Random programming method considering demand side and wind power permeability and related device

Abstract

The invention discloses a stochastic programming method considering demand side and wind power permeability and a related device. The method comprises the following steps: constructing a generator set model, a wind power plant output model and a demand side resource aggregator flexible resource model; introducing a conditional value-at-risk, and combining a generator set model, a wind power plant output model and a demand side resource aggregator flexible resource model to construct an objective function which takes system operation constraints into consideration to minimize total expected cost, and obtaining a market clearing model; respectively determining a first-stage constraint condition and a second-stage constraint condition of the market clearing model, and constructing an association constraint condition; and based on the constraint conditions, solving the market clearing model to obtain a scheduling result. According to the method provided by the invention, different types of demand side resources are scheduled more flexibly, and the fluctuation characteristics of renewable energy sources are better adapted; uncertainty is actively managed in scheduling, and the negative influence of wind power output fluctuation on system stability is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system resource scheduling, and in particular to a random programming method and related devices that take into account the demand side and wind power penetration rate. Background Art

[0002] As the proportion of renewable energy such as wind and solar energy in the power structure continues to increase, the volatility and uncertainty of power system operation have also increased significantly. The traditional system that relies on fossil fuel power generation is difficult to adapt to the randomness and intermittency of new energy, and an effective way is urgently needed to balance supply and demand and ensure that the system can still operate stably under the scenario of a high proportion of renewable energy.

[0003] At present, there are three main schemes for power system resource dispatch: 1. Conventional power generation reserve capacity: By increasing the reserve capacity of conventional power sources such as coal or gas, a rapid response is made when wind power output fluctuates to maintain the balance of power supply and demand; however, this method requires reliance on traditional fossil energy power generation, which cannot meet the needs of low-carbon development and increases operating costs and environmental burdens. In addition, the response speed of conventional power sources is limited, and it is difficult to fully match the rapid volatility of new energy sources such as wind power. 2. Centralized energy storage system: Use large-scale energy storage facilities (such as pumped storage and large-scale battery energy storage) to smooth out wind power output fluctuations; however, the construction cost of large-scale energy storage systems is high, and the requirements for grid infrastructure are high. Centralized energy storage often requires a large investment in land and equipment, especially in cities or load centers. In addition, its single layout model lacks sufficient flexibility when facing widespread fluctuations in demand across the entire network. 3. Demand response mechanism: By encouraging users to adjust their electricity demand during peak grid load periods or low wind power output periods to reduce the peak-to-valley difference of the power system, however, the traditional demand response mechanism is limited by consumers' willingness and flexibility to respond, and cannot guarantee adequate response at every moment of fluctuation. The response time of demand response is usually long, and it is difficult to accurately match short-term and rapid wind power fluctuations. Summary of the invention

[0004] The present invention provides a random programming method and related devices that take the demand side and wind power penetration rate into consideration, which can more flexibly dispatch different types of demand side resources and better adapt to the fluctuation characteristics of renewable energy. It can actively manage uncertainty in scheduling, reduce the negative impact of wind power output fluctuations on system stability, and ensure that the power system can still operate smoothly under conditions of a high proportion of renewable energy. It can also effectively improve the overall flexibility and reliability of the power system.

[0005] In view of this, the first aspect of the present application provides a stochastic programming method considering the demand side and wind power penetration rate, the method comprising:

[0006] Construct generator model, wind farm output model, and flexible resource model of demand-side resource aggregator;

[0007] Introducing conditional risk value, and combining the generator model, the wind farm output model, and the demand-side resource aggregator flexible resource model, constructing an objective function that minimizes the total expected cost by considering system operation constraints, and obtaining a market clearing model;

[0008] Determine the first-stage constraint conditions and the second-stage constraint conditions of the market clearing model respectively according to the generator set model, the wind farm output model and the demand-side resource aggregator flexible resource model, and construct associated constraint conditions;

[0009] Based on the first-stage constraints, the second-stage constraints, and the associated constraints, the market clearing model is solved to obtain a scheduling result.

[0010] Optionally, constructing the generator set model includes:

[0011] The multi-segment piecewise linear approximation and mixed integer linear programming structure are used to simulate the nonlinear cost function of the thermal unit and model the generator unit.

