A scheduling optimization method and system considering source-load bilateral uncertainty

Abstract

The application discloses a kind of scheduling optimization method and system considering source load bilateral uncertainty, the method includes: first, the planned data of power grid is brought into the security constrained unit commitment model constructed in advance to obtain the scheduling plan of conventional unit optimization calculation;Then the branch or section flow distribution when intermittent power is connected to power grid in the scheduling plan period of the conventional unit is calculated;Finally, the branch or section flow distribution when intermittent power is connected to power grid is based on the conventional unit scheduling plan optimization;The security constrained unit commitment model is constructed based on the uncertainty of demand response load under price incentive;The conventional unit includes coal-fired unit and gas turbine unit;The intermittent power includes wind turbine and photovoltaic unit.The present application overcomes the shortcomings that large-scale wind power integration does not specifically consider the mathematical characteristics and distribution law of wind power uncertainty, while considering the uncertainty factors of demand response.

Description

Technical Field

[0001] This invention relates to the field of power system automation, and more specifically to a scheduling optimization method and system that considers uncertainties on both the source and load sides. Background Technology

[0002] In actual production, to ensure that the power generation plan meets the system's various power safety constraints, it is necessary to combine it with planned data such as bus load forecasting and tie-line plans to form a daily planned power flow profile. Deterministic power flow calculation methods are then used to analyze whether the power flow in this profile exceeds limits or has weak points. Because large-scale intermittent grid-connected power sources (such as wind and solar power) are greatly affected by weather conditions, using conventional power flow methods for power system planning and operation analysis requires numerous calculations for various stochastic scenarios. This is not only computationally intensive and time-consuming, but also difficult to comprehensively reflect the situation. Considering the significant randomness and fluctuation in the output power of large-scale intermittent grid-connected power sources, traditional transmission margin models based on power flow based on determined grid impedance characteristics can only provide the branch power flow status and its relationship with safety thresholds under one or more specified system states. They cannot comprehensively assess the impact of such power sources on the overall grid's transmission safety and stability limits after grid connection.

[0003] Furthermore, changes in daily life and work patterns also affect load forecasting. Many countries' electricity markets are transitioning from traditional regulated models to competitive market models, leading to increased uncertainty in power systems under a greater emphasis on economic and social factors. On the one hand, electricity users, especially industrial users, are highly sensitive to electricity prices. They may schedule their work according to suitable prices, and some industrial users may relocate their production sites based on cost considerations, effectively altering the region's electricity consumption characteristics. On the other hand, power system equipment faces the risk of failure and maintenance, potentially leading to system outages or even changes in network topology. Traditional demand response does not consider these scenarios and therefore treats load as deterministic, leading to rapidly increasing uncertainty due to factors such as willingness to adjust, neglect of incentive policies, and communication delays.

[0004] In summary, the impact of uncertainties on both the source and load sides on power grid dispatching and operation is becoming increasingly serious, and there is an urgent need for a dispatching optimization method that takes into account uncertainties on both the source and load sides to improve dispatching efficiency. Summary of the Invention

[0005] To address the aforementioned shortcomings in the existing technology, this invention provides a scheduling optimization method considering uncertainties on both the source and load sides, comprising:

[0006] S1 inputs the power grid planning data into a pre-built safety-constrained unit combination model for optimization calculation to obtain the scheduling plan for conventional units;

[0007] S2 calculates the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid within the scheduling plan cycle of the conventional units;

[0008] S3 optimizes the conventional unit scheduling plan based on the branch or cross-sectional power flow distribution when the intermittent power source is connected to the grid;

[0009] The safety-constrained unit combination model is constructed based on the uncertainty of demand response load under price incentives; the conventional units include coal-fired units and gas-fired units; the intermittent power sources include wind turbines and photovoltaic units.

[0010] Preferably, the construction of the safety-constrained unit combination model includes:

[0011] The objective function is constructed with the goal of minimizing the start-up and operating costs of conventional generating units and the scheduling and operating costs of demand response loads.

[0012] Power balance constraints are constructed based on the response transfer of demand response load under price incentives;

[0013] The power balance constraint is a constraint condition of the objective function.

