Stochastic programming methods and related devices considering demand-side factors and wind power penetration rate

Abstract

This invention discloses a stochastic programming method and related apparatus that considers demand-side factors and wind power penetration. The method includes: constructing a generator set model, a wind farm output model, and a demand-side resource aggregator flexible resource model; introducing conditional value of risk (VoV) and combining the generator set model, wind farm output model, and demand-side resource aggregator flexible resource model to construct an objective function that minimizes the total expected cost while considering system operating constraints, thus obtaining a market clearing model; determining the first-stage and second-stage constraints of the market clearing model and constructing associated constraints; and solving the market clearing model based on the above constraints to obtain the scheduling result. This invention's method allows for more flexible scheduling of different types of demand-side resources, better adapting to the volatile characteristics of renewable energy; it proactively manages uncertainty during scheduling, reducing the negative impact of wind power output fluctuations on system stability.

Description

Technical Field

[0001] This invention relates to the field of power system resource scheduling technology, and in particular to a stochastic programming method and related apparatus that takes into account demand side and wind power penetration rate. Background Technology

[0002] As renewable energy sources such as wind and solar power continue to increase their share in the power mix, the volatility and uncertainty of power system operation have also increased significantly. Traditional systems relying on fossil fuel power generation are struggling to adapt to the randomness and intermittency of new energy sources, urgently requiring an effective way to balance supply and demand and ensure stable system operation even with a high proportion of renewable energy.

[0003] Currently, there are three main options for power system resource dispatch: 1. Conventional generation reserve capacity: This involves increasing the reserve capacity of conventional power sources such as coal or gas to respond quickly to fluctuations in wind power output and maintain a balance between power supply and demand. However, this method relies on traditional fossil fuel power generation, which cannot meet the needs of low-carbon development and increases operating costs and environmental burden. Furthermore, the response speed of conventional power sources is limited, making it difficult to fully match the rapid fluctuations of new energy sources such as wind power. 2. Centralized energy storage systems: This utilizes large-scale energy storage facilities (such as pumped hydro storage and large-scale battery storage) to smooth fluctuations in wind power output. However, large-scale energy storage systems have high construction costs and require significant grid infrastructure. Centralized energy storage often requires substantial land and equipment investment, especially in urban areas or load centers. Moreover, its single-location model lacks sufficient flexibility when facing widespread fluctuations in demand across the entire grid. 3. Demand response mechanisms: This incentivizes users to adjust their electricity demand during peak grid load periods or off-peak wind power output periods to reduce the peak-to-valley difference in the power system. However, traditional demand response mechanisms are limited by consumers' willingness and flexibility to respond, and cannot guarantee sufficient response at every fluctuation. Demand response typically has a long response time, making it difficult to accurately match short-term, rapid wind power fluctuations. Summary of the Invention

[0004] This invention provides a stochastic planning method and related apparatus that considers demand-side factors and wind power penetration, enabling more flexible scheduling of different types of demand-side resources and better adaptation to the volatile characteristics of renewable energy. It proactively manages uncertainty during scheduling, reduces the negative impact of wind power output fluctuations on system stability, and ensures that the power system can still operate smoothly under conditions of high proportion of renewable energy. Furthermore, it effectively improves the overall flexibility and reliability of the power system.

[0005] In view of this, the first aspect of this application provides a stochastic programming method that considers demand-side factors and wind power penetration, the method comprising:

[0006] Construct generator set models, wind farm output models, and flexible resource models for demand-side resource aggregators;

[0007] By introducing conditional value of risk and combining it with the generator set model, the wind farm output model, and the demand-side resource aggregator flexible resource model, an objective function is constructed to minimize the total expected cost while considering system operation constraints, thus obtaining a market clearing model.

[0008] Based on the generator set model, the wind farm output model, and the demand-side resource aggregator flexible resource model, the first-stage constraints and the second-stage constraints of the market clearing model are determined respectively, and related constraints are constructed.

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

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

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

[0012] Optionally, the wind farm output model is constructed, including:

[0013] The wind speed scenario is simulated by using a mixed integer linear programming method to model the power output of the wind farm.