[0012] Optionally, constructing the wind farm output model includes:

[0013] The wind power probability density function is programmed with mixed integer linear programming to simulate wind speed scenarios and model the wind farm output.

[0014] Optionally, the constructing of the demand-side resource aggregator flexible resource model includes: separately constructing a capacity market planning model and an energy storage system model.

[0015] Optionally, the objective function of minimizing the total expected cost by considering the system operation constraints is constructed to obtain a market clearing model, including:

[0016] A pool model is adopted, and a payment mechanism based on market settlement prices is constructed to minimize the total expected cost by considering system operation constraints, thus obtaining a market clearing model.

[0017] Optionally, the objective function is expressed as:

[0018] ;

[0019] In the formula, The total cost of operating the system, including energy costs and risk costs; is the expected cost; is the expected cost of day-ahead scheduling; is the expected cost of real-time scheduling; is a non-negative parameter, representing the weight coefficient of risk calculation; Adjust the system's sensitivity to risk aversion for the weight of risk cost in the total cost; It is the conditional value at risk, which is used to measure the expected loss after exceeding VaR (value at risk) and reflects the extreme risk of the system under uncertain conditions.

[0020] Optionally, solving the market clearing model to obtain a scheduling result includes:

[0021] The market clearing model is solved by an optimization solver, and the stochastic programming results considering the demand side and wind power penetration rate are output, including: generator unit scheduling, energy storage system scheduling, and demand side response scheduling plan.

[0022] A second aspect of the present application provides a stochastic programming system considering the demand side and wind power penetration rate, the system comprising:

[0023] The first construction unit is used to construct a generator set model, a wind farm output model, and a demand-side resource aggregator flexible resource model;

[0024] The second construction unit is used to introduce conditional risk value, and to construct an objective function that minimizes the total expected cost by considering system operation constraints in combination with the generator set model, the wind farm output model, and the demand-side resource aggregator flexible resource model, so as to obtain a market clearing model;

[0025] A third construction unit is used to determine the first-stage constraint conditions and the second-stage constraint conditions of the market clearing model respectively according to the generator set model, the wind farm output model and the demand-side resource aggregator flexible resource model, and to construct associated constraint conditions;

[0026] A solving unit is used to solve the market clearing model based on the first-stage constraint conditions, the second-stage constraint conditions, and the associated constraint conditions to obtain a scheduling result.

[0027] A third aspect of the present invention provides a random programming device considering the demand side and wind power penetration rate, the device comprising a processor and a memory:

[0028] The memory is used to store program code and transmit the program code to the processor;

[0029] The processor is used to execute the steps of the stochastic programming method considering the demand side and wind power penetration rate as described in the first aspect according to the instructions in the program code.

[0030] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program codes, and the program codes are used to execute the stochastic programming method considering the demand side and wind power penetration rate described in the first aspect above.

[0031] It can be seen from the above technical solutions that the present invention has the following advantages:

[0032] 1) More flexible demand-side resource integration: Distributed demand response resources are effectively integrated with energy storage systems through demand-side aggregators (DSPs), participating in power dispatch and backup resource allocation in a centralized manner. This integration method significantly improves the utilization efficiency of demand-side resources, enabling them to quickly respond to frequent fluctuations in the power system. Compared with traditional single demand response or centralized energy storage methods, this solution can more flexibly dispatch different types of demand-side resources and better adapt to the fluctuating characteristics of renewable energy such as wind power.

[0033] 2) Enhanced adaptability to wind power uncertainty: The conditional value at risk (CVaR) indicator is used to help system operators consider the risk exposure of wind power output when dispatching reserves, thereby optimizing dispatch decisions under uncertain conditions. Compared with the passive strategy of directly responding to fluctuations in traditional solutions, the risk management mechanism of the present invention can actively manage uncertainty in dispatching, reduce the negative impact of wind power output fluctuations on system stability, and ensure that the power system can still operate smoothly under the conditions of a high proportion of renewable energy.

