Power system scheduling method based on short-term and ultra-short-term operation optimization models

Abstract

The invention relates to a power system scheduling method based on short-term and ultra-short-term operation optimization models, and the method comprises the steps: analyzing the flexibility demands of a new energy high-permeability system, and configuring multiple types of flexible resources; the fluctuation of new energy power generation on different time scales is considered, a flexible transformation thermal power generating unit, a demand side controllable load and an energy storage unit are respectively selected as flexible resources under each time scale, and random uncertain changes of a power system are dealt with; establishing a new energy high-permeability comprehensive energy system; constructing a multi-time scale operation simulation framework; respectively establishing a typical weekly operation optimization model, a short-term operation optimization model and an ultra-short-term operation optimization model; and solving to obtain the flexibility capacity of the system under each time scale. According to the method, the operation optimization models of different time scales are constructed, and the system flexibility capacity under each time scale is solved, so that complementation and efficient utilization of various forms of energy are realized, and the clean energy consumption capability of a power system is improved.

Description

Technical Field

[0001] The invention belongs to the field of power system dispatching, and in particular relates to a power system dispatching method based on short-term and ultra-short-term operation optimization models. Background Art

[0002] At present, the global power system is transforming towards a low-carbon and environmentally friendly one. The high penetration of intermittent new energy represented by wind power and photovoltaic power has increased the uncertainty of power system operation, bringing huge challenges to the stable operation and power balance of the power system. This requires that in the process of optimizing the operation of the power system, not only long-term power and electricity balance should be guaranteed, but also short-term and even ultra-short-term power and electricity balance should be guaranteed. Therefore, how to give full play to the rapid adjustment ability of flexible resources of source, load and storage on multiple time scales and respond to the short-term flexibility demand of sudden net load has become an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to address the above-mentioned problems and provide a power system dispatching method based on short-term and ultra-short-term operation optimization models, establish a power system operation simulation mechanism with three layers of typical week, day and time period, construct typical week, short-term and ultra-short-term operation optimization models, and solve to obtain the system flexibility capacity at each time scale, make full use of source, load and storage flexibility resources, smooth out peaks and fill valleys, optimize load curves, improve the clean energy absorption capacity, realize energy complementarity and efficient utilization through multi-time scale coordinated optimization, enhance system flexibility, and promote the transformation of the energy system to diversification, cleanliness and low carbonization.

[0004] The technical solution of the present invention is a power system dispatching method based on short-term and ultra-short-term operation optimization models, comprising the following steps:

[0005] Step 1: Analyze the flexibility requirements of the new energy high penetration system, configure various types of flexible resources, and achieve flexibility supply and demand balance of the power system at various time scales;

[0006] Step 2: Considering the volatility of renewable energy generation at different time scales, we select flexible thermal power units, demand-side controllable loads, and energy storage units as flexible resources at different time scales to cope with random and uncertain changes in the power system.

[0007] Step 3: Establish a new energy high-penetration integrated energy system and initialize the parameters of the integrated energy system;

[0008] Step 4: Construct a multi-time scale operation simulation framework, which includes three stages: time series random production simulation, typical day operation simulation, and typical period operation simulation;

[0009] Step 5: Construct a typical weekly operation optimization model, focusing on system reliability, and taking carbon trading revenue and new energy curtailment penalty costs as optimization targets;

[0010] Step 6: Construct a short-term operation optimization model, focusing on the flexibility capacity of the system, with the goal of minimizing the penalty cost of insufficient flexibility and the benefit of flexibility resource regulation;

[0011] Step 7: Construct an ultra-short-term operation optimization model, focusing on the system's short-term peak-shaving and ramping capabilities, and considering the penalty costs for insufficient peak-shaving and insufficient ramping;

[0012] Step 8: Adopt the “multi-level coordination, step-by-step refinement” solution method to iteratively optimize the typical weekly operation optimization model, short-term operation optimization model and ultra-short-term operation optimization model;

[0013] Step 9: If the iteration termination conditions are met, the comprehensive cost of each stage and the system flexibility capacity at each time scale are output.

[0014] Preferably, in step 1, a flexibility deficiency penalty cost is introduced to quantify the flexibility shortfall.

[0015] Furthermore, in step 1, the upward and downward flexibility margins of the power system at time t are introduced and

[0016] In the formula represents the upward flexibility margin of the power system at time t, It represents the downward flexibility margin of the power system at time t.

[0017] When there is an upward flexibility gap When the system needs to consider the risk of load shedding; when there is a lack of downward flexibility When F SU,t 、F NE,t They represent the system flexibility supply and demand respectively.

[0018] In step 2, the renewable energy output and load are superimposed according to the time sequence relationship to form the net load

[0019] P NL,t =L t -P RE,t (2)

[0020] Where P NL,t represents the net load of the system at time t, P RE,t represents the renewable energy output of the system at time t, L t represents the load of the system at time t.

[0021] Different types of flexible resources have different output regulation characteristics at different time scales:

[0022] 1) Medium time scale: The flexible thermal power units refer to gas and oil units, which have low minimum technical output and fast ramp rate. By optimizing peak load regulation and ramp rate, the flexibility adjustment space on the power supply side of the system can be improved, and they can participate in the flexibility adjustment on the hourly time scale.