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

[0015] Optionally, the construction of the objective function that minimizes the total expected cost while considering system operating constraints, to obtain the market clearing model, includes:

[0016] By adopting a pooled model and a payment mechanism based on market settlement prices, an objective function is constructed to minimize the total expected cost while considering system operation constraints, thus obtaining a market clearing model.

[0017] Optionally, the expression for the objective function is:

[0018] ;

[0019] In the formula, The total cost of operating the system, including energy costs and risk costs; The expected cost; The expected cost of day-ahead scheduling; is the expected cost of real-time scheduling; is a non-negative parameter representing the weighting coefficient for risk calculation. Adjust the system's sensitivity to risk aversion by assigning a weight to risk costs in the total cost; Conditional Value at Risk (VaR) measures the expected loss beyond VaR (Value at Risk), reflecting the extreme risk of a system under uncertain conditions.

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

[0021] An optimization solver is used to solve the market clearing model, and the output is a stochastic programming result that takes into account demand and wind power penetration, including generator scheduling, energy storage system scheduling, and demand-side response scheduling plan.

[0022] A second aspect of this application provides a stochastic programming system that considers demand-side factors and wind power penetration, the system comprising:

[0023] The first building unit is used to build generator set models, wind farm output models, and demand-side resource aggregator flexible resource models.

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

[0025] The third construction unit is used to determine the first-stage constraints and the second-stage constraints of the market clearing model based on the generator set model, the wind farm output model, and the demand-side resource aggregator flexible resource model, and to construct associated constraints.

[0026] The solution unit is used to solve the market clearing model based on the first stage constraints, the second stage constraints, and the associated constraints to obtain the scheduling result.

[0027] A third aspect of the present invention provides a stochastic planning device that considers demand-side factors and wind power penetration, 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 configured to execute, according to instructions in the program code, the steps of the stochastic planning method considering demand side and wind power penetration as described in the first aspect above.

[0030] A fourth aspect of the present invention provides a computer-readable storage medium for storing program code for executing the stochastic planning method considering demand side and wind power penetration rate described in the first aspect above.

[0031] As can be seen from the above technical solutions, 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) to participate in power dispatch and reserve resource allocation in a centralized manner. This integration significantly improves the utilization efficiency of demand-side resources, enabling them to respond quickly to frequent fluctuations in the power system. Compared to 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 sources such as wind power.

[0033] 2) Enhanced adaptability to wind power uncertainty: The Conditional Value at Risk (CVaR) metric is adopted to help system operators consider the risk exposure of wind power output when scheduling reserves, thereby optimizing scheduling decisions under uncertain conditions. Compared with the passive strategy of directly dealing with fluctuations in traditional solutions, the risk management mechanism of this invention can proactively 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 high proportion of renewable energy.

[0034] 3) Efficient utilization 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 load centers, effectively improving the overall flexibility and reliability of the power system. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating a stochastic programming method that considers demand-side factors and wind power penetration rate, provided as an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of a stochastic programming system that considers demand-side factors and wind power penetration rate, provided as an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

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

[0040] To optimize energy and reserve allocation 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 generator sets;

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

[0044] Startup and shutdown costs;

[0045] Uphill / downhill climbing rate (MW / h);

[0046] The coefficients of the operating cost function;

[0047] 2. Load data:

[0048] Location of each load point and load curve;

[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] Impedance and Capacitance Limitations of Transmission Lines

[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 energy storage system;

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

[0061] Initial state of charge (%)

[0062] Charge / discharge efficiency (%)

[0063] 6. Demand-Side Response (DR) Data:

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

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

[0066] Incentive payments and penalty amounts;

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

[0068] 7. Market Data:

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

[0070] Real-time market prices: real-time market prices for energy and storage; costs of load shedding and wind energy spillover.

[0071] This invention provides a stochastic programming method considering demand-side factors and wind power penetration to explain the flexibility pricing problem for energy hubs (EHs) connected to electricity, heat, and gas networks. The proposed design is formulated as a two-level optimization. In the upper-level problem, profit maximization of EH-based flexibility resources (FSs) is modeled as a process that considers their flexibility constraints. Furthermore, the optimal operation of these networks with EHs is considered to minimize energy purchase costs in the lower-level problem. This problem is constrained by the network operation model and the operational-flexibility formulation of the EHs. Therefore, the scheme can be described as follows.