[0034] 3) Efficient use of distributed resources: The DSP mechanism proposed in the technical solution can effectively aggregate distributed resources, making up for the shortcomings of single energy storage or demand response in dealing with fluctuating demand across the entire network. The efficient integration of distributed resources enables the system to better utilize the distributed reserve resources of the load center, effectively improving the overall flexibility and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 A schematic flow chart of a random programming method considering the demand side and wind power penetration rate provided in an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of the structure of a stochastic programming system considering the demand side and wind power penetration rate provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] It should be noted that the present invention provides a two-stage stochastic programming method that takes into account the demand side and wind power penetration rate, aiming to simultaneously dispatch energy resources and demand-side aggregators for energy and backup supply, reduce the impact of wind power uncertainty, and help reduce the total cost of system operation.

[0040] To optimize the allocation of energy and reserves using a two-stage stochastic programming model, the following types of data are required:

[0041] 1. Power generation unit data:

[0042] The number and type of generating units;

[0043] Maximum and minimum output power of each unit (MW);

[0044] startup and downtime costs;

[0045] Up and down ramp rate (MW / h);

[0046] The coefficients of the operational cost function;

[0047] 2. Load data:

[0048] The location and load curve of each load point;

[0049] Maximum and minimum load variation range (MW);

[0050] the cost of load reduction;

[0051] 3. System topology:

[0052] Relationship between system nodes and transmission lines

[0053] Transmission Line Impedance and Capacity Limitations

[0054] 4. Wind energy data:

[0055] Wind speed data: historical wind speed data, used to generate wind speed scenarios;

[0056] Wind farm output data: location and installed capacity (MW) of each wind farm; wind farm output power curves at different wind speeds; random wind farm output power (MW) in the real-time market.

[0057] 5. Energy storage system data:

[0058] The number and location of energy storage systems;

[0059] Maximum and minimum energy range (MWh) for each storage system;

[0060] Maximum charge and discharge power (MW);

[0061] Initial charge state (%);

[0062] Charge and discharge efficiency (%);

[0063] 6. Demand response (DR) data:

[0064] the number and location of consumers participating in demand response;

[0065] Maximum and minimum load variation range for each consumer (MW);

[0066] incentive payments and penalty amounts;

[0067] Consumers’ willingness and flexibility to participate in demand response.

[0068] 7. Market data:

[0069] Day-ahead market prices: market prices for energy and reserves; market prices for start-up costs and operating costs;

[0070] Real-time market prices: Real-time market prices for energy and reserves; costs of load curtailment and wind spillover.

[0071] A stochastic programming approach considering the demand side and wind power penetration is provided in this paper to explain the flexibility pricing problem of energy hubs (EHs) connected to electricity, heat and gas networks. The proposed design is formulated as a two-level optimization. In the upper problem, the profit maximization of EH-based flexibility resources (FSs) is modeled as a process considering their flexibility constraints. In addition, the optimal operation of these networks and EHs is considered to minimize the energy purchase cost in the lower problem. The problem is constrained by the network operation model and the operation-flexibility formulation of EHs. Therefore, the scheme can be described as follows.

[0072] See also Figure 1 , a stochastic programming method considering the demand side and wind power penetration rate provided in an embodiment of the present invention includes:

[0073] Step 101: construct a generator set model, a wind farm output model, and a demand-side resource aggregator flexible resource model.

[0074] In one embodiment, step 101 includes:

[0075] Step 1011: Use multi-segment piecewise linear approximation and mixed integer linear programming structure to simulate the nonlinear cost function of the thermal unit and model the generator set.

[0076] It should be noted that this step uses multi-segment piecewise linear approximation and mixed integer linear programming (MILP) structure to simulate the nonlinear cost function of the thermal unit. The following formulas represent the cost function of the production unit and the production capacity of each production unit respectively.

[0077] Cost function: (1)

[0078] (2)

[0079] In the formula, , , is the coefficient of the fuel cost function of the conventional power generation unit (the power generation unit refers to the thermal power unit); is the minimum output power of each power generation unit; It is a binary variable for starting and stopping the unit; is the planned output power in the day-ahead market; It is the value of the energy mid-range f proposed by the unit.

[0080] Step 1012: Simulate wind speed scenarios through mixed integer linear programming wind power probability density function to model wind farm output.