[0023]

[0024] In the formula They are the upward and downward flexibility of the thermal power unit f supplied by the flexibility transformation in period t; They are the maximum and minimum outputs of the flexible thermal power unit f during period t; are the up and down ramp rates of the flexible transformation thermal power unit f, respectively, and Δt is the adjustment time interval; It represents the real-time output of the flexible thermal power unit f during period t.

[0025] 2) In the short time scale, the controllable load on the demand side can stimulate load interruption and transfer through compensation and policy management, smooth load fluctuations, reduce load peak-valley differences, and improve user-side flexibility. It can participate in flexibility adjustment in the short time scale of 15 to 60 minutes.

[0026]

[0027] In the formula They represent the upward and downward flexibility of the supply of the transferable load m during period t respectively; It represents the upward flexibility of the interruptible load n at time t; represents the transfer amount of the transferable load m during period t; They represent the maximum and minimum values ​​of the transfer amount of the transferable load m respectively; It represents the interruption amount of interruptible load n in time period t; Indicates the upper limit of the interruption amount of interruptible load n.

[0028] 3) Ultra-short time scale: Energy storage devices have the characteristics of short response time, and can achieve frequent charging and discharging within a short time scale. They are suitable for smoothing low-energy, high-frequency random fluctuations within 15 minutes or even 1 minute, and become a flexible power source for rapid adjustment.

[0029]

[0030] In the formula The flexibility of adjusting up and down the energy storage device s during time t; represents the output of energy storage device s during period t; They represent the maximum power of charging and discharging of energy storage device s during period t; E s,t E represents the storage capacity of energy storage device s during period t; s,max 、E s,min Indicates the upper and lower limits of the storage capacity of the energy storage device s; η C , η D are the charging and discharging efficiency of energy storage respectively, and Δt is the adjustment time interval.

[0031] The upward and downward flexibility supply capacity of the power system in period t

[0032]

[0033] Where N f 、N TL 、N IL 、N S They respectively represent the number of flexibly transformed thermal power units, transferable loads, interruptible loads and energy storage devices.

[0034] In step 4, for the new energy high penetration system, considering the time series fluctuation characteristics of net load and the drastic increase in flexibility demand, a multi-time scale power system operation simulation mechanism is constructed, which includes three stages: time series random production simulation, short-term operation simulation and ultra-short-term operation simulation, to describe the system operation characteristics at different time scales; the power supply reliability constraint is used to ensure the safe operation of the power system, and at the same time, the flexibility capacity, peak-shaving capacity and climbing residual capacity are used to gradually refine the flexibility of the power system at different time scales, and finally achieve environmental protection, economy and reliability on the basis of system flexibility, so as to ensure the safe and stable operation of the power system.

[0035] Multi-time scale operation simulation is a mixed integer linear programming problem. It adopts the "multi-level coordination, step-by-step refinement" solution method and relaxation technology. It iteratively optimizes the typical weekly operation optimization model, short-term operation optimization model and ultra-short-term operation optimization model, uses the MATLAB toolbox YALMIP for modeling, and uses the solver CPLEX to solve the model.

[0036] Compared with the prior art, the beneficial effects of the present invention include:

[0037] 1) The present invention constructs an operation optimization model for a typical week, day and typical period, solves the system flexibility capacity at each time scale, realizes the optimal configuration of power system source, load and storage, fully taps the regulation capacity of source, load and storage flexible resources, optimizes the load curve, realizes the complementarity and efficient utilization of various forms of energy, and improves the ability to absorb clean energy;

[0038] 2) The present invention establishes a power system operation simulation mechanism, which considers the flexibility of the power system in a step-by-step manner in terms of flexibility capacity, peak load regulation capacity, and ramp residual capacity, thereby improving the overall flexibility of the system;

[0039] 3) The dispatching method of the present invention fully considers the coordinated optimization of three time scales: weekly, short-term and ultra-short-term, which enhances the reliability and robustness of the power system and improves the ability of the power system to cope with uncertainty;

[0040] 4) The present invention introduces carbon trading benefits into the operation optimization model, and takes into account the penalty costs for insufficient flexibility capacity and the benefits of flexible resource regulation, thereby increasing the priority of flexible resources in the resource allocation of the power system, improving the system's flexibility regulation space, and enabling the system to have higher economic and environmental benefits.

[0041] 5) The present invention conducts flexibility balance analysis from the demand side and supply side of the new energy high penetration system, maintains the real-time power balance of the power system through a multi-time scale operation simulation mechanism, optimizes the overall flexibility of the system, and ensures the safe, stable and efficient operation of the power system.

[0042] 6) The load and the output of new energy are superimposed in accordance with the time series relationship to be equivalent to the net load, taking into account the time series fluctuations of the load and new energy, to ensure that the new energy high penetration system always has sufficient operating flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] Figure 1 The figure is a flowchart of a scheduling method according to an embodiment of the present invention.

[0045] Figure 2 Schematic diagram of the principle of power system flexibility.

[0046] Figure 3 Schematic diagram of resource configuration for flexibility at multiple time scales.

[0047] Figure 4 Schematic diagram of the regulation characteristics of flexibility resources at different time scales.

[0048] Figure 5 Schematic diagram of a power system operation simulation mechanism according to an embodiment of the present invention.

[0049] Figure 6 FIG. 4 is a power supply structure diagram of an embodiment of the present invention.

