[0141] contrast Figure 3 and Figure 4 It can be seen that after converting the hydropower output limit into a frequency constraint, not only the system frequency stability is guaranteed, but also the reserve reserve for hydropower is greatly reduced. After adding the demand response model and energy storage for peak regulation, the wind and solar power output can basically be fully absorbed, and the net load fluctuation of the system is also reduced.

[0142] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A clean energy system peak-shaving and frequency-regulating method based on demand-side management and multi-energy complementarity, characterized in that: The following steps are involved: Step 1: Based on the chance constraint method, describe the uncertainty of wind, solar output and load forecasting, and transform the constraints containing uncertain factors into deterministic equivalent classes; Step 2: Establish a load demand response model according to the price elasticity coefficient, and implement the first peak load regulation based on the load demand response model; Step 3: Perform the second peak load regulation through the energy storage device, which charges during the load valley period and discharges during the load peak period; Step 4: Determine the primary frequency regulation safety constraint index of the hydropower unit, change the method of reserving a predetermined proportion of frequency regulation reserve, use the primary frequency regulation capacity of the hydropower unit to establish the frequency regulation constraint index, and accurately calculate the primary frequency regulation reserve capacity; Step 5: Based on demand-side management and multi-energy complementarity, use the peak-shaving methods of steps 2 and 3, and the frequency regulation method described in step 4 to arrange the output of each energy source with the goal of minimizing the net load fluctuation of the system and minimizing the energy abandonment of the system.

2. The peak-shaving and frequency-regulating method of a clean energy system based on demand-side management and multi-energy complementarity according to claim 1 is characterized in that: The step 1 comprises the following steps: Step 1.1: Based on the theory of chance-constrained programming, the uncertainty of wind, solar output and load forecasting creates chance constraints for grid operation: Where: P r {˙} represents the probability that the formula in brackets is true; N I Represents the total number of thermal power units; P i,t is the output of thermal power unit i at time t; u i,t is a 0-1 variable, indicating the start / stop status of thermal power unit i at time t; P i,max , P i,min are the upper and lower limits of the output of thermal power unit i; P L,cut,t is the load cut-out amount at time t; P wp,cut,t is the amount of wind and solar power abandoned at time t; ΔP pre,t is the total forecast error of wind, solar power output and grid load at time t; ω1 and ω2 are the confidence levels of upper and lower reserve constraints respectively; Step 1.2: Based on the chance constraints in step 1.1, derive their deterministic equivalent constraints: Where: Φ λ (ω) represents the probability distribution function of the random variable λ, ω1 and ω2 are the confidence levels of the upper and lower backup constraints respectively. Represents the inverse function of the probability distribution function.

3. The peak-shaving and frequency-regulating method of a clean energy system based on demand-side management and multi-energy complementarity according to claim 1 is characterized in that: The step 2 comprises the following steps: Step 2.1: Determine the price elasticity coefficient based on the difference in consumer electricity consumption with changes in electricity prices: Y = (Δq.P) / (q.ΔP); Where: γ is the price elasticity coefficient; ΔP is the electricity price P The amount of change per unit time; Δq is the amount of change in the amount of electricity q per unit time based on the change in electricity price; Step 2.2: Obtain the load demand response model for period T based on the price elasticity coefficient: Where: γ mn is the elasticity coefficient, which is used to measure the scale of the change of user electricity consumption in time period m with the change of electricity price in time period n, m, n∈(1:T), T is the total scheduling period, m=n when γ mn is the elastic coefficient, when m≠n, γ mn is the cross elastic coefficient; Step 2.3: Use demand-side management to adjust the peak load and meet the load elasticity constraints, load upper and lower limit constraints, and user satisfaction constraints, as follows: Where: α is the proportion of elastic load in the load; P L1,t is the system load at time t before demand response; P L2,t is the load at time t after demand response; P L1,max , P L1,min are the maximum and minimum values ​​of the original load respectively; β q The user's satisfaction with electricity usage; q min is the minimum satisfaction level of users’ electricity usage; Δt is the time step.