[0081] It should be noted that due to the close relationship between the wind speed characteristics of a given location and the wind power probability density function (WPDF), mixed integer linear programming WPDF is used in this study to implement the wind speed scenario. At the same time, after calculating the wind speed scenario, the random output power of the wind farm per hour is expressed as follows:

[0082] (3)

[0083] In the formula, , , , and Respectively represent the predicted wind power, cut-in wind speed, rated power, rated wind speed and cut-out wind speed of scenario w and each wind farm wf at the tth hour.

[0084] Step 1013: construct a capacity market planning model and an energy storage system model respectively.

[0085] It should be noted that the demand-side resource aggregator DSP is responsible for providing flexible demand-side capacity in the form of an incentive plan based on CAP (Capacity Market Program) in the market environment, and managing the charging and discharging of the energy storage system ESS.

[0086] 1) Constructing a capacity market planning model:

[0087] The consumer participation level limits based on CAP change their hourly consumption are as follows:

[0088] (4)

[0089] In the formula, and is the maximum / minimum range of load change in CAP; is a variable that indicates the level of load change in the planned participation in the day-ahead market based on CAP.

[0090] In this scheme, consumers will receive certain rewards by maintaining their consumption reduction level under predefined flexible limits. On the other hand, they will receive participation value according to the contract in response to changes in their consumption level, and will be penalized if they do not participate. The following formulas show the contract cost, incentives, and penalty amounts imposed by the system operator, respectively.

[0091] (5)

[0092] (6)

[0093] (7)

[0094] In the formula, and Indicates the additional incentive value of up / down spinning reserve for participation in the CAP Capacity Reward Program; and is the up / down spinning reserve dispatch value of load j in the day-ahead market; represents the load reduction incentive payment value given to consumers in CAP; It is the penalty value for consumers who do not participate in CAP.

[0095] 2) Constructing energy storage system model:

[0096] The participation of energy storage systems (ESS) in the energy and reserve market is based on the following constraints. The constraints in the first stage include the charging and discharging equations, the operating status, and the allowed range of the upper and lower reserve limits of the ESS; in the second stage, they include the real-time upper and lower reserve limits, energy limits, the initial storage of the ESS, and the stored energy value.

[0097] (8)

[0098] (9)

[0099] (10)

[0100] (11)

[0101] (12)

[0102] (13)

[0103] (14)

[0104] (15)

[0105] In the formula, and Indicates the maximum charge / discharge power of the energy storage system in the day-ahead market; and Indicates up and down rotation standby; and is the charge / discharge power; and is the charge / discharge efficiency percentage; Indicates the energy storage level of the energy storage system; and To provide standby for the energy storage system to rotate up and down; and is a binary variable for the charge / discharge state; is the participation percentage of the energy storage system in the reserve allocation.

[0106] Step 102: introduce conditional risk value, and combine the generator model, wind farm output model and demand-side resource aggregator flexible resource model to construct an objective function that considers system operation constraints to minimize the total expected cost, and obtain a market clearing model.

[0107] In one embodiment, in step 102, an objective function is constructed to minimize the total expected cost by considering the system operation constraints, and a market clearing model is obtained, including:

[0108] A pool model is adopted, and a payment mechanism based on market settlement prices is constructed to minimize the total expected cost by considering system operation constraints, thus obtaining a market clearing model.

[0109] Step 103: Determine the first-stage constraint conditions and the second-stage constraint conditions of the market clearing model according to the generator set model, the wind farm output model, and the demand-side resource aggregator flexible resource model, and construct associated constraint conditions.

[0110] It should be noted that for steps 102-103, a notable feature of the stochastic programming problem is the ability to make optimal decisions under uncertainty. Since considering the risk index in the stochastic programming problem can help system decision makers manage costs, the CVaR (Conditional Value at Risk) indicator is used in the present invention to help risk-averse operators make optimal decisions. In fact, the risks of wrong decisions related to wind power uncertainty and the high costs imposed on system operators can be controlled by this index. At the same time, DSP (demand side supplier) as a flexible resource on the demand side also helps to reduce the risk level of ISO (independent system operator).