[0014] Preferably, the objective function is as follows:

[0015]

[0016] In the formula: F is the sum of the start-up and operating costs of conventional generating units and the dispatching and operating costs of demand response loads; N G N represents the total number of conventional generating units. T B represents the number of time periods. i (P i (t),t) represents the operating cost of conventional unit i during time period t, P i (t) represents the active power output of conventional unit i during time period t, Cu i (t) represents the startup cost of conventional unit i during time period t, f e1 This is the scheduling and operation cost function for demand response load.

[0017] Preferably, the startup cost Cu of the conventional unit i during time period t is... i (t), as shown in the following formula:

[0018]

[0019] In the formula: This represents the minimum operating time for conventional unit i. This refers to the time required for a conventional unit i to reach warm start-up after the minimum downtime. Cu is the time required for a conventional unit i to cold start after the minimum downtime. i,c Cu is the first constant of the start-up cost function for conventional unit i. i,w Cu is the second constant of the start-up cost function for conventional unit i. i,h It is the third constant in the start-up cost function of conventional unit i.

[0020] Preferably, the power balance constraint is as shown in the following formula:

[0021]

[0022] In the formula: P i (t) represents the active power output of conventional unit i during time period t, N G For the number of conventional generating units, P load (t) represents the system load forecast for time period t, ΔL t (t) represents the response transfer of demand response load under price incentives during time period t.

[0023] Preferably, the constraints further include:

[0024] Standby constraints, conventional unit output constraints, conventional unit minimum operating time constraints, conventional unit minimum downtime constraints, conventional unit load increase / decrease rate constraints, and network constraints.

[0025] Preferably, the step of calculating the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid within the scheduling planning cycle of the conventional units includes:

[0026] Obtain the power flow calculation parameters, generator and load nodes, and rated power of intermittent power sources within the scheduling plan cycle of the conventional units;

[0027] Based on the power flow calculation parameters, the rated power of generators and load nodes and intermittent power sources, a probabilistic power flow method using semi-invariants and series expansion is employed to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the power grid.

[0028] Preferably, the probabilistic power flow method employing semi-invariants and series expansion to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid includes:

[0029] Calculate the torques of the intermittent power source output using semi-invariants;

[0030] Based on the moments of the intermittent power supply output, the semi-invariants of the intermittent power supply output are obtained.

[0031] Based on the semi-invariants of the intermittent power output, the relationship matrix between nodes and branch power flows is obtained.

[0032] The relationship matrix between nodes and branch power flow is calculated to obtain the semi-invariants of each order of branch and cross-sectional power flow.

[0033] The power flow distribution of branches or sections when intermittent power sources are connected to the grid is obtained by performing series expansion of the semi-invariants of each order on the power flow of branches or sections.

[0034] Preferably, optimizing the conventional unit scheduling plan based on the branch or section power flow distribution when the intermittent power source is connected to the grid includes:

[0035] Check whether the power flow distribution of branches or sections meets the safety requirements of the confidence interval. When the power flow distribution of a branch or section does not meet the safety requirements of the confidence interval, feed back the power flow distribution results of the branch or section to the safety-constrained unit combination model, and execute S1 to optimize the conventional unit scheduling plan until the power flow distribution of all branches or transmission sections meets the safety requirements of the confidence interval. Obtain the start-up and shutdown status of conventional units, the output plan of conventional units, and the power flow curves of branches and sections.

[0036] Based on the same inventive concept, this invention also provides a scheduling optimization system considering uncertainties on both the source and load sides, comprising:

[0037] The scheduling planning module is used to input power grid planning data into a pre-built safety-constrained unit combination model for optimization calculation to obtain the scheduling plan for conventional units;

[0038] The power flow calculation module is used to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid within the scheduling plan cycle of the conventional units.

[0039] The optimization module is used to optimize the conventional unit scheduling plan based on the branch or cross-sectional power flow distribution when the intermittent power source is connected to the grid.

[0040] The safety-constrained unit combination model is constructed based on the uncertainty of demand response load under price incentives; the conventional units include coal-fired units and gas-fired units; the intermittent power sources include wind turbines and photovoltaic units.