4. The peak-shaving and frequency-regulating method of a clean energy system based on demand-side management and multi-energy complementarity according to claim 1 is characterized in that: The step 3 comprises the following steps: Step 3.1: Establish a charging and discharging model for chemical energy storage devices: Where: E s,t E is the remaining power of the energy storage battery at time t; s,t-1 is the remaining power of the energy storage battery at time t-1; μ is the energy storage energy loss rate; u t c is a 0-1 variable representing the energy storage charging state at time t, u t c When it is 1, it indicates energy storage charging; u t d is a 0-1 variable representing the energy storage discharge state at time t, u t d When it is 1, it indicates energy storage discharge; P s1,t , P s2,t Respectively represent the energy storage charging and discharging power at time t; η s1 , η s2 are the energy storage charging and discharging efficiency respectively; Step 3.2: Introduce the state of charge that represents the ratio of the remaining power of the energy storage to the rated capacity, and establish the output model of the energy storage based on SOC: Where: S oc,t E is the energy storage charge state at time t; s Indicates the capacity of the energy storage battery; P s1 Indicates the energy storage charging power; P s2 Indicates the energy storage discharge power; Step 3.3: Establish the upper and lower limit constraints of the state of charge of electrochemical energy storage, the single charge and discharge constraints of energy storage, and the working state conversion constraints, as follows: Where: S oc,min is the minimum value of energy storage charge state; S oc,t S is the energy storage charge state at time t; oc,max is the maximum value of the energy storage charge state; P s1,min is the minimum charging power of energy storage; P s1,max is the maximum charging power of energy storage; P s1,t is the charging power of the energy storage at time t; P s2,min is the minimum discharge power of energy storage; P s2,max is the maximum discharge power of energy storage; P s2,t is the discharge power of the energy storage at time t; Step 3.4: Establish the upper and lower power constraints, storage capacity constraints and working state transition constraints of the pumped storage unit during operation, as follows: Where: P ph1,t , P ph2,t are the pumping power and generating power of the pumped storage unit at time t respectively; P ph1,max is the maximum pumping power of the pumped storage unit; P ph2,max is the maximum power generation capacity of the pumped storage unit; V ph,t is the reservoir capacity at time t; V ph,t-1 is the reservoir capacity at time t-1; V ph,min is the minimum storage capacity of the reservoir; V ph,max is the maximum storage capacity of the reservoir; Q g,t , Q p,t are the generating flow and pumping flow of the unit respectively; η g , η p are the water-to-electricity conversion efficiency of the pumped storage unit when generating electricity and pumping water; u g,t and u p,t are 0-1 variables indicating that the unit is operating in power generation state and pumped storage state respectively; Step 3.5: Establish the power balance constraints of the pumped storage unit, as follows: Where: η ph is the pumping / power generation conversion efficiency; N PH is the total number of pumped storage units, T is the total time; ph is the serial number of the pumped storage unit; The above power balance constraint is transformed into the following formula: Where: χ represents the margin left for the pumping / power generation conversion efficiency.

5. The peak-shaving and frequency-regulating method of a clean energy system based on demand-side management and multi-energy complementarity according to claim 1 is characterized in that: The step 4 comprises the following steps: Step 4.1: Use the allowable frequency deviation and turbine regulation coefficient to evaluate the ability of the hydropower unit to adjust the frequency upward: Where: P h,up,t P is the ability of the hydropower unit h to adjust frequency upward at time t; h,max is the upper limit of the output of hydropower unit h; P h,t is the output of hydropower unit h at time t; Δf * is the per-unit value of the frequency deviation allowed by the system; σ h* is the regulation coefficient of the turbine unit; P h N Indicates the rated power of the hydropower unit; Step 4.2: Use the method in step 4.1 to evaluate the ability of the hydropower unit to adjust frequency to the next level: Where: P h,down,t is the next frequency regulation capability of hydropower unit h at time t; P h,min is the lower limit of the output of hydropower unit h; Step 4.3: Establish a frequency regulation capacity constraint: Where: N H is the total number of hydropower units; ΔP L,up,t , ΔP L,down,t They are load power adjustment upward and downward respectively; ΔP up,t , ΔP down,t They are the maximum excess power and maximum power shortage in the system respectively.

6. The peak-shaving and frequency-regulating method of a clean energy system based on demand-side management and multi-energy complementarity according to claim 1 is characterized in that: The step 5 comprises the following steps: Step 5.1: Establish the objective function with the goal of minimizing the system net load fluctuation and the system energy abandonment: Where: P net,t is the net load of the system at time t; P net,av is the average value of the system net load in a scheduling cycle, T is the total time; P wp,cut,t is the amount of wind and solar power abandoned at time t; the net load of the system P net,t and its average value P net,av The expression is as follows: Where: P L,t represents the load of the system at time t; P w,t represents the output of the wind turbine at time t; P p,t represents the output of the photovoltaic unit at time t; P s,t represents the output of chemical energy storage at time t; when the energy storage is charged, P s,t <0, when the energy storage is discharged, P s,t >0; Step 5.2: Establish the power balance constraint of the multi-energy complementary system: Where: N H is the total number of hydropower units; h is the serial number of the hydropower unit; P h,t is the output of hydropower unit h at time t; ph is the serial number of the pumped storage unit; N PH is the total number of pumped storage units; N I Represents the total number of thermal power units; P i,t is the output of thermal power unit i at time t.

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