[0050] Figure 7 The typical daily upward and downward adjustment flexibility capacity for different scenarios of the embodiments of the present invention.

[0051] Figure 8 The peak capacity is adjusted up or down during typical time periods of different scenarios in the embodiments of the present invention.

[0052] Fig. 9 It is the up-and-down ramp remaining capacity in typical time periods of different scenarios in the embodiments of the present invention. DETAILED DESCRIPTION

[0053] In the embodiment, considering the flexibility of thermal power, demand-side controllable load, energy storage and other types of resource regulation capabilities, the sudden flexibility demand of the new energy high penetration system is analyzed. Then, in order to focus on the flexibility of the system at different time scales, the flexibility deficiency penalty cost is introduced to quantify the flexibility shortage, and a multi-time scale operation simulation model is established, which are a time series random production simulation model, a typical day operation simulation model, and a typical time period operation simulation model; finally, the "multi-level coordination, step-by-step refinement" solution method and relaxation technology are adopted, and the flexibility capacity of the system at each time scale is obtained by iteratively optimizing the three models. Finally, on the basis of considering the flexibility of the system, while taking into account environmental protection and economy, it is achieved to ensure the safe and stable operation of the power system and promote the consumption of new energy. Finally, the effectiveness of the proposed method is verified by comparative examples.

[0054] like Figure 1 As shown, the power system dispatching method based on the short-term and ultra-short-term operation optimization model includes the following steps:

[0055] Step 1: Analyze the flexibility of the new energy high penetration system, optimize the configuration of various types of flexible resources such as source, load and storage, and achieve a balance between the supply and demand of flexibility of the system at all time scales; analyze the flexibility demand of the new energy high penetration system. After a high proportion of new energy is connected, the time series volatility and daily peak-to-valley difference of the system net load curve will increase significantly, the difficulty of peak regulation and climbing will increase, and the demand for system flexibility will surge.

[0056] The flexibility principle of power system is as follows Figure 2 Through the optimal configuration of various types of flexible resources, the system can achieve a balance between supply and demand of flexibility at various time scales, such as Figure 3 As shown in Figure 2, the flexibility of the power system has characteristics such as directionality, multi-time and space scale characteristics, state dependence, and bidirectional conversion. The upward and downward flexibility margins of the system at time t are introduced. and

[0057] When there is an upward flexibility gap When the system needs to consider the risk of load shedding; when there is a lack of downward flexibility When the system is running at 100%, the wind and solar power will be abandoned. SU,t and F NE,tThey represent the system flexibility supply and demand respectively.

[0058] Considering that the power generation of new energy sources such as photovoltaic and wind power has multi-time scale fluctuation characteristics. The volatility of net load in different directions and time scales has intensified the flexibility demand of the power system. Considering that the output of renewable energy is affected by the uncertainty of weather conditions and the need to prioritize the consumption of renewable energy, the output of renewable energy P RE,t With load L t The corresponding superposition according to the time sequence relationship is equivalent to the net load.

[0059] P NL,t =L t -P RE,t (2)

[0060] Step 2: Analyze the flexibility supply capacity of the new energy high penetration system, select source, load, and storage flexibility resources at various time scales to cope with random and uncertain changes in the power system.

[0061] Select flexible thermal power units, demand-side controllable loads and energy storage units to represent the source, load and storage flexibility resources at various time scales. The regulation characteristics are as follows: Figure 4 As shown in Figure 2, the size of the system's flexibility resources is related to the operating status of each unit and the unit's own output regulation characteristics. Different types of flexibility resources have different output regulation characteristics at different time scales.

[0062] 1) Medium time scale. The thermal power units that are transformed for flexibility refer to gas and oil units, which have low minimum technical output and fast ramp rate. By optimizing peak load regulation and ramp rate, the flexibility adjustment space on the power supply side of the system can be improved, and they can participate in the flexibility adjustment on the hourly time scale.

[0063]

[0064] They are the upward and downward flexibility provided by the thermal power unit f for flexibility transformation during period t; They are the output, maximum output and minimum output of the flexible thermal power unit f in period t respectively; are the up and down ramp rates of the flexible transformation thermal power unit f, and Δt is the adjustment time interval.

[0065] 2) Short time scale. The controllable load on the demand side can stimulate load interruption and transfer through economic compensation and policy management, smooth load fluctuations, reduce load peak-valley differences, and improve user-side flexibility. It can participate in flexibility adjustment on a short time scale of 15 to 60 minutes.

[0066]

[0067] They are the upward and downward flexibility of the supply of the transferable load m and the downward flexibility of the supply of the interruptible load n in period t respectively; are the transfer amount of the transferable load m in period t and its maximum and minimum values ​​respectively; is the interruption amount and upper limit of the interruptible load n in time period t.

[0068] 3) Ultra-short time scale. Energy storage devices have the characteristics of short response time and can achieve frequent charging and discharging in a short time scale. They are suitable for smoothing low-energy, high-frequency random fluctuations within 15 minutes or even 1 minute, and become a flexible power source with rapid adjustment.

[0069]

[0070] The flexibility of adjusting up and down the energy storage device s provided for time period t; are the output of energy storage device s and the maximum charging and discharging power during period t; E s,t ,E s,max ,E s,min are the storage capacity of energy storage device s and its upper and lower limits in time period t; η C and η D is the charging and discharging efficiency of energy storage, and Δt is the adjustment time interval.