[0041] Preferably, the power flow calculation module includes:

[0042] The acquisition unit is used to acquire power flow calculation parameters, generator and load nodes, and rated power of intermittent power sources within the scheduling plan cycle of the conventional unit.

[0043] The power flow calculation unit is used to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the power grid, based on the power flow calculation parameters, the rated power of generators and load nodes and intermittent power sources, using a probabilistic power flow method with semi-invariants and series expansion.

[0044] The technical solution provided by this invention has the following beneficial effects:

[0045] The technical solution provided by this invention first inputs grid planning data into a pre-constructed safety-constrained unit combination model for optimization calculation to obtain the scheduling plan for conventional units; then, within the scheduling plan period of the conventional units, the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid is calculated; finally, the scheduling plan for the conventional units is optimized based on the branch or cross-sectional power flow distribution when the intermittent power sources are connected to the grid. The safety-constrained unit combination model is constructed based on the uncertainty of demand response load under price incentives; the conventional units include coal-fired units and gas-fired units; the intermittent power sources include wind turbine units and photovoltaic units. This invention overcomes the shortcomings of current large-scale wind power grid connection methods that do not specifically consider the mathematical characteristics and distribution patterns of wind power uncertainty, while also considering the uncertainty factors of demand response, realizing a method for optimizing the scheduling plan considering both source and load uncertainties. Attached Figure Description

[0046] Figure 1 This is a flowchart of a scheduling optimization method considering uncertainties on both sides of the source and load in this invention;

[0047] Figure 2 This is a schematic diagram of a scheduling optimization method considering uncertainties on both sides of the source and load in an embodiment of the present invention.

[0048] Figure 3 This is the flowchart of the multi-time probabilistic power flow algorithm under intermittent power supply access in this embodiment of the invention. Detailed Implementation

[0049] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0050] Example 1: As Figure 1 As shown, the present invention provides a scheduling optimization method considering uncertainties on both sides of the source and load, comprising:

[0051] S1 inputs the power grid planning data into a pre-built safety-constrained unit combination model for optimization calculation to obtain the scheduling plan for conventional units;

[0052] S2 calculates the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid within the scheduling plan cycle of the conventional units;

[0053] S3 optimizes the conventional unit scheduling plan based on the branch or cross-sectional power flow distribution when the intermittent power source is connected to the grid;

[0054] The safety-constrained unit combination model is constructed based on the uncertainty of demand response load under price incentives; the conventional units include coal-fired units and gas-fired units; the intermittent power sources include wind turbines and photovoltaic units.

[0055] like Figure 2 As shown, for Figure 1 The technical solution shown will be described in detail, including:

[0056] Step 1: Basic data preparation, including power grid model, equipment parameters, unit operating parameters, and probability distribution description of intermittent energy sources.

[0057] Step 2: Use mixed integer programming to perform safety-constrained unit combination optimization calculations to obtain the start-up and shutdown states and output plans of conventional units that satisfy the objective function and various constraints. At this point, intermittent power sources such as wind power are assigned their expected values.

[0058] 1) Objective function:

[0059] The objective function is constructed with the goal of minimizing the start-up and operating costs of conventional generating units and the scheduling and operating costs of demand response loads, where the start-up and operating costs of conventional generating units are equivalent to power generation costs. This invention proposes a nested and coupled hierarchical scheduling optimization model, namely, an upper-level optimization model for unit start-up and shutdown and tie-line planning, and a lower-level optimization model for economic scheduling of the system under given start-up and shutdown methods and tie-line plans. To improve the acceptance capacity of intermittent energy sources, their corresponding costs are set to 0.

[0060]

[0061] Where, N G N represents the number of generator sets. T Here, i represents the time slot number (00:15-24:00, with each time slot lasting 15 minutes), t represents the time slot number, and f represents the period number. e1 This is the scheduling and operation cost function for demand response load.

[0062] Decision variable: P i (t) represents the active power output of unit i during time period t, Cu i (t) represents the startup cost of unit i during time period t.