[0111] The energy / reserve resource allocation model proposed in the present invention consists of two stages. According to this two-stage model, the independent system operator ISO makes the optimal energy and reserve decision in each stage based on relevant decision data and minimization of expected costs. In the first stage, ISO aims to determine the variables of the energy market without considering scenarios related to wind power uncertainty. After making decisions on H&N variables and applying wind power uncertainty, these decisions are used to calculate W&S variables in the reserve market. Among them, H&N (Here-and-Now) refers to the first-stage decision in the two-stage optimization model, that is, the preliminary decision made based on currently known information without fully considering future uncertainties. W&S (Wait-and-See) refers to the second-stage decision in the two-stage optimization model, that is, after uncertainty occurs, the preliminary decision of the first stage is adjusted according to the specific scenario.

[0112] The specific construction method of the market clearing model is as follows:

[0113] 1) Objective function construction:

[0114] The present invention adopts a pool model, and the payment mechanism is based on the market settlement price. According to the proposed model, the ISO settles both energy and reserve resources. In addition, the participation of generators is based on marginal costs, and the reserve cost is settled according to the actual delivered reserve volume. In this process, consumers participate in the DR (demand response) program through CAP, and after the market settlement, consumers are rewarded or punished according to their participation or non-participation. In general, the present invention realizes the participation of demand-side resources through DSP, which pools demand-side resources to provide the required network reserve.

[0115] The objective function aims to minimize the total expected cost by considering the system operation constraints as follows:

[0116] (16)

[0117] In the formula, TEC represents the total cost of system operation, including energy cost and risk cost; EC is the expected cost; is the expected cost of day-ahead scheduling; is the expected cost of real-time scheduling; U is a non-negative parameter, which represents the weight coefficient of risk calculation; It is the weight of risk cost in total cost, which adjusts the system's sensitivity to risk aversion; CVaR is conditional value at risk, which is used to measure the expected loss after exceeding VaR (risk value) and reflects the extreme risk of the system under uncertain conditions.

[0118] The specific cost calculation is as follows:

[0119] (17)

[0120] (18)

[0121] (19)

[0122] (20)

[0123] (twenty one)

[0124] (twenty two)

[0125] (twenty three)

[0126] In the formula, , and are auxiliary variables for CVaR calculations related to wind power generation scenarios, which are auxiliary variables for achieving the optimal response and confidence level, respectively; , , , and The startup costs, the value of up / down spinning reserve and non-spinning reserve, the energy value of ESS (energy storage system) in discharge mode, and the value of ESS up / down spinning reserve capacity are represented in the day-ahead market respectively; is the startup cost in the real-time market, the reserve, the value of up / down spinning reserve, the value of up / down reserve of ESS, the value of load reduction and wind curtailment, is the wind power abandonment volume and wind power scenario probability. Among them, Πω represents the probability of scenario ω. By analyzing historical data such as wind power output and load demand, a probability distribution model (such as normal distribution, Weibull distribution, etc.) is constructed. Then, random scenarios are generated based on the distribution, and the corresponding probability Πω is assigned to each scenario.

[0127] The costs involved in the first stage include operating costs, spinning and non-spinning reserve costs, energy supply and reserve costs of ESS, spinning reserve costs obtained through CAP, and incentive payments / consumer penalties for participating in or not participating in CAP. Equation (18) includes costs due to changes in generator start-up status, reserve costs, and spinning reserve costs of ESS and CAP in the real-time market. The last two terms include load reduction and wind power overflow costs.

[0128] 2) Construction of constraints in the first stage:

[0129] The constraints of the first stage are related to day-ahead scheduling, including constraints (1), (2), (5)-(7), (8)-(14), and (24). The uncertainty of wind power is not considered. The supply and demand balance constraint is expressed as follows:

[0130] (twenty four)

[0131] In the formula, is the planned output power of unit i in time period t in the day-ahead market (MW); is the random output power of the wind farm in time period t in the day-ahead market (MW); is the discharge power of the energy storage systems (ESSs) (MW); is the change in the planned participation level of load j in time period t based on the Capacity Market Agreement (CAP) in the day-ahead market (MW); is the charging power of the energy storage systems (ESSs) (MW).