[0072] Please see Figure 1 The present invention provides a stochastic programming method that considers demand-side factors and wind power penetration rate, comprising:

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

[0074] In one embodiment, step 101 includes:

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

[0076] It should be noted that this step uses a piecewise linear approximation and mixed-integer linear programming (MILP) structure to simulate the nonlinear cost function of the heat engine unit. The following equations 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, , , It is the coefficient of the fuel cost function of a conventional power generation unit (power generation unit refers to a thermal power unit); It is the minimum output power of each power generation unit; It is a binary variable for starting and stopping the generator set; This is the planned output power in the market at the moment; It is the value of the energy midpoint f proposed by the unit.

[0080] Step 1012: Simulate wind speed scenarios and model wind farm output by using mixed integer linear programming of wind power probability density function.

[0081] It should be noted that, since there is a close relationship between the wind speed characteristics at a given location and the wind power probability density function (WPDF), this study uses mixed-integer linear programming (WPDF) to implement the wind speed scenario. Furthermore, after calculating the wind speed scenario, the hourly random power output of the wind farm is expressed as follows:

[0082] (3)

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

[0084] Step 1013: Construct the capacity market planning model and the 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 market environment in the form of incentive programs based on CAP (Capacity Market Program), and for managing the charging and discharging of the energy storage system (ESS).

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

[0087] Based on CAP, the hourly consumer engagement level limit is changed as follows:

[0088] (4)

[0089] In the formula, and This represents the maximum / minimum range of load variation in CAP. This is a variable based on the level of load change planned for the day-ahead market by CAP.

[0090] In this program, consumers are rewarded for maintaining a reduced level of consumption within predefined, flexible limits. Conversely, they receive participation value based on contractual responses to changes in their consumption levels, and are penalized for non-participation. The following formulas illustrate the contract costs, incentives, and penalties imposed by the system operator.

[0091] (5)

[0092] (6)

[0093] (7)

[0094] In the formula, For contract costs, To motivate, The amount of the penalty imposed on the system operator, and This represents the additional incentive value for up / down spins in the CAP Capacity Bonus Program. and It is the on / off spinning reserve dispatch value of load j in the day-ahead market; This represents the reduced incentive pay value given to consumers in CAP; This is the consumer penalty value incurred for not participating in CAP.

[0095] 2) Construct an energy storage system model:

[0096] Energy storage systems (ESS) participate in the energy and backup markets based on the following constraints. The first phase of constraints includes charge and discharge equations, operating conditions, and allowable ranges for ESS upper and lower backup limits; the second phase includes real-time upper and lower backup limits, energy limits, 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 This indicates the maximum charging / discharging power of energy storage systems currently in the market; and Indicates up and down rotation for later use; and This refers to the charging / discharging power. and This represents the percentage of charge / discharge efficiency. Indicates the energy storage level of the energy storage system; and For backup in the energy storage system; and A binary variable representing the charging / discharging state; This represents the percentage of energy storage system participation in standby allocation.

[0106] Step 102: Introduce conditional value of risk and combine it with generator set model, wind farm output model and demand-side resource aggregator flexible resource model to construct an objective function that minimizes total expected cost considering system operation constraints, thus obtaining the market clearing model.

[0107] In one embodiment, step 102 involves constructing an objective function that minimizes the total expected cost while considering system operating constraints, resulting in a market clearing model, including:

[0108] By adopting a pooled model and a payment mechanism based on market settlement prices, an objective function is constructed to minimize the total expected cost while considering system operation constraints, thus obtaining a market clearing model.

[0109] Step 103: Determine the first-stage and second-stage constraints of the market clearing model based on the generator set model, wind farm output model, and demand-side resource aggregator flexible resource model, and construct related constraints.

[0110] For steps 102-103, it should be noted that a significant characteristic of stochastic programming problems is their ability to make optimal decisions under conditions of uncertainty. Since considering risk indices in stochastic programming problems can help system decision-makers manage costs, this invention uses the CVaR (Conditional Value at Risk) index to assist risk-averse operators in making optimal decisions. In fact, the risks associated with erroneous decisions related to wind power uncertainty, as well as the high costs imposed on system operators, can be controlled through this index. Simultaneously, DSPs (Demand-Side Suppliers), as flexible resources on the demand side, also help reduce the risk level of ISOs (Independent System Operators).