[0063] B i (P i (t),t) represents the operating cost of the i-th generator in time period t. The relevant function is described as follows:

[0064]

[0065]

[0066]

[0067] Among them, u i,j (t) represents the marker position of segment j of the segmented cost incremental rate curve for time period t. Let P be the initial output value of segment j of the incremental cost rate curve for segment i of unit. i,j (t) represents the output of unit i in segment j of the segmented cost incremental rate curve and The difference, K i,j (t) represents the slope of segment j of the multi-segment cost incremental rate curve for unit i, N L The number of segments in the multi-segment cost incremental rate curve of the unit. P i (t) represents the lower limit of unit i's output during time period t. If Then unit i is powered on, if Then unit i is shut down.

[0068] In this invention, the startup cost Cu of conventional unit i during time period t is... i (t), as shown in the following formula:

[0069]

[0070] in, Let i be the minimum operating time of unit i. This is the time required for unit i to reach warm start-up after the minimum downtime. Cu is the time required for unit i to cold start after the minimum downtime. i,c Cu i,w Cu i,h Let be a constant in the start-up cost function of unit i.

[0071] Decision variables: Let t be the operating time of unit i during time period t.

[0072] Considering the practical physical significance of the impact of generation planning and bus load forecasting on transmission margin, this study assumes that both conform to a probability distribution where the expected value is the planned value and the probability density function at the expected value is 100%. In light of current realities, flexible loads of the Direct Load Control (DLC) type are considered. The scheduling and operation cost function f of demand response load in this invention... e1 As shown in the following formula:

[0073]

[0074] In the formula: C il,i Let i be the scheduling operation cost function for demand response load i; The planned scheduling amount of demand response load for time period t; T is the total number of time periods; This represents the number of demand response loads.

[0075] 2) Constraints:

[0076] ① Power balance constraints:

[0077]

[0078] Where, N G P represents the number of generating units, including conventional units and intermittent energy units. load (t) represents the system load forecast for time period t, ΔL t (t) is the response transfer amount of demand response load under price incentives in time period t, and is a fuzzy variable.

[0079] In this invention, the load transfer rate generated by price incentives can be predicted by the consumer psychology curve. The fluctuation of response behavior caused by non-economic factors is described by fuzzy variables. The fuzzy parameter of the uncertain load response is ξ, which physically represents the deviation between the actual load transfer rate and the predicted load transfer rate.

[0080]

[0081] In the formula, Let ξ be the actual load transfer rate; λ be the predicted load transfer rate. Considering the fuzziness of the load transfer rate, the prediction error ξ is set as a trapezoidal (triangular) membership function. Based on the different time periods defined by the time-of-use (TOU) pricing, let T... p T f and T v The actual load values ​​are obtained based on the TOU consumer psychology curve model, representing peak, flat, and trough periods respectively:

[0082]

[0083] In the formula, L t For the initial load before TOU is implemented; The actual load after implementing TOU; and These represent the actual load transfer rates during peak-valley, peak-slow, and slow-valley periods, respectively; L pave L fave and L vave These represent the average initial load values ​​for peak, flat, and valley periods, respectively.

[0084] Combined Japanese style We can obtain:

[0085]

[0086]

[0087] ξ pv ξ pf and ξ fv These represent the load transfer rate deviations for peak-valley, peak-to-average, and average-valley periods, respectively. (Based on fuzzy variables...)

[0088] The nature of the quantity, the load response deviation obtained by linear operation of two fuzzy parameters. is a fuzzy variable representing the uncertainty of TOU user response behavior.

[0089] ②Alternative constraints:

[0090]

[0091]

[0092] in, and These represent positive and negative backup requirements, respectively. P i (t) represents the lower limit of the output of unit i in time period t. N represents the upper limit of unit i's output during time period t. T This refers to the number of conventional generating units.

[0093] ③ Unit output constraints:

[0094]

[0095] ④ Load increase / decrease rate constraints for conventional units:

[0096]

[0097]

[0098] in, and These represent the maximum output of unit i during time period t, when it is adjusted upwards and downwards respectively.

[0099] ⑤ Minimum operating time and minimum downtime constraints for conventional units:

[0100]

[0101]

[0102] in, and These represent the minimum operating time and minimum downtime of conventional unit i during time period t, respectively.