[0132] 3) Construction of constraints in the second stage:

[0133] The second stage constraints are related to the application of wind power uncertainty in the relevant context of the real-time market, including constraints (15), (25), and (26). The specific formula is as follows:

[0134] (25)

[0135] (26)

[0136] In the formula, is the power output of unit i in scenario w and time period t in the real-time market (MW); is the power consumed by load j in scenario w and time period t in the real-time market (MW); is the load reduction in the real-time market (MW); is the up / down reserve capacity of unit i in time period t of scenario w in the real-time market (MW); is the predicted output of wind farm wf in scenario w and time period t (MW); is the wind power spillover power in the real-time market (MW); is the power flow (MW) through line (n, r) in scenario w and time period t.

[0137] 4) Construction of associated constraints:

[0138] The following equations represent the output power and power consumption of the production unit, especially taking into account the uncertainty of wind power.

[0139] (27)

[0140] (28)

[0141] In the formula, is the power consumed by load j in scenario w and time period t in the real-time market (MW); is the change in the planned participation level of load j in time period t based on the Capacity Market Agreement (CAP) in the day-ahead market (MW); is the up / down reserve capacity of unit i in time period t of scenario w in the real-time market (MW); is the power output of unit i in scenario w and time period t in the real-time market (MW); is the planned output power of unit i in time period t in the day-ahead market (MW); is the non-spinning reserve capacity (MW) of unit i in time period t of scenario w in the real-time market.

[0142] The following formula considers the reserve capacity of each production unit segment in the real-time market under the condition of wind power uncertainty.

[0143] (29)

[0144] In the formula, is the up / down reserve capacity of unit i in time period t of scenario w in the real-time market (MW); is the non-spinning reserve capacity of unit i in time period t of scenario w in the real-time market (MW); is the reserve capacity (MW) provided by unit i in time period t according to segment f for scenario w in the real-time market.

[0145] Step 104: Based on the first-stage constraints, the second-stage constraints, and the associated constraints, the market clearing model is solved to obtain a scheduling result.

[0146] In one embodiment, solving the market clearing model to obtain a scheduling result in step 104 includes:

[0147] The market clearing model is solved by an optimization solver, and the stochastic programming results considering the demand side and wind power penetration rate are output, including: generator unit scheduling, energy storage system scheduling, and demand side response scheduling plan.

[0148] It should be noted that the model constructed in steps 102-103 can be solved using an optimization solver, and the output result is a random programming result that takes into account the demand side and wind power penetration rate, specifically including generator unit scheduling, energy storage system scheduling, and demand side response scheduling plan.

[0149] In summary, the present invention provides a random programming method that takes the demand side and wind power penetration rate into consideration. In order to cope with the uncertainty of wind power output, the present invention introduces the conditional value at risk (CVaR) indicator in the scheduling decision, which is used to evaluate and control the economic risk of the system under wind power fluctuation conditions. The CVaR indicator helps system operators to minimize risks when allocating reserve resources, so that scheduling strategies have better economy and safety when facing uncertainty. Furthermore, the present invention uses the wind speed probability distribution function (WPDF) to generate wind power output scenarios. This method can effectively simulate a variety of possible wind speed scenarios under wind power fluctuation conditions, and is an important step to ensure the accuracy of scheduling optimization results. Thereby solving the problem

[0150] The above is a random programming method considering the demand side and wind power penetration rate provided in an embodiment of the present invention. The following is a random programming system considering the demand side and wind power penetration rate provided in an embodiment of the present invention.

[0151] See also Figure 2 , a stochastic programming system considering the demand side and wind power penetration rate provided in an embodiment of the present invention includes:

[0152] The first construction unit 201 is used to construct a generator set model, a wind farm output model, and a demand-side resource aggregator flexible resource model.

[0153] The second construction unit 202 is used to introduce conditional risk value, and combine the generator model, wind farm output model and demand-side resource aggregator flexible resource model to construct an objective function that considers system operation constraints to minimize the total expected cost, and obtain a market clearing model.

[0154] The third construction unit 203 is used to determine the first-stage constraint conditions and the second-stage constraint conditions of the market clearing model according to the generator set model, the wind farm output model and the demand-side resource aggregator flexible resource model, and to construct associated constraint conditions.

[0155] The solving unit 204 is used to solve the market clearing model based on the first stage constraint conditions, the second stage constraint conditions, and the associated constraint conditions to obtain the scheduling result.