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

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

[0113] 1) Construction of the objective function:

[0114] This invention employs a pooled model, with a payment mechanism based on market settlement prices. According to the proposed model, the ISO simultaneously settles for both energy and reserve resources. Furthermore, generator participation is based on marginal cost, and reserve costs are settled based on the actual amount of reserve delivered. During this process, consumers participate in the DR (Demand Response) program through CAP, and after market settlement, consumers receive rewards or penalties based on their participation or non-participation. Overall, this invention achieves demand-side resource participation through DSP, which aggregates demand-side resources to provide the required network reserve.

[0115] The objective function aims to minimize the total expected cost by taking into account system operating constraints, as shown in the following equation:

[0116] (16)

[0117] In the formula, TEC represents the total cost of system operation, including energy costs and risk costs; EC represents the expected cost. The expected cost of day-ahead scheduling; The expected cost for real-time scheduling; U is a non-negative parameter representing the weighting coefficient for risk calculation; The risk cost is the weight of the total cost, adjusting the system's sensitivity to risk aversion; CVaR is the conditional value of risk, used to measure the expected loss after exceeding VaR (value of risk), reflecting the extreme risk of the system under uncertainty.

[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 These are auxiliary variables for calculating CVaR related to wind power generation scenarios, namely, auxiliary variables for achieving the optimal response and confidence level; , , , and Start-up costs, the value of spinning reserve and non-spinning reserve, the energy value of ESS (energy storage system) in discharge mode, and the value of ESS spinning reserve capacity are all expressed in the day-ahead market. It refers to the real-time market values ​​of start-up costs, reserves, spinning reserve, ESS (Emergency Shared Reserve), load shedding, and wind curtailment. It refers to wind curtailment and the probability of wind power scenarios.

[0127] Here, 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 this distribution, and a corresponding probability Πω is assigned to each scenario.

[0128] The costs involved in the first phase include operating costs, spinning reserve and non-spinning reserve costs, energy supply and reserve costs of the ESS, spinning reserve costs obtained through CAP, and the costs of incentive payments / consumer penalties arising from participation in or non-participation in CAP. Equation (18) includes costs arising from 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 shedding and wind power overflow costs.

[0129] 2) First-stage constraint construction:

[0130] The constraints for the first stage are related to day-ahead dispatch, including constraints (1), (2), (5)-(7), (8)-(14), and (24). Furthermore, the uncertainty of wind power output is not considered. The supply-demand balance constraint is expressed as follows:

[0131] (twenty four)

[0132] In the formula, In the day-ahead market, the planned output power (MW) of unit i in time period t. It is the random output power (MW) of a wind farm in the day-ahead market during time period t. It is the discharge power (MW) of the energy storage system (ESSs); It is the change in the planned participation level (MW) of load j during time period t, based on the Capacity Market Agreement (CAP) in the day-ahead market. It is the charging power (MW) of the energy storage system (ESSs).

[0133] 3) Second-stage constraint construction:

[0134] The second stage of constraints relates to the uncertainties of applying wind power in the context of the real-time market, including constraints (15), (25), and (26). The specific formulas are as follows:

[0135] (25)

[0136] (26)

[0137] In the formula, In the real-time market, the power output (MW) of unit i in scenario w and time period t. In the real-time market, the power (MW) consumed by load j in scenario w and time period t. This refers to load reduction (MW) in the real-time market. In the real-time market, within time period t of scenario w, the up / down reserve capacity (MW) of unit i. It is the predicted power output (MW) of the wind farm wf under scenario w and time period t; It is the wind power spillover power (MW) in the real-time market. It refers to the power flow (MW) through the line (n, r) within scenario w and time period t.

[0138] 4) Construction of associated constraints:

[0139] The following formula represents the output power and power consumption of the production unit, taking into account the uncertainty of wind power.