[0103] ⑥ Network constraints:

[0104]

[0105]

[0106] in, and These are the minimum and maximum limits for power flow in a branch or transmission section, respectively. and These represent the minimum and maximum values ​​of the probability power flow distribution range of the transmission line within the confidence interval α during time period t.

[0107] Step 3: Using a probabilistic power flow method based on semi-invariants and Gram-Charlier series expansion, calculate the impact of random fluctuations of intermittent power sources such as wind power on the power flow of the system in future time periods.

[0108] For a random variable ξ corresponding to a branch or transmission section, assume its mean is μ and its standard deviation is σ. Then its normalized random variable... for

[0109]

[0110] Normalizing random variables using Gram-Charlier series expansion theory probability density function f cum (x) and cumulative distribution function F cum (x) are respectively:

[0111]

[0112]

[0113] In the formula φ(x) and c are the probability density function and cumulative distribution function of a standard normally distributed random variable with an expected value of 0 and a standard deviation of 1, respectively. r Constant coefficients:

[0114] c0 = 1

[0115] c1 = c2 = 0

[0116]

[0117]

[0118] Step 4: Check whether the probability distribution range of the power flow of the branch or transmission section meets the requirements of the confidence interval α. If the power flow of any branch or section does not meet the requirements, the result is fed back to the safety constraint unit combination, that is, return to step 2 to re-perform the unit combination optimization calculation and adjust the conventional unit plan.

[0119] Let the upper and lower limits of power flow for branch lines or transmission sections be respectively and like or If the branch or transmission section does not meet the requirements of confidence interval α, then adjustments to the conventional unit plan are needed through safety-constrained unit combinations. Since the network model remains unchanged and the probabilistic characteristics of the wind turbines remain unchanged, only the expected value of the power flow in the branch or section needs to be adjusted accordingly to meet the safe operation requirements under confidence interval α. The adjustment of the power flow in the branch or transmission section can be achieved by adjusting the power flow limits of the corresponding branch or transmission section in the safety-constrained unit combination. Typically... Much larger Therefore, the adjustment amount ΔP of the limit is:

[0120] Reduced amount:

[0121] Increased amount:

[0122] When the power flow of a branch line or transmission section needs to be adjusted downwards, a temporary limit can be set for the power flow of the corresponding branch line or transmission section. When the power flow needs to be adjusted upwards, the lower limit of the power flow of the branch or transmission section can be set as a temporary limit. After determining the adjustment of the constraints, return to step 2 to recalculate the unit combination optimization until all branches or transmission sections meet the safety requirements under the confidence interval α.

[0123] Step 5: Results display and output, including unit start-up and shutdown status, unit output plan, and power flow curves of branches and transmission sections.

[0124] This invention addresses the optimization scheduling problem under uncertain source-load conditions. It establishes a probabilistic power flow model after intermittent energy grid connection and a scheduling model considering demand response (DR) uncertainty. Probabilistic power flow analysis considering wind power uncertainty is performed. Stochastic probabilistic models of conventional units and loads are constructed. A source-load coordinated optimization scheduling method based on probability distribution quantitative analysis is proposed. A multi-period stochastic power flow algorithm considering intermittent power source access is employed to achieve a nested and coupled hierarchical scheduling optimization model with the objective function of minimizing generation costs. This model optimizes the upper level for unit start-up and shutdown and tie-line planning, and the lower level for economic scheduling of the system under given start-up and shutdown methods and tie-line plans. It overcomes the shortcomings of current large-scale wind power grid connection methods that do not specifically consider the mathematical characteristics and distribution patterns of wind power uncertainty, and takes into account the uncertainty factors of demand response.

[0125] The multi-period stochastic power flow algorithm proposed in this invention, considering intermittent power source access, includes:

[0126] After obtaining the active power random power flow distribution of the entire network under the condition of large-scale intermittent power source access through planned random power flow, the branch power flow constraint information is fed back to the generation planning module according to the degree to which the discovered dangerous branches exceed the transmission margin safety probability threshold.