[0071] The system's upward and downward flexibility supply capacity in period t:

[0072]

[0073] Step 3: Establish a new energy high-penetration integrated energy system and initialize the parameters of the integrated energy system;

[0074] Step 4: Construct a multi-time scale operation simulation framework, which includes three stages: time series random production simulation, typical day operation simulation, and typical period operation simulation, to describe the system operation characteristics at different time scales, such as Figure 5 shown.

[0075] Power supply reliability constraints are used to ensure the safe operation of the system. At the same time, flexible capacity, peak-shaving capacity and climbing residual capacity are used to gradually refine the flexibility of the power system on different time scales. Ultimately, environmental protection, economy and reliability are taken into account while considering the flexibility of the system, ensuring the safe and stable operation of the power system.

[0076] Step 5: Construct a typical weekly operation simulation model, focusing on system reliability. Use a time series random operation simulation method to simulate the actual operation scenario of the power system on a weekly time scale, incorporate carbon trading revenue and new energy power abandonment penalty costs into the optimization target, obtain an operation result that takes into account both environmental protection and economy, and verify power supply reliability.

[0077] Typical weekly operation simulation objective function:

[0078] minC1=min(C CURT +C FE +C OP -B C ) (7)

[0079] Where C1 represents the cost of the production operation simulation stage, including the penalty cost C for renewable energy curtailment CURT , fuel and environmental costs C FE , production and operation cost C OP Carbon trading income B C .

[0080] 1) Penalty costs for abandoning new energy power

[0081]

[0082] Where Ω R A collection of new energy sources; and a r It represents the amount of power abandoned in period t and the unit penalty cost of power abandoned by renewable energy power plant r.

[0083] 2) Fuel and environmental costs

[0084] Fuel and environmental costs only consider CO2 pollution, penalty costs and coal costs generated by coal combustion in thermal power units.

[0085]

[0086] Where Ω G For the thermal power generation units; cf uel is the fuel cost of thermal power units; E gt is the power generation of thermal power units during period t; Characterizes CO2 emission intensity; ω pun and ω env They are the environmental penalty coefficient and the value coefficient respectively.

[0087] 3) Production and operation costs

[0088] The production and operation cost is composed of the power generation and start-up and shutdown costs of thermal power units C th , Fixed operation and maintenance costs of various power sources and flexibility resources C fix constitute.

[0089]

[0090] Where S g , D g and represents the start-up and shutdown costs and the start-up and shutdown states of the thermal power units respectively; C g (P g,t ) is the power generation cost function of thermal power units; Ω E For power supply collection; represents the fixed operation and maintenance cost of power unit k during period t.

[0091] 4) Carbon trading income

[0092] Carbon trading income refers to the cost of reducing carbon emissions from conventional thermal power units when new energy sources such as photovoltaics and wind power increase the same amount of power generation.

[0093] B C =E rt b c σ c μ c (11)

[0094] Where E rt represents the annual power generation of renewable energy; b c is the unit carbon trading fee; σ c The amount of coal consumed per unit of electricity produced by a thermal power plant; μ c It is the coefficient of converting unit coal into CO2.

[0095] Typical weekly operation simulation constraints:

[0096] 1. Power balance constraints

[0097]

[0098] Where P r,t , P s,t , P f,t and P g,t They are, respectively, the output of new energy, interruptible load, transferable load, energy storage, flexible transformation of thermal power and thermal power units within the simulated operating period t.

[0099] 2. Power unit operation constraints

[0100] 1) Power output constraints

[0101]

[0102] 2) Climbing limit constraints of thermal power units

[0103]

[0104] 3) Start-stop time and status constraints of thermal power units

[0105]

[0106] In the formula P r,max , P g,max and δ f,t , δ r,t , δ g,t Respectively represent the maximum output and operating status of flexible thermal power units, new energy and conventional thermal power; x f,t is the decision variable indicating whether the thermal power unit should undergo flexibility transformation; and It indicates the minimum technical output before and after the flexibility transformation of thermal power units; and They represent the up- and down-ramp variables before and after the flexibility transformation of thermal power units, respectively; and Characterize the shortest stop and start time and the maximum and minimum climbing limits of conventional thermal power units in turn; and Represents the minimum stop and start time of thermal power units before and after flexibility transformation.

[0107] 3. Energy storage operation constraints

[0108] 1) Storage power and balance constraints

[0109]

[0110] E s,min ≤E s,t ≤E s,max

[0111] E s,t＝0 =E s,t＝T (20)

[0112] 2) Charge and discharge power constraints

[0113]

[0114] P s,t =P D,s,t -P C,s,t (twenty two)

[0115] 3) Charge and discharge state constraints

[0116] c s,t +d s,t ≤1 (23)

[0117] Where η C , η D are the charging and discharging efficiency of energy storage; c s,t ,d s,t 0-1 variables representing the energy storage charging and discharging states; P D,s,max , P C,s,maxis the maximum discharge and charge power of the energy storage device s; E s,t＝0 、E s,t＝T Represents the storage energy of the energy storage device s in the initial and final states.