[0140] (27)

[0141] (28)

[0142] In the formula, In the real-time market, the power (MW) consumed by load j in scenario w and time period t. It is the change in the planned participation level (MW) of load j during time period t, based on the Capacity Market Agreement (CAP) in the day-ahead market. In the real-time market, within time period t of scenario w, the up / down reserve capacity (MW) of unit i. In the real-time market, the power output (MW) of unit i in scenario w and time period t. In the day-ahead market, the planned output power (MW) of unit i in time period t. In the real-time market, during time period t of scenario w, the non-spinning reserve capacity (MW) of unit i.

[0143] The following formula considers the reserve capacity of each production unit segment under the uncertainty of wind power in the real-time market.

[0144] (29)

[0145] In the formula, In the real-time market, within time period t of scenario w, the up / down reserve capacity (MW) of unit i. In the real-time market, during time period t of scenario w, the non-spinning reserve capacity (MW) of unit i. In the real-time market, for scenario w, unit i provides reserve capacity (MW) in time period t according to segment f.

[0146] Step 104: Based on the first-stage constraints, the second-stage constraints, and the associated constraints, solve the market clearing model to obtain the scheduling results.

[0147] In one embodiment, step 104, solving the market clearing model to obtain the scheduling result, includes:

[0148] An optimization solver is used to solve the market clearing model, and the output is a stochastic programming result that takes into account demand and wind power penetration, including generator scheduling, energy storage system scheduling, and demand-side response scheduling plan.

[0149] It should be noted that the model constructed in steps 102-103 can be solved using an optimization solver. The output results are stochastic programming results that take into account demand and wind power penetration, specifically including generator scheduling, energy storage system scheduling, and demand-side response scheduling plans.

[0150] In summary, this invention provides a stochastic programming method that considers both demand-side factors and wind power penetration. To address the uncertainty of wind power output, this invention introduces the Conditional Value at Risk (CVaR) index into the scheduling decision-making process to assess and control the economic risk of the system under fluctuating wind power conditions. The CVaR index helps system operators minimize risk when allocating reserve resources, making the scheduling strategy more economical and secure in the face of uncertainty. Furthermore, this invention utilizes the Wind Speed ​​Probability Distribution Function (WPDF) to generate wind power output scenarios. This method can effectively simulate multiple possible wind speed scenarios under fluctuating wind power conditions, which is an important step in ensuring the accuracy of scheduling optimization results. This solves the problem...

[0151] The above is a stochastic programming method considering demand and wind power penetration rate provided in the embodiments of the present invention. The following is a stochastic programming system considering demand and wind power penetration rate provided in the embodiments of the present invention.

[0152] Please see Figure 2 The present invention provides a stochastic programming system that considers demand-side factors and wind power penetration rate, comprising:

[0153] The first building unit 201 is used to build a generator set model, a wind farm output model, and a demand-side resource aggregator flexible resource model.

[0154] The second building unit 202 is used to introduce conditional value of risk and, in conjunction with the generator set model, wind farm output model, and demand-side resource aggregator flexible resource model, construct an objective function that minimizes the total expected cost while considering system operation constraints, thus obtaining a market clearing model.

[0155] The third building unit 203 is used to determine the first-stage constraints and the second-stage constraints of the market clearing model based on the generator set model, the wind farm output model, and the demand-side resource aggregator flexible resource model, and to construct the associated constraints.

[0156] Solver 204 is used to solve the market clearing model based on the first-stage constraints, the second-stage constraints, and the associated constraints to obtain the scheduling results.

[0157] Furthermore, this embodiment of the invention also provides a stochastic planning device that considers demand-side factors and wind power penetration rate, the device including a processor and a memory:

[0158] The memory is used to store program code and transmit the program code to the processor;

[0159] The processor is configured to execute, according to the instructions in the program code, the steps of the stochastic planning method considering demand side and wind power penetration rate as described in the above method embodiments.

[0160] Furthermore, this embodiment of the invention also provides a computer-readable storage medium for storing program code for executing the stochastic planning method considering demand side and wind power penetration rate described in the above method embodiments.