[0127] Feedback branch power flow constraints are based on two fundamental properties of the semi-invariant stochastic power flow method:

[0128] Property 1: If there are n independent random variables x (1) x (2) , ..., x (n) And each has r-order semi-invariants. If x exists, and x is the sum of these n random variables, then the r-th order semi-invariant of the random variable x is equal to the sum of the r-th order semi-invariants of each independent random variable, that is:

[0129]

[0130] Property 2: Random variable y is a random variable x A linear function, y = ax + b, Let x be the semi-invariants of each order, then let y be the semi-invariants of each order. for:

[0131]

[0132] We can assume that, after stochastic power flow calculation, the planned branch L at a certain moment... ij The transmission margin safety probability is lower than the safety threshold, and the corresponding P when the probability equals the safety threshold is calculated. ij With P limit Subtraction yields ΔP ij Based on property 2 and the linear relationship between branch power flow and sensitivity, we can obtain the result when the branch power flow changes by ΔP ij At that time, the random probability of branch power flow will satisfy the safe transmission margin probability threshold.

[0133] After a large proportion of intermittent power sources are connected to the power grid, certain imbalances will occur during operation due to the fluctuations and randomness of their output. In order to meet the power balance characteristics of the power grid, multi-machine balancing needs to be introduced to achieve real-time power balance. Therefore, in stochastic power flow calculation, some conventional power sources participating in AGC control need to be set as balancing units to meet the power flow convergence requirements.

[0134] Based on the established stochastic power flow probability model, the planned power flow algorithm for intermittent power supply grid connection is as follows: Figure 3 As shown, it can be described as:

[0135] Step 1: Obtain power grid planning data, which mainly includes power flow calculation parameters, generator and load nodes, and the rated power and expected power of wind farms.

[0136] Step 2: Calculate the mean and variance of the node injection amount. Simultaneously, considering load correlation, the covariance matrix C of the node injection amount should also be calculated. X Then, the covariance matrix C is obtained. X eigenvalues ​​λ i and eigenvector φ i (i = 1, 2, ..., m), use the formula to calculate x i Corresponding independent random variables The mean, λ i That is The variance;

[0137] Step 3: Calculate the wind speed probability distribution function based on the three-parameter Weibull distribution using wind speed statistics data from a wind farm over a certain period.

[0138] Step 4: Take the expected or average value of the above power parameters and perform deterministic power flow calculation to obtain the state variable X0, Jacobian matrix J0, and sensitivity matrix S0 at the reference operating point;

[0139] Step 5: Calculate the moments of the random variable of injected power at each node, and then find its semi-invariants. The semi-invariants of the wind farm output power can be solved using the following formulas:

[0140]

[0141] The above equation is derived by integrating the characteristic function. Then, using the relationship between the characteristic function and the moments of each order, the r-th moment of the output power can be derived as follows:

[0142]

[0143] In this way, the semi-invariants of each order of the wind turbine’s active power output can be obtained from the relationship between the semi-invariants and the matrix. It is generally believed that there is a linear relationship between reactive power and active power output, and similarly, its semi-invariants of each order can be obtained.

[0144] Step 6: At the nodes where the wind farm is connected, the semi-invariants of the injected power are obtained by adding the semi-invariants of the load power and the semi-invariants of the total power at each node, i.e.: and These are the k-order semi-invariants of wind farm output power and load-side power, respectively.

[0145] Step 7: From the invariants ΔS of the injected power of each order (k) Find the invariants ΔX of each order of the state variables of each node in the system. (k) ;

[0146] Step 8: Use Gram-Charlic expansion to fit the random distribution function and random probability density function of the node state variables.

[0147] This invention proposes a scheduling optimization method that considers uncertainties on both the source and load sides. For the optimization scheduling problem under uncertain source and load conditions, a probabilistic power flow model after the grid connection of intermittent energy sources and a scheduling model that takes into account the uncertainty of demand response (DR) are established, and a probabilistic power flow analysis considering the uncertainty of wind power is carried out.