[0118] 4. Demand response operation constraints

[0119] 1) Response potential constraints

[0120]

[0121] 2) Operational constraints

[0122]

[0123] In the formula They represent the upper limits of the response capacity of transferable and interruptible loads respectively.

[0124] 5. Reliable power supply constraints

[0125] L LOLPt ≤L LOLPmax (26)

[0126] Where L LOLPmax , L LOLPt are the limit value and load failure probability respectively.

[0127] Step 6: Build a typical day operation simulation model, focusing on the flexibility capacity of the system. Taking into account the time series fluctuation characteristics of net load and the balance characteristics of flexibility supply and demand, with the goal of minimizing the penalty cost of insufficient flexibility and the benefit of flexibility resource regulation, carefully analyze the short-term flexibility difficulties brought to the system by high-penetration new energy.

[0128] Objective function:

[0129]

[0130] Where C2 represents the typical day operation simulation phase cost, including the penalty cost C for insufficient flexibility VAC2 , flexibility resource response cost Compensation costs with flexibility resource calls

[0131] Insufficient flexibility penalty costs

[0132]

[0133] Where v dn and v up It indicates that the system lacks downward and upward flexibility and imposes penalty costs.

[0134] Flexibility Resource Response Cost

[0135]

[0136] Where P jt , R jt They represent the response capacity and the response cost per unit flexibility resource respectively.

[0137] Flexibility resource call compensation cost

[0138]

[0139] Where Ω FG ,Ω B ,Ω TL ,Ω IL The following are the collection of flexible modified thermal power units, energy storage systems, transferable loads and interruptible loads; r f 、r s , The response compensation costs are interruptible load, flexible transformation of thermal power units, energy storage and transferable load; E s,t 、E f,t , They respectively represent the amount of electricity required by energy storage, flexible thermal power units, interruptible loads and transferable loads in period t.

[0140] Typical day operation simulation constraints: The typical day operation simulation constraints include not only the system power plant units, intraday power balance, demand response and energy storage system operation constraints, unit start and stop constraints, but also the flexibility supply and demand capacity constraints. The flexibility supply and demand balance constraints are shown in formula (31):

[0141] F G,t +F FG,t +F DR,t +F S,t +|F MAR,t |≥|F NE,t | (31)

[0142] Where F S,t 、F DR,t 、F FG,t and F G,t The flexible supply capabilities of energy storage systems, demand response, flexible transformation of thermal power and conventional thermal power are respectively characterized.

[0143] Step 7: Construct a simulation model for typical period operation, focusing on the system's short-term peak load regulation and ramping capabilities. Take into account the penalty cost for insufficient peak load regulation and insufficient ramping in the comprehensive cost, and relax the ramping constraints based on the typical day constraints.

[0144] Objective function:

[0145] Where C3 represents the cost of the typical period operation simulation stage, including the penalty cost C for insufficient peak load regulation. RES , the penalty cost of insufficient climbing C RAM , flexibility resource response cost Compensation cost for flexible resource calls B F3 .

[0146] Penalty cost for insufficient peak load

[0147]

[0148] are the upper and lower peak power shortages respectively, and ρ1 and ρ2 are the corresponding penalty costs respectively.

[0149] Penalty cost for insufficient climbing

[0150]

[0151] are the uphill and downhill climbing power shortages respectively, ρ3 and ρ4 are the corresponding penalty costs respectively.

[0152] Constraints: The constraints of the typical period operation simulation are based on the typical day constraints. Considering that the flexible resource ramp capacity may be insufficient during the ultra-short-term operation simulation, the ramp constraints need to be relaxed:

[0153]

[0154] Where P t F is the output of the flexible unit F in period t, It is the up and down climbing capacity of the flexible unit F.

[0155] Step 8: Multi-time scale operation simulation is a mixed integer linear programming problem. The “multi-level coordination, step-by-step refinement” solution method and relaxation technology are used to iteratively optimize the three models of typical weekly operation optimization model, short-term operation optimization model and ultra-short-term operation optimization model. The MATLAB toolbox YALMIP is used for modeling, and the solver CPLEX is used to solve the model.

[0156] Step 9: If the iteration termination conditions are met, the comprehensive cost of each stage and the system flexibility capacity at each time scale are output.

[0157] In order to verify the effectiveness of the method of the present invention, a simulation analysis of the multi-time scale optimization operation of flexible resources under two forms is carried out through an actual example in a certain region. Figure 6 The basic parameters of the power supply unit are shown in Table 1.

[0158] Table 1 Basic parameters of power supply units

[0159]

[0160] Analysis of the impact of flexible resources: Based on the load and renewable energy timing output, the week with the largest fluctuation in summer net load timing was selected for timing random production simulation. As shown in Table 2, without considering the coordinated optimization of flexible resources, it is difficult to meet the system's surging flexibility needs by relying solely on the fixed reserve margin of conventional thermal power. At this time, the penalty cost of renewable energy power abandonment is the highest, and the system has a high risk of abandoning wind and solar power, which is not conducive to the grid connection of a high proportion of renewable energy. Incorporating carbon trading income and flexible resource call compensation costs into the flexible resource optimization operation goals is of environmental and economic significance. Considering the flexible transformation of units will slightly increase fuel and environmental costs, but the compensation costs obtained by calling flexible resources have greatly reduced the overall cost, promoted the consumption of new energy, and effectively promoted the realization of the "dual carbon" goals.