[0161] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0162] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0163] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0165] If the integrated unit is implemented as 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A stochastic programming method considering demand side and wind power penetration, characterized in that, The method comprises the following steps: a generator set model, a wind farm output model, and a demand side resource aggregator flexible resource model are constructed; a conditional value at risk is introduced, and a target function of minimizing total expected cost considering system operation constraints is constructed by combining the generator set model, the wind farm output model, and the demand side resource aggregator flexible resource model, to obtain a market clearing model; first stage constraint conditions and second stage constraint conditions of the market clearing model are determined respectively according to the generator set model, the wind farm output model, and the demand side resource aggregator flexible resource model, and an associated constraint condition is constructed; the market clearing model is solved based on the first stage constraint conditions, the second stage constraint conditions, and the associated constraint condition to obtain a scheduling result; an expression of the first stage constraint condition is as follows: ； an expression of the second stage constraint condition is as follows: ； ； an expression of the associated constraint condition is as follows: ； ； an expression of the target function of the market clearing model is as follows: ； wherein, is the planned output power of unit i at time period t in day-ahead market; is the random output power of wind farm wf at time period t in day-ahead market; is the discharging power of energy storage system; is the change of planned participation level of load j at time period t in day-ahead market based on capacity market agreement; is the charging power of energy storage system; is the power output of unit i at scenario w and time period t in real-time market; is the power consumed by load j at scenario w and time period t in real-time market; is the load curtailment in real-time market; is the up / down spinning reserve capacity of energy storage system at time period t of scenario w in real-time market; is the forecasted output of wind farm wf at scenario w and time period t; is the wind spill power in real-time market; is the power flow through line (n, r) at scenario w and time period t; is the non-spinning reserve capacity of unit i at time period t of scenario w in real-time market; is the total cost of system operation, including energy cost and risk cost; is the expected cost; is the day-ahead dispatch expected cost; is the real-time dispatch expected cost; is the weight of risk cost in total cost, adjusting the sensitivity of system to risk aversion; is the conditional value at risk, used to measure the expected loss beyond the value at risk, reflecting the extreme risk of system under uncertain conditions. 2.The method of claim 1, wherein, the generator set model is constructed, comprising: a multi-segment piecewise linear approximation and a mixed integer linear programming structure are used to simulate a nonlinear cost function of a thermal generator set, and the generator set is modeled. 3.The method of claim 1, wherein, the wind farm output model is constructed, comprising: a mixed integer linear programming wind power probability density function is used to simulate a wind speed scenario, and the wind farm output is modeled. 4.The method of claim 1, wherein, the demand side resource aggregator flexible resource model is constructed, comprising: 5.The method of claim 1, wherein, a capacity market planning model and an energy storage system model are respectively constructed. the target function of minimizing total expected cost considering system operation constraints is constructed to obtain the market clearing model, comprising: 6.The method of claim 1, wherein, a pool model is used to construct the target function of minimizing total expected cost considering system operation constraints based on a market settlement price payment mechanism, to obtain the market clearing model. the market clearing model is solved to obtain the scheduling result, comprising:

7. A stochastic programming system considering demand side and wind power penetration, characterized in that, an optimization solver is used to solve the market clearing model, and a random planning result considering demand side and wind power penetration is output, including generator set scheduling, energy storage system scheduling, and demand side response scheduling plan. A system for implementing the random planning method considering demand side and wind power penetration is provided, and the system comprises: a first construction unit configured to construct a generator set model, a wind farm output model, and a demand side resource aggregator flexible resource model; a second construction unit configured to introduce a conditional value at risk, and construct a target function of minimizing total expected cost considering system operation constraints by combining the generator set model, the wind farm output model, and the demand side resource aggregator flexible resource model, to obtain a market clearing model; a third construction unit configured to determine first stage constraint conditions and 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 an associated constraint condition; a solving unit configured to solve the market clearing model based on the first stage constraint conditions, the second stage constraint conditions, and the associated constraint condition to obtain a scheduling result.

8. A stochastic programming device considering demand side and wind power penetration, characterized in that, The device comprises a processor and a memory: The memory is configured to store program code and transmit the program code to the processor; The processor is configured to execute the random planning method considering demand side and wind power penetration according to instructions in the program code.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium is configured to store program code, and the program code is configured to execute the random planning method considering demand side and wind power penetration.

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