[0148] This invention constructs a stochastic probability model of conventional generating units and loads, proposes a source-load coordinated optimization scheduling method based on probability distribution quantitative analysis, and adopts a multi-period stochastic power flow algorithm that considers the access of intermittent power sources to achieve a nested and coupled hierarchical scheduling optimization model with the objective function of minimizing the sum of generation costs and scheduling costs. That is, the upper-level optimization is for the start-up and shutdown of generating units and tie-line plans, and the lower-level optimization model is for the economic scheduling of the system under the given start-up and shutdown methods and tie-line plans.

[0149] Example 2: Based on the same inventive concept, this embodiment of the invention also provides a scheduling optimization system considering uncertainties on both the source and load sides, including:

[0150] The scheduling planning module is used to input power grid planning data into a pre-built safety-constrained unit combination model for optimization calculation to obtain the scheduling plan for conventional units;

[0151] The power flow calculation module is used to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid within the scheduling plan cycle of the conventional units.

[0152] The optimization module is used to optimize the conventional unit scheduling plan based on the branch or cross-sectional power flow distribution when the intermittent power source is connected to the grid.

[0153] The safety-constrained unit combination model is constructed based on the uncertainty of demand response load under price incentives; the conventional units include coal-fired units and gas-fired units; the intermittent power sources include wind turbines and photovoltaic units.

[0154] In this embodiment, the power flow calculation module includes:

[0155] The acquisition unit is used to acquire power flow calculation parameters, generator and load nodes, and rated power of intermittent power sources within the scheduling plan cycle of the conventional unit.

[0156] The power flow calculation unit is used to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the power grid, based on the power flow calculation parameters, the rated power of generators and load nodes and intermittent power sources, using a probabilistic power flow method with semi-invariants and series expansion.

[0157] In this embodiment, the power flow calculation unit is specifically used for:

[0158] Calculate the torques of the intermittent power source output using semi-invariants;

[0159] Based on the moments of the intermittent power supply output, the semi-invariants of the intermittent power supply output are obtained.

[0160] Based on the semi-invariants of the intermittent power output, the relationship matrix between nodes and branch power flows is obtained.

[0161] The relationship matrix between nodes and branch power flow is calculated to obtain the semi-invariants of each order of branch and cross-sectional power flow.

[0162] The power flow distribution of branches or sections when intermittent power sources are connected to the grid is obtained by performing series expansion of the semi-invariants of each order on the power flow of branches or sections.

[0163] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A scheduling optimization method considering uncertainties on both sides of the source and load sides, characterized in that, include: S1 inputs the power grid planning data into a pre-built safety-constrained unit combination model for optimization calculation to obtain the scheduling plan for conventional units; S2 calculates the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid within the scheduling plan cycle of the conventional units. S3 optimizes the conventional unit scheduling plan based on the branch or cross-sectional power flow distribution when the intermittent power source is connected to the grid; The safety-constrained unit combination model is constructed based on the uncertainty of demand response load under price incentives; the conventional units include coal-fired units and gas-fired units; the intermittent power sources include wind turbines and photovoltaic units. The construction of the safety-constrained unit combination model includes: The objective function is constructed with the goal of minimizing the start-up and operating costs of conventional generating units and the scheduling and operating costs of demand response loads. Power balance constraints are constructed based on the response transfer of demand response load under price incentives; The power balance constraint is a constraint condition of the objective function; The objective function is shown in the following equation: In the formula: This is the sum of the start-up and operating costs of conventional generating units and the dispatching and operating costs of demand response loads. This represents the total number of conventional generating units. For the number of time periods, For conventional units i exist t Operating costs for a given period of time, For conventional units i exist t Contributions during a given period For conventional units i exist t Startup costs for a specific time period The scheduling and operation cost function for demand response load; The power balance constraint is shown in the following equation: In the formula: for t Regular units during the period i Those who have made contributions This is the number of conventional generating units. For time period t System load forecasting It is a time period t The amount of demand response load shifted under price incentives.

2. The method as described in claim 1, characterized in that, The conventional unit i exist t Startup costs for a given period As shown in the following formula: In the formula: For conventional units i Minimum runtime, For conventional units i The time required for warm start after minimum downtime. For conventional units i The time required for cold start after minimum downtime. For conventional units i The first constant of the startup cost function, For conventional units i The second constant of the startup cost function, For conventional units i The third constant of the startup cost function.