[0161] Table 2 Comparison of typical weekly operating costs in different scenarios

[0162]

[0163] Typical day operation flexibility analysis: The day with the largest net load time series fluctuation in a typical summer week is selected as the typical day. Figure 7 It can be seen that during the peak load periods of 0:00-5:00 and 17:00-19:00, without considering flexible resources, the average upward flexible capacity is 1105MW, and the existing power units have insufficient upward peak-shaving capacity, which is prone to the risk of load shedding. After considering the coordinated optimization of source, load and storage flexible resources, the average upward flexible capacity rises to 1560MW, which improves the problem of insufficient upward flexible capacity. During the peak wind power output period of 2:00-5:00 and the peak photovoltaic output period of 13:00-19:00, without considering flexible resources, the downward flexible capacity is too much. After considering the flexible resource adjustment capacity, the downward flexible capacity during the peak output period of new energy is reduced by about 500MW, avoiding resource waste, increasing the downward flexible capacity during the 6:00-12:00 period, and reducing the risk of new energy power abandonment. It can be seen from Table 3 that after comprehensively considering the coordinated optimization of source, load and storage flexible resources, the overall flexibility adjustment capacity of the system is improved, the penalty costs of insufficient flexibility in the upper and lower directions are reduced, and the overall economy is improved while promoting the consumption of new energy in the system.

[0164] Table 3 Comparison of typical day flexibility deficiency penalty costs in different scenarios

[0165]

[0166] Typical period operation peak load and ramp capacity analysis: In order to study the problem of ultra-short-term peak load and ramp capacity shortage of the system, the morning peak period of 7:00-9:00 when wind and light resources change dramatically was selected for research, and the time interval was selected as 5 minutes. Figure 8 It can be seen that in the time periods of 7:10-7:20, 7:35-8:00, and 8:45-8:55, when flexible resources are not considered, the peak-shaving capacity is insufficient, which is prone to load shedding risks and increases the penalty cost of insufficient peak-shaving capacity. After considering the coordinated optimization of flexible resources, the system peak-shaving capacity is effectively improved, and the peak-shaving capacity in the time periods of 7:00-7:05 and 8:05-8:40 is significantly improved, smoothing the peak-to-valley difference within the day and ensuring the safe and stable operation of the power system.

[0167] Depend on Fig. 9 It can be seen that without considering the scenario of flexible resource collaborative optimization, the up and down ramp remaining capacity is ±800MW / (5min) -1 , and at 8:00 the down-climbing remaining capacity is 0, the system down-climbing capacity is seriously insufficient, and at 8:30 the up-climbing remaining capacity is 0, the system up-climbing capacity is seriously insufficient. Considering the scenario of flexible resource collaborative optimization, for the periods of 7:00-8:05 and 8:35-8:55, the down-climbing remaining capacity increment is about 1500MW / (5min) -1 For the period from 8:05 to 8:30, the increase in the remaining capacity of the ramp is about 1200MW / (5min) -1. The overall system ramp-up residual capacity has been greatly improved, meeting the requirements for safe and stable operation of the power system in an ultra-short period of time.

Claims

1. A power system dispatching method based on short-term and ultra-short-term operation optimization models, characterized in that: The following steps are involved: Step 1: Analyze the flexibility requirements of the new energy high penetration system, configure various types of flexible resources, and achieve flexibility supply and demand balance of the power system at various time scales; Step 2: Considering the volatility of renewable energy generation at different time scales, we select flexible thermal power units, demand-side controllable loads, and energy storage units as flexible resources at different time scales to cope with random and uncertain changes in the power system. Step 3: Establish a new energy high-penetration integrated energy system and initialize the parameters of the integrated energy system; Step 4: Construct a multi-time scale operation simulation framework, which includes three stages: time series random production simulation, typical day operation simulation, and typical period operation simulation; Step 5: Construct a typical weekly operation optimization model, focusing on system reliability, and taking carbon trading revenue and new energy curtailment penalty costs as optimization targets; Step 6: Construct a short-term operation optimization model, focusing on the flexibility capacity of the system, with the goal of minimizing the penalty cost of insufficient flexibility and the benefit of flexibility resource regulation; Step 7: Construct an ultra-short-term operation optimization model, focusing on the system's short-term peak-shaving and ramping capabilities, and considering the penalty costs for insufficient peak-shaving and insufficient ramping; Step 8: Adopt the "multi-level coordination, step-by-step refinement" solution method to iteratively optimize the typical weekly operation optimization model, short-term operation optimization model and ultra-short-term operation optimization model; Step 9: If the iteration termination conditions are met, the comprehensive cost of each stage and the system flexibility capacity at each time scale are output.

2. The power system dispatching method according to claim 1, characterized in that: In step 1, a flexibility deficiency penalty cost is introduced to quantify the flexibility shortfall.

3. The power system dispatching method according to claim 1 or 2, characterized in that: In step 1, the upward and downward flexibility margins of the power system at time t are introduced. and In the formula represents the upward flexibility margin of the power system at time t, represents the downward flexibility margin of the power system at time t; When there is an upward flexibility gap When the system needs to consider the risk of load shedding; when there is a lack of downward flexibility When F SU,t 、F NE,t They represent the system flexibility supply and demand respectively.