[0156] Furthermore, an embodiment of the present invention also provides a random programming device that considers the demand side and wind power penetration rate, and the device includes a processor and a memory:

[0157] The memory is used to store program code and transmit the program code to the processor;

[0158] The processor is used to execute the steps of the stochastic programming method considering the demand side and wind power penetration rate as described in the above method embodiment according to the instructions in the program code.

[0159] Furthermore, a computer-readable storage medium is provided in an embodiment of the present invention, and the computer-readable storage medium is used to store program code, and the program code is used to execute the random programming method considering the demand side and wind power penetration rate described in the above method embodiment.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0162] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0164] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A stochastic programming method considering the demand side and wind power penetration rate, characterized in that: include: Construct generator model, wind farm output model, and flexible resource model of demand-side resource aggregator; Introducing conditional risk value, and combining the generator model, the wind farm output model, and the demand-side resource aggregator flexible resource model, constructing an objective function that minimizes the total expected cost by considering system operation constraints, and obtaining a market clearing model; Determine the first-stage constraint conditions and the second-stage constraint conditions of the market clearing model respectively according to the generator set model, the wind farm output model and the demand-side resource aggregator flexible resource model, and construct associated constraint conditions; Based on the first-stage constraints, the second-stage constraints, and the associated constraints, the market clearing model is solved to obtain a scheduling result.

2. The random programming method considering the demand side and wind power penetration rate according to claim 1 is characterized in that: Constructing the generator set model includes: The multi-segment piecewise linear approximation and mixed integer linear programming structure are used to simulate the nonlinear cost function of the thermal unit and model the generator unit.

3. The random programming method considering the demand side and wind power penetration rate according to claim 1 is characterized in that: Constructing the wind farm output model includes: The wind power probability density function is programmed with mixed integer linear programming to simulate wind speed scenarios and model the wind farm output.

4. The random programming method considering the demand side and wind power penetration rate according to claim 1 is characterized in that: The construction of the demand-side resource aggregator flexible resource model includes: separately constructing a capacity market planning model and an energy storage system model.

5. The random programming method considering the demand side and wind power penetration rate according to claim 1 is characterized in that: The objective function of minimizing the total expected cost by considering the system operation constraints is constructed to obtain a market clearing model, including: A pool model is adopted, and a payment mechanism based on market settlement prices is constructed to minimize the total expected cost by considering system operation constraints, thus obtaining a market clearing model.

6. The random programming method considering the demand side and wind power penetration rate according to claim 1 or 5, characterized in that: The expression of the objective function is: ； In the formula, The total cost of operating the system, including energy costs and risk costs; is the expected cost; is the expected cost of day-ahead scheduling; is the expected cost of real-time scheduling; is a non-negative parameter, representing the weight coefficient of risk calculation; Adjust the system's sensitivity to risk aversion for the weight of risk cost in the total cost; It is the conditional value at risk, which is used to measure the expected loss after exceeding VaR (value at risk) and reflects the extreme risk of the system under uncertain conditions.

7. The random programming method considering the demand side and wind power penetration rate according to claim 1 is characterized in that: Solving the market clearing model to obtain a scheduling result includes: The market clearing model is solved by an optimization solver, and the stochastic programming results considering the demand side and wind power penetration rate are output, including: generator unit scheduling, energy storage system scheduling, and demand side response scheduling plan.

8. A stochastic programming system considering demand side and wind power penetration rate, characterized in that: include: The first construction unit is used to construct a generator set model, a wind farm output model, and a demand-side resource aggregator flexible resource model; The second construction unit is used to introduce conditional risk value, and to construct an objective function that considers system operation constraints to minimize total expected costs by combining the generator set model, the wind farm output model, and the demand-side resource aggregator flexible resource model, thereby obtaining a market clearing model; A third construction unit is used to determine the first-stage constraint conditions and the second-stage constraint conditions of the market clearing model respectively according to the generator set model, the wind farm output model and the demand-side resource aggregator flexible resource model, and to construct associated constraint conditions; A solving unit is used to solve the market clearing model based on the first-stage constraint conditions, the second-stage constraint conditions, and the associated constraint conditions to obtain a scheduling result.

9. A random programming device considering the demand side and wind power penetration rate, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the stochastic programming method considering the demand side and wind power penetration rate as described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the stochastic programming method considering the demand side and wind power penetration rate as described in any one of claims 1-7.

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