3. The method as described in claim 1, characterized in that, The constraints also include: Standby constraints, conventional unit output constraints, conventional unit minimum operating time constraints, conventional unit minimum downtime constraints, conventional unit load increase / decrease rate constraints, and network constraints.

4. The method as described in claim 1, characterized in that, The calculation of branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid during the scheduling planning cycle of the conventional generating units includes: Obtain the power flow calculation parameters, generator and load nodes, and rated power of intermittent power sources within the scheduling plan cycle of the conventional units; Based on the power flow calculation parameters, the rated power of generators and load nodes and intermittent power sources, a probabilistic power flow method using semi-invariants and series expansion is employed to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the power grid.

5. The method as described in claim 4, characterized in that, The probabilistic power flow method employing semi-invariants and series expansions to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid includes: Calculate the torques of each order of the intermittent power source output using semi-invariants; Based on the moments of the intermittent power supply output, the semi-invariants of the intermittent power supply output are obtained. Based on the semi-invariants of the intermittent power supply output, the relationship matrix between nodes and branch power flows is obtained; The relationship matrix between nodes and branch power flow is calculated to obtain the semi-invariants of each order of branch and cross-sectional power flow. The power flow distribution of branches or sections when intermittent power sources are connected to the grid is obtained by performing series expansion of the semi-invariants of each order on the power flow of branches or sections.

6. The method as described in claim 1, characterized in that, The optimization of the conventional unit scheduling plan based on the branch or cross-sectional power flow distribution when the intermittent power source is connected to the grid includes: Check whether the power flow distribution of branches or sections meets the safety requirements of the confidence interval. When the power flow distribution of a branch or section does not meet the safety requirements of the confidence interval, feed back the power flow distribution results of the branch or section to the safety-constrained unit combination model, and execute S1 to optimize the conventional unit scheduling plan until the power flow distribution of all branches or transmission sections meets the safety requirements of the confidence interval. Obtain the start-up and shutdown status of conventional units, the output plan of conventional units, and the power flow curves of branches and sections.

7. A scheduling optimization system considering uncertainties on both the source and load sides, characterized in that, include: The scheduling planning module is used to input power grid planning data into a pre-built safety-constrained unit combination model for optimization calculation to obtain the scheduling plan for conventional units; The power flow calculation module is used to calculate the branch or cross-sectional power flow distribution when intermittent power sources are connected to the grid within the scheduling plan cycle of the conventional units. The optimization module is used to optimize the conventional unit scheduling plan based on the branch or cross-sectional power flow distribution when the intermittent power source is connected to the grid. The safety-constrained unit combination model is constructed based on the uncertainty of demand response load under price incentives; the conventional units include coal-fired units and gas-fired units; the intermittent power sources include wind turbines and photovoltaic units. The construction of the safety-constrained unit combination model includes: The objective function is constructed with the goal of minimizing the start-up and operating costs of conventional generating units and the scheduling and operating costs of demand response loads. Power balance constraints are constructed based on the response transfer of demand response load under price incentives; The power balance constraint is a constraint condition of the objective function; The objective function is shown in the following equation: In the formula: This is the sum of the start-up and operating costs of conventional generating units and the dispatching and operating costs of demand response loads. This represents the total number of conventional generating units. For the number of time periods, For conventional units i exist t Operating costs for a given period of time, For conventional units i exist t Contributions during a given period For conventional units i exist t Startup costs for a specific time period The scheduling and operation cost function for demand response load; The power balance constraint is shown in the following equation: In the formula: for t Regular units during the period i Those who have made contributions This is the number of conventional generating units. For time period t System load forecasting It is a time period t The amount of demand response load shifted under price incentives.

8. The system as described in claim 7, characterized in that, The power flow calculation module includes: The acquisition unit is used to acquire power flow calculation parameters, generator and load nodes, and rated power of intermittent power sources within the scheduling plan cycle of the conventional unit. The power flow calculation unit is used to calculate the branch or cross-sectional power flow distribution when the intermittent power source is connected to the power grid, based on the power flow calculation parameters, the rated power of generators and load nodes and intermittent power sources, using a probabilistic power flow method with semi-invariants and series expansion.

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