4. The power system dispatching method according to claim 3, characterized in that: In step 2, the renewable energy output and load are superimposed according to the time sequence relationship to form the net load P NL,t =L t -P RE,t (2) Where P NL,t represents the net load of the system at time t, P RE,t represents the renewable energy output of the system at time t, L t represents the load of the system at time t.

5. The power system dispatching method according to claim 4, characterized in that: Different types of flexible resources have different output regulation characteristics at different time scales: 1) Medium time scale: The flexible thermal power units refer to gas and oil units, which have low minimum technical output and fast ramp rate. By optimizing peak load regulation and ramp rate, the flexibility adjustment space on the power supply side of the system can be improved, and they can participate in the flexibility adjustment on the hourly time scale. In the formula They are the upward and downward flexibility of the thermal power unit f supplied by the flexibility transformation in period t; They are the maximum and minimum outputs of the flexible thermal power unit f during period t; are the up and down ramp rates of the flexible transformation thermal power unit f, respectively, and Δt is the adjustment time interval; It represents the real-time output of the flexible thermal power unit f in period t; 2) In the short term, the controllable load on the demand side can be used to stimulate load interruption and transfer through compensation and policy management, smooth load fluctuations, reduce load peak-valley differences, and improve user-side flexibility, and can participate in short-term flexibility regulation. In the formula They represent the upward and downward flexibility of the supply of the transferable load m during period t respectively; It represents the upward flexibility of the interruptible load n at time t; represents the transfer amount of the transferable load m during period t; They represent the maximum and minimum values ​​of the transfer amount of the transferable load m respectively; It represents the interruption amount of interruptible load n in time period t; Indicates the upper limit of the interruption amount of the interruptible load n; 3) Ultra-short time scale: Energy storage devices have the characteristics of short response time, and can realize frequent charging and discharging in a short time scale. They are suitable for smoothing low-energy, high-frequency random fluctuations in ultra-short time scales, and become a flexible power source for rapid adjustment. In the formula The flexibility of adjusting up and down the energy storage device s during time t; represents the output of energy storage device s during period t; They represent the maximum power of charging and discharging of energy storage device s during period t; E s,t E represents the storage capacity of energy storage device s during period t; s,max 、E s,min Indicates the upper and lower limits of the storage capacity of the energy storage device s; η C , η D are the charging and discharging efficiencies of energy storage, respectively, and Δt is the adjustment time interval; The upward and downward flexibility supply capacity of the power system in period t Where N f 、N TL 、N IL 、N S They respectively represent the number of flexibly transformed thermal power units, transferable loads, interruptible loads and energy storage.

6. The power system dispatching method according to claim 5, characterized in that: In step 5, the objective function of the optimization model for a typical week is: min C1=min(C CURT +C FE +C OP -B C ) (7) Where C CURT represents the penalty cost of renewable energy curtailment; C FE represents fuel and environmental costs; C OP represents the production and operation cost; B C represents the carbon trading income; C1 represents the cost of the production operation simulation stage, which includes the penalty cost of renewable energy abandonment, fuel and environmental costs, production and operation costs and carbon trading income; Penalty cost for abandoning new energy power: Where Ω R Represents a collection of new energy sources; represents the amount of power abandoned during period t; a r represents the unit power abandonment penalty cost of renewable energy power plant r; T1 represents the total time period of the typical weekly simulation stage; Fuel and environmental costs include CO2 pollution and penalty fees and coal costs generated by coal-fired power plants Where Ω G Represents the collection of thermal power units; c fuel is the fuel cost of thermal power units; E gt is the power generation of the thermal power unit in period t; Characterizes CO2 emission intensity; ω pun is the environmental penalty coefficient, ω env is the value coefficient; Production and operation costs include the cost of power generation and start-up and shutdown of thermal power units, and the fixed operation and maintenance costs of various power sources and flexible resources. Where C th represents the power generation and start-up and shutdown cost of thermal power units; C fix represents the fixed operation and maintenance costs of various power and flexibility resources; S g , D g They represent the start-up and shutdown costs of thermal power units respectively; Respectively represent the start and stop status of the thermal power unit; C g (P g,t ) is the power generation cost function of thermal power unit g in period t; Ω E Represents a power source collection; G represents the fixed operation and maintenance cost of power unit k during period t; kt Indicates the rated capacity of the power unit k; Carbon trading income is the cost of reducing carbon emissions from conventional thermal power units when photovoltaic and wind power increase the same amount of power generation. B C =E rt b c s c m c (11) Where B C represents the carbon trading income, E rt represents the annual power generation of renewable energy; b c is the unit carbon trading fee; σ c The amount of coal consumed per unit of electricity produced by a thermal power plant; μ c is the coefficient of unit coal conversion into CO2; Constraints of the typical weekly operation optimization model: (1) Power balance constraints: Where P r,t represents the output of the renewable energy power plant r during period t, represents the power of load n that can be interrupted during period t, represents the power of load m that can be transferred during period t, P s,t represents the output of energy storage device s during period t, P f,t represents the output of the flexible thermal power unit f during period t, P g,t represents the output of thermal power unit g during period t; L t represents the load during period t; N IL Indicates the number of interruptible loads; N TL Indicates the number of transferable loads; N s Indicates the number of energy storage devices; N f Indicates the number of thermal power units transformed with flexibility; N g Indicates the number of conventional thermal power units; (2) Power unit operation constraints: Power output constraints In the formula P r,max , P g,max They represent the maximum output of the flexible thermal power unit f, the new energy power plant r, and the conventional thermal power unit g respectively; x f,t is the decision variable for whether to carry out flexibility transformation of thermal power units; δ f,t , δ r,t , δ g,t They represent the operating status of the flexible thermal power unit f, the new energy power plant r, and the conventional thermal power unit g during period t respectively; Respectively represent the minimum technical output before and after the flexibility transformation of thermal power units; Climbing limit constraints of thermal power units In the formula Respectively represent the maximum and minimum climbing limits of conventional thermal power units; P g,t-1 represents the output of conventional thermal power units in the period t-1; They represent the ramp-up variables of the flexible transformation unit f before and after the transformation respectively; They represent the down-ramp variables of the flexible transformation unit f before and after the transformation respectively; Start and stop time and state constraints of thermal power units In the formula They represent the shortest stop and start time of conventional thermal power unit g; δ g,t-1 Indicates the operating status of the conventional thermal power unit g during the period t-1; They represent the minimum start and stop time of thermal power unit f before flexibility transformation, They represent the minimum start and stop time of thermal power unit f after flexibility transformation respectively; (3) Energy storage operation constraints: Storage capacity and balance constraints AND s,min ≤E s,t ≤E s,max AND s,t＝0 =And s,t＝T (20) Charge and discharge power constraints P s,t =P D,s,t -P C,s,t (22) Charge and discharge state constraints c s,t +d s,t ≤1 (23) Where η C , η D are the charging and discharging efficiency of energy storage; c s,t ,d s,t 0-1 variables representing the energy storage charging and discharging states; P D,s,max , P C,s,max is the maximum discharge and charge power of the energy storage device s; E s,t＝0 、E s,t＝T represents the storage energy of the energy storage device s in the initial and final states; (4) Demand response operation constraints: Response potential constraints In the formula They represent the upper limits of the response capacity of the transferable load and the interruptible load respectively; represents the response capacity of the interruptible load n in period t; represents the response capacity of the transferable load m in period t; Operational constraints (5) Reliable power supply constraints L LOLPt ≤L LOLPmax (26) Where L LOLPmax , L LOLPt are limit value and load failure probability respectively.

7. The power system dispatching method according to claim 6, characterized in that: In step 6, the objective function of the short-term optimization model is: In the formula Indicates the penalty cost of insufficient flexibility, represents the flexibility resource response cost, represents the flexibility resource call compensation cost, C2 represents the typical day operation simulation phase cost, which includes the flexibility deficiency penalty cost, flexibility resource response cost and flexibility resource call compensation cost; Insufficient flexibility penalty costs Where v dn 、v up It indicates that the system lacks downward and upward flexibility and imposes penalty costs; T2 Indicates the total time period of short-term operation; They represent upward and downward flexibility shortfalls respectively; Flexibility Resource Response Cost Where P jt , R jt denote the response capacity and the unit flexibility resource response cost respectively; Flexibility resource call compensation cost Where Ω FG ,Ω B ,Ω TL ,Ω IL They represent the collection of flexible thermal power units, energy storage systems, transferable loads and interruptible loads respectively; r f 、r s , are the response compensation costs of interruptible load n, flexible thermal power unit f, energy storage device s and transferable load m respectively; E s,t 、E f,t , They represent the power demanded by energy storage device s, flexible thermal power unit f, interruptible load n, and transferable load m in period t respectively; Constraints of the short-term operation optimization model: The constraints of the typical day operation simulation include not only the system power plant units, intraday power balance, demand response and energy storage system operation constraints, unit start and stop constraints, but also flexible supply and demand capacity constraints; Flexibility Supply and Demand Balance Constraints F G,t +F FG,t +F DR,t +F S,t +|F MAR,t |≥|F NE,t | (31) Where F S,t 、F DR,t 、F FG,t 、F G,t Respectively represent the flexibility supply capabilities of energy storage systems, demand response, flexibility transformation thermal power and conventional thermal power in period t.

8. The power system dispatching method according to claim 7, characterized in that: In step 7, the objective function of the ultra-short-term operation optimization model is: Where C RES represents the penalty cost for insufficient peak load regulation, C RAM represents the penalty cost for insufficient climbing, represents the flexibility resource response cost, represents the compensation cost for calling flexible resources, C3 represents the cost of the simulation phase of operation during the typical period, which includes the penalty cost for insufficient peak load regulation, the penalty cost for insufficient ramping, the response cost of flexible resources, and the compensation cost for calling flexible resources; Penalty cost for insufficient peak load In the formula are the upper and lower peak power shortages, respectively, and ρ1 and ρ2 are the corresponding penalty costs; Penalty cost for insufficient climbing In the formula are the uphill and downhill climbing power shortages, ρ3 and ρ4 are the corresponding penalty costs; Typical period operation simulation constraints: The constraints of the typical period operation simulation are based on the typical day constraints. Considering that the flexible resource ramping capacity may be insufficient during the ultra-short-term operation simulation, the ramping constraints need to be relaxed: Where P t F is the output of the flexible unit F at time t, It is the up and down climbing capacity of the flexible unit F.

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